Mutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing with Short Test Length

ERIC Educational Resources Information Center

Wang, Chun

2013-01-01

Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those…

A Computer Adaptive Testing Version of the Addiction Severity Index-Multimedia Version (ASI-MV): The Addiction Severity CAT

PubMed Central

Butler, Stephen F.; Black, Ryan A.; McCaffrey, Stacey A.; Ainscough, Jessica; Doucette, Ann M.

2017-01-01

The purpose of this study was to develop and validate a computer adaptive testing (CAT) version of the Addiction Severity Index-Multimedia Version (ASI-MV®), the Addiction Severity CAT. This goal was accomplished in four steps. First, new candidate items for Addiction Severity CAT domains were evaluated after brainstorming sessions with experts in substance abuse treatment. Next, this new item bank was psychometrically evaluated on a large non-clinical (n =4419) and substance abuse treatment sample (n =845). Based on these results, final items were selected and calibrated for the creation of the Addiction Severity CAT algorithms. Once the algorithms were developed for the entire assessment, a fully functioning prototype of an Addiction Severity CAT was created. CAT simulations were conducted and optimal termination criteria were selected for the Addiction Severity CAT algorithms. Finally, construct validity of the CAT algorithms was evaluated by examining convergent/discriminant validity and sensitivity to change. The Addiction Severity CAT was determined to be valid, sensitive to change, and reliable. Further, the Addiction Severity CAT’s time of administration was found to be significantly less than the average time of administration for the ASI-MV composite scores. This study represents the initial validation of an IRT-based Addiction Severity CAT, and further exploration of the Addiction Severity CAT is needed. PMID:28230387

Computerized adaptive testing: the capitalization on chance problem.

PubMed

Olea, Julio; Barrada, Juan Ramón; Abad, Francisco J; Ponsoda, Vicente; Cuevas, Lara

2012-03-01

This paper describes several simulation studies that examine the effects of capitalization on chance in the selection of items and the ability estimation in CAT, employing the 3-parameter logistic model. In order to generate different estimation errors for the item parameters, the calibration sample size was manipulated (N = 500, 1000 and 2000 subjects) as was the ratio of item bank size to test length (banks of 197 and 788 items, test lengths of 20 and 40 items), both in a CAT and in a random test. Results show that capitalization on chance is particularly serious in CAT, as revealed by the large positive bias found in the small sample calibration conditions. For broad ranges of theta, the overestimation of the precision (asymptotic Se) reaches levels of 40%, something that does not occur with the RMSE (theta). The problem is greater as the item bank size to test length ratio increases. Potential solutions were tested in a second study, where two exposure control methods were incorporated into the item selection algorithm. Some alternative solutions are discussed.

Overview and current management of computerized adaptive testing in licensing/certification examinations.

PubMed

Seo, Dong Gi

2017-01-01

Computerized adaptive testing (CAT) has been implemented in high-stakes examinations such as the National Council Licensure Examination-Registered Nurses in the United States since 1994. Subsequently, the National Registry of Emergency Medical Technicians in the United States adopted CAT for certifying emergency medical technicians in 2007. This was done with the goal of introducing the implementation of CAT for medical health licensing examinations. Most implementations of CAT are based on item response theory, which hypothesizes that both the examinee and items have their own characteristics that do not change. There are 5 steps for implementing CAT: first, determining whether the CAT approach is feasible for a given testing program; second, establishing an item bank; third, pretesting, calibrating, and linking item parameters via statistical analysis; fourth, determining the specification for the final CAT related to the 5 components of the CAT algorithm; and finally, deploying the final CAT after specifying all the necessary components. The 5 components of the CAT algorithm are as follows: item bank, starting item, item selection rule, scoring procedure, and termination criterion. CAT management includes content balancing, item analysis, item scoring, standard setting, practice analysis, and item bank updates. Remaining issues include the cost of constructing CAT platforms and deploying the computer technology required to build an item bank. In conclusion, in order to ensure more accurate estimations of examinees' ability, CAT may be a good option for national licensing examinations. Measurement theory can support its implementation for high-stakes examinations.

Overview and current management of computerized adaptive testing in licensing/certification examinations

PubMed Central

2017-01-01

Computerized adaptive testing (CAT) has been implemented in high-stakes examinations such as the National Council Licensure Examination-Registered Nurses in the United States since 1994. Subsequently, the National Registry of Emergency Medical Technicians in the United States adopted CAT for certifying emergency medical technicians in 2007. This was done with the goal of introducing the implementation of CAT for medical health licensing examinations. Most implementations of CAT are based on item response theory, which hypothesizes that both the examinee and items have their own characteristics that do not change. There are 5 steps for implementing CAT: first, determining whether the CAT approach is feasible for a given testing program; second, establishing an item bank; third, pretesting, calibrating, and linking item parameters via statistical analysis; fourth, determining the specification for the final CAT related to the 5 components of the CAT algorithm; and finally, deploying the final CAT after specifying all the necessary components. The 5 components of the CAT algorithm are as follows: item bank, starting item, item selection rule, scoring procedure, and termination criterion. CAT management includes content balancing, item analysis, item scoring, standard setting, practice analysis, and item bank updates. Remaining issues include the cost of constructing CAT platforms and deploying the computer technology required to build an item bank. In conclusion, in order to ensure more accurate estimations of examinees’ ability, CAT may be a good option for national licensing examinations. Measurement theory can support its implementation for high-stakes examinations. PMID:28811394

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

2006-01-01

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

Quantum partial search for uneven distribution of multiple target items

NASA Astrophysics Data System (ADS)

Zhang, Kun; Korepin, Vladimir

2018-06-01

Quantum partial search algorithm is an approximate search. It aims to find a target block (which has the target items). It runs a little faster than full Grover search. In this paper, we consider quantum partial search algorithm for multiple target items unevenly distributed in a database (target blocks have different number of target items). The algorithm we describe can locate one of the target blocks. Efficiency of the algorithm is measured by number of queries to the oracle. We optimize the algorithm in order to improve efficiency. By perturbation method, we find that the algorithm runs the fastest when target items are evenly distributed in database.

Item-focussed Trees for the Identification of Items in Differential Item Functioning.

PubMed

Tutz, Gerhard; Berger, Moritz

2016-09-01

A novel method for the identification of differential item functioning (DIF) by means of recursive partitioning techniques is proposed. We assume an extension of the Rasch model that allows for DIF being induced by an arbitrary number of covariates for each item. Recursive partitioning on the item level results in one tree for each item and leads to simultaneous selection of items and variables that induce DIF. For each item, it is possible to detect groups of subjects with different item difficulties, defined by combinations of characteristics that are not pre-specified. The way a DIF item is determined by covariates is visualized in a small tree and therefore easily accessible. An algorithm is proposed that is based on permutation tests. Various simulation studies, including the comparison with traditional approaches to identify items with DIF, show the applicability and the competitive performance of the method. Two applications illustrate the usefulness and the advantages of the new method.

Recommendation in evolving online networks

NASA Astrophysics Data System (ADS)

Hu, Xiao; Zeng, An; Shang, Ming-Sheng

2016-02-01

Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users' choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.

Post-hoc simulation study to adopt a computerized adaptive testing (CAT) for a Korean Medical License Examination.

PubMed

Seo, Dong Gi; Choi, Jeongwook

2018-05-17

Computerized adaptive testing (CAT) has been adopted in license examinations due to a test efficiency and accuracy. Many research about CAT have been published to prove the efficiency and accuracy of measurement. This simulation study investigated scoring method and item selection methods to implement CAT in Korean medical license examination (KMLE). This study used post-hoc (real data) simulation design. The item bank used in this study was designed with all items in a 2017 KMLE. All CAT algorithms for this study were implemented by a 'catR' package in R program. In terms of accuracy, Rasch and 2parametric logistic (PL) model performed better than 3PL model. Modal a Posteriori (MAP) or Expected a Posterior (EAP) provided more accurate estimates than MLE and WLE. Furthermore Maximum posterior weighted information (MPWI) or Minimum expected posterior variance (MEPV) performed better than other item selection methods. In terms of efficiency, Rasch model was recommended to reduce test length. Simulation study should be performed under varied test conditions before adopting a live CAT. Based on a simulation study, specific scoring and item selection methods should be predetermined before implementing a live CAT.

Personalized recommendation via unbalance full-connectivity inference

NASA Astrophysics Data System (ADS)

Ma, Wenping; Ren, Chen; Wu, Yue; Wang, Shanfeng; Feng, Xiang

2017-10-01

Recommender systems play an important role to help us to find useful information. They are widely used by most e-commerce web sites to push the potential items to individual user according to purchase history. Network-based recommendation algorithms are popular and effective in recommendation, which use two types of elements to represent users and items respectively. In this paper, based on consistence-based inference (CBI) algorithm, we propose a novel network-based algorithm, in which users and items are recognized with no difference. The proposed algorithm also uses information diffusion to find the relationship between users and items. Different from traditional network-based recommendation algorithms, information diffusion initializes from users and items, respectively. Experiments show that the proposed algorithm is effective compared with traditional network-based recommendation algorithms.

The improved Apriori algorithm based on matrix pruning and weight analysis

NASA Astrophysics Data System (ADS)

Lang, Zhenhong

2018-04-01

This paper uses the matrix compression algorithm and weight analysis algorithm for reference and proposes an improved matrix pruning and weight analysis Apriori algorithm. After the transactional database is scanned for only once, the algorithm will construct the boolean transaction matrix. Through the calculation of one figure in the rows and columns of the matrix, the infrequent item set is pruned, and a new candidate item set is formed. Then, the item's weight and the transaction's weight as well as the weight support for items are calculated, thus the frequent item sets are gained. The experimental result shows that the improved Apriori algorithm not only reduces the number of repeated scans of the database, but also improves the efficiency of data correlation mining.

Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™).

PubMed

Lloyd, Andrew; Kerr, Cicely; Breheny, Katie; Brazier, John; Ortiz, Aurora; Borg, Emma

2014-03-01

Condition-specific preference-based measures can offer utility data where they would not otherwise be available or where generic measures may lack sensitivity, although they lack comparability across conditions. This study aimed to develop an algorithm for estimating utilities from the short bowel syndrome health-related quality of life scale (SBS-QoL™). SBS-QoL™ items were selected based on factor and item performance analysis of a European SBS-QoL™ dataset and consultation with 3 SBS clinical experts. Six-dimension health states were developed using 8 SBS-QoL™ items (2 dimensions combined 2 SBS-QoL™ items). SBS health states were valued by a UK general population sample (N = 250) using the lead-time time trade-off method. Preference weights or 'utility decrements' for each severity level of each dimension were estimated by regression models and used to develop the scoring algorithm. Mean utilities for the SBS health states ranged from -0.46 (worst health state, very much affected on all dimensions) to 0.92 (best health state, not at all affected on all dimensions). The random effects model with maximum likelihood estimation regression had the best predictive ability and lowest root mean squared error and mean absolute error, and was used to develop the scoring algorithm. The preference-weighted scoring algorithm for the SBS-QoL™ developed is able to estimate a wide range of utility values from patient-level SBS-QoL™ data. This allows estimation of SBS HRQL impact for the purpose of economic evaluation of SBS treatment benefits.

Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

PubMed

Hollis, Geoff

2018-04-01

Best-worst scaling is a judgment format in which participants are presented with a set of items and have to choose the superior and inferior items in the set. Best-worst scaling generates a large quantity of information per judgment because each judgment allows for inferences about the rank value of all unjudged items. This property of best-worst scaling makes it a promising judgment format for research in psychology and natural language processing concerned with estimating the semantic properties of tens of thousands of words. A variety of different scoring algorithms have been devised in the previous literature on best-worst scaling. However, due to problems of computational efficiency, these scoring algorithms cannot be applied efficiently to cases in which thousands of items need to be scored. New algorithms are presented here for converting responses from best-worst scaling into item scores for thousands of items (many-item scoring problems). These scoring algorithms are validated through simulation and empirical experiments, and considerations related to noise, the underlying distribution of true values, and trial design are identified that can affect the relative quality of the derived item scores. The newly introduced scoring algorithms consistently outperformed scoring algorithms used in the previous literature on scoring many-item best-worst data.

Development of the 7-Item Binge-Eating Disorder Screener (BEDS-7)

PubMed Central

Deal, Linda S.; DiBenedetti, Dana B.; Nelson, Lauren; Fehnel, Sheri E.; Brown, T. Michelle

2016-01-01

Objective Develop a brief, patient-reported screening tool designed to identify individuals with probable binge-eating disorder (BED) for further evaluation or referral to specialists. Methods Items were developed on the basis of the DSM-5 diagnostic criteria, existing tools, and input from 3 clinical experts (January 2014). Items were then refined in cognitive debriefing interviews with participants self-reporting BED characteristics (March 2014) and piloted in a multisite, cross-sectional, prospective, noninterventional study consisting of a semistructured diagnostic interview (to diagnose BED) and administration of the pilot Binge-Eating Disorder Screener (BEDS), Binge Eating Scale (BES), and RAND 36-Item Short-Form Health Survey (RAND-36) (June 2014–July 2014). The sensitivity and specificity of classification algorithms (formed from the pilot BEDS item-level responses) in predicting BED diagnosis were evaluated. The final algorithm was selected to minimize false negatives and false positives, while utilizing the fewest number of BEDS items. Results Starting with the initial BEDS item pool (20 items), the 13-item pilot BEDS resulted from the cognitive debriefing interviews (n = 13). Of the 97 participants in the noninterventional study, 16 were diagnosed with BED (10/62 female, 16%; 6/35 male, 17%). Seven BEDS items (BEDS-7) yielded 100% sensitivity and 38.7% specificity. Participants correctly identified (true positives) had poorer BES scores and RAND-36 scores than participants identified as true negatives. Conclusions Implementation of the brief, patient-reported BEDS-7 in real-world clinical practice is expected to promote better understanding of BED characteristics and help physicians identify patients who may have BED. PMID:27486542

Development of the 7-Item Binge-Eating Disorder Screener (BEDS-7).

PubMed

Herman, Barry K; Deal, Linda S; DiBenedetti, Dana B; Nelson, Lauren; Fehnel, Sheri E; Brown, T Michelle

2016-01-01

Develop a brief, patient-reported screening tool designed to identify individuals with probable binge-eating disorder (BED) for further evaluation or referral to specialists. Items were developed on the basis of the DSM-5 diagnostic criteria, existing tools, and input from 3 clinical experts (January 2014). Items were then refined in cognitive debriefing interviews with participants self-reporting BED characteristics (March 2014) and piloted in a multisite, cross-sectional, prospective, noninterventional study consisting of a semistructured diagnostic interview (to diagnose BED) and administration of the pilot Binge-Eating Disorder Screener (BEDS), Binge Eating Scale (BES), and RAND 36-Item Short-Form Health Survey (RAND-36) (June 2014-July 2014). The sensitivity and specificity of classification algorithms (formed from the pilot BEDS item-level responses) in predicting BED diagnosis were evaluated. The final algorithm was selected to minimize false negatives and false positives, while utilizing the fewest number of BEDS items. Starting with the initial BEDS item pool (20 items), the 13-item pilot BEDS resulted from the cognitive debriefing interviews (n = 13). Of the 97 participants in the noninterventional study, 16 were diagnosed with BED (10/62 female, 16%; 6/35 male, 17%). Seven BEDS items (BEDS-7) yielded 100% sensitivity and 38.7% specificity. Participants correctly identified (true positives) had poorer BES scores and RAND-36 scores than participants identified as true negatives. Implementation of the brief, patient-reported BEDS-7 in real-world clinical practice is expected to promote better understanding of BED characteristics and help physicians identify patients who may have BED.

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Identification of Genetic Loci Underlying the Phenotypic Constructs of Autism Spectrum Disorders

ERIC Educational Resources Information Center

Liu, Xiao-Qing; Georgiades, Stelios; Duku, Eric; Thompson, Ann; Devlin, Bernie; Cook, Edwin H.; Wijsman, Ellen M.; Paterson, Andrew D.; Szatmari, Peter

2011-01-01

Objective: To investigate the underlying phenotypic constructs in autism spectrum disorders (ASD) and to identify genetic loci that are linked to these empirically derived factors. Method: Exploratory factor analysis was applied to two datasets with 28 selected Autism Diagnostic Interview-Revised (ADI-R) algorithm items. The first dataset was from…

Exploratory Item Classification Via Spectral Graph Clustering

PubMed Central

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2017-01-01

Large-scale assessments are supported by a large item pool. An important task in test development is to assign items into scales that measure different characteristics of individuals, and a popular approach is cluster analysis of items. Classical methods in cluster analysis, such as the hierarchical clustering, K-means method, and latent-class analysis, often induce a high computational overhead and have difficulty handling missing data, especially in the presence of high-dimensional responses. In this article, the authors propose a spectral clustering algorithm for exploratory item cluster analysis. The method is computationally efficient, effective for data with missing or incomplete responses, easy to implement, and often outperforms traditional clustering algorithms in the context of high dimensionality. The spectral clustering algorithm is based on graph theory, a branch of mathematics that studies the properties of graphs. The algorithm first constructs a graph of items, characterizing the similarity structure among items. It then extracts item clusters based on the graphical structure, grouping similar items together. The proposed method is evaluated through simulations and an application to the revised Eysenck Personality Questionnaire. PMID:29033476

Generalization of the Lord-Wingersky Algorithm to Computing the Distribution of Summed Test Scores Based on Real-Number Item Scores

ERIC Educational Resources Information Center

Kim, Seonghoon

2013-01-01

With known item response theory (IRT) item parameters, Lord and Wingersky provided a recursive algorithm for computing the conditional frequency distribution of number-correct test scores, given proficiency. This article presents a generalized algorithm for computing the conditional distribution of summed test scores involving real-number item…

Numerical Differentiation Methods for Computing Error Covariance Matrices in Item Response Theory Modeling: An Evaluation and a New Proposal

ERIC Educational Resources Information Center

Tian, Wei; Cai, Li; Thissen, David; Xin, Tao

2013-01-01

In item response theory (IRT) modeling, the item parameter error covariance matrix plays a critical role in statistical inference procedures. When item parameters are estimated using the EM algorithm, the parameter error covariance matrix is not an automatic by-product of item calibration. Cai proposed the use of Supplemented EM algorithm for…

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making.

PubMed

French, Robert M; Glady, Yannick; Thibaut, Jean-Pierre

2017-08-01

In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.

A method for feature selection of APT samples based on entropy

NASA Astrophysics Data System (ADS)

Du, Zhenyu; Li, Yihong; Hu, Jinsong

2018-05-01

By studying the known APT attack events deeply, this paper propose a feature selection method of APT sample and a logic expression generation algorithm IOCG (Indicator of Compromise Generate). The algorithm can automatically generate machine readable IOCs (Indicator of Compromise), to solve the existing IOCs logical relationship is fixed, the number of logical items unchanged, large scale and cannot generate a sample of the limitations of the expression. At the same time, it can reduce the redundancy and useless APT sample processing time consumption, and improve the sharing rate of information analysis, and actively respond to complex and volatile APT attack situation. The samples were divided into experimental set and training set, and then the algorithm was used to generate the logical expression of the training set with the IOC_ Aware plug-in. The contrast expression itself was different from the detection result. The experimental results show that the algorithm is effective and can improve the detection effect.

Feasibility of using a large Clinical Data Warehouse to automate the selection of diagnostic cohorts.

PubMed

Stephen, Reejis; Boxwala, Aziz; Gertman, Paul

2003-01-01

Data from Clinical Data Warehouses (CDWs) can be used for retrospective studies and for benchmarking. However, automated identification of cases from large datasets containing data items in free text fields is challenging. We developed an algorithm for categorizing pediatric patients presenting with respiratory distress into Bronchiolitis, Bacterial pneumonia and Asthma using clinical variables from a CDW. A feasibility study of this approach indicates that case selection may be automated.

Improved collaborative filtering recommendation algorithm of similarity measure

NASA Astrophysics Data System (ADS)

Zhang, Baofu; Yuan, Baoping

2017-05-01

The Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm in personalized recommender systems. The key is to find the nearest neighbor set of the active user by using similarity measure. However, the methods of traditional similarity measure mainly focus on the similarity of user common rating items, but ignore the relationship between the user common rating items and all items the user rates. And because rating matrix is very sparse, traditional collaborative filtering recommendation algorithm is not high efficiency. In order to obtain better accuracy, based on the consideration of common preference between users, the difference of rating scale and score of common items, this paper presents an improved similarity measure method, and based on this method, a collaborative filtering recommendation algorithm based on similarity improvement is proposed. Experimental results show that the algorithm can effectively improve the quality of recommendation, thus alleviate the impact of data sparseness.

Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly

ERIC Educational Resources Information Center

Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.

2013-01-01

Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…

An item-oriented recommendation algorithm on cold-start problem

NASA Astrophysics Data System (ADS)

Qiu, Tian; Chen, Guang; Zhang, Zi-Ke; Zhou, Tao

2011-09-01

Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.

High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Cai, Li

2010-01-01

A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The…

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis: The MusiQoL-MCAT.

PubMed

Michel, Pierre; Baumstarck, Karine; Ghattas, Badih; Pelletier, Jean; Loundou, Anderson; Boucekine, Mohamed; Auquier, Pascal; Boyer, Laurent

2016-04-01

The aim was to develop a multidimensional computerized adaptive short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL questionnaire for multiple sclerosis.A total of 1992 patients were enrolled in this international cross-sectional study. The development of the MusiQoL-MCAT was based on the assessment of between-items MIRT model fit followed by real-data simulations. The MCAT algorithm was based on Bayesian maximum a posteriori estimation of latent traits and Kullback-Leibler information item selection. We examined several simulations based on a fixed number of items. Accuracy was assessed using correlations (r) between initial IRT scores and MCAT scores. Precision was assessed using the standard error measurement (SEM) and the root mean square error (RMSE).The multidimensional graded response model was used to estimate item parameters and IRT scores. Among the MCAT simulations, the 16-item version of the MusiQoL-MCAT was selected because the accuracy and precision became stable with 16 items with satisfactory levels (r ≥ 0.9, SEM ≤ 0.55, and RMSE ≤ 0.3). External validity of the MusiQoL-MCAT was satisfactory.The MusiQoL-MCAT presents satisfactory properties and can individually tailor QoL assessment to each patient, making it less burdensome to patients and better adapted for use in clinical practice.

A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis

PubMed Central

Michel, Pierre; Baumstarck, Karine; Ghattas, Badih; Pelletier, Jean; Loundou, Anderson; Boucekine, Mohamed; Auquier, Pascal; Boyer, Laurent

2016-01-01

Abstract The aim was to develop a multidimensional computerized adaptive short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL questionnaire for multiple sclerosis. A total of 1992 patients were enrolled in this international cross-sectional study. The development of the MusiQoL-MCAT was based on the assessment of between-items MIRT model fit followed by real-data simulations. The MCAT algorithm was based on Bayesian maximum a posteriori estimation of latent traits and Kullback–Leibler information item selection. We examined several simulations based on a fixed number of items. Accuracy was assessed using correlations (r) between initial IRT scores and MCAT scores. Precision was assessed using the standard error measurement (SEM) and the root mean square error (RMSE). The multidimensional graded response model was used to estimate item parameters and IRT scores. Among the MCAT simulations, the 16-item version of the MusiQoL-MCAT was selected because the accuracy and precision became stable with 16 items with satisfactory levels (r ≥ 0.9, SEM ≤ 0.55, and RMSE ≤ 0.3). External validity of the MusiQoL-MCAT was satisfactory. The MusiQoL-MCAT presents satisfactory properties and can individually tailor QoL assessment to each patient, making it less burdensome to patients and better adapted for use in clinical practice. PMID:27057832

Development of the Brief Bipolar Disorder Symptom Scale for patients with bipolar disorder.

PubMed

Dennehy, Ellen B; Suppes, Trisha; Crismon, M Lynn; Toprac, Marcia; Carmody, Thomas J; Rush, A John

2004-06-30

The Brief Bipolar Disorder Symptom Scale (BDSS) is a 10-item measure of symptom severity that was derived from the 24-item Brief Psychiatric Rating Scale (BPRS24). It was developed for clinical use in settings where systematic evaluation is desired within the constraints of a brief visit. The psychometric properties of the BDSS were evaluated in 409 adult outpatients recruited from 19 clinics within the public mental health system of Texas, as part of the Texas Medication Algorithm Project (TMAP). The selection process for individual items is discussed in detail, and was based on multiple analyses, including principal components analysis with varimax rotation. Selection of the final items considered the statistical strength and factor loading of items within each of those factors as well as the need for comprehensive coverage of critical symptoms of bipolar disorder. The BDSS demonstrated good psychometric properties in this preliminary investigation. It demonstrated a strong association with the BPRS24 and performed similarly to the BPRS24 in its relationship to other symptom measures. The BDSS demonstrated superior sensitivity to symptom change, and an excellent level of agreement for classification of patients as either responders or non-responders with the BPRS24. Copyright 2004 Elsevier Ireland Ltd.

Data compression using adaptive transform coding. Appendix 1: Item 1. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rost, Martin Christopher

1988-01-01

Adaptive low-rate source coders are described in this dissertation. These coders adapt by adjusting the complexity of the coder to match the local coding difficulty of the image. This is accomplished by using a threshold driven maximum distortion criterion to select the specific coder used. The different coders are built using variable blocksized transform techniques, and the threshold criterion selects small transform blocks to code the more difficult regions and larger blocks to code the less complex regions. A theoretical framework is constructed from which the study of these coders can be explored. An algorithm for selecting the optimal bit allocation for the quantization of transform coefficients is developed. The bit allocation algorithm is more fully developed, and can be used to achieve more accurate bit assignments than the algorithms currently used in the literature. Some upper and lower bounds for the bit-allocation distortion-rate function are developed. An obtainable distortion-rate function is developed for a particular scalar quantizer mixing method that can be used to code transform coefficients at any rate.

Evidence-based algorithm for diagnosis and assessment in psoriatic arthritis: results by Italian DElphi in psoriatic Arthritis (IDEA).

PubMed

Lapadula, G; Marchesoni, A; Salaffi, F; Ramonda, R; Salvarani, C; Punzi, L; Costa, L; Caso, F; Simone, D; Baiocchi, G; Scioscia, C; Di Carlo, M; Scarpa, R; Ferraccioli, G

2016-12-16

Psoriatic arthritis (PsA) is a chronic inflammatory disease involving skin, peripheral joints, entheses, and axial skeleton. The disease is frequently associated with extrarticular manifestations (EAMs) and comorbidities. In order to create a protocol for PsA diagnosis and global assessment of patients with an algorithm based on anamnestic, clinical, laboratory and imaging procedures, we established a DElphi study on a national scale, named Italian DElphi in psoriatic Arthritis (IDEA). After a literature search, a Delphi poll, involving 52 rheumatologists, was performed. On the basis of the literature search, 202 potential items were identified. The steering committee planned at least two Delphi rounds. In the first Delphi round, the experts judged each of the 202 items using a score ranging from 1 to 9 based on its increasing clinical relevance. The questions posed to experts were How relevant is this procedure/observation/sign/symptom for assessment of a psoriatic arthritis patient? Proposals of additional items, not included in the questionnaire, were also encouraged. The results of the poll were discussed by the Steering Committee, which evaluated the necessity for removing selected procedures or adding additional ones, according to criteria of clinical appropriateness and sustainability. A total of 43 recommended diagnosis and assessment procedures, recognized as items, were derived by combination of the Delphi survey and two National Expert Meetings, and grouped in different areas. Favourable opinion was reached in 100% of cases for several aspects covering the following areas: medical (familial and personal) history, physical evaluation, imaging tool, second level laboratory tests, disease activity measurement and extrarticular manifestations. After performing PsA diagnosis, identification of specific disease activity scores and clinimetric approaches were suggested for assessing the different clinical subsets. Further, results showed the need for investigation on the presence of several EAMs and risk factors. In the context of any area, a rank was assigned for each item by Expert Committee members, in order to create the logical sequence of the algorithm. The final list of recommended diagnosis and assessment procedures, by the Delphi survey and the two National Expert Meetings, was also reported as an algorithm. This study shows results obtained by the combination of a DElphi survey of a group of Italian rheumatologists and two National Expert Meetings, created with the aim of establishing a clinical procedure and algorithm for the diagnosis and the assessment of PsA patients. In order to find accurate and practical diagnostic and assessment items in clinical practice, we have focused our attention on evaluating the different PsA domains. Hence, we conceived the IDEA algorithm in order to address PsA diagnosis and assessment in the context of daily clinical practice. The IDEA algorithm might eventually lead to a multidimensional approach and could represent a useful and practical tool for addressing diagnosis and for assessing the disease appropriately. However, the elaborated algorithm needs to be further investigated in daily practice, for evidencing and proving its eventual efficacy in detecting and staging PsA and its heterogeneous spectrum appropriately.

Issues in Real-Time Data Management.

DTIC Science & Technology

1991-07-01

2. Multiversion concurrency control [5] interprets write operations as the creation of new ver- sions of the items (in contrast to the update-in...features of optimistic (deferred writing, celayed selection of serialization order) and multiversion concurrency control. They do not present any...34 Multiversion Concurrency Control - Theory and Algorithms". ACM Transactions on Database Systems 8, 4 (December 1983), 465-484. 6. Buchman, A. P

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory.

ERIC Educational Resources Information Center

Kelderman, Henk

1992-01-01

Describes algorithms used in the computer program LOGIMO for obtaining maximum likelihood estimates of the parameters in loglinear models. These algorithms are also useful for the analysis of loglinear item-response theory models. Presents modified versions of the iterative proportional fitting and Newton-Raphson algorithms. Simulated data…

Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm. CRESST Report 834

ERIC Educational Resources Information Center

Monroe, Scott; Cai, Li

2013-01-01

In Ramsay curve item response theory (RC-IRT, Woods & Thissen, 2006) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's (1981) EM algorithm, which yields maximum marginal likelihood estimates. This method, however,…

Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Monroe, Scott; Cai, Li

2014-01-01

In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the…

Taboo search algorithm for item assignment in synchronized zone automated order picking system

NASA Astrophysics Data System (ADS)

Wu, Yingying; Wu, Yaohua

2014-07-01

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

LETTER TO THE EDITOR: Optimization of partial search

NASA Astrophysics Data System (ADS)

Korepin, Vladimir E.

2005-11-01

A quantum Grover search algorithm can find a target item in a database faster than any classical algorithm. One can trade accuracy for speed and find a part of the database (a block) containing the target item even faster; this is partial search. A partial search algorithm was recently suggested by Grover and Radhakrishnan. Here we optimize it. Efficiency of the search algorithm is measured by the number of queries to the oracle. The author suggests a new version of the Grover-Radhakrishnan algorithm which uses a minimal number of such queries. The algorithm can run on the same hardware that is used for the usual Grover algorithm.

Lord-Wingersky Algorithm Version 2.0 for Hierarchical Item Factor Models with Applications in Test Scoring, Scale Alignment, and Model Fit Testing. CRESST Report 830

ERIC Educational Resources Information Center

Cai, Li

2013-01-01

Lord and Wingersky's (1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined…

Improved Approximation Algorithms for Item Pricing with Bounded Degree and Valuation

NASA Astrophysics Data System (ADS)

Hamane, Ryoso; Itoh, Toshiya

When a store sells items to customers, the store wishes to decide the prices of the items to maximize its profit. If the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. It would be hard for the store to decide the prices of items. Assume that a store has a set V of n items and there is a set C of m customers who wish to buy those items. The goal of the store is to decide the price of each item to maximize its profit. We refer to this maximization problem as an item pricing problem. We classify the item pricing problems according to how many items the store can sell or how the customers valuate the items. If the store can sell every item i with unlimited (resp. limited) amount, we refer to this as unlimited supply (resp. limited supply). We say that the item pricing problem is single-minded if each customer j∈C wishes to buy a set ej⊆V of items and assigns valuation w(ej)≥0. For the single-minded item pricing problems (in unlimited supply), Balcan and Blum regarded them as weighted k-hypergraphs and gave several approximation algorithms. In this paper, we focus on the (pseudo) degree of k-hypergraphs and the valuation ratio, i. e., the ratio between the smallest and the largest valuations. Then for the single-minded item pricing problems (in unlimited supply), we show improved approximation algorithms (for k-hypergraphs, general graphs, bipartite graphs, etc.) with respect to the maximum (pseudo) degree and the valuation ratio.

Item Difficulty Modeling of Paragraph Comprehension Items

ERIC Educational Resources Information Center

Gorin, Joanna S.; Embretson, Susan E.

2006-01-01

Recent assessment research joining cognitive psychology and psychometric theory has introduced a new technology, item generation. In algorithmic item generation, items are systematically created based on specific combinations of features that underlie the processing required to correctly solve a problem. Reading comprehension items have been more…

Using qualitative research to inform development of a diagnostic algorithm for UTI in children.

PubMed

de Salis, Isabel; Whiting, Penny; Sterne, Jonathan A C; Hay, Alastair D

2013-06-01

Diagnostic and prognostic algorithms can help reduce clinical uncertainty. The selection of candidate symptoms and signs to be measured in case report forms (CRFs) for potential inclusion in diagnostic algorithms needs to be comprehensive, clearly formulated and relevant for end users. To investigate whether qualitative methods could assist in designing CRFs in research developing diagnostic algorithms. Specifically, the study sought to establish whether qualitative methods could have assisted in designing the CRF for the Health Technology Association funded Diagnosis of Urinary Tract infection in Young children (DUTY) study, which will develop a diagnostic algorithm to improve recognition of urinary tract infection (UTI) in children aged <5 years presenting acutely unwell to primary care. Qualitative methods were applied using semi-structured interviews of 30 UK doctors and nurses working with young children in primary care and a Children's Emergency Department. We elicited features that clinicians believed useful in diagnosing UTI and compared these for presence or absence and terminology with the DUTY CRF. Despite much agreement between clinicians' accounts and the DUTY CRFs, we identified a small number of potentially important symptoms and signs not included in the CRF and some included items that could have been reworded to improve understanding and final data analysis. This study uniquely demonstrates the role of qualitative methods in the design and content of CRFs used for developing diagnostic (and prognostic) algorithms. Research groups developing such algorithms should consider using qualitative methods to inform the selection and wording of candidate symptoms and signs.

Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis

ERIC Educational Resources Information Center

Cai, Li

2010-01-01

Item factor analysis (IFA), already well established in educational measurement, is increasingly applied to psychological measurement in research settings. However, high-dimensional confirmatory IFA remains a numerical challenge. The current research extends the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm, initially proposed for…

Manual and computer-aided materials selection for industrial production: An exercise in decision making

NASA Technical Reports Server (NTRS)

Bates, Seth P.

1990-01-01

Students are introduced to methods and concepts for systematic selection and evaluation of materials which are to be used to manufacture specific products in industry. For this laboratory exercise, students are asked to work in groups to identify and describe a product, then to proceed through the process to select a list of three candidates to make the item from. The exercise draws on knowledge of mechanical, physical, and chemical properties, common materials test techniques, and resource management skills in finding and assessing property data. A very important part of the exercise is the students' introduction to decision making algorithms, and learning how to apply them to a complex decision making process.

Efficient Algorithms for Segmentation of Item-Set Time Series

NASA Astrophysics Data System (ADS)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Fully Dynamic Bin Packing

NASA Astrophysics Data System (ADS)

Ivković, Zoran; Lloyd, Errol L.

Classic bin packing seeks to pack a given set of items of possibly varying sizes into a minimum number of identical sized bins. A number of approximation algorithms have been proposed for this NP-hard problem for both the on-line and off-line cases. In this chapter we discuss fully dynamic bin packing, where items may arrive (Insert) and depart (Delete) dynamically. In accordance with standard practice for fully dynamic algorithms, it is assumed that the packing may be arbitrarily rearranged to accommodate arriving and departing items. The goal is to maintain an approximately optimal solution of provably high quality in a total amount of time comparable to that used by an off-line algorithm delivering a solution of the same quality.

Equal Area Logistic Estimation for Item Response Theory

NASA Astrophysics Data System (ADS)

Lo, Shih-Ching; Wang, Kuo-Chang; Chang, Hsin-Li

2009-08-01

Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

Methods for reducing interference in the Complementary Learning Systems model: oscillating inhibition and autonomous memory rehearsal.

PubMed

Norman, Kenneth A; Newman, Ehren L; Perotte, Adler J

2005-11-01

The stability-plasticity problem (i.e. how the brain incorporates new information into its model of the world, while at the same time preserving existing knowledge) has been at the forefront of computational memory research for several decades. In this paper, we critically evaluate how well the Complementary Learning Systems theory of hippocampo-cortical interactions addresses the stability-plasticity problem. We identify two major challenges for the model: Finding a learning algorithm for cortex and hippocampus that enacts selective strengthening of weak memories, and selective punishment of competing memories; and preventing catastrophic forgetting in the case of non-stationary environments (i.e. when items are temporarily removed from the training set). We then discuss potential solutions to these problems: First, we describe a recently developed learning algorithm that leverages neural oscillations to find weak parts of memories (so they can be strengthened) and strong competitors (so they can be punished), and we show how this algorithm outperforms other learning algorithms (CPCA Hebbian learning and Leabra at memorizing overlapping patterns. Second, we describe how autonomous re-activation of memories (separately in cortex and hippocampus) during REM sleep, coupled with the oscillating learning algorithm, can reduce the rate of forgetting of input patterns that are no longer present in the environment. We then present a simple demonstration of how this process can prevent catastrophic interference in an AB-AC learning paradigm.

Efficiently measuring dimensions of the externalizing spectrum model: Development of the Externalizing Spectrum Inventory-Computerized Adaptive Test (ESI-CAT).

PubMed

Sunderland, Matthew; Slade, Tim; Krueger, Robert F; Markon, Kristian E; Patrick, Christopher J; Kramer, Mark D

2017-07-01

The development of the Externalizing Spectrum Inventory (ESI) was motivated by the need to comprehensively assess the interrelated nature of externalizing psychopathology and personality using an empirically driven framework. The ESI measures 23 theoretically distinct yet related unidimensional facets of externalizing, which are structured under 3 superordinate factors representing general externalizing, callous aggression, and substance abuse. One limitation of the ESI is its length at 415 items. To facilitate the use of the ESI in busy clinical and research settings, the current study sought to examine the efficiency and accuracy of a computerized adaptive version of the ESI. Data were collected over 3 waves and totaled 1,787 participants recruited from undergraduate psychology courses as well as male and female state prisons. A series of 6 algorithms with different termination rules were simulated to determine the efficiency and accuracy of each test under 3 different assumed distributions. Scores generated using an optimal adaptive algorithm evidenced high correlations (r > .9) with scores generated using the full ESI, brief ESI item-based factor scales, and the 23 facet scales. The adaptive algorithms for each facet administered a combined average of 115 items, a 72% decrease in comparison to the full ESI. Similarly, scores on the item-based factor scales of the ESI-brief form (57 items) were generated using on average of 17 items, a 70% decrease. The current study successfully demonstrates that an adaptive algorithm can generate similar scores for the ESI and the 3 item-based factor scales using a fraction of the total item pool. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Evaluation of Item-Based Top-N Recommendation Algorithms

DTIC Science & Technology

2000-09-15

Furthermore, one of the advantages of the item-based algorithm is that it has much smaller computational require- 11 0.0 0.1 0.2 0.3 0.4 0.5 0.6 ecommerce ...items, utilized by many e-commerce sites, cannot take advantage of pre-computed user-to-user similarities. Consequently, even though the throughput of...Non-Zeros ecommerce 6667 17491 91222 catalog 50918 39080 435524 ccard 42629 68793 398619 skills 4374 2125 82612 movielens 943 1682 100000 Table 1: The

Design of a Performance-Responsive Drill and Practice Algorithm for Computer-Based Training.

ERIC Educational Resources Information Center

Vazquez-Abad, Jesus; LaFleur, Marc

1990-01-01

Reviews criticisms of the use of drill and practice programs in educational computing and describes potentials for its use in instruction. Topics discussed include guidelines for developing computer-based drill and practice; scripted training courseware; item format design; item bank design; and a performance-responsive algorithm for item…

Extended Mixed-Efects Item Response Models with the MH-RM Algorithm

ERIC Educational Resources Information Center

Chalmers, R. Philip

2015-01-01

A mixed-effects item response theory (IRT) model is presented as a logical extension of the generalized linear mixed-effects modeling approach to formulating explanatory IRT models. Fixed and random coefficients in the extended model are estimated using a Metropolis-Hastings Robbins-Monro (MH-RM) stochastic imputation algorithm to accommodate for…

Computing Strongly Connected Components in the Streaming Model

NASA Astrophysics Data System (ADS)

Laura, Luigi; Santaroni, Federico

In this paper we present the first algorithm to compute the Strongly Connected Components of a graph in the datastream model (W-Stream), where the graph is represented by a stream of edges and we are allowed to produce intermediate output streams. The algorithm is simple, effective, and can be implemented with few lines of code: it looks at each edge in the stream, and selects the appropriate action with respect to a tree T, representing the graph connectivity seen so far. We analyze the theoretical properties of the algorithm: correctness, memory occupation (O(n logn)), per item processing time (bounded by the current height of T), and number of passes (bounded by the maximal height of T). We conclude by presenting a brief experimental evaluation of the algorithm against massive synthetic and real graphs that confirms its effectiveness: with graphs with up to 100M nodes and 4G edges, only few passes are needed, and millions of edges per second are processed.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

PubMed

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

ERIC Educational Resources Information Center

Aybek, Eren Can; Demirtasli, R. Nukhet

2017-01-01

This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response…

An evaluation of exact methods for the multiple subset maximum cardinality selection problem.

PubMed

Brusco, Michael J; Köhn, Hans-Friedrich; Steinley, Douglas

2016-05-01

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. © 2016 The British Psychological Society.

Pick-N Multiple Choice-Exams: A Comparison of Scoring Algorithms

ERIC Educational Resources Information Center

Bauer, Daniel; Holzer, Matthias; Kopp, Veronika; Fischer, Martin R.

