Sample records for popular feature selection
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ERIC Educational Resources Information Center
Groce, Stephen B.
1992-01-01
Discusses the use of feature films for courses on popular culture and the sociology of popular music. Suggests that films can illustrate topics such as culture, social groups, deviant behavior, racism, and sexism. Lists a selection of Hollywood feature films with accompanying readings and students' evaluations. (DK)
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Predicting Key Events in the Popularity Evolution of Online Information.
Hu, Ying; Hu, Changjun; Fu, Shushen; Fang, Mingzhe; Xu, Wenwen
2017-01-01
The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.
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Predicting Key Events in the Popularity Evolution of Online Information
Fu, Shushen; Fang, Mingzhe; Xu, Wenwen
2017-01-01
The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. PMID:28046121
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Quantum-enhanced feature selection with forward selection and backward elimination
NASA Astrophysics Data System (ADS)
He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen
2018-07-01
Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.
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Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography
Xu, Jian-Wu; Suzuki, Kenji
2014-01-01
We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058
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Max-AUC feature selection in computer-aided detection of polyps in CT colonography.
Xu, Jian-Wu; Suzuki, Kenji
2014-03-01
We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.
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Higher criticism thresholding: Optimal feature selection when useful features are rare and weak.
Donoho, David; Jin, Jiashun
2008-09-30
In important application fields today-genomics and proteomics are examples-selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, ..., p, let pi(i) denote the two-sided P-value associated with the ith feature Z-score and pi((i)) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p - pi((i)))/sqrt{i/p(1-i/p)}. We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT.
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Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
Donoho, David; Jin, Jiashun
2008-01-01
In important application fields today—genomics and proteomics are examples—selecting a small subset of useful features is crucial for success of Linear Classification Analysis. We study feature selection by thresholding of feature Z-scores and introduce a principle of threshold selection, based on the notion of higher criticism (HC). For i = 1, 2, …, p, let πi denote the two-sided P-value associated with the ith feature Z-score and π(i) denote the ith order statistic of the collection of P-values. The HC threshold is the absolute Z-score corresponding to the P-value maximizing the HC objective (i/p − π(i))/i/p(1−i/p). We consider a rare/weak (RW) feature model, where the fraction of useful features is small and the useful features are each too weak to be of much use on their own. HC thresholding (HCT) has interesting behavior in this setting, with an intimate link between maximizing the HC objective and minimizing the error rate of the designed classifier, and very different behavior from popular threshold selection procedures such as false discovery rate thresholding (FDRT). In the most challenging RW settings, HCT uses an unconventionally low threshold; this keeps the missed-feature detection rate under better control than FDRT and yields a classifier with improved misclassification performance. Replacing cross-validated threshold selection in the popular Shrunken Centroid classifier with the computationally less expensive and simpler HCT reduces the variance of the selected threshold and the error rate of the constructed classifier. Results on standard real datasets and in asymptotic theory confirm the advantages of HCT. PMID:18815365
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A real-time method to predict social media popularity
NASA Astrophysics Data System (ADS)
Chen, Xiao; Lu, Zhe-Ming
How to predict the future popularity of a message or video on online social media (OSM) has long been an attractive problem for researchers. Although many difficulties are still ahead, recent studies suggest that temporal and topological features of early adopters generally play a very important role. However, with the increase of the adopters, the feature space will grow explosively. How to select the most effective features is still an open issue. In this work, we investigate several feature extraction methods over the Twitter platform and find that most predictive power concentrates on the second half of the propagation period, and that not only a model trained on one platform generalizes well to others as previous works observed, but also a model trained on one dataset performs well on predicting the popularity for other datasets with different number of observed early adopters. According to these findings, at least for the best features by far, the data used to extract features can be halved without loss of evident accuracy and we provide a way to roughly predict the growth trend of a social-media item in real-time.
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Effective Feature Selection for Classification of Promoter Sequences.
K, Kouser; P G, Lavanya; Rangarajan, Lalitha; K, Acharya Kshitish
2016-01-01
Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.
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Feature and Region Selection for Visual Learning.
Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando
2016-03-01
Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.
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Relevance popularity: A term event model based feature selection scheme for text classification.
Feng, Guozhong; An, Baiguo; Yang, Fengqin; Wang, Han; Zhang, Libiao
2017-01-01
Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.
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featsel: A framework for benchmarking of feature selection algorithms and cost functions
NASA Astrophysics Data System (ADS)
Reis, Marcelo S.; Estrela, Gustavo; Ferreira, Carlos Eduardo; Barrera, Junior
In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and cost functions for benchmarking experiments. We also provide illustrative examples, in which featsel outperforms the popular Weka workbench in feature selection procedures on data sets from the UCI Machine Learning Repository.
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Feature Extraction and Selection Strategies for Automated Target Recognition
NASA Technical Reports Server (NTRS)
Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin
2010-01-01
Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
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Feature extraction and selection strategies for automated target recognition
NASA Astrophysics Data System (ADS)
Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin
2010-04-01
Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
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Passionate Virtue: Conceptions of Medical Professionalism in Popular Romance Fiction.
Miller, Jessica
2015-01-01
Medical romance fiction is a subgenre of popular romance fiction that features medical professionals in their work environment. This essay explores the way professionalism is portrayed in popular medical romance fiction written during the early twenty-first century, a period of significant disruption in both the public image and self-understanding of organized medicine. I analyze a selection of contemporary medical romance novels, published between 2008 and 2012, demonstrating that medical romance fiction is a form of public intervention in apparently insular debates over medical professionalism. I conclude that they promote "nostalgic professionalism," a vision of physicians as a select group of highly educated, self-regulated experts who provide, with a caring and altruistic attitude, a vitally important service to society, while at the same time generating implicit critiques of it.
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Selection of Server-Side Technologies for an E-Business Curriculum
ERIC Educational Resources Information Center
Sandvig, J. Christopher
2007-01-01
The rapid growth of e-business and e-commerce has made server-side programming an increasingly important topic in information systems (IS) and computer science (CS) curricula. This article presents an overview of the major features of several popular server-side programming technologies and discusses the factors that influence the selection of…
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An ant colony optimization based feature selection for web page classification.
Saraç, Esra; Özel, Selma Ayşe
2014-01-01
The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.
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AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.
Sun, Lei; Wang, Jun; Wei, Jinmao
2017-03-14
The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.
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Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification
Wu, Lin
2017-01-01
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images. Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories. PMID:28706534
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A robust dataset-agnostic heart disease classifier from Phonocardiogram.
Banerjee, Rohan; Dutta Choudhury, Anirban; Deshpande, Parijat; Bhattacharya, Sakyajit; Pal, Arpan; Mandana, K M
2017-07-01
Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature set for dataset-agnostic classification of normal and cardiac patients. The large and open access database, made available in Physionet 2016 challenge was used for feature selection, internal validation and creation of training models. A second dataset of 41 PCG segments, collected using our in-house smart phone based digital stethoscope from an Indian hospital was used for performance evaluation. Our proposed methodology yielded sensitivity and specificity scores of 0.76 and 0.75 respectively on the test dataset in classifying cardiovascular diseases. The methodology also outperformed three popular prior art approaches, when applied on the same dataset.
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A proto-architecture for innate directionally selective visual maps.
Adams, Samantha V; Harris, Chris M
2014-01-01
Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.
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Derivation of an artificial gene to improve classification accuracy upon gene selection.
Seo, Minseok; Oh, Sejong
2012-02-01
Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.
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An Ant Colony Optimization Based Feature Selection for Web Page Classification
2014-01-01
The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. PMID:25136678
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Bardus, Marco; van Beurden, Samantha B; Smith, Jane R; Abraham, Charles
2016-03-10
There are thousands of apps promoting dietary improvement, increased physical activity (PA) and weight management. Despite a growing number of reviews in this area, popular apps have not been comprehensively analysed in terms of features related to engagement, functionality, aesthetics, information quality, and content, including the types of change techniques employed. The databases containing information about all Health and Fitness apps on GP and iTunes (7,954 and 25,491 apps) were downloaded in April 2015. Database filters were applied to select the most popular apps available in both stores. Two researchers screened the descriptions selecting only weight management apps. Features, app quality and content were independently assessed using the Mobile App Rating Scale (MARS) and previously-defined categories of techniques relevant to behaviour change. Inter-coder reliabilities were calculated, and correlations between features explored. Of the 23 popular apps included in the review 16 were free (70%), 15 (65%) addressed weight control, diet and PA combined; 19 (83%) allowed behavioural tracking. On 5-point MARS scales, apps were of average quality (Md = 3.2, IQR = 1.4); "functionality" (Md = 4.0, IQR = 1.1) was the highest and "information quality" (Md = 2.0, IQR = 1.1) was the lowest domain. On average, 10 techniques were identified per app (range: 1-17) and of the 34 categories applied, goal setting and self-monitoring techniques were most frequently identified. App quality was positively correlated with number of techniques included (rho = .58, p < .01) and number of "technical" features (rho = .48, p < .05), which was also associated with the number of techniques included (rho = .61, p < .01). Apps that provided tracking used significantly more techniques than those that did not. Apps with automated tracking scored significantly higher in engagement, aesthetics, and overall MARS scores. Those that used change techniques previously associated with effectiveness (i.e., goal setting, self-monitoring and feedback) also had better "information quality". Popular apps assessed have overall moderate quality and include behavioural tracking features and a range of change techniques associated with behaviour change. These apps may influence behaviour, although more attention to information quality and evidence-based content are warranted to improve their quality.
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Internet food marketing on popular children's websites and food product websites in Australia.
Kelly, Bridget; Bochynska, Katarzyna; Kornman, Kelly; Chapman, Kathy
2008-11-01
The aim of the present study was to describe the nature and extent of food marketing on popular children's websites and food product websites in Australia. Food product websites (n 119) and popular children's websites (n 196) were selected based on website traffic data and previous research on frequently marketed food brands. Coding instruments were developed to capture food marketing techniques. All references to food on popular children's websites were also classified as either branded or non-branded and according to food categories. Websites contained a range of marketing features. On food product websites these marketing features included branded education (79.0% of websites), competitions (33.6%), promotional characters (35.3%), downloadable items (35.3%), branded games (28.6%) and designated children's sections (21.8%). Food references on popular children's websites were strongly skewed towards unhealthy foods (60.8% v. 39.2% healthy food references; P<0.001), with three times more branded food references for unhealthy foods. Branded food references displayed similar marketing features to those identified on food product websites. Internet food marketing uses a range of techniques to ensure that children are immersed in brand-related information and activities for extended periods, thereby increasing brand familiarity and exposure. The relatively unregulated marketing environment and increasing use of the Internet by children point to the potential increase in food marketing via this medium. Further research is required to investigate the impact of Internet food marketing on children's food preferences and consumption, and regulatory options to protect children.
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Zhang, Yu; Wu, Jianxin; Cai, Jianfei
2016-05-01
In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.
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Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.
Chen, Qi; Meng, Zhaopeng; Liu, Xinyi; Jin, Qianguo; Su, Ran
2018-06-15
Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.
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Popular Nutrition-Related Mobile Apps: A Feature Assessment
Fallaize, Rosalind; Lovegrove, Julie A; Hwang, Faustina
2016-01-01
Background A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. Objective This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback. Methods Apps were selected from the two largest online stores of the most popular mobile operating systems—the Google Play Store for Android and the iTunes App Store for iOS—based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription. Results A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary intake and PA. None of these nine apps offered features directly related to diet plans and motivational coaching. In contrast, the remaining four of the 13 apps focused on these opportunities, but without food diaries. One app—FatSecret—also had an innovative feature for connecting users with health professionals, and another—S Health—provided a nutrient balance score. Conclusions The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice. PMID:27480144
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Popular Nutrition-Related Mobile Apps: A Feature Assessment.
Franco, Rodrigo Zenun; Fallaize, Rosalind; Lovegrove, Julie A; Hwang, Faustina
2016-08-01
A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback. Apps were selected from the two largest online stores of the most popular mobile operating systems-the Google Play Store for Android and the iTunes App Store for iOS-based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription. A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary intake and PA. None of these nine apps offered features directly related to diet plans and motivational coaching. In contrast, the remaining four of the 13 apps focused on these opportunities, but without food diaries. One app-FatSecret-also had an innovative feature for connecting users with health professionals, and another-S Health-provided a nutrient balance score. The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice.
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A feature selection approach towards progressive vector transmission over the Internet
NASA Astrophysics Data System (ADS)
Miao, Ru; Song, Jia; Feng, Min
2017-09-01
WebGIS has been applied for visualizing and sharing geospatial information popularly over the Internet. In order to improve the efficiency of the client applications, the web-based progressive vector transmission approach is proposed. Important features should be selected and transferred firstly, and the methods for measuring the importance of features should be further considered in the progressive transmission. However, studies on progressive transmission for large-volume vector data have mostly focused on map generalization in the field of cartography, but rarely discussed on the selection of geographic features quantitatively. This paper applies information theory for measuring the feature importance of vector maps. A measurement model for the amount of information of vector features is defined based upon the amount of information for dealing with feature selection issues. The measurement model involves geometry factor, spatial distribution factor and thematic attribute factor. Moreover, a real-time transport protocol (RTP)-based progressive transmission method is then presented to improve the transmission of vector data. To clearly demonstrate the essential methodology and key techniques, a prototype for web-based progressive vector transmission is presented, and an experiment of progressive selection and transmission for vector features is conducted. The experimental results indicate that our approach clearly improves the performance and end-user experience of delivering and manipulating large vector data over the Internet.
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Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah
2018-02-01
Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.
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NASA Astrophysics Data System (ADS)
Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah
2018-02-01
Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.
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A Novel Feature Selection Technique for Text Classification Using Naïve Bayes.
Dey Sarkar, Subhajit; Goswami, Saptarsi; Agarwal, Aman; Aktar, Javed
2014-01-01
With the proliferation of unstructured data, text classification or text categorization has found many applications in topic classification, sentiment analysis, authorship identification, spam detection, and so on. There are many classification algorithms available. Naïve Bayes remains one of the oldest and most popular classifiers. On one hand, implementation of naïve Bayes is simple and, on the other hand, this also requires fewer amounts of training data. From the literature review, it is found that naïve Bayes performs poorly compared to other classifiers in text classification. As a result, this makes the naïve Bayes classifier unusable in spite of the simplicity and intuitiveness of the model. In this paper, we propose a two-step feature selection method based on firstly a univariate feature selection and then feature clustering, where we use the univariate feature selection method to reduce the search space and then apply clustering to select relatively independent feature sets. We demonstrate the effectiveness of our method by a thorough evaluation and comparison over 13 datasets. The performance improvement thus achieved makes naïve Bayes comparable or superior to other classifiers. The proposed algorithm is shown to outperform other traditional methods like greedy search based wrapper or CFS.
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Rios, Anthony; Kavuluru, Ramakanth
2013-09-01
Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.
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Stabilizing l1-norm prediction models by supervised feature grouping.
Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha
2016-02-01
Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making. Copyright © 2015 Elsevier Inc. All rights reserved.
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Collective feature selection to identify crucial epistatic variants.
Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D
2018-01-01
Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
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Kesharaju, Manasa; Nagarajah, Romesh
2015-09-01
The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.
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Celebrating Children's Choices: 25 Years of Children's Favorite Books.
ERIC Educational Resources Information Center
Post, Arden DeVries
This book provides the background and development of the Children's Choices project and highlights many of the best known and most popular books that have appeared on the Children's Choices list over the past 25 years. Each book selection features a picture of the book jacket, an annotation from the Choices list, a list of classroom applications,…
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Máthé, Koppány; Buşoniu, Lucian
2015-01-01
Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms and application fields where they are useful. We also highlight the sensor suites used where this information is available. We overview, among others, feature detection and tracking, optical flow and visual servoing, low-level stabilization and high-level planning methods. We then list popular low-cost UAVs, selecting mainly quadrotors. We discuss applications, restricting our focus to the field of infrastructure inspection. Finally, as an example, we formulate two use-cases for railway inspection, a less explored application field, and illustrate the usage of the vision and control techniques reviewed by selecting appropriate ones to tackle these use-cases. To select vision methods, we run a thorough set of experimental evaluations. PMID:26121608
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A hybrid approach to select features and classify diseases based on medical data
NASA Astrophysics Data System (ADS)
AbdelLatif, Hisham; Luo, Jiawei
2018-03-01
Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms
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Gurbaxani, Brian M; Jones, James F; Goertzel, Benjamin N; Maloney, Elizabeth M
2006-04-01
To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.
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Analysis of A Drug Target-based Classification System using Molecular Descriptors.
Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin
2016-01-01
Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.
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Landmarks selection in street map design
NASA Astrophysics Data System (ADS)
Kao, C. J.
2014-02-01
In Taiwan many electrical maps present their landmarks according to the category of the feature, a designer short of knowledge about mental representation of space, can cause the map to lose its communication effects. To resolve this map design problem, in this research through long-term memory recall, navigation and observation, and short-term memory processing 111 participants were asked to select the proper landmark from study area. The results reveal that in Taiwan convenience stores are the most popular local landmark in rural and urban areas. Their commercial signs have a unique design and bright color. Contrasted to their background, this makes the convenience store a salient feature. This study also developed a rule to assess the priority of the landmarks to design them in different scale maps.
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Feature weight estimation for gene selection: a local hyperlinear learning approach
2014-01-01
Background Modeling high-dimensional data involving thousands of variables is particularly important for gene expression profiling experiments, nevertheless,it remains a challenging task. One of the challenges is to implement an effective method for selecting a small set of relevant genes, buried in high-dimensional irrelevant noises. RELIEF is a popular and widely used approach for feature selection owing to its low computational cost and high accuracy. However, RELIEF based methods suffer from instability, especially in the presence of noisy and/or high-dimensional outliers. Results We propose an innovative feature weighting algorithm, called LHR, to select informative genes from highly noisy data. LHR is based on RELIEF for feature weighting using classical margin maximization. The key idea of LHR is to estimate the feature weights through local approximation rather than global measurement, which is typically used in existing methods. The weights obtained by our method are very robust in terms of degradation of noisy features, even those with vast dimensions. To demonstrate the performance of our method, extensive experiments involving classification tests have been carried out on both synthetic and real microarray benchmark datasets by combining the proposed technique with standard classifiers, including the support vector machine (SVM), k-nearest neighbor (KNN), hyperplane k-nearest neighbor (HKNN), linear discriminant analysis (LDA) and naive Bayes (NB). Conclusion Experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed feature selection method combined with supervised learning in three aspects: 1) high classification accuracy, 2) excellent robustness to noise and 3) good stability using to various classification algorithms. PMID:24625071
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Characterizing and modeling the dynamics of online popularity.
Ratkiewicz, Jacob; Fortunato, Santo; Flammini, Alessandro; Menczer, Filippo; Vespignani, Alessandro
2010-10-08
Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.
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A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques
NASA Astrophysics Data System (ADS)
Uswatun Khasanah, Annisa; Harwati
2017-06-01
Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.
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Popularity and adolescent friendship networks: selection and influence dynamics.
Dijkstra, Jan Kornelis; Cillessen, Antonius H N; Borch, Casey
2013-07-01
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher status adolescents, whereas higher status adolescents strive to maintain their status by keeping lower status adolescents at a distance. The results largely supported these expectations. Selection partially accounted for similarity in popularity among friends; adolescents preferred to affiliate with similar-status or higher status peers, reinforcing the attractiveness of popular adolescents and explaining stability of popularity at the individual level. Influence processes also accounted for similarity in popularity over time, showing that peers increase in popularity and become more similar to their friends. The results showed how selection and influence processes account for popularity dynamics in adolescent networks over time.
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A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.
Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A
2018-01-01
In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.
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Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej
2015-11-30
Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.
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A novel approach for dimension reduction of microarray.
Aziz, Rabia; Verma, C K; Srivastava, Namita
2017-12-01
This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Receptive fields selection for binary feature description.
Fan, Bin; Kong, Qingqun; Trzcinski, Tomasz; Wang, Zhiheng; Pan, Chunhong; Fua, Pascal
2014-06-01
Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG .accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.
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Familiarity effects in the construction of facial-composite images using modern software systems.
Frowd, Charlie D; Skelton, Faye C; Butt, Neelam; Hassan, Amal; Fields, Stephen; Hancock, Peter J B
2011-12-01
We investigate the effect of target familiarity on the construction of facial composites, as used by law enforcement to locate criminal suspects. Two popular software construction methods were investigated. Participants were shown a target face that was either familiar or unfamiliar to them and constructed a composite of it from memory using a typical 'feature' system, involving selection of individual facial features, or one of the newer 'holistic' types, involving repeated selection and breeding from arrays of whole faces. This study found that composites constructed of a familiar face were named more successfully than composites of an unfamiliar face; also, naming of composites of internal and external features was equivalent for construction of unfamiliar targets, but internal features were better named than the external features for familiar targets. These findings applied to both systems, although benefit emerged for the holistic type due to more accurate construction of internal features and evidence for a whole-face advantage. STATEMENT OF RELEVANCE: This work is of relevance to practitioners who construct facial composites with witnesses to and victims of crime, as well as for software designers to help them improve the effectiveness of their composite systems.
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Giovenco, Daniel P; Miller Lo, Erin J; Lewis, M Jane; Delnevo, Cristine D
2017-11-01
Cigarillo use is prevalent among young adults in the United States. Many young people use cigarillos as "blunts," a term for a cigar emptied of its tobacco and replaced with marijuana. Because cigars in the United States are not subject to the same regulations as cigarettes, they offer a diverse selection of flavors and packaging styles. It is unclear how these and other product attributes facilitate blunt use. Semi-structured telephone interviews were conducted with a sample of 40 young adult cigar or cigarillo users in the United States to assess patterns of use and perceptions about product features. Quotations from interview transcripts were coded for major themes and summarized across participants. Regardless of their preferred brand, participants felt that the brand Black & Mild is primarily smoked for the tobacco. There was a strong perception, however, that other popular cigarillo brands are almost always used to make blunts. Participants believed that cigarillo companies design their products to simplify blunt-making, with features such as perforated lines or wrappings that unroll easily. Resealable foil pouches, a popular packaging style, are often used to hold unused marijuana and mask its smell. Blunt use is pervasive among young adult cigarillo users in the United States, and certain cigar companies have developed products that facilitate blunt-making. Future surveillance measures should capture the extent to which cigarillo users are using these products as blunts. Continued surveillance of cigarillo sales and popular product attributes are needed. Cigarillo use is prevalent among young adults in the United States, many of whom are using the products as blunts. This study found that product features such as brand, flavor, packaging, and price influence the selection of cigarillos used for this purpose. There is also a strong perception among young adult cigarillo users that cigarillo companies design their products and packaging to make the blunt-making process simple and enjoyable. Better surveillance measures are needed to capture the extent to which cigarillos are used as blunts and which product features are driving category growth. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Rust, Nicole C.; DiCarlo, James J.
2012-01-01
While popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (V4 and IT) in the rhesus macaque monkey. We confirmed that IT neurons are indeed more selective for conjunctions of visual features than V4 neurons, and that this increase in feature conjunction selectivity is accompanied by an increase in tolerance (“invariance”) to identity-preserving transformations (e.g. shifting, scaling) of those features. We report here that V4 and IT neurons are, on average, tightly matched in their tuning breadth for natural images (“sparseness”), and that the average V4 or IT neuron will produce a robust firing rate response (over 50% of its peak observed firing rate) to ~10% of all natural images. We also observed that sparseness was positively correlated with conjunction selectivity and negatively correlated with tolerance within both V4 and IT, consistent with selectivity-building and invariance-building computations that offset one another to produce sparseness. Our results imply that the conjunction-selectivity-building and invariance-building computations necessary to support object recognition are implemented in a balanced fashion to maintain sparseness at each stage of processing. PMID:22836252
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Variable selection for marginal longitudinal generalized linear models.
Cantoni, Eva; Flemming, Joanna Mills; Ronchetti, Elvezio
2005-06-01
Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallows's C(p) (GC(p)) suitable for use with both parametric and nonparametric models. GC(p) provides an estimate of a measure of model's adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GC(p).
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Using deep learning for detecting gender in adult chest radiographs
NASA Astrophysics Data System (ADS)
Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.
2018-03-01
In this paper, we present a method for automatically identifying the gender of an imaged person using their frontal chest x-ray images. Our work is motivated by the need to determine missing gender information in some datasets. The proposed method employs the technique of convolutional neural network (CNN) based deep learning and transfer learning to overcome the challenge of developing handcrafted features in limited data. Specifically, the method consists of four main steps: pre-processing, CNN feature extractor, feature selection, and classifier. The method is tested on a combined dataset obtained from several sources with varying acquisition quality resulting in different pre-processing steps that are applied for each. For feature extraction, we tested and compared four CNN architectures, viz., AlexNet, VggNet, GoogLeNet, and ResNet. We applied a feature selection technique, since the feature length is larger than the number of images. Two popular classifiers: SVM and Random Forest, are used and compared. We evaluated the classification performance by cross-validation and used seven performance measures. The best performer is the VggNet-16 feature extractor with the SVM classifier, with accuracy of 86.6% and ROC Area being 0.932 for 5-fold cross validation. We also discuss several misclassified cases and describe future work for performance improvement.
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Use of genetic algorithm for the selection of EEG features
NASA Astrophysics Data System (ADS)
Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.
2015-09-01
Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.
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Evaluating the efficacy of fully automated approaches for the selection of eye blink ICA components
Pontifex, Matthew B.; Miskovic, Vladimir; Laszlo, Sarah
2017-01-01
Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eye blink artifacts from EEG signals. Manual identification of the eye blink ICA component by inspection of scalp map projections, however, is prone to error, particularly when non-artifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eye blink related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features [EyeCatch()], combined stereotypical spatial and temporal features [ADJUST()], and a novel method relying on time-series features alone [icablinkmetrics()] using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eye blink related ICA components at or above the level of trained human observers. However, icablinkmetrics(), in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for 1) identifying artifactual components via automated means and 2) reducing the accidental removal of signal-related ICA components are discussed. PMID:28191627
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Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging
NASA Astrophysics Data System (ADS)
Kohan, Zohreh; Azmi, Reza
2016-03-01
There are evidences in the literature that Temporal Lobe Epilepsy (TLE) causes some lateralized atrophy and deformation on hippocampus and other substructures of the brain. Magnetic Resonance Imaging (MRI), due to high-contrast soft tissue imaging, is one of the most popular imaging modalities being used in TLE diagnosis and treatment procedures. Using an algorithm to help clinicians for better and more effective shape deformations analysis could improve the diagnosis and treatment of the disease. In this project our purpose is to design, implement and test a classification algorithm for MRIs based on hippocampal asymmetry detection using shape and size-based features. Our method consisted of two main parts; (1) shape feature extraction, and (2) image classification. We tested 11 different shape and size features and selected four of them that detect the asymmetry in hippocampus significantly in a randomly selected subset of the dataset. Then, we employed a support vector machine (SVM) classifier to classify the remaining images of the dataset to normal and epileptic images using our selected features. The dataset contains 25 patient images in which 12 cases were used as a training set and the rest 13 cases for testing the performance of classifier. We measured accuracy, specificity and sensitivity of, respectively, 76%, 100%, and 70% for our algorithm. The preliminary results show that using shape and size features for detecting hippocampal asymmetry could be helpful in TLE diagnosis in MRI.
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Text Classification for Assisting Moderators in Online Health Communities
Huh, Jina; Yetisgen-Yildiz, Meliha; Pratt, Wanda
2013-01-01
Objectives Patients increasingly visit online health communities to get help on managing health. The large scale of these online communities makes it impossible for the moderators to engage in all conversations; yet, some conversations need their expertise. Our work explores low-cost text classification methods to this new domain of determining whether a thread in an online health forum needs moderators’ help. Methods We employed a binary classifier on WebMD’s online diabetes community data. To train the classifier, we considered three feature types: (1) word unigram, (2) sentiment analysis features, and (3) thread length. We applied feature selection methods based on χ2 statistics and under sampling to account for unbalanced data. We then performed a qualitative error analysis to investigate the appropriateness of the gold standard. Results Using sentiment analysis features, feature selection methods, and balanced training data increased the AUC value up to 0.75 and the F1-score up to 0.54 compared to the baseline of using word unigrams with no feature selection methods on unbalanced data (0.65 AUC and 0.40 F1-score). The error analysis uncovered additional reasons for why moderators respond to patients’ posts. Discussion We showed how feature selection methods and balanced training data can improve the overall classification performance. We present implications of weighing precision versus recall for assisting moderators of online health communities. Our error analysis uncovered social, legal, and ethical issues around addressing community members’ needs. We also note challenges in producing a gold standard, and discuss potential solutions for addressing these challenges. Conclusion Social media environments provide popular venues in which patients gain health-related information. Our work contributes to understanding scalable solutions for providing moderators’ expertise in these large-scale, social media environments. PMID:24025513
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A new approach to develop computer-aided detection schemes of digital mammograms
NASA Astrophysics Data System (ADS)
Tan, Maxine; Qian, Wei; Pu, Jiantao; Liu, Hong; Zheng, Bin
2015-06-01
The purpose of this study is to develop a new global mammographic image feature analysis based computer-aided detection (CAD) scheme and evaluate its performance in detecting positive screening mammography examinations. A dataset that includes images acquired from 1896 full-field digital mammography (FFDM) screening examinations was used in this study. Among them, 812 cases were positive for cancer and 1084 were negative or benign. After segmenting the breast area, a computerized scheme was applied to compute 92 global mammographic tissue density based features on each of four mammograms of the craniocaudal (CC) and mediolateral oblique (MLO) views. After adding three existing popular risk factors (woman’s age, subjectively rated mammographic density, and family breast cancer history) into the initial feature pool, we applied a sequential forward floating selection feature selection algorithm to select relevant features from the bilateral CC and MLO view images separately. The selected CC and MLO view image features were used to train two artificial neural networks (ANNs). The results were then fused by a third ANN to build a two-stage classifier to predict the likelihood of the FFDM screening examination being positive. CAD performance was tested using a ten-fold cross-validation method. The computed area under the receiver operating characteristic curve was AUC = 0.779 ± 0.025 and the odds ratio monotonically increased from 1 to 31.55 as CAD-generated detection scores increased. The study demonstrated that this new global image feature based CAD scheme had a relatively higher discriminatory power to cue the FFDM examinations with high risk of being positive, which may provide a new CAD-cueing method to assist radiologists in reading and interpreting screening mammograms.
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A graphical interface to CLIPS using SunView
NASA Technical Reports Server (NTRS)
Feagin, Terry
1990-01-01
The importance of the incorporation of various graphics-oriented features into CLIPS is discussed. These popular features, which have been implemented in a version of CLIPS developed for a popular workstation, are described and their usefulness in the development of expert systems is examined.
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Wong, Raymond
2013-01-01
Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684
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Classification of Microarray Data Using Kernel Fuzzy Inference System
Kumar Rath, Santanu
2014-01-01
The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543
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The Use of Popular Science Articles in Teaching Scientific Literacy
ERIC Educational Resources Information Center
Parkinson, Jean; Adendorff, Ralph
2004-01-01
This article considers the use of popular science articles in teaching scientific literacy. Comparing the discourse features of popular science with research article and textbook science--the last two being target forms for students--it argues that popular science articles cannot serve as models for scientific writing. It does, however, suggest…
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Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia
NASA Astrophysics Data System (ADS)
Javanmardi, Sara; McDonald, David W.; Caruana, Rich; Forouzan, Sholeh; Lopes, Cristina V.
A challenge facing user generated content systems is vandalism, i.e. edits that damage content quality. The high visibility and easy access to social networks makes them popular targets for vandals. Detecting and removing vandalism is critical for these user generated content systems. Because vandalism can take many forms, there are many different kinds of features that are potentially useful for detecting it. The complex nature of vandalism, and the large number of potential features, make vandalism detection difficult and time consuming for human editors. Machine learning techniques hold promise for developing accurate, tunable, and maintainable models that can be incorporated into vandalism detection tools. We describe a method for training classifiers for vandalism detection that yields classifiers that are more accurate on the PAN 2010 corpus than others previously developed. Because of the high turnaround in social network systems, it is important for vandalism detection tools to run in real-time. To this aim, we use feature selection to find the minimal set of features consistent with high accuracy. In addition, because some features are more costly to compute than others, we use cost-sensitive feature selection to reduce the total computational cost of executing our models. In addition to the features previously used for spam detection, we introduce new features based on user action histories. The user history features contribute significantly to classifier performance. The approach we use is general and can easily be applied to other user generated content systems.
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Padon, Alisa A; Rimal, Rajiv N; DeJong, William; Siegel, Michael; Jernigan, David
2018-02-01
Underage drinking is a serious public health problem in the United States, and youth exposure to alcohol advertising has been indicated as a possible contributing factor. Although a number of studies have identified advertising content features that youth find appealing, a key limitation of this research is the absence of a broader tool to examine those features, especially those used by alcohol brands that are popular with underage drinkers. We created an index of content elements found in the research literature to be appealing to youth, and then used this index in a content analysis to identify the degree to which youth-appealing content appeared in a sample of alcohol ads that aired on television shows popular among youth. Finally, using bivariate analysis, we tested the relationship between alcohol brands' use of this content and the popularity of those brands among youth. We found that many of the ads featured youth-appealing content, and that the ads for the alcohol brands most popular among youth had more youth-appealing content than the less popular brands.
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NASA Astrophysics Data System (ADS)
Arab, Yasser; Hassan, Ahmad Sanusi; Qanaa, Bushra
2017-10-01
This research analyzed the façade thermal performance of high-rise buildings with modern and neo-minimalist architectural style. Four high-rise apartment buildings in Penang Island are selected as case studies for this research. The modern architectural style, which was popular during the 1970s to 1990s, nearly disregarded the cultural identity of the country and used the basic geometric shapes in the design. Conversely, the neo-minimalist style is the popular style from the 2010s up to the present. This style is a result of the "less is more" concept, which means using minimal applications to obtain an efficient design. The four selected case studies are as follows: Halaman Kristal 2 and Mutiara Idaman 1 with modern architectural style and Light Linear and Baystar apartments with neo-minimalist style. The research uses Fluke Ti20 thermal imager to capture thermal images of the west façade of the selected case studies on an hourly basis from 12:00 to 6:00 P.M. on March 15, 2017. Results confirm that the neo-minimalist façade elements, such as balconies and recessed walls, as well as other shading elements, are effective in improving the performance of façade shading. Notably, façade shading causes low surface temperature and provides cool indoor atmosphere during the day when the temperature is extremely high outside. Accordingly, this distinct feature partly explains the current popularity of the neo-minimalist architectural style.
