A Sharing Mind Map-Oriented Approach to Enhance Collaborative Mobile Learning with Digital Archiving Systems

ERIC Educational Resources Information Center

Chang, Jui-Hung; Chiu, Po-Sheng; Huang, Yueh-Min

2018-01-01

With the advances in mobile network technology, the use of portable devices and mobile networks for learning is not limited by time and space. Such use, in combination with appropriate learning strategies, can achieve a better effect. Despite the effectiveness of mobile learning, students' learning direction, progress, and achievement may differ.…

The Nerdy Teacher: Pedagogical Identities for a Digital Age

ERIC Educational Resources Information Center

Hull, Glynda; Scott, John; Higgs, Jennifer

2014-01-01

Professional learning around digital media often focuses on tool use and neglects consideration of teachers as interested, creative producers of digital media artifacts. The best way to help teachers learn about and adapt technology in their classrooms is by immersing them in hands-on work in the same way their students use social networks and…

The Role of After-School Digital Media Clubs in Closing Participation Gaps and Expanding Social Networks

ERIC Educational Resources Information Center

Vickery, Jacqueline Ryan

2014-01-01

This article considers how after-school digital media clubs, as an example of informal learning, can provide meaningful opportunities for youth to participate in the creation of interest-driven learning ecologies through media production. Ethnographic research was conducted in two after-school digital media clubs at a large, ethnically diverse,…

Digital Doings: Curating Work-Learning Practices and Ecologies

ERIC Educational Resources Information Center

Thompson, Terrie Lynn

2016-01-01

Workers are faced with wider networks of knowledge generation amplified by the scale, diffusion, and critical mass of digital artefacts and web technologies globally. In this study of mobilities of work-learning practices, I draw on sociomaterial theorizing to explore how the work and everyday learning practices of self-employed workers or…

Informal Learning and Identity Formation in Online Social Networks

ERIC Educational Resources Information Center

Greenhow, Christine; Robelia, Beth

2009-01-01

All students today are increasingly expected to develop technological fluency, digital citizenship, and other twenty-first century competencies despite wide variability in the quality of learning opportunities schools provide. Social network sites (SNSs) available via the internet may provide promising contexts for learning to supplement…

Research and Policy: Can Online Learning Communities Foster Professional Development?

ERIC Educational Resources Information Center

Beach, Richard

2012-01-01

This column posits enhancing professional development through uses of digital tools to create professional learning communities (PLCs) designed to support collective inquiry and action research leading to schoolwide improvement. These digital tools include a social networking/discussion forum for teacher collaboration; teachers' individual…

Handwritten digits recognition based on immune network

NASA Astrophysics Data System (ADS)

Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

2011-11-01

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Building a Personal Learning Network for Intellectual Freedom: Join the Conversation

ERIC Educational Resources Information Center

Keuler, Annalisa

2012-01-01

Building a personal learning network (PLN) for intellectual freedom has long been an important role of a school librarian; however, in the steadily increasing onslaught of digital information that librarians face today, and in the future, the task has become mission-critical. Personal learning, it stands to reason, requires an appropriate dialogue…

Connectivism: A knowledge learning theory for the digital age?

PubMed

Goldie, John Gerard Scott

2016-10-01

The emergence of the internet, particularly Web 2.0 has provided access to the views and opinions of a wide range of individuals opening up opportunities for new forms of communication and knowledge formation. Previous ways of navigating and filtering available information are likely to prove ineffective in these new contexts. Connectivism is one of the most prominent of the network learning theories which have been developed for e-learning environments. It is beginning to be recognized by medical educators. This article aims to examine connectivism and its potential application. The conceptual framework and application of connectivism are presented along with an outline of the main criticisms. Its potential application in medical education is then considered. While connectivism provides a useful lens through which teaching and learning using digital technologies can be better understood and managed, further development and testing is required. There is unlikely to be a single theory that will explain learning in technological enabled networks. Educators have an important role to play in online network learning.