2011-01-01

To compare different scoring algorithms for Pick-N multiple correct answer multiple-choice (MC) exams regarding test reliability, student performance, total item discrimination and item difficulty. Data from six 3rd year medical students' end of term exams in internal medicine from 2005 to 2008 at Munich University were analysed (1,255 students,…

Stochastic Approximation Methods for Latent Regression Item Response Models

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2010-01-01

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…

Optimal segmentation and packaging process

DOEpatents

Kostelnik, Kevin M.; Meservey, Richard H.; Landon, Mark D.

1999-01-01

A process for improving packaging efficiency uses three dimensional, computer simulated models with various optimization algorithms to determine the optimal segmentation process and packaging configurations based on constraints including container limitations. The present invention is applied to a process for decontaminating, decommissioning (D&D), and remediating a nuclear facility involving the segmentation and packaging of contaminated items in waste containers in order to minimize the number of cuts, maximize packaging density, and reduce worker radiation exposure. A three-dimensional, computer simulated, facility model of the contaminated items are created. The contaminated items are differentiated. The optimal location, orientation and sequence of the segmentation and packaging of the contaminated items is determined using the simulated model, the algorithms, and various constraints including container limitations. The cut locations and orientations are transposed to the simulated model. The contaminated items are actually segmented and packaged. The segmentation and packaging may be simulated beforehand. In addition, the contaminated items may be cataloged and recorded.

Selecting Items for Criterion-Referenced Tests.

ERIC Educational Resources Information Center

Mellenbergh, Gideon J.; van der Linden, Wim J.

1982-01-01

Three item selection methods for criterion-referenced tests are examined: the classical theory of item difficulty and item-test correlation; the latent trait theory of item characteristic curves; and a decision-theoretic approach for optimal item selection. Item contribution to the standardized expected utility of mastery testing is discussed. (CM)

Hyperspectral data discrimination methods

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

2000-12-01

Hyperspectral data provides spectral response information that provides detailed chemical, moisture, and other description of constituent parts of an item. These new sensor data are useful in USDA product inspection. However, such data introduce problems such as the curse of dimensionality, the need to reduce the number of features used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). Several two-step methods are compared to a new and preferable single-step spectral decomposition algorithm. Initial results on hyperspectral data for good/bad almonds and for good/bad (aflatoxin infested) corn kernels are presented. The hyperspectral application addressed differs greatly from prior USDA work (PLS) in which the level of a specific channel constituent in food was estimated. A validation set (separate from the test set) is used in selecting algorithm parameters. Threshold parameters are varied to select the best Pc operating point. Initial results show that nonlinear features yield improved performance.

Detection and segmentation of multiple touching product inspection items

NASA Astrophysics Data System (ADS)

Casasent, David P.; Talukder, Ashit; Cox, Westley; Chang, Hsuan-Ting; Weber, David

1996-12-01

X-ray images of pistachio nuts on conveyor trays for product inspection are considered. The first step in such a processor is to locate each individual item and place it in a separate file for input to a classifier to determine the quality of each nut. This paper considers new techniques to: detect each item (each nut can be in any orientation, we employ new rotation-invariant filters to locate each item independent of its orientation), produce separate image files for each item [a new blob coloring algorithm provides this for isolated (non-touching) input items], segmentation to provide separate image files for touching or overlapping input items (we use a morphological watershed transform to achieve this), and morphological processing to remove the shell and produce an image of only the nutmeat. Each of these operations and algorithms are detailed and quantitative data for each are presented for the x-ray image nut inspection problem noted. These techniques are of general use in many different product inspection problems in agriculture and other areas.

A General Program for Item-Response Analysis That Employs the Stabilized Newton-Raphson Algorithm. Research Report. ETS RR-13-32

ERIC Educational Resources Information Center

Haberman, Shelby J.

2013-01-01

A general program for item-response analysis is described that uses the stabilized Newton-Raphson algorithm. This program is written to be compliant with Fortran 2003 standards and is sufficiently general to handle independent variables, multidimensional ability parameters, and matrix sampling. The ability variables may be either polytomous or…

Which method of posttraumatic stress disorder classification best predicts psychosocial function in children with traumatic brain injury?

PubMed

Iselin, Greg; Le Brocque, Robyne; Kenardy, Justin; Anderson, Vicki; McKinlay, Lynne

2010-10-01

Controversy surrounds the classification of posttraumatic stress disorder (PTSD), particularly in children and adolescents with traumatic brain injury (TBI). In these populations, it is difficult to differentiate TBI-related organic memory loss from dissociative amnesia. Several alternative PTSD classification algorithms have been proposed for use with children. This paper investigates DSM-IV-TR and alternative PTSD classification algorithms, including and excluding the dissociative amnesia item, in terms of their ability to predict psychosocial function following pediatric TBI. A sample of 184 children aged 6-14 years were recruited following emergency department presentation and/or hospital admission for TBI. PTSD was assessed via semi-structured clinical interview (CAPS-CA) with the child at 3 months post-injury. Psychosocial function was assessed using the parent report CHQ-PF50. Two alternative classification algorithms, the PTSD-AA and 2 of 3 algorithms, reached statistical significance. While the inclusion of the dissociative amnesia item increased prevalence rates across algorithms, it generally resulted in weaker associations with psychosocial function. The PTSD-AA algorithm appears to have the strongest association with psychosocial function following TBI in children and adolescents. Removing the dissociative amnesia item from the diagnostic algorithm generally results in improved validity. Copyright 2010 Elsevier Ltd. All rights reserved.

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Wagstaff, Kiri L.

2011-01-01

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

The Selection of Test Items for Decision Making with a Computer Adaptive Test.

ERIC Educational Resources Information Center

Spray, Judith A.; Reckase, Mark D.

The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…

Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering

PubMed Central

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term. PMID:25343243

Enhanced patient reported outcome measurement suitable for head and neck cancer follow-up clinics

PubMed Central

2012-01-01

Background The ‘Worse-Stable-Better’ (W-S-B) question was introduced to capture patient-perceived change in University of Washington Quality of Life (UW-QOL) domains. Methods 202 head and neck cancer patients in remission prospectively completed UW-QOL and Patients Concerns Inventory (PCI). For each UW-QOL domain, patients indicated whether over the last month things had worsened (W), remained stable (S) or were better (B). Results 202 patients at 448 attendances selected 1752 PCI items they wanted to discuss in consultation, and 58% (1024/1752) of these were not covered by the UW-QOL. UW-QOL algorithms highlighted another 440 significant problems that the patient did not want to discuss (i.e. the corresponding items on the PCI were not selected). After making allowance for UW-QOL algorithms to identify 'significant problems' and PCI selection of corresponding issues for discussion there remained clear residual and notable variation in W-S-B responses, in particular to identify patients with significant problems that were getting worse, and patients without significant problems that wanted to discuss issues that were getting worse. Changes in mean UW-QOL scores were notably lower for those getting worse on the W-S-B question, typically by 10 or more units a magnitude that suggests clinically important changes in score. Conclusions The W-S-B question adds little questionnaire burden and could help to better identify patients who might benefit from intervention. The results of this study suggest that the UW-QOL with the W-S-B modification should be used together with the PCI to allow optimal identification of issues for patient-clinician discussion during routine outpatient clinics. PMID:22695251

A hybrid personalized data recommendation approach for geoscience data sharing

NASA Astrophysics Data System (ADS)

WANG, M.; Wang, J.

2016-12-01

Recommender systems are effective tools helping Internet users overcome information overloading. The two most widely used recommendation algorithms are collaborating filtering (CF) and content-based filtering (CBF). A number of recommender systems based on those two algorithms were developed for multimedia, online sells, and other domains. Each of the two algorithms has its advantages and shortcomings. Hybrid approaches that combine these two algorithms are better choices in many cases. In geoscience data sharing domain, where the items (datasets) are more informative (in space and time) and domain-specific, no recommender system is specialized for data users. This paper reports a dynamic weighted hybrid recommendation algorithm that combines CF and CBF for geoscience data sharing portal. We first derive users' ratings on items with their historical visiting time by Jenks Natural Break. In the CBF part, we incorporate the space, time, and subject information of geoscience datasets to compute item similarity. Predicted ratings were computed with k-NN method separately using CBF and CF, and then combined with weights. With training dataset we attempted to find the best model describing ideal weights and users' co-rating numbers. A logarithmic function was confirmed to be the best model. The model was then used to tune the weights of CF and CBF on user-item basis with test dataset. Evaluation results show that the dynamic weighted approach outperforms either solo CF or CBF approach in terms of Precision and Recall.

A brief dementia screener suitable for use by non-specialists in resource poor settings—the cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia

PubMed Central

Prince, M; Acosta, D; Ferri, C P; Guerra, M; Huang, Y; Jacob, K S; Llibre Rodriguez, J J; Salas, A; Sosa, A L; Williams, J D; Hall, K S

2011-01-01

Objective Brief screening tools for dementia for use by non-specialists in primary care have yet to be validated in non-western settings where cultural factors and limited education may complicate the task. We aimed to derive a brief version of cognitive and informant scales from the Community Screening Instrument for Dementia (CSI-D) and to carry out initial assessments of their likely validity. Methods We applied Mokken analysis to CSI-D cognitive and informant scale data from 15 022 participants in representative population-based surveys in Latin America, India and China, to identify a subset of items from each that conformed optimally to item response theory scaling principles. The validity coefficients of the resulting brief scales (area under ROC curve, optimal cutpoint, sensitivity, specificity and Youden's index) were estimated from data collected in a previous cross-cultural validation of the full CSI-D. Results Seven cognitive items (Loevinger H coefficient 0.64) and six informant items (Loevinger H coefficient 0.69) were selected with excellent hierarchical scaling properties. For the brief cognitive scale, AUROC varied between 0.88 and 0.97, for the brief informant scale between 0.92 and 1.00, and for the combined algorithm between 0.94 and 1.00. Optimal cutpoints did not vary between regions. Youden's index for the combined algorithm varied between 0.78 and 1.00 by region. Conclusion A brief version of the full CSI-D appears to share the favourable culture- and education-fair screening properties of the full assessment, despite considerable abbreviation. The feasibility and validity of the brief version still needs to be established in routine primary care. Copyright © 2010 John Wiley & Sons, Ltd. PMID:21845592

A brief dementia screener suitable for use by non-specialists in resource poor settings--the cross-cultural derivation and validation of the brief Community Screening Instrument for Dementia.

PubMed

Prince, M; Acosta, D; Ferri, C P; Guerra, M; Huang, Y; Jacob, K S; Llibre Rodriguez, J J; Salas, A; Sosa, A L; Williams, J D; Hall, K S

2011-09-01

Brief screening tools for dementia for use by non-specialists in primary care have yet to be validated in non-western settings where cultural factors and limited education may complicate the task. We aimed to derive a brief version of cognitive and informant scales from the Community Screening Instrument for Dementia (CSI-D) and to carry out initial assessments of their likely validity. We applied Mokken analysis to CSI-D cognitive and informant scale data from 15 022 participants in representative population-based surveys in Latin America, India and China, to identify a subset of items from each that conformed optimally to item response theory scaling principles. The validity coefficients of the resulting brief scales (area under ROC curve, optimal cutpoint, sensitivity, specificity and Youden's index) were estimated from data collected in a previous cross-cultural validation of the full CSI-D. Seven cognitive items (Loevinger H coefficient 0.64) and six informant items (Loevinger H coefficient 0.69) were selected with excellent hierarchical scaling properties. For the brief cognitive scale, AUROC varied between 0.88 and 0.97, for the brief informant scale between 0.92 and 1.00, and for the combined algorithm between 0.94 and 1.00. Optimal cutpoints did not vary between regions. Youden's index for the combined algorithm varied between 0.78 and 1.00 by region. A brief version of the full CSI-D appears to share the favourable culture- and education-fair screening properties of the full assessment, despite considerable abbreviation. The feasibility and validity of the brief version still needs to be established in routine primary care. Copyright © 2010 John Wiley & Sons, Ltd.

Analyzing Multiple-Choice Questions by Model Analysis and Item Response Curves

NASA Astrophysics Data System (ADS)

Wattanakasiwich, P.; Ananta, S.

2010-07-01

In physics education research, the main goal is to improve physics teaching so that most students understand physics conceptually and be able to apply concepts in solving problems. Therefore many multiple-choice instruments were developed to probe students' conceptual understanding in various topics. Two techniques including model analysis and item response curves were used to analyze students' responses from Force and Motion Conceptual Evaluation (FMCE). For this study FMCE data from more than 1000 students at Chiang Mai University were collected over the past three years. With model analysis, we can obtain students' alternative knowledge and the probabilities for students to use such knowledge in a range of equivalent contexts. The model analysis consists of two algorithms—concentration factor and model estimation. This paper only presents results from using the model estimation algorithm to obtain a model plot. The plot helps to identify a class model state whether it is in the misconception region or not. Item response curve (IRC) derived from item response theory is a plot between percentages of students selecting a particular choice versus their total score. Pros and cons of both techniques are compared and discussed.

Accurate and scalable social recommendation using mixed-membership stochastic block models.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

2016-12-13

With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

Using genetic algorithm to determine the optimal order quantities for multi-item multi-period under warehouse capacity constraints in kitchenware manufacturing

NASA Astrophysics Data System (ADS)

Saraswati, D.; Sari, D. K.; Johan, V.

2017-11-01

The study was conducted on a manufacturer that produced various kinds of kitchenware with kitchen sink as the main product. There were four types of steel sheets selected as the raw materials of the kitchen sink. The problem was the manufacturer wanted to determine how much steel sheets to order from a single supplier to meet the production requirements in a way to minimize the total inventory cost. In this case, the economic order quantity (EOQ) model was developed using all-unit discount as the price of steel sheets and the warehouse capacity was limited. Genetic algorithm (GA) was used to find the minimum of the total inventory cost as a sum of purchasing cost, ordering cost, holding cost and penalty cost.

A Comparison of Three Types of Test Development Procedures Using Classical and Latent Trait Methods.

ERIC Educational Resources Information Center

Benson, Jeri; Wilson, Michael

Three methods of item selection were used to select sets of 38 items from a 50-item verbal analogies test and the resulting item sets were compared for internal consistency, standard errors of measurement, item difficulty, biserial item-test correlations, and relative efficiency. Three groups of 1,500 cases each were used for item selection. First…

Parameter Estimation in Rasch Models for Examinee-Selected Items

ERIC Educational Resources Information Center

Liu, Chen-Wei; Wang, Wen-Chung

2017-01-01

The examinee-selected-item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set of items (e.g., choose one item to respond from a pair of items), always yields incomplete data (i.e., only the selected items are answered and the others have missing data) that are likely nonignorable. Therefore, using…

Explaining and Controlling for the Psychometric Properties of Computer-Generated Figural Matrix Items

ERIC Educational Resources Information Center

Freund, Philipp Alexander; Hofer, Stefan; Holling, Heinz

2008-01-01

Figural matrix items are a popular task type for assessing general intelligence (Spearman's g). Items of this kind can be constructed rationally, allowing the implementation of computerized generation algorithms. In this study, the influence of different task parameters on the degree of difficulty in matrix items was investigated. A sample of N =…

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

NASA Technical Reports Server (NTRS)

Wang, Lui; Valenzuela-Rendon, Manuel

1993-01-01

The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

Optimal Item Selection with Credentialing Examinations.

ERIC Educational Resources Information Center

Hambleton, Ronald K.; And Others

The study compared two promising item response theory (IRT) item-selection methods, optimal and content-optimal, with two non-IRT item selection methods, random and classical, for use in fixed-length certification exams. The four methods were used to construct 20-item exams from a pool of approximately 250 items taken from a 1985 certification…

Optimal segmentation and packaging process

DOEpatents

Kostelnik, K.M.; Meservey, R.H.; Landon, M.D.

1999-08-10

A process for improving packaging efficiency uses three dimensional, computer simulated models with various optimization algorithms to determine the optimal segmentation process and packaging configurations based on constraints including container limitations. The present invention is applied to a process for decontaminating, decommissioning (D and D), and remediating a nuclear facility involving the segmentation and packaging of contaminated items in waste containers in order to minimize the number of cuts, maximize packaging density, and reduce worker radiation exposure. A three-dimensional, computer simulated, facility model of the contaminated items are created. The contaminated items are differentiated. The optimal location, orientation and sequence of the segmentation and packaging of the contaminated items is determined using the simulated model, the algorithms, and various constraints including container limitations. The cut locations and orientations are transposed to the simulated model. The contaminated items are actually segmented and packaged. The segmentation and packaging may be simulated beforehand. In addition, the contaminated items may be cataloged and recorded. 3 figs.

Mining Hesitation Information by Vague Association Rules

NASA Astrophysics Data System (ADS)

Lu, An; Ng, Wilfred

In many online shopping applications, such as Amazon and eBay, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold (for example, those items that are put into the basket but not checked out). We say that those almost sold items carry hesitation information, since customers are hesitating to buy them. The hesitation information of items is valuable knowledge for the design of good selling strategies. However, there is no conceptual model that is able to capture different statuses of hesitation information. Herein, we apply and extend vague set theory in the context of AR mining. We define the concepts of attractiveness and hesitation of an item, which represent the overall information of a customer's intent on an item. Based on the two concepts, we propose the notion of Vague Association Rules (VARs). We devise an efficient algorithm to mine the VARs. Our experiments show that our algorithm is efficient and the VARs capture more specific and richer information than do the traditional ARs.

Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data.

PubMed

Luna, Jose Maria; Padillo, Francisco; Pechenizkiy, Mykola; Ventura, Sebastian

2017-09-27

Pattern mining is one of the most important tasks to extract meaningful and useful information from raw data. This task aims to extract item-sets that represent any type of homogeneity and regularity in data. Although many efficient algorithms have been developed in this regard, the growing interest in data has caused the performance of existing pattern mining techniques to be dropped. The goal of this paper is to propose new efficient pattern mining algorithms to work in big data. To this aim, a series of algorithms based on the MapReduce framework and the Hadoop open-source implementation have been proposed. The proposed algorithms can be divided into three main groups. First, two algorithms [Apriori MapReduce (AprioriMR) and iterative AprioriMR] with no pruning strategy are proposed, which extract any existing item-set in data. Second, two algorithms (space pruning AprioriMR and top AprioriMR) that prune the search space by means of the well-known anti-monotone property are proposed. Finally, a last algorithm (maximal AprioriMR) is also proposed for mining condensed representations of frequent patterns. To test the performance of the proposed algorithms, a varied collection of big data datasets have been considered, comprising up to 3 · 10#x00B9;⁸ transactions and more than 5 million of distinct single-items. The experimental stage includes comparisons against highly efficient and well-known pattern mining algorithms. Results reveal the interest of applying MapReduce versions when complex problems are considered, and also the unsuitability of this paradigm when dealing with small data.

A novel multi-item joint replenishment problem considering multiple type discounts.

PubMed

Cui, Ligang; Zhang, Yajun; Deng, Jie; Xu, Maozeng

2018-01-01

In business replenishment, discount offers of multi-item may either provide different discount schedules with a single discount type, or provide schedules with multiple discount types. The paper investigates the joint effects of multiple discount schemes on the decisions of multi-item joint replenishment. In this paper, a joint replenishment problem (JRP) model, considering three discount (all-unit discount, incremental discount, total volume discount) offers simultaneously, is constructed to determine the basic cycle time and joint replenishment frequencies of multi-item. To solve the proposed problem, a heuristic algorithm is proposed to find the optimal solutions and the corresponding total cost of the JRP model. Numerical experiment is performed to test the algorithm and the computational results of JRPs under different discount combinations show different significance in the replenishment cost reduction.

Algorithme et enseignement de la grammaire (Algorithm and the Teaching of Grammar)

ERIC Educational Resources Information Center

Michiels, A.

1975-01-01

Binary algorithmic logic may prove useful for teaching grammar, especially in the case of 'closed-system items.' (Text is in French.) Available from Instituut voor Toegepaste Linguistiek, Vesaliusstraat 2, B. 3000 Leuven, Belgium. (TL)

IRT Item Parameter Recovery with Marginal Maximum Likelihood Estimation Using Loglinear Smoothing Models

ERIC Educational Resources Information Center

Casabianca, Jodi M.; Lewis, Charles

2015-01-01

Loglinear smoothing (LLS) estimates the latent trait distribution while making fewer assumptions about its form and maintaining parsimony, thus leading to more precise item response theory (IRT) item parameter estimates than standard marginal maximum likelihood (MML). This article provides the expectation-maximization algorithm for MML estimation…

PubMed Central

PANATTO, D.; ARATA, L.; BEVILACQUA, I.; APPRATO, L.; GASPARINI, R.; AMICIZIA, D.

2015-01-01

Summary Introduction. Health-related knowledge is often assessed through multiple-choice tests. Among the different types of formats, researchers may opt to use multiple-mark items, i.e. with more than one correct answer. Although multiple-mark items have long been used in the academic setting – sometimes with scant or inconclusive results – little is known about the implementation of this format in research on in-field health education and promotion. Methods. A study population of secondary school students completed a survey on nutrition-related knowledge, followed by a single- lecture intervention. Answers were scored by means of eight different scoring algorithms and analyzed from the perspective of classical test theory. The same survey was re-administered to a sample of the students in order to evaluate the short-term change in their knowledge. Results. In all, 286 questionnaires were analyzed. Partial scoring algorithms displayed better psychometric characteristics than the dichotomous rule. In particular, the algorithm proposed by Ripkey and the balanced rule showed greater internal consistency and relative efficiency in scoring multiple-mark items. A penalizing algorithm in which the proportion of marked distracters was subtracted from that of marked correct answers was the only one that highlighted a significant difference in performance between natives and immigrants, probably owing to its slightly better discriminatory ability. This algorithm was also associated with the largest effect size in the pre-/post-intervention score change. Discussion. The choice of an appropriate rule for scoring multiple- mark items in research on health education and promotion should consider not only the psychometric properties of single algorithms but also the study aims and outcomes, since scoring rules differ in terms of biasness, reliability, difficulty, sensitivity to guessing and discrimination. PMID:26900331

Data mining learning bootstrap through semantic thumbnail analysis

NASA Astrophysics Data System (ADS)

Battiato, Sebastiano; Farinella, Giovanni Maria; Giuffrida, Giovanni; Tribulato, Giuseppe

2007-01-01

The rapid increase of technological innovations in the mobile phone industry induces the research community to develop new and advanced systems to optimize services offered by mobile phones operators (telcos) to maximize their effectiveness and improve their business. Data mining algorithms can run over data produced by mobile phones usage (e.g. image, video, text and logs files) to discover user's preferences and predict the most likely (to be purchased) offer for each individual customer. One of the main challenges is the reduction of the learning time and cost of these automatic tasks. In this paper we discuss an experiment where a commercial offer is composed by a small picture augmented with a short text describing the offer itself. Each customer's purchase is properly logged with all relevant information. Upon arrival of new items we need to learn who the best customers (prospects) for each item are, that is, the ones most likely to be interested in purchasing that specific item. Such learning activity is time consuming and, in our specific case, is not applicable given the large number of new items arriving every day. Basically, given the current customer base we are not able to learn on all new items. Thus, we need somehow to select among those new items to identify the best candidates. We do so by using a joint analysis between visual features and text to estimate how good each new item could be, that is, whether or not is worth to learn on it. Preliminary results show the effectiveness of the proposed approach to improve classical data mining techniques.

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory. [Project Psychometric Aspects of Item Banking No. 53.] Research Report 91-1.

ERIC Educational Resources Information Center

Kelderman, Henk

In this paper, algorithms are described for obtaining the maximum likelihood estimates of the parameters in log-linear models. Modified versions of the iterative proportional fitting and Newton-Raphson algorithms are described that work on the minimal sufficient statistics rather than on the usual counts in the full contingency table. This is…

A Comparison of the One-and Three-Parameter Logistic Models on Measures of Test Efficiency.

ERIC Educational Resources Information Center

Benson, Jeri

Two methods of item selection were used to select sets of 40 items from a 50-item verbal analogies test, and the resulting item sets were compared for relative efficiency. The BICAL program was used to select the 40 items having the best mean square fit to the one parameter logistic (Rasch) model. The LOGIST program was used to select the 40 items…

Techniques of Acceleration for Association Rule Induction with Pseudo Artificial Life Algorithm

NASA Astrophysics Data System (ADS)

Kanakubo, Masaaki; Hagiwara, Masafumi

Frequent patterns mining is one of the important problems in data mining. Generally, the number of potential rules grows rapidly as the size of database increases. It is therefore hard for a user to extract the association rules. To avoid such a difficulty, we propose a new method for association rule induction with pseudo artificial life approach. The proposed method is to decide whether there exists an item set which contains N or more items in two transactions. If it exists, a series of item sets which are contained in the part of transactions will be recorded. The iteration of this step contributes to the extraction of association rules. It is not necessary to calculate the huge number of candidate rules. In the evaluation test, we compared the extracted association rules using our method with the rules using other algorithms like Apriori algorithm. As a result of the evaluation using huge retail market basket data, our method is approximately 10 and 20 times faster than the Apriori algorithm and many its variants.

Greedy algorithms and Zipf laws

NASA Astrophysics Data System (ADS)

Moran, José; Bouchaud, Jean-Philippe

2018-04-01

We consider a simple model of firm/city/etc growth based on a multi-item criterion: whenever entity B fares better than entity A on a subset of M items out of K, the agent originally in A moves to B. We solve the model analytically in the cases K  =  1 and . The resulting stationary distribution of sizes is generically a Zipf-law provided M  >  K/2. When , no selection occurs and the size distribution remains thin-tailed. In the special case M  =  K, one needs to regularize the problem by introducing a small ‘default’ probability ϕ. We find that the stationary distribution has a power-law tail that becomes a Zipf-law when . The approach to the stationary state can also be characterized, with strong similarities with a simple ‘aging’ model considered by Barrat and Mézard.

Use HypE to Hide Association Rules by Adding Items

PubMed Central

Cheng, Peng; Lin, Chun-Wei; Pan, Jeng-Shyang

2015-01-01

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases. PMID:26070130

A new method for E-government procurement using collaborative filtering and Bayesian approach.

PubMed

Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

PubMed Central

Wang, Yan

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach. PMID:24385869

Gamma signatures of the C-BORD Tagged Neutron Inspection System

NASA Astrophysics Data System (ADS)

Sardet, A.; Pérot, B.; Carasco, C.; Sannié, G.; Moretto, S.; Nebbia, G.; Fontana, C.; Pino, F.; Iovene, A.; Tintori, C.

2018-01-01

In the frame of C-BORD project (H2020 program of the EU), a Rapidly relocatable Tagged Neutron Inspection System (RRTNIS) is being developed to non-intrusively detect explosives, chemical threats, and other illicit goods in cargo containers. Material identification is performed through gamma spectroscopy, using twenty NaI detectors and four LaBr3 detectors, to determine the different elements composing the inspected item from their specific gamma signatures induced by fast neutrons. This is performed using an unfolding algorithm to decompose the energy spectrum of a suspect item, selected by X-ray radiography and on which the RRTNIS inspection is focused, on a database of pure element gamma signatures. This paper reports on simulated signatures for the NaI and LaBr3 detectors, constructed using the MCNP6 code. First experimental spectra of a few elements of interest are also presented.

Lord-Wingersky Algorithm Version 2.0 for Hierarchical Item Factor Models with Applications in Test Scoring, Scale Alignment, and Model Fit Testing.

PubMed

Cai, Li

2015-06-01

Lord and Wingersky's (Appl Psychol Meas 8:453-461, 1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined on a grid formed by direct products of quadrature points. However, the increase in computational burden remains exponential in the number of dimensions, making the implementation of the recursive algorithm cumbersome for truly high-dimensional models. In this paper, a dimension reduction method that is specific to the Lord-Wingersky recursions is developed. This method can take advantage of the restrictions implied by hierarchical item factor models, e.g., the bifactor model, the testlet model, or the two-tier model, such that a version of the Lord-Wingersky recursive algorithm can operate on a dramatically reduced set of quadrature points. For instance, in a bifactor model, the dimension of integration is always equal to 2, regardless of the number of factors. The new algorithm not only provides an effective mechanism to produce summed score to IRT scaled score translation tables properly adjusted for residual dependence, but leads to new applications in test scoring, linking, and model fit checking as well. Simulated and empirical examples are used to illustrate the new applications.

Data Clustering

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a particular application involves considerations of the kind of data being analyzed, algorithm runtime efficiency, and how much prior knowledge is available about the problem domain, which can dictate the nature of clusters sought. Fundamentally, the clustering method and its representations of clusters carries with it a definition of what a cluster is, and it is important that this be aligned with the analysis goals for the problem at hand. In this chapter, I emphasize this point by identifying for each algorithm the cluster representation as a model, m_j , even for algorithms that are not typically thought of as creating a “model.” This chapter surveys a basic collection of clustering methods useful to any practitioner who is interested in applying clustering to a new data set. The algorithms include k-means (Section 25.2), EM (Section 25.3), agglomerative (Section 25.4), and spectral (Section 25.5) clustering, with side mentions of variants such as kernel k-means and divisive clustering. The chapter also discusses each algorithm’s strengths and limitations and provides pointers to additional in-depth reading for each subject. Section 25.6 discusses methods for incorporating domain knowledge into the clustering process. This chapter concludes with a brief survey of interesting applications of clustering methods to astronomy data (Section 25.7). The chapter begins with k-means because it is both generally accessible and so widely used that understanding it can be considered a necessary prerequisite for further work in the field. EM can be viewed as a more sophisticated version of k-means that uses a generative model for each cluster and probabilistic item assignments. Agglomerative clustering is the most basic form of hierarchical clustering and provides a basis for further exploration of algorithms in that vein. Spectral clustering permits a departure from feature-vector-based clustering and can operate on data sets instead represented as affinity, or similarity matrices—cases in which only pairwise information is known. The list of algorithms covered in this chapter is representative of those most commonly in use, but it is by no means comprehensive. There is an extensive collection of existing books on clustering that provide additional background and depth. Three early books that remain useful today are Anderberg’s Cluster Analysis for Applications [3], Hartigan’s Clustering Algorithms [25], and Gordon’s Classification [22]. The latter covers basics on similarity measures, partitioning and hierarchical algorithms, fuzzy clustering, overlapping clustering, conceptual clustering, validations methods, and visualization or data reduction techniques such as principal components analysis (PCA),multidimensional scaling, and self-organizing maps. More recently, Jain et al. provided a useful and informative survey [27] of a variety of different clustering algorithms, including those mentioned here as well as fuzzy, graph-theoretic, and evolutionary clustering. Everitt’s Cluster Analysis [19] provides a modern overview of algorithms, similarity measures, and evaluation methods.

Controlling Item Exposure Conditional on Ability in Computerized Adaptive Testing.

ERIC Educational Resources Information Center

Stocking, Martha L.; Lewis, Charles

1998-01-01

Ensuring item and pool security in a continuous testing environment is explored through a new method of controlling exposure rate of items conditional on ability level in computerized testing. Properties of this conditional control on exposure rate, when used in conjunction with a particular adaptive testing algorithm, are explored using simulated…

Multidimensional CAT Item Selection Methods for Domain Scores and Composite Scores with Item Exposure Control and Content Constraints

ERIC Educational Resources Information Center

Yao, Lihua

2014-01-01

The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle;…

Comparing Methods for Item Analysis: The Impact of Different Item-Selection Statistics on Test Difficulty

ERIC Educational Resources Information Center

Jones, Andrew T.

2011-01-01

Practitioners often depend on item analysis to select items for exam forms and have a variety of options available to them. These include the point-biserial correlation, the agreement statistic, the B index, and the phi coefficient. Although research has demonstrated that these statistics can be useful for item selection, no research as of yet has…

Improving information filtering via network manipulation

NASA Astrophysics Data System (ADS)

Zhang, Fuguo; Zeng, An

2012-12-01

The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.

A Procedure to Detect Item Bias Present Simultaneously in Several Items

DTIC Science & Technology

1991-04-25

exhibit a coherent and major biasing influence at the test level. In partic- ular, this can be true even if each individual item displays only a minor...response functions (IRFs) without the use of item parameter estimation algorithms when the sample size is too small for their use. Thissen, Steinberg...convention). A random sample of examinees is drawn from each group, and a test of N items is administered to them. Typically it is suspected that a

Bayesian Estimation of Multidimensional Item Response Models. A Comparison of Analytic and Simulation Algorithms

ERIC Educational Resources Information Center

Martin-Fernandez, Manuel; Revuelta, Javier

2017-01-01

This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…

A Comparison of the One-, the Modified Three-, and the Three-Parameter Item Response Theory Models in the Test Development Item Selection Process.

ERIC Educational Resources Information Center

Eignor, Daniel R.; Douglass, James B.

This paper attempts to provide some initial information about the use of a variety of item response theory (IRT) models in the item selection process; its purpose is to compare the information curves derived from the selection of items characterized by several different IRT models and their associated parameter estimation programs. These…

The CSM testbed matrix processors internal logic and dataflow descriptions

NASA Technical Reports Server (NTRS)

Regelbrugge, Marc E.; Wright, Mary A.

1988-01-01

This report constitutes the final report for subtask 1 of Task 5 of NASA Contract NAS1-18444, Computational Structural Mechanics (CSM) Research. This report contains a detailed description of the coded workings of selected CSM Testbed matrix processors (i.e., TOPO, K, INV, SSOL) and of the arithmetic utility processor AUS. These processors and the current sparse matrix data structures are studied and documented. Items examined include: details of the data structures, interdependence of data structures, data-blocking logic in the data structures, processor data flow and architecture, and processor algorithmic logic flow.

Accurate and scalable social recommendation using mixed-membership stochastic block models

PubMed Central

Godoy-Lorite, Antonia; Moore, Cristopher

2016-01-01

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

PubMed

Goldstein, Markus; Uchida, Seiichi

2016-01-01

Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

Stochastic Approximation Methods for Latent Regression Item Response Models. Research Report. ETS RR-09-09

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2009-01-01

This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the…

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

2015-10-01

Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

Design and implementation of an audit trail in compliance with US regulations.

PubMed

Jiang, Keyuan; Cao, Xiang

2011-10-01

Audit trails have been used widely to ensure quality of study data and have been implemented in computerized clinical trials data systems. Increasingly, there is a need to audit access to study participant identifiable information to provide assurance that study participant privacy is protected and confidentiality is maintained. In the United States, several federal regulations specify how the audit trail function should be implemented. To describe the development and implementation of a comprehensive audit trail system that meets the regulatory requirements of assuring data quality and integrity and protecting participant privacy and that is also easy to implement and maintain. The audit trail system was designed and developed after we examined regulatory requirements, data access methods, prevailing application architecture, and good security practices. Our comprehensive audit trail system was developed and implemented at the database level using a commercially available database management software product. It captures both data access and data changes with the correct user identifier. Documentation of access is initiated automatically in response to either data retrieval or data change at the database level. Currently, our system has been implemented only on one commercial database management system. Although our audit trail algorithm does not allow for logging aggregate operations, aggregation does not reveal sensitive private participant information. Careful consideration must be given to data items selected for monitoring because selection of all data items using our system can dramatically increase the requirements for computer disk space. Evaluating the criticality and sensitivity of individual data items selected can control the storage requirements for clinical trial audit trail records. Our audit trail system is capable of logging data access and data change operations to satisfy regulatory requirements. Our approach is applicable to virtually any data that can be stored in a relational database.

A method for evaluating discoverability and navigability of recommendation algorithms.

PubMed

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis

2017-01-01

Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.

Using Mutual Information for Adaptive Item Comparison and Student Assessment

ERIC Educational Resources Information Center

Liu, Chao-Lin

2005-01-01

The author analyzes properties of mutual information between dichotomous concepts and test items. The properties generalize some common intuitions about item comparison, and provide principled foundations for designing item-selection heuristics for student assessment in computer-assisted educational systems. The proposed item-selection strategies…

Toward a Principled Sampling Theory for Quasi-Orders

PubMed Central

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets. PMID:27965601

Toward a Principled Sampling Theory for Quasi-Orders.

PubMed

Ünlü, Ali; Schrepp, Martin

2016-01-01

Quasi-orders, that is, reflexive and transitive binary relations, have numerous applications. In educational theories, the dependencies of mastery among the problems of a test can be modeled by quasi-orders. Methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data are sensitive to the underlying quasi-order structure. These data mining techniques have to be compared based on extensive simulation studies, with unbiased samples of randomly generated quasi-orders at their basis. In this paper, we develop techniques that can provide the required quasi-order samples. We introduce a discrete doubly inductive procedure for incrementally constructing the set of all quasi-orders on a finite item set. A randomization of this deterministic procedure allows us to generate representative samples of random quasi-orders. With an outer level inductive algorithm, we consider the uniform random extensions of the trace quasi-orders to higher dimension. This is combined with an inner level inductive algorithm to correct the extensions that violate the transitivity property. The inner level correction step entails sampling biases. We propose three algorithms for bias correction and investigate them in simulation. It is evident that, on even up to 50 items, the new algorithms create close to representative quasi-order samples within acceptable computing time. Hence, the principled approach is a significant improvement to existing methods that are used to draw quasi-orders uniformly at random but cannot cope with reasonably large item sets.

Stratified and Maximum Information Item Selection Procedures in Computer Adaptive Testing

ERIC Educational Resources Information Center

Deng, Hui; Ansley, Timothy; Chang, Hua-Hua

2010-01-01

In this study we evaluated and compared three item selection procedures: the maximum Fisher information procedure (F), the a-stratified multistage computer adaptive testing (CAT) (STR), and a refined stratification procedure that allows more items to be selected from the high a strata and fewer items from the low a strata (USTR), along with…

An NCME Instructional Module on Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

ERIC Educational Resources Information Center

Kim, Jee-Seon; Bolt, Daniel M.

2007-01-01

The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…

Bayesian Analysis of Item Response Curves. Research Report 84-1. Mathematical Sciences Technical Report No. 132.

ERIC Educational Resources Information Center

Tsutakawa, Robert K.; Lin, Hsin Ying

Item response curves for a set of binary responses are studied from a Bayesian viewpoint of estimating the item parameters. For the two-parameter logistic model with normally distributed ability, restricted bivariate beta priors are used to illustrate the computation of the posterior mode via the EM algorithm. The procedure is illustrated by data…

Estimation of Two-Parameter Logistic Item Response Curves. Research Report 83-1. Mathematical Sciences Technical Report No. 130.

ERIC Educational Resources Information Center

Tsutakawa, Robert K.

This paper presents a method for estimating certain characteristics of test items which are designed to measure ability, or knowledge, in a particular area. Under the assumption that ability parameters are sampled from a normal distribution, the EM algorithm is used to derive maximum likelihood estimates to item parameters of the two-parameter…

Item-Based Top-N Recommendation Algorithms

DTIC Science & Technology

2003-01-20

basket of items, utilized by many e-commerce sites, cannot take advantage of pre-computed user-to-user similarities. Finally, even though the...not discriminate between items that are present in frequent itemsets and items that are not, while still maintaining the computational advantages of...453219 0.02% 7.74 ccard 42629 68793 398619 0.01% 9.35 ecommerce 6667 17491 91222 0.08% 13.68 em 8002 1648 769311 5.83% 96.14 ml 943 1682 100000 6.31

Expertise sensitive item selection.

PubMed

Chow, P; Russell, H; Traub, R E

2000-12-01

In this paper we describe and illustrate a procedure for selecting items from a large pool for a certification test. The proposed procedure, which is intended to improve the alignment of the certification test with on-the-job performance, is based on an expertise sensitive index. This index for an item is the difference between the item's p values for experts and novices. An example is provided of the application of the index for selecting items to be used in certifying bakers.

Novel clustering of items from the Autism Diagnostic Interview-Revised to define phenotypes within autism spectrum disorders

PubMed Central

Hu, Valerie W.; Steinberg, Mara E.

2009-01-01

Heterogeneity in phenotypic presentation of ASD has been cited as one explanation for the difficulty in pinpointing specific genes involved in autism. Recent studies have attempted to reduce the “noise” in genetic and other biological data by reducing the phenotypic heterogeneity of the sample population. The current study employs multiple clustering algorithms on 123 item scores from the Autism Diagnostic Interview-Revised (ADI-R) diagnostic instrument of nearly 2000 autistic individuals to identify subgroups of autistic probands with clinically relevant behavioral phenotypes in order to isolate more homogeneous groups of subjects for gene expression analyses. Our combined cluster analyses suggest optimal division of the autistic probands into 4 phenotypic clusters based on similarity of symptom severity across the 123 selected item scores. One cluster is characterized by severe language deficits, while another exhibits milder symptoms across the domains. A third group possesses a higher frequency of savant skills while the fourth group exhibited intermediate severity across all domains. Grouping autistic individuals by multivariate cluster analysis of ADI-R scores reveals meaningful phenotypes of subgroups within the autistic spectrum which we show, in a related (accompanying) study, to be associated with distinct gene expression profiles. PMID:19455643

Approximation Algorithms for the Highway Problem under the Coupon Model

NASA Astrophysics Data System (ADS)

Hamane, Ryoso; Itoh, Toshiya; Tomita, Kouhei

When a store sells items to customers, the store wishes to decide the prices of items to maximize its profit. Intuitively, if the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. So it would be hard for the store to decide the prices of items. Assume that the store has a set V of n items and there is a set E of m customers who wish to buy the items, and also assume that each item i ∈ V has the production cost di and each customer ej ∈ E has the valuation vj on the bundle ej ⊆ V of items. When the store sells an item i ∈ V at the price ri, the profit for the item i is pi = ri - di. The goal of the store is to decide the price of each item to maximize its total profit. We refer to this maximization problem as the item pricing problem. In most of the previous works, the item pricing problem was considered under the assumption that pi ≥ 0 for each i ∈ V, however, Balcan, et al. [In Proc. of WINE, LNCS 4858, 2007] introduced the notion of “loss-leader, ” and showed that the seller can get more total profit in the case that pi < 0 is allowed than in the case that pi < 0 is not allowed. In this paper, we consider the line highway problem (in which each customer is interested in an interval on the line of the items) and the cycle highway problem (in which each customer is interested in an interval on the cycle of the items), and show approximation algorithms for the line highway problem and the cycle highway problem in which the smallest valuation is s and the largest valuation is l (this is called an [s, l]-valuation setting) or all valuations are identical (this is called a single valuation setting).