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Deconstructing continuous flash suppression
Yang, Eunice; Blake, Randolph
2012-01-01
In this paper, we asked to what extent the depth of interocular suppression engendered by continuous flash suppression (CFS) varies depending on spatiotemporal properties of the suppressed stimulus and CFS suppressor. An answer to this question could have implications for interpreting the results in which CFS influences the processing of different categories of stimuli to different extents. In a series of experiments, we measured the selectivity and depth of suppression (i.e., elevation in contrast detection thresholds) as a function of the visual features of the stimulus being suppressed and the stimulus evoking suppression, namely, the popular “Mondrian” CFS stimulus (N. Tsuchiya & C. Koch, 2005). First, we found that CFS differentially suppresses the spatial components of the suppressed stimulus: Observers' sensitivity for stimuli of relatively low spatial frequency or cardinally oriented features was more strongly impaired in comparison to high spatial frequency or obliquely oriented stimuli. Second, we discovered that this feature-selective bias primarily arises from the spatiotemporal structure of the CFS stimulus, particularly within information residing in the low spatial frequency range and within the smooth rather than abrupt luminance changes over time. These results imply that this CFS stimulus operates by selectively attenuating certain classes of low-level signals while leaving others to be potentially encoded during suppression. These findings underscore the importance of considering the contribution of low-level features in stimulus-driven effects that are reported under CFS. PMID:22408039
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Deconstructing continuous flash suppression.
Yang, Eunice; Blake, Randolph
2012-03-08
In this paper, we asked to what extent the depth of interocular suppression engendered by continuous flash suppression (CFS) varies depending on spatiotemporal properties of the suppressed stimulus and CFS suppressor. An answer to this question could have implications for interpreting the results in which CFS influences the processing of different categories of stimuli to different extents. In a series of experiments, we measured the selectivity and depth of suppression (i.e., elevation in contrast detection thresholds) as a function of the visual features of the stimulus being suppressed and the stimulus evoking suppression, namely, the popular "Mondrian" CFS stimulus (N. Tsuchiya & C. Koch, 2005). First, we found that CFS differentially suppresses the spatial components of the suppressed stimulus: Observers' sensitivity for stimuli of relatively low spatial frequency or cardinally oriented features was more strongly impaired in comparison to high spatial frequency or obliquely oriented stimuli. Second, we discovered that this feature-selective bias primarily arises from the spatiotemporal structure of the CFS stimulus, particularly within information residing in the low spatial frequency range and within the smooth rather than abrupt luminance changes over time. These results imply that this CFS stimulus operates by selectively attenuating certain classes of low-level signals while leaving others to be potentially encoded during suppression. These findings underscore the importance of considering the contribution of low-level features in stimulus-driven effects that are reported under CFS.
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Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.
Ginsburg, Shoshana; Ali, Sahirzeeshan; Lee, George; Basavanhally, Ajay; Madabhushi, Anant
2013-01-01
Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs). Due to the curse of dimensionality, building classifiers with large numbers of features requires feature selection (which may require a large training set) or dimensionality reduction (DR). DR methods map the original high-dimensional features in terms of eigenvectors and eigenvalues, which limits the potential for feature transparency or interpretability. Although methods exist for variable selection and ranking on embeddings obtained via linear DR schemes (e.g., principal components analysis (PCA)), similar methods do not yet exist for nonlinear DR (NLDR) methods. In this work we present a simple yet elegant method for approximating the mapping between the data in the original feature space and the transformed data in the kernel PCA (KPCA) embedding space; this mapping provides the basis for quantification of variable importance in nonlinear kernels (VINK). We show how VINK can be implemented in conjunction with the popular Isomap and Laplacian eigenmap algorithms. VINK is evaluated in the contexts of three different problems in digital pathology: (1) predicting five year PSA failure following radical prostatectomy, (2) predicting Oncotype DX recurrence risk scores for ER+ breast cancers, and (3) distinguishing good and poor outcome p16+ oropharyngeal tumors. We demonstrate that subsets of features identified by VINK provide similar or better classification or regression performance compared to the original high dimensional feature sets.
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Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform
NASA Astrophysics Data System (ADS)
Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah
2017-02-01
Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.
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Efficient robust conditional random fields.
Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A
2015-10-01
Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.
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A review of channel selection algorithms for EEG signal processing
NASA Astrophysics Data System (ADS)
Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq
2015-12-01
Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.
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Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics
ERIC Educational Resources Information Center
Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey
2013-01-01
This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher…
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Pan, Xue; Liu, Kecheng
2017-01-01
Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the ‘rich get richer’ dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one’s friends have significant influence over the one’s selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one’s selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems. PMID:28406984
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Pan, Xue; Hou, Lei; Liu, Kecheng
2017-01-01
Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the 'rich get richer' dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one's friends have significant influence over the one's selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one's selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems.
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Li, Ziyi; Safo, Sandra E; Long, Qi
2017-07-11
Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.
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An Evaluation of Feature Learning Methods for High Resolution Image Classification
NASA Astrophysics Data System (ADS)
Tokarczyk, P.; Montoya, J.; Schindler, K.
2012-07-01
Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.
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Convolutional neural network features based change detection in satellite images
NASA Astrophysics Data System (ADS)
Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong
2016-07-01
With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.
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Spanish Federation of Popular Universities (FEUP)
ERIC Educational Resources Information Center
Serrano, Isabel Garcia-Longoria
2006-01-01
This article features the Spanish Popular Universities, which are defined as "a project of cultural development that acts in the municipality, whose objective is to promote social participation, education, training, and culture in order to improve life quality" (Federation of Popular Education Universities, 2000). A century of history of…
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Finding the top influential bloggers based on productivity and popularity features
NASA Astrophysics Data System (ADS)
Khan, Hikmat Ullah; Daud, Ali
2017-07-01
A blog acts as a platform of virtual communication to share comments or views about products, events and social issues. Like other social web activities, blogging actions spread to a large number of people. Users influence others in many ways, such as buying a product, having a particular political or social opinion or initiating new activity. Finding the top influential bloggers is an active research domain as it helps us in various fields, such as online marketing, e-commerce, product search and e-advertisements. There exist various models to find the influential bloggers, but they consider limited features using non-modular approach. This paper proposes a new model, Popularity and Productivity Model (PPM), based on a modular approach to find the top influential bloggers. It consists of popularity and productivity modules which exploit various features. We discuss the role of each proposed and existing features and evaluate the proposed model against the standard baseline models using datasets from the real-world blogs. The analysis using standard performance evaluation measures verifies that both productivity and popularity modules play a vital role to find influential bloggers in blogging community in an effective manner.
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Lex-SVM: exploring the potential of exon expression profiling for disease classification.
Yuan, Xiongying; Zhao, Yi; Liu, Changning; Bu, Dongbo
2011-04-01
Exon expression profiling technologies, including exon arrays and RNA-Seq, measure the abundance of every exon in a gene. Compared with gene expression profiling technologies like 3' array, exon expression profiling technologies could detect alterations in both transcription and alternative splicing, therefore they are expected to be more sensitive in diagnosis. However, exon expression profiling also brings higher dimension, more redundancy, and significant correlation among features. Ignoring the correlation structure among exons of a gene, a popular classification method like L1-SVM selects exons individually from each gene and thus is vulnerable to noise. To overcome this limitation, we present in this paper a new variant of SVM named Lex-SVM to incorporate correlation structure among exons and known splicing patterns to promote classification performance. Specifically, we construct a new norm, ex-norm, including our prior knowledge on exon correlation structure to regularize the coefficients of a linear SVM. Lex-SVM can be solved efficiently using standard linear programming techniques. The advantage of Lex-SVM is that it can select features group-wisely, force features in a subgroup to take equal weihts and exclude the features that contradict the majority in the subgroup. Experimental results suggest that on exon expression profile, Lex-SVM is more accurate than existing methods. Lex-SVM also generates a more compact model and selects genes more consistently in cross-validation. Unlike L1-SVM selecting only one exon in a gene, Lex-SVM assigns equal weights to as many exons in a gene as possible, lending itself easier for further interpretation.
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Teaching India with Popular Feature Films: A Guide for High School and College Teachers
ERIC Educational Resources Information Center
Parameswaran, Gowri
2010-01-01
Popular films say a lot about the culture where they are seen and enjoyed even though they may not reflect "reality" in the way that academics may want to portray a country. Popular films get at the deepest longings and fears of their viewership and their biggest hopes for the future. Teaching India through popular films is particularly…
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McStas 1.7 - a new version of the flexible Monte Carlo neutron scattering package
NASA Astrophysics Data System (ADS)
Willendrup, Peter; Farhi, Emmanuel; Lefmann, Kim
2004-07-01
Current neutron instrumentation is both complex and expensive, and accurate simulation has become essential both for building new instruments and for using them effectively. The McStas neutron ray-trace simulation package is a versatile tool for producing such simulations, developed in collaboration between Risø and ILL. The new version (1.7) has many improvements, among these added support for the popular Microsoft Windows platform. This presentation will demonstrate a selection of the new features through a simulation of the ILL IN6 beamline.
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Ding, Huijun; He, Qing; Zhou, Yongjin; Dan, Guo; Cui, Song
2017-01-01
Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results. PMID:29167655
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Popularity and user diversity of online objects
NASA Astrophysics Data System (ADS)
Wang, Jia-Hua; Guo, Qiang; Yang, Kai; Zhang, Yi-Lu; Han, Jingti; Liu, Jian-Guo
2016-11-01
The popularity has been widely used to describe the object property of online user-object bipartite networks regardless of the user characteristics. In this paper, we introduce a measurement namely user diversity to measure diversity of users who select or rate one type of objects by using the information entropy. We empirically calculate the user diversity of objects with specific degree for both MovieLens and Diggs data sets. The results indicate that more types of users select normal-degree objects than those who select large-degree and small-degree objects. Furthermore, small-degree objects are usually selected by large-degree users while large-degree objects are usually selected by small-degree users. Moreover, we define 15% objects of smallest degrees as unpopular objects and 10% ones of largest degrees as popular objects. The timestamp is introduced to help further analyze the evolution of user diversity of popular objects and unpopular objects. The dynamic analysis shows that as objects become popular gradually, they are more likely accepted by small-degree users but lose attention among the large-degree users.
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Warped document image correction method based on heterogeneous registration strategies
NASA Astrophysics Data System (ADS)
Tong, Lijing; Zhan, Guoliang; Peng, Quanyao; Li, Yang; Li, Yifan
2013-03-01
With the popularity of digital camera and the application requirement of digitalized document images, using digital cameras to digitalize document images has become an irresistible trend. However, the warping of the document surface impacts on the quality of the Optical Character Recognition (OCR) system seriously. To improve the warped document image's vision quality and the OCR rate, this paper proposed a warped document image correction method based on heterogeneous registration strategies. This method mosaics two warped images of the same document from different viewpoints. Firstly, two feature points are selected from one image. Then the two feature points are registered in the other image base on heterogeneous registration strategies. At last, image mosaics are done for the two images, and the best mosaiced image is selected by OCR recognition results. As a result, for the best mosaiced image, the distortions are mostly removed and the OCR results are improved markedly. Experimental results show that the proposed method can resolve the issue of warped document image correction more effectively.
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Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh
2018-02-27
Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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ERIC Educational Resources Information Center
Kettle, Margaret; Yuan, Yifeng; Luke, Allan; Ewing, Robyn; Shen, Huizhong
2012-01-01
As increasing numbers of Chinese language learners choose to learn English online, there is a need to investigate popular websites and their language learning designs. This paper reports on the first stage of a study that analyzed the pedagogical, linguistic, and content features of 25 Chinese English Language Learning (ELL) websites ranked…
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The Effect of Selection Bias in Studies of Fads and Fashions
Denrell, Jerker; Kovács, Balázs
2015-01-01
Most studies of fashion and fads focus on objects and practices that once were popular. We argue that limiting the sample to such trajectories generates a selection bias that obscures the underlying process and generates biased estimates. Through simulations and the analysis of a data set that has previously not been used to analyze the rise and fall of cultural practices, the New York Times text archive, we show that studying a whole range of cultural objects, both popular and less popular, is essential for understanding the drivers of popularity. In particular, we show that estimates of statistical models of the drivers of popularity will be biased if researchers use only trajectories of those practices that once were popular. PMID:25886158
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StochKit2: software for discrete stochastic simulation of biochemical systems with events.
Sanft, Kevin R; Wu, Sheng; Roh, Min; Fu, Jin; Lim, Rone Kwei; Petzold, Linda R
2011-09-01
StochKit2 is the first major upgrade of the popular StochKit stochastic simulation software package. StochKit2 provides highly efficient implementations of several variants of Gillespie's stochastic simulation algorithm (SSA), and tau-leaping with automatic step size selection. StochKit2 features include automatic selection of the optimal SSA method based on model properties, event handling, and automatic parallelism on multicore architectures. The underlying structure of the code has been completely updated to provide a flexible framework for extending its functionality. StochKit2 runs on Linux/Unix, Mac OS X and Windows. It is freely available under GPL version 3 and can be downloaded from http://sourceforge.net/projects/stochkit/. petzold@engineering.ucsb.edu.
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Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque
2017-01-01
Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).
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Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed
2017-01-01
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.
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Food marketing on popular children's web sites: a content analysis.
Alvy, Lisa M; Calvert, Sandra L
2008-04-01
In 2006 the Institute of Medicine (IOM) concluded that food marketing was a contributor to childhood obesity in the United States. One recommendation of the IOM committee was for research on newer marketing venues, such as Internet Web sites. The purpose of this cross-sectional study was to answer the IOM's call by examining food marketing on popular children's Web sites. Ten Web sites were selected based on market research conducted by KidSay, which identified favorite sites of children aged 8 to 11 years during February 2005. Using a standardized coding form, these sites were examined page by page for the existence, type, and features of food marketing. Web sites were compared using chi2 analyses. Although food marketing was not pervasive on the majority of the sites, seven of the 10 Web sites contained food marketing. The products marketed were primarily candy, cereal, quick serve restaurants, and snacks. Candystand.com, a food product site, contained a significantly greater amount of food marketing than the other popular children's Web sites. Because the foods marketed to children are not consistent with a healthful diet, nutrition professionals should consider joining advocacy groups to pressure industry to reduce online food marketing directed at youth.
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Designing and implementing an authentic science research program
NASA Astrophysics Data System (ADS)
Rosvally, Harry Edward, Jr.
Science research programs have become a popular elective course in high schools around the country. As the popularity of these programs grows, school districts need a guide by which to implement science research in their own schools. This study sought to provide this information by answering the following questions: (1) What are the most important features in existing research program models? (2) How do schools that have an existing research program define "success"? (3) How do different factors (i.e., budget, professional development, scheduling, recruitment effort, curriculum, and mentors) affect the scope and implementation of a research program? (4) Which features and factors support inclusiveness as a goal for a research program? (5) What kinds of indicators are appropriate for assessing the progress toward an inclusive science research program? After reviewing the literature, six sites with existing research programs were selected for participation in the study. Interviews with teachers and students were conducted during site visits. Interviews with mentors were conducted by telephone. Although the six models in this study were different from one another, there were common characteristics. Students conducted their own review of the literature. Upon completion of the actual research, students published or otherwise communicated their findings to the larger scientific community through regional and national competitions and non-competitive science symposia. This study was also able to identify significant elements that contribute to successful programs. These included: teacher selection; budget requirements; mentor qualities; recruitment and retention practices; and overall structure. As a result of the findings during the research, this study makes recommendations for the successful implementation of a research program.
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Dog Movie Stars and Dog Breed Popularity: A Case Study in Media Influence on Choice
Ghirlanda, Stefano; Acerbi, Alberto; Herzog, Harold
2014-01-01
Fashions and fads are important phenomena that influence many individual choices. They are ubiquitous in human societies, and have recently been used as a source of data to test models of cultural dynamics. Although a few statistical regularities have been observed in fashion cycles, their empirical characterization is still incomplete. Here we consider the impact of mass media on popular culture, showing that the release of movies featuring dogs is often associated with an increase in the popularity of featured breeds, for up to 10 years after movie release. We also find that a movie's impact on breed popularity correlates with the estimated number of viewers during the movie's opening weekend—a proxy of the movie's reach among the general public. Movies' influence on breed popularity was strongest in the early 20th century, and has declined since. We reach these conclusions through a new, widely applicable method to measure the cultural impact of events, capable of disentangling the event's effect from ongoing cultural trends. PMID:25208271
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Dog movie stars and dog breed popularity: a case study in media influence on choice.
Ghirlanda, Stefano; Acerbi, Alberto; Herzog, Harold
2014-01-01
Fashions and fads are important phenomena that influence many individual choices. They are ubiquitous in human societies, and have recently been used as a source of data to test models of cultural dynamics. Although a few statistical regularities have been observed in fashion cycles, their empirical characterization is still incomplete. Here we consider the impact of mass media on popular culture, showing that the release of movies featuring dogs is often associated with an increase in the popularity of featured breeds, for up to 10 years after movie release. We also find that a movie's impact on breed popularity correlates with the estimated number of viewers during the movie's opening weekend--a proxy of the movie's reach among the general public. Movies' influence on breed popularity was strongest in the early 20th century, and has declined since. We reach these conclusions through a new, widely applicable method to measure the cultural impact of events, capable of disentangling the event's effect from ongoing cultural trends.
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ERIC Educational Resources Information Center
McIntosh, Phyllis
2014-01-01
This feature article highlights dog breeds that are popular in the United States and explores the health benefits and services that dogs provide to people. The article also discusses dog shows and dogs in popular culture.
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ERIC Educational Resources Information Center
Taber, Nancy; Woloshyn, Vera; Munn, Caitlin; Lane, Laura
2014-01-01
In this article, we discuss how our analysis of several popular culture artifacts featuring "super" women characters (superheroes and supernatural) provided the foundation for a media discussion group for female college students with learning exceptionalities. We explore the use of popular culture in discussion groups as well as discuss…
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Adult Services. Twenty-Eight Quick Recipes for Stretching Your Popular Adult Materials Budget.
ERIC Educational Resources Information Center
Baker, Sharon L., Ed.
1995-01-01
Describes methods for stretching a popular adult library materials budget. Highlights include prepurchase evaluation and selection; acquisitions; fund raising to support selection funds; distribution; weeding; promotion; and lobbying. (Author/AEF)
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Yang, Qinghua; Sangalang, Angeline; Rooney, Molly; Maloney, Erin; Emery, Sherry; Cappella, Joseph N
2018-01-01
The purpose of the study is to investigate how vaping marijuana, a novel but emerging risky health behavior, is portrayed on YouTube, and how the content and features of these YouTube videos influence their popularity and retransmission. A content analysis of vaping marijuana YouTube videos published between July 2014 to June 2015 (n = 214) was conducted. Video genre, valence, promotional and warning arguments, emotional appeals, message sensation value, presence of misinformation and misleading information, and user-generated statistics, including number of views, comments, shares, likes and dislikes, were coded. The results showed that these videos were predominantly pro-marijuana-vaping, with the most frequent videos being user-sharing. The genre and message features influenced the popularity, evaluations, and retransmission of vaping marijuana YouTube videos. Theoretical and practical implications are discussed.
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Gooding, Lori F; Mori-Inoue, Satoko
2011-01-01
The purpose of this study was to examine the effect of video exposure on music therapy students' perceptions of clinical applications of popular music in the field of music therapy. Fifty-one participants were randomly divided into two groups and exposed to a popular song in either audio-only or music video format. Participants were asked to indicate clinical applications; specifically, participants chose: (a) possible population(s), (b) most appropriate population(s), (c) possible age range(s), (d) most appropriate age ranges, (e) possible goal area(s) and (f) most appropriate goal area. Data for each of these categories were compiled and analyzed, with no significant differences found in the choices made by the audio-only and video groups. Three items, (a) selection of the bereavement population, (b) selection of bereavement as the most appropriate population and (c) selection of the age ranges of pre teen/mature adult, were additionally selected for further analysis due to their relationship to the video content. Analysis results revealed a significant difference between the video and audio-only groups for the selection of these specific items, with the video group's selections more closely aligned to the video content. Results of this pilot study suggest that music video exposure to popular music can impact how students choose to implement popular songs in the field of music therapy.
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Autism Spectrum Disorder in Popular Media: Storied Reflections of Societal Views
ERIC Educational Resources Information Center
Belcher, Christina; Maich, Kimberly
2014-01-01
This article explores how storied representations of characters with Autism Spectrum Disorder (ASD) are typified in a world that is increasingly influenced by popular media. Twenty commercially published children's picture books, popular novels, mainstream television programs, and popular movies from 2006-2012 were selected using purposive,…
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Makowski, Dale
2016-01-01
This paper sets out the basics for approaching the selection and implementation of a cloud-based communication system to support a business continuity programme, including: • consideration for how a cloud-based communication system can enhance a business continuity programme; • descriptions of some of the more popular features of a cloud-based communication system; • options to evaluate when selecting a cloud-based communication system; • considerations for how to design a system to be most effective for an organisation; • best practices for how to conduct the initial load of data to a cloud-based communication system; • best practices for how to conduct an initial validation of the data loaded to a cloud-based communication system; • considerations for how to keep contact information in the cloud-based communication system current and accurate; • best practices for conducting ongoing system testing; • considerations for how to conduct user training; • review of other potential uses of a cloud-based communication system; and • review of other tools and features many cloud-based communication systems may offer.
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Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul
2013-01-01
Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.
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van Gemert, Jan C; Veenman, Cor J; Smeulders, Arnold W M; Geusebroek, Jan-Mark
2010-07-01
This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features. Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been successfully applied for some years. In this paper, we investigate four types of soft assignment of visual words to image features. We demonstrate that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model. The traditional codebook model is compared against our method for five well-known data sets: 15 natural scenes, Caltech-101, Caltech-256, and Pascal VOC 2007/2008. We demonstrate that large codebook vocabulary sizes completely deteriorate the performance of the traditional model, whereas the proposed model performs consistently. Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories.
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Shin, Huiyoung
2017-11-01
Interactions with friends are a salient part of adolescents' experience at school. Adolescents tend to form friendships with similar peers and, in turn, their friends influence adolescents' behaviors and beliefs. The current study investigated early adolescents' selection of friends and friends' influence with regard to physical aggression, prosocial behavior, and popularity and social preference (i.e., likeability) among fifth and sixth graders (N = 736, 52% girls at wave1, N = 677, 52% girls at wave 2) in elementary schools in South Korea. The moderating role of gender on early adolescents' friend selection and influence was also examined. With longitudinal social network analysis (RSiena), we found that youth tended to select friends with similar levels of physical aggression and popularity, and their friends influenced their own physical aggression and popularity over time. The higher youth were in social preference, the less likely they chose physically aggressive peers as friends. Boys were more likely to select highly popular peers as friends compared to girls, and influence effects for physical aggression and popularity were stronger for boys compared to girls. The results underscore the importance of gender in friendship dynamics among Asian early adolescents.
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Effects of individual popularity on information spreading in complex networks
NASA Astrophysics Data System (ADS)
Gao, Lei; Li, Ruiqi; Shu, Panpan; Wang, Wei; Gao, Hui; Cai, Shimin
2018-01-01
In real world, human activities often exhibit preferential selection mechanism based on the popularity of individuals. However, this mechanism is seldom taken into account by previous studies about spreading dynamics on networks. Thus in this work, an information spreading model is proposed by considering the preferential selection based on individuals' current popularity, which is defined as the number of individuals' cumulative contacts with informed neighbors. A mean-field theory is developed to analyze the spreading model. Through systematically studying the information spreading dynamics on uncorrelated configuration networks as well as real-world networks, we find that the popularity preference has great impacts on the information spreading. On the one hand, the information spreading is facilitated, i.e., a larger final prevalence of information and a smaller outbreak threshold, if nodes with low popularity are preferentially selected. In this situation, the effective contacts between informed nodes and susceptible nodes are increased, and nodes almost have uniform probabilities of obtaining the information. On the other hand, if nodes with high popularity are preferentially selected, the final prevalence of information is reduced, the outbreak threshold is increased, and even the information cannot outbreak. In addition, the heterogeneity of the degree distribution and the structure of real-world networks do not qualitatively affect the results. Our research can provide some theoretical supports for the promotion of spreading such as information, health related behaviors, and new products, etc.
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2012-01-01
Background Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have analyzed single variants one at a time, it has been proposed that the genetic basis of these disorders could be determined through detailed analysis of the genetic variants themselves and in conjunction with one another. The construction of models that account for these subsets of variants requires methodologies that generate predictions based on the total risk of a particular group of polymorphisms. However, due to the excessive number of variants, constructing these types of models has so far been computationally infeasible. Results We have implemented an algorithm, known as greedy RLS, that we use to perform the first known wrapper-based feature selection on the genome-wide level. The running time of greedy RLS grows linearly in the number of training examples, the number of features in the original data set, and the number of selected features. This speed is achieved through computational short-cuts based on matrix calculus. Since the memory consumption in present-day computers can form an even tighter bottleneck than running time, we also developed a space efficient variation of greedy RLS which trades running time for memory. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm. As a proof of concept, we apply greedy RLS to the Hypertension – UK National Blood Service WTCCC dataset and select the most predictive variants using 3-fold external cross-validation in less than 26 minutes on a high-end desktop. On this dataset, we also show that greedy RLS has a better classification performance on independent test data than a classifier trained using features selected by a statistical p-value-based filter, which is currently the most popular approach for constructing predictive models in GWAS. Conclusions Greedy RLS is the first known implementation of a machine learning based method with the capability to conduct a wrapper-based feature selection on an entire GWAS containing several thousand examples and over 400,000 variants. In our experiments, greedy RLS selected a highly predictive subset of genetic variants in a fraction of the time spent by wrapper-based selection methods used together with SVM classifiers. The proposed algorithms are freely available as part of the RLScore software library at http://users.utu.fi/aatapa/RLScore/. PMID:22551170
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ERIC Educational Resources Information Center
Robe, Stanley L., Ed.
A variety of oral folk material from Mexican sources is presented in this anthology. The 114 selections are derived from the various genres available and from traditional as well as newer formations. The selections include folktales, jests and anecdotes, legends and beliefs, beliefs about popular medicine, prayers, verses, children's games and…
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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi
2015-12-01
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.
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NASA Astrophysics Data System (ADS)
Gómez, Breogán; Miguez-Macho, Gonzalo
2017-04-01
Nudging techniques are commonly used to constrain the evolution of numerical models to a reference dataset that is typically of a lower resolution. The nudged model retains some of the features of the reference field while incorporating its own dynamics to the solution. These characteristics have made nudging very popular in dynamic downscaling applications that cover from shot range, single case studies, to multi-decadal regional climate simulations. Recently, a variation of this approach called Spectral Nudging, has gained popularity for its ability to maintain the higher temporal and spatial variability of the model results, while forcing the large scales in the solution with a coarser resolution field. In this work, we focus on a not much explored aspect of this technique: the impact of selecting different cut-off wave numbers and spin-up times. We perform four-day long simulations with the WRF model, daily for three different one-month periods that include a free run and several Spectral Nudging experiments with cut-off wave numbers ranging from the smallest to the largest possible (full Grid Nudging). Results show that Spectral Nudging is very effective at imposing the selected scales onto the solution, while allowing the limited area model to incorporate finer scale features. The model error diminishes rapidly as the nudging expands over broader parts of the spectrum, but this decreasing trend ceases sharply at cut-off wave numbers equivalent to a length scale of about 1000 km, and the error magnitude changes minimally thereafter. This scale corresponds to the Rossby Radius of deformation, separating synoptic from convective scales in the flow. When nudging above this value is applied, a shifting of the synoptic patterns can occur in the solution, yielding large model errors. However, when selecting smaller scales, the fine scale contribution of the model is damped, thus making 1000 km the appropriate scale threshold to nudge in order to balance both effects. Finally, we note that longer spin-up times are needed for model errors to stabilize when using Spectral Nudging than with Grid Nudging. Our results suggest that this time is between 36 and 48 hours.
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2017-01-01
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002
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Pro-smoking apps: where, how and who are most at risk.
BinDhim, Nasser F; Freeman, Becky; Trevena, Lyndal
2015-03-01
Pro-smoking applications (app) provide information about brands of tobacco products, where to buy them, and encourage their use. It is unclear in which countries these apps are being downloaded, or whether app stores play a role in promoting or regulating these apps, particularly those that appear to target children. The lifetime popularity of 107 pro-smoking apps was investigated, using a third-party app metrics service that aggregates data from app stores about app download popularity by country. Apps were deemed popular if at any time in their lifespan they achieved a top 25 ranking overall across all apps, or a top 25 ranking in any particular category of apps, such as 'educational games'. Fifty-eight pro-smoking apps reached 'popularity' status in Apple and Android stores in one or more of 49 countries, particularly Italy, Egypt, Germany, Belgium and the USA. The daily downloads in each country ranged from approximately 2000 to 80 000. The Apple store featured five of the pro-smoking apps in various categories, and two apps were featured by the Android market. Two pro-smoking apps in the Apple store were extremely popular in the 'Educational Games' and 'Kids' Games' categories. Pro-smoking apps were popular in many countries. Most apps were assigned to entertainment and games categories, with some apps specifically targeting children through placement in categories directed at children. App stores that feature pro-smoking apps may be in violation of tobacco control laws. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
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NASA Astrophysics Data System (ADS)
Taşkin Kaya, Gülşen
2013-10-01
Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The HDMR method is developed to improve the efficiency of the deducing high dimensional behaviors. The method is formed by a particular organization of low dimensional component functions, in which each function is the contribution of one or more input variables to the output variables.
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A comprehensive overview of the applications of artificial life.
Kim, Kyung-Joong; Cho, Sung-Bae
2006-01-01
We review the applications of artificial life (ALife), the creation of synthetic life on computers to study, simulate, and understand living systems. The definition and features of ALife are shown by application studies. ALife application fields treated include robot control, robot manufacturing, practical robots, computer graphics, natural phenomenon modeling, entertainment, games, music, economics, Internet, information processing, industrial design, simulation software, electronics, security, data mining, and telecommunications. In order to show the status of ALife application research, this review primarily features a survey of about 180 ALife application articles rather than a selected representation of a few articles. Evolutionary computation is the most popular method for designing such applications, but recently swarm intelligence, artificial immune network, and agent-based modeling have also produced results. Applications were initially restricted to the robotics and computer graphics, but presently, many different applications in engineering areas are of interest.
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Learning discriminative features from RGB-D images for gender and ethnicity identification
NASA Astrophysics Data System (ADS)
Azzakhnini, Safaa; Ballihi, Lahoucine; Aboutajdine, Driss
2016-11-01
The development of sophisticated sensor technologies gave rise to an interesting variety of data. With the appearance of affordable devices, such as the Microsoft Kinect, depth-maps and three-dimensional data became easily accessible. This attracted many computer vision researchers seeking to exploit this information in classification and recognition tasks. In this work, the problem of face classification in the context of RGB images and depth information (RGB-D images) is addressed. The purpose of this paper is to study and compare some popular techniques for gender recognition and ethnicity classification to understand how much depth data can improve the quality of recognition. Furthermore, we investigate which combination of face descriptors, feature selection methods, and learning techniques is best suited to better exploit RGB-D images. The experimental results show that depth data improve the recognition accuracy for gender and ethnicity classification applications in many use cases.
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Is it time for a paradigm shift in understanding embryo selection?
Gleicher, Norbert; Kushnir, Vitaly A; Barad, David H
2015-01-11
Embryo selection has been an integral feature of in vitro fertilization (IVF) almost since its inception. Since the advent of extended blastocyst stage embryo culture, and especially with increasing popularity of elective single embryo transfer (eSET), the concept of embryo selection has increasingly become a mainstay of routine IVF. We here, however, argue that embryo selection via blastocyst stage embryo transfer (BSET), as currently practiced, at best improves IVF outcomes only for a small minority of patients undergoing IVF cycles. For a large majority BSET is either ineffective or, indeed, may actually be harmful by decreasing IVF pregnancy chances. Overall, only a small minority of patients, thus, benefit from prolonged embryo culture, while BSET, as a tool to enhance IVF outcomes, is increasingly utilized as routine care in IVF for all patients. Since newer methods of embryo selection, like preimplantation genetic screening (PGS) and closed system embryo incubation with time-lapse photography are practically dependent on BSET, these concepts of embryo selection, currently increasingly adopted in mainstream IVF, require reconsideration. They, automatically, transfer the downsides of BSET, including decreases in IVF pregnancy chances in some patients, to these new procedures, and in addition raise serious questions about cost-effectiveness.
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Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.
Liu, Li; Shao, Ling; Li, Xuelong; Lu, Ke
2016-01-01
Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.
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Mining online e-liquid reviews for opinion polarities about e-liquid features.
Chen, Zhipeng; Zeng, Daniel D
2017-07-07
In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users' preference on this feature. The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.
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Pseudo CT estimation from MRI using patch-based random forest
NASA Astrophysics Data System (ADS)
Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian
2017-02-01
Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.
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Automatic detection of atrial fibrillation in cardiac vibration signals.
Brueser, C; Diesel, J; Zink, M D H; Winter, S; Schauerte, P; Leonhardt, S
2013-01-01
We present a study on the feasibility of the automatic detection of atrial fibrillation (AF) from cardiac vibration signals (ballistocardiograms/BCGs) recorded by unobtrusive bedmounted sensors. The proposed system is intended as a screening and monitoring tool in home-healthcare applications and not as a replacement for ECG-based methods used in clinical environments. Based on BCG data recorded in a study with 10 AF patients, we evaluate and rank seven popular machine learning algorithms (naive Bayes, linear and quadratic discriminant analysis, support vector machines, random forests as well as bagged and boosted trees) for their performance in separating 30 s long BCG epochs into one of three classes: sinus rhythm, atrial fibrillation, and artifact. For each algorithm, feature subsets of a set of statistical time-frequency-domain and time-domain features were selected based on the mutual information between features and class labels as well as first- and second-order interactions among features. The classifiers were evaluated on a set of 856 epochs by means of 10-fold cross-validation. The best algorithm (random forests) achieved a Matthews correlation coefficient, mean sensitivity, and mean specificity of 0.921, 0.938, and 0.982, respectively.
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NASA Astrophysics Data System (ADS)
Perconti, Philip; Loew, Murray
2006-03-01
Automatic classification of the density of breast parenchyma is shown using a measure that is correlated to the human observer performance, and compared against the BI-RADS density rating. Increasingly popular in the United States, the Breast Imaging Reporting and Data System (BI-RADS) is used to draw attention to the increased screening difficulty associated with greater breast density; however, the BI-RADS rating scheme is subjective and is not intended as an objective measure of breast density. So, while popular, BI-RADS does not define density classes using a standardized measure, which leads to increased variability among observers. The adaptive thresholding technique is a more quantitative approach for assessing the percentage breast density, but considerable reader interaction is required. We calculate an objective density rating that is derived using a measure of local feature salience. Previously, this measure was shown to correlate well with radiologists' localization and discrimination of true positive and true negative regions-of-interest. Using conspicuous spatial frequency features, an objective density rating is obtained and correlated with adaptive thresholding, and the subjectively ascertained BI-RADS density ratings. Using 100 cases, obtained from the University of South Florida's DDSM database, we show that an automated breast density measure can be derived that is correlated with the interactive thresholding method for continuous percentage breast density, but not with the BI-RADS density rating categories for the selected cases. Comparison between interactive thresholding and the new salience percentage density resulted in a Pearson correlation of 76.7%. Using a four-category scale equivalent to the BI-RADS density categories, a Spearman correlation coefficient of 79.8% was found.