The Effect of Social Interaction on Learning Engagement in a Social Networking Environment

ERIC Educational Resources Information Center

Lu, Jie; Churchill, Daniel

2014-01-01

This study investigated the impact of social interactions among a class of undergraduate students on their learning engagement in a social networking environment. Thirteen undergraduate students enrolled in a course in a university in Hong Kong used an Elgg-based social networking platform throughout a semester to develop their digital portfolios…

Mathematics Education & Digital Technologies: Facing the Challenge of Networking European Research Teams

ERIC Educational Resources Information Center

Bottino, Rosa Maria; Kynigos, Chronis

2009-01-01

This paper introduces the "IJCML" Special Issue dedicated to digital technologies and mathematics education and, in particular, to the work performed by the European Research Team TELMA (Technology Enhanced Learning in Mathematics). TELMA was one of the initiatives of the Kaleidoscope Network of Excellence established by the European…

Cross-domain and multi-task transfer learning of deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

We propose a cross-domain, multi-task transfer learning framework to transfer knowledge learned from non-medical images by a deep convolutional neural network (DCNN) to medical image recognition task while improving the generalization by multi-task learning of auxiliary tasks. A first stage cross-domain transfer learning was initiated from ImageNet trained DCNN to mammography trained DCNN. 19,632 regions-of-interest (ROI) from 2,454 mass lesions were collected from two imaging modalities: digitized-screen film mammography (SFM) and full-field digital mammography (DM), and split into training and test sets. In the multi-task transfer learning, the DCNN learned the mass classification task simultaneously from the training set of SFM and DM. The best transfer network for mammography was selected from three transfer networks with different number of convolutional layers frozen. The performance of single-task and multitask transfer learning on an independent SFM test set in terms of the area under the receiver operating characteristic curve (AUC) was 0.78+/-0.02 and 0.82+/-0.02, respectively. In the second stage cross-domain transfer learning, a set of 12,680 ROIs from 317 mass lesions on DBT were split into validation and independent test sets. We first studied the data requirements for the first stage mammography trained DCNN by varying the mammography training data from 1% to 100% and evaluated its learning on the DBT validation set in inference mode. We found that the entire available mammography set provided the best generalization. The DBT validation set was then used to train only the last four fully connected layers, resulting in an AUC of 0.90+/-0.04 on the independent DBT test set.

Transformational Leadership & Professional Development for Digitally Rich Learning Environments: A Case Study of the Galileo Educational Network.

ERIC Educational Resources Information Center

Jacobsen, Michele; Clifford, Pat; Friesen, Sharon

The Galileo Educational Network is an innovative educational reform initiative that brings learning to learners. Expert teachers work alongside teachers and students in schools to create new images of engaged learning, technology integration and professional development. This case study is based on the nine schools involved with Galileo in…

Putting ECD into Practice: The Interplay of Theory and Data in Evidence Models within a Digital Learning Environment

ERIC Educational Resources Information Center

Rupp, Andre A.; Levy, Roy; Dicerbo, Kristen E.; Sweet, Shauna J.; Crawford, Aaron V.; Calico, Tiago; Benson, Martin; Fay, Derek; Kunze, Katie L.; Mislevy, Robert J.; Behrens, John T.

2012-01-01

In this paper we describe the development and refinement of "evidence rules" and "measurement models" within the "evidence model" of the "evidence-centered design" (ECD) framework in the context of the "Packet Tracer" digital learning environment of the "Cisco Networking Academy." Using…

What Size Is Your Digital Footprint?

ERIC Educational Resources Information Center

Hewson, Kurtis

2013-01-01

The Professional Learning Network (PLN) is gaining momentum in the education lexicon. It records and reflects the personal development of a community of learners--primarily online through a variety of platforms and social networks--in which educators share resources, provide support, introduce and debate ideas and celebrate learning. These…

Are Digital Natives a Myth or Reality? University Students' Use of Digital Technologies

ERIC Educational Resources Information Center

Margaryan, Anoush; Littlejohn, Allison; Vojt, Gabrielle

2011-01-01

This study investigated the extent and nature of university students' use of digital technologies for learning and socialising. The findings show that students use a limited range of mainly established technologies. Use of collaborative knowledge creation tools, virtual worlds, and social networking sites was low. "Digital natives" and students of…

An adaptive deep Q-learning strategy for handwritten digit recognition.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Edmodo social learning network for elementary school mathematics learning

NASA Astrophysics Data System (ADS)

Ariani, Y.; Helsa, Y.; Ahmad, S.; Prahmana, RCI

2017-12-01

A developed instructional media can be as printed media, visual media, audio media, and multimedia. The development of instructional media can also take advantage of technological development by utilizing Edmodo social network. This research aims to develop a digital classroom learning model using Edmodo social learning network for elementary school mathematics learning which is practical, valid and effective in order to improve the quality of learning activities. The result of this research showed that the prototype of mathematics learning device for elementary school students using Edmodo was in good category. There were 72% of students passed the assessment as a result of Edmodo learning. Edmodo has become a promising way to engage students in a collaborative learning process.

Developing Digital Portfolios for Childhood Education. Research Reports.