Selection of multiple cued items is possible during visual short-term memory maintenance.

PubMed

Matsukura, Michi; Vecera, Shaun P

2015-07-01

Recent neuroimaging studies suggest that maintenance of a selected object feature held in visual short-term/working memory (VSTM/VWM) is supported by the same neural mechanisms that encode the sensory information. If VSTM operates by retaining "reasonable copies" of scenes constructed during sensory processing (Serences, Ester, Vogel, & Awh, 2009, p. 207, the sensory recruitment hypothesis), then attention should be able to select multiple items represented in VSTM as long as the number of these attended items does not exceed the typical VSTM capacity. It is well known that attention can select at least two noncontiguous locations at the same time during sensory processing. However, empirical reports from the studies that examined this possibility are inconsistent. In the present study, we demonstrate that (1) attention can indeed select more than a single item during VSTM maintenance when observers are asked to recognize a set of items in the manner that these items were originally attended, and (2) attention can select multiple cued items regardless of whether these items are perceptually organized into a single group (contiguous locations) or not (noncontiguous locations). The results also replicate and extend the recent finding that selective attention that operates during VSTM maintenance is sensitive to the observers' goal and motivation to use the cueing information.

Mining algorithm for association rules in big data based on Hadoop

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Wang, Xiaojing; Zhang, Lijun; Qiao, Liying

2018-04-01

In order to solve the problem that the traditional association rules mining algorithm has been unable to meet the mining needs of large amount of data in the aspect of efficiency and scalability, take FP-Growth as an example, the algorithm is realized in the parallelization based on Hadoop framework and Map Reduce model. On the basis, it is improved using the transaction reduce method for further enhancement of the algorithm's mining efficiency. The experiment, which consists of verification of parallel mining results, comparison on efficiency between serials and parallel, variable relationship between mining time and node number and between mining time and data amount, is carried out in the mining results and efficiency by Hadoop clustering. Experiments show that the paralleled FP-Growth algorithm implemented is able to accurately mine frequent item sets, with a better performance and scalability. It can be better to meet the requirements of big data mining and efficiently mine frequent item sets and association rules from large dataset.

Are all quantitative postmarketing signal detection methods equal? Performance characteristics of logistic regression and Multi-item Gamma Poisson Shrinker.

PubMed

Berlin, Conny; Blanch, Carles; Lewis, David J; Maladorno, Dionigi D; Michel, Christiane; Petrin, Michael; Sarp, Severine; Close, Philippe

2012-06-01

The detection of safety signals with medicines is an essential activity to protect public health. Despite widespread acceptance, it is unclear whether recently applied statistical algorithms provide enhanced performance characteristics when compared with traditional systems. Novartis has adopted a novel system for automated signal detection on the basis of disproportionality methods within a safety data mining application (Empirica™ Signal System [ESS]). ESS uses two algorithms for routine analyses: empirical Bayes Multi-item Gamma Poisson Shrinker and logistic regression (LR). A model was developed comprising 14 medicines, categorized as "new" or "established." A standard was prepared on the basis of safety findings selected from traditional sources. ESS results were compared with the standard to calculate the positive predictive value (PPV), specificity, and sensitivity. PPVs of the lower one-sided 5% and 0.05% confidence limits of the Bayes geometric mean (EB05) and of the LR odds ratio (LR0005) almost coincided for all the drug-event combinations studied. There was no obvious difference comparing the PPV of the leading Medical Dictionary for Regulatory Activities (MedDRA) terms to the PPV for all terms. The PPV of narrow MedDRA query searches was higher than that for broad searches. The widely used threshold value of EB05 = 2.0 or LR0005 = 2.0 together with more than three spontaneous reports of the drug-event combination produced balanced results for PPV, sensitivity, and specificity. Consequently, performance characteristics were best for leading terms with narrow MedDRA query searches irrespective of applying Multi-item Gamma Poisson Shrinker or LR at a threshold value of 2.0. This research formed the basis for the configuration of ESS for signal detection at Novartis. Copyright © 2011 John Wiley & Sons, Ltd.

Influence of Fallible Item Parameters on Test Information During Adaptive Testing.

ERIC Educational Resources Information Center

Wetzel, C. Douglas; McBride, James R.

Computer simulation was used to assess the effects of item parameter estimation errors on different item selection strategies used in adaptive and conventional testing. To determine whether these effects reduced the advantages of certain optimal item selection strategies, simulations were repeated in the presence and absence of item parameter…

Intelligent topical sentiment analysis for the classification of e-learners and their topics of interest.

PubMed

Ravichandran, M; Kulanthaivel, G; Chellatamilan, T

2015-01-01

Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows: (1) lexicon based sentiment polarity of tweet messages; (2) the bigram cooccurrence relationship using naïve Bayesian; (3) the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.

PubMed

Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm

PubMed Central

Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

2016-01-01

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function. PMID:27893845

Utilizing Response Time Distributions for Item Selection in CAT

ERIC Educational Resources Information Center

Fan, Zhewen; Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey

2012-01-01

Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two…

Procedures for Selecting Items for Computerized Adaptive Tests.

ERIC Educational Resources Information Center

Kingsbury, G. Gage; Zara, Anthony R.

1989-01-01

Several classical approaches and alternative approaches to item selection for computerized adaptive testing (CAT) are reviewed and compared. The study also describes procedures for constrained CAT that may be added to classical item selection approaches to allow them to be used for applied testing. (TJH)

Algorithm for designing smart factory Industry 4.0

NASA Astrophysics Data System (ADS)

Gurjanov, A. V.; Zakoldaev, D. A.; Shukalov, A. V.; Zharinov, I. O.

2018-03-01

The designing task of production division of the Industry 4.0 item designing company is being studied. The authors proposed an algorithm, which is based on the modified V L Volkovich method. This algorithm allows generating options how to arrange the production with robotized technological equipment functioning in the automatic mode. The optimization solution of the multi-criteria task for some additive criteria is the base of the algorithm.

[Egypt: Selected Readings, Egyptian Mummies, and the Egyptian Pyramid.

ERIC Educational Resources Information Center

National Museum of Natural History, Washington, DC.

This resource packet presents information and resources on ancient Egypt. The bibliography includes readings divided into five sections: (1) "General Information" (46 items); (2) "Religion" (8 items); (3) "Art" (8 items); (4) "Hieroglyphics" (6 items); and (5) selections "For Young Readers" (11…

A Graph Summarization Algorithm Based on RFID Logistics

NASA Astrophysics Data System (ADS)

Sun, Yan; Hu, Kongfa; Lu, Zhipeng; Zhao, Li; Chen, Ling

Radio Frequency Identification (RFID) applications are set to play an essential role in object tracking and supply chain management systems. The volume of data generated by a typical RFID application will be enormous as each item will generate a complete history of all the individual locations that it occupied at every point in time. The movement trails of such RFID data form gigantic commodity flowgraph representing the locations and durations of the path stages traversed by each item. In this paper, we use graph to construct a warehouse of RFID commodity flows, and introduce a database-style operation to summarize graphs, which produces a summary graph by grouping nodes based on user-selected node attributes, further allows users to control the hierarchy of summaries. It can cut down the size of graphs, and provide convenience for users to study just on the shrunk graph which they interested. Through extensive experiments, we demonstrate the effectiveness and efficiency of the proposed method.

Implementation and Initial Testing of Advanced Processing and Analysis Algorithms for Correlated Neutron Counting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santi, Peter Angelo; Cutler, Theresa Elizabeth; Favalli, Andrea

In order to improve the accuracy and capabilities of neutron multiplicity counting, additional quantifiable information is needed in order to address the assumptions that are present in the point model. Extracting and utilizing higher order moments (Quads and Pents) from the neutron pulse train represents the most direct way of extracting additional information from the measurement data to allow for an improved determination of the physical properties of the item of interest. The extraction of higher order moments from a neutron pulse train required the development of advanced dead time correction algorithms which could correct for dead time effects inmore » all of the measurement moments in a self-consistent manner. In addition, advanced analysis algorithms have been developed to address specific assumptions that are made within the current analysis model, namely that all neutrons are created at a single point within the item of interest, and that all neutrons that are produced within an item are created with the same energy distribution. This report will discuss the current status of implementation and initial testing of the advanced dead time correction and analysis algorithms that have been developed in an attempt to utilize higher order moments to improve the capabilities of correlated neutron measurement techniques.« less

An effective trust-based recommendation method using a novel graph clustering algorithm

NASA Astrophysics Data System (ADS)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

PubMed Central

Goldstein, Markus; Uchida, Seiichi

2016-01-01

Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

Use of Jackknifing to Evaluate Effects of Anchor Item Selection on Equating with the Nonequivalent Groups with Anchor Test (NEAT) Design. Research Report. ETS RR-15-10

ERIC Educational Resources Information Center

Lu, Ru; Haberman, Shelby; Guo, Hongwen; Liu, Jinghua

2015-01-01

In this study, we apply jackknifing to anchor items to evaluate the impact of anchor selection on equating stability. In an ideal world, the choice of anchor items should have little impact on equating results. When this ideal does not correspond to reality, selection of anchor items can strongly influence equating results. This influence does not…

Visualization of Documents and Concepts in Neuroinformatics with the 3D-SE Viewer

PubMed Central

Naud, Antoine; Usui, Shiro; Ueda, Naonori; Taniguchi, Tatsuki

2007-01-01

A new interactive visualization tool is proposed for mining text data from various fields of neuroscience. Applications to several text datasets are presented to demonstrate the capability of the proposed interactive tool to visualize complex relationships between pairs of lexical entities (with some semantic contents) such as terms, keywords, posters, or papers' abstracts. Implemented as a Java applet, this tool is based on the spherical embedding (SE) algorithm, which was designed for the visualization of bipartite graphs. Items such as words and documents are linked on the basis of occurrence relationships, which can be represented in a bipartite graph. These items are visualized by embedding the vertices of the bipartite graph on spheres in a three-dimensional (3-D) space. The main advantage of the proposed visualization tool is that 3-D layouts can convey more information than planar or linear displays of items or graphs. Different kinds of information extracted from texts, such as keywords, indexing terms, or topics are visualized, allowing interactive browsing of various fields of research featured by keywords, topics, or research teams. A typical use of the 3D-SE viewer is quick browsing of topics displayed on a sphere, then selecting one or several item(s) displays links to related terms on another sphere representing, e.g., documents or abstracts, and provides direct online access to the document source in a database, such as the Visiome Platform or the SfN Annual Meeting. Developed as a Java applet, it operates as a tool on top of existing resources. PMID:18974802

Visualization of Documents and Concepts in Neuroinformatics with the 3D-SE Viewer.

PubMed

Naud, Antoine; Usui, Shiro; Ueda, Naonori; Taniguchi, Tatsuki

2007-01-01

A new interactive visualization tool is proposed for mining text data from various fields of neuroscience. Applications to several text datasets are presented to demonstrate the capability of the proposed interactive tool to visualize complex relationships between pairs of lexical entities (with some semantic contents) such as terms, keywords, posters, or papers' abstracts. Implemented as a Java applet, this tool is based on the spherical embedding (SE) algorithm, which was designed for the visualization of bipartite graphs. Items such as words and documents are linked on the basis of occurrence relationships, which can be represented in a bipartite graph. These items are visualized by embedding the vertices of the bipartite graph on spheres in a three-dimensional (3-D) space. The main advantage of the proposed visualization tool is that 3-D layouts can convey more information than planar or linear displays of items or graphs. Different kinds of information extracted from texts, such as keywords, indexing terms, or topics are visualized, allowing interactive browsing of various fields of research featured by keywords, topics, or research teams. A typical use of the 3D-SE viewer is quick browsing of topics displayed on a sphere, then selecting one or several item(s) displays links to related terms on another sphere representing, e.g., documents or abstracts, and provides direct online access to the document source in a database, such as the Visiome Platform or the SfN Annual Meeting. Developed as a Java applet, it operates as a tool on top of existing resources.

Layout Study and Application of Mobile App Recommendation Approach Based On Spark Streaming Framework

NASA Astrophysics Data System (ADS)

Wang, H. T.; Chen, T. T.; Yan, C.; Pan, H.

2018-05-01

For App recommended areas of mobile phone software, made while using conduct App application recommended combined weighted Slope One algorithm collaborative filtering algorithm items based on further improvement of the traditional collaborative filtering algorithm in cold start, data matrix sparseness and other issues, will recommend Spark stasis parallel algorithm platform, the introduction of real-time streaming streaming real-time computing framework to improve real-time software applications recommended.

Can We Retrieve the Information Which Was Intentionally Forgotten? Electrophysiological Correlates of Strategic Retrieval in Directed Forgetting.

PubMed

Mao, Xinrui; Tian, Mengxi; Liu, Yi; Li, Bingcan; Jin, Yan; Wu, Yanhong; Guo, Chunyan

2017-01-01

Retrieval inhibition hypothesis of directed forgetting effects assumed TBF (to-be-forgotten) items were not retrieved intentionally, while selective rehearsal hypothesis assumed the memory representation of retrieved TBF (to-be-forgotten) items was weaker than TBR (to-be-remembered) items. Previous studies indicated that directed forgetting effects of item-cueing method resulted from selective rehearsal at encoding, but the mechanism of retrieval inhibition that affected directed forgetting of TBF (to-be-forgotten) items was not clear. Strategic retrieval is a control process allowing the selective retrieval of target information, which includes retrieval orientation and strategic recollection. Retrieval orientation via the comparison of tasks refers to the specific form of processing resulted by retrieval efforts. Strategic recollection is the type of strategies to recollect studied items for the retrieval success of targets. Using a "directed forgetting" paradigm combined with a memory exclusion task, our investigation of strategic retrieval in directed forgetting assisted to explore how retrieval inhibition played a role on directed forgetting effects. When TBF items were targeted, retrieval orientation showed more positive ERPs to new items, indicating that TBF items demanded more retrieval efforts. The results of strategic recollection indicated that: (a) when TBR items were retrieval targets, late parietal old/new effects were only evoked by TBR items but not TBF items, indicating the retrieval inhibition of TBF items; (b) when TBF items were retrieval targets, the late parietal old/new effect were evoked by both TBR items and TBF items, indicating that strategic retrieval could overcome retrieval inhibition of TBF items. These findings suggested the modulation of strategic retrieval on retrieval inhibition of directed forgetting, supporting that directed forgetting effects were not only caused by selective rehearsal, but also retrieval inhibition.

Can We Retrieve the Information Which Was Intentionally Forgotten? Electrophysiological Correlates of Strategic Retrieval in Directed Forgetting

PubMed Central

Mao, Xinrui; Tian, Mengxi; Liu, Yi; Li, Bingcan; Jin, Yan; Wu, Yanhong; Guo, Chunyan

2017-01-01

Retrieval inhibition hypothesis of directed forgetting effects assumed TBF (to-be-forgotten) items were not retrieved intentionally, while selective rehearsal hypothesis assumed the memory representation of retrieved TBF (to-be-forgotten) items was weaker than TBR (to-be-remembered) items. Previous studies indicated that directed forgetting effects of item-cueing method resulted from selective rehearsal at encoding, but the mechanism of retrieval inhibition that affected directed forgetting of TBF (to-be-forgotten) items was not clear. Strategic retrieval is a control process allowing the selective retrieval of target information, which includes retrieval orientation and strategic recollection. Retrieval orientation via the comparison of tasks refers to the specific form of processing resulted by retrieval efforts. Strategic recollection is the type of strategies to recollect studied items for the retrieval success of targets. Using a “directed forgetting” paradigm combined with a memory exclusion task, our investigation of strategic retrieval in directed forgetting assisted to explore how retrieval inhibition played a role on directed forgetting effects. When TBF items were targeted, retrieval orientation showed more positive ERPs to new items, indicating that TBF items demanded more retrieval efforts. The results of strategic recollection indicated that: (a) when TBR items were retrieval targets, late parietal old/new effects were only evoked by TBR items but not TBF items, indicating the retrieval inhibition of TBF items; (b) when TBF items were retrieval targets, the late parietal old/new effect were evoked by both TBR items and TBF items, indicating that strategic retrieval could overcome retrieval inhibition of TBF items. These findings suggested the modulation of strategic retrieval on retrieval inhibition of directed forgetting, supporting that directed forgetting effects were not only caused by selective rehearsal, but also retrieval inhibition. PMID:28900411

Auditory attentional capture during serial recall: violations at encoding of an algorithm-based neural model?

PubMed

Hughes, Robert W; Vachon, François; Jones, Dylan M

2005-07-01

A novel attentional capture effect is reported in which visual-verbal serial recall was disrupted if a single deviation in the interstimulus interval occurred within otherwise regularly presented task-irrelevant spoken items. The degree of disruption was the same whether the temporal deviant was embedded in a sequence made up of a repeating item or a sequence of changing items. Moreover, the effect was evident during the presentation of the to-be-remembered sequence but not during rehearsal just prior to recall, suggesting that the encoding of sequences is particularly susceptible. The results suggest that attentional capture is due to a violation of an algorithm rather than an aggregate-based neural model and further undermine an attentional capture-based account of the classical changing-state irrelevant sound effect. ((c) 2005 APA, all rights reserved).

Item Selection and Ability Estimation Procedures for a Mixed-Format Adaptive Test

ERIC Educational Resources Information Center

Ho, Tsung-Han; Dodd, Barbara G.

2012-01-01

In this study we compared five item selection procedures using three ability estimation methods in the context of a mixed-format adaptive test based on the generalized partial credit model. The item selection procedures used were maximum posterior weighted information, maximum expected information, maximum posterior weighted Kullback-Leibler…

Bayesian Item Selection in Constrained Adaptive Testing Using Shadow Tests

ERIC Educational Resources Information Center

Veldkamp, Bernard P.

2010-01-01

Application of Bayesian item selection criteria in computerized adaptive testing might result in improvement of bias and MSE of the ability estimates. The question remains how to apply Bayesian item selection criteria in the context of constrained adaptive testing, where large numbers of specifications have to be taken into account in the item…

A Feedback Control Strategy for Enhancing Item Selection Efficiency in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Weissman, Alexander

2006-01-01

A computerized adaptive test (CAT) may be modeled as a closed-loop system, where item selection is influenced by trait level ([theta]) estimation and vice versa. When discrepancies exist between an examinee's estimated and true [theta] levels, nonoptimal item selection is a likely result. Nevertheless, examinee response behavior consistent with…

An Incremental High-Utility Mining Algorithm with Transaction Insertion

PubMed Central

Gan, Wensheng; Zhang, Binbin

2015-01-01

Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns. PMID:25811038

Finding community structure in very large networks

NASA Astrophysics Data System (ADS)

Clauset, Aaron; Newman, M. E. J.; Moore, Cristopher

2004-12-01

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(mdlogn) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with mtilde n and dtilde logn , in which case our algorithm runs in essentially linear time, O(nlog2n) . As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2×106 edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

Comparison of scoring approaches for the NEI VFQ-25 in low vision.

PubMed

Dougherty, Bradley E; Bullimore, Mark A

2010-08-01

The aim of this study was to evaluate different approaches to scoring the National Eye Institute Visual Functioning Questionnaire-25 (NEI VFQ-25) in patients with low vision including scoring by the standard method, by Rasch analysis, and by use of an algorithm created by Massof to approximate Rasch person measure. Subscale validity and use of a 7-item short form instrument proposed by Ryan et al. were also investigated. NEI VFQ-25 data from 50 patients with low vision were analyzed using the standard method of summing Likert-type scores and calculating an overall average, Rasch analysis using Winsteps software, and the Massof algorithm in Excel. Correlations between scores were calculated. Rasch person separation reliability and other indicators were calculated to determine the validity of the subscales and of the 7-item instrument. Scores calculated using all three methods were highly correlated, but evidence of floor and ceiling effects was found with the standard scoring method. None of the subscales investigated proved valid. The 7-item instrument showed acceptable person separation reliability and good targeting and item performance. Although standard scores and Rasch scores are highly correlated, Rasch analysis has the advantages of eliminating floor and ceiling effects and producing interval-scaled data. The Massof algorithm for approximation of the Rasch person measure performed well in this group of low-vision patients. The validity of the subscales VFQ-25 should be reconsidered.

Applying Bayesian Item Selection Approaches to Adaptive Tests Using Polytomous Items

ERIC Educational Resources Information Center

Penfield, Randall D.

2006-01-01

This study applied the maximum expected information (MEI) and the maximum posterior-weighted information (MPI) approaches of computer adaptive testing item selection to the case of a test using polytomous items following the partial credit model. The MEI and MPI approaches are described. A simulation study compared the efficiency of ability…

Missouri Assessment Program (MAP), Spring 2000: Elementary Health/Physical Education, Released Items, Grade 5.

ERIC Educational Resources Information Center

Missouri State Dept. of Elementary and Secondary Education, Jefferson City.

This document presents 10 released items from the Health/Physical Education Missouri Assessment Program (MAP) test given in the spring of 2000 to fifth graders. Items from the test sessions include: selected-response (multiple choice), constructed-response, and a performance event. The selected-response items consist of individual questions…

Inductive Selectivity in Children’s Cross-classified Concepts

PubMed Central

Nguyen, Simone P.

2012-01-01

Cross-classified items pose an interesting challenge to children’s induction since these items belong to many different categories, each of which may serve as a basis for a different type of inference. Inductive selectivity is the ability to appropriately make different types of inferences about a single cross-classifiable item based on its different category memberships. This research includes five experiments that examine the development of inductive selectivity in 3-, 4-, and 5-year-olds (N = 272). Overall, the results show that by age 4 years, children have inductive selectivity with taxonomic and script categories. That is, children use taxonomic categories to make biochemical inferences about an item whereas children use script categories to make situational inferences about an item. PMID:22803510

An algorithm for calculating exam quality as a basis for performance-based allocation of funds at medical schools.

PubMed

Kirschstein, Timo; Wolters, Alexander; Lenz, Jan-Hendrik; Fröhlich, Susanne; Hakenberg, Oliver; Kundt, Günther; Darmüntzel, Martin; Hecker, Michael; Altiner, Attila; Müller-Hilke, Brigitte

2016-01-01

The amendment of the Medical Licensing Act (ÄAppO) in Germany in 2002 led to the introduction of graded assessments in the clinical part of medical studies. This, in turn, lent new weight to the importance of written tests, even though the minimum requirements for exam quality are sometimes difficult to reach. Introducing exam quality as a criterion for the award of performance-based allocation of funds is expected to steer the attention of faculty members towards more quality and perpetuate higher standards. However, at present there is a lack of suitable algorithms for calculating exam quality. In the spring of 2014, the students' dean commissioned the "core group" for curricular improvement at the University Medical Center in Rostock to revise the criteria for the allocation of performance-based funds for teaching. In a first approach, we developed an algorithm that was based on the results of the most common type of exam in medical education, multiple choice tests. It included item difficulty and discrimination, reliability as well as the distribution of grades achieved. This algorithm quantitatively describes exam quality of multiple choice exams. However, it can also be applied to exams involving short assay questions and the OSCE. It thus allows for the quantitation of exam quality in the various subjects and - in analogy to impact factors and third party grants - a ranking among faculty. Our algorithm can be applied to all test formats in which item difficulty, the discriminatory power of the individual items, reliability of the exam and the distribution of grades are measured. Even though the content validity of an exam is not considered here, we believe that our algorithm is suitable as a general basis for performance-based allocation of funds.

Use of Management Pathways or Algorithms in Children With Chronic Cough: Systematic Reviews.

PubMed

Chang, Anne B; Oppenheimer, John J; Weinberger, Miles; Weir, Kelly; Rubin, Bruce K; Irwin, Richard S

2016-01-01

Use of appropriate cough pathways or algorithms may reduce the morbidity of chronic cough, lead to earlier diagnosis of chronic underlying illness, and reduce unnecessary costs and medications. We undertook three systematic reviews to examine three related key questions (KQ): In children aged ?14 years with chronic cough (> 4 weeks' duration), KQ1, do cough management protocols (or algorithms) improve clinical outcomes? KQ2, should the cough management or testing algorithm differ depending on the duration and/or severity? KQ3, should the cough management or testing algorithm differ depending on the associated characteristics of the cough and clinical history? We used the CHEST expert cough panel's protocol. Two authors screened searches and selected and extracted data. Only systematic reviews, randomized controlled trials (RCTs), and cohort studies published in English were included. Data were presented in Preferred Reporting Items for Systematic Reviews and Meta-analyses flowcharts and summary tabulated. Nine studies were included in KQ1 (RCT = 1; cohort studies = 7) and eight in KQ3 (RCT = 2; cohort = 6), but none in KQ2. There is high-quality evidence that in children aged ?14 years with chronic cough (> 4 weeks' duration), the use of cough management protocols (or algorithms) improves clinical outcomes and cough management or the testing algorithm should differ depending on the associated characteristics of the cough and clinical history. It remains uncertain whether the management or testing algorithm should depend on the duration or severity of chronic cough. Pending new data, chronic cough in children should be defined as > 4 weeks' duration and children should be systematically evaluated with treatment targeted to the underlying cause irrespective of the cough severity. Copyright © 2016 American College of Chest Physicians. All rights reserved.

Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

NASA Astrophysics Data System (ADS)

Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

2018-05-01

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

Restricted interests and teacher presentation of items.

PubMed

Stocco, Corey S; Thompson, Rachel H; Rodriguez, Nicole M

2011-01-01

Restricted and repetitive behavior (RRB) is more pervasive, prevalent, frequent, and severe in individuals with autism spectrum disorders (ASDs) than in their typical peers. One subtype of RRB is restricted interests in items or activities, which is evident in the manner in which individuals engage with items (e.g., repetitious wheel spinning), the types of items or activities they select (e.g., preoccupation with a phone book), or the range of items or activities they select (i.e., narrow range of items). We sought to describe the relation between restricted interests and teacher presentation of items. Overall, we observed 5 teachers interacting with 2 pairs of students diagnosed with an ASD. Each pair included 1 student with restricted interests. During these observations, teachers were free to present any items from an array of 4 stimuli selected by experimenters. We recorded student responses to teacher presentation of items and analyzed the data to determine the relation between teacher presentation of items and the consequences for presentation provided by the students. Teacher presentation of items corresponded with differential responses provided by students with ASD, and those with restricted preferences experienced a narrower array of items.

Missouri Assessment Program (MAP), Spring 2000: High School Health/Physical Education, Released Items, Grade 9.

ERIC Educational Resources Information Center

Missouri State Dept. of Elementary and Secondary Education, Jefferson City.

This document presents 10 released items from the Health/Physical Education Missouri Assessment Program (MAP) test given in the spring of 2000 to ninth graders. Items from the test sessions include: selected-response (multiple choice), constructed-response, and a performance event. The selected-response items consist of individual questions…

[Effect of algorithms for calibration set selection on quantitatively determining asiaticoside content in Centella total glucosides by near infrared spectroscopy].

PubMed

Zhan, Xue-yan; Zhao, Na; Lin, Zhao-zhou; Wu, Zhi-sheng; Yuan, Rui-juan; Qiao, Yan-jiang

2014-12-01

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.

Information mining in weighted complex networks with nonlinear rating projection

NASA Astrophysics Data System (ADS)

Liao, Hao; Zeng, An; Zhou, Mingyang; Mao, Rui; Wang, Bing-Hong

2017-10-01

Weighted rating networks are commonly used by e-commerce providers nowadays. In order to generate an objective ranking of online items' quality according to users' ratings, many sophisticated algorithms have been proposed in the complex networks domain. In this paper, instead of proposing new algorithms we focus on a more fundamental problem: the nonlinear rating projection. The basic idea is that even though the rating values given by users are linearly separated, the real preference of users to items between the different given values is nonlinear. We thus design an approach to project the original ratings of users to more representative values. This approach can be regarded as a data pretreatment method. Simulation in both artificial and real networks shows that the performance of the ranking algorithms can be improved when the projected ratings are used.

High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models. Research Report. ETS RR-16-34

ERIC Educational Resources Information Center

von Davier, Matthias

2016-01-01

This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

On selecting evidence to test hypotheses: A theory of selection tasks.

PubMed

Ragni, Marco; Kola, Ilir; Johnson-Laird, Philip N

2018-05-21

How individuals choose evidence to test hypotheses is a long-standing puzzle. According to an algorithmic theory that we present, it is based on dual processes: individuals' intuitions depending on mental models of the hypothesis yield selections of evidence matching instances of the hypothesis, but their deliberations yield selections of potential counterexamples to the hypothesis. The results of 228 experiments using Wason's selection task corroborated the theory's predictions. Participants made dependent choices of items of evidence: the selections in 99 experiments were significantly more redundant (using Shannon's measure) than those of 10,000 simulations of each experiment based on independent selections. Participants tended to select evidence corresponding to instances of hypotheses, or to its counterexamples, or to both. Given certain contents, instructions, or framings of the task, they were more likely to select potential counterexamples to the hypothesis. When participants received feedback about their selections in the "repeated" selection task, they switched from selections of instances of the hypothesis to selection of potential counterexamples. These results eliminated most of the 15 alternative theories of selecting evidence. In a meta-analysis, the model theory yielded a better fit of the results of 228 experiments than the one remaining theory based on reasoning rather than meaning. We discuss the implications of the model theory for hypothesis testing and for a well-known paradox of confirmation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer’s Quality of Life Questionnaire C30: a confirmatory versus exploratory approach

PubMed Central

Costa, Daniel SJ; Aaronson, Neil K; Fayers, Peter M; Grimison, Peter S; Janda, Monika; Pallant, Julie F; Rowen, Donna; Velikova, Galina; Viney, Rosalie; Young, Tracey A; King, Madeleine T

2014-01-01

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure. PMID:25395875

Image processing for x-ray inspection of pistachio nuts

NASA Astrophysics Data System (ADS)

Casasent, David P.

2001-03-01

A review is provided of image processing techniques that have been applied to the inspection of pistachio nuts using X-ray images. X-ray sensors provide non-destructive internal product detail not available from other sensors. The primary concern in this data is detecting the presence of worm infestations in nuts, since they have been linked to the presence of aflatoxin. We describe new techniques for segmentation, feature selection, selection of product categories (clusters), classifier design, etc. Specific novel results include: a new segmentation algorithm to produce images of isolated product items; preferable classifier operation (the classifier with the best probability of correct recognition Pc is not best); higher-order discrimination information is present in standard features (thus, high-order features appear useful); classifiers that use new cluster categories of samples achieve improved performance. Results are presented for X-ray images of pistachio nuts; however, all techniques have use in other product inspection applications.

A Generalized Partial Credit Model: Application of an EM Algorithm.

ERIC Educational Resources Information Center

Muraki, Eiji

1992-01-01

The partial credit model with a varying slope parameter is developed and called the generalized partial credit model (GPCM). Analysis results for simulated data by this and other polytomous item-response models demonstrate that the rating formulation of the GPCM is adaptable to the analysis of polytomous item responses. (SLD)

New Theory and Algorithms for Scalable Data Fusion

DTIC Science & Technology

2013-07-14

neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience , 7(5), May 2004. [11] T. Cai, W. Liu, and X. Luo. A...in which the goal is to predict users’ preferences for items (such as movies or music ) based on their and other users’ ratings of related items. The

Strategy Execution in Cognitive Skill Learning: An Item-Level Test of Candidate Models

ERIC Educational Resources Information Center

Rickard, Timothy C.

2004-01-01

This article investigates the transition to memory-based performance that commonly occurs with practice on tasks that initially require use of a multistep algorithm. In an alphabet arithmetic task, item response times exhibited pronounced step-function decreases after moderate practice that were uniquely predicted by T. C. Rickard's (1997)…

RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

PubMed

Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

2015-01-01

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

Item Selection and Pre-equating with Empirical Item Characteristic Curves.

ERIC Educational Resources Information Center

Livingston, Samuel A.

An empirical item characteristic curve shows the probability of a correct response as a function of the student's total test score. These curves can be estimated from large-scale pretest data. They enable test developers to select items that discriminate well in the score region where decisions are made. A similar set of curves can be used to…

Design and Evaluation of Perceptual-based Object Group Selection Techniques

NASA Astrophysics Data System (ADS)

Dehmeshki, Hoda

Selecting groups of objects is a frequent task in graphical user interfaces. It is required prior to many standard operations such as deletion, movement, or modification. Conventional selection techniques are lasso, rectangle selection, and the selection and de-selection of items through the use of modifier keys. These techniques may become time-consuming and error-prone when target objects are densely distributed or when the distances between target objects are large. Perceptual-based selection techniques can considerably improve selection tasks when targets have a perceptual structure, for example when arranged along a line. Current methods to detect such groups use ad hoc grouping algorithms that are not based on results from perception science. Moreover, these techniques do not allow selecting groups with arbitrary arrangements or permit modifying a selection. This dissertation presents two domain-independent perceptual-based systems that address these issues. Based on established group detection models from perception research, the proposed systems detect perceptual groups formed by the Gestalt principles of good continuation and proximity. The new systems provide gesture-based or click-based interaction techniques for selecting groups with curvilinear or arbitrary structures as well as clusters. Moreover, the gesture-based system is adapted for the graph domain to facilitate path selection. This dissertation includes several user studies that show the proposed systems outperform conventional selection techniques when targets form salient perceptual groups and are still competitive when targets are semi-structured.

One portion size of foods frequently consumed by Korean adults

PubMed Central

Choi, Mi-Kyeong; Hyun, Wha-Jin; Lee, Sim-Yeol; Park, Hong-Ju; Kim, Se-Na

2010-01-01

This study aimed to define a one portion size of food items frequently consumed for convenient use by Koreans in food selection, diet planning, and nutritional evaluation. We analyzed using the original data on 5,436 persons (60.87%) aged 20 ~ 64 years among 8,930 persons to whom NHANES 2005 and selected food items consumed by the intake frequency of 30 or higher among the 500 most frequently consumed food items. A total of 374 varieties of food items of regular use were selected. And the portion size of food items was set on the basis of the median (50th percentile) of the portion size for a single intake by a single person was analyzed. In cereals, the portion size of well polished rice was 80 g. In meats, the portion size of Korean beef cattle was 25 g. Among vegetable items, the portion size of Baechukimchi was 40 g. The portion size of the food items of regular use set in this study will be conveniently and effectively used by general consumers in selecting food items for a nutritionally balanced diet. In addition, these will be used as the basic data in setting the serving size in meal planning. PMID:20198213

[Mokken scaling of the Cognitive Screening Test].

PubMed

Diesfeldt, H F A

2009-10-01

The Cognitive Screening Test (CST) is a twenty-item orientation questionnaire in Dutch, that is commonly used to evaluate cognitive impairment. This study applied Mokken Scale Analysis, a non-parametric set of techniques derived from item response theory (IRT), to CST-data of 466 consecutive participants in psychogeriatric day care. The full item set and the standard short version of fourteen items both met the assumptions of the monotone homogeneity model, with scalability coefficient H = 0.39, which is considered weak. In order to select items that would fulfil the assumption of invariant item ordering or the double monotonicity model, the subjects were randomly partitioned into a training set (50% of the sample) and a test set (the remaining half). By means of an automated item selection eleven items were found to measure one latent trait, with H = 0.67 and item H coefficients larger than 0.51. Cross-validation of the item analysis in the remaining half of the subjects gave comparable values (H = 0.66; item H coefficients larger than 0.56). The selected items involve year, place of residence, birth date, the monarch's and prime minister's names, and their predecessors. Applying optimal discriminant analysis (ODA) it was found that the full set of twenty CST items performed best in distinguishing two predefined groups of patients of lower or higher cognitive ability, as established by an independent criterion derived from the Amsterdam Dementia Screening Test. The chance corrected predictive value or prognostic utility was 47.5% for the full item set, 45.2% for the fourteen items of the standard short version of the CST, and 46.1% for the homogeneous, unidimensional set of selected eleven items. The results of the item analysis support the application of the CST in cognitive assessment, and revealed a more reliable 'short' version of the CST than the standard short version (CST14).

Contextual behavior and neural circuits

PubMed Central

Lee, Inah; Lee, Choong-Hee

2013-01-01

Animals including humans engage in goal-directed behavior flexibly in response to items and their background, which is called contextual behavior in this review. Although the concept of context has long been studied, there are differences among researchers in defining and experimenting with the concept. The current review aims to provide a categorical framework within which not only the neural mechanisms of contextual information processing but also the contextual behavior can be studied in more concrete ways. For this purpose, we categorize contextual behavior into three subcategories as follows by considering the types of interactions among context, item, and response: contextual response selection, contextual item selection, and contextual item–response selection. Contextual response selection refers to the animal emitting different types of responses to the same item depending on the context in the background. Contextual item selection occurs when there are multiple items that need to be chosen in a contextual manner. Finally, when multiple items and multiple contexts are involved, contextual item–response selection takes place whereby the animal either chooses an item or inhibits such a response depending on item–context paired association. The literature suggests that the rhinal cortical regions and the hippocampal formation play key roles in mnemonically categorizing and recognizing contextual representations and the associated items. In addition, it appears that the fronto-striatal cortical loops in connection with the contextual information-processing areas critically control the flexible deployment of adaptive action sets and motor responses for maximizing goals. We suggest that contextual information processing should be investigated in experimental settings where contextual stimuli and resulting behaviors are clearly defined and measurable, considering the dynamic top-down and bottom-up interactions among the neural systems for contextual behavior. PMID:23675321

Constructing the Exact Significance Level for a Person-Fit Statistic.

ERIC Educational Resources Information Center

Liou, Michelle; Chang, Chih-Hsin

1992-01-01

An extension is proposed for the network algorithm introduced by C.R. Mehta and N.R. Patel to construct exact tail probabilities for testing the general hypothesis that item responses are distributed according to the Rasch model. A simulation study indicates the efficiency of the algorithm. (SLD)

Memory capacity, selective control, and value-directed remembering in children with and without attention-deficit/hyperactivity disorder (ADHD).

PubMed

Castel, Alan D; Lee, Steve S; Humphreys, Kathryn L; Moore, Amy N

2011-01-01

The ability to select what is important to remember, to attend to this information, and to recall high-value items leads to the efficient use of memory. The present study examined how children with and without attention-deficit/hyperactivity disorder (ADHD) performed on an incentive-based selectivity task in which to-be-remembered items were worth different point values. Participants were 6-9 year old children with ADHD (n = 57) and without ADHD (n = 59). Using a selectivity task, participants studied words paired with point values and were asked to maximize their score, which was the overall value of the items they recalled. This task allows for measures of memory capacity and the ability to selectively remember high-value items. Although there were no significant between-groups differences in the number of words recalled (memory capacity), children with ADHD were less selective than children in the control group in terms of the value of the items they recalled (control of memory). All children recalled more high-value items than low-value items and showed some learning with task experience, but children with ADHD Combined type did not efficiently maximize memory performance (as measured by a selectivity index) relative to children with ADHD Inattentive type and healthy controls, who did not differ significantly from one another. Children with ADHD Combined type exhibit impairments in the strategic and efficient encoding and recall of high-value items. The findings have implications for theories of memory dysfunction in childhood ADHD and the key role of metacognition, cognitive control, and value-directed remembering when considering the strategic use of memory. (c) 2010 APA, all rights reserved

Markov prior-based block-matching algorithm for superdimension reconstruction of porous media

NASA Astrophysics Data System (ADS)

Li, Yang; He, Xiaohai; Teng, Qizhi; Feng, Junxi; Wu, Xiaohong

2018-04-01

A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation. Hence, in this study, regular items are adopted as prior knowledge in the reconstruction process, and a Markov prior-based block-matching algorithm for superdimension reconstruction is developed for more accurate reconstruction. The algorithm simultaneously takes into consideration the probabilistic relationship between the already reconstructed blocks in three different perpendicular directions (x , y , and z ) and the block to be reconstructed, and the maximum value of the probability product of the blocks to be reconstructed (as found in the dictionary for the three directions) is adopted as the basis for the final block selection. Using this approach, the problem of an imprecise spatial structure caused by a point simulation can be overcome. The problem of artifacts in the reconstructed structure is also addressed through the addition of hard data and by neighborhood matching. To verify the improved reconstruction accuracy of the proposed method, the statistical and morphological features of the results from the proposed method and traditional superdimension reconstruction method are compared with those of the target system. The proposed superdimension reconstruction algorithm is confirmed to enable a more accurate reconstruction of the target system while also eliminating artifacts.

Clinical vs. Self-report Versions of the Quick Inventory of Depressive Symptomatology in a Public Sector Sample

PubMed Central

Bernstein, Ira H.; Rush, A. John; Carmody, Thomas J.; Woo, Ada; Trivedi, Madhukar H.

2007-01-01

Objectives Recent work using classical test theory (CTT) and item response theory (IRT) has found that the self-report (QIDS-SR16) and clinician-rated (QIDS-C16) versions of the 16-item Quick Inventory of Depressive Symptomatology were generally comparable in outpatients with nonpsychotic major depressive disorder (MDD). This report extends this comparison to a less well-educated, more treatment-resistant sample that included more ethnic/racial minorities using IRT and selected classical test analyses. Methods The QIDS-SR16 and QIDS-C16 were obtained in a sample of 441 outpatients with nonpsychotic MDD seen in the public sector in the Texas Medication Algorithm Project (TMAP). The Samejima graded response IRT model was used to compare the QIDS-SR16 and QIDS-C16. Results The nine symptom domains in the QIDS-SR16 and QIDS-C16 related well to overall depression. The slopes of the item response functions a), which index the strength of relationship between overall depression and each symptom, were extremely similar with the two measures. Likewise, the CTT and IRT indices of symptom frequency (item means and locations of the item response functions, bi) were also similar with these two measures. For example, sad mood and difficulty with concentration/decision making were highly related to the overall depression severity with both the QIDS-C16 and QIDS-SR16. Likewise, sleeping difficulties were commonly reported, even though they were not as strongly related to overall magnitude of depression. Conclusion In this less educated, socially disadvantaged sample, differences between the QIDS-C16 and QIDS-SR16 were minor. The QIDS-SR16 is a satisfactory substitute for the more time-consuming QIDS-C16 in a broad range of adult, nonpsychotic, depressed outpatients. PMID:16716351

Clinical vs. self-report versions of the quick inventory of depressive symptomatology in a public sector sample.