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Drawing a Line in Water: Constructing the School Censorship Frame in Popular Music Education
ERIC Educational Resources Information Center
Kallio, Alexis Anja
2015-01-01
The apparent ideological tensions between popular musics and formal school contexts raise significant issues regarding teachers' popular repertoire selection processes. Such decision-making may be seen to take place within a school censorship frame, through which certain musics and their accompanying values are promoted, whilst others are…
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NASA Astrophysics Data System (ADS)
Kim, Junghoe; Lee, Jong-Hwan
2014-03-01
A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.
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Group selection and the development of the biological species concept
Mallet, James
2010-01-01
The development of what became known as the biological species concept began with a paper by Theodosius Dobzhansky in 1935, and was amplified by a mutualistic interaction between Dobzhansky, Alfred Emerson and Ernst Mayr after the second world war. By the 1950s and early 1960s, these authors had developed an influential concept of species as coadapted genetic complexes at equilibrium. At this time many features of species were seen as group advantages maintained by selection to avoid breakdown of beneficial coadaptation and the ‘gene pool’. Speciation thus seemed difficult. It seemed to require, more so than today, an external deus ex machina, such as allopatry or the founder effect, rather than ordinary within-species processes of natural selection, sexual selection, drift and gene flow. In the mid-1960s, the distinctions between group and individual selection were clarified. Dobzhansky and Mayr both understood the implications, but their views on species changed little. These group selectionist ideas now seem peculiar, and are becoming distinctly less popular today. Few vestiges of group selectionism and species-level adaptationism remain in recent reviews of speciation. One wonders how many of our own cherished views on evolution will seem as odd to future biologists. PMID:20439286
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Status Quo and Prospective of WeChat in Improving Chinese English Learners' Pronunciation
ERIC Educational Resources Information Center
Wang, Kanghui
2017-01-01
With the ubiquitous usage of wireless, portable, and handheld devices gaining popularity in 21st century, the revolutionary mobile technology introduces digital new media to educational settings, which has changed the way of traditional teaching and learning. WeChat is one of the most popular social networking applications in China featured by its…
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Sport in Germany. Basis-Info: Social Policy. In-Press.
ERIC Educational Resources Information Center
Beitz, Steffen
This report describes sports in Germany, explaining that sport is part of Germany's culture. Popular sports are enjoyed by both the public and private sector. Germany has a well-developed club and association sector. One in three Germans belongs to a sports organization. A major feature of sport in Germany is its autonomy. Popular sports begin in…
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ERIC Educational Resources Information Center
Garandeau, Claire F.; Ahn, Hai-Jeong; Rodkin, Philip C.
2011-01-01
This study tested the effects of 5 classroom contextual features on the social status (perceived popularity and social preference) that peers accord to aggressive students in late elementary school, including classroom peer status hierarchy (whether within-classroom differences in popularity are large or small), classroom academic level, and grade…
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Effective Alumni Marketing Research: Theory Put to Use or, Practicing What We Preach.
ERIC Educational Resources Information Center
Greene, Robert C., Jr.; Weldon, Peter K.
1996-01-01
A telephone survey of 328 graduates of a major Canadian university strongly supports continuation of the alumni newspaper, and found that: features are popular with specific subgroups; willingness to pay for the publication shows support for the paper but not a subscription fee; paid advertisements are acceptable; the university is popular in…
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Geographies of American Popular Music: Introducing Students to Basic Geographic Concepts
ERIC Educational Resources Information Center
McClain, Stephen S.
2010-01-01
Popular music can be used to study many subjects and issues related to the social sciences. "Geographies of American Popular Music" was a workshop that not only examined the history and development of select genres of American music, it also introduced students to basic geographic concepts such as the culture hearth and spatial diffusion. Through…
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ERIC Educational Resources Information Center
O'Reilly, Frances L.; Matt, John J.
2012-01-01
Both gifted educators and parents need to be vigilant as to how gifted education is characterized in the popular media. Gifted educators spend countless resources to meet the needs of gifted students using very limited fiscal resources, and it is imperative that those efforts are not undermined in the popular media by unsubstantiated statements.…
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Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter J E; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong
2017-08-03
Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.
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Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F
2015-01-01
Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.
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The Role of Semantics in Next-Generation Online Virtual World-Based Retail Store
NASA Astrophysics Data System (ADS)
Sharma, Geetika; Anantaram, C.; Ghosh, Hiranmay
Online virtual environments are increasingly becoming popular for entrepreneurship. While interactions are primarily between avatars, some interactions could occur through intelligent chatbots. Such interactions require connecting to backend business applications to obtain information, carry out real-world transactions etc. In this paper, we focus on integrating business application systems with virtual worlds. We discuss the probable features of a next-generation online virtual world-based retail store and the technologies involved in realizing the features of such a store. In particular, we examine the role of semantics in integrating popular virtual worlds with business applications to provide natural language based interactions.
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The Distance from Isolation: Why Communities Are the Logical Conclusion in e-Learning
ERIC Educational Resources Information Center
Weller, Martin
2007-01-01
This paper argues that the internet is built around key technology design features of openness, robustness and decentralisation. These design features have transformed into social features, which are embodied within the cultural values of the internet. By examining applications that have become popular on the net, the importance of these values is…
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ERIC Educational Resources Information Center
Seifried, Scott
2006-01-01
The purpose of this study was to better understand the impact of rock and popular teenage music on public school music education programs. "Frankstown Secondary School" is a large suburban public school offering a guitar program that includes a strong popular music component. Subjects in this study were selected from students enrolled in…
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Ronald E. McRoberts; Erkki O. Tomppo; Andrew O. Finley; Heikkinen Juha
2007-01-01
The k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest...
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ERIC Educational Resources Information Center
Hagerstown Junior Coll., MD.
This colloquium book review (occasioned by Andrew Scott's "Pirates of the Cell") contains seven selected readings from popular periodicals and research journals. It is designed to eliminate some of the mental barriers that many have to topics like molecular biology and virology. Included are: (1) "What Is A Virus?" (William D. Elliot); (2) "The…
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Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire
2017-12-01
Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.
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What are the most promising strategies for the therapeutic immunomodulation of allergic diseases?
Tokura, Y; Röcken, M; Clark, R A; Haliasos, E; Takigawa, M; Sinha, A A
2001-04-01
Specific immunotherapy and other immunomodulatory strategies have long been a stronghold in the management of allergic diseases. In particular, "immunodeviation-therapy" or "vaccination for allergies", i.e. the redirection of Th2-type immune responses towards a Th1-response pattern, has become an ever more popular concept. The present feature of CONTROVERSIES complements our previous discussion of atopy (Röcken et al., Exp Dermatol 7: 97--104, 1998), and is dedicated to a critical analysis of the general problems and limitations one faces with the main immunomodulatory strategies traditionally considered in this context. We also explore alternative approaches that appear promising in order to achieve both a more effective and/or a more specific immunotherapy of allergic diseases. Given that the mast cell remains a key protagonist in the pathogenesis of allergic diseases finally, this feature examines how innovative, more selectively mast cell-targeted strategies may be developed for the management of allergic diseases.
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Segmenting texts from outdoor images taken by mobile phones using color features
NASA Astrophysics Data System (ADS)
Liu, Zongyi; Zhou, Hanning
2011-01-01
Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.
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ERIC Educational Resources Information Center
Anderson, Ashlee; Aronson, Brittany; Ellison, Scott; Fairchild-Keyes, Sherrie
2015-01-01
With this article, we work to identify the limit-horizon of possible ideas, practices, and ways of talking about education reform and schooling via a critical discourse analysis of selected popular political and governmental texts. To do so, we explore the popular discourse of education reform in the United States through our analyses of three…
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ERIC Educational Resources Information Center
Smith, Donna Ridley, Comp.
Over 700 reference materials, songbooks, and recordings on pop, rock, country, folk, blues, and soul music from the 1950s to the present are listed. The bibliography was compiled because the study of popular music is becoming increasingly important to disciplines such as history, communications, and popular culture as well as music. Entries are…
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A data-driven approach to modeling physical fatigue in the workplace using wearable sensors.
Sedighi Maman, Zahra; Alamdar Yazdi, Mohammad Ali; Cavuoto, Lora A; Megahed, Fadel M
2017-11-01
Wearable sensors are currently being used to manage fatigue in professional athletics, transportation and mining industries. In manufacturing, physical fatigue is a challenging ergonomic/safety "issue" since it lowers productivity and increases the incidence of accidents. Therefore, physical fatigue must be managed. There are two main goals for this study. First, we examine the use of wearable sensors to detect physical fatigue occurrence in simulated manufacturing tasks. The second goal is to estimate the physical fatigue level over time. In order to achieve these goals, sensory data were recorded for eight healthy participants. Penalized logistic and multiple linear regression models were used for physical fatigue detection and level estimation, respectively. Important features from the five sensors locations were selected using Least Absolute Shrinkage and Selection Operator (LASSO), a popular variable selection methodology. The results show that the LASSO model performed well for both physical fatigue detection and modeling. The modeling approach is not participant and/or workload regime specific and thus can be adopted for other applications. Copyright © 2017 Elsevier Ltd. All rights reserved.
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An Adaptive Genetic Association Test Using Double Kernel Machines.
Zhan, Xiang; Epstein, Michael P; Ghosh, Debashis
2015-10-01
Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study.
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Curated Collection for Educators: Five Key Papers about the Flipped Classroom Methodology.
King, Andrew; Boysen-Osborn, Megan; Cooney, Robert; Mitzman, Jennifer; Misra, Asit; Williams, Jennifer; Dulani, Tina; Gottlieb, Michael
2017-10-25
The flipped classroom (FC) pedagogy is becoming increasingly popular in medical education due to its appeal to the millennial learner and potential benefits in knowledge acquisition. Despite its popularity and effectiveness, the FC educational method is not without challenges. In this article, we identify and summarize several key papers relevant to medical educators interested in exploring the FC teaching methodology. The authors identified an extensive list of papers relevant to FC pedagogy via online discussions within the Academic Life in Emergency Medicine (ALiEM) Faculty Incubator. This list was augmented by an open call on Twitter (utilizing the #meded, #FOAMed, and #flippedclassroom hashtags) yielding a list of 33 papers. We then conducted a three-round modified Delphi process within the authorship group, which included both junior and senior clinician educators, to identify the most impactful papers for educators interested in FC pedagogy. The three-round modified Delphi process ranked all of the selected papers and selected the five most highly-rated papers for inclusion. The authorship group reviewed and summarized these papers with specific consideration given to their value to junior faculty educators and faculty developers interested in the flipped classroom approach. The list of papers featured in this article serves as a key reading list for junior clinician educators and faculty developers interested in the flipped classroom technique. The associated commentaries contextualize the importance of these papers for medical educators aiming to optimize their understanding and implementation of the flipped classroom methodology in their teaching and through faculty development.
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MSAViewer: interactive JavaScript visualization of multiple sequence alignments.
Yachdav, Guy; Wilzbach, Sebastian; Rauscher, Benedikt; Sheridan, Robert; Sillitoe, Ian; Procter, James; Lewis, Suzanna E; Rost, Burkhard; Goldberg, Tatyana
2016-11-15
The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is 'web ready': written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/Supplementary information: Supplementary data are available at Bioinformatics online. msa@bio.sh. © The Author 2016. Published by Oxford University Press.
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MSAViewer: interactive JavaScript visualization of multiple sequence alignments
Yachdav, Guy; Wilzbach, Sebastian; Rauscher, Benedikt; Sheridan, Robert; Sillitoe, Ian; Procter, James; Lewis, Suzanna E.; Rost, Burkhard; Goldberg, Tatyana
2016-01-01
Summary: The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is ‘web ready’: written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. Availability and Implementation: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: msa@bio.sh PMID:27412096
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Friend suggestion in social network based on user log
NASA Astrophysics Data System (ADS)
Kaviya, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.
2017-11-01
Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.
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Visual Foraging With Fingers and Eye Gaze
Thornton, Ian M.; Smith, Irene J.; Chetverikov, Andrey; Kristjánsson, Árni
2016-01-01
A popular model of the function of selective visual attention involves search where a single target is to be found among distractors. For many scenarios, a more realistic model involves search for multiple targets of various types, since natural tasks typically do not involve a single target. Here we present results from a novel multiple-target foraging paradigm. We compare finger foraging where observers cancel a set of predesignated targets by tapping them, to gaze foraging where observers cancel items by fixating them for 100 ms. During finger foraging, for most observers, there was a large difference between foraging based on a single feature, where observers switch easily between target types, and foraging based on a conjunction of features where observers tended to stick to one target type. The pattern was notably different during gaze foraging where these condition differences were smaller. Two conclusions follow: (a) The fact that a sizeable number of observers (in particular during gaze foraging) had little trouble switching between different target types raises challenges for many prominent theoretical accounts of visual attention and working memory. (b) While caveats must be noted for the comparison of gaze and finger foraging, the results suggest that selection mechanisms for gaze and pointing have different operational constraints. PMID:27433323
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Mutual information estimation reveals global associations between stimuli and biological processes
Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun
2009-01-01
Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155
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Formation enthalpies for transition metal alloys using machine learning
NASA Astrophysics Data System (ADS)
Ubaru, Shashanka; Miedlar, Agnieszka; Saad, Yousef; Chelikowsky, James R.
2017-06-01
The enthalpy of formation is an important thermodynamic property. Developing fast and accurate methods for its prediction is of practical interest in a variety of applications. Material informatics techniques based on machine learning have recently been introduced in the literature as an inexpensive means of exploiting materials data, and can be used to examine a variety of thermodynamics properties. We investigate the use of such machine learning tools for predicting the formation enthalpies of binary intermetallic compounds that contain at least one transition metal. We consider certain easily available properties of the constituting elements complemented by some basic properties of the compounds, to predict the formation enthalpies. We show how choosing these properties (input features) based on a literature study (using prior physics knowledge) seems to outperform machine learning based feature selection methods such as sensitivity analysis and LASSO (least absolute shrinkage and selection operator) based methods. A nonlinear kernel based support vector regression method is employed to perform the predictions. The predictive ability of our model is illustrated via several experiments on a dataset containing 648 binary alloys. We train and validate the model using the formation enthalpies calculated using a model by Miedema, which is a popular semiempirical model used for the prediction of formation enthalpies of metal alloys.
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Local Variation of Hashtag Spike Trains and Popularity in Twitter
Sanlı, Ceyda; Lambiotte, Renaud
2015-01-01
We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650
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ERIC Educational Resources Information Center
Roberts, Sherron Killingsworth
Jessica and Elizabeth are two female characters, twins, featured throughout Francine Pascal's Sweet Valley series, the Bantam Publishers popular series for girls from elementary school through junior high, high school, university, and well into adulthood. This paper notes that these books are a part of the same formula that are used for romance…
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McKinney, Brett A.; White, Bill C.; Grill, Diane E.; Li, Peter W.; Kennedy, Richard B.; Poland, Gregory A.; Oberg, Ann L.
2013-01-01
Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php. PMID:24339943
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Executing SADI services in Galaxy.
Aranguren, Mikel Egaña; González, Alejandro Rodríguez; Wilkinson, Mark D
2014-01-01
In recent years Galaxy has become a popular workflow management system in bioinformatics, due to its ease of installation, use and extension. The availability of Semantic Web-oriented tools in Galaxy, however, is limited. This is also the case for Semantic Web Services such as those provided by the SADI project, i.e. services that consume and produce RDF. Here we present SADI-Galaxy, a tool generator that deploys selected SADI Services as typical Galaxy tools. SADI-Galaxy is a Galaxy tool generator: through SADI-Galaxy, any SADI-compliant service becomes a Galaxy tool that can participate in other out-standing features of Galaxy such as data storage, history, workflow creation, and publication. Galaxy can also be used to execute and combine SADI services as it does with other Galaxy tools. Finally, we have semi-automated the packing and unpacking of data into RDF such that other Galaxy tools can easily be combined with SADI services, plugging the rich SADI Semantic Web Service environment into the popular Galaxy ecosystem. SADI-Galaxy bridges the gap between Galaxy, an easy to use but "static" workflow system with a wide user-base, and SADI, a sophisticated, semantic, discovery-based framework for Web Services, thus benefiting both user communities.
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Ground-based cloud classification by learning stable local binary patterns
NASA Astrophysics Data System (ADS)
Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua
2018-07-01
Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.
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Peer influence on marijuana use in different types of friendships.
Tucker, Joan S; de la Haye, Kayla; Kennedy, David P; Green, Harold D; Pollard, Michael S
2014-01-01
Although several social network studies have demonstrated peer influence effects on adolescent substance use, findings for marijuana use have been equivocal. This study examines whether structural features of friendships moderate friends' influence on adolescent marijuana use over time. Using 1-year longitudinal data from the National Longitudinal Study of Adolescent Health, this article examines whether three structural features of friendships moderate friends' influence on adolescent marijuana use: whether the friendship is reciprocated, the popularity of the nominated friend, and the popularity/status difference between the nominated friend and the adolescent. The sample consists of students in grade 10/11 at wave I, who were in grade 11/12 at wave II, from two large schools with complete grade-based friendship network data (N = 1,612). In one school, friends' influence on marijuana use was more likely to occur within mutual, reciprocated friendships compared with nonreciprocated relationships. In the other school, friends' influence was stronger when the friends were relatively popular within the school setting or much more popular than the adolescents themselves. Friends' influence on youth marijuana use may play out in different ways, depending on the school context. In one school, influence occurred predominantly within reciprocated relationships that are likely characterized by closeness and trust, whereas in the other school adopting friends' drug use behaviors appeared to be a strategy to attain social status. Further research is needed to better understand the conditions under which structural features of friendships moderate friends' influence on adolescent marijuana use. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.
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GuiTope: an application for mapping random-sequence peptides to protein sequences.
Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert
2012-01-03
Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.
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Will genomic selection be a practical method for plant breeding?
Nakaya, Akihiro; Isobe, Sachiko N
2012-11-01
Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use. In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed. Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.
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Temperature-Centric Evaluation of Sensor Transients
NASA Astrophysics Data System (ADS)
Ayhan, Tuba; Muezzinoglu, Kerem; Vergara, Alexander; Yalcin, Mustak
2011-09-01
Controllable sensing conditions provide the means for diversifying sensor response and achieving better selectivity. Modulating the sensing layer temperature of metal-oxide sensors is a popular method for multiplexing the limited number of sensing elements that can be employed in a practical array. Time limitations in many applications, however, cannot tolerate an ad-hoc, one-size-fits-all modulation pattern. When the response pattern is itself non-stationary, as in the transient phase, a temperature program also becomes infeasible. We consider the problem of determining and tuning into a fixed optimum temperature in a sensor array. For this purpose, we present an empirical analysis of the temperature's role on the performance of a metal-oxide gas sensor array in the identification of odorants along the response transient. We show that the optimal temperature in this sense depends heavily on the selection of (i) the set of candidate analytes, (ii) the time-window of the analysis, (iii) the feature extracted from the sensor response, and (iv) the computational identification method used.
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NASA Astrophysics Data System (ADS)
Cao, Kunlin; Bhagalia, Roshni; Sood, Anup; Brogi, Edi; Mellinghoff, Ingo K.; Larson, Steven M.
2015-03-01
Positron emission tomography (PET) using uorodeoxyglucose (18F-FDG) is commonly used in the assessment of breast lesions by computing voxel-wise standardized uptake value (SUV) maps. Simple metrics derived from ensemble properties of SUVs within each identified breast lesion are routinely used for disease diagnosis. The maximum SUV within the lesion (SUVmax) is the most popular of these metrics. However these simple metrics are known to be error-prone and are susceptible to image noise. Finding reliable SUV map-based features that correlate to established molecular phenotypes of breast cancer (viz. estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) expression) will enable non-invasive disease management. This study investigated 36 SUV features based on first and second order statistics, local histograms and texture of segmented lesions to predict ER and PR expression in 51 breast cancer patients. True ER and PR expression was obtained via immunohistochemistry (IHC) of tissue samples from each lesion. A supervised learning, adaptive boosting-support vector machine (AdaBoost-SVM), framework was used to select a subset of features to classify breast lesions into distinct phenotypes. Performance of the trained multi-feature classifier was compared against the baseline single-feature SUVmax classifier using receiver operating characteristic (ROC) curves. Results show that texture features encoding local lesion homogeneity extracted from gray-level co-occurrence matrices are the strongest discriminator of lesion ER expression. In particular, classifiers including these features increased prediction accuracy from 0.75 (baseline) to 0.82 and the area under the ROC curve from 0.64 (baseline) to 0.75.
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Zhang, Puhong; Dong, Le; Chen, Huan; Chai, Yanling; Liu, Jianbo
2018-06-08
Mobile health services are thriving in the field of maternal and child health in China due to expansions in the field of electronic health and the introduction of the two-child policy. There are numerous maternal and child health apps in computer stores, but the exact number of apps, number of downloads, and features of these apps is not known. This study aimed to explore the use of maternal and child health apps in Android and iOS app stores and to describe the key functional features of the most popular apps, with the purpose of providing insight into further research and development of maternal and child health mobile health products. The researchers conducted a search in the 3 most popular Android app stores (Tencent MyApp, Baidu Mobile Assistant, and 360 Mobile Assistant) and the iTunes App Store in China. All apps regarding family planning (contraception and preparing for pregnancy), pregnancy and perinatal care, neonatal care and health, and development for children under 6 years were included in the initial analysis. Maternal and child health mobile apps with predominant features of product marketing, children's songs, animation, or games were excluded from the study. The 50 most frequently used apps in each of the Android stores as well as the iTunes store (a total of 78 deduplicated apps) were selected and downloaded for an in-depth analysis. A total of 5276 Android apps and 877 iOS apps developed for maternal and child health care were identified. Of the 78 most frequently used apps, 43 (55%) apps focused on one stage of MCH care, mainly targeting child care (25 apps) and before pregnancy care (11 apps), whereas 35 (45%) of the apps covered 2 or more stages, most of which (32 apps) included both pregnancy and child care services. The app features that were commonly adopted by the popular apps were health education, communication, health status self-monitoring, a diary, reminders, and counseling. Within the app feature of "health status self-monitoring," the researchers found 47 specific tools supporting activities such as pregnancy preparation, fetal heart monitoring, blood glucose and blood pressure monitoring, and doctor visits. A few apps were equipped with external devices (n=3) or sensors. No app with intelligent decision-support features to support disease management for conditions such as gestational diabetes and pregnancy-induced hypertension was found. A small number of apps (n=5) had a Web connection with hospital information systems to support appointment making, payments, hospital service guidance, or checking of laboratory results. There are thousands of maternal and child health apps in the Chinese market. Child care, pregnancy, and before pregnancy were the mostly covered maternal and child health stages, in that order. Various app features and tools were adopted by maternal and child health apps, but the use of internal or external sensors, intelligent decision support, and tethering with existing hospital information systems was rare and these features need more research and development. ©Puhong Zhang, Le Dong, Huan Chen, Yanling Chai, Jianbo Liu. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 08.06.2018.
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Packer, Rowena M. A.; Sordo, Lorena; Chen, Ruoning; Caney, Sarah M. A.
2018-01-01
Simple Summary Recently, there has been an increase in popularity of cats with different skull shapes, including shortened or lengthened muzzles. Skull shape, like other physical features, may affect human preferences; however, it is also more likely to have an impact on the welfare of the cat. We asked people to score their preference for 15 pictures of cats across two surveys. Extreme face shapes (those that were very short or very long) were least preferred. Short-faced cats were less popular amongst cat owners from animal related jobs as opposed to other people. Respondents that had a short or long-faced cat preferred cats with the same skull shape, but also had lower preferences for the opposite skull shape. Respondents from Asia, as compared to those from elsewhere, gave higher preference scores to both long and short-faced cats. Amongst the other features, green eyes, a ginger coat color and medium length coat were most preferred, although the ability to draw conclusions around these features is limited, given they are not necessarily independent of skull shape. This study provides the first evidence that preferences for cat breeds, and their associated skull morphologies, are driven by both culture and owner experience. This information may inform future research concerning the preferences of cat owners. Abstract Changes in the popularity of cat breeds are largely driven by human perceptions of, and selection for, phenotypic traits including skull morphology. The popularity of breeds with altered skull shapes appears to be increasing, and owner preferences are an important part of this dynamic. This study sought to establish how and why a range of phenotypic attributes, including skull shape, affect preferences shown by cat owners. Two questionnaires were distributed on-line to cat owners who were asked to rate preferences for pictures of cats on a 0–10 scale. Veterinarian consensus established the skull types of the cats pictured (i.e., level of brachycephaly (BC) or dolichocephaly (DC)). Preferences were then explored relative to cat skull type, coat and eye color, and coat length. Generalized estimating equations identified relationships between physical characteristics and respondent ratings. Further sub-analyses explored effects of respondents’ occupation, location and previous cat ownership on rating scores. Overall, cats with extreme changes in skull morphology (both BC and DC) were significantly less preferred than mesocephalic cats. Green eyes, ginger coat color and medium length coat were most preferred. Current owners of a BC or DC pure bred cat showed significantly greater preference for cats with similar features and significantly lower preference for the opposite extreme. Respondents from Asia were significantly more likely to prefer both BC and DC cats as compared to respondents from other locations. Finally, those in an animal care profession, as compared to other professions, provided a significantly lower preference rating for BC cats but not for DC cats. This work, despite the acknowledged limitations, provides preliminary evidence that preferences for cat breeds, and their associated skull morphologies, are driven by both cultural and experiential parameters. This information may allow for better targeting of educational materials concerning cat breeds. PMID:29461472
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Moller, Arlen C; Majewski, Sara; Standish, Melanie; Agarwal, Pooja; Podowski, Aleksandra; Carson, Rebecca; Eyesus, Biruk; Shah, Aakash; Schneider, Kristin L
2014-11-25
The popularity of active video games (AVGs) has skyrocketed over the last decade. However, research suggests that the most popular AVGs, which rely on synchronous integration between players' activity and game features, fail to promote physical activity outside of the game or for extended periods of engagement. This limitation has led researchers to consider AVGs that involve asynchronous integration of players' ongoing physical activity with game features. Rather than build an AVG de novo, we selected an established sedentary video game uniquely well suited for the incorporation of asynchronous activity: online fantasy sports. The primary aim of this study was to explore the feasibility of a new asynchronous AVG-active fantasy sports-designed to promote physical activity. We conducted two pilot studies of an active fantasy sports game designed to promote physical activity. Participants wore a low cost triaxial accelerometer and participated in an online fantasy baseball (Study 1, n=9, 13-weeks) or fantasy basketball (Study 2, n=10, 17-weeks) league. Privileges within the game were made contingent on meeting weekly physical activity goals (eg, averaging 10,000 steps/day). Across the two studies, the feasibility of integrating physical activity contingent features and privileges into online fantasy sports games was supported. Participants found the active fantasy sports game enjoyable, as or more enjoyable than traditional (sedentary) online fantasy sports (Study 1: t8=4.43, P<.01; Study 2: t9=2.09, P=.07). Participants in Study 1 increased their average steps/day, t8=2.63, P<.05, while participants in Study 2 maintained (ie, did not change) their activity, t9=1.57, P=.15). In postassessment interviews, social support within the game was cited as a key motivating factor for increasing physical activity. Preliminary evidence supports potential for the active fantasy sports system as a sustainable and scalable intervention for promoting adult physical activity.
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Majewski, Sara; Standish, Melanie; Agarwal, Pooja; Podowski, Aleksandra; Carson, Rebecca; Eyesus, Biruk; Shah, Aakash; Schneider, Kristin L
2014-01-01
Background The popularity of active video games (AVGs) has skyrocketed over the last decade. However, research suggests that the most popular AVGs, which rely on synchronous integration between players’ activity and game features, fail to promote physical activity outside of the game or for extended periods of engagement. This limitation has led researchers to consider AVGs that involve asynchronous integration of players’ ongoing physical activity with game features. Rather than build an AVG de novo, we selected an established sedentary video game uniquely well suited for the incorporation of asynchronous activity: online fantasy sports. Objective The primary aim of this study was to explore the feasibility of a new asynchronous AVG—active fantasy sports—designed to promote physical activity. Methods We conducted two pilot studies of an active fantasy sports game designed to promote physical activity. Participants wore a low cost triaxial accelerometer and participated in an online fantasy baseball (Study 1, n=9, 13-weeks) or fantasy basketball (Study 2, n=10, 17-weeks) league. Privileges within the game were made contingent on meeting weekly physical activity goals (eg, averaging 10,000 steps/day). Results Across the two studies, the feasibility of integrating physical activity contingent features and privileges into online fantasy sports games was supported. Participants found the active fantasy sports game enjoyable, as or more enjoyable than traditional (sedentary) online fantasy sports (Study 1: t 8=4.43, P<.01; Study 2: t 9=2.09, P=.07). Participants in Study 1 increased their average steps/day, t 8=2.63, P<.05, while participants in Study 2 maintained (ie, did not change) their activity, t 9=1.57, P=.15). In postassessment interviews, social support within the game was cited as a key motivating factor for increasing physical activity. Conclusions Preliminary evidence supports potential for the active fantasy sports system as a sustainable and scalable intervention for promoting adult physical activity. PMID:25654304
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Popular Glucose Tracking Apps and Use of mHealth by Latinos With Diabetes: Review
Williams, John Patrick
2015-01-01
Background Diabetes mellitus in the United States is an increasingly common chronic disease, costing hundreds of billions of dollars and contributing to hundreds of thousands of deaths each year. The prevalence of diabetes is over 50% higher in Latinos than in the general population, and this group also suffers from higher rates of complications and diabetes-related mortality than NHWs. mHealth is a promising new treatment modality for diabetes, though few smartphone apps have been designed specifically for Latinos. Objective The objectives of our study were: (1) to identify the most common features of the most popular diabetes apps and consider how such features may be improved to meet the needs of Latinos; (2) to determine the use of diabetes apps among a sample of online Hispanics in the US. Methods Our study consisted of two parts. First, 20 of the most popular diabetes apps were reviewed in order to ascertain the most prevalent features and functionalities. Second, an online survey was fielded through a popular health website for Latinos (HolaDoctor) inquiring about respondents’ use of diabetes apps. Results Approximately one-third of apps reviewed were available in Spanish. The most common features were blood glucose recording/annotation and activity logs. The majority of apps permitted exportation of data via e-mail but only a third enabled uploading to an online account. Twenty percent of apps reviewed could connect directly with a glucometer, and 30% had reminder functionalities prompting patients to take medications or check blood glucose levels. Over 1600 online surveys were completed during the second half of April 2014. More than 90% of respondents were from the United States, including Puerto Rico. The majority of respondents used a device running on an Android platform while only a quarter used an iPhone. Use of diabetes apps was approximately 3% among diabetic respondents and 3.6% among diabetic respondents who also had a smartphone. Among app users, blood glucose and medication diaries were the most frequently used functionalities while hemoglobin A1c and insulin diaries were the least used. A significant majority of app users did not share their progress on social media though many of these were willing to share it with their doctor. Conclusions Latino diabetics have unique needs and this should be reflected in diabetes apps designed for this population. Existing research as well as our survey results suggest that many Latinos do not possess the prerequisite diabetes knowledge or self-awareness to fully benefit from the most prevalent functionalities offered by the most popular diabetes apps. We recommend developers incorporate more basic features such as diabetes education, reminders to check blood glucose levels or take medications, Spanish language interfaces, and glucometer connectivities, which are relatively underrepresented in the most popular diabetes apps currently available in Spanish. PMID:26307533
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Popular Glucose Tracking Apps and Use of mHealth by Latinos With Diabetes: Review.
Williams, John Patrick; Schroeder, Dirk
2015-08-25
Diabetes mellitus in the United States is an increasingly common chronic disease, costing hundreds of billions of dollars and contributing to hundreds of thousands of deaths each year. The prevalence of diabetes is over 50% higher in Latinos than in the general population, and this group also suffers from higher rates of complications and diabetes-related mortality than NHWs. mHealth is a promising new treatment modality for diabetes, though few smartphone apps have been designed specifically for Latinos. The objectives of our study were: (1) to identify the most common features of the most popular diabetes apps and consider how such features may be improved to meet the needs of Latinos; (2) to determine the use of diabetes apps among a sample of online Hispanics in the US. Our study consisted of two parts. First, 20 of the most popular diabetes apps were reviewed in order to ascertain the most prevalent features and functionalities. Second, an online survey was fielded through a popular health website for Latinos (HolaDoctor) inquiring about respondents' use of diabetes apps. Approximately one-third of apps reviewed were available in Spanish. The most common features were blood glucose recording/annotation and activity logs. The majority of apps permitted exportation of data via e-mail but only a third enabled uploading to an online account. Twenty percent of apps reviewed could connect directly with a glucometer, and 30% had reminder functionalities prompting patients to take medications or check blood glucose levels. Over 1600 online surveys were completed during the second half of April 2014. More than 90% of respondents were from the United States, including Puerto Rico. The majority of respondents used a device running on an Android platform while only a quarter used an iPhone. Use of diabetes apps was approximately 3% among diabetic respondents and 3.6% among diabetic respondents who also had a smartphone. Among app users, blood glucose and medication diaries were the most frequently used functionalities while hemoglobin A1c and insulin diaries were the least used. A significant majority of app users did not share their progress on social media though many of these were willing to share it with their doctor. Latino diabetics have unique needs and this should be reflected in diabetes apps designed for this population. Existing research as well as our survey results suggest that many Latinos do not possess the prerequisite diabetes knowledge or self-awareness to fully benefit from the most prevalent functionalities offered by the most popular diabetes apps. We recommend developers incorporate more basic features such as diabetes education, reminders to check blood glucose levels or take medications, Spanish language interfaces, and glucometer connectivities, which are relatively underrepresented in the most popular diabetes apps currently available in Spanish.
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Ghost Hunting as a Means to Illustrate Scientific Methodology and Enhance Critical Thinking
ERIC Educational Resources Information Center
Rockwell, Steven C.