ERIC Educational Resources Information Center

Kankaanranta, Marja

This action research study developed, explored, and analyzed the use of digital portfolios as a multiperspective ecological assessment method in primary education learning environments in Finland. Participating in the study were kindergarten and primary school teachers who were challenged and encouraged to utilize networking and digital portfolios…

Information Resources Usage in Project Management Digital Learning System

ERIC Educational Resources Information Center

Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

2017-01-01

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…

Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

PubMed

Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

2017-11-01

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neuromorphic Learning From Noisy Data

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Troudet, Terry

1993-01-01

Two reports present numerical study of performance of feedforward neural network trained by back-propagation algorithm in learning continuous-valued mappings from data corrupted by noise. Two types of noise considered: plant noise which affects dynamics of controlled process and data-processing noise, which occurs during analog processing and digital sampling of signals. Study performed with view toward use of neural networks as neurocontrollers to substitute for, or enhance, performances of human experts in controlling mechanical devices in presence of sensor and actuator noise and to enhance performances of more-conventional digital feedback electronic process controllers in noisy environments.

Convergent Technologies in Distance Learning Delivery.

ERIC Educational Resources Information Center

Wheeler, Steve

1999-01-01

Describes developments in British education in distance learning technologies. Highlights include networking the rural areas; communication, community, and paradigm shifts; digital compression techniques and telematics; Web-based material delivered over the Internet; system flexibility; social support; learning support; videoconferencing; and…

Teachers' Self-Initiated Professional Learning through Personal Learning Networks

ERIC Educational Resources Information Center

Tour, Ekaterina

2017-01-01

It is widely acknowledged that to be able to teach language and literacy with digital technologies, teachers need to engage in relevant professional learning. Existing formal models of professional learning are often criticised for being ineffective. In contrast, informal and self-initiated forms of learning have been recently recognised as…

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Exploratory Study on the Digital Identity Formation of Korean University EFL Learners

ERIC Educational Resources Information Center

Lee, Jang Ho; Kim, Heyoung

2014-01-01

The present study aims to sketch the contours of new media ecology for Korean university students as well as to examine how these learners shape and negotiate their digital identity by using social networking services and digital devices. It also investigates their use of digital media for learning English as a foreign language (EFL). In total,…

The Effectiveness of Social Media Network Telegram in Teaching English Language Pronunciation to Iranian EFL Learners

ERIC Educational Resources Information Center

Xodabande, Ismail

2017-01-01

In recent years, the expansion of digital technologies, multimedia, and social networks, dramatically transformed our lives. Education in general and the area of foreign language teaching and learning have also benefited hugely from those developments and advances. As a result, the face of language learning is changing and new technologies provide…

Digital Technologies: From Vision to Action

ERIC Educational Resources Information Center

Armistead, Stuart

2016-01-01

The interest and uptake in utilising digital technologies in education appears to be exponential. With the rollout of ultrafast broadband and the development of the Network for Learning in New Zealand, school leaders face the challenges and opportunity of deciding when, what and how they go about implementing digital technologies in their schools.…

Applications of deep convolutional neural networks to digitized natural history collections.

PubMed

Schuettpelz, Eric; Frandsen, Paul B; Dikow, Rebecca B; Brown, Abel; Orli, Sylvia; Peters, Melinda; Metallo, Adam; Funk, Vicki A; Dorr, Laurence J

2017-01-01

Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools.

Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.

PubMed

Li, Hui; Giger, Maryellen L; Huynh, Benjamin Q; Antropova, Natalia O

2017-10-01

To evaluate deep learning in the assessment of breast cancer risk in which convolutional neural networks (CNNs) with transfer learning are used to extract parenchymal characteristics directly from full-field digital mammographic (FFDM) images instead of using computerized radiographic texture analysis (RTA), 456 clinical FFDM cases were included: a "high-risk" BRCA1/2 gene-mutation carriers dataset (53 cases), a "high-risk" unilateral cancer patients dataset (75 cases), and a "low-risk dataset" (328 cases). Deep learning was compared to the use of features from RTA, as well as to a combination of both in the task of distinguishing between high- and low-risk subjects. Similar classification performances were obtained using CNN [area under the curve [Formula: see text]; standard error [Formula: see text

Designing Personalized Learning Products for Middle School Mathematics: The Case for Networked Learning Games

ERIC Educational Resources Information Center

Evans, Michael A.; Pruett, Jordan; Chang, Mido; Nino, Miguel

2014-01-01

Middle school mathematics education is subject to ongoing reform based on advances in digital instructional technologies, especially learning games, leading to recent calls for investment in "personalized learning." Through an extensive literature review, this investigation identified three priority areas that should be taken into…

Digital books.