PubMed

Bernstein, Ira H; Rush, A John; Carmody, Thomas J; Woo, Ada; Trivedi, Madhukar H

2007-01-01

Recent work using classical test theory (CTT) and item response theory (IRT) has found that the self-report (QIDS-SR(16)) and clinician-rated (QIDS-C(16)) versions of the 16-item quick inventory of depressive symptomatology were generally comparable in outpatients with nonpsychotic major depressive disorder (MDD). This report extends this comparison to a less well-educated, more treatment-resistant sample that included more ethnic/racial minorities using IRT and selected classical test analyses. The QIDS-SR(16) and QIDS-C(16) were obtained in a sample of 441 outpatients with nonpsychotic MDD seen in the public sector in the Texas Medication Algorithm Project (TMAP). The Samejima graded response IRT model was used to compare the QIDS-SR(16) and QIDS-C(16). The nine symptom domains in the QIDS-SR(16) and QIDS-C(16) related well to overall depression. The slopes of the item response functions, a, which index the strength of relationship between overall depression and each symptom, were extremely similar with the two measures. Likewise, the CTT and IRT indices of symptom frequency (item means and locations of the item response functions, b(i) were also similar with these two measures. For example, sad mood and difficulty with concentration/decision making were highly related to the overall depression severity with both the QIDS-C(16) and QIDS-SR(16). Likewise, sleeping difficulties were commonly reported, even though they were not as strongly related to overall magnitude of depression. In this less educated, socially disadvantaged sample, differences between the QIDS-C(16) and QIDS-SR(16) were minor. The QIDS-SR(16) is a satisfactory substitute for the more time-consuming QIDS-C(16) in a broad range of adult, nonpsychotic, depressed outpatients.

Implementing AORN recommended practices for selection and use of packaging systems for sterilization.

PubMed

Morton, Paula J; Conner, Ramona

2014-04-01

The delivery of sterile products to the sterile field is essential to perioperative practice. The use of protective packaging for sterilized items is crucial to helping ensure that patients receive sterile items for surgical procedures. AORN's "Recommended practices for selection and use of packaging systems for sterilization" offers guidance to perioperative team members in evaluating, selecting, and using packaging systems that permit sterilization of the contents, prevent contamination of sterilized items until the package is opened for use, protect the items from damage during transport and storage, and permit aseptic delivery of the items to the sterile field. Copyright © 2014 AORN, Inc. Published by Elsevier Inc. All rights reserved.

Load Balancing in Structured P2P Networks

NASA Astrophysics Data System (ADS)

Zhu, Yingwu

In this chapter we start by addressing the importance and necessity of load balancing in structured P2P networks, due to three main reasons. First, structured P2P networks assume uniform peer capacities while peer capacities are heterogeneous in deployed P2P networks. Second, resorting to pseudo-uniformity of the hash function used to generate node IDs and data item keys leads to imbalanced overlay address space and item distribution. Lastly, placement of data items cannot be randomized in some applications (e.g., range searching). We then present an overview of load aggregation and dissemination techniques that are required by many load balancing algorithms. Two techniques are discussed including tree structure-based approach and gossip-based approach. They make different tradeoffs between estimate/aggregate accuracy and failure resilience. To address the issue of load imbalance, three main solutions are described: virtual server-based approach, power of two choices, and address-space and item balancing. While different in their designs, they all aim to improve balance on the address space and data item distribution. As a case study, the chapter discusses a virtual server-based load balancing algorithm that strives to ensure fair load distribution among nodes and minimize load balancing cost in bandwidth. Finally, the chapter concludes with future research and a summary.

Effect of individual thinking styles on item selection during study time allocation.

PubMed

Jia, Xiaoyu; Li, Weijian; Cao, Liren; Li, Ping; Shi, Meiling; Wang, Jingjing; Cao, Wei; Li, Xinyu

2018-04-01

The influence of individual differences on learners' study time allocation has been emphasised in recent studies; however, little is known about the role of individual thinking styles (analytical versus intuitive). In the present study, we explored the influence of individual thinking styles on learners' application of agenda-based and habitual processes when selecting the first item during a study-time allocation task. A 3-item cognitive reflection test (CRT) was used to determine individuals' degree of cognitive reliance on intuitive versus analytical cognitive processing. Significant correlations between CRT scores and the choices of first item selection were observed in both Experiment 1a (study time was 5 seconds per triplet) and Experiment 1b (study time was 20 seconds per triplet). Furthermore, analytical decision makers constructed a value-based agenda (prioritised high-reward items), whereas intuitive decision makers relied more upon habitual responding (selected items from the leftmost of the array). The findings of Experiment 1a were replicated in Experiment 2 notwithstanding ruling out the possible effects from individual intelligence and working memory capacity. Overall, the individual thinking style plays an important role on learners' study time allocation and the predictive ability of CRT is reliable in learners' item selection strategy. © 2016 International Union of Psychological Science.

Quantum algorithm for association rules mining

NASA Astrophysics Data System (ADS)

Yu, Chao-Hua; Gao, Fei; Wang, Qing-Le; Wen, Qiao-Yan

2016-10-01

Association rules mining (ARM) is one of the most important problems in knowledge discovery and data mining. Given a transaction database that has a large number of transactions and items, the task of ARM is to acquire consumption habits of customers by discovering the relationships between itemsets (sets of items). In this paper, we address ARM in the quantum settings and propose a quantum algorithm for the key part of ARM, finding frequent itemsets from the candidate itemsets and acquiring their supports. Specifically, for the case in which there are Mf(k ) frequent k -itemsets in the Mc(k ) candidate k -itemsets (Mf(k )≤Mc(k ) ), our algorithm can efficiently mine these frequent k -itemsets and estimate their supports by using parallel amplitude estimation and amplitude amplification with complexity O (k/√{Mc(k )Mf(k ) } ɛ ) , where ɛ is the error for estimating the supports. Compared with the classical counterpart, i.e., the classical sampling-based algorithm, whose complexity is O (k/Mc(k ) ɛ2) , our quantum algorithm quadratically improves the dependence on both ɛ and Mc(k ) in the best case when Mf(k )≪Mc(k ) and on ɛ alone in the worst case when Mf(k )≈Mc(k ) .

A protocol for the Hamilton Rating Scale for Depression: Item scoring rules, Rater training, and outcome accuracy with data on its application in a clinical trial.

PubMed

Rohan, Kelly J; Rough, Jennifer N; Evans, Maggie; Ho, Sheau-Yan; Meyerhoff, Jonah; Roberts, Lorinda M; Vacek, Pamela M

2016-08-01

We present a fully articulated protocol for the Hamilton Rating Scale for Depression (HAM-D), including item scoring rules, rater training procedures, and a data management algorithm to increase accuracy of scores prior to outcome analyses. The latter involves identifying potentially inaccurate scores as interviews with discrepancies between two independent raters on the basis of either scores >=5-point difference) or meeting threshold for depression recurrence status, a long-term treatment outcome with public health significance. Discrepancies are resolved by assigning two new raters, identifying items with disagreement per an algorithm, and reaching consensus on the most accurate scores for those items. These methods were applied in a clinical trial where the primary outcome was the Structured Interview Guide for the Hamilton Rating Scale for Depression-Seasonal Affective Disorder version (SIGH-SAD), which includes the 21-item HAM-D and 8 items assessing atypical symptoms. 177 seasonally depressed adult patients were enrolled and interviewed at 10 time points across treatment and the 2-year followup interval for a total of 1589 completed interviews with 1535 (96.6%) archived. Inter-rater reliability ranged from ICCs of .923-.967. Only 86 (5.6%) interviews met criteria for a between-rater discrepancy. HAM-D items "Depressed Mood", "Work and Activities", "Middle Insomnia", and "Hypochondriasis" and Atypical items "Fatigability" and "Hypersomnia" contributed most to discrepancies. Generalizability beyond well-trained, experienced raters in a clinical trial is unknown. Researchers might want to consider adopting this protocol in part or full. Clinicians might want to tailor it to their needs. Copyright © 2016 Elsevier B.V. All rights reserved.

Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements

PubMed Central

Kriegeskorte, Nikolaus; Mur, Marieke

2012-01-01

The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request. PMID:22848204

How Can Multivariate Item Response Theory Be Used in Reporting of Susbcores? Research Report. ETS RR-10-09

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Recently, there has been increasing interest in reporting diagnostic scores. This paper examines reporting of subscores using multidimensional item response theory (MIRT) models. An MIRT model is fitted using a stabilized Newton-Raphson algorithm (Haberman, 1974, 1988) with adaptive Gauss-Hermite quadrature (Haberman, von Davier, & Lee, 2008).…

Assessing Conceptual and Algorithmic Knowledge in General Chemistry with ACS Exams

ERIC Educational Resources Information Center

Holme, Thomas; Murphy, Kristen

2011-01-01

In 2005, the ACS Examinations Institute released an exam for first-term general chemistry in which items are intentionally paired with one conceptual and one traditional item. A second-term, paired-questions exam was released in 2007. This paper presents an empirical study of student performances on these two exams based on national samples of…

The effects of relative food item size on optimal tooth cusp sharpness during brittle food item processing

PubMed Central

Berthaume, Michael A.; Dumont, Elizabeth R.; Godfrey, Laurie R.; Grosse, Ian R.

2014-01-01

Teeth are often assumed to be optimal for their function, which allows researchers to derive dietary signatures from tooth shape. Most tooth shape analyses normalize for tooth size, potentially masking the relationship between relative food item size and tooth shape. Here, we model how relative food item size may affect optimal tooth cusp radius of curvature (RoC) during the fracture of brittle food items using a parametric finite-element (FE) model of a four-cusped molar. Morphospaces were created for four different food item sizes by altering cusp RoCs to determine whether optimal tooth shape changed as food item size changed. The morphospaces were also used to investigate whether variation in efficiency metrics (i.e. stresses, energy and optimality) changed as food item size changed. We found that optimal tooth shape changed as food item size changed, but that all optimal morphologies were similar, with one dull cusp that promoted high stresses in the food item and three cusps that acted to stabilize the food item. There were also positive relationships between food item size and the coefficients of variation for stresses in food item and optimality, and negative relationships between food item size and the coefficients of variation for stresses in the enamel and strain energy absorbed by the food item. These results suggest that relative food item size may play a role in selecting for optimal tooth shape, and the magnitude of these selective forces may change depending on food item size and which efficiency metric is being selected. PMID:25320068

Random one-of-N selector

DOEpatents

Kronberg, J.W.

1993-04-20

An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

Random one-of-N selector

DOEpatents

Kronberg, James W.

1993-01-01

An apparatus for selecting at random one item of N items on the average comprising counter and reset elements for counting repeatedly between zero and N, a number selected by the user, a circuit for activating and deactivating the counter, a comparator to determine if the counter stopped at a count of zero, an output to indicate an item has been selected when the count is zero or not selected if the count is not zero. Randomness is provided by having the counter cycle very often while varying the relatively longer duration between activation and deactivation of the count. The passive circuit components of the activating/deactivating circuit and those of the counter are selected for the sensitivity of their response to variations in temperature and other physical characteristics of the environment so that the response time of the circuitry varies. Additionally, the items themselves, which may be people, may vary in shape or the time they press a pushbutton, so that, for example, an ultrasonic beam broken by the item or person passing through it will add to the duration of the count and thus to the randomness of the selection.

Combining computer adaptive testing technology with cognitively diagnostic assessment.

PubMed

McGlohen, Meghan; Chang, Hua-Hua

2008-08-01

A major advantage of computerized adaptive testing (CAT) is that it allows the test to home in on an examinee's ability level in an interactive manner. The aim of the new area of cognitive diagnosis is to provide information about specific content areas in which an examinee needs help. The goal of this study was to combine the benefit of specific feedback from cognitively diagnostic assessment with the advantages of CAT. In this study, three approaches to combining these were investigated: (1) item selection based on the traditional ability level estimate (theta), (2) item selection based on the attribute mastery feedback provided by cognitively diagnostic assessment (alpha), and (3) item selection based on both the traditional ability level estimate (theta) and the attribute mastery feedback provided by cognitively diagnostic assessment (alpha). The results from these three approaches were compared for theta estimation accuracy, attribute mastery estimation accuracy, and item exposure control. The theta- and alpha-based condition outperformed the alpha-based condition regarding theta estimation, attribute mastery pattern estimation, and item exposure control. Both the theta-based condition and the theta- and alpha-based condition performed similarly with regard to theta estimation, attribute mastery estimation, and item exposure control, but the theta- and alpha-based condition has an additional advantage in that it uses the shadow test method, which allows the administrator to incorporate additional constraints in the item selection process, such as content balancing, item type constraints, and so forth, and also to select items on the basis of both the current theta and alpha estimates, which can be built on top of existing 3PL testing programs.

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

PubMed

Su, Qiang; Wang, Yina; Jiang, Xiaobing; Chen, Fuxue; Lu, Wen-Cong

2017-01-01

To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selection (CFS) principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. We adopted support vector machines (SVM) as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR), and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

Effects of adding an Italian theme to a restaurant on the perceived ethnicity, acceptability, and selection of foods.

PubMed

Bell, R; Meiselman, H L; Pierson, B J; Reeve, W G

1994-02-01

We investigated whether a change in the perceived ethnicity of a food can be produced without manipulating the food item itself, and if that change in ethnic perception is accompanied by a change in acceptability and food selection behavior. Italian and British foods were offered in a British restaurant for four days. Foods were offered for 2 days under control conditions, when the restaurant was decorated as usual. The identical foods then were offered in the restaurant for 2 more days under experimental conditions, when ethnic names were used on the menu to describe foods, and the restaurant was decorated with an Italian theme. Perceived ethnicity and acceptability of items were rated by customers each day, and item selection was tracked. The Italian theme increased selection of pasta and dessert items, and decreased the selection of fish. The Italian theme also increased the perceived Italian ethnicity of British pasta items, fish and veal, and increased the perceived Italian ethnicity of the meal overall. These findings show that changes in perceived ethnicity and food selection can be accomplished without altering food items, but merely by manipulating the environment, and may imply a unique strategy for increasing perceived menu variety.

Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm*

NASA Astrophysics Data System (ADS)

Xiang, LI

In order to analysis car crash test in C-NCAP, an improved algorithm is given based on Apriori algorithm in this paper. The new algorithm is implemented with vertical data layout, breadth first searching, and intersecting. It takes advantage of the efficiency of vertical data layout and intersecting, and prunes candidate frequent item sets like Apriori. Finally, the new algorithm is applied in simulation of car crash test analysis system. The result shows that the relations will affect the C-NCAP test results, and it can provide a reference for the automotive design.

Specifying the role of the left prefrontal cortex in word selection

PubMed Central

Ries, S. K; Karzmark, C. R.; Navarrete, E.; Knight, R. T.; Dronkers, N. F.

2015-01-01

Word selection allows us to choose words during language production. This is often viewed as a competitive process wherein a lexical representation is retrieved among semantically-related alternatives. The left prefrontal cortex (LPFC) is thought to help overcome competition for word selection through top-down control. However, whether the LPFC is always necessary for word selection remains unclear. We tested 6 LPFC-injured patients and controls in two picture naming paradigms varying in terms of item repetition. Both paradigms elicited the expected semantic interference effects (SIE), reflecting interference caused by semantically-related representations in word selection. However, LPFC patients as a group showed a larger SIE than controls only in the paradigm involving item repetition. We argue that item repetition increases interference caused by semantically-related alternatives, resulting in increased LPFC-dependent cognitive control demands. The remaining network of brain regions associated with word selection appears to be sufficient when items are not repeated. PMID:26291289

The relative price of healthy and less healthy foods available in Australian school canteens.

PubMed

Billich, Natassja; Adderley, Marijke; Ford, Laura; Keeton, Isabel; Palermo, Claire; Peeters, Anna; Woods, Julie; Backholer, Kathryn

2018-04-12

School canteens have an important role in modelling a healthy food environment. Price is a strong predictor of food and beverage choice. This study compared the relative price of healthy and less healthy lunch and snack items sold within Australian school canteens. A convenience sample of online canteen menus from five Australian states were selected (100 primary and 100 secondary schools). State-specific canteen guidelines were used to classify menu items into 'green' (eat most), 'amber' (select carefully) and 'red' (not recommended in schools). The price of the cheapest 'healthy' lunch (vegetable-based 'green') and snack ('green' fruit) item was compared to the cheapest 'less healthy' ('amber/red') lunch and snack item, respectively, using an un-paired t-test. The relative price of the 'healthy' items and the 'less healthy' items was calculated to determine the proportion of schools that sold the 'less healthy' item cheaper. The mean cost of the 'healthy' lunch items was greater than the 'less healthy' lunch items for both primary (AUD $0.70 greater) and secondary schools ($0.50 greater; p < 0.01). For 75% of primary and 57% of secondary schools, the selected 'less healthy' lunch item was cheaper than the 'healthy' lunch item. For 41% of primary and 48% of secondary schools, the selected 'less healthy' snack was cheaper than the 'healthy' snack. These proportions were greatest for primary schools located in more, compared to less, disadvantaged areas. The relative price of foods sold within Australian school canteens appears to favour less healthy foods. School canteen healthy food policies should consider the price of foods sold.

Item Selection in Multidimensional Computerized Adaptive Testing--Gaining Information from Different Angles

ERIC Educational Resources Information Center

Wang, Chun; Chang, Hua-Hua

2011-01-01

Over the past thirty years, obtaining diagnostic information from examinees' item responses has become an increasingly important feature of educational and psychological testing. The objective can be achieved by sequentially selecting multidimensional items to fit the class of latent traits being assessed, and therefore Multidimensional…

Direction of Wording Effects in Balanced Scales.

ERIC Educational Resources Information Center

Miller, Timothy R.; Cleary, T. Anne

1993-01-01

The degree to which statistical item selection reduces direction-of-wording effects in balanced affective measures developed from relatively small item pools was investigated with 171 male and 228 female undergraduate and graduate students at 2 U.S. universities. Clearest direction-of-wording effects result from selection of items with high…

A Comparison Study of Item Exposure Control Strategies in MCAT

ERIC Educational Resources Information Center

Mao, Xiuzhen; Ozdemir, Burhanettin; Wang, Yating; Xiu, Tao

2016-01-01

Four item selection indexes with and without exposure control are evaluated and compared in multidimensional computerized adaptive testing (CAT). The four item selection indices are D-optimality, Posterior expectation Kullback-Leibler information (KLP), the minimized error variance of the linear combination score with equal weight (V1), and the…

Assessing Correspondence Following Acquisition of an Exchange-Based Communication System

ERIC Educational Resources Information Center

Sigafoos, Jeff; Ganz, Jennifer B.; O'Reilly, Mark; Lancioni, Giulio E.; Schlosser, Ralf W.

2007-01-01

Two students with developmental disabilities were taught to request six snack items. Requesting involved giving a graphic symbol to the trainer in exchange for the matching snack item. Following acquisition, we assessed the correspondence between requests and subsequent item selections by requiring the student to select the previously requested…

A Comparison of Item Selection Techniques for Testlets

ERIC Educational Resources Information Center

Murphy, Daniel L.; Dodd, Barbara G.; Vaughn, Brandon K.

2010-01-01

This study examined the performance of the maximum Fisher's information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the…

Decision making: rational or hedonic?

PubMed Central

Cabanac, Michel; Bonniot-Cabanac, Marie-Claude

2007-01-01

Three experiments studied the hedonicity of decision making. Participants rated their pleasure/displeasure while reading item-sentences describing political and social problems followed by different decisions (Questionnaire 1). Questionnaire 2 was multiple-choice, grouping the items from Questionnaire 1. In Experiment 1, participants answered Questionnaire 2 rapidly or slowly. Both groups selected what they had rated as pleasant, but the 'leisurely' group maximized pleasure less. In Experiment 2, participants selected the most rational responses. The selected behaviors were pleasant but less than spontaneous behaviors. In Experiment 3, Questionnaire 2 was presented once with items grouped by theme, and once with items shuffled. Participants maximized the pleasure of their decisions, but the items selected on Questionnaires 2 were different when presented in different order. All groups maximized pleasure equally in their decisions. These results support that decisions are made predominantly in the hedonic dimension of consciousness. PMID:17848195

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

A Monte Carlo Approach for Adaptive Testing with Content Constraints

ERIC Educational Resources Information Center

Belov, Dmitry I.; Armstrong, Ronald D.; Weissman, Alexander

2008-01-01

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…

Automated segmentation and feature extraction of product inspection items

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-03-01

X-ray film and linescan images of pistachio nuts on conveyor trays for product inspection are considered. The final objective is the categorization of pistachios into good, blemished and infested nuts. A crucial step before classification is the separation of touching products and the extraction of features essential for classification. This paper addresses new detection and segmentation algorithms to isolate touching or overlapping items. These algorithms employ a new filter, a new watershed algorithm, and morphological processing to produce nutmeat-only images. Tests on a large database of x-ray film and real-time x-ray linescan images of around 2900 small, medium and large nuts showed excellent segmentation results. A new technique to detect and segment dark regions in nutmeat images is also presented and tested on approximately 300 x-ray film and approximately 300 real-time linescan x-ray images with 95-97 percent detection and correct segmentation. New algorithms are described that determine nutmeat fill ratio and locate splits in nutmeat. The techniques formulated in this paper are of general use in many different product inspection and computer vision problems.

SPMBR: a scalable algorithm for mining sequential patterns based on bitmaps

NASA Astrophysics Data System (ADS)

Xu, Xiwei; Zhang, Changhai

2013-12-01

Now some sequential patterns mining algorithms generate too many candidate sequences, and increase the processing cost of support counting. Therefore, we present an effective and scalable algorithm called SPMBR (Sequential Patterns Mining based on Bitmap Representation) to solve the problem of mining the sequential patterns for large databases. Our method differs from previous related works of mining sequential patterns. The main difference is that the database of sequential patterns is represented by bitmaps, and a simplified bitmap structure is presented firstly. In this paper, First the algorithm generate candidate sequences by SE(Sequence Extension) and IE(Item Extension), and then obtain all frequent sequences by comparing the original bitmap and the extended item bitmap .This method could simplify the problem of mining the sequential patterns and avoid the high processing cost of support counting. Both theories and experiments indicate that the performance of SPMBR is predominant for large transaction databases, the required memory size for storing temporal data is much less during mining process, and all sequential patterns can be mined with feasibility.

Selecting Soldiers and Civilians into the U.S. Army Officer Candidate School : Developing Empirical Selection Composites

DTIC Science & Technology

2014-07-01

a biographical instrument measuring personality ; (b) a Work Values instrument representing work preferences investigated in prior officer and...items used in SelectOCS Phase 2 (see Table 2.5). TAPAS uses multidimensional pairwise preference (MDPP) personality items scored using item response...presented respondents with a list of 30 traits and 30 skills (derived from leadership and personality literature) and instructed them to rate the

Directed forgetting of visual symbols: evidence for nonverbal selective rehearsal.

PubMed

Hourihan, Kathleen L; Ozubko, Jason D; MacLeod, Colin M

2009-12-01

Is selective rehearsal possible for nonverbal information? Two experiments addressed this question using the item method directed forgetting paradigm, where the advantage of remember items over forget items is ascribed to selective rehearsal favoring the remember items. In both experiments, difficult-to-name abstract symbols were presented for study, followed by a recognition test. Directed forgetting effects were evident for these symbols, regardless of whether they were or were not spontaneously named. Critically, a directed forgetting effect was observed for unnamed symbols even when the symbols were studied under verbal suppression to prevent verbal rehearsal. This pattern indicates that a form of nonverbal rehearsal can be used strategically (i.e., selectively) to enhance memory, even when verbal rehearsal is not possible.

Development of a short version of the new brief job stress questionnaire.

PubMed

Inoue, Akiomi; Kawakami, Norito; Shimomitsu, Teruichi; Tsutsumi, Akizumi; Haratani, Takashi; Yoshikawa, Toru; Shimazu, Akihito; Odagiri, Yuko

2014-01-01

This study was aimed to investigate the test-retest reliability and validity of a short version of the New Brief Job Stress Questionnaire (New BJSQ) whose scales have one item selected from a standard version. Based on the results from an anonymous web-based questionnaire of occupational health staffs and personnel/labor staffs, we selected higher-priority scales from the standard version. After selecting one item with highest item-total correlation coefficient from each scale, a 23-item questionnaire was developed. A nationally representative survey was administered to Japanese employees (n=1,633) to examine test-retest reliability and validity. Most scales (or items) showed modest but adequate levels of test-retest reliability (r>0.50). Furthermore, job demands and job resources scales (or items) were associated with mental and physical stress reactions while job resources scales (or items) were also associated with positive outcomes. These findings provided a piece of evidence that the short version of the New BJSQ is reliable and valid.

Development of a Short Version of the New Brief Job Stress Questionnaire

PubMed Central

INOUE, Akiomi; KAWAKAMI, Norito; SHIMOMITSU, Teruichi; TSUTSUMI, Akizumi; HARATANI, Takashi; YOSHIKAWA, Toru; SHIMAZU, Akihito; ODAGIRI, Yuko

2014-01-01

This study was aimed to investigate the test-retest reliability and validity of a short version of the New Brief Job Stress Questionnaire (New BJSQ) whose scales have one item selected from a standard version. Based on the results from an anonymous web-based questionnaire of occupational health staffs and personnel/labor staffs, we selected higher-priority scales from the standard version. After selecting one item with highest item-total correlation coefficient from each scale, a 23-item questionnaire was developed. A nationally representative survey was administered to Japanese employees (n=1,633) to examine test-retest reliability and validity. Most scales (or items) showed modest but adequate levels of test-retest reliability (r>0.50). Furthermore, job demands and job resources scales (or items) were associated with mental and physical stress reactions while job resources scales (or items) were also associated with positive outcomes. These findings provided a piece of evidence that the short version of the New BJSQ is reliable and valid. PMID:24975108

TEDS-M 2008 User Guide for the International Database. Supplement 4: TEDS-M Released Mathematics and Mathematics Pedagogy Knowledge Assessment Items

ERIC Educational Resources Information Center

Brese, Falk, Ed.

2012-01-01

The goal for selecting the released set of test items was to have approximately 25% of each of the full item sets for mathematics content knowledge (MCK) and mathematics pedagogical content knowledge (MPCK) that would represent the full range of difficulty, content, and item format used in the TEDS-M study. The initial step in the selection was to…

Methodology for Developing and Evaluating the PROMIS® Smoking Item Banks

PubMed Central

Cai, Li; Stucky, Brian D.; Tucker, Joan S.; Shadel, William G.; Edelen, Maria Orlando

2014-01-01

Introduction: This article describes the procedures used in the PROMIS® Smoking Initiative for the development and evaluation of item banks, short forms (SFs), and computerized adaptive tests (CATs) for the assessment of 6 constructs related to cigarette smoking: nicotine dependence, coping expectancies, emotional and sensory expectancies, health expectancies, psychosocial expectancies, and social motivations for smoking. Methods: Analyses were conducted using response data from a large national sample of smokers. Items related to each construct were subjected to extensive item factor analyses and evaluation of differential item functioning (DIF). Final item banks were calibrated, and SF assessments were developed for each construct. The performance of the SFs and the potential use of the item banks for CAT administration were examined through simulation study. Results: Item selection based on dimensionality assessment and DIF analyses produced item banks that were essentially unidimensional in structure and free of bias. Simulation studies demonstrated that the constructs could be accurately measured with a relatively small number of carefully selected items, either through fixed SFs or CAT-based assessment. Illustrative results are presented, and subsequent articles provide detailed discussion of each item bank in turn. Conclusions: The development of the PROMIS smoking item banks provides researchers with new tools for measuring smoking-related constructs. The use of the calibrated item banks and suggested SF assessments will enhance the quality of score estimates, thus advancing smoking research. Moreover, the methods used in the current study, including innovative approaches to item selection and SF construction, may have general relevance to item bank development and evaluation. PMID:23943843

Inductive Selectivity in Children's Cross-Classified Concepts

ERIC Educational Resources Information Center

Nguyen, Simone P.

2012-01-01

Cross-classified items pose an interesting challenge to children's induction as these items belong to many different categories, each of which may serve as a basis for a different type of inference. Inductive selectivity is the ability to appropriately make different types of inferences about a single cross-classifiable item based on its different…

Automated Test-Form Generation

ERIC Educational Resources Information Center

van der Linden, Wim J.; Diao, Qi

2011-01-01

In automated test assembly (ATA), the methodology of mixed-integer programming is used to select test items from an item bank to meet the specifications for a desired test form and optimize its measurement accuracy. The same methodology can be used to automate the formatting of the set of selected items into the actual test form. Three different…

Implementation of Rosenbrock methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shampine, L. F.

1980-11-01

Rosenbrock formulas have shown promise in research codes for the solution of initial-value problems for stiff systems of ordinary differential equations (ODEs). To help assess their practical value, the author wrote an item of mathematical software based on such a formula. This required a variety of algorithmic and software developments. Those of general interest are reported in this paper. Among them is a way to select automatically, at every step, an explicit Runge-Kutta formula or a Rosenbrock formula according to the stiffness of the problem. Solving linear systems is important to methods for stiff ODEs, and is rather special formore » Rosenbrock methods. A cheap, effective estimate of the condition of the linear systems is derived. Some numerical results are presented to illustrate the developments.« less

Optimal pricing and replenishment policies for instantaneous deteriorating items with backlogging and trade credit under inflation

NASA Astrophysics Data System (ADS)

Sundara Rajan, R.; Uthayakumar, R.

2017-12-01

In this paper we develop an economic order quantity model to investigate the optimal replenishment policies for instantaneous deteriorating items under inflation and trade credit. Demand rate is a linear function of selling price and decreases negative exponentially with time over a finite planning horizon. Shortages are allowed and partially backlogged. Under these conditions, we model the retailer's inventory system as a profit maximization problem to determine the optimal selling price, optimal order quantity and optimal replenishment time. An easy-to-use algorithm is developed to determine the optimal replenishment policies for the retailer. We also provide optimal present value of profit when shortages are completely backlogged as a special case. Numerical examples are presented to illustrate the algorithm provided to obtain optimal profit. And we also obtain managerial implications from numerical examples to substantiate our model. The results show that there is an improvement in total profit from complete backlogging rather than the items being partially backlogged.

A hybrid binary particle swarm optimization for large capacitated multi item multi level lot sizing (CMIMLLS) problem

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Sahithi, V. V. D.; Rao, C. S. P.

2016-09-01

The lot sizing problem deals with finding optimal order quantities which minimizes the ordering and holding cost of product mix. when multiple items at multiple levels with all capacity restrictions are considered, the lot sizing problem become NP hard. Many heuristics were developed in the past have inevitably failed due to size, computational complexity and time. However the authors were successful in the development of PSO based technique namely iterative improvement binary particles swarm technique to address very large capacitated multi-item multi level lot sizing (CMIMLLS) problem. First binary particle Swarm Optimization algorithm is used to find a solution in a reasonable time and iterative improvement local search mechanism is employed to improvise the solution obtained by BPSO algorithm. This hybrid mechanism of using local search on the global solution is found to improve the quality of solutions with respect to time thus IIBPSO method is found best and show excellent results.

Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

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

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

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.

Research in VLSI Systems. Heuristic Programming Project and VLSI Theory Project. A Fast Turn Around Facility for Very Large Scale Integration (VLSI)

DTIC Science & Technology

1982-11-01

to occur). When a rectangle is inserted, all currently selected items are de -selected, and the newly inserted rectangle is selected. This makes it...Items are de - * selected before the selection takes place. A selected symbol instance is displayed with a bold outline, and a selected rectangle edge...symbol instance or set of rectangle edges, everything previously selected is first de -selected. If the selected object is a reference point the old

Clustervision: Visual Supervision of Unsupervised Clustering.

PubMed

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it?

PubMed

Smits, Niels; van der Ark, L Andries; Conijn, Judith M

2017-11-02

Two important goals when using questionnaires are (a) measurement: the questionnaire is constructed to assign numerical values that accurately represent the test taker's attribute, and (b) prediction: the questionnaire is constructed to give an accurate forecast of an external criterion. Construction methods aimed at measurement prescribe that items should be reliable. In practice, this leads to questionnaires with high inter-item correlations. By contrast, construction methods aimed at prediction typically prescribe that items have a high correlation with the criterion and low inter-item correlations. The latter approach has often been said to produce a paradox concerning the relation between reliability and validity [1-3], because it is often assumed that good measurement is a prerequisite of good prediction. To answer four questions: (1) Why are measurement-based methods suboptimal for questionnaires that are used for prediction? (2) How should one construct a questionnaire that is used for prediction? (3) Do questionnaire-construction methods that optimize measurement and prediction lead to the selection of different items in the questionnaire? (4) Is it possible to construct a questionnaire that can be used for both measurement and prediction? An empirical data set consisting of scores of 242 respondents on questionnaire items measuring mental health is used to select items by means of two methods: a method that optimizes the predictive value of the scale (i.e., forecast a clinical diagnosis), and a method that optimizes the reliability of the scale. We show that for the two scales different sets of items are selected and that a scale constructed to meet the one goal does not show optimal performance with reference to the other goal. The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, but it is very costly. Implications for the construction of patient-reported outcome questionnaires are discussed.

Effects of Content Balancing and Item Selection Method on Ability Estimation in Computerized Adaptive Tests

ERIC Educational Resources Information Center

Sahin, Alper; Ozbasi, Durmus

2017-01-01

Purpose: This study aims to reveal effects of content balancing and item selection method on ability estimation in computerized adaptive tests by comparing Fisher's maximum information (FMI) and likelihood weighted information (LWI) methods. Research Methods: Four groups of examinees (250, 500, 750, 1000) and a bank of 500 items with 10 different…

Using Response Times for Item Selection in Adaptive Testing

ERIC Educational Resources Information Center

van der Linden, Wim J.

2008-01-01

Response times on items can be used to improve item selection in adaptive testing provided that a probabilistic model for their distribution is available. In this research, the author used a hierarchical modeling framework with separate first-level models for the responses and response times and a second-level model for the distribution of the…

Optimizing the Use of Response Times for Item Selection in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Choe, Edison M.; Kern, Justin L.; Chang, Hua-Hua

2018-01-01

Despite common operationalization, measurement efficiency of computerized adaptive testing should not only be assessed in terms of the number of items administered but also the time it takes to complete the test. To this end, a recent study introduced a novel item selection criterion that maximizes Fisher information per unit of expected response…

A Comparison of Four Item-Selection Methods for Severely Constrained CATs

ERIC Educational Resources Information Center

He, Wei; Diao, Qi; Hauser, Carl

2014-01-01

This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several…

Specifying the role of the left prefrontal cortex in word selection.

PubMed

Riès, S K; Karzmark, C R; Navarrete, E; Knight, R T; Dronkers, N F

2015-10-01

Word selection allows us to choose words during language production. This is often viewed as a competitive process wherein a lexical representation is retrieved among semantically-related alternatives. The left prefrontal cortex (LPFC) is thought to help overcome competition for word selection through top-down control. However, whether the LPFC is always necessary for word selection remains unclear. We tested 6 LPFC-injured patients and controls in two picture naming paradigms varying in terms of item repetition. Both paradigms elicited the expected semantic interference effects (SIE), reflecting interference caused by semantically-related representations in word selection. However, LPFC patients as a group showed a larger SIE than controls only in the paradigm involving item repetition. We argue that item repetition increases interference caused by semantically-related alternatives, resulting in increased LPFC-dependent cognitive control demands. The remaining network of brain regions associated with word selection appears to be sufficient when items are not repeated. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour

PubMed Central

Arnulf, Jan Ketil; Larsen, Kai Rune; Martinsen, Øyvind Lund; Bong, Chih How

2014-01-01

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60–86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain. PMID:25184672

Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour.

PubMed

Arnulf, Jan Ketil; Larsen, Kai Rune; Martinsen, Øyvind Lund; Bong, Chih How

2014-01-01

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60-86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.

Development of Elderly Quality of Life Index – Eqoli: Item Reduction and Distribution into Dimensions

PubMed Central

Paschoal, Sérgio Márcio Pacheco; Filho, Wilson Jacob; Litvoc, Júlio

2008-01-01

OBJECTIVE To describe item reduction and its distribution into dimensions in the construction process of a quality of life evaluation instrument for the elderly. METHODS The sampling method was chosen by convenience through quotas, with selection of elderly subjects from four programs to achieve heterogeneity in the “health status”, “functional capacity”, “gender”, and “age” variables. The Clinical Impact Method was used, consisting of the spontaneous and elicited selection by the respondents of relevant items to the construct Quality of Life in Old Age from a previously elaborated item pool. The respondents rated each item’s importance using a 5-point Likert scale. The product of the proportion of elderly selecting the item as relevant (frequency) and the mean importance score they attributed to it (importance) represented the overall impact of that item in their quality of life (impact). The items were ordered according to their impact scores and the top 46 scoring items were grouped in dimensions by three experts. A review of the negative items was performed. RESULTS One hundred and ninety three people (122 women and 71 men) were interviewed. Experts distributed the 46 items into eight dimensions. Closely related items were grouped and dimensions not reaching the minimum expected number of items received additional items resulting in eight dimensions and 43 items. DISCUSSION The sample was heterogeneous and similar to what was expected. The dimensions and items demonstrated the multidimensionality of the construct. The Clinical Impact Method was appropriate to construct the instrument, which was named Elderly Quality of Life Index - EQoLI. An accuracy process will be examined in the future. PMID:18438571

Methodology for developing and evaluating the PROMIS smoking item banks.

PubMed

Hansen, Mark; Cai, Li; Stucky, Brian D; Tucker, Joan S; Shadel, William G; Edelen, Maria Orlando

2014-09-01

This article describes the procedures used in the PROMIS Smoking Initiative for the development and evaluation of item banks, short forms (SFs), and computerized adaptive tests (CATs) for the assessment of 6 constructs related to cigarette smoking: nicotine dependence, coping expectancies, emotional and sensory expectancies, health expectancies, psychosocial expectancies, and social motivations for smoking. Analyses were conducted using response data from a large national sample of smokers. Items related to each construct were subjected to extensive item factor analyses and evaluation of differential item functioning (DIF). Final item banks were calibrated, and SF assessments were developed for each construct. The performance of the SFs and the potential use of the item banks for CAT administration were examined through simulation study. Item selection based on dimensionality assessment and DIF analyses produced item banks that were essentially unidimensional in structure and free of bias. Simulation studies demonstrated that the constructs could be accurately measured with a relatively small number of carefully selected items, either through fixed SFs or CAT-based assessment. Illustrative results are presented, and subsequent articles provide detailed discussion of each item bank in turn. The development of the PROMIS smoking item banks provides researchers with new tools for measuring smoking-related constructs. The use of the calibrated item banks and suggested SF assessments will enhance the quality of score estimates, thus advancing smoking research. Moreover, the methods used in the current study, including innovative approaches to item selection and SF construction, may have general relevance to item bank development and evaluation. © The Author 2013. 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.

Measurement equivalence of seven selected items of posttraumatic growth between black and white adult survivors of Hurricane Katrina.

PubMed

Rhodes, Alison M; Tran, Thanh V

2013-02-01

This study examined the equivalence or comparability of the measurement properties of seven selected items measuring posttraumatic growth among self-identified Black (n = 270) and White (n = 707) adult survivors of Hurricane Katrina, using data from the Baseline Survey of the Hurricane Katrina Community Advisory Group Study. Internal consistency reliability was equally good for both groups (Cronbach's alphas = .79), as were correlations between individual scale items and their respective overall scale. Confirmatory factor analysis of a congeneric measurement model of seven selected items of posttraumatic growth showed adequate measures of fit for both groups. The results showed only small variation in magnitude of factor loadings and measurement errors between the two samples. Tests of measurement invariance showed mixed results, but overall indicated that factor loading, error variance, and factor variance were similar between the two samples. These seven selected items can be useful for future large-scale surveys of posttraumatic growth.

A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

NASA Astrophysics Data System (ADS)

Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

Evolution of a Test Item

ERIC Educational Resources Information Center

Spaan, Mary

2007-01-01

This article follows the development of test items (see "Language Assessment Quarterly", Volume 3 Issue 1, pp. 71-79 for the article "Test and Item Specifications Development"), beginning with a review of test and item specifications, then proceeding to writing and editing of items, pretesting and analysis, and finally selection of an item for a…

A Model-Based Analysis of Semi-Automated Data Discovery and Entry Using Automated Content Extraction

DTIC Science & Technology

2011-02-01

Accomplish Goal) to (a) visually search the contents of a file folder until the icon corresponding to the desired file is located (Choose...Item_from_set), and (b) move the mouse to that icon and double click to open it (Double_select Object). Note that Choose Item_from_set and Double_select...argument, which Open File fills with <found_item>, a working memory pointer to the file icon that Choose_item_from Set finds. Look_at, Point_to

Selective attention and recognition: effects of congruency on episodic learning.

PubMed

Rosner, Tamara M; D'Angelo, Maria C; MacLellan, Ellen; Milliken, Bruce

2015-05-01

Recent research on cognitive control has focused on the learning consequences of high selective attention demands in selective attention tasks (e.g., Botvinick, Cognit Affect Behav Neurosci 7(4):356-366, 2007; Verguts and Notebaert, Psychol Rev 115(2):518-525, 2008). The current study extends these ideas by examining the influence of selective attention demands on remembering. In Experiment 1, participants read aloud the red word in a pair of red and green spatially interleaved words. Half of the items were congruent (the interleaved words had the same identity), and the other half were incongruent (the interleaved words had different identities). Following the naming phase, participants completed a surprise recognition memory test. In this test phase, recognition memory was better for incongruent than for congruent items. In Experiment 2, context was only partially reinstated at test, and again recognition memory was better for incongruent than for congruent items. In Experiment 3, all of the items contained two different words, but in one condition the words were presented close together and interleaved, while in the other condition the two words were spatially separated. Recognition memory was better for the interleaved than for the separated items. This result rules out an interpretation of the congruency effects on recognition in Experiments 1 and 2 that hinges on stronger relational encoding for items that have two different words. Together, the results support the view that selective attention demands for incongruent items lead to encoding that improves recognition.