2012-01-01
The increasing popularity of television shows featuring paranormal investigations has led to a renewed enthusiasm in ghost hunting activities, and belief in the paranormal in general. These shows typically feature a group of investigators who, while claiming to utilize proper scientifically correct methodologies, violate many core scientific…
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Reading Men Differently: Alternative Portrayals of Masculinity in Contemporary Young Adult Fiction
ERIC Educational Resources Information Center
Bean, Thomas W.; Harper, Helen
2007-01-01
This study explored the nature and performance of masculinity as portrayed in three popular young adult novels: two novels featuring male protagonists and one featuring a female protagonist. Drawing on emerging theory and scholarship, researchers view masculinity, like femininity, as a gendered performance, socially scripted but amenable to…
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From tiger to panda: animal head detection.
Zhang, Weiwei; Sun, Jian; Tang, Xiaoou
2011-06-01
Robust object detection has many important applications in real-world online photo processing. For example, both Google image search and MSN live image search have integrated human face detector to retrieve face or portrait photos. Inspired by the success of such face filtering approach, in this paper, we focus on another popular online photo category--animal, which is one of the top five categories in the MSN live image search query log. As a first attempt, we focus on the problem of animal head detection of a set of relatively large land animals that are popular on the internet, such as cat, tiger, panda, fox, and cheetah. First, we proposed a new set of gradient oriented feature, Haar of Oriented Gradients (HOOG), to effectively capture the shape and texture features on animal head. Then, we proposed two detection algorithms, namely Bruteforce detection and Deformable detection, to effectively exploit the shape feature and texture feature simultaneously. Experimental results on 14,379 well labeled animals images validate the superiority of the proposed approach. Additionally, we apply the animal head detector to improve the image search result through text based online photo search result filtering.
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The Science and Art of Eyebrow Transplantation by Follicular Unit Extraction
Gupta, Jyoti; Kumar, Amrendra; Chouhan, Kavish; Ariganesh, C; Nandal, Vinay
2017-01-01
Eyebrows constitute a very important and prominent feature of the face. With growing information, eyebrow transplant has become a popular procedure. However, though it is a small area it requires a lot of precision and knowledge regarding anatomy, designing of brows, extraction and implantation technique. This article gives a comprehensive view regarding eyebrow transplant with special emphasis on follicular unit extraction technique, which has become the most popular technique. PMID:28852290
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NASA Astrophysics Data System (ADS)
Houston Jones, J.; Alice Wessen, Manager Of Solar System Eduction; Public Engagement
2010-12-01
NASA's What's Up video podcast supports the Year of the Solar System (YSS) October 2010 - August 2012. During YSS each podcast pairs a popular night sky viewing target (Moon, Comet, Planets, solar system features) with a mission event (launch, flyby, orbit insertion, landing). This product has proven popular with public, formal and informal audiences and will compliment and augment other programming material.
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ERIC Educational Resources Information Center
Wong, Meng Ee; Tan, Stacey S. K.
2012-01-01
Among the smart phones, the iPhone has emerged as one of the more popular smart phones. A feature that makes the iPhone popular to the user is the growing number of apps available through The App Store. Among the many apps, a number are designed for people with visual impairments. Some are free of charge, while others require payment. Compared to…
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NASA Astrophysics Data System (ADS)
Wang, Dong
2016-03-01
Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.
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Curated Collection for Educators: Five Key Papers about the Flipped Classroom Methodology
Boysen-Osborn, Megan; Cooney, Robert; Mitzman, Jennifer; Misra, Asit; Williams, Jennifer; Dulani, Tina; Gottlieb, Michael
2017-01-01
The flipped classroom (FC) pedagogy is becoming increasingly popular in medical education due to its appeal to the millennial learner and potential benefits in knowledge acquisition. Despite its popularity and effectiveness, the FC educational method is not without challenges. In this article, we identify and summarize several key papers relevant to medical educators interested in exploring the FC teaching methodology. The authors identified an extensive list of papers relevant to FC pedagogy via online discussions within the Academic Life in Emergency Medicine (ALiEM) Faculty Incubator. This list was augmented by an open call on Twitter (utilizing the #meded, #FOAMed, and #flippedclassroom hashtags) yielding a list of 33 papers. We then conducted a three-round modified Delphi process within the authorship group, which included both junior and senior clinician educators, to identify the most impactful papers for educators interested in FC pedagogy. The three-round modified Delphi process ranked all of the selected papers and selected the five most highly-rated papers for inclusion. The authorship group reviewed and summarized these papers with specific consideration given to their value to junior faculty educators and faculty developers interested in the flipped classroom approach. The list of papers featured in this article serves as a key reading list for junior clinician educators and faculty developers interested in the flipped classroom technique. The associated commentaries contextualize the importance of these papers for medical educators aiming to optimize their understanding and implementation of the flipped classroom methodology in their teaching and through faculty development. PMID:29282445
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Teaching Theory through Popular Culture Texts
ERIC Educational Resources Information Center
Trier, James
2007-01-01
In this article, the author describes a pedagogical approach to teaching theory to pre-service teachers. This approach involves articulating academic texts that introduce theoretical ideas and tools with carefully selected popular culture texts that can be taken up to illustrate the elements of a particular theory. Examples of the theories…
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ERIC Educational Resources Information Center
Stahre, Sven-Arne
The chief components of Swedish formal adult education are (1) independent lectures, which stress popularization of public affairs and of selected aspects of culture, science, and technology; (2) the folk high schools, whose object is to impart to young adults a general and civic education; (3) special schools operated by the popular movements;…
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A Popularized Version of 21 Doctoral Dissertations. R & D Monograph 70.
ERIC Educational Resources Information Center
Klein, Lawrence R.; Ghozeil, Susan
This volume makes available to a broad readership dissertation findings from social and behavioral sciences research supported by the Employment and Training Administration. Each of twenty-one dissertations, rewritten in the vernacular, is presented in condensed form for primarily nonacademic readers. These popularized selections, which have been…
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Unhealthy food marketing to New Zealand children and adolescents through the internet.
Vandevijvere, Stefanie; Sagar, Karuna; Kelly, Bridget; Swinburn, Boyd
2017-02-17
To assess the extent and nature of unhealthy food marketing to New Zealand children and adolescents through the internet. Internet traffic data for January 2014 was purchased from AC Nielsen to identify the most popular websites (n=110) among children and adolescents aged 6-17 years. In addition, websites (n=70) of food and beverage brands most frequently marketed to children through television, sports, magazines and Facebook were included. Marketing techniques and features on those websites were analysed. The extent of food marketing on popular non-food websites was low. A wide range of marketing techniques and features was, however, identified on food brand websites, including advercation (87%), viral marketing (64%), cookies (54%), free downloadable items (43%), promotional characters (39%), designated children's sections (19%) and advergaming (13%). Most techniques appeared more frequently on websites specifically targeting children and adolescents, than on other websites targeting the general public. Compared to traditional media, the internet allows food marketers to use engaging techniques to directly interact with children. While the range of marketing techniques and features identified on food brand websites was extensive, the most popular websites among children and adolescents were non-food related, and the extent of food marketing on those websites was found to be low. Additional assessment of food marketing to children through social and other digital media is recommended.
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Lansu, Tessa A M; Cillessen, Antonius H N; Karremans, Johan C
2014-01-01
Previous research has shown that adolescents' attention for a peer is determined by the peer's status. This study examined how it is also determined by the status of the perceiving adolescent, and the gender of both parties involved (perceiver and perceived). Participants were 122 early adolescents (M age = 11.0 years) who completed sociometric measures and eye-tracking recordings of visual fixations at pictures of high-status (popular) and low-status (unpopular) classmates. Automatic attention (first-gaze preference) and controlled attention (total gaze time) were measured. Target popularity was associated with both measures of attention. These associations were further moderated by perceiver popularity and perceiver and target gender. Popular adolescents attracted attention especially from other popular adolescents. Popular boys attracted attention especially from girls. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.
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Robust visual tracking via multiple discriminative models with object proposals
NASA Astrophysics Data System (ADS)
Zhang, Yuanqiang; Bi, Duyan; Zha, Yufei; Li, Huanyu; Ku, Tao; Wu, Min; Ding, Wenshan; Fan, Zunlin
2018-04-01
Model drift is an important reason for tracking failure. In this paper, multiple discriminative models with object proposals are used to improve the model discrimination for relieving this problem. Firstly, the target location and scale changing are captured by lots of high-quality object proposals, which are represented by deep convolutional features for target semantics. And then, through sharing a feature map obtained by a pre-trained network, ROI pooling is exploited to wrap the various sizes of object proposals into vectors of the same length, which are used to learn a discriminative model conveniently. Lastly, these historical snapshot vectors are trained by different lifetime models. Based on entropy decision mechanism, the bad model owing to model drift can be corrected by selecting the best discriminative model. This would improve the robustness of the tracker significantly. We extensively evaluate our tracker on two popular benchmarks, the OTB 2013 benchmark and UAV20L benchmark. On both benchmarks, our tracker achieves the best performance on precision and success rate compared with the state-of-the-art trackers.
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CW-SSIM kernel based random forest for image classification
NASA Astrophysics Data System (ADS)
Fan, Guangzhe; Wang, Zhou; Wang, Jiheng
2010-07-01
Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.
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Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.
Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo
2015-08-01
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
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A new approach to enhance the performance of decision tree for classifying gene expression data.
Hassan, Md; Kotagiri, Ramamohanarao
2013-12-20
Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.
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Modeling perceived stress via HRV and accelerometer sensor streams.
Wu, Min; Cao, Hong; Nguyen, Hai-Long; Surmacz, Karl; Hargrove, Caroline
2015-08-01
Discovering and modeling of stress patterns of human beings is a key step towards achieving automatic stress monitoring, stress management and healthy lifestyle. As various wearable sensors become popular, it becomes possible for individuals to acquire their own relevant sensory data and to automatically assess their stress level on the go. Previous studies for stress analysis were conducted in the controlled laboratory and clinic settings. These studies are not suitable for stress monitoring in one's daily life as various physical activities may affect the physiological signals. In this paper, we address such issue by integrating two modalities of sensors, i.e., HRV sensors and accelerometers, to monitor the perceived stress levels in daily life. We gathered both the heart and the motion data from 8 participants continuously for about 2 weeks. We then extracted features from both sensory data and compared the existing machine learning methods for learning personalized models to interpret the perceived stress levels. Experimental results showed that Bagging classifier with feature selection is able to achieve a prediction accuracy 85.7%, indicating our stress monitoring on daily basis is fairly practical.
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Neural classifier in the estimation process of maturity of selected varieties of apples
NASA Astrophysics Data System (ADS)
Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Wojcieszak, D.; Zbytek, Z.; Ludwiczak, A.; Przybylak, A.; Lewicki, A.
2015-07-01
This paper seeks to present methods of neural image analysis aimed at estimating the maturity state of selected varieties of apples which are popular in Poland. An identification of the degree of maturity of selected varieties of apples has been conducted on the basis of information encoded in graphical form, presented in the digital photos. The above process involves the application of the BBCH scale, used to determine the maturity of apples. The aforementioned scale is widely used in the EU and has been developed for many species of monocotyledonous plants and dicotyledonous plants. It is also worth noticing that the given scale enables detailed determinations of development stage of a given plant. The purpose of this work is to identify maturity level of selected varieties of apples, which is supported by the use of image analysis methods and classification techniques represented by artificial neural networks. The analysis of graphical representative features based on image analysis method enabled the assessment of the maturity of apples. For the utilitarian purpose the "JabVis 1.1" neural IT system was created, in accordance with requirements of the software engineering dedicated to support the decision-making processes occurring in broadly understood production process and processing of apples.
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An Adaptive Genetic Association Test Using Double Kernel Machines
Zhan, Xiang; Epstein, Michael P.; Ghosh, Debashis
2014-01-01
Recently, gene set-based approaches have become very popular in gene expression profiling studies for assessing how genetic variants are related to disease outcomes. Since most genes are not differentially expressed, existing pathway tests considering all genes within a pathway suffer from considerable noise and power loss. Moreover, for a differentially expressed pathway, it is of interest to select important genes that drive the effect of the pathway. In this article, we propose an adaptive association test using double kernel machines (DKM), which can both select important genes within the pathway as well as test for the overall genetic pathway effect. This DKM procedure first uses the garrote kernel machines (GKM) test for the purposes of subset selection and then the least squares kernel machine (LSKM) test for testing the effect of the subset of genes. An appealing feature of the kernel machine framework is that it can provide a flexible and unified method for multi-dimensional modeling of the genetic pathway effect allowing for both parametric and nonparametric components. This DKM approach is illustrated with application to simulated data as well as to data from a neuroimaging genetics study. PMID:26640602
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Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.
Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling
2018-04-01
In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.
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Bentley, R Alexander
2008-08-27
The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example.
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Random Drift versus Selection in Academic Vocabulary: An Evolutionary Analysis of Published Keywords
Bentley, R. Alexander
2008-01-01
The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded in the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example. PMID:18728786
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Neural Basis of Video Gaming: A Systematic Review
Palaus, Marc; Marron, Elena M.; Viejo-Sobera, Raquel; Redolar-Ripoll, Diego
2017-01-01
Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass. Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games. Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence. Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies. PMID:28588464
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Neural Basis of Video Gaming: A Systematic Review.
Palaus, Marc; Marron, Elena M; Viejo-Sobera, Raquel; Redolar-Ripoll, Diego
2017-01-01
Background: Video gaming is an increasingly popular activity in contemporary society, especially among young people, and video games are increasing in popularity not only as a research tool but also as a field of study. Many studies have focused on the neural and behavioral effects of video games, providing a great deal of video game derived brain correlates in recent decades. There is a great amount of information, obtained through a myriad of methods, providing neural correlates of video games. Objectives: We aim to understand the relationship between the use of video games and their neural correlates, taking into account the whole variety of cognitive factors that they encompass. Methods: A systematic review was conducted using standardized search operators that included the presence of video games and neuro-imaging techniques or references to structural or functional brain changes. Separate categories were made for studies featuring Internet Gaming Disorder and studies focused on the violent content of video games. Results: A total of 116 articles were considered for the final selection. One hundred provided functional data and 22 measured structural brain changes. One-third of the studies covered video game addiction, and 14% focused on video game related violence. Conclusions: Despite the innate heterogeneity of the field of study, it has been possible to establish a series of links between the neural and cognitive aspects, particularly regarding attention, cognitive control, visuospatial skills, cognitive workload, and reward processing. However, many aspects could be improved. The lack of standardization in the different aspects of video game related research, such as the participants' characteristics, the features of each video game genre and the diverse study goals could contribute to discrepancies in many related studies.
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Biomedical microfluidic devices by using low-cost fabrication techniques: A review.
Faustino, Vera; Catarino, Susana O; Lima, Rui; Minas, Graça
2016-07-26
One of the most popular methods to fabricate biomedical microfluidic devices is by using a soft-lithography technique. However, the fabrication of the moulds to produce microfluidic devices, such as SU-8 moulds, usually requires a cleanroom environment that can be quite costly. Therefore, many efforts have been made to develop low-cost alternatives for the fabrication of microstructures, avoiding the use of cleanroom facilities. Recently, low-cost techniques without cleanroom facilities that feature aspect ratios more than 20, for fabricating those SU-8 moulds have been gaining popularity among biomedical research community. In those techniques, Ultraviolet (UV) exposure equipment, commonly used in the Printed Circuit Board (PCB) industry, replaces the more expensive and less available Mask Aligner that has been used in the last 15 years for SU-8 patterning. Alternatively, non-lithographic low-cost techniques, due to their ability for large-scale production, have increased the interest of the industrial and research community to develop simple, rapid and low-cost microfluidic structures. These alternative techniques include Print and Peel methods (PAP), laserjet, solid ink, cutting plotters or micromilling, that use equipment available in almost all laboratories and offices. An example is the xurography technique that uses a cutting plotter machine and adhesive vinyl films to generate the master moulds to fabricate microfluidic channels. In this review, we present a selection of the most recent lithographic and non-lithographic low-cost techniques to fabricate microfluidic structures, focused on the features and limitations of each technique. Only microfabrication methods that do not require the use of cleanrooms are considered. Additionally, potential applications of these microfluidic devices in biomedical engineering are presented with some illustrative examples. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Mobile application for diabetes self-management in China: Do they fit for older adults?
Gao, Chenchen; Zhou, Lanshu; Liu, Zhihui; Wang, Haocen; Bowers, Barbara
2017-05-01
Despite the exponential proliferation of Chinese diabetes applications, none are designed to meet the needs of the largest potential user population. The purpose of this study is to examine the features and contents of Chinese diabetes mobile applications in terms of their suitability for use by older adults with diabetes. A search of the Apple application store and the 360 Mobile Assistant was conducted to identify Chinese diabetes applications. Next, we compared the features and contents of all the included and most popular diabetes applications with both the International Diabetes Federation (IDF) clinical guideline and recommended usability criteria for older adults respectively. Seventy-one diabetes apps were randomly selected (from a pool of 552 diabetes apps) and reviewed. The features of most apps failed to include content areas of known importance for managing diabetes in older adults. Usability of all tested applications was rated moderate to good. Designing maximally effective medical applications would benefit from attention to both usability and content guidelines targeted for the largest potential user population. Despite the preponderance of older adults in the potential user group, failing to consider the relevance of content, in addition to usability for the specific population will ultimately limit the usefulness of the app. Copyright © 2017 Elsevier B.V. All rights reserved.
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The guitar chord-generating algorithm based on complex network
NASA Astrophysics Data System (ADS)
Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais
2016-02-01
This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.
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Investigation of Relationship between Aggression and Sociometric Popularity in Adolescents
ERIC Educational Resources Information Center
Yavuzer, Yasemin
2013-01-01
In this study, it was aimed to determine the linear and curvilinear relationships between adolescent aggression and sociometric popularity. 524 adolescents randomly selected from 20 elementary schools in Nigde city center formed the study group. The participants were from 8th grade in 20 different classrooms. The research data were collected by…
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ERIC Educational Resources Information Center
Martin, Nelly
2017-01-01
This study explores the relationship between language selection and identity construction in contemporary Indonesia through an examination of the function of English, a language that still receives stigma from many Indonesians and the government, particularly in Indonesian popular texts published after 1998. Utilizing hybrid critical approaches…
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Popular Nonfiction Authors for Children. A Biographical and Thematic Guide.
ERIC Educational Resources Information Center
Wyatt, Flora R.; Coggins, Margaret; Imber, Jane Hunter
This sourcebook provides brief biographies of 65 popular children's nonfiction authors. Each profile focuses on how authors go about writing information books by giving a firsthand account of the challenges and rewards of nonfiction writing. Each biography provides a selected bibliography of the author's work, a photo, and a message to the student…
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Definition and Measurement of Selection Bias: From Constant Ratio to Constant Difference
ERIC Educational Resources Information Center
Cahan, Sorel; Gamliel, Eyal
2006-01-01
Despite its intuitive appeal and popularity, Thorndike's constant ratio (CR) model for unbiased selection is inherently inconsistent in "n"-free selection. Satisfaction of the condition for unbiased selection, when formulated in terms of success/acceptance probabilities, usually precludes satisfaction by the converse probabilities of…
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Will genomic selection be a practical method for plant breeding?
Nakaya, Akihiro; Isobe, Sachiko N.
2012-01-01
Background Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use. Scope In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed. Conclusions Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory. PMID:22645117
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Teen Screen: Take a Walk on the Wild Side.
ERIC Educational Resources Information Center
Flowers, Sarah
2002-01-01
Reviews seven videos that involve some aspect of extreme sports, feature loud rock music, and are popular with teens. Includes snowboarding, Gravity Games, BMX bikes, skateboarding, and skydiving. (LRW)
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Subtle Nonlinearity in Popular Album Charts
NASA Astrophysics Data System (ADS)
Bentley, R. Alexander; Maschner, Herbert D. G.
Large-scale patterns of culture change may be explained by models of self organized criticality, or alternatively, by multiplicative processes. We speculate that popular album activity may be similar to critical models of extinction in that interconnected agents compete to survive within a limited space. Here we investigate whether popular music albums as listed on popular album charts display evidence of self-organized criticality, including a self-affine time series of activity and power-law distributions of lifetimes and exit activity in the chart. We find it difficult to distinguish between multiplicative growth and critical model hypotheses for these data. However, aspects of criticality may be masked by the selective sampling that a "Top 200" listing necessarily implies.
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Influence of licensed characters on children's taste and snack preferences.
Roberto, Christina A; Baik, Jenny; Harris, Jennifer L; Brownell, Kelly D
2010-07-01
The goal was to study how popular licensed cartoon characters appearing on food packaging affect young children's taste and snack preferences. Forty 4- to 6-year-old children tasted 3 pairs of identical foods (graham crackers, gummy fruit snacks, and carrots) presented in packages either with or without a popular cartoon character. Children tasted both food items in each pair and indicated whether the 2 foods tasted the same or one tasted better. Children then selected which of the food items they would prefer to eat for a snack. Children significantly preferred the taste of foods that had popular cartoon characters on the packaging, compared with the same foods without characters. The majority of children selected the food sample with a licensed character on it for their snack, but the effects were weaker for carrots than for gummy fruit snacks and graham crackers. Branding food packages with licensed characters substantially influences young children's taste preferences and snack selection and does so most strongly for energy-dense, nutrient-poor foods. These findings suggest that the use of licensed characters to advertise junk food to children should be restricted.
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ERIC Educational Resources Information Center
Major, Raymond L.
1998-01-01
Presents a technique for developing a knowledge-base of information to use in an expert system. Proposed approach employs a popular machine-learning algorithm along with a method for forming a finite number of features or conjuncts of at most n primitive attributes. Illustrates this procedure by examining qualitative information represented in a…
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NASA Astrophysics Data System (ADS)
Rhodes, Andrew P.; Christian, John A.; Evans, Thomas
2017-12-01
With the availability and popularity of 3D sensors, it is advantageous to re-examine the use of point cloud descriptors for the purpose of pose estimation and spacecraft relative navigation. One popular descriptor is the oriented unique repeatable clustered viewpoint feature histogram (
OUR-CVFH ), which is most often utilized in personal and industrial robotics to simultaneously recognize and navigate relative to an object. Recent research into using theOUR-CVFH descriptor for spacecraft navigation has produced favorable results. SinceOUR-CVFH is the most recent innovation in a large family of feature histogram point cloud descriptors, discussions of parameter settings and insights into its functionality are spread among various publications and online resources. This paper organizes the history of feature histogram point cloud descriptors for a straightforward explanation of their evolution. This article compiles all the requisite information needed to implementOUR-CVFH into one location, as well as providing useful suggestions on how to tune the generation parameters. This work is beneficial for anyone interested in using this histogram descriptor for object recognition or navigation - may it be personal robotics or spacecraft navigation. -
Kavuluru, Ramakanth; Rios, Anthony; Lu, Yuan
2015-01-01
Background Diagnosis codes are assigned to medical records in healthcare facilities by trained coders by reviewing all physician authored documents associated with a patient's visit. This is a necessary and complex task involving coders adhering to coding guidelines and coding all assignable codes. With the popularity of electronic medical records (EMRs), computational approaches to code assignment have been proposed in the recent years. However, most efforts have focused on single and often short clinical narratives, while realistic scenarios warrant full EMR level analysis for code assignment. Objective We evaluate supervised learning approaches to automatically assign international classification of diseases (ninth revision) - clinical modification (ICD-9-CM) codes to EMRs by experimenting with a large realistic EMR dataset. The overall goal is to identify methods that offer superior performance in this task when considering such datasets. Methods We use a dataset of 71,463 EMRs corresponding to in-patient visits with discharge date falling in a two year period (2011–2012) from the University of Kentucky (UKY) Medical Center. We curate a smaller subset of this dataset and also use a third gold standard dataset of radiology reports. We conduct experiments using different problem transformation approaches with feature and data selection components and employing suitable label calibration and ranking methods with novel features involving code co-occurrence frequencies and latent code associations. Results Over all codes with at least 50 training examples we obtain a micro F-score of 0.48. On the set of codes that occur at least in 1% of the two year dataset, we achieve a micro F-score of 0.54. For the smaller radiology report dataset, the classifier chaining approach yields best results. For the smaller subset of the UKY dataset, feature selection, data selection, and label calibration offer best performance. Conclusions We show that datasets at different scale (size of the EMRs, number of distinct codes) and with different characteristics warrant different learning approaches. For shorter narratives pertaining to a particular medical subdomain (e.g., radiology, pathology), classifier chaining is ideal given the codes are highly related with each other. For realistic in-patient full EMRs, feature and data selection methods offer high performance for smaller datasets. However, for large EMR datasets, we observe that the binary relevance approach with learning-to-rank based code reranking offers the best performance. Regardless of the training dataset size, for general EMRs, label calibration to select the optimal number of labels is an indispensable final step. PMID:26054428
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Kavuluru, Ramakanth; Rios, Anthony; Lu, Yuan
2015-10-01
Diagnosis codes are assigned to medical records in healthcare facilities by trained coders by reviewing all physician authored documents associated with a patient's visit. This is a necessary and complex task involving coders adhering to coding guidelines and coding all assignable codes. With the popularity of electronic medical records (EMRs), computational approaches to code assignment have been proposed in the recent years. However, most efforts have focused on single and often short clinical narratives, while realistic scenarios warrant full EMR level analysis for code assignment. We evaluate supervised learning approaches to automatically assign international classification of diseases (ninth revision) - clinical modification (ICD-9-CM) codes to EMRs by experimenting with a large realistic EMR dataset. The overall goal is to identify methods that offer superior performance in this task when considering such datasets. We use a dataset of 71,463 EMRs corresponding to in-patient visits with discharge date falling in a two year period (2011-2012) from the University of Kentucky (UKY) Medical Center. We curate a smaller subset of this dataset and also use a third gold standard dataset of radiology reports. We conduct experiments using different problem transformation approaches with feature and data selection components and employing suitable label calibration and ranking methods with novel features involving code co-occurrence frequencies and latent code associations. Over all codes with at least 50 training examples we obtain a micro F-score of 0.48. On the set of codes that occur at least in 1% of the two year dataset, we achieve a micro F-score of 0.54. For the smaller radiology report dataset, the classifier chaining approach yields best results. For the smaller subset of the UKY dataset, feature selection, data selection, and label calibration offer best performance. We show that datasets at different scale (size of the EMRs, number of distinct codes) and with different characteristics warrant different learning approaches. For shorter narratives pertaining to a particular medical subdomain (e.g., radiology, pathology), classifier chaining is ideal given the codes are highly related with each other. For realistic in-patient full EMRs, feature and data selection methods offer high performance for smaller datasets. However, for large EMR datasets, we observe that the binary relevance approach with learning-to-rank based code reranking offers the best performance. Regardless of the training dataset size, for general EMRs, label calibration to select the optimal number of labels is an indispensable final step. Copyright © 2015 Elsevier B.V. All rights reserved.
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The popularity of neurology in Spain: An analysis of specialty selection.
Curbelo, J; Romeo, J M; Galván-Román, J M; Vega-Villar, J; Martinez-Lapiscina, E H; Jiménez-Fonseca, P; Villacampa, T; Sánchez-Lasheras, F; Fernández-Somoano, A; Baladrón, J
2017-12-23
Neurology is one of the medical specialties offered each year to residency training candidates. This project analyses the data associated with candidates choosing neurology residency programmes in recent years. Data related to specialty selection were obtained from official reports by the Spanish Ministry of Health, Social Services, and Equality. Information was collected on several characteristics of teaching centres: availability of stroke units, endovascular intervention, national reference clinics for neurology, specific on-call shifts for neurology residents, and links with medical schools or national research networks. The median selection list position of candidates selecting neurology training has been higher year on year; neurology was among the 4 most popular residency programmes in 2016. Potential residents were mainly female, Spanish, and had good academic results. The median number of hospitals with higher numbers of beds, endovascular intervention, stroke units, and national reference clinics for neurology is significantly lower. This is also true when centers are analysed by presence of specific on-call shifts for neurology residents and association with medical schools or national research networks. The centres selected by candidates with the highest median selection list position in 2012-2016 were the Clínico San Carlos, 12 de Octubre, and Vall d'Hebron university hospitals. Neurology has gradually improved in residency selection choices and is now one of the 4 most popular options. Potential residents prefer larger centres which are more demanding in terms of patient care and which perform more research activity. Copyright © 2017 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.
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Featured Image | Galaxy of Images
our most popular images is that of renowned female scientist (and the first recipient of two Nobel cameras as the perfect way to capture summer memories. This adventurous female copilot attempts to
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VirGO: A Visual Browser for the ESO Science Archive Facility
NASA Astrophysics Data System (ADS)
Chéreau, Fabien
2012-04-01
VirGO is the next generation Visual Browser for the ESO Science Archive Facility developed by the Virtual Observatory (VO) Systems Department. It is a plug-in for the popular open source software Stellarium adding capabilities for browsing professional astronomical data. VirGO gives astronomers the possibility to easily discover and select data from millions of observations in a new visual and intuitive way. Its main feature is to perform real-time access and graphical display of a large number of observations by showing instrumental footprints and image previews, and to allow their selection and filtering for subsequent download from the ESO SAF web interface. It also allows the loading of external FITS files or VOTables, the superimposition of Digitized Sky Survey (DSS) background images, and the visualization of the sky in a `real life' mode as seen from the main ESO sites. All data interfaces are based on Virtual Observatory standards which allow access to images and spectra from external data centers, and interaction with the ESO SAF web interface or any other VO applications supporting the PLASTIC messaging system.
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The use of teetaimed in Estonia, 1880s-1990s.
Sõukand, Renata; Kalle, Raivo
2012-10-01
This research contributes to a better understanding of the criteria used for the selection of plants for making beverages. Worldwide, not only the leaves of Camellia sinensis, but also various other plants are used for making tea. We argue that the selection of plants for making tea (in Estonian teetaimed) depends on specific features possessed by or attributed to the plants. 54 plant taxa and one lichen were identified as being used for making tea, based on the analysis of Estonian historical handwritten archival records on plant use for the period from 1887 to 1994. The influence of popular literature on the use of plants for making tea was also assessed. The suitability of a plant for making tea depends on a combination of factors like multifunctional use, mild taste and attributed medicinal properties. The variety of medicinal properties attributed to teetaimed in folk medicine allowed herbal tea drinking to be considered as mild disease prevention. Hence, the roots of the Estonian tea tradition lie in the medicinal use of the plants, not oriental ceremonial tea drinking. Copyright © 2012 Elsevier Ltd. All rights reserved.
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The neuropsychiatric aspects of influenza/swine flu: A selective review
Manjunatha, Narayana; Math, Suresh Bada; Kulkarni, Girish Baburao; Chaturvedi, Santosh Kumar
2011-01-01
The world witnessed the influenza virus during the seasonal epidemics and pandemics. The current strain of H1N1 (swine flu) pandemic is believed to be the legacy of the influenza pandemic (1918-19). The influenza virus has been implicated in many neuropsychiatric disorders. In view of the recent pandemic, it would be interesting to review the neuropsychiatric aspects of influenza, specifically swine flu. Author used popular search engine ‘PUBMED’ to search for published articles with different MeSH terms using Boolean operator (AND). Among these, a selective review of the published literature was done. Acute manifestations of swine flu varied from behavioral changes, fear of misdiagnosis during outbreak, neurological features like seizures, encephalopathy, encephalitis, transverse myelitis, aseptic meningitis, multiple sclerosis, and Guillian-Barre Syndrome. Among the chronic manifestations, schizophrenia, Parkinson's disease, mood disorder, dementia, and mental retardation have been hypothesized. Further research is required to understand the etiological hypothesis of the chronic manifestations of influenza. The author urges neuroscientists around the world to make use of the current swine flu pandemic as an opportunity for further research. PMID:23271861
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The neuropsychiatric aspects of influenza/swine flu: A selective review.
Manjunatha, Narayana; Math, Suresh Bada; Kulkarni, Girish Baburao; Chaturvedi, Santosh Kumar
2011-07-01
The world witnessed the influenza virus during the seasonal epidemics and pandemics. The current strain of H1N1 (swine flu) pandemic is believed to be the legacy of the influenza pandemic (1918-19). The influenza virus has been implicated in many neuropsychiatric disorders. In view of the recent pandemic, it would be interesting to review the neuropsychiatric aspects of influenza, specifically swine flu. Author used popular search engine 'PUBMED' to search for published articles with different MeSH terms using Boolean operator (AND). Among these, a selective review of the published literature was done. Acute manifestations of swine flu varied from behavioral changes, fear of misdiagnosis during outbreak, neurological features like seizures, encephalopathy, encephalitis, transverse myelitis, aseptic meningitis, multiple sclerosis, and Guillian-Barre Syndrome. Among the chronic manifestations, schizophrenia, Parkinson's disease, mood disorder, dementia, and mental retardation have been hypothesized. Further research is required to understand the etiological hypothesis of the chronic manifestations of influenza. The author urges neuroscientists around the world to make use of the current swine flu pandemic as an opportunity for further research.
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Cascaded face alignment via intimacy definition feature
NASA Astrophysics Data System (ADS)
Li, Hailiang; Lam, Kin-Man; Chiu, Man-Yau; Wu, Kangheng; Lei, Zhibin
2017-09-01
Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a random-forest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.
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2013-01-01
Background The 2009–10 influenza pandemic was a major public health concern. Vaccination was recommended by the health authorities, but compliance was not optimal and perception of the presumed associated risks was high among the public. The Internet is increasingly being used as a source of health information and advice. The aim of the study was to investigate the characteristics of websites providing information about flu vaccine and the quality of the information provided. Methods Website selection was performed in autumn 2010 by entering eight keywords in two of the most commonly used search engines (Google.com and Yahoo.com). The first three result pages were analysed for each search, giving a total of 480 occurrences. Page rank was evaluated to assess visibility. Websites based on Web 2.0 philosophy, websites merely displaying popular news/articles and single files were excluded from the subsequent analysis. We analysed the selected websites (using WHO criteria) as well as the information provided, using a codebook for pro/neutral websites and a qualitative approach for the adverse ones. Results Of the 89 websites selected, 54 dealt with seasonal vaccination, three with anti-H1N1 vaccination and 32 with both. Rank analysis showed that only classic websites (ones not falling in any other category) and one social network were provided on the first pages by Yahoo; 21 classic websites, six displaying popular news/articles and one blog by Google. Analysis of the selected websites revealed that the majority of them (88.8%) had a positive/neutral attitude to flu vaccination. Pro/neutral websites distinguished themselves from the adverse ones by some revealing features like greater transparency, credibility and privacy protection. Conclusions We found that the majority of the websites providing information on flu vaccination were pro/neutral and gave sufficient information. We suggest that antivaccinationist information may have been spread by a different route, such as via Web 2.0 tools, which may be more prone to the dissemination of “viral” information. The page ranking analysis revealed the crucial role of search engines regarding access to information on the Internet. PMID:23360311
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Covolo, Loredana; Mascaretti, Silvia; Caruana, Anna; Orizio, Grazia; Caimi, Luigi; Gelatti, Umberto
2013-01-29
The 2009-10 influenza pandemic was a major public health concern. Vaccination was recommended by the health authorities, but compliance was not optimal and perception of the presumed associated risks was high among the public. The Internet is increasingly being used as a source of health information and advice. The aim of the study was to investigate the characteristics of websites providing information about flu vaccine and the quality of the information provided. Website selection was performed in autumn 2010 by entering eight keywords in two of the most commonly used search engines (Google.com and Yahoo.com). The first three result pages were analysed for each search, giving a total of 480 occurrences. Page rank was evaluated to assess visibility. Websites based on Web 2.0 philosophy, websites merely displaying popular news/articles and single files were excluded from the subsequent analysis. We analysed the selected websites (using WHO criteria) as well as the information provided, using a codebook for pro/neutral websites and a qualitative approach for the adverse ones. Of the 89 websites selected, 54 dealt with seasonal vaccination, three with anti-H1N1 vaccination and 32 with both. Rank analysis showed that only classic websites (ones not falling in any other category) and one social network were provided on the first pages by Yahoo; 21 classic websites, six displaying popular news/articles and one blog by Google. Analysis of the selected websites revealed that the majority of them (88.8%) had a positive/neutral attitude to flu vaccination. Pro/neutral websites distinguished themselves from the adverse ones by some revealing features like greater transparency, credibility and privacy protection. We found that the majority of the websites providing information on flu vaccination were pro/neutral and gave sufficient information. We suggest that antivaccinationist information may have been spread by a different route, such as via Web 2.0 tools, which may be more prone to the dissemination of "viral" information. The page ranking analysis revealed the crucial role of search engines regarding access to information on the Internet.