The emotion-induced memory trade-off: more than an effect of overt attention?

PubMed

Steinmetz, Katherine R Mickley; Kensinger, Elizabeth A

2013-01-01

Although it has been suggested that many effects of emotion on memory are attributable to attention, in the present study we addressed the hypothesis that such effects may relate to a number of different factors during encoding or postencoding. One way to look at the effects of emotion on memory is by examining the emotion-induced memory trade-off, whereby enhanced memory for emotional items often comes at the cost of memory for surrounding background information. We present evidence that this trade-off cannot be explained solely by overt attention (measured via eyetracking) directed to the emotional items during encoding. Participants did not devote more overt attention to emotional than to neutral items when those items were selectively remembered (at the expense of their backgrounds). Only when participants were asked to answer true/false questions about the items and the backgrounds--a manipulation designed to affect both overt attention and poststimulus elaboration--was there a reduction in selective emotional item memory due to an increase in background memory. These results indicate that the allocation of overt visual attention during encoding is not sufficient to predict the occurrence of selective item memory for emotional items.

Set of Frequent Word Item sets as Feature Representation for Text with Indonesian Slang

NASA Astrophysics Data System (ADS)

Sa'adillah Maylawati, Dian; Putri Saptawati, G. A.

2017-01-01

Indonesian slang are commonly used in social media. Due to their unstructured syntax, it is difficult to extract their features based on Indonesian grammar for text mining. To do so, we propose Set of Frequent Word Item sets (SFWI) as text representation which is considered match for Indonesian slang. Besides, SFWI is able to keep the meaning of Indonesian slang with regard to the order of appearance sentence. We use FP-Growth algorithm with adding separation sentence function into the algorithm to extract the feature of SFWI. The experiments is done with text data from social media such as Facebook, Twitter, and personal website. The result of experiments shows that Indonesian slang were more correctly interpreted based on SFWI.

The Impact of Presentation Format on Younger and Older Adults' Self-Regulated Learning.

PubMed

Price, Jodi

2017-01-01

Background/Study Context: Self-regulated learning involves deciding what to study and for how long. Debate surrounds whether individuals' selections are influenced more by item complexity, point values, or if instead people select in a left-to-right reading order, ignoring item complexity and value. The present study manipulated whether point values and presentation format favored selection of simple or complex Chinese-English pairs to assess the impact on younger and older adults' selection behaviors. One hundred and five younger (M age  = 20.26, SD = 2.38) and 102 older adults (M age  = 70.28, SD = 6.37) participated in the experiment. Participants studied four different 3 × 3 grids (two per trial), each containing three simple, three medium, and three complex Chinese-English vocabulary pairs presented in either a simple-first or complex-first order, depending on condition. Point values were assigned in either a 2-4-8 or 8-4-2 order so that either simple or complex items were favored. Points did not influence the order in which either age group selected items, whereas presentation format did. Younger and older adults selected more simple or complex items when they appeared in the first column. However, older adults selected and allocated more time to simpler items but recalled less overall than did younger adults. Memory beliefs and working memory capacity predicted study time allocation, but not item selection, behaviors. Presentation format must be considered when evaluating which theory of self-regulated learning best accounts for younger and older adults' study behaviors and whether there are age-related differences in self-regulated learning. The results of the present study combine with others to support the importance of also considering the role of external factors (e.g., working memory capacity and memory beliefs) in each age group's self-regulated learning decisions.

Varying levels of difficulty index of skills-test items randomly selected by examinees on the Korean emergency medical technician licensing examination.

PubMed

Koh, Bongyeun; Hong, Sunggi; Kim, Soon-Sim; Hyun, Jin-Sook; Baek, Milye; Moon, Jundong; Kwon, Hayran; Kim, Gyoungyong; Min, Seonggi; Kang, Gu-Hyun

2016-01-01

The goal of this study was to characterize the difficulty index of the items in the skills test components of the class I and II Korean emergency medical technician licensing examination (KEMTLE), which requires examinees to select items randomly. The results of 1,309 class I KEMTLE examinations and 1,801 class II KEMTLE examinations in 2013 were subjected to analysis. Items from the basic and advanced skills test sections of the KEMTLE were compared to determine whether some were significantly more difficult than others. In the class I KEMTLE, all 4 of the items on the basic skills test showed significant variation in difficulty index (P<0.01), as well as 4 of the 5 items on the advanced skills test (P<0.05). In the class II KEMTLE, 4 of the 5 items on the basic skills test showed significantly different difficulty index (P<0.01), as well as all 3 of the advanced skills test items (P<0.01). In the skills test components of the class I and II KEMTLE, the procedure in which examinees randomly select questions should be revised to require examinees to respond to a set of fixed items in order to improve the reliability of the national licensing examination.

Two-dimensional shape recognition using oriented-polar representation

NASA Astrophysics Data System (ADS)

Hu, Neng-Chung; Yu, Kuo-Kan; Hsu, Yung-Li

1997-10-01

To deal with such a problem as object recognition of position, scale, and rotation invariance (PSRI), we utilize some PSRI properties of images obtained from objects, for example, the centroid of the image. The corresponding position of the centroid to the boundary of the image is invariant in spite of rotation, scale, and translation of the image. To obtain the information of the image, we use the technique similar to Radon transform, called the oriented-polar representation of a 2D image. In this representation, two specific points, the centroid and the weighted mean point, are selected to form an initial ray, then the image is sampled with N angularly equispaced rays departing from the initial rays. Each ray contains a number of intersections and the distance information obtained from the centroid to the intersections. The shape recognition algorithm is based on the least total error of these two items of information. Together with a simple noise removal and a typical backpropagation neural network, this algorithm is simple, but the PSRI is achieved with a high recognition rate.

Gimbal Control Algorithms for the Global Precipitation Measurement Core Observatory

NASA Technical Reports Server (NTRS)

Welter, Gary L.; Liu, Kuo Chia; Blaurock, Carl

2012-01-01

There are two gimbaled systems on the Global Precipitation Measurement Core Observatory: two single-degree-of-freedom solar arrays (SAs) and one two-degree-of-freedom high gain antenna (HGA). The guidance, navigation, and control analysis team was presented with the following challenges regarding SA orientation control during periods of normal mission science: (1) maximize solar flux on the SAs during orbit day, subject to battery charging limits, (2) minimize atmospheric drag during orbit night to reduce frequency of orbit maintenance thruster usage, (3) minimize atmospheric drag during orbits for which solar flux is nearly independent of SA orientation, and (4) keep array-induced spacecraft attitude disturbances within allocated tolerances. The team was presented with the following challenges regarding HGA control during mission science periods: (1) while tracking a ground-selected Tracking Data and Relay Satellite (TDRS), keep HGA control error below about 4', (2) keep array-induced spacecraft attitude disturbances small, and (3) minimize transition time between TDRSs subject to constraints imposed by item 2. This paper describes the control algorithms developed to achieve these goals and certain analysis done as part of that work.

Using the Self-Directed Search in Research: Selecting a Representative Pool of Items to Measure Vocational Interests

ERIC Educational Resources Information Center

Poitras, Sarah-Caroline; Guay, Frederic; Ratelle, Catherine F.

2012-01-01

Using Item Response Theory (IRT) and Confirmatory Factor Analysis (CFA), the goal of this study was to select a reduced pool of items from the French Canadian version of the Self-Directed Search--Activities Section (Holland, Fritzsche, & Powell, 1994). Two studies were conducted. Results of Study 1, involving 727 French Canadian students,…

Evidence against global attention filters selective for absolute bar-orientation in human vision.

PubMed

Inverso, Matthew; Sun, Peng; Chubb, Charles; Wright, Charles E; Sperling, George

2016-01-01

The finding that an item of type A pops out from an array of distractors of type B typically is taken to support the inference that human vision contains a neural mechanism that is activated by items of type A but not by items of type B. Such a mechanism might be expected to yield a neural image in which items of type A produce high activation and items of type B low (or zero) activation. Access to such a neural image might further be expected to enable accurate estimation of the centroid of an ensemble of items of type A intermixed with to-be-ignored items of type B. Here, it is shown that as the number of items in stimulus displays is increased, performance in estimating the centroids of horizontal (vertical) items amid vertical (horizontal) distractors degrades much more quickly and dramatically than does performance in estimating the centroids of white (black) items among black (white) distractors. Together with previous findings, these results suggest that, although human vision does possess bottom-up neural mechanisms sensitive to abrupt local changes in bar-orientation, and although human vision does possess and utilize top-down global attention filters capable of selecting multiple items of one brightness or of one color from among others, it cannot use a top-down global attention filter capable of selecting multiple bars of a given absolute orientation and filtering bars of the opposite orientation in a centroid task.

Item Response Models for Examinee-Selected Items

ERIC Educational Resources Information Center

Wang, Wen-Chung; Jin, Kuan-Yu; Qiu, Xue-Lan; Wang, Lei

2012-01-01

In some tests, examinees are required to choose a fixed number of items from a set of given items to answer. This practice creates a challenge to standard item response models, because more capable examinees may have an advantage by making wiser choices. In this study, we developed a new class of item response models to account for the choice…

Investigating Item Exposure Control Methods in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Ozturk, Nagihan Boztunc; Dogan, Nuri

2015-01-01

This study aims to investigate the effects of item exposure control methods on measurement precision and on test security under various item selection methods and item pool characteristics. In this study, the Randomesque (with item group sizes of 5 and 10), Sympson-Hetter, and Fade-Away methods were used as item exposure control methods. Moreover,…

ATTDES: An Expert System for Satellite Attitude Determination and Control. 2

NASA Technical Reports Server (NTRS)

Mackison, Donald L.; Gifford, Kevin

1996-01-01

The design, analysis, and flight operations of satellite attitude determintion and attitude control systems require extensive mathematical formulations, optimization studies, and computer simulation. This is best done by an analyst with extensive education and experience. The development of programs such as ATTDES permit the use of advanced techniques by those with less experience. Typical tasks include the mission analysis to select stabilization and damping schemes, attitude determination sensors and algorithms, and control system designs to meet program requirements. ATTDES is a system that includes all of these activities, including high fidelity orbit environment models that can be used for preliminary analysis, parameter selection, stabilization schemes, the development of estimators covariance analyses, and optimization, and can support ongoing orbit activities. The modification of existing simulations to model new configurations for these purposes can be an expensive, time consuming activity that becomes a pacing item in the development and operation of such new systems. The use of an integrated tool such as ATTDES significantly reduces the effort and time required for these tasks.

Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

ERIC Educational Resources Information Center

Zijlstra, Wobbe P.; van der Ark, L. Andries; Sijtsma, Klaas

2011-01-01

Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to…

Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matulef, Kevin Michael

The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewermore » resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.« less

[Development of a cell phone addiction scale for korean adolescents].

PubMed

Koo, Hyun Young

2009-12-01

This study was done to develop a cell phone addiction scale for Korean adolescents. The process included construction of a conceptual framework, generation of initial items, verification of content validity, selection of secondary items, preliminary study, and extraction of final items. The participants were 577 adolescents in two middle schools and three high schools. Item analysis, factor analysis, criterion related validity, and internal consistency were used to analyze the data. Twenty items were selected for the final scale, and categorized into 3 factors explaining 55.45% of total variance. The factors were labeled as withdrawal/tolerance (7 items), life dysfunction (6 items), and compulsion/persistence (7 items). The scores for the scale were significantly correlated with self-control, impulsiveness, and cell phone use. Cronbach's alpha coefficient for the 20 items was .92. Scale scores identified students as cell phone addicted, heavy users, or average users. The above findings indicate that the cell phone addiction scale has good validity and reliability when used with Korean adolescents.

Development of a Multidimensional Functional Health Scale for Older Adults in China.

PubMed

Mao, Fanzhen; Han, Yaofeng; Chen, Junze; Chen, Wei; Yuan, Manqiong; Alicia Hong, Y; Fang, Ya

2016-05-01

A first step to achieve successful aging is assessing functional wellbeing of older adults. This study reports the development of a culturally appropriate brief scale (the Multidimensional Functional Health Scale for Chinese Elderly, MFHSCE) to assess the functional health of Chinese elderly. Through systematic literature review, Delphi method, cultural adaptation, synthetic statistical item selection, Cronbach's alpha and confirmatory factor analysis, we conducted development of item pool, two rounds of item selection, and psychometric evaluation. Synthetic statistical item selection and psychometric evaluation was processed among 539 and 2032 older adults, separately. The MFHSCE consists of 30 items, covering activities of daily living, social relationships, physical health, mental health, cognitive function, and economic resources. The Cronbach's alpha was 0.92, and the comparative fit index was 0.917. The MFHSCE has good internal consistency and construct validity; it is also concise and easy to use in general practice, especially in communities in China.

Effects of promotional materials on vending sales of low-fat items in teachers' lounges.

PubMed

Fiske, Amy; Cullen, Karen Weber

2004-01-01

This study examined the impact of an environmental intervention in the form of promotional materials and increased availability of low-fat items on vending machine sales. Ten vending machines were selected and randomly assigned to one of three conditions: control, or one of two experimental conditions. Vending machines in the two intervention conditions received three additional low-fat selections. Low-fat items were promoted at two levels: labels (intervention I), and labels plus signs (intervention II). The number of individual items sold and the total revenue generated was recorded weekly for each machine for 4 weeks. Use of promotional materials resulted in a small, but not significant, increase in the number of low-fat items sold, although machine sales were not significantly impacted by the change in product selection. Results of this study, although not statistically significant, suggest that environmental change may be a realistic means of positively influencing consumer behavior.

Assessment of the Item Selection and Weighting in the Birmingham Vasculitis Activity Score for Wegener's Granulomatosis

PubMed Central

MAHR, ALFRED D.; NEOGI, TUHINA; LAVALLEY, MICHAEL P.; DAVIS, JOHN C.; HOFFMAN, GARY S.; MCCUNE, W. JOSEPH; SPECKS, ULRICH; SPIERA, ROBERT F.; ST.CLAIR, E. WILLIAM; STONE, JOHN H.; MERKEL, PETER A.

2013-01-01

Objective To assess the Birmingham Vasculitis Activity Score for Wegener's Granulomatosis (BVAS/WG) with respect to its selection and weighting of items. Methods This study used the BVAS/WG data from the Wegener's Granulomatosis Etanercept Trial. The scoring frequencies of the 34 predefined items and any “other” items added by clinicians were calculated. Using linear regression with generalized estimating equations in which the physician global assessment (PGA) of disease activity was the dependent variable, we computed weights for all predefined items. We also created variables for clinical manifestations frequently added as other items, and computed weights for these as well. We searched for the model that included the items and their generated weights yielding an activity score with the highest R2 to predict the PGA. Results We analyzed 2,044 BVAS/WG assessments from 180 patients; 734 assessments were scored during active disease. The highest R2 with the PGA was obtained by scoring WG activity based on the following items: the 25 predefined items rated on ≥5 visits, the 2 newly created fatigue and weight loss variables, the remaining minor other and major other items, and a variable that signified whether new or worse items were present at a specific visit. The weights assigned to the items ranged from 1 to 21. Compared with the original BVAS/WG, this modified score correlated significantly more strongly with the PGA. Conclusion This study suggests possibilities to enhance the item selection and weighting of the BVAS/WG. These changes may increase this instrument's ability to capture the continuum of disease activity in WG. PMID:18512722

Focused, Unfocused, and Defocused Information in Working Memory

ERIC Educational Resources Information Center

Rerko, Laura; Oberauer, Klaus

2013-01-01

The study investigated the effect of selection cues in working memory (WM) on the fate of not-selected contents of WM. Experiments 1A and 1B showed that focusing on 1 cued item in WM does not impair memory for the remaining items. The nonfocused items are maintained in WM even when this is not required by the task. Experiments 2 and 3 showed that…

American College Student Values: Their Relationship to Selected Personal and Academic Variables.

ERIC Educational Resources Information Center

Ritter, Carolyn E.

A 20-item chi-square test of independence was administered to a selected sample of college students that was stratified 50% male and 50% female. Male and female responses showed a significant difference on 18 of the 20 items. The 2 items on which attitudes of both sexes were the same were the role of government in business and a solution to the…

Development and clinical application of an evidence-based pharmaceutical care service algorithm in acute coronary syndrome.

PubMed

Kang, J E; Yu, J M; Choi, J H; Chung, I-M; Pyun, W B; Kim, S A; Lee, E K; Han, N Y; Yoon, J-H; Oh, J M; Rhie, S J

2018-06-01

Drug therapies are critical for preventing secondary complications in acute coronary syndrome (ACS). The purpose of this study was to develop and apply a pharmaceutical care service (PCS) algorithm for ACS and confirm that it is applicable through a prospective clinical trial. The ACS-PCS algorithm was developed according to extant evidence-based treatment and pharmaceutical care guidelines. Quality assurance was conducted through two methods: literature comparison and expert panel evaluation. The literature comparison was used to compare the content of the algorithm with the referenced guidelines. Expert evaluations were conducted by nine experts for 75 questionnaire items. A trial was conducted to confirm its effectiveness. Seventy-nine patients were assigned to either the pharmacist-included multidisciplinary team care (MTC) group or the usual care (UC) group. The endpoints of the trial were the prescription rate of two important drugs, readmission, emergency room (ER) visit and mortality. The main frame of the algorithm was structured with three tasks: medication reconciliation, medication optimization and transition of care. The contents and context of the algorithm were compliant with class I recommendations and the main service items from the evidence-based guidelines. Opinions from the expert panel were mostly positive. There were significant differences in beta-blocker prescription rates in the overall period (P = .013) and ER visits (four cases, 9.76%, P = .016) in the MTC group compared to the UC group, respectively. We developed a PCS algorithm for ACS based on the contents of evidence-based drug therapy and the core concept of pharmacist services. © 2018 John Wiley & Sons Ltd.

Algorithms for the Construction of Parallel Tests by Zero-One Programming. Project Psychometric Aspects of Item Banking No. 7. Research Report 86-7.

ERIC Educational Resources Information Center

Boekkooi-Timminga, Ellen

Nine methods for automated test construction are described. All are based on the concepts of information from item response theory. Two general kinds of methods for the construction of parallel tests are presented: (1) sequential test design; and (2) simultaneous test design. Sequential design implies that the tests are constructed one after the…

Research Directions in Database Security IV

DTIC Science & Technology

1993-07-01

second algorithm, which is based on multiversion timestamp ordering, is that high level transactions can be forced to read arbitrarily old data values...system. The first, the single ver- sion model, stores only the latest veision of each data item, while the second, the 88 multiversion model, stores... Multiversion Database Model In the standard database model, where there is only one version of each data item, all transactions compete for the most recent

A Maximin Model for Test Design with Practical Constraints. Project Psychometric Aspects of Item Banking No. 25. Research Report 87-10.

ERIC Educational Resources Information Center

van der Linden, Wim J.; Boekkooi-Timminga, Ellen

A "maximin" model for item response theory based test design is proposed. In this model only the relative shape of the target test information function is specified. It serves as a constraint subject to which a linear programming algorithm maximizes the information in the test. In the practice of test construction there may be several…

Software For Nearly Optimal Packing Of Cargo

NASA Technical Reports Server (NTRS)

Fennel, Theron R.; Daughtrey, Rodney S.; Schwaab, Doug G.

1994-01-01

PACKMAN computer program used to find nearly optimal arrangements of cargo items in storage containers, subject to such multiple packing objectives as utilization of volumes of containers, utilization of containers up to limits on weights, and other considerations. Automatic packing algorithm employed attempts to find best positioning of cargo items in container, such that volume and weight capacity of container both utilized to maximum extent possible. Written in Common LISP.

Determining the Capacity of Time-Based Selection

ERIC Educational Resources Information Center

Watson, Derrick G.; Kunar, Melina A.

2012-01-01

In visual search, a set of distractor items can be suppressed from future selection if they are presented (previewed) before a second set of search items arrive. This "visual marking" mechanism provides a top-down way of prioritizing the selection of new stimuli, at the expense of old stimuli already in the field (Watson & Humphreys,…

IRT Model Selection Methods for Dichotomous Items

ERIC Educational Resources Information Center

Kang, Taehoon; Cohen, Allan S.

2007-01-01

Fit of the model to the data is important if the benefits of item response theory (IRT) are to be obtained. In this study, the authors compared model selection results using the likelihood ratio test, two information-based criteria, and two Bayesian methods. An example illustrated the potential for inconsistency in model selection depending on…

Relevance of Item Analysis in Standardizing an Achievement Test in Teaching of Physical Science in B.Ed Syllabus

ERIC Educational Resources Information Center

Marie, S. Maria Josephine Arokia; Edannur, Sreekala

2015-01-01

This paper focused on the analysis of test items constructed in the paper of teaching Physical Science for B.Ed. class. It involved the analysis of difficulty level and discrimination power of each test item. Item analysis allows selecting or omitting items from the test, but more importantly item analysis is a tool to help the item writer improve…

Arctic Small Rodents Have Diverse Diets and Flexible Food Selection

PubMed Central

Soininen, Eeva M.; Ravolainen, Virve T.; Bråthen, Kari Anne; Yoccoz, Nigel G.; Gielly, Ludovic; Ims, Rolf A.

2013-01-01

The ecology of small rodent food selection is poorly understood, as mammalian herbivore food selection theory has mainly been developed by studying ungulates. Especially, the effect of food availability on food selection in natural habitats where a range of food items are available is unknown. We studied diets and selectivity of grey-sided voles (Myodes rufocanus) and tundra voles (Microtus oeconomus), key herbivores in European tundra ecosystems, using DNA metabarcoding, a novel method enabling taxonomically detailed diet studies. In order to cover the range of food availabilities present in the wild, we employed a large-scale study design for sampling data on food availability and vole diets. Both vole species had ingested a range of plant species and selected particularly forbs and grasses. Grey-sided voles also selected ericoid shrubs and tundra voles willows. Availability of a food item rarely affected its utilization directly, although seasonal changes of diets and selection suggest that these are positively correlated with availability. Moreover, diets and selectivity were affected by availability of alternative food items. These results show that the focal sub-arctic voles have diverse diets and flexible food preferences and rarely compensate low availability of a food item with increased searching effort. Diet diversity itself is likely to be an important trait and has previously been underrated owing to methodological constraints. We suggest that the roles of alternative food item availability and search time limitations for small rodent feeding ecology should be investigated. Nomenclature Annotated Checklist of the Panarctic Flora (PAF), Vascular plants. Available at: http://nhm2.uio.no/paf/, accessed 15.6.2012. PMID:23826371

Varying levels of difficulty index of skills-test items randomly selected by examinees on the Korean emergency medical technician licensing examination

PubMed Central

2016-01-01

Purpose: The goal of this study was to characterize the difficulty index of the items in the skills test components of the class I and II Korean emergency medical technician licensing examination (KEMTLE), which requires examinees to select items randomly. Methods: The results of 1,309 class I KEMTLE examinations and 1,801 class II KEMTLE examinations in 2013 were subjected to analysis. Items from the basic and advanced skills test sections of the KEMTLE were compared to determine whether some were significantly more difficult than others. Results: In the class I KEMTLE, all 4 of the items on the basic skills test showed significant variation in difficulty index (P<0.01), as well as 4 of the 5 items on the advanced skills test (P<0.05). In the class II KEMTLE, 4 of the 5 items on the basic skills test showed significantly different difficulty index (P<0.01), as well as all 3 of the advanced skills test items (P<0.01). Conclusion: In the skills test components of the class I and II KEMTLE, the procedure in which examinees randomly select questions should be revised to require examinees to respond to a set of fixed items in order to improve the reliability of the national licensing examination. PMID:26883810

Designing P-Optimal Item Pools in Computerized Adaptive Tests with Polytomous Items

ERIC Educational Resources Information Center

Zhou, Xuechun

2012-01-01

Current CAT applications consist of predominantly dichotomous items, and CATs with polytomously scored items are limited. To ascertain the best approach to polytomous CAT, a significant amount of research has been conducted on item selection, ability estimation, and impact of termination rules based on polytomous IRT models. Few studies…

Comparison of Alternate and Original Items on the Montreal Cognitive Assessment.

PubMed

Lebedeva, Elena; Huang, Mei; Koski, Lisa

2016-03-01

The Montreal Cognitive Assessment (MoCA) is a screening tool for mild cognitive impairment (MCI) in elderly individuals. We hypothesized that measurement error when using the new alternate MoCA versions to monitor change over time could be related to the use of items that are not of comparable difficulty to their corresponding originals of similar content. The objective of this study was to compare the difficulty of the alternate MoCA items to the original ones. Five selected items from alternate versions of the MoCA were included with items from the original MoCA administered adaptively to geriatric outpatients (N = 78). Rasch analysis was used to estimate the difficulty level of the items. None of the five items from the alternate versions matched the difficulty level of their corresponding original items. This study demonstrates the potential benefits of a Rasch analysis-based approach for selecting items during the process of development of parallel forms. The results suggest that better match of the items from different MoCA forms by their difficulty would result in higher sensitivity to changes in cognitive function over time.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

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.

Aging, Memory Efficiency and the Strategic Control of Attention at Encoding: Impairments of Value-Directed Remembering in Alzheimer's Disease

PubMed Central

Castel, Alan D.; Balota, David A.; McCabe, David P.

2009-01-01

Selecting what is important to remember, attending to this information, and then later recalling it can be thought of in terms of the strategic control of attention and the efficient use of memory. In order to examine whether aging and Alzheimer's disease (AD) influenced this ability, the present study used a selectivity task, where studied items were worth various point values and participants were asked to maximize the value of the items they recalled. Relative to younger adults (N=35) and healthy older adults (N=109), individuals with very mild AD (N=41) and mild AD (N=13) showed impairments in the strategic and efficient encoding and recall of high value items. Although individuals with AD recalled more high value items than low value items, they did not efficiently maximize memory performance (as measured by a selectivity index) relative to healthy older adults. Performance on complex working memory span tasks was related to the recall of the high value items but not low value items. This pattern suggests that relative to healthy aging, AD leads to impairments in strategic control at encoding and value-directed remembering. PMID:19413444

McTwo: a two-step feature selection algorithm based on maximal information coefficient.

PubMed

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.

Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

PubMed Central

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

Optimization of Contrast Detection Power with Probabilistic Behavioral Information

PubMed Central

Cordes, Dietmar; Herzmann, Grit; Nandy, Rajesh; Curran, Tim

2012-01-01

Recent progress in the experimental design for event-related fMRI experiments made it possible to find the optimal stimulus sequence for maximum contrast detection power using a genetic algorithm. In this study, a novel algorithm is proposed for optimization of contrast detection power by including probabilistic behavioral information, based on pilot data, in the genetic algorithm. As a particular application, a recognition memory task is studied and the design matrix optimized for contrasts involving the familiarity of individual items (pictures of objects) and the recollection of qualitative information associated with the items (left/right orientation). Optimization of contrast efficiency is a complicated issue whenever subjects’ responses are not deterministic but probabilistic. Contrast efficiencies are not predictable unless behavioral responses are included in the design optimization. However, available software for design optimization does not include options for probabilistic behavioral constraints. If the anticipated behavioral responses are included in the optimization algorithm, the design is optimal for the assumed behavioral responses, and the resulting contrast efficiency is greater than what either a block design or a random design can achieve. Furthermore, improvements of contrast detection power depend strongly on the behavioral probabilities, the perceived randomness, and the contrast of interest. The present genetic algorithm can be applied to any case in which fMRI contrasts are dependent on probabilistic responses that can be estimated from pilot data. PMID:22326984

Non-ignorable missingness item response theory models for choice effects in examinee-selected items.

PubMed

Liu, Chen-Wei; Wang, Wen-Chung

2017-11-01

Examinee-selected item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set, always yields incomplete data (i.e., when only the selected items are answered, data are missing for the others) that are likely non-ignorable in likelihood inference. Standard item response theory (IRT) models become infeasible when ESI data are missing not at random (MNAR). To solve this problem, the authors propose a two-dimensional IRT model that posits one unidimensional IRT model for observed data and another for nominal selection patterns. The two latent variables are assumed to follow a bivariate normal distribution. In this study, the mirt freeware package was adopted to estimate parameters. The authors conduct an experiment to demonstrate that ESI data are often non-ignorable and to determine how to apply the new model to the data collected. Two follow-up simulation studies are conducted to assess the parameter recovery of the new model and the consequences for parameter estimation of ignoring MNAR data. The results of the two simulation studies indicate good parameter recovery of the new model and poor parameter recovery when non-ignorable missing data were mistakenly treated as ignorable. © 2017 The British Psychological Society.

ADSORPTIVE MEDIA TECHNOLOGIES: MEDIA SELECTION

EPA Science Inventory

The presentation provides information on six items to be considered when selecting an adsorptive media for removing arsenic from drinking water; performance, EBCT, pre-treatment, regeneration, residuals, and cost. Each item is discussed in general and data and photographs from th...

Measuring User Similarity Using Electric Circuit Analysis: Application to Collaborative Filtering

PubMed Central

Yang, Joonhyuk; Kim, Jinwook; Kim, Wonjoon; Kim, Young Hwan

2012-01-01

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user–item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems PMID:23145095

Measuring user similarity using electric circuit analysis: application to collaborative filtering.

PubMed

Yang, Joonhyuk; Kim, Jinwook; Kim, Wonjoon; Kim, Young Hwan

2012-01-01

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user-item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems.

The Structured Interview & Scoring Tool-Massachusetts Alzheimer's Disease Research Center (SIST-M): development, reliability, and cross-sectional validation of a brief structured clinical dementia rating interview.

PubMed

Okereke, Olivia I; Copeland, Maura; Hyman, Bradley T; Wanggaard, Taylor; Albert, Marilyn S; Blacker, Deborah

2011-03-01

The Clinical Dementia Rating (CDR) and CDR Sum-of-Boxes can be used to grade mild but clinically important cognitive symptoms of Alzheimer disease. However, sensitive clinical interview formats are lengthy. To develop a brief instrument for obtaining CDR scores and to assess its reliability and cross-sectional validity. Using legacy data from expanded interviews conducted among 347 community-dwelling older adults in a longitudinal study, we identified 60 questions (from a possible 131) about cognitive functioning in daily life using clinical judgment, inter-item correlations, and principal components analysis. Items were selected in 1 cohort (n=147), and a computer algorithm for generating CDR scores was developed in this same cohort and re-run in a replication cohort (n=200) to evaluate how well the 60 items retained information from the original 131 items. Short interviews based on the 60 items were then administered to 50 consecutively recruited older individuals, with no symptoms or mild cognitive symptoms, at an Alzheimer's Disease Research Center. Clinical Dementia Rating scores based on short interviews were compared with those from independent long interviews. In the replication cohort, agreement between short and long CDR interviews ranged from κ=0.65 to 0.79, with κ=0.76 for Memory, κ=0.77 for global CDR, and intraclass correlation coefficient for CDR Sum-of-Boxes=0.89. In the cross-sectional validation, short interview scores were slightly lower than those from long interviews, but good agreement was observed for global CDR and Memory (κ≥0.70) as well as for CDR Sum-of-Boxes (intraclass correlation coefficient=0.73). The Structured Interview & Scoring Tool-Massachusetts Alzheimer's Disease Research Center is a brief, reliable, and sensitive instrument for obtaining CDR scores in persons with symptoms along the spectrum of mild cognitive change.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Self-Regulated Learning in Younger and Older Adults: Does Aging Affect Metacognitive Control?

PubMed Central

Price, Jodi; Hertzog, Christopher; Dunlosky, John

2011-01-01

Two experiments examined whether younger and older adults’ self-regulated study (item selection and study time) conformed to the region of proximal learning (RPL) model when studying normatively easy, medium, and difficult vocabulary pairs. Experiment 2 manipulated the value of recalling different pairs and provided learning goals for words recalled and points earned. Younger and older adults in both experiments selected items for study in an easy-to-difficult order, indicating the RPL model applies to older adults’ self-regulated study. Individuals allocated more time to difficult items, but prioritized easier items when given less time or point values favoring difficult items. Older adults studied more items for longer but realized lower recall than did younger adults. Older adults’ lower memory self-efficacy and perceived control correlated with their greater item restudy and avoidance of difficult items with high point values. Results are discussed in terms of RPL and agenda-based regulation models. PMID:19866382

Digital Library Selection: Maximum Access, Not Buying the Best Titles: Libraries Should Become Full-Text Amazon.coms's.

ERIC Educational Resources Information Center

Ferguson, Anthony W.

2000-01-01

Discusses new ways of selecting information for digital libraries. Topics include increasing the quantity of information acquired versus item by item selection that is more costly than the value it adds; library-publisher relationships; netLibrary; electronic journals; and the SPARC (Scholarly Publishing and Academic Resources Coalition)…

The Empirical Selection of Anchor Items Using a Multistage Approach

ERIC Educational Resources Information Center

Craig, Brandon

2017-01-01

The purpose of this study was to determine if using a multistage approach for the empirical selection of anchor items would lead to more accurate DIF detection rates than the anchor selection methods proposed by Kopf, Zeileis, & Strobl (2015b). A simulation study was conducted in which the sample size, percentage of DIF, and balance of DIF…

Customizing FP-growth algorithm to parallel mining with Charm++ library

NASA Astrophysics Data System (ADS)

Puścian, Marek

2017-08-01

This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Implementation of the EM Algorithm in the Estimation of Item Parameters: The BILOG Computer Program.

ERIC Educational Resources Information Center

Mislevy, Robert J.; Bock, R. Darrell

This paper reviews the basic elements of the EM approach to estimating item parameters and illustrates its use with one simulated and one real data set. In order to illustrate the use of the BILOG computer program, runs for 1-, 2-, and 3-parameter models are presented for the two sets of data. First is a set of responses from 1,000 persons to five…

China Report, Science and Technology, No. 188

DTIC Science & Technology

1983-02-18

parenthetical notes within the body of an item originate with the source. Times within items are as given by source. The contents of this publication in...the model-oriented algorithm and, especially,.this system does not need the time -consuming work of mathematical statistics during summarization of the...the fact that the users can see the samples at the exibit, several times the number of expected orders have been taken to demon- strate that a

Use of Matrix Sampling Procedures to Assess Achievement in Solving Open Addition and Subtraction Sentences.

ERIC Educational Resources Information Center

Montague, Margariete A.

This study investigated the feasibility of concurrently and randomly sampling examinees and items in order to estimate group achievement. Seven 32-item tests reflecting a 640-item universe of simple open sentences were used such that item selection (random, systematic) and assignment (random, systematic) of items (four, eight, sixteen) to forms…

The associative memory deficit in aging is related to reduced selectivity of brain activity during encoding

PubMed Central

Saverino, Cristina; Fatima, Zainab; Sarraf, Saman; Oder, Anita; Strother, Stephen C.; Grady, Cheryl L.

2016-01-01

Human aging is characterized by reductions in the ability to remember associations between items, despite intact memory for single items. Older adults also show less selectivity in task-related brain activity, such that patterns of activation become less distinct across multiple experimental tasks. This reduced selectivity, or dedifferentiation, has been found for episodic memory, which is often reduced in older adults, but not for semantic memory, which is maintained with age. We used functional magnetic resonance imaging (fMRI) to investigate whether there is a specific reduction in selectivity of brain activity during associative encoding in older adults, but not during item encoding, and whether this reduction predicts associative memory performance. Healthy young and older adults were scanned while performing an incidental-encoding task for pictures of objects and houses under item or associative instructions. An old/new recognition test was administered outside the scanner. We used agnostic canonical variates analysis and split-half resampling to detect whole brain patterns of activation that predicted item vs. associative encoding for stimuli that were later correctly recognized. Older adults had poorer memory for associations than did younger adults, whereas item memory was comparable across groups. Associative encoding trials, but not item encoding trials, were predicted less successfully in older compared to young adults, indicating less distinct patterns of associative-related activity in the older group. Importantly, higher probability of predicting associative encoding trials was related to better associative memory after accounting for age and performance on a battery of neuropsychological tests. These results provide evidence that neural distinctiveness at encoding supports associative memory and that a specific reduction of selectivity in neural recruitment underlies age differences in associative memory. PMID:27082043

Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography.

PubMed

Yilmaz, E; Kayikcioglu, T; Kayipmaz, S

2017-07-01

In this article, we propose a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT provides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed. We employed 50 CBCT 3D image dataset files as the full dataset of our study. These datasets were identified by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer software that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classifiers were used for the classification experiments. The Support Vector Machine (SVM) classifier achieved the best classification performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross validation method was used with a forward feature selection algorithm. SVM achieved the best classification performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample validation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm. Based on the results, we determined that periapical cyst and KCOT lesions can be classified with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classifiers, comprise an important contribution to the field of computer-aided diagnosis of dental apical lesions. Copyright © 2017 Elsevier B.V. All rights reserved.

An affine projection algorithm using grouping selection of input vectors

NASA Astrophysics Data System (ADS)

Shin, JaeWook; Kong, NamWoong; Park, PooGyeon

2011-10-01

This paper present an affine projection algorithm (APA) using grouping selection of input vectors. To improve the performance of conventional APA, the proposed algorithm adjusts the number of the input vectors using two procedures: grouping procedure and selection procedure. In grouping procedure, the some input vectors that have overlapping information for update is grouped using normalized inner product. Then, few input vectors that have enough information for for coefficient update is selected using steady-state mean square error (MSE) in selection procedure. Finally, the filter coefficients update using selected input vectors. The experimental results show that the proposed algorithm has small steady-state estimation errors comparing with the existing algorithms.

XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

PubMed Central

2015-01-01

Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results. PMID:26328893

Bin Packing, Number Balancing, and Rescaling Linear Programs

NASA Astrophysics Data System (ADS)

Hoberg, Rebecca

This thesis deals with several important algorithmic questions using techniques from diverse areas including discrepancy theory, machine learning and lattice theory. In Chapter 2, we construct an improved approximation algorithm for a classical NP-complete problem, the bin packing problem. In this problem, the goal is to pack items of sizes si ∈ [0,1] into as few bins as possible, where a set of items fits into a bin provided the sum of the item sizes is at most one. We give a polynomial-time rounding scheme for a standard linear programming relaxation of the problem, yielding a packing that uses at most OPT + O(log OPT) bins. This makes progress towards one of the "10 open problems in approximation algorithms" stated in the book of Shmoys and Williamson. In fact, based on related combinatorial lower bounds, Rothvoss conjectures that theta(logOPT) may be a tight bound on the additive integrality gap of this LP relaxation. In Chapter 3, we give a new polynomial-time algorithm for linear programming. Our algorithm is based on the multiplicative weights update (MWU) method, which is a general framework that is currently of great interest in theoretical computer science. An algorithm for linear programming based on MWU was known previously, but was not polynomial time--we remedy this by alternating between a MWU phase and a rescaling phase. The rescaling methods we introduce improve upon previous methods by reducing the number of iterations needed until one can rescale, and they can be used for any algorithm with a similar rescaling structure. Finally, we note that the MWU phase of the algorithm has a simple interpretation as gradient descent of a particular potential function, and we show we can speed up this phase by walking in a direction that decreases both the potential function and its gradient. In Chapter 4, we show that an approximate oracle for Minkowski's Theorem gives an approximate oracle for the number balancing problem, and conversely. Number balancing is the problem of minimizing | 〈a,x〉 | over x ∈ {-1,0,1}n \\ { 0}, given a ∈ [0,1]n. While an application of the pigeonhole principle shows that there always exists x with | 〈a,x〉| ≤ O(√ n/2n), the best known algorithm only guarantees |〈a,x〉| ≤ 2-ntheta(log n). We show that an oracle for Minkowski's Theorem with approximation factor rho would give an algorithm for NBP that guarantees | 〈a,x〉 | ≤ 2-ntheta(1/rho). In particular, this would beat the bound of Karmarkar and Karp provided rho ≤ O(logn/loglogn). In the other direction, we prove that any polynomial time algorithm for NBP that guarantees a solution of difference at most 2√n/2 n would give a polynomial approximation for Minkowski as well as a polynomial factor approximation algorithm for the Shortest Vector Problem.

A study of metaheuristic algorithms for high dimensional feature selection on microarray data

NASA Astrophysics Data System (ADS)

Dankolo, Muhammad Nasiru; Radzi, Nor Haizan Mohamed; Sallehuddin, Roselina; Mustaffa, Noorfa Haszlinna

2017-11-01

Microarray systems enable experts to examine gene profile at molecular level using machine learning algorithms. It increases the potentials of classification and diagnosis of many diseases at gene expression level. Though, numerous difficulties may affect the efficiency of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data pre-processing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper discusses the application of the metaheuristics algorithms for feature selection in microarray dataset. This study reveals that, the algorithms have yield an interesting result with limited resources thereby saving computational expenses of machine learning algorithms.

Web Cast on Arsenic Demonstration Program: Lessons Learned

EPA Science Inventory

Web cast presentation covered 10 Lessons Learned items selected from the Arsenic Demonstration Program with supporting information. The major items discussed include system design and performance items and the cost of the technologies.

Computerized Adaptive Testing: Overview and Introduction.

ERIC Educational Resources Information Center

Meijer, Rob R.; Nering, Michael L.

1999-01-01

Provides an overview of computerized adaptive testing (CAT) and introduces contributions to this special issue. CAT elements discussed include item selection, estimation of the latent trait, item exposure, measurement precision, and item-bank development. (SLD)

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

Reliability of a store observation tool in measuring availability of alcohol and selected foods.