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ERIC Educational Resources Information Center
Smith, Donna Ridley, Comp.
The bibliography lists over 400 works in the California State University Library, Sacramento, on pop, rock, country, folk, blues, and soul music from 1950 to the present. Books, periodicals, and non-book materials noted in the bibliography are appropriate for history, communication studies, and popular culture studies as well as for music. Items…
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ERIC Educational Resources Information Center
Selden, Steven
1987-01-01
Presents an essay review of three recent books on eugenics, a once popular quasiscientific and politically conservative social movement devoted to the improvement of humankind through programs of selective breeding and marriage restriction. States that educators must study and come to grips with the meaning of this movement in order to appreciate…
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ERIC Educational Resources Information Center
Logan, Rochelle; Halverstadt, Julie
This book, which is intended as a reference for teenagers and educators, profiles the lives and professional paths of the 100 most popular business leaders for young adults. Those profiled were selected for a number of reasons, including the following: their names are recognizable; they are associated with businesses and industries that are…
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ERIC Educational Resources Information Center
Sun, Chyng; Bridges, Ana; Wosnitzer, Robert; Scharrer, Erica; Liberman, Rachael
2008-01-01
Pornography is a lucrative business. Increasingly, women have participated in both its production, direction, and consumption. This study investigated how the content in popular pornographic videos created by female directors differs from that of their male counterparts. We conducted a quantitative analysis of 122 randomly selected scenes from 44…
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Improved EEG Event Classification Using Differential Energy.
Harati, A; Golmohammadi, M; Lopez, S; Obeid, I; Picone, J
2015-12-01
Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24 % absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.
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Increasing Verbal Behavior of a Student Who Is Selectively Mute
ERIC Educational Resources Information Center
Beare, Paul; Torgerson, Colleen; Creviston, Cindy
2008-01-01
"Selective mutism" is the term used to describe a disorder in which a person speaks only in restricted stimulus situations. Examination of single-subject research concerning selective mutism reveals the most popular and successful interventions to instate speech involve a combination of behavior modification procedures. The present research…
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Drug Dependence and Abuse: A Selected Bibliography.
ERIC Educational Resources Information Center
National Inst. on Drug Abuse (DHEW/PHS), Rockville, MD. National Clearinghouse for Drug Abuse Information.
This selected list of references is designed to provide an introduction to both scientific and popular drug abuse literature. Criteria for selection are presented and include: (1) 1969 or 1970 books by recognized and authoritative writers, (2) current and responsible research, (3) classic books, articles and studies, and (4) factual popular…
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A Critical Review on Prosthetic Features Available for Reversed Total Shoulder Arthroplasty
De Wilde, Lieven
2016-01-01
Reversed total shoulder arthroplasty is a popular treatment in rotator cuff arthropathy and in displaced proximal humeral fractures in elderly. In 2016, 29 models of commercially available designs express this popularity. This study describes all the different design parameters available on the market. Prosthetic differences are found for the baseplate, glenosphere, polyethylene, and humeral component and these differences need to be weighed out carefully for each patient knowing that a gain in one mechanical parameter can balance the loss of another. Patient specific implants may help in the future. PMID:28105417
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Self-organizing map classifier for stressed speech recognition
NASA Astrophysics Data System (ADS)
Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav
2016-05-01
This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.
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Boyd, Hope; Murnen, Sarah K
2017-06-01
We examined the extent to which popular dolls and action figures were portrayed with gendered body proportions, and the extent to which these gendered ideals were associated with heterosexual "success." We coded internet depictions of 72 popular female dolls and 71 popular male action figures from the websites of three national stores in the United States. Sixty-two percent of dolls had a noticeably thin body, while 42.3% of action figures had noticeably muscular bodies. Further, more thin dolls were portrayed with more sex object features than less thin dolls, including revealing, tight clothing and high-heeled shoes; bodies positioned with a curved spine, bent knee, and head cant; and with a sexually appealing facial expression. More muscular male action figures were more likely than less muscular ones to be shown with hands in fists and with an angry, emotional expression, suggesting male dominance. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Operant psychology goes to the fair: Marian and Keller Breland in the popular press, 1947-1966
Bailey, Robert E.; Gillaspy, J. Arthur
2005-01-01
Marian and Keller Breland pioneered the application of operant psychology to commercial animal training during the 1940s and 1950s. The Brelands' story is relatively unknown in the history of behavior analysis. Using information from the Breland-Bailey papers, this paper describes the development and activities of Animal Behavior Enterprises (ABE), the Brelands' animal training business. We also review popular press coverage of the Brelands between 1947 and 1966 to investigate the level of public exposure to ABE-trained animals and to the principles and methods of operant psychology. An examination of 308 popular print articles featuring the Brelands indicates that there was public exposure of behavior analysis through the popular press coverage of ABE-trained animals. Furthermore, the expansion of operant methods to the marine mammal and bird training industries can be linked to the Brelands' mass media exposure. ImagesFigure 1Figure 2Figure 3Figure 4Figure 5 PMID:22478446
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Podoll, K
2000-11-01
Based on a survey of a variety of sources from medical and film history, an account is given of the history of scientific and popular educational films in neurology and psychiatry in Germany in the era of the silent film 1895-1929. A central event for the centralization of the production and distribution of medical scientific educational films was the foundation, in 1918, of the 'cultural department' of the Ufa film company which established, under the direction of the neurologist Curt Thomalla, a large medical film archive. Curt Thomalla was also the first who developed a dramatic type of popular educational film amalgamating medical and melodramatical features, thereby greatly increasing its mass impact, but also anticipating central elements of its later misuse by the Nazi film propaganda.
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How Many Votes Are Needed to Be Elected President?
ERIC Educational Resources Information Center
Mahoney, John F.
2004-01-01
The presidential election that frequently features the results of political polling is presented. These polls attempt to estimate the popular vote that each candidate would receive as they could predict who would win the elections.
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Special Feature on Some Popular Pastimes: Wilderness Skills, Handcrafts, Mini and Trail Biking
ERIC Educational Resources Information Center
Meiklejohn-Moz, Jane; And Others
1976-01-01
In an effort to stimulate teacher-readers to find out what specialized activities their own students may be absorbed in, the authors describe some less commonly known student recreational activities. (MB)
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Building of fuzzy decision trees using ID3 algorithm
NASA Astrophysics Data System (ADS)
Begenova, S. B.; Avdeenko, T. V.
2018-05-01
Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.
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Warpage optimization on a mobile phone case using response surface methodology (RSM)
NASA Astrophysics Data System (ADS)
Lee, X. N.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.; Shazzuan, S.
2017-09-01
Plastic injection moulding is a popular manufacturing method not only it is reliable, but also efficient and cost saving. It able to produce plastic part with detailed features and complex geometry. However, defects in injection moulding process degrades the quality and aesthetic of the injection moulded product. The most common defect occur in the process is warpage. Inappropriate process parameter setting of injection moulding machine is one of the reason that leads to the occurrence of warpage. The aims of this study were to improve the quality of injection moulded part by investigating the optimal parameters in minimizing warpage using Response Surface Methodology (RSM). Subsequent to this, the most significant parameter was identified and recommended parameters setting was compared with the optimized parameter setting using RSM. In this research, the mobile phone case was selected as case study. The mould temperature, melt temperature, packing pressure, packing time and cooling time were selected as variables whereas warpage in y-direction was selected as responses in this research. The simulation was carried out by using Autodesk Moldflow Insight 2012. In addition, the RSM was performed by using Design Expert 7.0. The warpage in y direction recommended by RSM were reduced by 70 %. RSM performed well in solving warpage issue.
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Dashtban, M; Balafar, Mohammadali
2017-03-01
Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.
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Forensic Analysis of the Sony Playstation Portable
NASA Astrophysics Data System (ADS)
Conrad, Scott; Rodriguez, Carlos; Marberry, Chris; Craiger, Philip
The Sony PlayStation Portable (PSP) is a popular portable gaming device with features such as wireless Internet access and image, music and movie playback. As with most systems built around a processor and storage, the PSP can be used for purposes other than it was originally intended - legal as well as illegal. This paper discusses the features of the PSP browser and suggests best practices for extracting digital evidence.
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Indexing and retrieval of MPEG compressed video
NASA Astrophysics Data System (ADS)
Kobla, Vikrant; Doermann, David S.
1998-04-01
To keep pace with the increased popularity of digital video as an archival medium, the development of techniques for fast and efficient analysis of ideo streams is essential. In particular, solutions to the problems of storing, indexing, browsing, and retrieving video data from large multimedia databases are necessary to a low access to these collections. Given that video is often stored efficiently in a compressed format, the costly overhead of decompression can be reduced by analyzing the compressed representation directly. In earlier work, we presented compressed domain parsing techniques which identified shots, subshots, and scenes. In this article, we present efficient key frame selection, feature extraction, indexing, and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame type independent representation which normalizes spatial and temporal features including frame type, frame size, macroblock encoding, and motion compensation vectors. Features for indexing are derived directly from this representation and mapped to a low- dimensional space where they can be accessed using standard database techniques. Spatial information is used as primary index into the database and temporal information is used to rank retrieved clips and enhance the robustness of the system. The techniques presented enable efficient indexing, querying, and retrieval of compressed video as demonstrated by our system which typically takes a fraction of a second to retrieve similar video scenes from a database, with over 95 percent recall.
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Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip
2015-11-01
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Alcohol marketing in televised international football: frequency analysis.
Adams, Jean; Coleman, James; White, Martin
2014-05-20
Alcohol marketing includes sponsorship of individuals, organisations and sporting events. Football (soccer) is one of the most popular spectator sports worldwide. No previous studies have quantified the frequency of alcohol marketing in a high profile international football tournament. The aims were to determine: the frequency and nature of visual references to alcohol in a representative sample of EURO2012 matches broadcast in the UK; and if frequency or nature varied between matches broadcast on public service and commercial channels, or between matches that did and did not feature England. Eight matches selected by stratified random sampling were recorded. All visual references to alcohol were identified using a tool with high inter-rater reliability. 1846 visual references to alcohol were identified over 1487 minutes of broadcast--an average of 1.24 references per minute. The mean number of references per minute was higher in matches that did vs did not feature England (p = 0.004), but did not differ between matches broadcast on public service vs commercial channels (p = 0.92). The frequency of visual references to alcohol was universally high and higher in matches featuring the only UK home team--England--suggesting that there may be targeting of particularly highly viewed matches. References were embedded in broadcasts, and not particular to commercial channels including paid-for advertising. New UK codes-of-conduct on alcohol marketing at sporting events will not reduce the level of marketing reported here.
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Considering Object Oriented Technology in Aviation Applications
NASA Technical Reports Server (NTRS)
Hayhurst, Kelly J.; Holloway, C. Michael
2003-01-01
Few developers of commercial aviation software products are using object-oriented technology (OOT), despite its popularity in some other industries. Safety concerns about using OOT in critical applications, uncertainty about how to comply with regulatory requirements, and basic conservatism within the aviation community have been factors behind this caution. The Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA) have sponsored research to investigate and workshops to discuss safety and certification concerns about OOT and to develop recommendations for safe use. Two Object Oriented Technology in Aviation (OOTiA) workshops have been held and numerous issues and comments about the effect of OOT features and languages have been collected. This paper gives a high level overview of the OOTiA project, and discusses selected specific results from the March 2003 workshop. In particular, results in the form of questions to consider before making the decision to use OOT are presented.
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Astronomy in the Digital Universe
NASA Astrophysics Data System (ADS)
Haisch, Bernard M.; Lindblom, J.; Terzian, Y.
2006-12-01
The Digital Universe is an Internet project whose mission is to provide free, accurate, unbiased information covering all aspects of human knowledge, and to inspire humans to learn, make use of, and expand this knowledge. It is planned to be a decades long effort, inspired by the Encyclopedia Galactica concept popularized by Carl Sagan, and is being developed by the non-profit Digital Universe Foundation. A worldwide network of experts is responsible for selecting content featured within the Digital Universe. The first publicly available content is the Encyclopedia of Earth, a Boston University project headed by Prof. Cutler Cleveland, which will be part of the Earth Portal. The second major content area will be an analogous Encyclopedia of the Cosmos to be part of the Cosmos Portal. It is anticipated that this will evolve into a major resource for astronomy education. Authors and topic editors are now being recruited for the Encyclopedia of the Cosmos.
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New algorithms for optimal reduction of technical risks
NASA Astrophysics Data System (ADS)
Todinov, M. T.
2013-06-01
The article features exact algorithms for reduction of technical risk by (1) optimal allocation of resources in the case where the total potential loss from several sources of risk is a sum of the potential losses from the individual sources; (2) optimal allocation of resources to achieve a maximum reduction of system failure; and (3) making an optimal choice among competing risky prospects. The article demonstrates that the number of activities in a risky prospect is a key consideration in selecting the risky prospect. As a result, the maximum expected profit criterion, widely used for making risk decisions, is fundamentally flawed, because it does not consider the impact of the number of risk-reward activities in the risky prospects. A popular view, that if a single risk-reward bet with positive expected profit is unacceptable then a sequence of such identical risk-reward bets is also unacceptable, has been analysed and proved incorrect.
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A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.
Li, Shan; Kang, Liying; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.
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Skirts in the lab: Madame Curie and the image of the woman scientist in the feature film.
Elena, A
1997-07-01
Recent research has appropriately emphasized the significant role played by feature films in the creation (as well as the reflection) of popular stereotypes of the scientist. However, no particular study has yet been devoted to the depiction of women scientists in the cinema, even though it is quite clear that this presents its own distinctive features. Taking the influential Madame Curie (Mervyn LeRoy, 1943) as a starting point, this paper attempts to give a first overview of the subject.
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Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
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Feature Selection for Chemical Sensor Arrays Using Mutual Information
Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.
2014-01-01
We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058
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Portrayal of Alcohol Brands Popular Among Underage Youth on YouTube: A Content Analysis.
Primack, Brian A; Colditz, Jason B; Rosen, Eva B; Giles, Leila M; Jackson, Kristina M; Kraemer, Kevin L
2017-09-01
We characterized leading YouTube videos featuring alcohol brand references and examined video characteristics associated with each brand and video category. We systematically captured the 137 most relevant and popular videos on YouTube portraying alcohol brands that are popular among underage youth. We used an iterative process to codebook development. We coded variables within domains of video type, character sociodemographics, production quality, and negative and positive associations with alcohol use. All variables were double coded, and Cohen's kappa was greater than .80 for all variables except age, which was eliminated. There were 96,860,936 combined views for all videos. The most common video type was "traditional advertisements," which comprised 40% of videos. Of the videos, 20% were "guides" and 10% focused on chugging a bottle of distilled spirits. While 95% of videos featured males, 40% featured females. Alcohol intoxication was present in 19% of videos. Aggression, addiction, and injuries were uncommonly identified (2%, 3%, and 4%, respectively), but 47% of videos contained humor. Traditional advertisements represented the majority of videos related to Bud Light (83%) but only 18% of Grey Goose and 8% of Hennessy videos. Intoxication was most present in chugging demonstrations (77%), whereas addiction was only portrayed in music videos (22%). Videos containing humor ranged from 11% for music-related videos to 77% for traditional advertisements. YouTube videos depicting the alcohol brands favored by underage youth are heavily viewed, and the majority are traditional or narrative advertisements. Understanding characteristics associated with different brands and video categories may aid in intervention development.
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Rough sets and Laplacian score based cost-sensitive feature selection
Yu, Shenglong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884
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Rough sets and Laplacian score based cost-sensitive feature selection.
Yu, Shenglong; Zhao, Hong
2018-01-01
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
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Doubting Thomas: Reading Between the Lines.
ERIC Educational Resources Information Center
Carrington, Bruce; Denscombe, Martyn
1987-01-01
Explains the continuing popularity of the Reverend W. Awdry's "Railway Series" featuring Thomas the Tank Engine. Argues that though the settings are anachronistic, the ideology expressed through Thomas the Tank Engine is congruent with that of the New Right. (SRT)
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Design of converging stepped spillways
USDA-ARS?s Scientific Manuscript database
Roller compacted concrete (RCC) stepped spillways are growing in popularity for providing overtopping protection for aging watershed dams with inadequate auxiliary spillway capacity and for the construction of new dams. Unobtainable land rights, topographic features, and land use changes caused by ...
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Subject/Author Index 1968-1992.
ERIC Educational Resources Information Center
Kupidura, Eva, Ed.; Kupidura, Peter, Ed.
1993-01-01
This 25-year index contains annotations of feature articles by subject and by author. Representative subjects include basic education, development education, empowerment, human rights, lifelong education, peace education, popular education, rural development, social/political action, technological advancement, and transformative research. Articles…
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Astronomy Popularization via Sci-fi Movies
NASA Astrophysics Data System (ADS)
Li, Qingkang
2015-08-01
It is astronomers’ duty to let more and more young people know a bit astronomy and be interested in astronomy and appreciate the beauty and great achievements in astronomy. One of the most effective methods to popularize astronomy to young people nowadays might be via enjoying some brilliant sci-fi movies related to astronomy with some guidance from astronomers. Firstly, we will introduce the basic information of our selective course “Appreciation of Sci-fi Movies in Astronomy” for the non-major astronomy students in our University, which is surely unique in China, then we will show its effect on astronomy popularization based on several rounds of teaching.
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ERIC Educational Resources Information Center
O'Grady, Terence J.
1979-01-01
The author offers an analysis of musical techniques found in the major rock trends of the 1960s. An annotated list of selected readings and a subject-indexed list of selected recordings are appended. This article is part of a theme issue on popular music. (Editor/SJL)
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ERIC Educational Resources Information Center
Touchstone, Allison J. L.
2010-01-01
Dual credit programs have become increasingly popular with 71% U.S. public high schools offering dual credit courses in 2002-2003. As this popularity has grown, so have concerns regarding academic rigor, course quality, parity with college courses, and effects on higher education. Determining actual dual credit course equivalent in higher…
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Hu, Ze; Zhang, Zhan; Yang, Haiqin; Chen, Qing; Zuo, Decheng
2017-07-01
Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users' votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework. Copyright © 2017 Elsevier Inc. All rights reserved.
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ERIC Educational Resources Information Center
Shalvoy, Mary Lee
1985-01-01
Describes range of options and features in gradebook programs and reviews most popular and readily available software for organizing, calculating, and updating grades: Master Grades, EA Gradebook, Gradeaid, Grader, Graphic Gradebook, Classic Plus Gradekeeping System, Records, Gradecalc, Grade Master, Report Card, Electronic Gradebook for…
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ERIC Educational Resources Information Center
White, Kerry A.
1996-01-01
Chicago's High School for Agricultural Sciences is a popular and successful urban school devoted to agriculture. This agriculturally focused high school features a tough academic curriculum and hands-on learning designed to prepare the predominantly Black and Hispanic student body for college and careers in agriculture. (SM)
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ERIC Educational Resources Information Center
Hafner, Lawrence E.
A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…
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Cape, G S
2003-03-01
To identify common character stereotypes of alcohol and other drug users as portrayed in motion pictures. A selective review of a number of movies prominently portraying alcohol and other drug use and misuse. The great majority of popular films portray alcohol and drug use whether as a routinized background, routinized foreground or exceptional foreground. Four main stereotypes of alcohol and other drug users appear to be prevalent - the tragic hero, the demonized user, the rebellious free spirit and the comedic user. A number of movies are selected which portray alcohol and other drug use as a prominent theme. Movies, as a medium for mass communication, have a powerful influence on the public and perpetuate popular mythologies regarding alcohol and other drug use.
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Action recognition using mined hierarchical compound features.
Gilbert, Andrew; Illingworth, John; Bowden, Richard
2011-05-01
The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.
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Tolan, Patrick H.; Henry, David B.; Schoeny, Michael S.; Lovegrove, Peter; Nichols, Emily
2013-01-01
Objectives To conduct a meta-analytic review of selective and indicated mentoring interventions for effects for youth at risk on delinquency and key associated outcomes (aggression, drug use, academic functioning). We also undertook the first systematic evaluation of intervention implementation features and organization and tested for effects of theorized key processes of mentor program effects. Methods Campbell Collaboration review inclusion criteria and procedures were used to search and evaluate the literature. Criteria included a sample defined as at-risk for delinquency due to individual behavior such as aggression or conduct problems or environmental characteristics such as residence in high-crime community. Studies were required to be random assignment or strong quasi-experimental design. Of 163 identified studies published 1970 - 2011, 46 met criteria for inclusion. Results Mean effects sizes were significant and positive for each outcome category (ranging form d =.11 for Academic Achievement to d = .29 for Aggression). Heterogeneity in effect sizes was noted for all four outcomes. Stronger effects resulted when mentor motivation was professional development but not by other implementation features. Significant improvements in effects were found when advocacy and emotional support mentoring processes were emphasized. Conclusions This popular approach has significant impact on delinquency and associated outcomes for youth at-risk for delinquency. While evidencing some features may relate to effects, the body of literature is remarkably lacking in details about specific program features and procedures. This persistent state of limited reporting seriously impedes understanding about how mentoring is beneficial and ability to maximize its utility. PMID:25386111
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Dijkstra, Jan Kornelis; Berger, Christian
2018-01-01
The present study examined to what extent selection and influence processes for physical aggression and prosociality in friendship networks differed between sex-specific contexts (i.e., all-male, all-female, and mixed-sex classrooms), while controlling for perceived popularity. Whereas selection processes reflect how behaviors shape friendships, influence processes reveal the reversed pattern by indicating how friends affect individual behaviors. Data were derived from a longitudinal sample of early adolescents from Chile. Four all-male classrooms ( n = 150 male adolescents), four all-female classrooms ( n = 190 female adolescents), and eight mixed-sex classrooms ( n = 272 students) were followed one year from grades 5 to 6 ( M age = 13). Analyses were conducted by means of stochastic-actor-based modeling as implemented in RSIENA. Although it was expected that selection and influence effects for physical aggression and prosociality would vary by context, these effects showed remarkably similar trends across all-male, all-female, and mixed-sex classrooms, with physical aggression reducing and with prosociality increasing the number of nominations received as best friend in all-male and particularly all-female classrooms. Further, perceived popularity increased the number of friendship nominations received in all contexts. Influence processes were only found for perceived popularity, but not for physical aggression and prosociality in any of the three contexts. Together, these findings highlight the importance of both behaviors for friendship selection independent of sex-specific contexts, attenuating the implications of these gendered behaviors for peer relations.
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Choi, Eunjung; Kwon, Sunghyuk; Lee, Donghun; Lee, Hogin; Chung, Min K
2014-07-01
Various studies that derived gesture commands from users have used the frequency ratio to select popular gestures among the users. However, the users select only one gesture from a limited number of gestures that they could imagine during an experiment, and thus, the selected gesture may not always be the best gesture. Therefore, two experiments including the same participants were conducted to identify whether the participants maintain their own gestures after observing other gestures. As a result, 66% of the top gestures were different between the two experiments. Thus, to verify the changed gestures between the two experiments, a third experiment including another set of participants was conducted, which showed that the selected gestures were similar to those from the second experiment. This finding implies that the method of using the frequency in the first step does not necessarily guarantee the popularity of the gestures. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.
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Online feature selection with streaming features.
Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan
2013-05-01
We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.
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VirGO: A Visual Browser for the ESO Science Archive Facility
NASA Astrophysics Data System (ADS)
Chéreau, F.
2008-08-01
VirGO is the next generation Visual Browser for the ESO Science Archive Facility developed by the Virtual Observatory (VO) Systems Department. It is a plug-in for the popular open source software Stellarium adding capabilities for browsing professional astronomical data. VirGO gives astronomers the possibility to easily discover and select data from millions of observations in a new visual and intuitive way. Its main feature is to perform real-time access and graphical display of a large number of observations by showing instrumental footprints and image previews, and to allow their selection and filtering for subsequent download from the ESO SAF web interface. It also allows the loading of external FITS files or VOTables, the superimposition of Digitized Sky Survey (DSS) background images, and the visualization of the sky in a `real life' mode as seen from the main ESO sites. All data interfaces are based on Virtual Observatory standards which allow access to images and spectra from external data centers, and interaction with the ESO SAF web interface or any other VO applications supporting the PLASTIC messaging system. The main website for VirGO is at http://archive.eso.org/cms/virgo.
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Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia
2018-06-12
In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.
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Optimizing Conferencing Freeware
ERIC Educational Resources Information Center
Baggaley, Jon; Klaas, Jim; Wark, Norine; Depow, Jim
2005-01-01
The increasing range of options provided by two popular conferencing freeware products, "Yahoo Messenger" and "MSN Messenger," are discussed. Each tool contains features designed primarily for entertainment purposes, which can be customized for use in online education. This report provides suggestions for optimizing the educational potential of…
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Link Prediction in Evolving Networks Based on Popularity of Nodes.
Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian
2017-08-02
Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.
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Roivainen, Eka
2015-12-01
Vocabularies of natural languages evolve over time. Useful words become more popular and useless concepts disappear. In this study, the frequency of the use of 295 English, 100 German, and 114 French personality adjectives in book texts and Twitter messages as qualifiers of the words person, woman, homme, femme, and Person was studied. Word frequency data were compared to factor loadings from previous factor analytic studies on personality terms. The correlation between the popularity of an adjective and its highest primary loading in five- and six-factor models was low (-0.12 to 0.17). The Big five (six) marker adjectives were not more popular than "blended" adjectives that had moderate loadings on several factors. This finding implies that laymen consider "blended" adjectives as equally useful descriptors compared to adjectives that represent core features of the five (six) factors. These results are compatible with three hypotheses: 1) laymen are not good at describing personality, 2) the five (six) factors are artifacts of research methods, 3) the interaction of the five (six) factors is not well understood.
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Optimized feature-detection for on-board vision-based surveillance
NASA Astrophysics Data System (ADS)
Gond, Laetitia; Monnin, David; Schneider, Armin
2012-06-01
The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.
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ERIC Educational Resources Information Center
Jakiel, Stanley James
The problem approached in this study was to ascertain what objectives should be included in the study of literature in the ninth grade and to analyze some popular anthologies to find if appropriate goals and materials to meet these goals are included. Research on the subject of objectives for literature study was examined as were the writings of…
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Science and Facebook: The same popularity law!
Néda, Zoltán; Varga, Levente; Biró, Tamás S
2017-01-01
The distribution of scientific citations for publications selected with different rules (author, topic, institution, country, journal, etc…) collapse on a single curve if one plots the citations relative to their mean value. We find that the distribution of "shares" for the Facebook posts rescale in the same manner to the very same curve with scientific citations. This finding suggests that citations are subjected to the same growth mechanism with Facebook popularity measures, being influenced by a statistically similar social environment and selection mechanism. In a simple master-equation approach the exponential growth of the number of publications and a preferential selection mechanism leads to a Tsallis-Pareto distribution offering an excellent description for the observed statistics. Based on our model and on the data derived from PubMed we predict that according to the present trend the average citations per scientific publications exponentially relaxes to about 4.
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Science and Facebook: The same popularity law!
Varga, Levente; Biró, Tamás S.
2017-01-01
The distribution of scientific citations for publications selected with different rules (author, topic, institution, country, journal, etc…) collapse on a single curve if one plots the citations relative to their mean value. We find that the distribution of “shares” for the Facebook posts rescale in the same manner to the very same curve with scientific citations. This finding suggests that citations are subjected to the same growth mechanism with Facebook popularity measures, being influenced by a statistically similar social environment and selection mechanism. In a simple master-equation approach the exponential growth of the number of publications and a preferential selection mechanism leads to a Tsallis-Pareto distribution offering an excellent description for the observed statistics. Based on our model and on the data derived from PubMed we predict that according to the present trend the average citations per scientific publications exponentially relaxes to about 4. PMID:28678796
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The origin of the criticality in meme popularity distribution on complex networks.
Kim, Yup; Park, Seokjong; Yook, Soon-Hyung
2016-03-24
Previous studies showed that the meme popularity distribution is described by a heavy-tailed distribution or a power-law, which is a characteristic feature of the criticality. Here, we study the origin of the criticality on non-growing and growing networks based on the competition induced criticality model. From the direct Mote Carlo simulations and the exact mapping into the position dependent biased random walk (PDBRW), we find that the meme popularity distribution satisfies a very robust power- law with exponent α = 3/2 if there is an innovation process. On the other hand, if there is no innovation, then we find that the meme popularity distribution is bounded and highly skewed for early transient time periods, while it satisfies a power-law with exponent α ≠ 3/2 for intermediate time periods. The exact mapping into PDBRW clearly shows that the balance between the creation of new memes by the innovation process and the extinction of old memes is the key factor for the criticality. We confirm that the balance for the criticality sustains for relatively small innovation rate. Therefore, the innovation processes with significantly influential memes should be the simple and fundamental processes which cause the critical distribution of the meme popularity in real social networks.
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The origin of the criticality in meme popularity distribution on complex networks
Kim, Yup; Park, Seokjong; Yook, Soon-Hyung
2016-01-01
Previous studies showed that the meme popularity distribution is described by a heavy-tailed distribution or a power-law, which is a characteristic feature of the criticality. Here, we study the origin of the criticality on non-growing and growing networks based on the competition induced criticality model. From the direct Mote Carlo simulations and the exact mapping into the position dependent biased random walk (PDBRW), we find that the meme popularity distribution satisfies a very robust power- law with exponent α = 3/2 if there is an innovation process. On the other hand, if there is no innovation, then we find that the meme popularity distribution is bounded and highly skewed for early transient time periods, while it satisfies a power-law with exponent α ≠ 3/2 for intermediate time periods. The exact mapping into PDBRW clearly shows that the balance between the creation of new memes by the innovation process and the extinction of old memes is the key factor for the criticality. We confirm that the balance for the criticality sustains for relatively small innovation rate. Therefore, the innovation processes with significantly influential memes should be the simple and fundamental processes which cause the critical distribution of the meme popularity in real social networks. PMID:27009399
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The origin of the criticality in meme popularity distribution on complex networks
NASA Astrophysics Data System (ADS)
Kim, Yup; Park, Seokjong; Yook, Soon-Hyung
2016-03-01
Previous studies showed that the meme popularity distribution is described by a heavy-tailed distribution or a power-law, which is a characteristic feature of the criticality. Here, we study the origin of the criticality on non-growing and growing networks based on the competition induced criticality model. From the direct Mote Carlo simulations and the exact mapping into the position dependent biased random walk (PDBRW), we find that the meme popularity distribution satisfies a very robust power- law with exponent α = 3/2 if there is an innovation process. On the other hand, if there is no innovation, then we find that the meme popularity distribution is bounded and highly skewed for early transient time periods, while it satisfies a power-law with exponent α ≠ 3/2 for intermediate time periods. The exact mapping into PDBRW clearly shows that the balance between the creation of new memes by the innovation process and the extinction of old memes is the key factor for the criticality. We confirm that the balance for the criticality sustains for relatively small innovation rate. Therefore, the innovation processes with significantly influential memes should be the simple and fundamental processes which cause the critical distribution of the meme popularity in real social networks.
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Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.
Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei
2016-01-13
An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.
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Group selection - problems and possibilities and for the more shade-intolerant species
Paul A. Murphy; Michael G. Shelton; David L. Graney
1993-01-01
The group selection method is a hybrid, drawing key elements from both even- and uneven-aged silviculture. It is perhaps the least used and understood of all the reproductive cutting methods, but it is gaining popularity because of the current disfavor of even-aged silviculture. The group selection method appears promising for regenerating shade-intolerant and...
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Group selection - problems and possibilities and for the more shade-intolerant species
Paul A. Murphy; Michael G. Shelton; David L. Graney
1993-01-01
The group selection method is a hybrid, drawing key elements from both even- and uneven-aged silviculture. It is perhaps the least used and understood of all reproductive cutting methods, but it is gaining popularity because of the current disfavor of even-aged silviculture. The group selection method appears promising for regenerating shade-intolerant and intermediate...
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The effect of friend selection on social influences in obesity.
Trogdon, Justin G; Allaire, Benjamin T
2014-12-01
We present an agent-based model of weight choice and peer selection that simulates the effect of peer selection on social multipliers for weight loss interventions. The model generates social clustering around weight through two mechanisms: a causal link from others' weight to an individual's weight and the propensity to select peers based on weight. We simulated weight loss interventions and tried to identify intervention targets that maximized the spillover of weight loss from intervention participants to nonparticipants. Social multipliers increase with the number of intervention participants' friends. For example, when friend selection was based on a variable exogenous to weight, the weight lost among non-participants increased by 23% (14.3lb vs. 11.6lb) when targeting the most popular obese. Holding constant the number of participants' friends, multipliers increase with increased weight clustering due to selection, up to a point. For example, among the most popular obese, social multipliers when matching on a characteristic correlated with weight (1.189) were higher than when matching on the exogenous characteristic (1.168) and when matching on weight (1.180). Increased weight clustering also implies more obese "friends of friends" of participants, who reduce social multipliers. Copyright © 2014 Elsevier B.V. All rights reserved.
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Mason, Matthew J.; Cornwall, Hannah L.; Smith, Ewan St. J.