PubMed

Cohen, Deborah A; Schoeff, Diane; Farley, Thomas A; Bluthenthal, Ricky; Scribner, Richard; Overton, Adrian

2007-11-01

Alcohol and food items can compromise or contribute to health, depending on the quantity and frequency with which they are consumed. How much people consume may be influenced by product availability and promotion in local retail stores. We developed and tested an observational tool to objectively measure in-store availability and promotion of alcoholic beverages and selected food items that have an impact on health. Trained observers visited 51 alcohol outlets in Los Angeles and southeastern Louisiana. Using a standardized instrument, two independent observations were conducted documenting the type of outlet, the availability and shelf space for alcoholic beverages and selected food items, the purchase price of standard brands, the placement of beer and malt liquor, and the amount of in-store alcohol advertising. Reliability of the instrument was excellent for measures of item availability, shelf space, and placement of malt liquor. Reliability was lower for alcohol advertising, beer placement, and items that measured the "least price" of apples and oranges. The average kappa was 0.87 for categorical items and the average intraclass correlation coefficient was 0.83 for continuous items. Overall, systematic observation of the availability and promotion of alcoholic beverages and food items was feasible, acceptable, and reliable. Measurement tools such as the one we evaluated should be useful in studies of the impact of availability of food and beverages on consumption and on health outcomes.

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

PubMed Central

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc PMID:26894358

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

PubMed

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.

TrustRank: a Cold-Start tolerant recommender system

NASA Astrophysics Data System (ADS)

Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi

2015-02-01

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users' trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.

PIBAS FedSPARQL: a web-based platform for integration and exploration of bioinformatics datasets.

PubMed

Djokic-Petrovic, Marija; Cvjetkovic, Vladimir; Yang, Jeremy; Zivanovic, Marko; Wild, David J

2017-09-20

There are a huge variety of data sources relevant to chemical, biological and pharmacological research, but these data sources are highly siloed and cannot be queried together in a straightforward way. Semantic technologies offer the ability to create links and mappings across datasets and manage them as a single, linked network so that searching can be carried out across datasets, independently of the source. We have developed an application called PIBAS FedSPARQL that uses semantic technologies to allow researchers to carry out such searching across a vast array of data sources. PIBAS FedSPARQL is a web-based query builder and result set visualizer of bioinformatics data. As an advanced feature, our system can detect similar data items identified by different Uniform Resource Identifiers (URIs), using a text-mining algorithm based on the processing of named entities to be used in Vector Space Model and Cosine Similarity Measures. According to our knowledge, PIBAS FedSPARQL was unique among the systems that we found in that it allows detecting of similar data items. As a query builder, our system allows researchers to intuitively construct and run Federated SPARQL queries across multiple data sources, including global initiatives, such as Bio2RDF, Chem2Bio2RDF, EMBL-EBI, and one local initiative called CPCTAS, as well as additional user-specified data source. From the input topic, subtopic, template and keyword, a corresponding initial Federated SPARQL query is created and executed. Based on the data obtained, end users have the ability to choose the most appropriate data sources in their area of interest and exploit their Resource Description Framework (RDF) structure, which allows users to select certain properties of data to enhance query results. The developed system is flexible and allows intuitive creation and execution of queries for an extensive range of bioinformatics topics. Also, the novel "similar data items detection" algorithm can be particularly useful for suggesting new data sources and cost optimization for new experiments. PIBAS FedSPARQL can be expanded with new topics, subtopics and templates on demand, rendering information retrieval more robust.

Effect of a promotional campaign on heart-healthy menu choices in community restaurants.

PubMed

Fitzgerald, Catherine M; Kannan, Srimathi; Sheldon, Sharon; Eagle, Kim Allen

2004-03-01

The research question examined in this study was: Does a promotional campaign impact the sales of heart-healthy menu items at community restaurants? The 8-week promotional campaign used professionally developed advertisements in daily and monthly print publications and posters and table tents in local restaurants. Nine restaurants tracked the sales of selected heart-healthy menu items and comparable menu items sold before and after a promotional campaign. The percentage of heart-healthy items sold after the campaign showed a trend toward a slight increase in heart-healthy menu item selections, although it was not statistically significant. This study and others indicate that dietetics professionals must continue to develop strategies to promote heart-healthy food choices in community restaurants.

Promoting Cold-Start Items in Recommender Systems

PubMed Central

Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

2014-01-01

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs. PMID:25479013

Promoting cold-start items in recommender systems.

PubMed

Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

2014-01-01

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.

A multi-factor Rasch scale for artistic judgment.

PubMed

Bezruczko, Nikolaus

2002-01-01

Measurement properties are reported for a combined scale of abstract and figurative artistic judgment aptitude items. Abstract items are synthetic, rule-based images from Visual Designs Test which implements a statistical algorithm to control design complexity and redundancy, and figurative items are canvas paintings in five styles, Fauvism, Post-Impressionism, Surrealism, Renaissance, and Baroque especially created for this research. The paintings integrate syntactic structure from VDT Abstract designs with thematic content for each style at four levels of complexity while controlling redundancy. Trained test administrators collected preference for synthetic abstract designs and authentic figurative art from 462 examinees in Johnson O'Connor Research Foundation testing offices in Boston, New York, Chicago, and Dallas. The Rasch model replicated measurement properties for VDT Abstract items and identified an item hierarchy that was statistically invariant between genders and generally stable across age for new, authentic figurative items. Further examination of the figurative item hierarchy revealed that complexity interacts with style and meaning. Sound measurement properties for a combined VDT Abstract and Figurative scale shows promise for a comprehensive artistic judgment construct.

Efforts Toward the Development of Unbiased Selection and Assessment Instruments.

ERIC Educational Resources Information Center

Rudner, Lawrence M.

Investigations into item bias provide an empirical basis for the identification and elimination of test items which appear to measure different traits across populations or cultural groups. The Psychometric rationales for six approaches to the identification of biased test items are reviewed: (1) Transformed item difficulties: within-group…

Developing and investigating the use of single-item measures in organizational research.

PubMed

Fisher, Gwenith G; Matthews, Russell A; Gibbons, Alyssa Mitchell

2016-01-01

The validity of organizational research relies on strong research methods, which include effective measurement of psychological constructs. The general consensus is that multiple item measures have better psychometric properties than single-item measures. However, due to practical constraints (e.g., survey length, respondent burden) there are situations in which certain single items may be useful for capturing information about constructs that might otherwise go unmeasured. We evaluated 37 items, including 18 newly developed items as well as 19 single items selected from existing multiple-item scales based on psychometric characteristics, to assess 18 constructs frequently measured in organizational and occupational health psychology research. We examined evidence of reliability; convergent, discriminant, and content validity assessments; and test-retest reliabilities at 1- and 3-month time lags for single-item measures using a multistage and multisource validation strategy across 3 studies, including data from N = 17 occupational health subject matter experts and N = 1,634 survey respondents across 2 samples. Items selected from existing scales generally demonstrated better internal consistency reliability and convergent validity, whereas these particular new items generally had higher levels of content validity. We offer recommendations regarding when use of single items may be more or less appropriate, as well as 11 items that seem acceptable, 14 items with mixed results that might be used with caution due to mixed results, and 12 items we do not recommend using as single-item measures. Although multiple-item measures are preferable from a psychometric standpoint, in some circumstances single-item measures can provide useful information. (c) 2016 APA, all rights reserved).

Location Indices for Ordinal Polytomous Items Based on Item Response Theory. Research Report. ETS RR-15-20

ERIC Educational Resources Information Center

Ali, Usama S.; Chang, Hua-Hua; Anderson, Carolyn J.

2015-01-01

Polytomous items are typically described by multiple category-related parameters; situations, however, arise in which a single index is needed to describe an item's location along a latent trait continuum. Situations in which a single index would be needed include item selection in computerized adaptive testing or test assembly. Therefore single…

Comparison of Alternate and Original Items on the Montreal Cognitive Assessment

PubMed Central

Lebedeva, Elena; Huang, Mei; Koski, Lisa

2016-01-01

Background The Montreal Cognitive Assessment (MoCA) is a screening tool for mild cognitive impairment (MCI) in elderly individuals. We hypothesized that measurement error when using the new alternate MoCA versions to monitor change over time could be related to the use of items that are not of comparable difficulty to their corresponding originals of similar content. The objective of this study was to compare the difficulty of the alternate MoCA items to the original ones. Methods Five selected items from alternate versions of the MoCA were included with items from the original MoCA administered adaptively to geriatric outpatients (N = 78). Rasch analysis was used to estimate the difficulty level of the items. Results None of the five items from the alternate versions matched the difficulty level of their corresponding original items. Conclusions This study demonstrates the potential benefits of a Rasch analysis-based approach for selecting items during the process of development of parallel forms. The results suggest that better match of the items from different MoCA forms by their difficulty would result in higher sensitivity to changes in cognitive function over time. PMID:27076861

Optimization of a large-scale microseismic monitoring network in northern Switzerland

NASA Astrophysics Data System (ADS)

Kraft, Toni; Mignan, Arnaud; Giardini, Domenico

2013-10-01

We have developed a network optimization method for regional-scale microseismic monitoring networks and applied it to optimize the densification of the existing seismic network in northeastern Switzerland. The new network will build the backbone of a 10-yr study on the neotectonic activity of this area that will help to better constrain the seismic hazard imposed on nuclear power plants and waste repository sites. This task defined the requirements regarding location precision (0.5 km in epicentre and 2 km in source depth) and detection capability [magnitude of completeness Mc = 1.0 (ML)]. The goal of the optimization was to find the geometry and size of the network that met these requirements. Existing stations in Switzerland, Germany and Austria were considered in the optimization procedure. We based the optimization on the simulated annealing approach proposed by Hardt & Scherbaum, which aims to minimize the volume of the error ellipsoid of the linearized earthquake location problem (D-criterion). We have extended their algorithm to: calculate traveltimes of seismic body waves using a finite difference ray tracer and the 3-D velocity model of Switzerland, calculate seismic body-wave amplitudes at arbitrary stations assuming the Brune source model and using scaling and attenuation relations recently derived for Switzerland, and estimate the noise level at arbitrary locations within Switzerland using a first-order ambient seismic noise model based on 14 land-use classes defined by the EU-project CORINE and open GIS data. We calculated optimized geometries for networks with 10-35 added stations and tested the stability of the optimization result by repeated runs with changing initial conditions. Further, we estimated the attainable magnitude of completeness (Mc) for the different sized optimal networks using the Bayesian Magnitude of Completeness (BMC) method introduced by Mignan et al. The algorithm developed in this study is also applicable to smaller optimization problems, for example, small local monitoring networks. Possible applications are volcano monitoring, the surveillance of induced seismicity associated with geotechnical operations and many more. Our algorithm is especially useful to optimize networks in populated areas with heterogeneous noise conditions and if complex velocity structures or existing stations have to be considered.

Investigating a memory-based account of negative priming: support for selection-feature mismatch.

PubMed

MacDonald, P A; Joordens, S

2000-08-01

Using typical and modified negative priming tasks, the selection-feature mismatch account of negative priming was tested. In the modified task, participants performed selections on the basis of a semantic feature (e.g., referent size). This procedure has been shown to enhance negative priming (P. A. MacDonald, S. Joordens, & K. N. Seergobin, 1999). Across 3 experiments, negative priming occurred only when the repeated item mismatched in terms of the feature used as the basis for selections. When the repeated item was congruent on the selection feature across the prime and probe displays, positive priming arose. This pattern of results appeared in both the ignored- and the attended-repetition conditions. Negative priming does not result from previously ignoring an item. These findings strongly support the selection-feature mismatch account of negative priming and refute both the distractor inhibition and the episodic-retrieval explanations.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

5 CFR 591.215 - Where does OPM collect prices in the COLA and DC areas?

Code of Federal Regulations, 2010 CFR

2010-01-01

...-housing data throughout the survey area, and for selected items such as golf, snow skiing, and air travel..., City of Manassas, and City of Manassas Park. 1 1 For selected items, such as golf, snow skiing, and air...

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

PubMed

Jin, Haizhe; Qu, Qingxing; Munechika, Masahiko; Sano, Masataka; Kajihara, Chisato; Duffy, Vincent G; Chen, Han

2018-05-03

Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a difficult and time-consuming task and requires an analyst with a professional medical background. The issues of identifying a method to extract medical error factors and reduce the extraction difficulty need to be resolved. In this research, a systematic methodology to extract and identify error factors in the medical administration process was proposed. The design of the error report, extraction of the error factors, and identification of the error factors were analyzed. Based on 624 medical error cases across four medical institutes in both Japan and China, 19 error-related items and their levels were extracted. After which, they were closely related to 12 error factors. The relational model between the error-related items and error factors was established based on a genetic algorithm (GA)-back-propagation neural network (BPNN) model. Additionally, compared to GA-BPNN, BPNN, partial least squares regression and support vector regression, GA-BPNN exhibited a higher overall prediction accuracy, being able to promptly identify the error factors from the error-related items. The combination of "error-related items, their different levels, and the GA-BPNN model" was proposed as an error-factor identification technology, which could automatically identify medical error factors.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

Approximation Preserving Reductions among Item Pricing Problems

NASA Astrophysics Data System (ADS)

Hamane, Ryoso; Itoh, Toshiya; Tomita, Kouhei

When a store sells items to customers, the store wishes to determine the prices of the items to maximize its profit. Intuitively, if the store sells the items with low (resp. high) prices, the customers buy more (resp. less) items, which provides less profit to the store. So it would be hard for the store to decide the prices of items. Assume that the store has a set V of n items and there is a set E of m customers who wish to buy those items, and also assume that each item i ∈ V has the production cost di and each customer ej ∈ E has the valuation vj on the bundle ej ⊆ V of items. When the store sells an item i ∈ V at the price ri, the profit for the item i is pi = ri - di. The goal of the store is to decide the price of each item to maximize its total profit. We refer to this maximization problem as the item pricing problem. In most of the previous works, the item pricing problem was considered under the assumption that pi ≥ 0 for each i ∈ V, however, Balcan, et al. [In Proc. of WINE, LNCS 4858, 2007] introduced the notion of “loss-leader, ” and showed that the seller can get more total profit in the case that pi < 0 is allowed than in the case that pi < 0 is not allowed. In this paper, we derive approximation preserving reductions among several item pricing problems and show that all of them have algorithms with good approximation ratio.

Generating constrained randomized sequences: item frequency matters.

PubMed

French, Robert M; Perruchet, Pierre

2009-11-01

All experimental psychologists understand the importance of randomizing lists of items. However, randomization is generally constrained, and these constraints-in particular, not allowing immediately repeated items-which are designed to eliminate particular biases, frequently engender others. We describe a simple Monte Carlo randomization technique that solves a number of these problems. However, in many experimental settings, we are concerned not only with the number and distribution of items but also with the number and distribution of transitions between items. The algorithm mentioned above provides no control over this. We therefore introduce a simple technique that uses transition tables for generating correctly randomized sequences. We present an analytic method of producing item-pair frequency tables and item-pair transitional probability tables when immediate repetitions are not allowed. We illustrate these difficulties and how to overcome them, with reference to a classic article on word segmentation in infants. Finally, we provide free access to an Excel file that allows users to generate transition tables with up to 10 different item types, as well as to generate appropriately distributed randomized sequences of any length without immediately repeated elements. This file is freely available from http://leadserv.u-bourgogne.fr/IMG/xls/TransitionMatrix.xls.

Feature-based and spatial attentional selection in visual working memory.

PubMed

Heuer, Anna; Schubö, Anna

2016-05-01

The contents of visual working memory (VWM) can be modulated by spatial cues presented during the maintenance interval ("retrocues"). Here, we examined whether attentional selection of representations in VWM can also be based on features. In addition, we investigated whether the mechanisms of feature-based and spatial attention in VWM differ with respect to parallel access to noncontiguous locations. In two experiments, we tested the efficacy of valid retrocues relying on different kinds of information. Specifically, participants were presented with a typical spatial retrocue pointing to two locations, a symbolic spatial retrocue (numbers mapping onto two locations), and two feature-based retrocues: a color retrocue (a blob of the same color as two of the items) and a shape retrocue (an outline of the shape of two of the items). The two cued items were presented at either contiguous or noncontiguous locations. Overall retrocueing benefits, as compared to a neutral condition, were observed for all retrocue types. Whereas feature-based retrocues yielded benefits for cued items presented at both contiguous and noncontiguous locations, spatial retrocues were only effective when the cued items had been presented at contiguous locations. These findings demonstrate that attentional selection and updating in VWM can operate on different kinds of information, allowing for a flexible and efficient use of this limited system. The observation that the representations of items presented at noncontiguous locations could only be reliably selected with feature-based retrocues suggests that feature-based and spatial attentional selection in VWM rely on different mechanisms, as has been shown for attentional orienting in the external world.

Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.; Clare, Loren P.

2013-01-01

Contact Graph Routing (CGR) for Delay/Disruption Tolerant Networking (DTN) space-based networks makes use of the predictable nature of node contacts to make real-time routing decisions given unpredictable traffic patterns. The contact graph will have been disseminated to all nodes before the start of route computation. CGR was designed for space-based networking environments where future contact plans are known or are independently computable (e.g., using known orbital dynamics). For each data item (known as a bundle in DTN), a node independently performs route selection by examining possible paths to the destination. Route computation could conceivably run thousands of times a second, so computational load is important. This work refers to the simulation software model of Enhanced Contact Graph Routing (ECGR) for DTN Bundle Protocol in JPL's MACHETE simulation tool. The simulation model was used for performance analysis of CGR and led to several performance enhancements. The simulation model was used to demonstrate the improvements of ECGR over CGR as well as other routing methods in space network scenarios. ECGR moved to using earliest arrival time because it is a global monotonically increasing metric that guarantees the safety properties needed for the solution's correctness since route re-computation occurs at each node to accommodate unpredicted changes (e.g., traffic pattern, link quality). Furthermore, using earliest arrival time enabled the use of the standard Dijkstra algorithm for path selection. The Dijkstra algorithm for path selection has a well-known inexpensive computational cost. These enhancements have been integrated into the open source CGR implementation. The ECGR model is also useful for route metric experimentation and comparisons with other DTN routing protocols particularly when combined with MACHETE's space networking models and Delay Tolerant Link State Routing (DTLSR) model.

Investigating Measurement Invariance in Computer-Based Personality Testing: The Impact of Using Anchor Items on Effect Size Indices

ERIC Educational Resources Information Center

Egberink, Iris J. L.; Meijer, Rob R.; Tendeiro, Jorge N.

2015-01-01

A popular method to assess measurement invariance of a particular item is based on likelihood ratio tests with all other items as anchor items. The results of this method are often only reported in terms of statistical significance, and researchers proposed different methods to empirically select anchor items. It is unclear, however, how many…

Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems

NASA Astrophysics Data System (ADS)

Kim, Jungja; Ceong, Heetaek; Won, Yonggwan

In market-basket analysis, weighted association rule (WAR) discovery can mine the rules that include more beneficial information by reflecting item importance for special products. In the point-of-sale database, each transaction is composed of items with similar properties, and item weights are pre-defined and fixed by a factor such as the profit. However, when items are divided into more than one group and the item importance must be measured independently for each group, traditional weighted association rule discovery cannot be used. To solve this problem, we propose a new weighted association rule mining methodology. The items should be first divided into subgroups according to their properties, and the item importance, i.e. item weight, is defined or calculated only with the items included in the subgroup. Then, transaction weight is measured by appropriately summing the item weights from each subgroup, and the weighted support is computed as the fraction of the transaction weights that contains the candidate items relative to the weight of all transactions. As an example, our proposed methodology is applied to assess the vulnerability to threats of computer systems that provide networked services. Our algorithm provides both quantitative risk-level values and qualitative risk rules for the security assessment of networked computer systems using WAR discovery. Also, it can be widely used for new applications with many data sets in which the data items are distinctly separated.

40 CFR 721.63 - Protection in the workplace.

Code of Federal Regulations, 2010 CFR

2010-07-01

... wear, personal protective equipment that provides a barrier to prevent dermal exposure to the substance in the specific work area where it is selected for use. Each such item of personal protective... other personal protective equipment selected in paragraph (a)(1) of this section, the following items...

Developing a Strategy for Using Technology-Enhanced Items in Large-Scale Standardized Tests

ERIC Educational Resources Information Center

Bryant, William

2017-01-01

As large-scale standardized tests move from paper-based to computer-based delivery, opportunities arise for test developers to make use of items beyond traditional selected and constructed response types. Technology-enhanced items (TEIs) have the potential to provide advantages over conventional items, including broadening construct measurement,…

The Application of Strength of Association Statistics to the Item Analysis of an In-Training Examination in Diagnostic Radiology.

ERIC Educational Resources Information Center

Diamond, James J.; McCormick, Janet

1986-01-01

Using item responses from an in-training examination in diagnostic radiology, the application of a strength of association statistic to the general problem of item analysis is illustrated. Criteria for item selection, general issues of reliability, and error of measurement are discussed. (Author/LMO)

Assessing Patients’ Experiences with Communication Across the Cancer Care Continuum

PubMed Central

Mazor, Kathleen M.; Street, Richard L.; Sue, Valerie M.; Williams, Andrew E.; Rabin, Borsika A.; Arora, Neeraj K.

2016-01-01

Objective To evaluate the relevance, performance and potential usefulness of the Patient Assessment of cancer Communication Experiences (PACE) items. Methods Items focusing on specific communication goals related to exchanging information, fostering healing relationships, responding to emotions, making decisions, enabling self-management, and managing uncertainty were tested via a retrospective, cross-sectional survey of adults who had been diagnosed with cancer. Analyses examined response frequencies, inter-item correlations, and coefficient alpha. Results A total of 366 adults were included in the analyses. Relatively few selected “Does Not Apply”, suggesting that items tap relevant communication experiences. Ratings of whether specific communication goals were achieved were strongly correlated with overall ratings of communication, suggesting item content reflects important aspects of communication. Coefficient alpha was ≥.90 for each item set, indicating excellent reliability. Variations in the percentage of respondents selecting the most positive response across items suggest results can identify strengths and weaknesses. Conclusion The PACE items tap relevant, important aspects of communication during cancer care, and may be useful to cancer care teams desiring detailed feedback. PMID:26979476

Effects of aging on neural connectivity underlying selective memory for emotional scenes

PubMed Central

Waring, Jill D.; Addis, Donna Rose; Kensinger, Elizabeth A.

2012-01-01

Older adults show age-related reductions in memory for neutral items within complex visual scenes, but just like young adults, older adults exhibit a memory advantage for emotional items within scenes compared with the background scene information. The present study examined young and older adults’ encoding-stage effective connectivity for selective memory of emotional items versus memory for both the emotional item and its background. In a functional magnetic resonance imaging (fMRI) study, participants viewed scenes containing either positive or negative items within neutral backgrounds. Outside the scanner, participants completed a memory test for items and backgrounds. Irrespective of scene content being emotionally positive or negative, older adults had stronger positive connections among frontal regions and from frontal regions to medial temporal lobe structures than did young adults, especially when items and backgrounds were subsequently remembered. These results suggest there are differences between young and older adults’ connectivity accompanying the encoding of emotional scenes. Older adults may require more frontal connectivity to encode all elements of a scene rather than just encoding the emotional item. PMID:22542836

Effects of aging on neural connectivity underlying selective memory for emotional scenes.

PubMed

Waring, Jill D; Addis, Donna Rose; Kensinger, Elizabeth A

2013-02-01

Older adults show age-related reductions in memory for neutral items within complex visual scenes, but just like young adults, older adults exhibit a memory advantage for emotional items within scenes compared with the background scene information. The present study examined young and older adults' encoding-stage effective connectivity for selective memory of emotional items versus memory for both the emotional item and its background. In a functional magnetic resonance imaging (fMRI) study, participants viewed scenes containing either positive or negative items within neutral backgrounds. Outside the scanner, participants completed a memory test for items and backgrounds. Irrespective of scene content being emotionally positive or negative, older adults had stronger positive connections among frontal regions and from frontal regions to medial temporal lobe structures than did young adults, especially when items and backgrounds were subsequently remembered. These results suggest there are differences between young and older adults' connectivity accompanying the encoding of emotional scenes. Older adults may require more frontal connectivity to encode all elements of a scene rather than just encoding the emotional item. Published by Elsevier Inc.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

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

Using wound care algorithms: a content validation study.

PubMed

Beitz, J M; van Rijswijk, L

1999-09-01

Valid and reliable heuristic devices facilitating optimal wound care are lacking. The objectives of this study were to establish content validation data for a set of wound care algorithms, to identify their associated strengths and weaknesses, and to gain insight into the wound care decision-making process. Forty-four registered nurse wound care experts were surveyed and interviewed at national and regional educational meetings. Using a cross-sectional study design and an 83-item, 4-point Likert-type scale, this purposive sample was asked to quantify the degree of validity of the algorithms' decisions and components. Participants' comments were tape-recorded, transcribed, and themes were derived. On a scale of 1 to 4, the mean score of the entire instrument was 3.47 (SD +/- 0.87), the instrument's Content Validity Index was 0.86, and the individual Content Validity Index of 34 of 44 participants was > 0.8. Item scores were lower for those related to packing deep wounds (P < .001). No other significant differences were observed. Qualitative data analysis revealed themes of difficulty associated with wound assessment and care issues, that is, the absence of valid and reliable definitions. The wound care algorithms studied proved valid. However, the lack of valid and reliable wound assessment and care definitions hinders optimal use of these instruments. Further research documenting their clinical use is warranted. Research-based practice recommendations should direct the development of future valid and reliable algorithms designed to help nurses provide optimal wound care.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

An item-response theory approach to safety climate measurement: The Liberty Mutual Safety Climate Short Scales.

PubMed

Huang, Yueng-Hsiang; Lee, Jin; Chen, Zhuo; Perry, MacKenna; Cheung, Janelle H; Wang, Mo

2017-06-01

Zohar and Luria's (2005) safety climate (SC) scale, measuring organization- and group- level SC each with 16 items, is widely used in research and practice. To improve the utility of the SC scale, we shortened the original full-length SC scales. Item response theory (IRT) analysis was conducted using a sample of 29,179 frontline workers from various industries. Based on graded response models, we shortened the original scales in two ways: (1) selecting items with above-average discriminating ability (i.e. offering more than 6.25% of the original total scale information), resulting in 8-item organization-level and 11-item group-level SC scales; and (2) selecting the most informative items that together retain at least 30% of original scale information, resulting in 4-item organization-level and 4-item group-level SC scales. All four shortened scales had acceptable reliability (≥0.89) and high correlations (≥0.95) with the original scale scores. The shortened scales will be valuable for academic research and practical survey implementation in improving occupational safety. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Image segmentation algorithm based on improved PCNN

NASA Astrophysics Data System (ADS)

Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

2017-11-01

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

Improved Monkey-King Genetic Algorithm for Solving Large Winner Determination in Combinatorial Auction

NASA Astrophysics Data System (ADS)

Li, Yuzhong

Using GA solve the winner determination problem (WDP) with large bids and items, run under different distribution, because the search space is large, constraint complex and it may easy to produce infeasible solution, would affect the efficiency and quality of algorithm. This paper present improved MKGA, including three operator: preprocessing, insert bid and exchange recombination, and use Monkey-king elite preservation strategy. Experimental results show that improved MKGA is better than SGA in population size and computation. The problem that traditional branch and bound algorithm hard to solve, improved MKGA can solve and achieve better effect.

Study on store-space assignment based on logistic AGV in e-commerce goods to person picking pattern

NASA Astrophysics Data System (ADS)

Xu, Lijuan; Zhu, Jie

2017-10-01

This paper studied on the store-space assignment based on logistic AGV in E-commerce goods to person picking pattern, and established the store-space assignment model based on the lowest picking cost, and design for store-space assignment algorithm after the cluster analysis based on similarity coefficient. And then through the example analysis, compared the picking cost between store-space assignment algorithm this paper design and according to item number and storage according to ABC classification allocation, and verified the effectiveness of the design of the store-space assignment algorithm.

ERP markers of target selection discriminate children with high vs. low working memory capacity.

PubMed

Shimi, Andria; Nobre, Anna Christina; Scerif, Gaia

2015-01-01

Selective attention enables enhancing a subset out of multiple competing items to maximize the capacity of our limited visual working memory (VWM) system. Multiple behavioral and electrophysiological studies have revealed the cognitive and neural mechanisms supporting adults' selective attention of visual percepts for encoding in VWM. However, research on children is more limited. What are the neural mechanisms involved in children's selection of incoming percepts in service of VWM? Do these differ from the ones subserving adults' selection? Ten-year-olds and adults used a spatial arrow cue to select a colored item for later recognition from an array of four colored items. The temporal dynamics of selection were investigated through EEG signals locked to the onset of the memory array. Both children and adults elicited significantly more negative activity over posterior scalp locations contralateral to the item to-be-selected for encoding (N2pc). However, this activity was elicited later and for longer in children compared to adults. Furthermore, although children as a group did not elicit a significant N2pc during the time-window in which N2pc was elicited in adults, the magnitude of N2pc during the "adult time-window" related to their behavioral performance during the later recognition phase of the task. This in turn highlights how children's neural activity subserving attention during encoding relates to better subsequent VWM performance. Significant differences were observed when children were divided into groups of high vs. low VWM capacity as a function of cueing benefit. Children with large cue benefits in VWM capacity elicited an adult-like contralateral negativity following attentional selection of the to-be-encoded item, whereas children with low VWM capacity did not. These results corroborate the close coupling between selective attention and VWM from childhood and elucidate further the attentional mechanisms constraining VWM performance in children.

Item generation and design testing of a questionnaire to assess degenerative joint disease-associated pain in cats.

PubMed

Zamprogno, Helia; Hansen, Bernie D; Bondell, Howard D; Sumrell, Andrea Thomson; Simpson, Wendy; Robertson, Ian D; Brown, James; Pease, Anthony P; Roe, Simon C; Hardie, Elizabeth M; Wheeler, Simon J; Lascelles, B Duncan X

2010-12-01

To determine the items (question topics) for a subjective instrument to assess degenerative joint disease (DJD)-associated chronic pain in cats and determine the instrument design most appropriate for use by cat owners. 100 randomly selected client-owned cats from 6 months to 20 years old. Cats were evaluated to determine degree of radiographic DJD and signs of pain throughout the skeletal system. Two groups were identified: high DJD pain and low DJD pain. Owner-answered questions about activity and signs of pain were compared between the 2 groups to define items relating to chronic DJD pain. Interviews with 45 cat owners were performed to generate items. Fifty-three cat owners who had not been involved in any other part of the study, 19 veterinarians, and 2 statisticians assessed 6 preliminary instrument designs. 22 cats were selected for each group; 19 important items were identified, resulting in 12 potential items for the instrument; and 3 additional items were identified from owner interviews. Owners and veterinarians selected a 5-point descriptive instrument design over 11-point or visual analogue scale formats. Behaviors relating to activity were substantially different between healthy cats and cats with signs of DJD-associated pain. Fifteen items were identified as being potentially useful, and the preferred instrument design was identified. This information could be used to construct an owner-based questionnaire to assess feline DJD-associated pain. Once validated, such a questionnaire would assist in evaluating potential analgesic treatments for these patients.

Item Selection Criteria with Practical Constraints for Computerized Classification Testing

ERIC Educational Resources Information Center

Lin, Chuan-Ju

2011-01-01

This study compares four item selection criteria for a two-category computerized classification testing: (1) Fisher information (FI), (2) Kullback-Leibler information (KLI), (3) weighted log-odds ratio (WLOR), and (4) mutual information (MI), with respect to the efficiency and accuracy of classification decision using the sequential probability…

Selecting Lower Priced Items.

ERIC Educational Resources Information Center

Kleinert, Harold L.; And Others

1988-01-01

A program used to teach moderately to severely mentally handicapped students to select the lower priced items in actual shopping activities is described. Through a five-phase process, students are taught to compare prices themselves as well as take into consideration variations in the sizes of containers and varying product weights. (VW)

ITEM SELECTION TECHNIQUES AND EVALUATION OF INSTRUCTIONAL OBJECTIVES.

ERIC Educational Resources Information Center

COX, RICHARD C.

THE VALIDITY OF AN EDUCATIONAL ACHIEVEMENT TEST DEPENDS UPON THE CORRESPONDENCE BETWEEN SPECIFIED EDUCATIONAL OBJECTIVES AND THE EXTENT TO WHICH THESE OBJECTIVES ARE MEASURED BY THE EVALUATION INSTRUMENT. THIS STUDY IS DESIGNED TO EVALUATE THE EFFECT OF STATISTICAL ITEM SELECTION ON THE STRUCTURE OF THE FINAL EVALUATION INSTRUMENT AS COMPARED WITH…

Mutual Information Item Selection in Adaptive Classification Testing

ERIC Educational Resources Information Center

Weissman, Alexander

2007-01-01

A general approach for item selection in adaptive multiple-category classification tests is provided. The approach uses mutual information (MI), a special case of the Kullback-Leibler distance, or relative entropy. MI works efficiently with the sequential probability ratio test and alleviates the difficulties encountered with using other local-…

Model Selection Indices for Polytomous Items

ERIC Educational Resources Information Center

Kang, Taehoon; Cohen, Allan S.; Sung, Hyun-Jung

2009-01-01

This study examines the utility of four indices for use in model selection with nested and nonnested polytomous item response theory (IRT) models: a cross-validation index and three information-based indices. Four commonly used polytomous IRT models are considered: the graded response model, the generalized partial credit model, the partial credit…

ARBA Guide to Biographical Resources 1986-1997.

ERIC Educational Resources Information Center

Wick, Robert L., Ed.; Mood, Terry Ann, Ed.

This guide provides a representative selection of biographical dictionaries and related works useful to the reference and collection development processes in all types of libraries. Three criteria were used in selection: (1) each item included was published within the past 12 years; (2) each item has been included in American Reference Books…

Taking Turns

ERIC Educational Resources Information Center

Hopkins, Brian

2010-01-01

Two people take turns selecting from an even number of items. Their relative preferences over the items can be described as a permutation, then tools from algebraic combinatorics can be used to answer various questions. We describe each person's optimal selection strategies including how each could make use of knowing the other's preferences. We…

The Performance of IRT Model Selection Methods with Mixed-Format Tests

ERIC Educational Resources Information Center

Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.

2012-01-01

When tests consist of multiple-choice and constructed-response items, researchers are confronted with the question of which item response theory (IRT) model combination will appropriately represent the data collected from these mixed-format tests. This simulation study examined the performance of six model selection criteria, including the…

Supercomputing '91; Proceedings of the 4th Annual Conference on High Performance Computing, Albuquerque, NM, Nov. 18-22, 1991

NASA Technical Reports Server (NTRS)

1991-01-01

Various papers on supercomputing are presented. The general topics addressed include: program analysis/data dependence, memory access, distributed memory code generation, numerical algorithms, supercomputer benchmarks, latency tolerance, parallel programming, applications, processor design, networks, performance tools, mapping and scheduling, characterization affecting performance, parallelism packaging, computing climate change, combinatorial algorithms, hardware and software performance issues, system issues. (No individual items are abstracted in this volume)

Selecting materialized views using random algorithm

NASA Astrophysics Data System (ADS)

Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

2007-04-01

The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

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.

The effects of 'does not apply' on measurement of temperament with the Infant Behavior Questionnaire-Revised: A cautionary tale for very young infants.

PubMed

Giesbrecht, Gerald F; Dewey, Deborah

2014-10-01

The Infant Behavior Questionnaire-Revised (IBQ-R) is a widely used parent report measure of infant temperament. Items marked 'does not apply' (NA) are treated as missing data when calculating scale scores, but the effect of this practice on assessment of infant temperament has not been reported. To determine the effect of NA responses on assessment of infant temperament and to evaluate the remedy offered by several missing data strategies. A prospective, community-based longitudinal cohort study. 401 infants who were born>37 weeks of gestation. Mothers completed the short form of the IBQ-R when infants were 3-months and 6-months of age. The rate of NA responses at the 3-month assessment was three times as high (22%) as the rate at six months (7%). Internal consistency was appreciably reduced and scale means were inflated in the presence of NA responses, especially at 3-months. The total number of NA items endorsed by individual parents was associated with infant age and parity. None of the missing data strategies completely eliminated problems related to NA responses but the Expectation Maximization algorithm greatly reduced these problems. The findings suggest that researchers should exercise caution when interpreting results obtained from infants at 3 months of age. Careful selection of scales, selecting a full length version of the IBQ-R, and use of a modern missing data technique may help to maintain the quality of data obtained from very young infants. Copyright © 2014 Elsevier Ltd. All rights reserved.

Affine Projection Algorithm with Improved Data-Selective Method Using the Condition Number

NASA Astrophysics Data System (ADS)

Ban, Sung Jun; Lee, Chang Woo; Kim, Sang Woo

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

An Evaluation of "Intentional" Weighting of Extended-Response or Constructed-Response Items in Tests with Mixed Item Types.

ERIC Educational Resources Information Center

Ito, Kyoko; Sykes, Robert C.

This study investigated the practice of weighting a type of test item, such as constructed response, more than other types of items, such as selected response, to compute student scores for a mixed-item type of test. The study used data from statewide writing field tests in grades 3, 5, and 8 and considered two contexts, that in which a single…

Embedded control system for computerized franking machine

NASA Astrophysics Data System (ADS)

Shi, W. M.; Zhang, L. B.; Xu, F.; Zhan, H. W.

2007-12-01

This paper presents a novel control system for franking machine. A methodology for operating a franking machine using the functional controls consisting of connection, configuration and franking electromechanical drive is studied. A set of enabling technologies to synthesize postage management software architectures driven microprocessor-based embedded systems is proposed. The cryptographic algorithm that calculates mail items is analyzed to enhance the postal indicia accountability and security. The study indicated that the franking machine is reliability, performance and flexibility in printing mail items.

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

Science Library of Test Items. Volume Two.

ERIC Educational Resources Information Center

New South Wales Dept. of Education, Sydney (Australia).

The second volume of test items in the Science Library of Test Items is intended as a resource to assist teachers in implementing and evaluating science courses in the first 4 years of Australian secondary school. The items were selected from questions submitted to the School Certificate Development Unit by teachers in New South Wales. Only the…

Integrating Test-Form Formatting into Automated Test Assembly

ERIC Educational Resources Information Center

Diao, Qi; van der Linden, Wim J.

2013-01-01

Automated test assembly uses the methodology of mixed integer programming to select an optimal set of items from an item bank. Automated test-form generation uses the same methodology to optimally order the items and format the test form. From an optimization point of view, production of fully formatted test forms directly from the item pool using…

Ordinal-To-Interval Scale Conversion Tables and National Items for the New Zealand Version of the WHOQOL-BREF

PubMed Central

Billington, D. Rex; Hsu, Patricia Hsien-Chuan; Feng, Xuan Joanna; Medvedev, Oleg N.; Kersten, Paula; Landon, Jason; Siegert, Richard J.

2016-01-01

The World Health Organisation Quality of Life (WHOQOL) questionnaires are widely used around the world and can claim strong cross-cultural validity due to their development in collaboration with international field centres. To enhance conceptual equivalence of quality of life across cultures, optional national items are often developed for use alongside the core instrument. The present study outlines the development of national items for the New Zealand WHOQOL-BREF. Focus groups with members of the community as well as health experts discussed what constitutes quality of life in their opinion. Based on themes extracted of aspects not contained in the existing WHOQOL instrument, 46 candidate items were generated and subsequently rated for their importance by a random sample of 585 individuals from the general population. Applying importance criteria reduced these items to 24, which were then sent to another large random sample (n = 808) to be rated alongside the existing WHOQOL-BREF. A final set of five items met the criteria for national items. Confirmatory factor analysis identified four national items as belonging to the psychological domain of quality of life, and one item to the social domain. Rasch analysis validated these results and generated ordinal-to-interval conversion algorithms to allow use of parametric statistics for domain scores with and without national items. PMID:27812203

The Subset Sum game.

PubMed

Darmann, Andreas; Nicosia, Gaia; Pferschy, Ulrich; Schauer, Joachim

2014-03-16

In this work we address a game theoretic variant of the Subset Sum problem, in which two decision makers (agents/players) compete for the usage of a common resource represented by a knapsack capacity. Each agent owns a set of integer weighted items and wants to maximize the total weight of its own items included in the knapsack. The solution is built as follows: Each agent, in turn, selects one of its items (not previously selected) and includes it in the knapsack if there is enough capacity. The process ends when the remaining capacity is too small for including any item left. We look at the problem from a single agent point of view and show that finding an optimal sequence of items to select is an [Formula: see text]-hard problem. Therefore we propose two natural heuristic strategies and analyze their worst-case performance when (1) the opponent is able to play optimally and (2) the opponent adopts a greedy strategy. From a centralized perspective we observe that some known results on the approximation of the classical Subset Sum can be effectively adapted to the multi-agent version of the problem.

The Subset Sum game☆

PubMed Central

Darmann, Andreas; Nicosia, Gaia; Pferschy, Ulrich; Schauer, Joachim

2014-01-01

In this work we address a game theoretic variant of the Subset Sum problem, in which two decision makers (agents/players) compete for the usage of a common resource represented by a knapsack capacity. Each agent owns a set of integer weighted items and wants to maximize the total weight of its own items included in the knapsack. The solution is built as follows: Each agent, in turn, selects one of its items (not previously selected) and includes it in the knapsack if there is enough capacity. The process ends when the remaining capacity is too small for including any item left. We look at the problem from a single agent point of view and show that finding an optimal sequence of items to select is an NP-hard problem. Therefore we propose two natural heuristic strategies and analyze their worst-case performance when (1) the opponent is able to play optimally and (2) the opponent adopts a greedy strategy. From a centralized perspective we observe that some known results on the approximation of the classical Subset Sum can be effectively adapted to the multi-agent version of the problem. PMID:25844012

Revisiting negative selection algorithms.

PubMed

Ji, Zhou; Dasgupta, Dipankar

2007-01-01

This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion.

DRD4 long allele carriers show heightened attention to high-priority items relative to low-priority items.

PubMed

Gorlick, Marissa A; Worthy, Darrell A; Knopik, Valerie S; McGeary, John E; Beevers, Christopher G; Maddox, W Todd

2015-03-01

Humans with seven or more repeats in exon III of the DRD4 gene (long DRD4 carriers) sometimes demonstrate impaired attention, as seen in attention-deficit hyperactivity disorder, and at other times demonstrate heightened attention, as seen in addictive behavior. Although the clinical effects of DRD4 are the focus of much work, this gene may not necessarily serve as a "risk" gene for attentional deficits, but as a plasticity gene where attention is heightened for priority items in the environment and impaired for minor items. Here we examine the role of DRD4 in two tasks that benefit from selective attention to high-priority information. We examine a category learning task where performance is supported by focusing on features and updating verbal rules. Here, selective attention to the most salient features is associated with good performance. In addition, we examine the Operation Span (OSPAN) task, a working memory capacity task that relies on selective attention to update and maintain items in memory while also performing a secondary task. Long DRD4 carriers show superior performance relative to short DRD4 homozygotes (six or less tandem repeats) in both the category learning and OSPAN tasks. These results suggest that DRD4 may serve as a "plasticity" gene where individuals with the long allele show heightened selective attention to high-priority items in the environment, which can be beneficial in the appropriate context.

DRD4 Long Allele Carriers Show Heightened Attention to High-Priority Items Relative to Low-Priority Items

PubMed Central

Gorlick, Marissa A.; Worthy, Darrell A.; Knopik, Valerie S.; McGeary, John E.; Beevers, Christopher G.; Maddox, W. Todd

2014-01-01

Humans with 7 or more repeats in exon III of the DRD4 gene (long DRD4 carriers) sometimes demonstrate impaired attention, as seen in ADHD, and at other times demonstrate heightened attention, as seen in addictive behavior. Though the clinical effects of DRD4 are the focus of much work, this gene may not necessarily serve as a ‘risk’ gene for attentional deficits, but as a plasticity gene where attention is heightened for priority items in the environment and impaired for minor items. Here we examine the role of DRD4 in two tasks that benefit from selective attention to high-priority information. We examine a category learning task where performance is supported by focusing on features and updating verbal rules. Here selective attention to the most salient features is associated with good performance. In addition, we examine the Operation Span Task (OSPAN), a working memory capacity task that relies on selective attention to update and maintain items in memory while also performing a secondary task. Long DRD4 carriers show superior performance relative to short DRD4 homozygotes (six or less tandem repeats) in both the category learning and OSPAN tasks. These results suggest that DRD4 may serve as a ‘plasticity’ gene where individuals with the long allele show heightened selective attention to high-priority items in the environment, which can be beneficial in the appropriate context. PMID:25244120

New decision criteria for selecting delta check methods based on the ratio of the delta difference to the width of the reference range can be generally applicable for each clinical chemistry test item.

PubMed

Park, Sang Hyuk; Kim, So-Young; Lee, Woochang; Chun, Sail; Min, Won-Ki

2012-09-01

Many laboratories use 4 delta check methods: delta difference, delta percent change, rate difference, and rate percent change. However, guidelines regarding decision criteria for selecting delta check methods have not yet been provided. We present new decision criteria for selecting delta check methods for each clinical chemistry test item. We collected 811,920 and 669,750 paired (present and previous) test results for 27 clinical chemistry test items from inpatients and outpatients, respectively. We devised new decision criteria for the selection of delta check methods based on the ratio of the delta difference to the width of the reference range (DD/RR). Delta check methods based on these criteria were compared with those based on the CV% of the absolute delta difference (ADD) as well as those reported in 2 previous studies. The delta check methods suggested by new decision criteria based on the DD/RR ratio corresponded well with those based on the CV% of the ADD except for only 2 items each in inpatients and outpatients. Delta check methods based on the DD/RR ratio also corresponded with those suggested in the 2 previous studies, except for 1 and 7 items in inpatients and outpatients, respectively. The DD/RR method appears to yield more feasible and intuitive selection criteria and can easily explain changes in the results by reflecting both the biological variation of the test item and the clinical characteristics of patients in each laboratory. We suggest this as a measure to determine delta check methods.

A framework for diversifying recommendation lists by user interest expansion.

PubMed

Zhang, Zhu; Zheng, Xiaolong; Zeng, Daniel Dajun

2016-08-01

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users' interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users' preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

A framework for diversifying recommendation lists by user interest expansion

PubMed Central

Zhang, Zhu; Zeng, Daniel Dajun

2017-01-01

Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation. PMID:28959089

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

PubMed

Al-Rajab, Murad; Lu, Joan; Xu, Qiang

2017-07-01

This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

SamSelect: a sample sequence selection algorithm for quorum planted motif search on large DNA datasets.

PubMed

Yu, Qiang; Wei, Dingbang; Huo, Hongwei

2018-06-18

Given a set of t n-length DNA sequences, q satisfying 0 < q ≤ 1, and l and d satisfying 0 ≤ d < l < n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., t = 20 and n = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more. We analyze the effects of t and q on the time performance of qPMS algorithms and find that a large t or a small q causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set D' with a small t and a large q from the large input dataset D and then executing qPMS algorithms on D'. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select D' efficiently and (2) that the qPMS algorithms executed on D' can find implanted or real motifs in a significantly shorter time than when executed on D. We improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set D', rather than take an unfeasibly long time to search the original sequence set D. Our motif discovery method is an approximate algorithm.

Algorithm Analysis of the DSM-5 Alcohol Withdrawal Symptom.

PubMed

Martin, Christopher S; Vergés, Alvaro; Langenbucher, James W; Littlefield, Andrew; Chung, Tammy; Clark, Duncan B; Sher, Kenneth J

2018-06-01

Alcohol withdrawal (AW) is an important clinical and diagnostic feature of alcohol dependence. AW has been found to predict a worsened course of illness in clinical samples, but in some community studies, AW endorsement rates are strikingly high, suggesting false-positive symptom assignments. Little research has examined the validity of the DSM-5 algorithm for AW, which requires either the presence of at least 2 of 8 subcriteria (i.e., autonomic hyperactivity, tremulousness, insomnia, nausea, hallucinations, psychomotor agitation, anxiety, and grand mal seizures), or, the use of alcohol to avoid or relieve these symptoms. We used item and algorithm analyses of data from waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (current drinkers, n = 26,946 at wave 1) to study the validity of DSM-5 AW as operationalized by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV (AUDADIS-IV). A substantial proportion of individuals given the AW symptom reported only modest to moderate levels of alcohol use and alcohol problems. Alternative AW algorithms were superior to DSM-5 in terms of levels of alcohol use and alcohol problem severity among those with AW, group difference effect sizes, and predictive validity at a 3-year follow-up. The superior alternative algorithms included those that excluded the nausea subcriterion; required withdrawal-related distress or impairment; increased the AW subcriteria threshold from 2 to 3 items; and required tremulousness for AW symptom assignment. The results indicate that the DSM-5 definition of AW, as assessed by the AUDADIS-IV, has low specificity. This shortcoming can be addressed by making the algorithm for symptom assignment more stringent. Copyright © 2018 by the Research Society on Alcoholism.

Evaluating the healthiness of chain-restaurant menu items using crowdsourcing: a new method.

PubMed

Lesser, Lenard I; Wu, Leslie; Matthiessen, Timothy B; Luft, Harold S

2017-01-01

To develop a technology-based method for evaluating the nutritional quality of chain-restaurant menus to increase the efficiency and lower the cost of large-scale data analysis of food items. Using a Modified Nutrient Profiling Index (MNPI), we assessed chain-restaurant items from the MenuStat database with a process involving three steps: (i) testing 'extreme' scores; (ii) crowdsourcing to analyse fruit, nut and vegetable (FNV) amounts; and (iii) analysis of the ambiguous items by a registered dietitian. In applying the approach to assess 22 422 foods, only 3566 could not be scored automatically based on MenuStat data and required further evaluation to determine healthiness. Items for which there was low agreement between trusted crowd workers, or where the FNV amount was estimated to be >40 %, were sent to a registered dietitian. Crowdsourcing was able to evaluate 3199, leaving only 367 to be reviewed by the registered dietitian. Overall, 7 % of items were categorized as healthy. The healthiest category was soups (26 % healthy), while desserts were the least healthy (2 % healthy). An algorithm incorporating crowdsourcing and a dietitian can quickly and efficiently analyse restaurant menus, allowing public health researchers to analyse the healthiness of menu items.

Selecting, Evaluating and Creating Policies for Computer-Based Resources in the Behavioral Sciences and Education.

ERIC Educational Resources Information Center

Richardson, Linda B., Comp.; And Others

This collection includes four handouts: (1) "Selection Critria Considerations for Computer-Based Resources" (Linda B. Richardson); (2) "Software Collection Policies in Academic Libraries" (a 24-item bibliography, Jane W. Johnson); (3) "Circulation and Security of Software" (a 19-item bibliography, Sara Elizabeth Williams); and (4) "Bibliography of…

Comparing the Performance of Five Multidimensional CAT Selection Procedures with Different Stopping Rules

ERIC Educational Resources Information Center

Yao, Lihua

2013-01-01

Through simulated data, five multidimensional computerized adaptive testing (MCAT) selection procedures with varying test lengths are examined and compared using different stopping rules. Fixed item exposure rates are used for all the items, and the Priority Index (PI) method is used for the content constraints. Two stopping rules, standard error…

Emotional Intelligence in Applicant Selection for Care-Related Academic Programs

ERIC Educational Resources Information Center

Zysberg, Leehu; Levy, Anat; Zisberg, Anna

2011-01-01

Two studies describe the development of the Audiovisual Test of Emotional Intelligence (AVEI), aimed at candidate selection in educational settings. Study I depicts the construction of the test and the preliminary examination of its psychometric properties in a sample of 92 college students. Item analysis allowed the modification of problem items,…

A Selected Bibliography on International Education.

ERIC Educational Resources Information Center

Foreign Policy Association, New York, NY.

This unannotated bibliography is divided into four major sections; 1) General Background Readings for Teachers; 2) Approaches and Methods; 3) Materials for the Classroom; and, 4) Sources of Information and Materials. It offers a highly selective list of items which provide wide coverage of the field. Included are items on foreign policy, war and…

2 CFR Appendix B to Part 230 - Selected Items of Cost

Code of Federal Regulations, 2010 CFR

2010-01-01

... PRINCIPLES FOR NON-PROFIT ORGANIZATIONS (OMB CIRCULAR A-122) Pt. 230, App. B Appendix B to Part 230—Selected... use of patents and copyrights 45. Selling and marketing 46. Specialized service facilities 47. Taxes... of this appendix provide principles to be applied in establishing the allowability of certain items...

An Attempt to Influence Selected Portions of Student Learning.

ERIC Educational Resources Information Center

Anderson, Edwin R.

In an attempt to selectively improve student performance, one-half of a set of difficult test items from a FORTRAN programming class had handouts explaining the concepts underlying the items distributed to the students. Each handout contained a written learning objective, a short prose passage explaining the objective, and one or more practice…

Informed and Uninformed Naïve Assessment Constructors' Strategies for Item Selection

ERIC Educational Resources Information Center

Fives, Helenrose; Barnes, Nicole

2017-01-01

We present a descriptive analysis of 53 naïve assessment constructors' explanations for selecting test items to include on a summative assessment. We randomly assigned participants to an informed and uninformed condition (i.e., informed participants read an article describing a Table of Specifications). Through recursive thematic analyses of…

Dual-Objective Item Selection Criteria in Cognitive Diagnostic Computerized Adaptive Testing

ERIC Educational Resources Information Center

Kang, Hyeon-Ah; Zhang, Susu; Chang, Hua-Hua

2017-01-01

The development of cognitive diagnostic-computerized adaptive testing (CD-CAT) has provided a new perspective for gaining information about examinees' mastery on a set of cognitive attributes. This study proposes a new item selection method within the framework of dual-objective CD-CAT that simultaneously addresses examinees' attribute mastery…

Audiovisual Materials for Teaching Economics. Third Edition.

ERIC Educational Resources Information Center

Harter, Charlotte T.; And Others

The third edition of this catalog, which expands and revises earlier editions, annotates audiovisual items for economic education in kindergarten through college. The purpose of the catalog is to help teachers select sound economic materials for classroom use. A selective listing, the catalog cites over 700 items out of more than 1200 items…

The Relationship between Attitudes toward Censorship and Selected Academic Variables.

ERIC Educational Resources Information Center

Dwyer, Edward J.; Summy, Mary K.

1989-01-01

To examine characteristics of subjects relative to their attitudes toward censorship, a study surveyed 98 college students selected from students in a public university in the southeastern United States. A 24-item Likert-style censorship scale was used to measure attitudes toward censorship. Strong agreement with affirmative items would suggest…

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Shilling attack detection for recommender systems based on credibility of group users and rating time series.

PubMed

Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

Shilling attack detection for recommender systems based on credibility of group users and rating time series

PubMed Central

Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. PMID:29742134

Is selective attention the basis for selective imitation in infants? An eye-tracking study of deferred imitation with 12-month-olds.

PubMed

Kolling, Thorsten; Oturai, Gabriella; Knopf, Monika

2014-08-01

Infants and children do not blindly copy every action they observe during imitation tasks. Research demonstrated that infants are efficient selective imitators. The impact of selective perceptual processes (selective attention) for selective deferred imitation, however, is still poorly described. The current study, therefore, analyzed 12-month-old infants' looking behavior during demonstration of two types of target actions: arbitrary versus functional actions. A fully automated remote eye tracker was used to assess infants' looking behavior during action demonstration. After a 30-min delay, infants' deferred imitation performance was assessed. Next to replicating a memory effect, results demonstrate that infants do imitate significantly more functional actions than arbitrary actions (functionality effect). Eye-tracking data show that whereas infants do not fixate significantly longer on functional actions than on arbitrary actions, amount of fixations and amount of saccades differ between functional and arbitrary actions, indicating different encoding mechanisms. In addition, item-level findings differ from overall findings, indicating that perceptual and conceptual item features influence looking behavior. Looking behavior on both the overall and item levels, however, does not relate to deferred imitation performance. Taken together, the findings demonstrate that, on the one hand, selective imitation is not explainable merely by selective attention processes. On the other hand, notwithstanding this reasoning, attention processes on the item level are important for encoding processes during target action demonstration. Limitations and future studies are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Fast object detection algorithm based on HOG and CNN

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Wang, Dandan; Zhang, Yanduo

2018-04-01

In the field of computer vision, object classification and object detection are widely used in many fields. The traditional object detection have two main problems:one is that sliding window of the regional selection strategy is high time complexity and have window redundancy. And the other one is that Robustness of the feature is not well. In order to solve those problems, Regional Proposal Network (RPN) is used to select candidate regions instead of selective search algorithm. Compared with traditional algorithms and selective search algorithms, RPN has higher efficiency and accuracy. We combine HOG feature and convolution neural network (CNN) to extract features. And we use SVM to classify. For TorontoNet, our algorithm's mAP is 1.6 percentage points higher. For OxfordNet, our algorithm's mAP is 1.3 percentage higher.

Tier One Performance Screen Initial Operational Test and Evaluation: 2012 Interim Report

DTIC Science & Technology

2013-12-01

are known to predict outcomes in work settings. Because the TAPAS uses item response theory (IRT) methods to construct and score items, it can be...Qualification Test (AFQT), to select new Soldiers. Although the AFQT is useful for selecting new Soldiers, other personal attributes are important to...to be and will continue to serve as a useful metric for selecting new Soldiers, other personal attributes, in particular non-cognitive attributes

The Comparative Effectiveness of Different Item Analysis Techniques in Increasing Change Score Reliability.

ERIC Educational Resources Information Center

Crocker, Linda M.; Mehrens, William A.

Four new methods of item analysis were used to select subsets of items which would yield measures of attitude change. The sample consisted of 263 students at Michigan State University who were tested on the Inventory of Beliefs as freshmen and retested on the same instrument as juniors. Item change scores and total change scores were computed for…

Anchor Selection Strategies for DIF Analysis: Review, Assessment, and New Approaches

ERIC Educational Resources Information Center

Kopf, Julia; Zeileis, Achim; Strobl, Carolin

2015-01-01

Differential item functioning (DIF) indicates the violation of the invariance assumption, for instance, in models based on item response theory (IRT). For item-wise DIF analysis using IRT, a common metric for the item parameters of the groups that are to be compared (e.g., for the reference and the focal group) is necessary. In the Rasch model,…

Computerized Adaptive Testing for Polytomous Motivation Items: Administration Mode Effects and a Comparison with Short Forms

ERIC Educational Resources Information Center

Hol, A. Michiel; Vorst, Harrie C. M.; Mellenbergh, Gideon J.

2007-01-01

In a randomized experiment (n = 515), a computerized and a computerized adaptive test (CAT) are compared. The item pool consists of 24 polytomous motivation items. Although items are carefully selected, calibration data show that Samejima's graded response model did not fit the data optimally. A simulation study is done to assess possible…

Objective and Item Banking Computer Software and Its Use in Comprehensive Achievement Monitoring.

ERIC Educational Resources Information Center

Schriber, Peter E.; Gorth, William P.

The current emphasis on objectives and test item banks for constructing more effective tests is being augmented by increasingly sophisticated computer software. Items can be catalogued in numerous ways for retrieval. The items as well as instructional objectives can be stored and test forms can be selected and printed by the computer. It is also…

Wisconsin Title I Migrant Education. Section 143 Project: Development of an Item Bank. Summary Report.

ERIC Educational Resources Information Center

Brown, Frank N.; And Others

The successful Wisconsin Title 1 project item bank offers a valid, flexible, and efficient means of providing migrant student tests in reading and mathematics tailored to instructor curricula. The item bank system consists of nine PASCAL computer programs which maintain, search, and select from approximately 1,000 test items stored on floppy disks…

Novel and efficient tag SNPs selection algorithms.

PubMed

Chen, Wen-Pei; Hung, Che-Lun; Tsai, Suh-Jen Jane; Lin, Yaw-Ling

2014-01-01

SNPs are the most abundant forms of genetic variations amongst species; the association studies between complex diseases and SNPs or haplotypes have received great attention. However, these studies are restricted by the cost of genotyping all SNPs; thus, it is necessary to find smaller subsets, or tag SNPs, representing the rest of the SNPs. In fact, the existing tag SNP selection algorithms are notoriously time-consuming. An efficient algorithm for tag SNP selection was presented, which was applied to analyze the HapMap YRI data. The experimental results show that the proposed algorithm can achieve better performance than the existing tag SNP selection algorithms; in most cases, this proposed algorithm is at least ten times faster than the existing methods. In many cases, when the redundant ratio of the block is high, the proposed algorithm can even be thousands times faster than the previously known methods. Tools and web services for haplotype block analysis integrated by hadoop MapReduce framework are also developed using the proposed algorithm as computation kernels.

Assessing patients' experiences with communication across the cancer care continuum.

PubMed

Mazor, Kathleen M; Street, Richard L; Sue, Valerie M; Williams, Andrew E; Rabin, Borsika A; Arora, Neeraj K

2016-08-01

To evaluate the relevance, performance and potential usefulness of the Patient Assessment of cancer Communication Experiences (PACE) items. Items focusing on specific communication goals related to exchanging information, fostering healing relationships, responding to emotions, making decisions, enabling self-management, and managing uncertainty were tested via a retrospective, cross-sectional survey of adults who had been diagnosed with cancer. Analyses examined response frequencies, inter-item correlations, and coefficient alpha. A total of 366 adults were included in the analyses. Relatively few selected Does Not Apply, suggesting that items tap relevant communication experiences. Ratings of whether specific communication goals were achieved were strongly correlated with overall ratings of communication, suggesting item content reflects important aspects of communication. Coefficient alpha was ≥.90 for each item set, indicating excellent reliability. Variations in the percentage of respondents selecting the most positive response across items suggest results can identify strengths and weaknesses. The PACE items tap relevant, important aspects of communication during cancer care, and may be useful to cancer care teams desiring detailed feedback. The PACE is a new tool for eliciting patients' perspectives on communication during cancer care. It is freely available online for practitioners, researchers and others. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Automated Item Generation with Recurrent Neural Networks.

PubMed

von Davier, Matthias

2018-03-12

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven's progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

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

Online selective kernel-based temporal difference learning.

PubMed

Chen, Xingguo; Gao, Yang; Wang, Ruili

2013-12-01

In this paper, an online selective kernel-based temporal difference (OSKTD) learning algorithm is proposed to deal with large scale and/or continuous reinforcement learning problems. OSKTD includes two online procedures: online sparsification and parameter updating for the selective kernel-based value function. A new sparsification method (i.e., a kernel distance-based online sparsification method) is proposed based on selective ensemble learning, which is computationally less complex compared with other sparsification methods. With the proposed sparsification method, the sparsified dictionary of samples is constructed online by checking if a sample needs to be added to the sparsified dictionary. In addition, based on local validity, a selective kernel-based value function is proposed to select the best samples from the sample dictionary for the selective kernel-based value function approximator. The parameters of the selective kernel-based value function are iteratively updated by using the temporal difference (TD) learning algorithm combined with the gradient descent technique. The complexity of the online sparsification procedure in the OSKTD algorithm is O(n). In addition, two typical experiments (Maze and Mountain Car) are used to compare with both traditional and up-to-date O(n) algorithms (GTD, GTD2, and TDC using the kernel-based value function), and the results demonstrate the effectiveness of our proposed algorithm. In the Maze problem, OSKTD converges to an optimal policy and converges faster than both traditional and up-to-date algorithms. In the Mountain Car problem, OSKTD converges, requires less computation time compared with other sparsification methods, gets a better local optima than the traditional algorithms, and converges much faster than the up-to-date algorithms. In addition, OSKTD can reach a competitive ultimate optima compared with the up-to-date algorithms.

Toward DSM-V: an item response theory analysis of the diagnostic process for DSM-IV alcohol abuse and dependence in adolescents.

PubMed

Gelhorn, Heather; Hartman, Christie; Sakai, Joseph; Stallings, Michael; Young, Susan; Rhee, Soo Hyun; Corley, Robin; Hewitt, John; Hopfer, Christian; Crowley, Thomas

2008-11-01

Item response theory analyses were used to examine alcohol abuse and dependence symptoms and diagnoses in adolescents. Previous research suggests that the DSM-IV alcohol use disorder (AUD) symptoms in adolescents may be characterized by a single dimension. The present study extends prior research with a larger and more comprehensive sample and an examination of an alternative diagnostic algorithm for AUDs. Approximately 5,587 adolescents between the ages of 12 and 18 years from adjudicated, clinical, and community samples were administered structured clinical interviews. Analyses were conducted to examine the severity of alcohol abuse and dependence symptoms and the severity of alcohol use problems (AUDs) within the diagnostic categories created by the DSM-IV. Although the DSM-IV diagnostic categories differ in severity of AUDs, there is substantial overlap and inconsistency in AUD severity of persons across these categories. Item Response Theory-based AUD severity estimates suggest that many persons diagnosed with abuse have AUD severity greater than persons with dependence. Similarly, many persons who endorse some symptoms but do not qualify for a diagnosis (i.e., diagnostic orphans) have more severe AUDs than persons with an abuse diagnosis. Additionally, two dependence items, "tolerance" and "larger/longer," show differences in severity between samples. The distinction between DSM-IV abuse and dependence based on severity can be improved using an alternative diagnostic algorithm that considers all of the alcohol abuse and dependence symptoms conjointly.

Development of the PROMIS health expectancies of smoking item banks.

PubMed

Edelen, Maria Orlando; Tucker, Joan S; Shadel, William G; Stucky, Brian D; Cerully, Jennifer; Li, Zhen; Hansen, Mark; Cai, Li

2014-09-01

Smokers' health-related outcome expectancies are associated with a number of important constructs in smoking research, yet there are no measures currently available that focus exclusively on this domain. This paper describes the development and evaluation of item banks for assessing the health expectancies of smoking. Using data from a sample of daily (N = 4,201) and nondaily (N = 1,183) smokers, we conducted a series of item factor analyses, item response theory analyses, and differential item functioning analyses (according to gender, age, and race/ethnicity) to arrive at a unidimensional set of health expectancies items for daily and nondaily smokers. We also evaluated the performance of short forms (SFs) and computer adaptive tests (CATs) to efficiently assess health expectancies. A total of 24 items were included in the Health Expectancies item banks; 13 items are common across daily and nondaily smokers, 6 are unique to daily, and 5 are unique to nondaily. For both daily and nondaily smokers, the Health Expectancies item banks are unidimensional, reliable (reliability = 0.95 and 0.96, respectively), and perform similarly across gender, age, and race/ethnicity groups. A SF common to daily and nondaily smokers consists of 6 items (reliability = 0.87). Results from simulated CATs showed that health expectancies can be assessed with good precision with an average of 5-6 items adaptively selected from the item banks. Health expectancies of smoking can be assessed on the basis of these item banks via SFs, CATs, or through a tailored set of items selected for a specific research purpose. © The Author 2014. 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.

Development of the PROMIS nicotine dependence item banks.

PubMed

Shadel, William G; Edelen, Maria Orlando; Tucker, Joan S; Stucky, Brian D; Hansen, Mark; Cai, Li

2014-09-01

Nicotine dependence is a core construct important for understanding cigarette smoking and smoking cessation behavior. This article describes analyses conducted to develop and evaluate item banks for assessing nicotine dependence among daily and nondaily smokers. Using data from a sample of daily (N = 4,201) and nondaily (N =1,183) smokers, we conducted a series of item factor analyses, item response theory analyses, and differential item functioning analyses (according to gender, age, and race/ethnicity) to arrive at a unidimensional set of nicotine dependence items for daily and nondaily smokers. We also evaluated performance of short forms (SFs) and computer adaptive tests (CATs) to efficiently assess dependence. A total of 32 items were included in the Nicotine Dependence item banks; 22 items are common across daily and nondaily smokers, 5 are unique to daily smokers, and 5 are unique to nondaily smokers. For both daily and nondaily smokers, the Nicotine Dependence item banks are strongly unidimensional, highly reliable (reliability = 0.97 and 0.97, respectively), and perform similarly across gender, age, and race/ethnicity groups. SFs common to daily and nondaily smokers consist of 8 and 4 items (reliability = 0.91 and 0.81, respectively). Results from simulated CATs showed that dependence can be assessed with very good precision for most respondents using fewer than 6 items adaptively selected from the item banks. Nicotine dependence on cigarettes can be assessed on the basis of these item banks via one of the SFs, by using CATs, or through a tailored set of items selected for a specific research purpose. © The Author 2014. 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.

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

Gene selection in cancer classification using sparse logistic regression with Bayesian regularization.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2006-10-01

Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but efficient training procedure. The degree of sparsity obtained is determined by the value of a regularization parameter, which must be carefully tuned in order to optimize performance. This normally involves a model selection stage, based on a computationally intensive search for the minimizer of the cross-validation error. In this paper, we demonstrate that a simple Bayesian approach can be taken to eliminate this regularization parameter entirely, by integrating it out analytically using an uninformative Jeffrey's prior. The improved algorithm (BLogReg) is then typically two or three orders of magnitude faster than the original algorithm, as there is no longer a need for a model selection step. The BLogReg algorithm is also free from selection bias in performance estimation, a common pitfall in the application of machine learning algorithms in cancer classification. The SLogReg, BLogReg and Relevance Vector Machine (RVM) gene selection algorithms are evaluated over the well-studied colon cancer and leukaemia benchmark datasets. The leave-one-out estimates of the probability of test error and cross-entropy of the BLogReg and SLogReg algorithms are very similar, however the BlogReg algorithm is found to be considerably faster than the original SLogReg algorithm. Using nested cross-validation to avoid selection bias, performance estimation for SLogReg on the leukaemia dataset takes almost 48 h, whereas the corresponding result for BLogReg is obtained in only 1 min 24 s, making BLogReg by far the more practical algorithm. BLogReg also demonstrates better estimates of conditional probability than the RVM, which are of great importance in medical applications, with similar computational expense. A MATLAB implementation of the sparse logistic regression algorithm with Bayesian regularization (BLogReg) is available from http://theoval.cmp.uea.ac.uk/~gcc/cbl/blogreg/

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

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.

Multi-task feature selection in microarray data by binary integer programming.

PubMed

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.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

PubMed Central

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems. PMID:25961028

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

PubMed

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Weapon Performance Testing and Analysis: The MODI-PAC Round, the Number 4 Lead-Shot Round, and the Flying Baton

DTIC Science & Technology

1976-01-01

items. The items tested were the MODI-PAC, a proprietary item of Reming)on Arms Company, a standard 12 - gauge round of No. 4 lead shot, and an...to refrain from testing this item. Therefore, the final selection of items for testing were (1) the MODI-PAC, (2) a standard 12 - gauge shotgun round of...The first item evaluated was the MODI-PAC5. The MOQ1-PAC which standsfor “modified impact “ is a 12 - gauge shotgun shell loaded with approximately 320

Method of data mining including determining multidimensional coordinates of each item using a predetermined scalar similarity value for each item pair

DOEpatents

Meyers, Charles E.; Davidson, George S.; Johnson, David K.; Hendrickson, Bruce A.; Wylie, Brian N.

1999-01-01

A method of data mining represents related items in a multidimensional space. Distance between items in the multidimensional space corresponds to the extent of relationship between the items. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the items.

Threshold automatic selection hybrid phase unwrapping algorithm for digital holographic microscopy

NASA Astrophysics Data System (ADS)

Zhou, Meiling; Min, Junwei; Yao, Baoli; Yu, Xianghua; Lei, Ming; Yan, Shaohui; Yang, Yanlong; Dan, Dan

2015-01-01

Conventional quality-guided (QG) phase unwrapping algorithm is hard to be applied to digital holographic microscopy because of the long execution time. In this paper, we present a threshold automatic selection hybrid phase unwrapping algorithm that combines the existing QG algorithm and the flood-filled (FF) algorithm to solve this problem. The original wrapped phase map is divided into high- and low-quality sub-maps by selecting a threshold automatically, and then the FF and QG unwrapping algorithms are used in each level to unwrap the phase, respectively. The feasibility of the proposed method is proved by experimental results, and the execution speed is shown to be much faster than that of the original QG unwrapping algorithm.

Network analysis of online bidding activity

NASA Astrophysics Data System (ADS)

Yang, I.; Oh, E.; Kahng, B.

2006-07-01

With the advent of digital media, people are increasingly resorting to online channels for commercial transactions. The online auction is a prototypical example. In such online transactions, the pattern of bidding activity is more complex than traditional offline transactions; this is because the number of bidders participating in a given transaction is not bounded and the bidders can also easily respond to the bidding instantaneously. By using the recently developed network theory, we study the interaction patterns between bidders (items) who (that) are connected when they bid for the same item (if the item is bid by the same bidder). The resulting network is analyzed by using the hierarchical clustering algorithm, which is used for clustering analysis for expression data from DNA microarrays. A dendrogram is constructed for the item subcategories; this dendrogram is compared to a traditional classification scheme. The implication of the difference between the two is discussed.

Development, Validation, and Use of an Item Bank for Police Promotion Examinations.

ERIC Educational Resources Information Center

Enger, John M.

In Arkansas, in reaction to complaints about traditional methods of selection for promotion, the civil service commission has chosen to base promotions in the police department solely on scores on locally-developed objective tests. Items developed and loaded into a computerized test bank were selected from six areas of responsibility: (1) criminal…

Multidimensional CAT Item Selection Methods for Domain Scores and Composite Scores: Theory and Applications

ERIC Educational Resources Information Center

Yao, Lihua

2012-01-01

Multidimensional computer adaptive testing (MCAT) can provide higher precision and reliability or reduce test length when compared with unidimensional CAT or with the paper-and-pencil test. This study compared five item selection procedures in the MCAT framework for both domain scores and overall scores through simulation by varying the structure…

Variable stars around selected open clusters in the VVV area: Young Stellar Objects

NASA Astrophysics Data System (ADS)

Medina, Nicolas; Borissova, Jura; Bayo, Amelia; Kurtev, Radostin; Lucas, Philip

2017-09-01

Time-varying phenomena are one of the most substantial sources of astrophysical information, and led to many fundamental discoveries in modern astronomy. We have developed an automated tool to search and analyze variable sources in the near infrared Ks band, using the data from the Vista Variables in the Vía Láctea (VVV) ESO Public Survey ([5, 8]). One of our main goals is to investigate the Young Stellar Objects (YSOs) in the Galactic star forming regions, looking for: •Variability. •New pre-main sequence star clusters. Here we present the newly discovered YSOs within some selected stellar clusters in our Galaxy.

Optimizing data collection for public health decisions: a data mining approach

PubMed Central

2014-01-01

Background Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. Methods The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Results Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. Conclusions While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost. PMID:24919484

Optimizing data collection for public health decisions: a data mining approach.

PubMed

Partington, Susan N; Papakroni, Vasil; Menzies, Tim

2014-06-12

Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost.

Selection of Common Items as an Unrecognized Source of Variability in Test Equating: A Bootstrap Approximation Assuming Random Sampling of Common Items

ERIC Educational Resources Information Center

Michaelides, Michalis P.; Haertel, Edward H.

2014-01-01

The standard error of equating quantifies the variability in the estimation of an equating function. Because common items for deriving equated scores are treated as fixed, the only source of variability typically considered arises from the estimation of common-item parameters from responses of samples of examinees. Use of alternative, equally…

Development of a wheelchair mobility skills test for children and adolescents: combining evidence with clinical expertise.

PubMed

Sol, Marleen Elisabeth; Verschuren, Olaf; de Groot, Laura; de Groot, Janke Frederike

2017-02-13

Wheelchair mobility skills (WMS) training is regarded by children using a manual wheelchair and their parents as an important factor to improve participation and daily physical activity. Currently, there is no outcome measure available for the evaluation of WMS in children. Several wheelchair mobility outcome measures have been developed for adults, but none of these have been validated in children. Therefore the objective of this study is to develop a WMS outcome measure for children using the current knowledge from literature in combination with the clinical expertise of health care professionals, children and their parents. Mixed methods approach. Phase 1: Item identification of WMS items through a systematic review using the 'COnsensus-based Standards for the selection of health Measurement Instruments' (COSMIN) recommendations. Phase 2: Item selection and validation of relevant WMS items for children, using a focus group and interviews with children using a manual wheelchair, their parents and health care professionals. Phase 3: Feasibility of the newly developed Utrecht Pediatric Wheelchair Mobility Skills Test (UP-WMST) through pilot testing. Phase 1: Data analysis and synthesis of nine WMS related outcome measures showed there is no widely used outcome measure with levels of evidence across all measurement properties. However, four outcome measures showed some levels of evidence on reliability and validity for adults. Twenty-two WMS items with the best clinimetric properties were selected for further analysis in phase 2. Phase 2: Fifteen items were deemed as relevant for children, one item needed adaptation and six items were considered not relevant for assessing WMS in children. Phase 3: Two health care professionals administered the UP-WMST in eight children. The instructions of the UP-WMST were clear, but the scoring method of the height difference items needed adaptation. The outdoor items for rolling over soft surface and the side slope item were excluded in the final version of the UP-WMST due to logistic reasons. The newly developed 15 item UP-WMST is a validated outcome measure which is easy to administer in children using a manual wheelchair. More research regarding reliability, construct validity and responsiveness is warranted before the UP-WMST can be used in practice.

Prey selection by the Lake Superior fish community

USGS Publications Warehouse

Isaac, Edmund J.; Hrabik, Thomas R.; Stockwell, Jason D.; Gamble, Allison E.

2012-01-01

Mysis diluviana is an important prey item to the Lake Superior fish community as found through a recent diet study. We further evaluated this by relating the quantity of prey found in fish diets to the quantity of prey available to fish, providing insight into feeding behavior and prey preferences. We describe the seasonal prey selection of major fish species collected across 18 stations in Lake Superior in spring, summer, and fall of 2005. Of the major nearshore fish species, bloater (Coregonus hoyi), rainbow smelt (Osmerus mordax), and lake whitefish (Coregonus clupeaformis) consumed Mysis, and strongly selected Mysis over other prey items each season. However, lake whitefish also selected Bythotrephes in the fall when Bythotrephes were numerous. Cisco (Coregonus artedi), a major nearshore and offshore species, fed largely on calanoid copepods, and selected calanoid copepods (spring) and Bythotrephes (summer and fall). Cisco also targeted prey similarly across bathymetric depths. Other major offshore fish species such as kiyi (Coregonus kiyi) and deepwater sculpin (Myoxocephalus thompsoni) fed largely on Mysis, with kiyi targeting Mysis exclusively while deepwater sculpin did not prefer any single prey organism. The major offshore predator siscowet lake trout (Salvelinus namaycush siscowet) consumed deepwater sculpin and coregonines, but selected deepwater sculpin and Mysis each season, with juveniles having a higher selection for Mysis than adults. Our results suggest that Mysis is not only a commonly consumed prey item, but a highly preferred prey item for pelagic, benthic, and piscivorous fishes in nearshore and offshore waters of Lake Superior.

Development of Methodologic Tools for Planning and Managing Library Services: IV. Bibliography of Studies Selected for Methods and Data Useful to Biomedical Libraries *

PubMed Central

Orr, Richard H.

1970-01-01

This selective bibliography is intended to serve as a guide to empirical studies reporting data and methods that can be used by medical librarians to assess their own efforts objectively and to improve the effectiveness and efficiency of services they offer. The decision rules that governed selection of items for the bibliography are specified in detail. A total of 178 items published between 1915 and mid-1968 met the selection criteria. The list of items is supplemented by a keyword index derived from titles. Half the items are journal articles; a third of these articles appeared in the Bulletin, and most of the remainder in thirteen other library and information science journals. Most of the non-journal items are technical reports issued by the organization that conducted or sponsored the work. The characteristics of this literature suggest that few medical libraries, unless they are part of a university system that includes the collection of a library or information science school, are likely to have quick access to the literature base needed to support a comprehensive program of self-evaluation studies and the continuing education of their own staff. Regional medical libraries might well undertake to ensure both ready access to, and awareness of, literature on the scientific aspects of librarianship. PMID:4912761

Guidelines for the selection of chemical-protective clothing. Volume 1. (3rd Edition). Report for January 1985-May 1987

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwope, A.D.; Costas, P.P.; Jackson, J.O.

1987-02-01

A variety of protective-clothing items are commerically available for emergency response and other applications where chemical hazards may be encountered. Data and information for selecting chemical-protective clothing is either not available or is inconsistant from source to source. In 1983, the U.S. Environmental Protection Agency sponsored the development of chemical-protective clothing selection guidelines to assist their own Office of Health and Safety in providing guidance to personnel, primarily EPA employees and contractors, working on hazardous-waste sites. These guidelines allowed a user to select an appropriate protective material for a specific chemical, select a clothing item (glove, suit, etc.) and thenmore » determine which manufacturers offered the clothing item in the selected material. The U.S. Coast Guard Office of Research and Development and the EPA have supplemented these guidelines with additional data on material chemical resistance, material physical properties, clothing design features, and specific-vendor products. A chapter has been added for selecting chemical protective suits. These guidelines contain data for over 750 chemicals and 700 clothing products. Volume I provides the performance information and recommendations for selecting different types of protective clothing.« less

General relaxation schemes in multigrid algorithms for higher order singularity methods

NASA Technical Reports Server (NTRS)

Oskam, B.; Fray, J. M. J.

1981-01-01

Relaxation schemes based on approximate and incomplete factorization technique (AF) are described. The AF schemes allow construction of a fast multigrid method for solving integral equations of the second and first kind. The smoothing factors for integral equations of the first kind, and comparison with similar results from the second kind of equations are a novel item. Application of the MD algorithm shows convergence to the level of truncation error of a second order accurate panel method.

[The effects of cue upon selective memorization].

PubMed

Watanabe, I

1982-10-01

The subjects listened to a list of 21 words read aloud and memorized eight words among them indicated by the cue. In VC (voice-cue) condition, where the change of voice between male and female cued the difference between the to-be-memorized and not-to-be-memorized items, the percentage of correct recall was higher and the number of intrusion-errors was fewer than in NVC (non-voice-cue) condition, where the cue was the sound of chime given immediately before the to-be-memorized item. The results suggest that the physical characteristics of cue facilitate the selective memorization, but do not necessarily support the early-selection theory of attention. Next, in order to confirm Watanabe's (1976) assertion that the transformation of to-be-memorized items into long-term memory and the exclusion of not-to-be-memorized items take place in parallel, the subjects were required to rehearse aloud every word as it was presented. However, it was found that the method of voiced rehearsal was inadequate to test the assertion.

Severity of Organized Item Theft in Computerized Adaptive Testing: A Simulation Study

ERIC Educational Resources Information Center

Yi, Qing; Zhang, Jinming; Chang, Hua-Hua

2008-01-01

Criteria had been proposed for assessing the severity of possible test security violations for computerized tests with high-stakes outcomes. However, these criteria resulted from theoretical derivations that assumed uniformly randomized item selection. This study investigated potential damage caused by organized item theft in computerized adaptive…

Assessment of Preference for Edible and Leisure Items in Individuals with Dementia

ERIC Educational Resources Information Center

Ortega, Javier Virues; Iwata, Brian A.; Nogales-Gonzalez, Celia; Frades, Belen

2012-01-01

We conducted 2 studies on reinforcer preference in patients with dementia. Results of preference assessments yielded differential selections by 14 participants. Unlike prior studies with individuals with intellectual disabilities, all participants showed a noticeable preference for leisure items over edible items. Results of a subsequent analysis…

Evaluating brain-computer interface performance using color in the P300 checkerboard speller.

PubMed

Ryan, D B; Townsend, G; Gates, N A; Colwell, K; Sellers, E W

2017-10-01

Current Brain-Computer Interface (BCI) systems typically flash an array of items from grey to white (GW). The objective of this study was to evaluate BCI performance using uniquely colored stimuli. In addition to the GW stimuli, the current study tested two types of color stimuli (grey to color [GC] and color intensification [CI]). The main hypotheses were that in a checkboard paradigm, unique color stimuli will: (1) increase BCI performance over the standard GW paradigm; (2) elicit larger event-related potentials (ERPs); and, (3) improve offline performance with an electrode selection algorithm (i.e., Jumpwise). Online results (n=36) showed that GC provides higher accuracy and information transfer rate than the CI and GW conditions. Waveform analysis showed that GC produced higher amplitude ERPs than CI and GW. Information transfer rate was improved by the Jumpwise-selected channel locations in all conditions. Unique color stimuli (GC) improved BCI performance and enhanced ERPs. Jumpwise-selected electrode locations improved offline performance. These results show that in a checkerboard paradigm, unique color stimuli increase BCI performance, are preferred by participants, and are important to the design of end-user applications; thus, could lead to an increase in end-user performance and acceptance of BCI technology. Copyright © 2017 International Federation of Clinical Neurophysiology. All rights reserved.