2016-01-01
Although increasingly popular as a laboratory species, very little is known about the peripheral auditory system of the naked mole-rat, Heterocephalus glaber. In this study, middle and inner ears of naked mole-rats of a range of ages were examined using micro-computed tomography and dissection. The ears of five other bathyergid species (Bathyergus suillus, Cryptomys hottentotus, Fukomys micklemi, Georychus capensis and Heliophobius argenteocinereus) were examined for comparative purposes. The middle ears of bathyergids show features commonly found in other members of the Ctenohystrica rodent clade, including a fused malleus and incus, a synovial stapedio-vestibular articulation and the loss of the stapedius muscle. Heterocephalus deviates morphologically from the other bathyergids examined in that it has a more complex mastoid cavity structure, poorly-ossified processes of the malleus and incus, a ‘columelliform’ stapes and fewer cochlear turns. Bathyergids have semicircular canals with unusually wide diameters relative to their radii of curvature. How the lateral semicircular canal reaches the vestibule differs between species. Heterocephalus has much more limited high-frequency hearing than would be predicted from its small ear structures. The spongy bone forming its ossicular processes, the weak incudo-stapedial articulation, the columelliform stapes and (compared to other bathyergids) reduced cochlear coiling are all potentially degenerate features which might reflect a lack of selective pressure on its peripheral auditory system. Substantial intraspecific differences were found in certain middle and inner ear structures, which might also result from relaxed selective pressures. However, such interpretations must be treated with caution in the absence of experimental evidence. PMID:27926945
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EEG feature selection method based on decision tree.
Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun
2015-01-01
This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.
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Patterson, Brent R.; Anderson, Morgan L.; Rodgers, Arthur R.; Vander Vennen, Lucas M.; Fryxell, John M.
2017-01-01
Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism–a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors–has negative consequences for the viability of woodland caribou. PMID:29117234
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Newton, Erica J; Patterson, Brent R; Anderson, Morgan L; Rodgers, Arthur R; Vander Vennen, Lucas M; Fryxell, John M
2017-01-01
Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism-a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors-has negative consequences for the viability of woodland caribou.
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McTwo: a two-step feature selection algorithm based on maximal information coefficient.
Ge, Ruiquan; Zhou, Manli; Luo, Youxi; Meng, Qinghan; Mai, Guoqin; Ma, Dongli; Wang, Guoqing; Zhou, Fengfeng
2016-03-23
High-throughput bio-OMIC technologies are producing high-dimension data from bio-samples at an ever increasing rate, whereas the training sample number in a traditional experiment remains small due to various difficulties. This "large p, small n" paradigm in the area of biomedical "big data" may be at least partly solved by feature selection algorithms, which select only features significantly associated with phenotypes. Feature selection is an NP-hard problem. Due to the exponentially increased time requirement for finding the globally optimal solution, all the existing feature selection algorithms employ heuristic rules to find locally optimal solutions, and their solutions achieve different performances on different datasets. This work describes a feature selection algorithm based on a recently published correlation measurement, Maximal Information Coefficient (MIC). The proposed algorithm, McTwo, aims to select features associated with phenotypes, independently of each other, and achieving high classification performance of the nearest neighbor algorithm. Based on the comparative study of 17 datasets, McTwo performs about as well as or better than existing algorithms, with significantly reduced numbers of selected features. The features selected by McTwo also appear to have particular biomedical relevance to the phenotypes from the literature. McTwo selects a feature subset with very good classification performance, as well as a small feature number. So McTwo may represent a complementary feature selection algorithm for the high-dimensional biomedical datasets.
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Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo
2016-02-01
Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.
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Xu, Pei; Wu, Xinyi; Muñoz-Amatriaín, María; Wang, Baogen; Wu, Xiaohua; Hu, Yaowen; Huynh, Bao-Lam; Close, Timothy J; Roberts, Philip A; Zhou, Wen; Lu, Zhongfu; Li, Guojing
2017-05-01
Cowpea (V. unguiculata L. Walp) is a climate resilient legume crop important for food security. Cultivated cowpea (V. unguiculata L) generally comprises the bushy, short-podded grain cowpea dominant in Africa and the climbing, long-podded vegetable cowpea popular in Asia. How selection has contributed to the diversification of the two types of cowpea remains largely unknown. In the current study, a novel genotyping assay for over 50 000 SNPs was employed to delineate genomic regions governing pod length. Major, minor and epistatic QTLs were identified through QTL mapping. Seventy-two SNPs associated with pod length were detected by genome-wide association studies (GWAS). Population stratification analysis revealed subdivision among a cowpea germplasm collection consisting of 299 accessions, which is consistent with pod length groups. Genomic scan for selective signals suggested that domestication of vegetable cowpea was accompanied by selection of multiple traits including pod length, while the further improvement process was featured by selection of pod length primarily. Pod growth kinetics assay demonstrated that more durable cell proliferation rather than cell elongation or enlargement was the main reason for longer pods. Transcriptomic analysis suggested the involvement of sugar, gibberellin and nutritional signalling in regulation of pod length. This study establishes the basis for map-based cloning of pod length genes in cowpea and for marker-assisted selection of this trait in breeding programmes. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.
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19. Photographer unknown, circa 1950 'THE LOOP OVER' BRIDGE WAS ...
19. Photographer unknown, circa 1950 'THE LOOP OVER' BRIDGE WAS CONSTRUCTED ON NEWFOUND GAP ROAD TO REPLACE A SERIES OF DANGEROUS SWITCHBACKS. THIS ENGINEERING FEATURE QUICKLY BECAME A POPULAR TOURIST ATTRACTION. - Great Smoky Mountains National Park Roads & Bridges, Gatlinburg, Sevier County, TN
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Videlier, P; Piras, P
1990-01-01
Strip cartoons are among the most vivid means of communication at our disposal, and they are particularly popular with the young. Medical matters have featured in many stories, though usually in a peripheral role. Could more be done to use this powerful medium, or would deliberate exploitation destroy it?
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The Effect of Blended Instruction on Accelerated Learning
ERIC Educational Resources Information Center
Patchan, Melissa M.; Schunn, Christian D.; Sieg, Wilfried; McLaughlin, Dawn
2016-01-01
While online instructional technologies are becoming more popular in higher education, educators' opinions about online learning tend to be generally negative. Furthermore, many studies have failed to systematically examine the features that distinguish one instructional mode from another, which weakens possible explanations for why online…
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The ESA/ESO/NASA Photoshop FITS Liberator 3: Have your say on new features
NASA Astrophysics Data System (ADS)
Nielsen, L. H.; Christensen, L. L.; Hurt, R. L.; Nielsen, K.; Johansen, T.
2008-06-01
The popular, free ESA/ESO/NASA Photoshop FITS Liberator image processing software (a plugin for Adobe Photoshop) is about to get simpler, faster and more user-friendly! Here we would like to solicit inputs from the community of users.
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Project Achievement: An After-School Success Story.
ERIC Educational Resources Information Center
Mercure, Christine M.
1993-01-01
To improve its school failure rate, a Virginia intermediate school instituted Project Achievement, a privately funded program helping at-risk students complete homework assignments. Structured into three one-hour sessions featuring tutoring, interdisciplinary study groups, and special activities, the project is immensely popular. During the summer…
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Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.
Haoliang Yuan; Yuan Yan Tang
2017-04-01
Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.
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The effect of feature selection methods on computer-aided detection of masses in mammograms
NASA Astrophysics Data System (ADS)
Hupse, Rianne; Karssemeijer, Nico
2010-05-01
In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.
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Earl C. Leatherberry; David W. Lime; Jerrilyn Lavarre Thompson
1980-01-01
Participation in river recreation has been expanding at a rapid rate. This paper reviews selected phenomenon associated with the growing popularity of rivers as recreational resources. The paper will: (1) describe the river recreation resource (the supply situation); (2) present selected indicators of increased river recreation use (the demand situation); (3) present...
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A Selected Bibliography of Herbert Marshall McLuhan (1911-1973).
ERIC Educational Resources Information Center
Katula, Richard, Comp.
Spanning Herbert Marshall McLuhan's writing career, this selected bibliography covers his development as a scholar, beginning with his education and scholarly growth in the classical and literary traditions, continuing with his turning toward society and more popular concerns--especially communication, and concluding with his synthesizing of these…
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5 CFR 591.217 - In which outlets does OPM collect prices?
Code of Federal Regulations, 2010 CFR
2010-01-01
... popular outlets in each survey area. OPM selects these outlets based on their proximity to the housing... that reflect sales volume. To the extent practical, OPM prices like items in the same types of outlets... surveys and select outlets based on the results of those surveys. ...
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Random forest (RF) is popular in ecological and environmental modeling, in part, because of its insensitivity to correlated predictors and resistance to overfitting. Although variable selection has been proposed to improve both performance and interpretation of RF models, it is u...
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Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network
DOE Office of Scientific and Technical Information (OSTI.GOV)
Wang Xiaojia; Mao Qirong; Zhan Yongzhao
There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less
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Alcohol marketing in televised international football: frequency analysis
2014-01-01
Background Alcohol marketing includes sponsorship of individuals, organisations and sporting events. Football (soccer) is one of the most popular spectator sports worldwide. No previous studies have quantified the frequency of alcohol marketing in a high profile international football tournament. The aims were to determine: the frequency and nature of visual references to alcohol in a representative sample of EURO2012 matches broadcast in the UK; and if frequency or nature varied between matches broadcast on public service and commercial channels, or between matches that did and did not feature England. Methods Eight matches selected by stratified random sampling were recorded. All visual references to alcohol were identified using a tool with high inter-rater reliability. Results 1846 visual references to alcohol were identified over 1487 minutes of broadcast - an average of 1.24 references per minute. The mean number of references per minute was higher in matches that did vs did not feature England (p = 0.004), but did not differ between matches broadcast on public service vs commercial channels (p = 0.92). Conclusions The frequency of visual references to alcohol was universally high and higher in matches featuring the only UK home team - England - suggesting that there may be targeting of particularly highly viewed matches. References were embedded in broadcasts, and not particular to commercial channels including paid-for advertising. New UK codes-of-conduct on alcohol marketing at sporting events will not reduce the level of marketing reported here. PMID:24885718
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Petros, Ryan; Solomon, Phyllis
2015-11-01
Illness self-management (ISM) programs for adults with serious mental illness offer strategies to increase self-directed recovery activities to maximize wellness and increase independence from the service delivery system. This article describes five of the most popular ISM programs: Pathways to Recovery, The Recovery Workbook, Building Recovery of Individual Dreams and Goals through Education and Support, Wellness and Recovery Action Planning, and Illness Management and Recovery. It provides guidance for administrators, practitioners, and consumers for the purposes of selecting the program or programs providing the best fit. The framework for describing the five programs encompasses four contextual domains that supplement empirical evidence for a more comprehensive evaluation: structure, value orientation toward recovery, methods of teaching, and educational content. Contextual domains distinguish programs from one another, including length and time commitment, requisite resources, inclusion of group support, utilization of medical language and pathology, degree of traditional didactic education, and prioritization of consumer-driven self-exploration. The authors also searched PsycINFO, Google Scholar, and Cochrane Reviews for empirical evidence and evaluated the five programs on the strength of the evidence and the effectiveness of the intervention. Evidence of program effectiveness was found to range from low to moderate. However, empirical evidence alone is insufficient for selecting among the five programs, and contextual domains may offer the most relevant guidance by matching program features with goals of consumers, practitioners, and administrators.
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Zhao, Yu-Xiang; Chou, Chien-Hsing
2016-01-01
In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters. PMID:27314346
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Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention
Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei
2016-01-01
An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features. PMID:26759193
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Speech-Language Pathology production regarding voice in popular singing.
Drumond, Lorena Badaró; Vieira, Naymme Barbosa; Oliveira, Domingos Sávio Ferreira de
2011-12-01
To present a literature review about the Brazilian scientific production in Speech-Language Pathology and Audiology regarding voice in popular singing in the last decade, as for number of publications, musical styles studied, focus of the researches, and instruments used for data collection. Cross-sectional descriptive study carried out in two stages: search in databases and publications encompassing the last decade of researches in this area in Brazil, and reading of the material obtained for posterior categorization. The databases LILACS and SciELO, the Databasis of Dissertations and Theses organized by CAPES, the online version of Acta ORL, and the online version of OPUS were searched, using the following uniterms: voice, professional voice, singing voice, dysphonia, voice disorders, voice training, music, dysodia. Articles published between the years 2000 and 2010 were selected. The researches found were classified and categorized after reading their abstracts and, when necessary, the whole study. Twenty researches within the proposed theme were selected, all of which were descriptive, involving several musical styles. Twelve studies focused on the evaluation of the popular singer's voice, and the most frequently used data collection instrument was the auditory-perceptual evaluation. The results of the publications found corroborate the objectives proposed by the authors and the different methodologies. The number of studies published is still restricted when compared to the diversity of musical genres and the uniqueness of popular singer.
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Natural image classification driven by human brain activity
NASA Astrophysics Data System (ADS)
Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao
2016-03-01
Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.
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EFS: an ensemble feature selection tool implemented as R-package and web-application.
Neumann, Ursula; Genze, Nikita; Heider, Dominik
2017-01-01
Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.
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NASA's Sentinels Monitoring Weather and Climate: Past, Present, and Future
NASA Technical Reports Server (NTRS)
Shepherd, J. Marshall; Herring, David; Gutro, Rob; Huffman, George; Halverson, Jeff
2002-01-01
Weatherwise is probably the most popular newstand magazine focusing on the subject of weather. It is published six times per year and includes features on weather, climate, and technology. This article (to appear in the January/February Issue) provides a comprehensive review of NASA s past, present, and future contributions in satellite remote sensing for weather and climate processes. The article spans the historical strides of the TIROS program through the scientific and technological innovation of Earth Observer-3 and Global Precipitation Measurement (GPM). It is one of the most thorough reviews of NASA s weather and climate satellite efforts to appear in the popular literature.
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Ma, Haitao; Fang, Chuanglin; Pang, Bo; Li, Guangdong
2014-01-01
Background The relations between geographical proximity and spatial distance constitute a popular topic of concern. Thus, how geographical proximity affects scientific cooperation, and whether geographically proximate scientific cooperation activities in fact exhibit geographic scale features should be investigated. Methodology Selected statistics from the ISI database on cooperatively authored papers, the authors of which resided in 60 typical cites in China, and which were published in the years 1990, 1995, 2000, 2005, and 2010, were used to establish matrices of geographic distance and cooperation levels between cities. By constructing a distance-cooperation model, the degree of scientific cooperation based on spatial distance was calculated. The relationship between geographical proximity and scientific cooperation, as well as changes in that relationship, was explored using the fitting function. Result (1) Instead of declining, the role of geographical proximity in inter-city scientific cooperation has increased gradually but significantly with the popularization of telecommunication technologies; (2) the relationship between geographical proximity and scientific cooperation has not followed a perfect declining curve, and at certain spatial scales, the distance-decay regularity does not work; (3) the Chinese scientific cooperation network gathers around different regional center cities, showing a trend towards a regional network; within this cooperation network the amount of inter-city cooperation occurring at close range increased greatly. Conclusion The relationship between inter-city geographical distance and scientific cooperation has been enhanced and strengthened over time. PMID:25365449
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Derewenda, Zygmunt S; Godzik, Adam
2017-01-01
Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e., crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure, and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular "sticky patch" model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer "sticky patches" which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the "sticky patch" model. We discuss state-of-the-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.
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Polydopamine--a nature-inspired polymer coating for biomedical science.
Lynge, Martin E; van der Westen, Rebecca; Postma, Almar; Städler, Brigitte
2011-12-01
Polymer coatings are of central importance for many biomedical applications. In the past few years, poly(dopamine) (PDA) has attracted considerable interest for various types of biomedical applications. This feature article outlines the basic chemistry and material science regarding PDA and discusses its successful application from coatings for interfacing with cells, to drug delivery and biosensing. Although many questions remain open, the primary aim of this feature article is to illustrate the advent of PDA on its way to become a popular polymer for bioengineering purposes.
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Object recognition with hierarchical discriminant saliency networks.
Han, Sunhyoung; Vasconcelos, Nuno
2014-01-01
The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.
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Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo
2015-01-01
Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease. PMID:26106884
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Disciplinary Literacy from a Speech-Language Pathologist's Perspective
ERIC Educational Resources Information Center
Ehren, Barbara J.; Murza, Kimberly A.; Malani, Melissa D.
2012-01-01
Disciplinary literacy is an increasingly popular focal area in adolescent literacy. In disciplinary literacy, the discourse features of specific knowledge domains (e.g., literature, history, science, and math) assume major importance in understanding and constructing meaning in each discipline. Because language plays a significant role in…
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Picture Books in the Digital Age
ERIC Educational Resources Information Center
Serafini, Frank; Kachorsky, Danielle; Aguilera, Earl
2016-01-01
The design, publication, and features of contemporary narrative picturebooks have been impacted by the digital revolution and the emerging popularity of digital reading devices. Considering both the affordances and the limitations of digital picturebook apps can help readers, teachers, parents, and other educators make better decisions about the…
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Good Student Questions in Inquiry Learning
ERIC Educational Resources Information Center
Lombard, François E.; Schneider, Daniel K.
2013-01-01
Acquisition of scientific reasoning is one of the big challenges in education. A popular educational strategy advocated for acquiring deep knowledge is inquiry-based learning, which is driven by emerging "good questions". This study will address the question: "Which design features allow learners to refine questions while preserving…
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ERIC Educational Resources Information Center
Chai, David; Garcia, Alejandro L.
2011-01-01
Animation has become enormously popular in feature films, television, and video games. Art departments and film schools at universities as well as animation programs at high schools have expanded in recent years to meet the growing demands for animation artists. Professional animators identify the technological facet as the most rapidly advancing…
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ERIC Educational Resources Information Center
Bhattacharya, Banhi
2013-01-01
School choice has gained considerable popularity in recent decades as governments struggle to improve quality and reduce the cost of education by increasing competition among schools and decreasing the level of bureaucracy (Chubb & Moe, 1990). The trend towards reorganization of public service allocation for education has been a feature of…
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Feature selection methods for big data bioinformatics: A survey from the search perspective.
Wang, Lipo; Wang, Yaoli; Chang, Qing
2016-12-01
This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures. Copyright © 2016 Elsevier Inc. All rights reserved.
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NASA Astrophysics Data System (ADS)
Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na
2016-10-01
Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.
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Sparse alignment for robust tensor learning.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming
2014-10-01
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.
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Teeter, Matthew G; Kopacz, Alexander J; Nikolov, Hristo N; Holdsworth, David W
2015-01-01
Additive manufacturing continues to increase in popularity and is being used in applications such as biomaterial ingrowth that requires sub-millimeter dimensional accuracy. The purpose of this study was to design a metrology test object for determining the capabilities of additive manufacturing systems to produce common objects, with a focus on those relevant to medical applications. The test object was designed with a variety of features of varying dimensions, including holes, cylinders, rectangles, gaps, and lattices. The object was built using selective laser melting, and the produced dimensions were compared to the target dimensions. Location of the test objects on the build plate did not affect dimensions. Features with dimensions less than 0.300 mm did not build or were overbuilt to a minimum of 0.300 mm. The mean difference between target and measured dimensions was less than 0.100 mm in all cases. The test object is applicable to multiple systems and materials, tests the effect of location on the build, uses a minimum of material, and can be measured with a variety of efficient metrology tools (including measuring microscopes and micro-CT). Investigators can use this test object to determine the limits of systems and adjust build parameters to achieve maximum accuracy. © IMechE 2014.
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Fusing visual and behavioral cues for modeling user experience in games.
Shaker, Noor; Asteriadis, Stylianos; Yannakakis, Georgios N; Karpouzis, Kostas
2013-12-01
Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91%, 92%, and 88% for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player.
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NASA Astrophysics Data System (ADS)
Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang
2017-01-01
Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.
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Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang
2017-01-01
Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883
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Multi-channel feature dictionaries for RGB-D object recognition
NASA Astrophysics Data System (ADS)
Lan, Xiaodong; Li, Qiming; Chong, Mina; Song, Jian; Li, Jun
2018-04-01
Hierarchical matching pursuit (HMP) is a popular feature learning method for RGB-D object recognition. However, the feature representation with only one dictionary for RGB channels in HMP does not capture sufficient visual information. In this paper, we propose multi-channel feature dictionaries based feature learning method for RGB-D object recognition. The process of feature extraction in the proposed method consists of two layers. The K-SVD algorithm is used to learn dictionaries in sparse coding of these two layers. In the first-layer, we obtain features by performing max pooling on sparse codes of pixels in a cell. And the obtained features of cells in a patch are concatenated to generate patch jointly features. Then, patch jointly features in the first-layer are used to learn the dictionary and sparse codes in the second-layer. Finally, spatial pyramid pooling can be applied to the patch jointly features of any layer to generate the final object features in our method. Experimental results show that our method with first or second-layer features can obtain a comparable or better performance than some published state-of-the-art methods.
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Integrated feature extraction and selection for neuroimage classification
NASA Astrophysics Data System (ADS)
Fan, Yong; Shen, Dinggang
2009-02-01
Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.
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Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.
Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor
2010-08-01
Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.
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Williams, Rebecca S; Derrick, Jason; Liebman, Aliza K; LaFleur, Kevin
2017-11-03
To identify the population of Internet e-cigarette vendors (IEVs) and conduct content analysis of products sold and IEVs' promotional, claims and pricing practices. Multiple sources were used to identify IEV websites, primarily complex search algorithms scanning over 180 million websites. In 2013, 32 446 websites were manually screened, identifying 980 IEVs, with the 281 most popular selected for content analysis. This methodology yielded 31 239 websites for manual screening in 2014, identifying 3096 IEVs, with 283 selected for content analysis. While the majority of IEVs (71.9%) were US based in 2013, this dropped to 64.3% in 2014 (p<0.01), with IEVs located in at least 38 countries, and 12% providing location indicators reflecting two or more countries, complicating jurisdictional determinations.Reflecting the retail market, IEVs are transitioning from offering disposable and 'cigalike' e-cigarettes to larger tank and "mod" systems. Flavored e-cigarettes were available from 85.9% of IEVs in 2014, with fruit and candy flavors being most popular. Most vendors (76.5%) made health claims in 2013, dropping to 43.1% in 2014. Some IEVs featured conflicting claims about whether or not e-cigarettes aid in smoking cessation. There was wide variation in pricing, with e-cigarettes available as inexpensive as one dollar, well within the affordable range for adults and teens. The number of Internet e-cigarette vendors grew threefold from 2013 to 2014, far surpassing the number of Internet cigarette vendors (N=775) at the 2004 height of that industry. New and expanded regulations for online e-cigarette sales are needed, including restrictions on flavors and marketing claims. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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Dark victory: cancer and popular Hollywood film.
Lederer, Susan E
2007-01-01
This paper explores the cultural representations of cancer in popular Hollywood films released between 1930 and 1970. These cinematic treatments were not representative of the types of cancer that increasingly afflicted Americans, nor were filmmakers and studios concerned with realistic representations of the disease, its treatment, and its outcomes. As in the "epidemic entertainments" of the early twentieth century that portrayed diseases as cultural commodities, popular filmmakers selectively projected some cancers rather than others, favoring those that were less offensive and more photogenic. Although the characters became weak and died, they did so without gross transformations of their bodies. This paper argues that such representations nonetheless informed American attitudes about cancer and the role of medical research in overcoming the disease.
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Advice offered by practitioners of complementary/ alternative medicine: an important ethical issue.
Ernst, E
2009-12-01
The current popularity of complementary/alternative medicine (CAM) generates many challenges to medical ethics. The one discussed here is the advice offered by CAM practitioners. Using selected examples, the author tries to demonstrate that some of the advice issued through the popular media or provided by acupuncturists, chiropractors, herbalists, homeopaths, pharmacists, and doctors is misleading or dangerous. This, the author argues, can impinge on the main principle of medical ethics: beneficence, nonmaleficence, and autonomy. We should work toward correcting this deplorable situation.
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USDA-ARS?s Scientific Manuscript database
Changes in temperature can result in fundamental changes in plant physiology. This study investigated the impact of different temperatures from 14 to 26 °C on the resistance or susceptibility to the Hessian fly, Mayetiola destructor, of selected wheat cultivars that are either currently popular in ...
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Recordings for Children. A Selected List of Records and Cassettes. Fourth Edition.
ERIC Educational Resources Information Center
Thomas, Elaine E., Comp.; And Others
This booklet, compiled by three experts in children's recordings, provides a selected list of records and cassettes which can accommodate a broad range of informational and recreational requirements of children. Some of the subjects covered are children's songs and folk music, orchestral music, popular music, holidays, natural sciences and space…
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Selectivity of Content and Language Integrated Learning Programmes in German Secondary Schools
ERIC Educational Resources Information Center
Dallinger, Sara; Jonkmann, Kathrin; Hollm, Jan
2018-01-01
Despite its increasing popularity and adoption across Europe, Content and Language Integrated Learning (CLIL) is not without its critics. It has been argued that CLIL programmes are highly selective, that is, the students possess more favourable learning prerequisites than their monolingually taught peers. The present study contributes to this…
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Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression
ERIC Educational Resources Information Center
Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.
2013-01-01
Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…
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ERIC Educational Resources Information Center
Brittin, Ruth V.
2014-01-01
Listeners ("N" = 543) in grades 4, 5, and 6 rated their preference for 10 instrumental and vocal selections from various styles, including four popular music selections with versions performed in English, Spanish, or an Asian language. Participants estimated their identification with Spanish/Hispanic/Latino and Asian cultures, the number…
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Disk Memories: What You Should Know before You Buy Them.
ERIC Educational Resources Information Center
Bursky, Dave
1981-01-01
Explains the basic features of floppy disk and hard disk computer storage systems and the purchasing decisions which must be made, particularly in relation to certain popular microcomputers. A disk vendors directory is included. Journal availability: Hayden Publishing Company, 50 Essex Street, Rochelle Park, NJ 07662. (SJL)
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"I'm Proud to Be Me": Health, Community and Schooling
ERIC Educational Resources Information Center
Burrrows, Lisette
2011-01-01
Health reportage in New Zealand's popular and professional media regularly features large, avowedly inactive, indigenous and/or "poor" people failing to nurture their children properly on account of their size. While well-meaning government and school-based initiatives explicitly target these so-called "high-need" communities,…
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Learning to Learn Cooperatively
ERIC Educational Resources Information Center
Byrd, Anne Hammond
2009-01-01
Cooperative learning, put quite simply, is a type of instruction whereby students work together in small groups to achieve a common goal. Cooperative learning has become increasingly popular as a feature of Communicative Language Teaching (CLT) with benefits that include increased student interest due to the quick pace of cooperative tasks,…
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ERIC Educational Resources Information Center
Whelan, James
2005-01-01
Community-based environmental education is an important part of the sustainability project. Along with regulation and market-based instruments, adult learning and education in non-formal settings consistently features in the sustainability strategies advocated and implemented by government, community and industry entities. Community-situated…
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A Matter of Perspective: Teaching International Relations in the Middle East
ERIC Educational Resources Information Center
Burns, Sean
2014-01-01
In this article, the author looks at several popular international relations textbooks in light of his experience teaching students in the Middle East. He finds that, for their many strengths, most of the books lack some key features that would make them more useful for students abroad.
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Death as Insight into Life: Adolescents' Gothic Text Encounters
ERIC Educational Resources Information Center
Del Nero, Jennifer
2017-01-01
This qualitative case study explores adolescents' responses to texts containing death and destruction, a seminal trope of the Gothic literary genre. Participants read both classic and popular culture texts featuring characters grappling with death in their seventh grade reading classroom. Observations, interviews, and documents were collected and…
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Listening to Estuary English in Singapore
ERIC Educational Resources Information Center
Deterding, David
2005-01-01
In Singapore, many people are not familiar with Estuary English (EE), the variety of English becoming popular in much of southern England. In the current study, when students listened to interviews with EE speakers and were asked to transcribe orthographically what they heard, most of them had severe problems. Features of pronunciation that…
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Internal Consistency Reliability of the Self-Report Antisocial Process Screening Device
ERIC Educational Resources Information Center
Poythress, Norman G.; Douglas, Kevin S.; Falkenbach, Diana; Cruise, Keith; Lee, Zina; Murrie, Daniel C.; Vitacco, Michael
2006-01-01
The self-report version of the Antisocial Process Screening Device (APSD) has become a popular measure for assessing psychopathic features in justice-involved adolescents. However, the internal consistency reliability of its component scales (Narcissism, Callous-Unemotional, and Impulsivity) has been questioned in several studies. This study…
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An Investigation on Instructors' Knowledge, Belief and Practices towards Distance Education
ERIC Educational Resources Information Center
Yildiz, Merve; Erdem, Mukaddes
2018-01-01
Distance education systems have emerged as increasingly accessible and indispensable features in education owing to the development and spread of communication technologies and the transformation of individual characteristics, needs and demands. With the growing popularity of distance education programs, detailed analysis of their actual success…
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Meteor Beliefs Project: Musical Meteors, meteoric imagery as used in near-contemporary song lyrics
NASA Astrophysics Data System (ADS)
McBeath, Alastair; Gheorghe, Andrei Dorian
2010-01-01
Items collected from contemporary song lyrics featuring meteoric imagery, or inspired by meteors, are given, with some discussion. While not a major part of the Meteor Beliefs Project, there are points of interest in how such usage may become passed into popular beliefs about meteors.
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Happy Children: A Challenge to Parents.
ERIC Educational Resources Information Center
Dreikurs, Rudolf; Soltz, Vicki
The present edition brings back into print a popular parenting book that appeared in 1964 and which sold over half a million copies. It features Anglicized spelling and provides examples that fit the British rather than American culture. The book instructs parents in democratic methods for raising children and it addresses contemporary dilemmas…
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Parabolic Mirror: Focusing on Science, Technology, Engineering, and Math
ERIC Educational Resources Information Center
Smith, Karianne; Hughes, William
2013-01-01
In the fall of 2011, Park Forest Middle School (PFMS) students approached the STEM faculty with numerous questions regarding the popular television show Myth Busters, which detailed Greek mathematician, physicist, engineer, and inventor, Archimedes. Two episodes featured attempts to test historical accounts that Archimedes developed a death ray…
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Molecular Characterization of Kastamonu Garlic: An Economically Important Garlic Clone in Turkey
USDA-ARS?s Scientific Manuscript database
This study was conducted to assess genetic relationship of Kastamonu garlic, which is very popular in Turkey due to its high quality features, along with some previously characterized garlic clones collected from different regions of the world using AFLP and locus specific DNA markers. UPGMA cluste...
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Spotlight on Transition to Teaching Music
ERIC Educational Resources Information Center
MENC: The National Association for Music Education, 2004
2004-01-01
The latest title in the popular Spotlight series, this timely book focuses on issues involving recruitment and retention of music teachers, a crucial issue in these days of budget constraints. Arranged chronologically, it features a collection of articles from state journals focusing on issues such as mentoring, teacher shortages, burnout, and…
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Literary Experience and Literature Teaching since the Growth Model
ERIC Educational Resources Information Center
Reid, Ian
2016-01-01
By the late 70s the "growth through English" slogan, derived from John Dixon's account of the Dartmouth conference, had become popular around Australia. In 1980 the Sydney IFTE conference featured several Dartmouth veterans; but during that conference, Dartmouth-linked ideas from overseas mingled with lines of local influence, especially…
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Studying Masculinity(ies) in Books about Girls
ERIC Educational Resources Information Center
Harper, Helen
2007-01-01
This study explored the nature and performance of masculinity portrayed in popular young adult novels featuring female protagonists. Although all had their limitations, the novels offered more complex renderings of gendered identity in the lives of female and male adolescent characters, addressed the effects of enforced traditional masculinity,…
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Using Popular Film as a Teaching Resource in Accounting Classes
ERIC Educational Resources Information Center
Bay, Darlene; Felton, Sandra
2012-01-01
This paper describes a pedagogical experiment that used feature films in a senior accounting class to stimulate development of student competencies and raise ethical issues. Rather than being content driven, this active learning technique focuses on skills development, while engaging the students' emotions in the learning process. Encompassing…
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Multi-Phase Combustion and Transport Processes Under the Influence of Acoustic Excitation
2014-01-01
membrane and fibre - type regime. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 1.9 A convective shear layer diagrammed in the...Experimental and Theoretical Resonant Frequencies . . . . . . . . . . . . 73 2.2 Fuel Properties ...the presence of acoustic forcing as their usage is increasingly diversified. Although the IC engine, the most popular biofuels application, features
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A National Assessment of Change in Green Infrastructure Using Mathematical Morphology
Green infrastructure is a popular framework for conservation planning. The main elements of green infrastructure are hubs and links. Hubs tend to be large areas of natural vegetation and links tend to be linear features (e.g., streams) that connect hubs. Within the United States...
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Health Website's Games and Features Evaluation by Middle Schoolers
ERIC Educational Resources Information Center
Chapman-Novakofski, Karen; Muzaffar, Henna; Castelli, Darla; Scherer, Jane
2016-01-01
Health information on the Internet is popular for both adults and adolescents. Providing this information in an enjoyable manner during school may provide an alternative to teacher-led education. However, there are advantages and disadvantages of "edutainment". The objective of this study was to explore these advantages and disadvantages…
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NASA Astrophysics Data System (ADS)
Mohan, C.
In this paper, I survey briefly some of the recent and emerging trends in hardware and software features which impact high performance transaction processing and data analytics applications. These features include multicore processor chips, ultra large main memories, flash storage, storage class memories, database appliances, field programmable gate arrays, transactional memory, key-value stores, and cloud computing. While some applications, e.g., Web 2.0 ones, were initially built without traditional transaction processing functionality in mind, slowly system architects and designers are beginning to address such previously ignored issues. The availability, analytics and response time requirements of these applications were initially given more importance than ACID transaction semantics and resource consumption characteristics. A project at IBM Almaden is studying the implications of phase change memory on transaction processing, in the context of a key-value store. Bitemporal data management has also become an important requirement, especially for financial applications. Power consumption and heat dissipation properties are also major considerations in the emergence of modern software and hardware architectural features. Considerations relating to ease of configuration, installation, maintenance and monitoring, and improvement of total cost of ownership have resulted in database appliances becoming very popular. The MapReduce paradigm is now quite popular for large scale data analysis, in spite of the major inefficiencies associated with it.
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Multi-task feature selection in microarray data by binary integer programming.
Lan, Liang; Vucetic, Slobodan
2013-12-20
A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.
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Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A
2015-07-08
Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those features separately. This result is key to understanding attentional selection in complex (natural) scenes, where relevant stimuli are likely to be defined by a combination of stimulus features. Copyright © 2015 the authors 0270-6474/15/359912-08$15.00/0.