Modeling and system design for the LOFAR station digital processing

NASA Astrophysics Data System (ADS)

Alliot, Sylvain; van Veelen, Martijn

2004-09-01

In the context of the LOFAR preliminary design phase and in particular for the specification of the Station Digital Processing (SDP), a performance/cost model of the system was used. We present here the framework and the trajectory followed in this phase when going from requirements to specification. In the phased array antenna concepts for the next generation of radio telescopes (LOFAR, ATA, SKA) signal processing (multi-beaming and RFI mitigation) replaces the large antenna dishes. The embedded systems for these telescopes are major infrastructure cost items. Moreover, the flexibility and overall performance of the instrument depend greatly on them, therefore alternative solutions need to be investigated. In particular, the technology and the various data transport selections play a fundamental role in the optimization of the architecture. We proposed a formal method [1] of exploring these alternatives that has been followed during the SDP developments. Different scenarios were compared for the specification of the application (selection of the algorithms as well as detailed signal processing techniques) and in the specification of the system architecture (selection of high level topologies, platforms and components). It gave us inside knowledge on the possible trade-offs in the application and architecture domains. This was successful in providing firm basis for the design choices that are demanded by technical review committees.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

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.

Assessing food selection in a health promotion program: validation of a brief instrument for American Indian children in the southwest United States.

PubMed

Koehler, K M; Cunningham-Sabo, L; Lambert, L C; McCalman, R; Skipper, B J; Davis, S M

2000-02-01

Brief dietary assessment instruments are needed to evaluate behavior changes of participants in dietary intervention programs. The purpose of this project was to design and validate an instrument for children participating in Pathways to Health, a culturally appropriate, cancer prevention curriculum. Validation of a brief food selection instrument, Yesterday's Food Choices (YFC), which contained 33 questions about foods eaten the previous day with response choices of yes, no, or not sure. Reference data for validation were 24-hour dietary recalls administered individually to 120 students selected randomly. The YFC and 24-hour dietary recalls were administered to American Indian children in fifth- and seventh-grade classes in the Southwest United States. Dietary recalls were coded for food items in the YFC and results were compared for each item using percentage agreement and the kappa statistic. Percentage agreement for all items was greater than 60%; for most items it was greater than 70%, and for several items it was greater than 80%. The amount of agreement beyond that explained by chance (kappa statistic) was generally small. Three items showed substantial agreement beyond chance (kappa > or = 0.6); 2 items showed moderate agreement (kappa = 0.40 to 0.59) most items showed fair agreement (kappa = 0.20 to 0.39). The food items showing substantial agreement were hot or cold cereal, low-fat milk, and mutton or chile stew. Fried or scrambled eggs and deep-fried foods showed moderate agreement beyond chances. Previous development and validation of brief food selection instruments for children participating in health promotion programs has had limited success. In this study, instrument-related factors that apparently contributed to poor agreement between data from the YFC and 24-hour dietary recall were inclusion of categories of foods vs specific foods; food knowledge, preparation, and vocabulary, item length, and overreporting of attractive foods. Collecting and scoring the 24-hour recall data may also have contributed to poor agreement. Further development of brief instruments for evaluating changes in children's behavior in dietary programs is necessary. Factors related to the YFC that need further development may be issues that are also important in the development of effective, brief dietary assessments for children as individual clients or patients.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Leveling the playing field: attention mitigates the effects of intelligence on memory.

PubMed

Markant, Julie; Amso, Dima

2014-05-01

Effective attention and memory skills are fundamental to typical development and essential for achievement during the formal education years. It is critical to identify the specific mechanisms linking efficiency of attentional selection of an item and the quality of its memory retention. The present study capitalized on the spatial cueing paradigm to examine the role of selection via suppression in modulating children and adolescents' memory encoding. By varying a single parameter, the spatial cueing task can elicit either a simple orienting mechanism (i.e., facilitation) or one that involves both target selection and simultaneous suppression of competing information (i.e., IOR). We modified this paradigm to include images of common items in target locations. Participants were not instructed to learn the items and were not told they would be completing a memory test later. Following the cueing task, we imposed a 7-min delay and then asked participants to complete a recognition memory test. Results indicated that selection via suppression promoted recognition memory among 7-17year-olds. Moreover, individual differences in the extent of suppression during encoding predicted recognition memory accuracy. When basic cueing facilitated orienting to target items during encoding, IQ was the best predictor of recognition memory performance for the attended items. In contrast, engaging suppression (i.e., IOR) during encoding counteracted individual differences in intelligence, effectively improving recognition memory performance among children with lower IQs. This work demonstrates that engaging selection via suppression during learning and encoding improves memory retention and has broad implications for developing effective educational techniques. Copyright © 2014 Elsevier B.V. All rights reserved.

Leveling the playing field: Attention mitigates the effects of intelligence on memory

PubMed Central

Markant, Julie; Amso, Dima

2014-01-01

Effective attention and memory skills are fundamental to typical development and essential for achievement during the formal education years. It is critical to identify the specific mechanisms linking efficiency of attentional selection of an item and the quality of its memory retention. The present study capitalized on the spatial cueing paradigm to examine the role of selection via suppression in modulating children and adolescents’ memory encoding. By varying a single parameter, the spatial cueing task can elicit either a simple orienting mechanism (i.e., facilitation) or one that involves both target selection and simultaneous suppression of competing information (i.e., IOR). We modified this paradigm to include images of common items in target locations. Participants were not instructed to learn the items and were not told they would be completing a memory test later. Following the cueing task, we imposed a seven-minute delay and then asked participants to complete a recognition memory test. Results indicated that selection via suppression promoted recognition memory among 7-17 year-olds. Moreover, individual differences in the extent of suppression during encoding predicted recognition memory accuracy. When basic cueing facilitated orienting to target items during encoding, IQ was the best predictor of recognition memory performance for the attended items. In contrast, engaging suppression (i.e, IOR) during encoding counteracted individual differences in intelligence, effectively improving recognition memory performance among children with lower IQs. This work demonstrates that engaging selection via suppression during learning and encoding improves memory retention and has broad implications for developing effective educational techniques. PMID:24549142

The Development of Memory Efficiency and Value-Directed Remembering Across the Lifespan: A Cross-Sectional Study of Memory and Selectivity

PubMed Central

Castel, Alan D.; Humphreys, Kathryn L.; Lee, Steve S.; Galván, Adriana; Balota, David A.; McCabe, David P.

2012-01-01

Although attentional control and memory change considerably across the lifespan, no research has examined how the ability to strategically remember important information (i.e., value-directed remembering) changes from childhood to old age. The present study examined this in different age groups across the lifespan (N=320, 5 to 96 years old). We employed a selectivity task where participants were asked to study and recall items worth different point values in order to maximize their point score. This procedure allowed for measures of memory quantity/capacity (number of words recalled) and memory efficiency/selectivity (the recall of high-value items relative to low-value items). Age-related differences were found for memory capacity, as young adults recalled more words than the other groups. However, in terms of selectivity, younger and older adults were more selective than adolescents and children. The dissociation between these measures across the lifespan illustrates important age-related differences in terms of memory capacity and the ability to selectively remember high-value information. PMID:21942664

The development of memory efficiency and value-directed remembering across the life span: a cross-sectional study of memory and selectivity.

PubMed

Castel, Alan D; Humphreys, Kathryn L; Lee, Steve S; Galván, Adriana; Balota, David A; McCabe, David P

2011-11-01

Although attentional control and memory change considerably across the life span, no research has examined how the ability to strategically remember important information (i.e., value-directed remembering) changes from childhood to old age. The present study examined this in different age groups across the life span (N = 320, 5-96 years old). A selectivity task was used in which participants were asked to study and recall items worth different point values in order to maximize their point score. This procedure allowed for measures of memory quantity/capacity (number of words recalled) and memory efficiency/selectivity (the recall of high-value items relative to low-value items). Age-related differences were found for memory capacity, as young adults recalled more words than the other groups. However, in terms of selectivity, younger and older adults were more selective than adolescents and children. The dissociation between these measures across the life span illustrates important age-related differences in terms of memory capacity and the ability to selectively remember high-value information.

Which kind of psychometrics is adequate for patient satisfaction questionnaires?

PubMed

Konerding, Uwe

2016-01-01

The construction and psychometric analysis of patient satisfaction questionnaires are discussed. The discussion is based upon the classification of multi-item questionnaires into scales or indices. Scales consist of items that describe the effects of the latent psychological variable to be measured, and indices consist of items that describe the causes of this variable. Whether patient satisfaction questionnaires should be constructed and analyzed as scales or as indices depends upon the purpose for which these questionnaires are required. If the final aim is improving care with regard to patients' preferences, then these questionnaires should be constructed and analyzed as indices. This implies two requirements: 1) items for patient satisfaction questionnaires should be selected in such a way that the universe of possible causes of patient satisfaction is covered optimally and 2) Cronbach's alpha, principal component analysis, exploratory factor analysis, confirmatory factor analysis, and analyses with models from item response theory, such as the Rasch Model, should not be applied for psychometric analyses. Instead, multivariate regression analyses with a direct rating of patient satisfaction as the dependent variable and the individual questionnaire items as independent variables should be performed. The coefficients produced by such an analysis can be applied for selecting the best items and for weighting the selected items when a sum score is determined. The lower boundaries of the validity of the unweighted and the weighted sum scores can be estimated by their correlations with the direct satisfaction rating. While the first requirement is fulfilled in the majority of the previous patient satisfaction questionnaires, the second one deviates from previous practice. Hence, if patient satisfaction is actually measured with the final aim of improving care with regard to patients' preferences, then future practice should be changed so that the second requirement is also fulfilled.

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

NASA Astrophysics Data System (ADS)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

Bibliographie annotee 4-6--Francais langue seconde-immersion: Selection d'ouvrages de la litterature jeunesse. Supplement 2001 (Annotated Bibliography 4-6--French as a Second Language-Immersion: Selection of Works from Children's Literature. Supplement 2001).

ERIC Educational Resources Information Center

Alberta Learning, Edmonton. Direction de l'education francaise.

The document comprises 42 inserts to be included in the List of Books (4-6) for French as a Second Language (Immersion)--a selection of works of youth literature--published by Alberta Learning in 2000. Thirty-seven of the items appeal to the world of imagination and esthetics, and five items are of an informative nature. An appendix, entitled "How…

Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm

PubMed Central

Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya

2014-01-01

A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. PMID:25093195

Automated identification of retained surgical items in radiological images

NASA Astrophysics Data System (ADS)

Agam, Gady; Gan, Lin; Moric, Mario; Gluncic, Vicko

2015-03-01

Retained surgical items (RSIs) in patients is a major operating room (OR) patient safety concern. An RSI is any surgical tool, sponge, needle or other item inadvertently left in a patients body during the course of surgery. If left undetected, RSIs may lead to serious negative health consequences such as sepsis, internal bleeding, and even death. To help physicians efficiently and effectively detect RSIs, we are developing computer-aided detection (CADe) software for X-ray (XR) image analysis, utilizing large amounts of currently available image data to produce a clinically effective RSI detection system. Physician analysis of XRs for the purpose of RSI detection is a relatively lengthy process that may take up to 45 minutes to complete. It is also error prone due to the relatively low acuity of the human eye for RSIs in XR images. The system we are developing is based on computer vision and machine learning algorithms. We address the problem of low incidence by proposing synthesis algorithms. The CADe software we are developing may be integrated into a picture archiving and communication system (PACS), be implemented as a stand-alone software application, or be integrated into portable XR machine software through application programming interfaces. Preliminary experimental results on actual XR images demonstrate the effectiveness of the proposed approach.

Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms.

PubMed

Cheng, Lu; Zhu, Mu; Poss, Jeffrey W; Hirdes, John P; Glenny, Christine; Stolee, Paul

2015-10-09

Resources for home care rehabilitation are limited, and many home care clients who could benefit do not receive rehabilitation therapy. The interRAI Contact Assessment (CA) is a new screening instrument comprised of a subset of interRAI Home Care (HC) items, designed to be used as a preliminary assessment to identify which potential home care clients should be referred for a full assessment, or for services such as rehabilitation. We investigated which client characteristics are most relevant in predicting rehabilitation use in the full interRAI HC assessment. We applied two algorithms from machine learning and data mining - the LASSO and the random forest - to frequency matched interRAI HC and service utilization data for home care clients in Ontario, Canada. Analyses confirmed the importance of functional decline and mobility variables in targeting rehabilitation services, but suggested that other items in use as potential predictors may be less relevant. Six of the most highly ranked items related to ambulation. Diagnosis of cancer was highly associated with decreased rehabilitation use; however, cognitive status was not. Inconsistencies between variables considered important for classifying clients who need rehabilitation and those identified in this study based on use may indicate a discrepancy in the client characteristics considered relevant in theory versus actual practice.

Rare itemsets mining algorithm based on RP-Tree and spark framework

NASA Astrophysics Data System (ADS)

Liu, Sainan; Pan, Haoan

2018-05-01

For the issues of the rare itemsets mining in big data, this paper proposed a rare itemsets mining algorithm based on RP-Tree and Spark framework. Firstly, it arranged the data vertically according to the transaction identifier, in order to solve the defects of scan the entire data set, the vertical datasets are divided into frequent vertical datasets and rare vertical datasets. Then, it adopted the RP-Tree algorithm to construct the frequent pattern tree that contains rare items and generate rare 1-itemsets. After that, it calculated the support of the itemsets by scanning the two vertical data sets, finally, it used the iterative process to generate rare itemsets. The experimental show that the algorithm can effectively excavate rare itemsets and have great superiority in execution time.

Quantum search algorithms on a regular lattice

NASA Astrophysics Data System (ADS)

Hein, Birgit; Tanner, Gregor

2010-07-01

Quantum algorithms for searching for one or more marked items on a d-dimensional lattice provide an extension of Grover’s search algorithm including a spatial component. We demonstrate that these lattice search algorithms can be viewed in terms of the level dynamics near an avoided crossing of a one-parameter family of quantum random walks. We give approximations for both the level splitting at the avoided crossing and the effectively two-dimensional subspace of the full Hilbert space spanning the level crossing. This makes it possible to give the leading order behavior for the search time and the localization probability in the limit of large lattice size including the leading order coefficients. For d=2 and d=3, these coefficients are calculated explicitly. Closed form expressions are given for higher dimensions.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Attainment of Selected Earth Science Concepts by Texas High School Seniors.

ERIC Educational Resources Information Center

Rollins, Mavis M.; And Others

The purpose of this study was to determine whether high school seniors (N=492) had attained each of five selected earth science concepts and if said attainment was influenced by the number of science courses completed. A 72-item, multiple-choice format test (12 items for each concept) was developed and piloted previous to this study to measure…

An Information Analysis of 2-, 3-, and 4-Word Verbal Discrimination Learning.

ERIC Educational Resources Information Center

Arima, James K.; Gray, Francis D.

Information theory was used to qualify the difficulty of verbal discrimination (VD) learning tasks and to measure VD performance. Words for VD items were selected with high background frequency and equal a priori probabilities of being selected as a first response. Three VD lists containing only 2-, 3-, or 4-word items were created and equated for…

Bias in Testing: A Presentation of Selected Methods.

ERIC Educational Resources Information Center

Merz, William R.; Rudner, Lawrence M.

A variety of terms related to test bias or test fairness have been used in a variety of ways, but in this document the "fair use of tests" is defined as equitable selection procedures by means of intact tests, and "test item bias" refers to the study of separate items with respect to the tests of which they are a part. Seven…

A Method for the Comparison of Item Selection Rules in Computerized Adaptive Testing

ERIC Educational Resources Information Center

Barrada, Juan Ramon; Olea, Julio; Ponsoda, Vicente; Abad, Francisco Jose

2010-01-01

In a typical study comparing the relative efficiency of two item selection rules in computerized adaptive testing, the common result is that they simultaneously differ in accuracy and security, making it difficult to reach a conclusion on which is the more appropriate rule. This study proposes a strategy to conduct a global comparison of two or…

Brief Report: Impaired Flexible Item Selection Task (FIST) in School-Age Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Yerys, Benjamin E.; Wolff, Brian C.; Moody, Eric; Pennington, Bruce F.; Hepburn, Susan L.

2012-01-01

Cognitive flexibility has been measured with inductive reasoning or explicit rule tasks in individuals with autism spectrum disorders (ASD). The "Flexible Item Selection Task" (FIST) differs from previous cognitive flexibility tasks in ASD research by giving children an abstract, ambiguous rule to switch. The ASD group (N = 22; Mean age = 8.28…

Sexual Assault Prevention and Response Climate DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

DEOCS, (7) examining variance and descriptive statistics (8) examining the relationship among items/areas to reduce multicollinearity, and (9...selecting items that demonstrate the strongest scale properties. Included is a review of the 4.0 description and items, followed by the proposed...Tables 1 – 7 for the description of each measure and corresponding items. Table 1. DEOCS 4.0 Perceptions of Safety Measure Description

Effect of Item Response Theory (IRT) Model Selection on Testlet-Based Test Equating. Research Report. ETS RR-14-19

ERIC Educational Resources Information Center

Cao, Yi; Lu, Ru; Tao, Wei

2014-01-01

The local item independence assumption underlying traditional item response theory (IRT) models is often not met for tests composed of testlets. There are 3 major approaches to addressing this issue: (a) ignore the violation and use a dichotomous IRT model (e.g., the 2-parameter logistic [2PL] model), (b) combine the interdependent items to form a…

Development of Test Items Related to Selected Concepts Within the Scheme the Particle Nature of Matter.

ERIC Educational Resources Information Center

Doran, Rodney L.; Pella, Milton O.

The purpose of this study was to develop tests items with a minimum reading demand for use with pupils at grade levels two through six. An item was judged to be acceptable if the item satisfied at least four of six criteria. Approximately 250 students in grades 2-6 participated in the study. Half of the students were given instruction to develop…

Structured reporting for fibrosing lung disease: a model shared by radiologist and pulmonologist.

PubMed

Sverzellati, Nicola; Odone, Anna; Silva, Mario; Polverosi, Roberta; Florio, Carlo; Cardinale, Luciano; Cortese, Giancarlo; Addonisio, Giancarlo; Zompatori, Maurizio; Dalpiaz, Giorgia; Piciucchi, Sara; Larici, Anna Rita

2018-04-01

To apply the Delphi exercise with iterative involvement of radiologists and pulmonologists with the aim of defining a structured reporting template for high-resolution computed tomography (HRCT) of patients with fibrosing lung disease (FLD). The writing committee selected the HRCT criteria-the Delphi items-for rating from both radiology panelists (RP) and pulmonology panelists (PP). The Delphi items were first rated by RPs as "essential", "optional", or "not relevant". The items rated "essential" by < 80% of the RP were selected for the PP rating. The format of reporting was rated by both RP and PP. A total of 42 RPs and 12 PPs participated to the survey. In both Delphi round 1 and 2, 10/27 (37.7%) items were rated "essential" by more than 80% of RP. The remaining 17/27 (63.3%) items were rated by the PP in round 3, with 2/17 items (11.7%) rated "essential" by the PP. PP proposed additional items for conclusion domain, which were rated by RPs in the fourth round. Poor consensus was observed for the format of reporting. This study provides a template for structured report of FLD that features essential items as agreed by expert thoracic radiologists and pulmonologists.

Item response theory - A first approach

NASA Astrophysics Data System (ADS)

Nunes, Sandra; Oliveira, Teresa; Oliveira, Amílcar

2017-07-01

The Item Response Theory (IRT) has become one of the most popular scoring frameworks for measurement data, frequently used in computerized adaptive testing, cognitively diagnostic assessment and test equating. According to Andrade et al. (2000), IRT can be defined as a set of mathematical models (Item Response Models - IRM) constructed to represent the probability of an individual giving the right answer to an item of a particular test. The number of Item Responsible Models available to measurement analysis has increased considerably in the last fifteen years due to increasing computer power and due to a demand for accuracy and more meaningful inferences grounded in complex data. The developments in modeling with Item Response Theory were related with developments in estimation theory, most remarkably Bayesian estimation with Markov chain Monte Carlo algorithms (Patz & Junker, 1999). The popularity of Item Response Theory has also implied numerous overviews in books and journals, and many connections between IRT and other statistical estimation procedures, such as factor analysis and structural equation modeling, have been made repeatedly (Van der Lindem & Hambleton, 1997). As stated before the Item Response Theory covers a variety of measurement models, ranging from basic one-dimensional models for dichotomously and polytomously scored items and their multidimensional analogues to models that incorporate information about cognitive sub-processes which influence the overall item response process. The aim of this work is to introduce the main concepts associated with one-dimensional models of Item Response Theory, to specify the logistic models with one, two and three parameters, to discuss some properties of these models and to present the main estimation procedures.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

NASA Astrophysics Data System (ADS)

Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

2013-06-01

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Geometrical pose and structural estimation from a single image for automatic inspection of filter components

NASA Astrophysics Data System (ADS)

Liu, Yonghuai; Rodrigues, Marcos A.

2000-03-01

This paper describes research on the application of machine vision techniques to a real time automatic inspection task of air filter components in a manufacturing line. A novel calibration algorithm is proposed based on a special camera setup where defective items would show a large calibration error. The algorithm makes full use of rigid constraints derived from the analysis of geometrical properties of reflected correspondence vectors which have been synthesized into a single coordinate frame and provides a closed form solution to the estimation of all parameters. For a comparative study of performance, we also developed another algorithm based on this special camera setup using epipolar geometry. A number of experiments using synthetic data have shown that the proposed algorithm is generally more accurate and robust than the epipolar geometry based algorithm and that the geometric properties of reflected correspondence vectors provide effective constraints to the calibration of rigid body transformations.

Item selection via Bayesian IRT models.

PubMed

Arima, Serena

2015-02-10

With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan. Copyright © 2014 John Wiley & Sons, Ltd.

A Comparative Study of Optimization Algorithms for Engineering Synthesis.

DTIC Science & Technology

1983-03-01

the ADS program demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular...demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular problem. 4 TABLE OF...algorithm to suit a particular problem. The ADS library of design optimization algorithms was . developed by Vanderplaats in response to the first

Development and validation of a socioculturally competent trust in physician scale for a developing country setting.

PubMed

Gopichandran, Vijayaprasad; Wouters, Edwin; Chetlapalli, Satish Kumar

2015-05-03

Trust in physicians is the unwritten covenant between the patient and the physician that the physician will do what is in the best interest of the patient. This forms the undercurrent of all healthcare relationships. Several scales exist for assessment of trust in physicians in developed healthcare settings, but to our knowledge none of these have been developed in a developing country context. To develop and validate a new trust in physician scale for a developing country setting. Dimensions of trust in physicians, which were identified in a previous qualitative study in the same setting, were used to develop a scale. This scale was administered among 616 adults selected from urban and rural areas of Tamil Nadu, south India, using a multistage sampling cross sectional survey method. The individual items were analysed using a classical test approach as well as item response theory. Cronbach's α was calculated and the item to total correlation of each item was assessed. After testing for unidimensionality and absence of local dependence, a 2 parameter logistic Semajima's graded response model was fit and item characteristics assessed. Competence, assurance of treatment, respect for the physician and loyalty to the physician were important dimensions of trust. A total of 31 items were developed using these dimensions. Of these, 22 were selected for final analysis. The Cronbach's α was 0.928. The item to total correlations were acceptable for all the 22 items. The item response analysis revealed good item characteristic curves and item information for all the items. Based on the item parameters and item information, a final 12 item scale was developed. The scale performs optimally in the low to moderate trust range. The final 12 item trust in physician scale has a good construct validity and internal consistency. 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.

Development and validation of a socioculturally competent trust in physician scale for a developing country setting

PubMed Central

Gopichandran, Vijayaprasad; Wouters, Edwin; Chetlapalli, Satish Kumar

2015-01-01

Trust in physicians is the unwritten covenant between the patient and the physician that the physician will do what is in the best interest of the patient. This forms the undercurrent of all healthcare relationships. Several scales exist for assessment of trust in physicians in developed healthcare settings, but to our knowledge none of these have been developed in a developing country context. Objectives To develop and validate a new trust in physician scale for a developing country setting. Methods Dimensions of trust in physicians, which were identified in a previous qualitative study in the same setting, were used to develop a scale. This scale was administered among 616 adults selected from urban and rural areas of Tamil Nadu, south India, using a multistage sampling cross sectional survey method. The individual items were analysed using a classical test approach as well as item response theory. Cronbach's α was calculated and the item to total correlation of each item was assessed. After testing for unidimensionality and absence of local dependence, a 2 parameter logistic Semajima's graded response model was fit and item characteristics assessed. Results Competence, assurance of treatment, respect for the physician and loyalty to the physician were important dimensions of trust. A total of 31 items were developed using these dimensions. Of these, 22 were selected for final analysis. The Cronbach's α was 0.928. The item to total correlations were acceptable for all the 22 items. The item response analysis revealed good item characteristic curves and item information for all the items. Based on the item parameters and item information, a final 12 item scale was developed. The scale performs optimally in the low to moderate trust range. Conclusions The final 12 item trust in physician scale has a good construct validity and internal consistency. PMID:25941182

‘Forget me (not)?’ – Remembering Forget-Items Versus Un-Cued Items in Directed Forgetting

PubMed Central

Zwissler, Bastian; Schindler, Sebastian; Fischer, Helena; Plewnia, Christian; Kissler, Johanna M.

2015-01-01

Humans need to be able to selectively control their memories. This capability is often investigated in directed forgetting (DF) paradigms. In item-method DF, individual items are presented and each is followed by either a forget- or remember-instruction. On a surprise test of all items, memory is then worse for to-be-forgotten items (TBF) compared to to-be-remembered items (TBR). This is thought to result mainly from selective rehearsal of TBR, although inhibitory mechanisms also appear to be recruited by this paradigm. Here, we investigate whether the mnemonic consequences of a forget instruction differ from the ones of incidental encoding, where items are presented without a specific memory instruction. Four experiments were conducted where un-cued items (UI) were interspersed and recognition performance was compared between TBR, TBF, and UI stimuli. Accuracy was encouraged via a performance-dependent monetary bonus. Experiments varied the number of items and their presentation speed and used either letter-cues or symbolic cues. Across all experiments, including perceptually fully counterbalanced variants, memory accuracy for TBF was reduced compared to TBR, but better than for UI. Moreover, participants made consistently fewer false alarms and used a very conservative response criterion when responding to TBF stimuli. Thus, the F-cue results in active processing and reduces false alarm rate, but this does not impair recognition memory beyond an un-cued baseline condition, where only incidental encoding occurs. Theoretical implications of these findings are discussed. PMID:26635657

Identifying relevant hyperspectral bands using Boruta: a temporal analysis of water hyacinth biocontrol

NASA Astrophysics Data System (ADS)

Agjee, Na'eem Hoosen; Ismail, Riyad; Mutanga, Onisimo

2016-10-01

Water hyacinth plants (Eichhornia crassipes) are threatening freshwater ecosystems throughout Africa. The Neochetina spp. weevils are seen as an effective solution that can combat the proliferation of the invasive alien plant. We aimed to determine if multitemporal hyperspectral data could be utilized to detect the efficacy of the biocontrol agent. The random forest (RF) algorithm was used to classify variable infestation levels for 6 weeks using: (1) all the hyperspectral bands, (2) bands selected by the recursive feature elimination (RFE) algorithm, and (3) bands selected by the Boruta algorithm. Results showed that the RF model using all the bands successfully produced low-classification errors (12.50% to 32.29%) for all 6 weeks. However, the RF model using Boruta selected bands produced lower classification errors (8.33% to 15.62%) than the RF model using all the bands or bands selected by the RFE algorithm (11.25% to 21.25%) for all 6 weeks, highlighting the utility of Boruta as an all relevant band selection algorithm. All relevant bands selected by Boruta included: 352, 754, 770, 771, 775, 781, 782, 783, 786, and 789 nm. It was concluded that RF coupled with Boruta band-selection algorithm can be utilized to undertake multitemporal monitoring of variable infestation levels on water hyacinth plants.

A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection

PubMed Central

Sale, Mark; Sherer, Eric A

2015-01-01

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection. PMID:23772792

Developing operation algorithms for vision subsystems in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Shikhman, M. V.; Shidlovskiy, S. V.

2018-05-01

The paper analyzes algorithms for selecting keypoints on the image for the subsequent automatic detection of people and obstacles. The algorithm is based on the histogram of oriented gradients and the support vector method. The combination of these methods allows successful selection of dynamic and static objects. The algorithm can be applied in various autonomous mobile robots.

What I Wish I Knew about Assessment.

ERIC Educational Resources Information Center

Guion, Robert M.

Reflecting on a career spent in assessment in personnel selection and managment, the author lists 12 things (items) that he wishes he knew about assessment. The first four items are underdeveloped ideas set aside because of the field's preoccupation with equal employment opportunity. A second set (items five through eight) comes from intellectual…

Decisions that Make a Difference in Detecting Differential Item Functioning

ERIC Educational Resources Information Center

Sireci, Stephen G.; Rios, Joseph A.

2013-01-01

There are numerous statistical procedures for detecting items that function differently across subgroups of examinees that take a test or survey. However, in endeavouring to detect items that may function differentially, selection of the statistical method is only one of many important decisions. In this article, we discuss the important decisions…

A Multilevel Testlet Model for Dual Local Dependence

ERIC Educational Resources Information Center

Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying

2012-01-01

The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced.…

Communication Apprehension: A Preliminary Bibliography of Research.

ERIC Educational Resources Information Center

Daly, John A.

This 243-item bibliography represents a preliminary step in the collection and synthesis of research in communication apprehension. No qualitative criteria were used in the selection of items for this bibliography, except that the item had to deal with apprehension as a major part of the report. Basic texts that mention apprehension but that do…

Are Learning Disabled Students "Test-Wise?": An Inquiry into Reading Comprehension Test Items.

ERIC Educational Resources Information Center

Scruggs, Thomas E.; Lifson, Steve

The ability to correctly answer reading comprehension test items, without having read the accompanying reading passage, was compared for third grade learning disabled students and their peers from a regular classroom. In the first experiment, fourteen multiple choice items were selected from the Stanford Achievement Test. No reading passages were…

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

Development of a gambling addictive behavior scale for adolescents in Korea.

PubMed

Park, Hyun Sook; Jung, Sun Young

2012-12-01

This study was conducted to develop a gambling addictive behavior scale for adolescents. The process involved construction of a conceptual framework, initial item search, verification of content validity, selection of secondary items, and extraction of final items. The participants were 299 adolescents from two middle schools and four high schools. Item analysis, factor analysis, criterion validity, internal consistency, and ROC curve were used to analyze the data. For the final scale, 25 items were selected, and categorized into 4 factors which accounted for 54.9% of the total variance. The factors were labeled as loss of control, life dysfunction from gambling addiction, gambling experience, and social dysfunction from problem gambling. The scores for the scale were significantly correlated with addictive personality, irrational gambling belief, and adolescent's gambling addictive behavior. Cronbach's alpha coefficient for the 25 items was .94. Scale scores identified adolescents as being in a problem gambling group, a non-problem gambling group, and a non-gambling group by the ROC curve. The above findings indicate that the gambling addictive behavior scale has good validity and reliability and can be used with adolescents in Korea.

A new algorithm to build bridges between two patient-reported health outcome instruments: the MOS SF-36® and the VR-12 Health Survey.

PubMed

Selim, Alfredo; Rogers, William; Qian, Shirley; Rothendler, James A; Kent, Erin E; Kazis, Lewis E

2018-04-19

To develop bridging algorithms to score the Veterans Rand-12 (VR-12) scales for comparability to those of the SF-36® for facilitating multi-cohort studies using data from the National Cancer Institute Surveillance, Epidemiology, and End Results Program (SEER) linked to Medicare Health Outcomes Survey (MHOS), and to provide a model for minimizing non-statistical error in pooled analyses stemming from changes to survey instruments over time. Observational study of MHOS cohorts 1-12 (1998-2011). We modeled 2-year follow-up SF-36 scale scores from cohorts 1-6 based on baseline SF-36 scores, age, and gender, yielding 100 clusters using Classification and Regression Trees. Within each cluster, we averaged follow-up SF-36 scores. Using the same cluster specifications, expected follow-up SF-36 scores, based on cohorts 1-6, were computed for cohorts 7-8 (where the VR-12 was the follow-up survey). We created a new criterion validity measure, termed "extensibility," calculated from the square root of the mean square difference between expected SF-36 scale averages and observed VR-12 item score from cohorts 7-8, weighted by cluster size. VR-12 items were rescored to minimize this quantity. Extensibility of rescored VR-12 items and scales was considerably improved from the "simple" scoring method for comparability to the SF-36 scales. The algorithms are appropriate across a wide range of potential subsamples within the MHOS and provide robust application for future studies that span the SF-36 and VR-12 eras. It is possible that these surveys in a different setting outside the MHOS, especially in younger age groups, could produce somewhat different results.

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Indicators of Family Care for Development for Use in Multicountry Surveys

PubMed Central

Kariger, Patricia; Engle, Patrice; Britto, Pia M. Rebello; Sywulka, Sara M.; Menon, Purnima

2012-01-01

Indicators of family care for development are essential for ascertaining whether families are providing their children with an environment that leads to positive developmental outcomes. This project aimed to develop indicators from a set of items, measuring family care practices and resources important for caregiving, for use in epidemiologic surveys in developing countries. A mixed method (quantitative and qualitative) design was used for item selection and evaluation. Qualitative and quantitative analyses were conducted to examine the validity of candidate items in several country samples. Qualitative methods included the use of global expert panels to identify and evaluate the performance of each candidate item as well as in-country focus groups to test the content validity of the items. The quantitative methods included analyses of item-response distributions, using bivariate techniques. The selected items measured two family care practices (support for learning/stimulating environment and limit-setting techniques) and caregiving resources (adequacy of the alternate caregiver when the mother worked). Six play-activity items, indicative of support for learning/stimulating environment, were included in the core module of UNICEF's Multiple Cluster Indictor Survey 3. The other items were included in optional modules. This project provided, for the first time, a globally-relevant set of items for assessing family care practices and resources in epidemiological surveys. These items have multiple uses, including national monitoring and cross-country comparisons of the status of family care for development used globally. The obtained information will reinforce attention to efforts to improve the support for development of children. PMID:23304914

Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation.

PubMed

Berthon, Beatrice; Spezi, Emiliano; Galavis, Paulina; Shepherd, Tony; Apte, Aditya; Hatt, Mathieu; Fayad, Hadi; De Bernardi, Elisabetta; Soffientini, Chiara D; Ross Schmidtlein, C; El Naqa, Issam; Jeraj, Robert; Lu, Wei; Das, Shiva; Zaidi, Habib; Mawlawi, Osama R; Visvikis, Dimitris; Lee, John A; Kirov, Assen S

2017-08-01

The aim of this paper is to define the requirements and describe the design and implementation of a standard benchmark tool for evaluation and validation of PET-auto-segmentation (PET-AS) algorithms. This work follows the recommendations of Task Group 211 (TG211) appointed by the American Association of Physicists in Medicine (AAPM). The recommendations published in the AAPM TG211 report were used to derive a set of required features and to guide the design and structure of a benchmarking software tool. These items included the selection of appropriate representative data and reference contours obtained from established approaches and the description of available metrics. The benchmark was designed in a way that it could be extendable by inclusion of bespoke segmentation methods, while maintaining its main purpose of being a standard testing platform for newly developed PET-AS methods. An example of implementation of the proposed framework, named PETASset, was built. In this work, a selection of PET-AS methods representing common approaches to PET image segmentation was evaluated within PETASset for the purpose of testing and demonstrating the capabilities of the software as a benchmark platform. A selection of clinical, physical, and simulated phantom data, including "best estimates" reference contours from macroscopic specimens, simulation template, and CT scans was built into the PETASset application database. Specific metrics such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity (S), were included to allow the user to compare the results of any given PET-AS algorithm to the reference contours. In addition, a tool to generate structured reports on the evaluation of the performance of PET-AS algorithms against the reference contours was built. The variation of the metric agreement values with the reference contours across the PET-AS methods evaluated for demonstration were between 0.51 and 0.83, 0.44 and 0.86, and 0.61 and 1.00 for DSC, PPV, and the S metric, respectively. Examples of agreement limits were provided to show how the software could be used to evaluate a new algorithm against the existing state-of-the art. PETASset provides a platform that allows standardizing the evaluation and comparison of different PET-AS methods on a wide range of PET datasets. The developed platform will be available to users willing to evaluate their PET-AS methods and contribute with more evaluation datasets. © 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

A polarized low-coherence interferometry demodulation algorithm by recovering the absolute phase of a selected monochromatic frequency.

PubMed

Jiang, Junfeng; Wang, Shaohua; Liu, Tiegen; Liu, Kun; Yin, Jinde; Meng, Xiange; Zhang, Yimo; Wang, Shuang; Qin, Zunqi; Wu, Fan; Li, Dingjie

2012-07-30

A demodulation algorithm based on absolute phase recovery of a selected monochromatic frequency is proposed for optical fiber Fabry-Perot pressure sensing system. The algorithm uses Fourier transform to get the relative phase and intercept of the unwrapped phase-frequency linear fit curve to identify its interference-order, which are then used to recover the absolute phase. A simplified mathematical model of the polarized low-coherence interference fringes was established to illustrate the principle of the proposed algorithm. Phase unwrapping and the selection of monochromatic frequency were discussed in detail. Pressure measurement experiment was carried out to verify the effectiveness of the proposed algorithm. Results showed that the demodulation precision by our algorithm could reach up to 0.15kPa, which has been improved by 13 times comparing with phase slope based algorithm.

Quantum Search in Hilbert Space

NASA Technical Reports Server (NTRS)

Zak, Michail

2003-01-01

A proposed quantum-computing algorithm would perform a search for an item of information in a database stored in a Hilbert-space memory structure. The algorithm is intended to make it possible to search relatively quickly through a large database under conditions in which available computing resources would otherwise be considered inadequate to perform such a task. The algorithm would apply, more specifically, to a relational database in which information would be stored in a set of N complex orthonormal vectors, each of N dimensions (where N can be exponentially large). Each vector would constitute one row of a unitary matrix, from which one would derive the Hamiltonian operator (and hence the evolutionary operator) of a quantum system. In other words, all the stored information would be mapped onto a unitary operator acting on a quantum state that would represent the item of information to be retrieved. Then one could exploit quantum parallelism: one could pose all search queries simultaneously by performing a quantum measurement on the system. In so doing, one would effectively solve the search problem in one computational step. One could exploit the direct- and inner-product decomposability of the unitary matrix to make the dimensionality of the memory space exponentially large by use of only linear resources. However, inasmuch as the necessary preprocessing (the mapping of the stored information into a Hilbert space) could be exponentially expensive, the proposed algorithm would likely be most beneficial in applications in which the resources available for preprocessing were much greater than those available for searching.

Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

PubMed

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-03-12

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

Mathematical Optimization Algorithm for Minimizing the Cost Function of GHG Emission in AS/RS Using Positive Selection Based Clonal Selection Principle

NASA Astrophysics Data System (ADS)

Mahalakshmi; Murugesan, R.

2018-04-01

This paper regards with the minimization of total cost of Greenhouse Gas (GHG) efficiency in Automated Storage and Retrieval System (AS/RS). A mathematical model is constructed based on tax cost, penalty cost and discount cost of GHG emission of AS/RS. A two stage algorithm namely positive selection based clonal selection principle (PSBCSP) is used to find the optimal solution of the constructed model. In the first stage positive selection principle is used to reduce the search space of the optimal solution by fixing a threshold value. In the later stage clonal selection principle is used to generate best solutions. The obtained results are compared with other existing algorithms in the literature, which shows that the proposed algorithm yields a better result compared to others.

Use of non-parametric item response theory to develop a shortened version of the Positive and Negative Syndrome Scale (PANSS).

PubMed

Khan, Anzalee; Lewis, Charles; Lindenmayer, Jean-Pierre

2011-11-16

Nonparametric item response theory (IRT) was used to examine (a) the performance of the 30 Positive and Negative Syndrome Scale (PANSS) items and their options ((levels of severity), (b) the effectiveness of various subscales to discriminate among differences in symptom severity, and (c) the development of an abbreviated PANSS (Mini-PANSS) based on IRT and a method to link scores to the original PANSS. Baseline PANSS scores from 7,187 patients with Schizophrenia or Schizoaffective disorder who were enrolled between 1995 and 2005 in psychopharmacology trials were obtained. Option characteristic curves (OCCs) and Item Characteristic Curves (ICCs) were constructed to examine the probability of rating each of seven options within each of 30 PANSS items as a function of subscale severity, and summed-score linking was applied to items selected for the Mini-PANSS. The majority of items forming the Positive and Negative subscales (i.e. 19 items) performed very well and discriminate better along symptom severity compared to the General Psychopathology subscale. Six of the seven Positive Symptom items, six of the seven Negative Symptom items, and seven out of the 16 General Psychopathology items were retained for inclusion in the Mini-PANSS. Summed score linking and linear interpolation was able to produce a translation table for comparing total subscale scores of the Mini-PANSS to total subscale scores on the original PANSS. Results show scores on the subscales of the Mini-PANSS can be linked to scores on the original PANSS subscales, with very little bias. The study demonstrated the utility of non-parametric IRT in examining the item properties of the PANSS and to allow selection of items for an abbreviated PANSS scale. The comparisons between the 30-item PANSS and the Mini-PANSS revealed that the shorter version is comparable to the 30-item PANSS, but when applying IRT, the Mini-PANSS is also a good indicator of illness severity.