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From the West Wing to Pink Floyd to Einstein Advertising: Astronomy in Popular Culture
NASA Astrophysics Data System (ADS)
Fraknoi, Andrew
2007-12-01
In what popular movie does Darryl Hannah play an astronomer? What Japanese car company is named after a well-known star cluster? Can you name at least two murder mysteries that take place at an observatory? What national astronomy education project was mentioned on The West Wing television show (which had several "stealth astronomy” episodes)? What piece of classical music begins with a Big Bang and has the players expanding on stage and into the concert hall? Can you recite the most famous neutrino poem and name the poet? What science fiction story, written by an astronomer under a pseudonym, features an H-R diagram? What rock group had its members’ names included in a reference in the Astrophysical Journal, unbeknownst to the editor? How many astronomy related operas can you name? How many astronomers does it take to screw in a light bulb? Join in on an exploration of astronomy in popular culture, from stamp collecting to advertising, from science fiction (with accurate astronomy) to rock music, from Broadway musicals to modern poetry. Learn which astronomy colleagues have been writing fiction and poetry while you were busy publishing in the research literature. Bring your favorite example of astronomy in popular culture and we'll take the time at the end to share ideas and have some fun. A resource guide for exploring astronomy and popular culture will be available.
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Audio feature extraction using probability distribution function
NASA Astrophysics Data System (ADS)
Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.
2015-05-01
Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.
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MixDroid: A multi-features and multi-classifiers bagging system for Android malware detection
NASA Astrophysics Data System (ADS)
Huang, Weiqing; Hou, Erhang; Zheng, Liang; Feng, Weimiao
2018-05-01
In the past decade, Android platform has rapidly taken over the mobile market for its superior convenience and open source characteristics. However, with the popularity of Android, malwares targeting on Android devices are increasing rapidly, while the conventional rule-based and expert-experienced approaches are no longer able to handle such explosive growth. In this paper, combining with the theory of natural language processing and machine learning, we not only implement the basic feature extraction of permission application features, but also propose two innovative schemes of feature extraction: Dalvik opcode features and malicious code image, and implement an automatic Android malware detection system MixDroid which is based on multi-features and multi-classifiers. According to our experiment results on 20,000 Android applications, detection accuracy of MixDroid is 98.1%, which proves our schemes' effectiveness in Android malware detection.
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Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan
2017-12-01
This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.
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A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.
Ni, Qianwu; Chen, Lei
2017-01-01
Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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On the use of continuous flash suppression for the study of visual processing outside of awareness
Yang, Eunice; Brascamp, Jan; Kang, Min-Suk; Blake, Randolph
2014-01-01
The interocular suppression technique termed continuous flash suppression (CFS) has become an immensely popular tool for investigating visual processing outside of awareness. The emerging picture from studies using CFS is that extensive processing of a visual stimulus, including its semantic and affective content, occurs despite suppression from awareness of that stimulus by CFS. However, the current implementation of CFS in many studies examining processing outside of awareness has several drawbacks that may be improved upon for future studies using CFS. In this paper, we address some of those shortcomings, particularly ones that affect the assessment of unawareness during CFS, and ones to do with the use of “visible” conditions that are often included as a comparison to a CFS condition. We also discuss potential biases in stimulus processing as a result of spatial attention and feature-selective suppression. We suggest practical guidelines that minimize the effects of those limitations in using CFS to study visual processing outside of awareness. PMID:25071685
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Spectral Regression Discriminant Analysis for Hyperspectral Image Classification
NASA Astrophysics Data System (ADS)
Pan, Y.; Wu, J.; Huang, H.; Liu, J.
2012-08-01
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learning methods are popular for dimensionality reduction, such as Locally Linear Embedding, Isomap, and Laplacian Eigenmap. However, a disadvantage of many manifold learning methods is that their computations usually involve eigen-decomposition of dense matrices which is expensive in both time and memory. In this paper, we introduce a new dimensionality reduction method, called Spectral Regression Discriminant Analysis (SRDA). SRDA casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. It can make efficient use of data points to discover the intrinsic discriminant structure in the data. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral data sets demonstrate the effectiveness of the proposed method.
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Non-negative matrix factorization in texture feature for classification of dementia with MRI data
NASA Astrophysics Data System (ADS)
Sarwinda, D.; Bustamam, A.; Ardaneswari, G.
2017-07-01
This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).
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NASA Astrophysics Data System (ADS)
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
2017-04-01
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
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Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System
NASA Technical Reports Server (NTRS)
Penaloza, Mauel A.; Welch, Ronald M.
1996-01-01
Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.
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[Popular education in health and nutrition: literature review].
Mueses De Molina, C
1993-01-01
This literature review of popular education in health and nutrition is intended to provide the necessary theoretical framework for proposals and programs for human resource development in food and nutrition. The work contains a summary of the objectives, purposes, and methodology of popular education in general, a discussion of applications of popular education techniques to health and nutrition education, and a description of some projects based on popular education. Popular education was developed in Latin America by Paulo Freire and others as a response to political domination. Its basic objective was to make the oppressed masses aware of their condition and able to struggle for the transformation of society. Popular education views community participation, development of consciousness, and integration with social and economic activity as fundamental attributes. Participation should be developed through community organizations and should continue for the duration of the educational intervention. The right of all persons to participate in a plane of equality should be recognized. Community or popular education should be conceived as a process of permanent education that will continue throughout the lifetime of individuals and groups. Popular education is directed toward population sectors excluded from participation in employment, family, community, mass communications, education, and leisure activities. Such population sectors are concentrated in the urban periphery and in rural areas. Abandonment of traditional educational techniques and assumption of an active role by community members are elements in development of the methodology of popular education. Steps in the methodology include investigation of possible themes, selection of themes to serve as points of departure, definition of the problem, and action programs. Popular education in nutrition and health begins by asking what problems need to be remedied. The entire process of training and education in health should be centered in the community and be accessible, timely, and gradual, based on the experience of the population. Health personnel should be informed about the strategies and techniques of popular education. A wide variety of teaching materials has been prepared for popular education in health and nutrition. Although some earlier initiatives involving health and nutrition education have had short-term and limited success in Latin America, more successful programs based on the principals of popular education have also been developed.
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Ohl, Michael; Lohrmann, Volker; Breitkreuz, Laura; Kirschey, Lukas; Krause, Stefanie
2014-01-01
Taxonomy, the science of describing and naming of the living world, is recognized as an important and relevant field in modern biological science. While there is wide agreement on the importance of a complete inventory of all organisms on Earth, the public is partly unaware of the amount of known and unknown biodiversity. Out of the enormous number of undescribed (but already recognized) species in natural history museum collections, we selected an attractive example of a wasp, which was presented to museum visitors at a special museum event. We asked 300 visitors to vote on a name for the new species and out of four preselected options, Ampulex dementor Ohl n. sp. was selected. The name, derived from the 'soul sucking' dementors from the popular Harry Potter books is an allusion to the wasps' behavior to selectively paralyze its cockroach prey. In this example, public voting on a scientific name has been shown to be an appropriate way to link museum visitors emotionally to biodiversity and its discovery.
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Unbiased feature selection in learning random forests for high-dimensional data.
Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi
2015-01-01
Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.
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Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques
NASA Astrophysics Data System (ADS)
Jiang, Feng; McComas, William F.
2014-09-01
This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were analyzed: Scientific American, Discover magazine, winners of the Royal Society Winton Prize for Science Books, and books from NSTA's list of Outstanding Science Trade Books. Computer analysis categorized passages in the selected documents based on their inclusions of NOS. Human analysis assessed the frequency, context, coverage, and accuracy of the inclusions of NOS within computer identified NOS passages. NOS was rarely addressed in selected document sets but somewhat more frequently addressed in the letters section of the two magazines. This result suggests that readers seem interested in the discussion of NOS-related themes. In the popular science books analyzed, NOS presentations were found more likely to be aggregated in the beginning and the end of the book, rather than scattered throughout. The most commonly addressed NOS elements in the analyzed documents are science and society and empiricism in science. Only one inaccurate presentation of NOS were identified in all analyzed documents. The text mining technique demonstrated exciting performance, which invites more applications of the technique to analyze other aspects of science textbooks, popular science writing, or other materials involved in science teaching and learning.
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NASA Astrophysics Data System (ADS)
Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin
2018-02-01
This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.
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The social dynamics of genetic testing: the case of Fragile-X.
Nelkin, D
1996-12-01
This article considers a program to screen school children for Fragile-X Syndrome as a way to explore several features of the growing practice of genetic testing in American society. These include the common practice of predictive testing in nonclinical settings; the economic, entrepreneurial, and policy interests that are driving the development of genetic screening programs; and the public support for genetic testing even when there are no effective therapeutic interventions. Drawing from research on popular images of genetics, I argue that cultural beliefs and expectations, widely conveyed through popular narratives, are encouraging the search for diagnostic information and enhancing the appeal of genetic explanations for a growing range of conditions.
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Women and smoking in Hollywood movies: a content analysis.
Escamilla, G; Cradock, A L; Kawachi, I
2000-03-01
We analyzed the portrayal of smoking in Hollywood films starring 10 popular actressess. Five movies were randomly sampled for each actress, for a total of 96 hours of film footage that was analyzed in 1116 5-minute intervals. Leading female actors were as likely to smoke in movies aimed at juvenile audiences (PG/PG-13) as in R-rated movies, whereas male actors were 2.5 times more likely to smoke in R-rated movies. PG/PG-13-rated movies were less likely than R-rated movies to contain negative messages about smoking. Smoking is highly prevalent in Hollywood films featuring popular actressess and may influence young audiences for whom movie stars serve as role models.
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Explanations - Styles of explanation in science
NASA Astrophysics Data System (ADS)
Cornwell, John
2004-06-01
Our lives, states of health, relationships, behavior, experiences of the natural world, and the technologies that shape our contemporary existence are subject to a superfluity of competing, multi-faceted and sometimes incompatible explanations. Widespread confusion about the nature of "explanation" and its scope and limits pervades popular exposition of the natural sciences, popular history and philosophy of science. This fascinating book explores the way explanations work, why they vary between disciplines, periods, and cultures, and whether they have any necessary boundaries. In other words, Explanations aims to achieve a better understanding of explanation, both within the sciences and the humanities. It features contributions from expert writers from a wide range of disciplines, including science, philosophy, mathematics, and social anthropology.
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Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
2016-07-30
In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.
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Smart, Otis; Burrell, Lauren
2014-01-01
Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059
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Evaluating and Selecting Online Magazines for Children. ERIC Digest.
ERIC Educational Resources Information Center
Lu, Mei-Yu
This Digest provides an overview of children's online magazines, also known as e-zines. It begins with a brief review of factors that contribute to the popularity of these publications, followed by a list of criteria for selecting high-quality online magazines for children. Samples of high-quality children's e-zines are also included in this…
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A Mutant Hunt Using the C-Fern (Ceratopteris Richardii)
ERIC Educational Resources Information Center
Calie, Patrick J.
2005-01-01
A modification of the popular C-Fern system, the tropical fern Ceratopteris richardii is developed in which students plate out a genetically mixed set of fern spores and then select for specific mutants. This exercise can provide students with an experience in plant mutant selection and can be used as a platform to expose students to a diverse…
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a Region-Based Multi-Scale Approach for Object-Based Image Analysis
NASA Astrophysics Data System (ADS)
Kavzoglu, T.; Yildiz Erdemir, M.; Tonbul, H.
2016-06-01
Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Rizzardi, M.; Mohr, M.S.; Merrill, D.W.
1992-07-01
In 1990, the United States Bureau of the Census released detailed geographic base files known as TIGER/Line (Topologically Integrated Geographic Encoding and Referencing) which contain detail on the physical features and census tract boundaries of every county in the United States. The TIGER database is attractive for two reasons. First, it is publicly available through the Bureau of the Census on tape or cd-rom for a minimal fee. Second, it contains 24 billion characters of data which describe geographic features of interest to the Census Bureau such as coastlines, hydrography, transportation networks, political boundaries, etc. Unfortunately, the large TIGER databasemore » only provides raw alphanumeric data; no utility software, graphical or otherwise, is included. On the other hand New S, a popular statistical software package by AT T, has easily operated functions that permit advanced graphics in conjunction with data analysis. New S has the ability to plot contours, lines, segments, and points. However, of special interest is the New S function map and its options. Using the map function, which requires polygons as input, census tracts can be quickly selected, plotted, shaded, etc. New S graphics combined with the TIGER database has obvious potential. This paper reports on our efforts to use the TIGER map files with New S, especially to construct census tract maps of counties. While census tract boundaries are inherently polygonal, they are not organized as such in the TIGER database. This conversion of the TIGER line'' format into New S polygon/polyline'' format is one facet of the work reported here. Also we discuss the selection and extraction of auxiliary geographic information from TIGER files for graphical display using New S.« less
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Interfacing 1990 US Census TIGER map files with New S graphics software
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rizzardi, M.; Mohr, M.S.; Merrill, D.W.
1992-07-01
In 1990, the United States Bureau of the Census released detailed geographic base files known as TIGER/Line (Topologically Integrated Geographic Encoding and Referencing) which contain detail on the physical features and census tract boundaries of every county in the United States. The TIGER database is attractive for two reasons. First, it is publicly available through the Bureau of the Census on tape or cd-rom for a minimal fee. Second, it contains 24 billion characters of data which describe geographic features of interest to the Census Bureau such as coastlines, hydrography, transportation networks, political boundaries, etc. Unfortunately, the large TIGER databasemore » only provides raw alphanumeric data; no utility software, graphical or otherwise, is included. On the other hand New S, a popular statistical software package by AT&T, has easily operated functions that permit advanced graphics in conjunction with data analysis. New S has the ability to plot contours, lines, segments, and points. However, of special interest is the New S function map and its options. Using the map function, which requires polygons as input, census tracts can be quickly selected, plotted, shaded, etc. New S graphics combined with the TIGER database has obvious potential. This paper reports on our efforts to use the TIGER map files with New S, especially to construct census tract maps of counties. While census tract boundaries are inherently polygonal, they are not organized as such in the TIGER database. This conversion of the TIGER ``line`` format into New S ``polygon/polyline`` format is one facet of the work reported here. Also we discuss the selection and extraction of auxiliary geographic information from TIGER files for graphical display using New S.« less
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Wittevrongel, Benjamin; Van Hulle, Marc M
2017-01-01
Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection). In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.
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Assessment of Cellular Mutagenicity of Americano Coffees from Popular Coffee Chains.
Liu, Zhen-Shu; Chen, Po-Wen; Wang, Jung-Yu; Kuo, Tai-Chen
2017-09-01
Coffee is a popular beverage worldwide, but coffee beans can be contaminated with carcinogens. The Ames Salmonella mutagenicity test is often used for analysis of carcinogens for mutagenicity. However, previous studies have provided controversial data about the direct mutagenicity of coffee beans based on Ames test results. This study was conducted to determine the mutagenicity of popular Americano coffee based on results from the Ames test. Coffee samples without additives that were served by five international coffee chain restaurants were subjected to the analysis using Salmonella Typhimurium tester strains TA98, TA100, and TA1535. The levels of bacterial revertants in samples from coffee chains were lower than the twofold criterion of the control sets, and no significant dose-response effect was observed with or without rat liver enzyme activation. These data indicate that Americano coffees from the selected coffee chains possessed no direct mutagenic activity with or without enzyme activation. These findings suggest a low mutagenic risk from Americano coffees served by the selected coffee chains and support the use of other methods to confirm the nonmutagenicity of coffee products. These results are consistent with most recent epidemiological reports.
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NASA Astrophysics Data System (ADS)
Wÿss Rudge, David
H.B.D. Kettlewell's investigations on the phenomenon of industrial melanism are generally referred to in textbooks and other popularizations of science as the classic demonstration of natural selection (Majerus 1989). A central question for historians of this episode is accounting for why public perceptions of the importance of Kettlewell's work have diverged from those of researchers who actually work on the phenomenon. In a recent paper published in Biology and Philosophy, Joel Hagen draws attention to the role Kettlewell and his colleagues played in idealizing his investigations as an example of controlled experimentation in their several retrospective popular accounts. The present essay discusses the important role photographic and film depictions of differential bird predation played in Kettlewell's popularizations. This analysis supports Hagen's contentions that Kettlewell deftly and strategically used these visual representations to command assent to his interpretation of the phenomenon and shore up claims about the scientific legitimacy and importance of his work. It nevertheless disputes that these images were intended to portray Kettlewell's experiments as an example of controlled experimentation. In a concluding section, the essay draws several morals from this analysis regarding the use of popularized articles and visual images to teach science.
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What science are you singing? A study of the science image in the mainstream music of Taiwan.
Huang, Chun-Ju; Allgaier, Joachim
2015-01-01
Previous research showed that pop music bands in the Western world have sometimes included science imagery in their lyrics. Their songs could potentially be helpful facilitators for science communication and public engagement purposes. However, so far no systematic research has been conducted for investigating science in popular music in Eastern cultures. This study explores whether science has been regarded as an element in the creation of popular mainstream music, and examines the content and quantity of distribution through an analysis of mainstream music lyrics, to reflect on the conditions of the absorption of science into popular culture. The results indicate that expressions related to astronomy and space science feature very prominently. Most of the lyrics are connected to emotional states and mood expressions and they are only very rarely related to actual issues of science. The implications for science communication and further research are discussed in the final section. © The Author(s) 2014.
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Garandeau, Claire F; Ahn, Hai-Jeong; Rodkin, Philip C
2011-11-01
This study tested the effects of 5 classroom contextual features on the social status (perceived popularity and social preference) that peers accord to aggressive students in late elementary school, including classroom peer status hierarchy (whether within-classroom differences in popularity are large or small), classroom academic level, and grade level as the main predictors of interest as well as classroom aggression and ethnic composition as controls. Multilevel analyses were conducted on an ethnically diverse sample of 968 fourth- and fifth-graders from 46 classrooms in 9 schools. Associations between aggression and status varied greatly from one classroom to another. Aggressive students were more popular and better liked in classrooms with higher levels of peer status hierarchy. Aggressive students had higher social status in Grade 5 than in Grade 4 and lower social preference in classrooms of higher academic level. Classroom aggression and ethnic composition did not moderate aggression-status associations. Limitations and practical implications of these findings are discussed.
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Method of generating features optimal to a dataset and classifier
DOE Office of Scientific and Technical Information (OSTI.GOV)
Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.
A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.
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Gomez-Ramirez, Manuel; Trzcinski, Natalie K.; Mihalas, Stefan; Niebur, Ernst
2014-01-01
Studies in vision show that attention enhances the firing rates of cells when it is directed towards their preferred stimulus feature. However, it is unknown whether other sensory systems employ this mechanism to mediate feature selection within their modalities. Moreover, whether feature-based attention modulates the correlated activity of a population is unclear. Indeed, temporal correlation codes such as spike-synchrony and spike-count correlations (rsc) are believed to play a role in stimulus selection by increasing the signal and reducing the noise in a population, respectively. Here, we investigate (1) whether feature-based attention biases the correlated activity between neurons when attention is directed towards their common preferred feature, (2) the interplay between spike-synchrony and rsc during feature selection, and (3) whether feature attention effects are common across the visual and tactile systems. Single-unit recordings were made in secondary somatosensory cortex of three non-human primates while animals engaged in tactile feature (orientation and frequency) and visual discrimination tasks. We found that both firing rate and spike-synchrony between neurons with similar feature selectivity were enhanced when attention was directed towards their preferred feature. However, attention effects on spike-synchrony were twice as large as those on firing rate, and had a tighter relationship with behavioral performance. Further, we observed increased rsc when attention was directed towards the visual modality (i.e., away from touch). These data suggest that similar feature selection mechanisms are employed in vision and touch, and that temporal correlation codes such as spike-synchrony play a role in mediating feature selection. We posit that feature-based selection operates by implementing multiple mechanisms that reduce the overall noise levels in the neural population and synchronize activity across subpopulations that encode the relevant features of sensory stimuli. PMID:25423284
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A comparative study of programming languages for next-generation astrodynamics systems
NASA Astrophysics Data System (ADS)
Eichhorn, Helge; Cano, Juan Luis; McLean, Frazer; Anderl, Reiner
2018-03-01
Due to the computationally intensive nature of astrodynamics tasks, astrodynamicists have relied on compiled programming languages such as Fortran for the development of astrodynamics software. Interpreted languages such as Python, on the other hand, offer higher flexibility and development speed thereby increasing the productivity of the programmer. While interpreted languages are generally slower than compiled languages, recent developments such as just-in-time (JIT) compilers or transpilers have been able to close this speed gap significantly. Another important factor for the usefulness of a programming language is its wider ecosystem which consists of the available open-source packages and development tools such as integrated development environments or debuggers. This study compares three compiled languages and three interpreted languages, which were selected based on their popularity within the scientific programming community and technical merit. The three compiled candidate languages are Fortran, C++, and Java. Python, Matlab, and Julia were selected as the interpreted candidate languages. All six languages are assessed and compared to each other based on their features, performance, and ease-of-use through the implementation of idiomatic solutions to classical astrodynamics problems. We show that compiled languages still provide the best performance for astrodynamics applications, but JIT-compiled dynamic languages have reached a competitive level of speed and offer an attractive compromise between numerical performance and programmer productivity.
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Selection-Fusion Approach for Classification of Datasets with Missing Values
Ghannad-Rezaie, Mostafa; Soltanian-Zadeh, Hamid; Ying, Hao; Dong, Ming
2010-01-01
This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values. PMID:20212921
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PMLB: a large benchmark suite for machine learning evaluation and comparison.
Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H
2017-01-01
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.
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ERIC Educational Resources Information Center
Schul, James E.
2012-01-01
The increased popularity of desktop documentary making among both teachers and students in history classrooms warrants an examination of its integration into classroom instruction. This multiple case study focused on two secondary students in an AP European History course during a unit that featured desktop documentary making. Employing Cultural…
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Green infrastructure is a popular framework for conservation planning. The main elements of green infrastructure are hubs and links. Hubs tend to be large areas of ‘natural’ vegetation and links tend to be linear features (e.g., streams) that connect hubs. Within the United State...
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Distinguished Educators on Reading: Contributions That Have Shaped Effective Literacy Instruction.
ERIC Educational Resources Information Center
Padak, Nancy D., Ed.; Rasinski, Timothy V., Ed.; Peck, Jacqueline K., Ed.; Church, Brenda Weible, Ed.; Fawcett, Gay, Ed.; Hendershot, Judith M., Ed.; Henry, Justina M., Ed.; Moss, Barbara G., Ed.; Pryor, Elizabeth, Ed.; Roskos, Kathleen A., Ed.; Baumann, James F., Ed.; Dillon, Deborah R., Ed.; Hopkins, Carol J., Ed.; Humphrey, Jack W., Ed.; O'Brien, David G., Ed.
Drawing from the popular "Distinguished Educator" series of articles in the journal "The Reading Teacher," this book presents 33 essays by respected scholars in nearly every field of reading research and instruction. In addition to the original articles, almost all featured educators have included a professional biography written especially for…
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Cobra Strikes! High-Performance Car Inspires Students, Markets Program
ERIC Educational Resources Information Center
Jenkins, Bonita
2008-01-01
Nestled in the Lower Piedmont region of upstate South Carolina, Piedmont Technical College (PTC) is one of 16 technical colleges in the state. Automotive technology is one of its most popular programs. The program features an instructive, motivating activity that the author describes in this article: building a high-performance car. The Cobra…
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Whenever You Use a Computer You Are Using a Program Called an Operating System.
ERIC Educational Resources Information Center
Cook, Rick
1984-01-01
Examines design, features, and shortcomings of eight disk-based operating systems designed for general use that are popular or most likely to affect the future of microcomputing. Included are the CP/M family, MS-DOS, Apple DOS/ProDOS, Unix, Pick, the p-System, TRSDOS, and Macintosh/Lisa. (MBR)
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Geometrical Simplification of the Dipole-Dipole Interaction Formula
ERIC Educational Resources Information Center
Kocbach, Ladislav; Lubbad, Suhail
2010-01-01
Many students meet dipole-dipole potential energy quite early on when they are taught electrostatics or magnetostatics and it is also a very popular formula, featured in encyclopedias. We show that by a simple rewriting of the formula it becomes apparent that, for example, by reorienting the two dipoles, their attraction can become exactly twice…
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Algeria: Revolution, Army and Political Power
ERIC Educational Resources Information Center
Zeraoui, Zidane
2012-01-01
Despite the numerous similarities among the Arab countries that explain the rapid popular movements since the end of 2010, the case of Algeria presents particular features. It shares the same inequalities and social challenges as the rest of the countries in the region. However, the revolutionary process in Algeria between 1954 and 1962 and the…
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Sites Mimicking Social Networks Set Up for Staff Development
ERIC Educational Resources Information Center
Sawchuk, Stephen
2008-01-01
As support and professional-development opportunities for teachers begin to move from conference rooms to chat rooms, a burgeoning number of states and districts are drawing on features from course-management software and popular social-networking sites to establish online networks connecting teachers to peers who may live dozens or even hundreds…
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The LENR-CANR.ORG Website, its Past and Future
NASA Astrophysics Data System (ADS)
Rothwell, J.; Storms, E.
2005-12-01
The LENR-CANR.org web site has proven to be a popular source of information about cold fusion. This site has distributed more full text papers about LENR than any other source. In addition, it contains many features that allow easy search and insertion of the discovered references into a document.
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Proposing a Mathematical Software Tool in Physics Secondary Education
ERIC Educational Resources Information Center
Baltzis, Konstantinos B.
2009-01-01
MathCad® is a very popular software tool for mathematical and statistical analysis in science and engineering. Its low cost, ease of use, extensive function library, and worksheet-like user interface distinguish it among other commercial packages. Its features are also well suited to educational process. The use of natural mathematical notation…
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The Dimensions of the Solar System
ERIC Educational Resources Information Center
Schneider, Stephen E.; Davis, Kathleen S.
2007-01-01
A few new wrinkles have been added to the popular activity of building a scale model of the solar system. Students can learn about maps and scaling using easily accessible online resources that include satellite images. This is accomplished by taking advantage of some of the special features of Google Earth. This activity gives students a much…
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Untangling the Mother Knot: Some Thoughts on Parents, Children and Philosophers of Education
ERIC Educational Resources Information Center
Suissa, Judith
2006-01-01
Although children and parents often feature in philosophical literature on education, the nature of the parent-child relationship remains occluded by the language of rights, duties and entitlements. Likewise, talk of "parenting" in popular literature and culture implies that being a parent is primarily about performing tasks. Drawing on popular…
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The Evolution of Notification Systems
ERIC Educational Resources Information Center
DeVoe, Jeanne Jackson
2008-01-01
This article reports that the American public's favorite methods of notification are still phone and e-mail, but advancements in technology over the past several years have changed the way many district leaders contact parents when an emergency arises at school. The latest tech feature popular in the general public--text messages--is taking hold…
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ERIC Educational Resources Information Center
Gorsevski, Ellen W.; Schuck, Raymond I.; Lin, Canchu
2012-01-01
Using rhetorical analysis in the form of an autoethnographically informed biocritique, this study applies and expands the concept of rhetorical plasticity to examine the popular museum exhibit "Bodies: The Exhibition," which is arguably the most controversial of a series of contemporary museum exhibits that feature deceased human bodies…
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The Mentoring Relationship in Action.
ERIC Educational Resources Information Center
IUME Briefs, 1992
1992-01-01
Mentoring is now a very popular, but loosely defined, feature of many programs for youth. The heart of mentoring is the relationship between the youth and the mentor, but little is actually known about this relationship. Mentoring should not be limited to at-risk youth, since many average students or underachievers from stable backgrounds may…
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The Effects of Videoconferenced Distance-Learning Instruction in a Taiwanese Company
ERIC Educational Resources Information Center
Lin, Chin-Hung; Yang, Shu-Ching
2011-01-01
Distance learning, where instruction is given to students despite wide separations of students and teachers, is increasingly popular. Videoconferencing, which is examined in this study, is a distance learning mode of featuring real-time interaction of students and teachers and provides sequence, real-time, vision, and actual interaction. This…
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Gaussian-Type Orbitals versus Slater-Type Orbitals: A Comparison
ERIC Educational Resources Information Center
Magalha~es, Alexandre L.
2014-01-01
The advantages of Gaussian-type orbitals (GTO) over Slater-type orbitals (STO) in quantum chemistry calculations are clarified here by means of a holistic approach. The popular Microsoft Office Excel program was used to create an interactive application with which students are able to explore the features of GTO, including automatic calculations…
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Prime-Time Television: Assessing Violence during the Most Popular Viewing Hours.
ERIC Educational Resources Information Center
Smith, Stacy L.; Nathanson, Amy I.; Wilson, Barbara J.
2002-01-01
Assesses the prevalence and context of violence in prime-time television programming using a random, representative sample. Shows that, regardless of the time of day, viewers are likely to encounter violence in roughly 2 out of 3 programs. Identifies specific channel types and genres that feature potentially harmful depictions of violence during…
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The Racialization of Hinduism, Islam, and Sikhism in the United States
ERIC Educational Resources Information Center
Joshi, Khyati Y.
2006-01-01
In this article I posit the "racialization" of religion, a process that begins when certain phenotypical features associated with a group and attached to race in popular discourse become associated with a particular religion or religions. By examining the experiences of Indian Americans--a group made up primarily of Hindus, Muslims, and…
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Indexed Captioned Searchable Videos: A Learning Companion for STEM Coursework
ERIC Educational Resources Information Center
Tuna, Tayfun; Subhlok, Jaspal; Barker, Lecia; Shah, Shishir; Johnson, Olin; Hovey, Christopher
2017-01-01
Videos of classroom lectures have proven to be a popular and versatile learning resource. A key shortcoming of the lecture video format is accessing the content of interest hidden in a video. This work meets this challenge with an advanced video framework featuring topical indexing, search, and captioning (ICS videos). Standard optical character…
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Performing Play: Cultural Production on Twitch.tv
ERIC Educational Resources Information Center
Pellicone, Anthony James
2017-01-01
Streaming is an emerging practice of videogame culture, where a player broadcasts a live capture of their game-play to an audience. Every day Twitch.tv, the most popular streaming platform, features thousands of streams broadcast to millions of viewers. Streams are detailed multimedia artifacts, and their study allows us to understand how the…
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The Interactive Classroom: An Overview of Smart Notebook Software
ERIC Educational Resources Information Center
Nichols, Bryan E.
2015-01-01
Interactive whiteboards are increasingly used in school classrooms. SMART Boards have been adopted in many schools, including music classes taught by specialists. This article provides specific tips for using the most popular whiteboard application. The main features of the software as well as specific strategies for maximizing their use in the…
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Feature Selection Methods for Zero-Shot Learning of Neural Activity.
Caceres, Carlos A; Roos, Matthew J; Rupp, Kyle M; Milsap, Griffin; Crone, Nathan E; Wolmetz, Michael E; Ratto, Christopher R
2017-01-01
Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.
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Heterogeneous compute in computer vision: OpenCL in OpenCV
NASA Astrophysics Data System (ADS)
Gasparakis, Harris
2014-02-01
We explore the relevance of Heterogeneous System Architecture (HSA) in Computer Vision, both as a long term vision, and as a near term emerging reality via the recently ratified OpenCL 2.0 Khronos standard. After a brief review of OpenCL 1.2 and 2.0, including HSA features such as Shared Virtual Memory (SVM) and platform atomics, we identify what genres of Computer Vision workloads stand to benefit by leveraging those features, and we suggest a new mental framework that replaces GPU compute with hybrid HSA APU compute. As a case in point, we discuss, in some detail, popular object recognition algorithms (part-based models), emphasizing the interplay and concurrent collaboration between the GPU and CPU. We conclude by describing how OpenCL has been incorporated in OpenCV, a popular open source computer vision library, emphasizing recent work on the Transparent API, to appear in OpenCV 3.0, which unifies the native CPU and OpenCL execution paths under a single API, allowing the same code to execute either on CPU or on a OpenCL enabled device, without even recompiling.
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Content management systems and E-commerce: a comparative case study
NASA Astrophysics Data System (ADS)
Al Rasheed, Amal A.; El-Masri, Samir D.
2011-12-01
The need for CMS's to create and edit e-commerce websites has increased with the growing importance of e-commerce. In this paper, the various features essential for e-commerce CMS's are explored. The aim of the paper was to find the best CMS solution for e-commerce which includes the best of both CMS and store management. Accordingly, we conducted a study on three popular open source CMS's for e-commerce: VirtueMart from Joomla!, Ubercart from Drupal, and Magento. We took into account features like hosting and installation, performance, support/community, content management, add on modules and functional features. We concluded with improvements that could be made in order to alleviate problems.
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Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong; Fan, Xiaoming
2015-01-01
Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge.
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Effect of feature-selective attention on neuronal responses in macaque area MT
Chen, X.; Hoffmann, K.-P.; Albright, T. D.
2012-01-01
Attention influences visual processing in striate and extrastriate cortex, which has been extensively studied for spatial-, object-, and feature-based attention. Most studies exploring neural signatures of feature-based attention have trained animals to attend to an object identified by a certain feature and ignore objects/displays identified by a different feature. Little is known about the effects of feature-selective attention, where subjects attend to one stimulus feature domain (e.g., color) of an object while features from different domains (e.g., direction of motion) of the same object are ignored. To study this type of feature-selective attention in area MT in the middle temporal sulcus, we trained macaque monkeys to either attend to and report the direction of motion of a moving sine wave grating (a feature for which MT neurons display strong selectivity) or attend to and report its color (a feature for which MT neurons have very limited selectivity). We hypothesized that neurons would upregulate their firing rate during attend-direction conditions compared with attend-color conditions. We found that feature-selective attention significantly affected 22% of MT neurons. Contrary to our hypothesis, these neurons did not necessarily increase firing rate when animals attended to direction of motion but fell into one of two classes. In one class, attention to color increased the gain of stimulus-induced responses compared with attend-direction conditions. The other class displayed the opposite effects. Feature-selective activity modulations occurred earlier in neurons modulated by attention to color compared with neurons modulated by attention to motion direction. Thus feature-selective attention influences neuronal processing in macaque area MT but often exhibited a mismatch between the preferred stimulus dimension (direction of motion) and the preferred attention dimension (attention to color). PMID:22170961
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Effect of feature-selective attention on neuronal responses in macaque area MT.
Chen, X; Hoffmann, K-P; Albright, T D; Thiele, A
2012-03-01
Attention influences visual processing in striate and extrastriate cortex, which has been extensively studied for spatial-, object-, and feature-based attention. Most studies exploring neural signatures of feature-based attention have trained animals to attend to an object identified by a certain feature and ignore objects/displays identified by a different feature. Little is known about the effects of feature-selective attention, where subjects attend to one stimulus feature domain (e.g., color) of an object while features from different domains (e.g., direction of motion) of the same object are ignored. To study this type of feature-selective attention in area MT in the middle temporal sulcus, we trained macaque monkeys to either attend to and report the direction of motion of a moving sine wave grating (a feature for which MT neurons display strong selectivity) or attend to and report its color (a feature for which MT neurons have very limited selectivity). We hypothesized that neurons would upregulate their firing rate during attend-direction conditions compared with attend-color conditions. We found that feature-selective attention significantly affected 22% of MT neurons. Contrary to our hypothesis, these neurons did not necessarily increase firing rate when animals attended to direction of motion but fell into one of two classes. In one class, attention to color increased the gain of stimulus-induced responses compared with attend-direction conditions. The other class displayed the opposite effects. Feature-selective activity modulations occurred earlier in neurons modulated by attention to color compared with neurons modulated by attention to motion direction. Thus feature-selective attention influences neuronal processing in macaque area MT but often exhibited a mismatch between the preferred stimulus dimension (direction of motion) and the preferred attention dimension (attention to color).
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A social network approach to the interplay between adolescents' bullying and likeability over time.
Sentse, Miranda; Kiuru, Noona; Veenstra, René; Salmivalli, Christina
2014-09-01
Our knowledge on adolescents' bullying behavior has rapidly increased over the past decade and it is widely recognized that bullying is a group process and, consequently, context-dependent. Only since recently, though, researchers have had access to statistical programs to study these group processes appropriately. The current 1-year longitudinal study examined the interplay between adolescents' bullying and likeability from a social network perspective. Data came from the evaluation of the Finnish KiVa antibullying program, consisting of students in grades 7-9 (N = 9,183, M age at wave 1 = 13.96 years; 49.2% boys; M classroom size = 19.47) from 37 intervention and 30 control schools. Perceived popularity, gender, and structural network effects were additionally controlled. Longitudinal social network analysis with SIENA revealed that, overall, the higher the students' level of bullying, the less they were liked by their peers. Second, students liked peers with similar levels of bullying and this selection-similarity effect was stronger at low levels of bullying. This selection effect held after controlling for selection-similarity in perceived popularity and gender. Third, students were likely to increase in bullying when they liked peers high on bullying and to decrease in bullying when they liked peers low on bullying. Again, this influence effect held after controlling for the effects of perceived popularity and gender on changes in bullying behavior. No significant differences between control and intervention schools appeared in the effects. The results are discussed in light of their theoretical and methodological implications.
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Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo
2016-01-01
Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.
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NASA Astrophysics Data System (ADS)
Liu, Wanjun; Liang, Xuejian; Qu, Haicheng
2017-11-01
Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.
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Haque, Mohammad Nazmul; Noman, Nasimul; Berretta, Regina; Moscato, Pablo
2016-01-01
Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases. PMID:26764911
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Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.
Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki
2016-07-01
We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.
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Jeyasingh, Suganthi; Veluchamy, Malathi
2017-05-01
Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License
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Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
2016-01-01
In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285
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Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing
2018-06-01
Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Sentiment analysis of feature ranking methods for classification accuracy
NASA Astrophysics Data System (ADS)
Joseph, Shashank; Mugauri, Calvin; Sumathy, S.
2017-11-01
Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.
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Learning Scene Categories from High Resolution Satellite Image for Aerial Video Analysis
DOE Office of Scientific and Technical Information (OSTI.GOV)
Cheriyadat, Anil M
2011-01-01
Automatic scene categorization can benefit various aerial video processing applications. This paper addresses the problem of predicting the scene category from aerial video frames using a prior model learned from satellite imagery. We show that local and global features in the form of line statistics and 2-D power spectrum parameters respectively can characterize the aerial scene well. The line feature statistics and spatial frequency parameters are useful cues to distinguish between different urban scene categories. We learn the scene prediction model from highresolution satellite imagery to test the model on the Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) dataset ollected bymore » high-altitude wide area UAV sensor platform. e compare the proposed features with the popular Scale nvariant Feature Transform (SIFT) features. Our experimental results show that proposed approach outperforms te SIFT model when the training and testing are conducted n disparate data sources.« less
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Gmz: a Gml Compression Model for Webgis
NASA Astrophysics Data System (ADS)
Khandelwal, A.; Rajan, K. S.
2017-09-01
Geography markup language (GML) is an XML specification for expressing geographical features. Defined by Open Geospatial Consortium (OGC), it is widely used for storage and transmission of maps over the Internet. XML schemas provide the convenience to define custom features profiles in GML for specific needs as seen in widely popular cityGML, simple features profile, coverage, etc. Simple features profile (SFP) is a simpler subset of GML profile with support for point, line and polygon geometries. SFP has been constructed to make sure it covers most commonly used GML geometries. Web Feature Service (WFS) serves query results in SFP by default. But it falls short of being an ideal choice due to its high verbosity and size-heavy nature, which provides immense scope for compression. GMZ is a lossless compression model developed to work for SFP compliant GML files. Our experiments indicate GMZ achieves reasonably good compression ratios and can be useful in WebGIS based applications.
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Hswen, Yulin; Murti, Vaidhy; Vormawor, Adenugbe A.; Bhattacharjee, Robbie; Naslund, John A.
2014-01-01
Background Rapid growth in Smartphone use among children affords potential opportunities to target health behaviors such as dietary habits; however, few mobile health applications are specifically designed with these individuals in mind. This brief report describes our step-by-step approach towards developing a mobile health application for targeting nutrition behaviors among children. Methods Descriptions of the 10 most popular paid and 10 most popular free Smartphone applications available on the Apple iTunes store for ages 4 and up as of March 2012 were qualitatively analyzed. The relevance of key characteristics found in these applications was then further explored for their potential to improve dietary behaviours amongst children, and a mobile application was developed. Results Three prominent characteristics of the most popular applications emerged: 1) virtual avatars or characters (observed in 50% of the applications); 2) gaming (observed in 75% of the applications); and 3) social media (observed in 45% of the applications). These features were then incorporated into the design of a mobile health application called Avafeed, which uses a virtual avatar and gaming to help make choosing healthier food options easier among children. The application was successfully released onto the Apple iTunes Store in September 2012. Conclusions In this unconventional approach, evidence-based research was combined with information procured from a qualitative review of popular applications available on the Apple iTunes Store in order to design a potentially relevant and popular mobile health application for use among children. PMID:25419244
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NASA Astrophysics Data System (ADS)
Diamant, Idit; Shalhon, Moran; Goldberger, Jacob; Greenspan, Hayit
2016-03-01
Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we present a novel method for feature selection based on mutual information (MI) criterion for automatic classification of microcalcifications. We explored the MI based feature selection for various texture features. The proposed method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness and the advantage of using the MI-based feature selection to obtain the most relevant features for the task and thus to provide for improved performance as compared to using all features.
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Teen Alcohol Use and Social Networks: The Contributions of Friend Influence and Friendship Selection
Cheadle, Jacob E; Walsemann, Katrina M; Goosby, Bridget J
2015-01-01
Background We evaluated the contributions of teen alcohol use to the formation and continuation of new and existing friendships while in turn estimating the influence of friend drinking on individuals’ regular use and heavy drinking. Method Longitudinal network analysis was used to assess the mutual influences between teen drinking and social networks among adolescents in two large Add Health schools where full network data was collected three times. Friendship processes were disaggregated into the formation of new friendships and the continuation of existing friendships in a joint model isolating friendship selection and friend influences. Results Friends have a modest influence on one another when selection is controlled. Selection is more complicated than prior studies suggest, and is only related to new friendships and not their duration in the largest school. Alcohol use predicts decreasing popularity in some cases, and popularity does not predict alcohol consumption. Conclusion Intervention efforts should continue pursuing strategies that mitigate negative peer influences. The development of socializing opportunities that facilitate relationship opportunities to select on healthy behaviors also appears promising. Future work preventing teen substance use should incorporate longitudinal network assessments to determine whether programs promote protective peer relationships in addition to how treatment effects diffuse through social networks. PMID:26692436
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Derewenda, Zygmunt S.; Godzik, Adam
2017-01-01
Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e. crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular ‘sticky patch’ model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer ‘sticky patches’ which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the ‘sticky patch’ model. We discuss state-of-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design of variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis. PMID:28573570
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Feature Selection Methods for Zero-Shot Learning of Neural Activity
Caceres, Carlos A.; Roos, Matthew J.; Rupp, Kyle M.; Milsap, Griffin; Crone, Nathan E.; Wolmetz, Michael E.; Ratto, Christopher R.
2017-01-01
Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy. PMID:28690513
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ERIC Educational Resources Information Center
Workers Educational Association, Sunderland (England). Northern District.
Four adult educators from the North East/Brazil Project spent three months in Brazil in 1986 on "pilot" education ventures. The areas selected for collaboration were health and safety for lay officers and induction/leadership training for women. "Methods" courses were selected as the means to train lay officers as discussion…
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MIIB: A Metric to Identify Top Influential Bloggers in a Community.
Khan, Hikmat Ullah; Daud, Ali; Malik, Tahir Afzal
2015-01-01
Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.
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Mobile object retrieval in server-based image databases
NASA Astrophysics Data System (ADS)
Manger, D.; Pagel, F.; Widak, H.
2013-05-01
The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.
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Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection
NASA Astrophysics Data System (ADS)
Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu
Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.
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The fate of task-irrelevant visual motion: perceptual load versus feature-based attention.
Taya, Shuichiro; Adams, Wendy J; Graf, Erich W; Lavie, Nilli
2009-11-18
We tested contrasting predictions derived from perceptual load theory and from recent feature-based selection accounts. Observers viewed moving, colored stimuli and performed low or high load tasks associated with one stimulus feature, either color or motion. The resultant motion aftereffect (MAE) was used to evaluate attentional allocation. We found that task-irrelevant visual features received less attention than co-localized task-relevant features of the same objects. Moreover, when color and motion features were co-localized yet perceived to belong to two distinct surfaces, feature-based selection was further increased at the expense of object-based co-selection. Load theory predicts that the MAE for task-irrelevant motion would be reduced with a higher load color task. However, this was not seen for co-localized features; perceptual load only modulated the MAE for task-irrelevant motion when this was spatially separated from the attended color location. Our results suggest that perceptual load effects are mediated by spatial selection and do not generalize to the feature domain. Feature-based selection operates to suppress processing of task-irrelevant, co-localized features, irrespective of perceptual load.
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Classification Influence of Features on Given Emotions and Its Application in Feature Selection
NASA Astrophysics Data System (ADS)
Xing, Yin; Chen, Chuang; Liu, Li-Long
2018-04-01
In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.
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Mala, S.; Latha, K.
2014-01-01
Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185
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Mala, S; Latha, K
2014-01-01
Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.
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Review of influential articles in surgical education: 2002-2012.
Wohlauer, Max V; George, Brian; Lawrence, Peter F; Pugh, Carla M; Van Eaton, Erik G; Darosa, Debra
2013-06-01
Exploring the trends in surgical education research offers insight into concerns, developments, and questions researchers are exploring that are relevant to teaching and learning in surgical specialties. We conducted a review of the surgical education literature published between 2002 and 2012. The purpose was 2-fold: to provide an overview of the most frequently cited articles in the field of surgical education during the last decade and to describe the study designs and themes featured in these articles. Articles were identified through Web of Science by using "surgical education" and "English language" as search terms. Using a feature in Web of Science, we tracked the number of citations of any publication. Of the 800 articles produced by the initial search, we initially selected 23 articles with 45 or more citations, and ultimately chose the 20 articles that were most frequently cited for our analysis. Analysis of the most frequently cited articles published in US journals between the years 2002-2012 identified 7 research themes and presented them in order of frequency with which they appear: use of simulation, issues in student/resident assessment, specialty choice, patient safety, team training, clinical competence assessment, and teaching the clinical sciences, with surgical simulation being the central theme. Researchers primarily used descriptive methods. Popular themes in surgical education research illuminate the information needs of surgical educators as well as topics of high interest to the surgical community.
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A data-driven dynamics simulation framework for railway vehicles
NASA Astrophysics Data System (ADS)
Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun
2018-03-01
The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.
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Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models
NASA Astrophysics Data System (ADS)
Shahid, Muhammad; Pandremmenou, Katerina; Kondi, Lisimachos P.; Rossholm, Andreas; Lövström, Benny
2016-09-01
Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.
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Feature Grouping and Selection Over an Undirected Graph.
Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping
2012-01-01
High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.
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Atypical viral dynamics from transport through popular places
NASA Astrophysics Data System (ADS)
Manrique, Pedro D.; Xu, Chen; Hui, Pak Ming; Johnson, Neil F.
2016-08-01
The flux of visitors through popular places undoubtedly influences viral spreading—from H1N1 and Zika viruses spreading through physical spaces such as airports, to rumors and ideas spreading through online spaces such as chat rooms and social media. However, there is a lack of understanding of the types of viral dynamics that can result. Here we present a minimal dynamical model that focuses on the time-dependent interplay between the mobility through and the occupancy of such spaces. Our generic model permits analytic analysis while producing a rich diversity of infection profiles in terms of their shapes, durations, and intensities. The general features of these theoretical profiles compare well to real-world data of recent social contagion phenomena.
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Ensemble training to improve recognition using 2D ear
NASA Astrophysics Data System (ADS)
Middendorff, Christopher; Bowyer, Kevin W.
2009-05-01
The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an ensemble, but none present a method for designing the individual parts that comprise the ensemble. In this work, we introduce a method of modifying the ensemble shapes to improve performance. We determine how different properties of an ensemble training system can affect overall performance. We show that ensembles built from small parts will outperform ensembles built with larger parts, and that incorporating a large number of parts improves the performance of the ensemble.
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Women and smoking in Hollywood movies: a content analysis.
Escamilla, G; Cradock, A L; Kawachi, I
2000-01-01
OBJECTIVES: We analyzed the portrayal of smoking in Hollywood films starring 10 popular actressess. METHODS: Five movies were randomly sampled for each actress, for a total of 96 hours of film footage that was analyzed in 1116 5-minute intervals. RESULTS: Leading female actors were as likely to smoke in movies aimed at juvenile audiences (PG/PG-13) as in R-rated movies, whereas male actors were 2.5 times more likely to smoke in R-rated movies. PG/PG-13-rated movies were less likely than R-rated movies to contain negative messages about smoking. CONCLUSIONS: Smoking is highly prevalent in Hollywood films featuring popular actressess and may influence young audiences for whom movie stars serve as role models. PMID:10705861
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The barefoot debate: can minimalist shoes reduce running-related injuries?
Rixe, Jeffrey A; Gallo, Robert A; Silvis, Matthew L
2012-01-01
Running has evolved throughout history from a necessary form of locomotion to an athletic and recreational pursuit. During this transition, our barefoot ancestors developed footwear. By the late 1970s, running popularity surged, and footwear manufacturers developed the running shoe. Despite new shoe technology and expert advice, runners still face high injury rates, which have yet to decline. Recently, "minimalist" running, marked by a soft forefoot strike and shorter, quicker strides, has become increasingly popular within the running community. Biomechanical studies have suggested that these features of barefoot-style running may lead to a reduction in injury rates. After conducting more outcomes-based research, minimalist footwear and gait retraining may serve as new methods to reduce injuries within the running population.
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Surfing the web and parkinson's law.
Baldwin, F D
1996-05-01
The World Wide Web accounts for much of the popular interest in the Internet and offers a rich and variegated source of medical information. It's where you'll find online attractions ranging from "The Visible Human" to collections of lawyer jokes, as well as guides to clinical materials. Here's a basic introduction to the Web, its features, and its vocabulary.
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Richard M. DeGraaf; Jay B. Hestbeck; Mariko Yamasaki
1998-01-01
Assessment of faunal distribution in relation to landscape features is becoming increasingly popular. Technological advances in remote sensing have encouraged regional analyses of the distributions of terrestrial vertebrates. Comparisons of the strength of association of habitat characteristics at various scales of measurement of habitat structure are rare. We compared...
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Passion Play: Will Wright and Games for Science Learning
ERIC Educational Resources Information Center
Ching, Dixie
2012-01-01
Researchers and instructional designers are exploring the possibilities of using video games to support STEM education in the U.S., not only because they are a popular media form among youth, but also because well-designed games often leverage the best features of inquiry learning. Those interested in using games in an educational capacity may…
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Incorporation of National Universities in Korea: Dynamic Forces, Key Features, and Challenges
ERIC Educational Resources Information Center
Rhee, Byung-Shik
2007-01-01
Education reform has become more popular than ever, and the incorporation of public institutions of higher education is one such reform. Eventually, Korea will see its national universities being incorporated. The legislature passed a law on March 7, 2007 that requires a new national university to be established as a National University…
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ERIC Educational Resources Information Center
King, Michael A.
2009-01-01
Business intelligence derived from data warehousing and data mining has become one of the most strategic management tools today, providing organizations with long-term competitive advantages. Business school curriculums and popular database textbooks cover data warehousing, but the examples and problem sets typically are small and unrealistic. The…
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ERIC Educational Resources Information Center
Toth, Eva Erdosne; Ludvico, Lisa R.; Morrow, Becky L.
2014-01-01
This study examined the characteristics of virtual and hands-on inquiry environments for the development of blended learning in a popular domain of bio-nanotechnology: the separation of different-sized DNA fragments using gel-electrophoresis, also known as DNA-fingerprinting. Since the latest scientific developments in nano- and micro-scale tools…
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Why Do Academics Love Mysteries, and Vice-Versa?
ERIC Educational Resources Information Center
Svoboda, Frederic J.
A course on detective fiction proved to be very popular at the University of Michigan, Flint. Fifty students signed up for the class, which was supposed to be limited to 45. Surprisingly, though, only 10 of these identified themselves as readers of detective fiction; those remaining were mainly curious. The course featured a range of works…
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A Longitudinal Assessment of an Initial Cohort in a Psychology Learning Community
ERIC Educational Resources Information Center
Buch, Kim; Spaulding, Sue
2008-01-01
Discipline-based learning communities have become a popular strategy for improving student performance and satisfaction. This article describes the goals and features of a university-based, first-year psychology learning community (PLC) implemented in Fall 2003. We also report the results of a longitudinal assessment of the impact of the PLC on…
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Contemporary Ghost Stories: Cyberspace in Fiction for Children and Young Adults
ERIC Educational Resources Information Center
Harris, Marla
2005-01-01
This essay identifies a genre of popular fiction for children and young adults, prevalent in the 1990s and continuing into the early twenty-first century, that incorporates computers and the internet, e-mails and chat rooms, into its plots. However, along with a focus on technology, this fiction frequently features the supernatural. So, too,…
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ERIC Educational Resources Information Center
Moore, Kimberly
2018-01-01
Minecraft is an open world survival computer game that was originally created in Sweden in 2011 and later purchased by Microsoft® in 2014. It is among the most popular computer games with students of all ages because it gives players a sense of ownership and opportunities for creative exploration. The game has three unique features: (1) no clear…
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Online K-12 Teachers' Perceptions and Practices of Supporting Self-Regulated Learning
ERIC Educational Resources Information Center
Huh, Yeol; Reigeluth, Charles M.
2018-01-01
With growing interest in and popularity of online learning and lifelong learners, students' ability to be engaged in self-regulated learning (SRL) has become more important. Moreover, online learning is becoming an important feature of K-12 education. Although SRL is known to be important and teachable, little research has been conducted on…
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ERIC Educational Resources Information Center
Wallace, Edward
2017-01-01
Background and Purpose: Racial and ethnic minority populations are disproportionally affected by obesity. Text messaging is a major feature of mobile phones and is popular because it allows people to receive information effectively, unobtrusively, and privately. However, the willingness to exercise and eat healthy to prevent obesity by receiving…
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Vintage Films as Primary Sources in the History Classroom
ERIC Educational Resources Information Center
Knickerbocker, Joan L.
2014-01-01
Feature films provide a significant form of historical evidence about a culture even when they are fictional. Like books, films are texts that can be analyzed, questioned, and discussed. Vintage films can, therefore, add a valuable new dimension to the history classroom. This article examines how popular films of the 1940s reveal the values,…
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ERIC Educational Resources Information Center
Wathington, Heather D.; Pretlow, Joshua; Mitchell, Claire
2011-01-01
Learning communities, a small cohort of students enrolled together in two or more linked courses, have become a popular intervention to help underprepared students succeed in college. Though learning communities abound in practice, the key structural feature of a learning community--the cohort--may not be fully understood. Authors posit that a…
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ERIC Educational Resources Information Center
Al-Azawei, Ahmed; Lundqvist, Karsten
2015-01-01
Online learning constitutes the most popular distance-learning method, with flexibility, accessibility, visibility, manageability and availability as its core features. However, current research indicates that its efficacy is not consistent across all learners. This study aimed to modify and extend the factors of the Technology Acceptance Model…
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Noise impact issues on the Great Walks of New Zealand
Gordon R. Cessford
2000-01-01
This paper describes the features of recreational noise impacts and presents examples from popular New Zealand backcountry trails. Some noise effects were noticed at very high levels, and a varied range of tolerance for these was noted. Aircraft noise provided the most extreme impact example, while noise impacts from motorboats and social behaviour in huts were also...
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WhatsApp Goes to School: Mobile Instant Messaging between Teachers and Students
ERIC Educational Resources Information Center
Bouhnik, Dan; Deshen, Mor
2014-01-01
WhatsApp is a Smartphone application for instant messaging. Lately the application's popularity has risen. One of the unique features of the application is its ability to enhance communication within a group. Classroom communication between teaching faculty and high school students using WhatsApp has not yet, to our knowledge, been researched…
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The "Life-Long Draught": From Learning to Teaching and Back
ERIC Educational Resources Information Center
Gardner, Philip
2007-01-01
A significant but seldom explored feature of social change brought about by popular education in the modern period lies in its intimate and complex association with the humanizing idea of the "lifelong". At a moment when the idea of "lifelong learning" exercises a considerable policy influence, it is perhaps timely to reflect on the relation of…
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Online Features of Qzone Weblog for Critical Peer Feedback to Facilitate Business English Writing
ERIC Educational Resources Information Center
Gao, Xianwei; Samuel, Moses; Asmawi, Adelina
2016-01-01
Qzone weblog is one of the most popular weblogs in China. This study explores Qzone weblog for critical peer feedback to facilitate Business English writing among the Chinese undergraduates. A qualitative case study is conducted by NVivo 8 to analyze the three research data of semistructured interviews, Business English writing assignments, and…
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ERIC Educational Resources Information Center
Ghanbari, Batoul; Rasekh, Abbas Eslami
2012-01-01
English for specific purposes (ESP), the popular catchphrase of presently English language teaching programs, has been investigated from different perspectives. However, there have been occasional forays in to the role of ESP practitioner as one of the most distinctive features in the literature. In addition to fulfilling the usual role of a…
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Common Core Taught through the Arts
ERIC Educational Resources Information Center
Robelen, Erik W.
2012-01-01
At the Isabella Stewart Gardner Museum in Boston, the painting "El Jaleo"--a canvas spanning 11 feet that features a flamenco dancer--is a popular starting point for getting students to spend time with a work of art. But viewing and discussing the 1882 piece by the American artist John Singer Sargent isn't just a cultural experience. It…
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Reviving Reification: Education, Indoctrination, and Anxiety in "The Graduate"
ERIC Educational Resources Information Center
Cooley, Aaron
2009-01-01
This article takes its inspiration and method from Slavoj Zizek's work that reads and integrates social theory with popular culture through the medium of film. I use the film "The Graduate" (Nichols 1967) as a prism to illuminate the concept of reification as a fundamental, defining feature of modern societies and their educational systems. The…
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Game on: The Impact of Game Features in Computer-Based Training
ERIC Educational Resources Information Center
DeRouin-Jessen, Renee E.
2008-01-01
The term "serious games" became popularized in 2002 as a result of an initiative to promote the use of games for education, training, and other purposes. Today, many companies are using games for training and development, often with hefty price tags. For example, the development budget for the U.S. Army recruiting game, "America's…
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Peak with Books: An Early Childhood Resource for Balanced Literacy. Third Edition.
ERIC Educational Resources Information Center
Nelsen, Marjorie R.; Nelsen-Parish, Jan
This book shows how to use popular children's literature to build reading, writing, and cognitive skills in an inquiry-based environment. This third edition has been expanded to include first and second grades. New features include: (1) new emphasis on culturally diverse storybooks; (2) a description of the experiential learning inquiry process;…
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Maximizing the Educational Power of History Movies in the Classroom
ERIC Educational Resources Information Center
Metzger, Scott Alan
2010-01-01
Cinematic feature films are a big part of youth popular culture. When blockbuster movies are about historical topics, it is reasonable for teachers to be drawn to using them in the classroom to motivate students interest. This article overviews research on film in the history classroom and describes three learning functions that history movies can…
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76 FR 42677 - Notice of Intent To Seek Approval To Collect Information
Federal Register 2010, 2011, 2012, 2013, 2014
2011-07-19
... and maintains an on-line recipe database, the Recipe Finder, as a popular feature to the SNAP-Ed Connection Web site. The purpose of the Recipe Finder database is to provide SNAP-Ed providers with low-cost... inclusion in the database. SNAP-Ed staff and providers benefit from collecting and posting feedback on...
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Using formative research to conceptualize and develop a marketing plan for student health services.
Stephenson, M T
1999-03-01
Conceptualization and development of a health services awareness campaign at the University of Kentucky followed the steps in a communication process called formative research. Preproduction surveys and subsequent testing of possible initiatives led to creation of a popular video featuring the university mascot that is being used in new-student orientation.
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Design for Social Presence and Exploring Its Mediating Effect in Mobile Data Communication Services
ERIC Educational Resources Information Center
Ogara, Solomon Omondi
2011-01-01
The mobility, flexibility, convenience, and ubiquity of mobile data services (MDS) have contributed to their enormous growth and popularity with users. MDS allow users to communicate through mobile texting (mTexting), mobile Instant Messaging (mIM), multimedia messaging services (MMS), and email. A unique feature of MDS that enhances its…
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The Lancasterian Monitorial System as an Education Industry with a Logic of Capitalist Valorisation
ERIC Educational Resources Information Center
Mesquita, Leopoldo
2012-01-01
The business side of the Lancasterian system of mass schooling has been highlighted by some researchers. However, this feature is usually considered of minor importance compared to other dimensions of that system, namely the social control role of popular education in early nineteenth-century Britain. The present surge of projects and mechanisms…
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The Effects of Routing and Scoring within a Computer Adaptive Multi-Stage Framework
ERIC Educational Resources Information Center
Dallas, Andrew
2014-01-01
This dissertation examined the overall effects of routing and scoring within a computer adaptive multi-stage framework (ca-MST). Testing in a ca-MST environment has become extremely popular in the testing industry. Testing companies enjoy its efficiency benefits as compared to traditionally linear testing and its quality-control features over…
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Contextual Multi-armed Bandits under Feature Uncertainty
DOE Office of Scientific and Technical Information (OSTI.GOV)
Yun, Seyoung; Nam, Jun Hyun; Mo, Sangwoo
We study contextual multi-armed bandit problems under linear realizability on rewards and uncertainty (or noise) on features. For the case of identical noise on features across actions, we propose an algorithm, coined NLinRel, having O(T⁷/₈(log(dT)+K√d)) regret bound for T rounds, K actions, and d-dimensional feature vectors. Next, for the case of non-identical noise, we observe that popular linear hypotheses including NLinRel are impossible to achieve such sub-linear regret. Instead, under assumption of Gaussian feature vectors, we prove that a greedy algorithm has O(T²/₃√log d)regret bound with respect to the optimal linear hypothesis. Utilizing our theoretical understanding on the Gaussian case,more » we also design a practical variant of NLinRel, coined Universal-NLinRel, for arbitrary feature distributions. It first runs NLinRel for finding the ‘true’ coefficient vector using feature uncertainties and then adjust it to minimize its regret using the statistical feature information. We justify the performance of Universal-NLinRel on both synthetic and real-world datasets.« less
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Natural image statistics and low-complexity feature selection.
Vasconcelos, Manuela; Vasconcelos, Nuno
2009-02-01
Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.
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Cross-Modal Retrieval With CNN Visual Features: A New Baseline.
Wei, Yunchao; Zhao, Yao; Lu, Canyi; Wei, Shikui; Liu, Luoqi; Zhu, Zhenfeng; Yan, Shuicheng
2017-02-01
Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.
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Leonti, Marco
2011-04-12
Apart from empirically learned medicinal and pharmacological properties, the selection of medicinal plants is dependent on cognitive features, ecological factors and cultural history. In literate societies the transmission of medicinal plant knowledge through texts and, more recently, other media containing local as well as non-local knowledge has a more immediate and a more prolonged effect than oral transmission. Therefore, I try to visualize how field based studies in ethnobiology and especially medical ethnobotany and ethnopharmacology run the risk of repeating information and knowledge and illustrate the importance of differentiating and acknowledging the origin, transmission and rationale of plant use made by humans. Reviewing literature dealing with the traditional parameters (e.g. hot/cold dichotomy, organoleptic properties, doctrine of signatures) influencing the selection and transmission of plant use in a juxtaposition to our recent finding of causal influence of text on local plant use. Discussing the passing down of knowledge by text as a special case of oblique/one-to-many knowledge transmission. Historical texts on materia medica, popular books on plant use, clinical studies, and informants of ethnobotanical field studies generate a circle of information and knowledge, which progressively conditions the results of ethnobotanical field studies. While text reporting on phytotherapeutical trends may cause innovation through the introduction of "new" applications to local customs, persistently repeating well established folk remedies leads to the consolidation of such uses adding a conservative dimension to a local pharmacopoeia, which might not actually be there to that extent. Such a "shaping" of what might appear to be the results of a field investigation is clearly outside the ordinary principles of scientific enquiry. The traditional pillars of ethnobotanical field studies - that is, "input to drug discovery" and "conservation of cultural heritage" - are also incompatible with this process. Ethnobotancial field studies aimed at a contribution to natural products research and/or the conservation of cultural heritage, as well as those aimed at an assessment and validation of local pharmacopoeias should differentiate between local plant use and widespread as well as modern knowledge reported in popular textbooks and scientific literature. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
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Effective traffic features selection algorithm for cyber-attacks samples
NASA Astrophysics Data System (ADS)
Li, Yihong; Liu, Fangzheng; Du, Zhenyu
2018-05-01
By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.
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Hybrid feature selection for supporting lightweight intrusion detection systems
NASA Astrophysics Data System (ADS)
Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin
2017-08-01
Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.
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Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui
2016-06-01
Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.
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Hua Loo-Keng's Popularization of Mathematics and the Cultural Revolution.
Hudeček, Jiří
2017-09-01
Before 1966, Chinese mathematician Hua Loo-Keng had singled out "Two Methods" as a way to truly applied and useful mathematics. The Overall Planning Method, based on the Critical Path Method widely used in USA, mostly appealed to middle and upper management. This limited its spread during the Cultural Revolution. The Optimum Selection Method, also of US origin, was more mass-oriented and ready for popularization. Nevertheless, Hua met resistance from leftist radicals, whose ideological objections sprang from an underlying power struggle. Hua built popularization teams, mostly from talented younger people whose careers were disrupted by the Cultural Revolution, and thus opened a path for many of them to important roles in China's scientific infrastructure after 1976. Hua Loo-Keng's efforts, while interrupted during the Cultural Revolution and the subsequent political campaigns, were also helped by the populist ethos of the movement, and by the lack of other non-political endeavors at that time. In this sense, the Cultural Revolution gave Hua Loo-Keng's popularization its importance and long-term impact. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Teaching with Historical Novels: A Four-Step Approach.
ERIC Educational Resources Information Center
Smith, John A; Dobson, Dorothy
1993-01-01
Asserts that the use of historical novels in the elementary curriculum is becoming increasingly popular. Provides a four-step process that guides instruction using novels. Includes recommendations for selecting the novels, preteaching activities, and enrichment activities. (CFR)
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Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong
2017-04-01
Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l 2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Romantic Partner Selection and Socialization during Early Adolescence
Simon, Valerie A.; Aikins, Julie Wargo; Prinstein, Mitchell J.
2012-01-01
This prospective study examined romantic partner selection and socialization among a sample of 78 young adolescents (6th–8th graders). Independent assessments of adolescent and romantic partner adjustment were collected before and after relationships initiated via peer nomination and self-report. Prior to their relationship, adolescents and partners were significantly alike on popularity, physical attraction, and depressive symptoms. Controlling for initial similarity, partners' popularity, depressive symptoms, relational aggression and relational victimization significantly predicted changes in adolescents' functioning in these areas over time. However, the magnitude and direction of change varied according to adolescents' and partners' pre-relationship functioning. In general, adolescents who dated high-functioning partners changed more than those who dated low-functioning partners, and partner characteristics predicted greater change among low versus high-functioning adolescents. Results were consistent even when controlling for best friend characteristics. The current findings are among the first to demonstrate unique contributions of romantic partner characteristics to adolescents' psychosocial functioning. PMID:19037942
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Ohl, Michael; Lohrmann, Volker; Breitkreuz, Laura; Kirschey, Lukas; Krause, Stefanie
2014-01-01
Taxonomy, the science of describing and naming of the living world, is recognized as an important and relevant field in modern biological science. While there is wide agreement on the importance of a complete inventory of all organisms on Earth, the public is partly unaware of the amount of known and unknown biodiversity. Out of the enormous number of undescribed (but already recognized) species in natural history museum collections, we selected an attractive example of a wasp, which was presented to museum visitors at a special museum event. We asked 300 visitors to vote on a name for the new species and out of four preselected options, Ampulex dementor Ohl n. sp. was selected. The name, derived from the ‘soul sucking’ dementors from the popular Harry Potter books is an allusion to the wasps' behavior to selectively paralyze its cockroach prey. In this example, public voting on a scientific name has been shown to be an appropriate way to link museum visitors emotionally to biodiversity and its discovery. PMID:24755672
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Evolution of the violin: The law of effect in action.
Wasserman, Edward A; Cullen, Patrick
2016-01-01
As is true for most other human inventions, the origin of the violin is unknown. What is known is that this popular and versatile instrument has notably changed over the course of several hundred years. At issue is whether those evolutionary changes in the construction of the violin are the result of premeditated, intelligent design or whether they arose through a trial-and-error process. Recent scientific evidence favors the latter account. Our perspective piece puts these recent empirical findings into a comprehensive selectionist framework. According to this view, the many things we do and make--like violins--arise from a process of variation and selection which accords with the law of effect. Contrary to popular opinion, there is neither mystique nor romance in this process; it is as fundamental and ubiquitous as the law of natural selection. As with the law of natural selection in the evolution of organisms, there is staunch resistance to the role of the law of effect in the evolution of human inventions. We conclude our piece by considering several objections to our perspective. (c) 2016 APA, all rights reserved).
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Toolkits and Libraries for Deep Learning.
Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth
2017-08-01
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.