PubMed

Wink, Diane M

2011-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, and collaborative writing tools; social networking and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. This article describes digital books.

Writing/Thinking in Real Time: Digital Video and Corpus Query Analysis

ERIC Educational Resources Information Center

Park, Kwanghyun; Kinginger, Celeste

2010-01-01

The advance of digital video technology in the past two decades facilitates empirical investigation of learning in real time. The focus of this paper is the combined use of real-time digital video and a networked linguistic corpus for exploring the ways in which these technologies enhance our capability to investigate the cognitive process of…

Digital Technology Snapshot of the Literacy and Essential Skills Field 2013. Summary Report

ERIC Educational Resources Information Center

Trottier, Vicki

2013-01-01

From January to March 2013, "Canadian Literacy and Learning Network" (CLLN) conducted a snapshot to provide information about how digital technology tools are being used in the Literacy and Essential Skills (L/ES) field. The snapshot focused primarily on digital tools and activities that meet the organizational needs of provincial and…

Turning the Digital Divide into Digital Dividends through Free Content and Open Networks: WikiEducator Learning4Content (L4C) Initiative

ERIC Educational Resources Information Center

Schlicht, Patricia

2013-01-01

In today's world where tuition fees continue to rise rapidly and the demand for higher education increases in both the developing and developed world, it is important to find additional and alternative learning pathways that learners can afford. Traditional education as we have known it has begun to change, allowing for new parallel learning…

The New Engines of Learning.

ERIC Educational Resources Information Center

Negroponto, Nicholas

1995-01-01

According to the author's book "Being Digital," our world is shifting from atoms to bits. Digitally rendered information, combined with personal computing power and networks, will make computers active participants in our everyday lives. "Teaching-disabled" classrooms will move from passivity to active participation and…

Applications of deep convolutional neural networks to digitized natural history collections

PubMed Central

Frandsen, Paul B.; Dikow, Rebecca B.; Brown, Abel; Orli, Sylvia; Peters, Melinda; Metallo, Adam; Funk, Vicki A.; Dorr, Laurence J.

2017-01-01

Abstract Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools. PMID:29200929

The Best of All Worlds: Immersive Interfaces for Art Education in Virtual and Real World Teaching and Learning Environments

ERIC Educational Resources Information Center

Grenfell, Janette

2013-01-01

Selected ubiquitous technologies encourage collaborative participation between higher education students and educators within a virtual socially networked e-learning landscape. Multiple modes of teaching and learning, ranging from real world experiences, to text and digital images accessed within the Deakin studies online learning management…

Interactive Distance Learning in Connecticut.

ERIC Educational Resources Information Center

Pietras, Jesse John; Murphy, Robert J.

This paper provides an overview of distance learning activities in Connecticut and addresses the feasibility of such activities. Distance education programs have evolved from the one dimensional electronic mail systems to the use of sophisticated digital fiber networks. The Middlesex Distance Learning Consortium has developed a long-range plan to…

Integrating Social Networks in Teaching in Higher Education

ERIC Educational Resources Information Center

Abousoliman, Onsy

2017-01-01

In response to the emerging and swiftly developing digital tools, this dissertation investigated integrating a specific category of these tools, social networks, in teaching in higher education. The study focused on exploring how social networks integration might impact the teaching/learning process and on investigating the challenges that could…

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

2017-12-01

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

Top Ten Technology Breakthroughs for Schools.

ERIC Educational Resources Information Center

Bateman, Bill; Crystal, Jerry; Davidson, Hall; Holzberg, Carol S.; McIntire, Todd; McLester, Susan; Ohler, Jason; Rose, Ray; Shields, Jean; Warlick, David

2001-01-01

Contributors discuss the top ten technologies that allow for thinking in new and innovative ways about the concept of "school": virtual learning; wireless networking; collaboration tools; digital video; Application Service Providers; handheld devices; optical networking; videoconferencing; XML; and simulations. (AEF)

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.

PubMed

Liu, Da; Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

PubMed Central

Xu, Ming; Niu, Dongxiao; Wang, Shoukai; Liang, Sai

2016-01-01

Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012. PMID:27281032

Do pre-trained deep learning models improve computer-aided classification of digital mammograms?

NASA Astrophysics Data System (ADS)

Aboutalib, Sarah S.; Mohamed, Aly A.; Zuley, Margarita L.; Berg, Wendie A.; Luo, Yahong; Wu, Shandong

2018-02-01

Digital mammography screening is an important exam for the early detection of breast cancer and reduction in mortality. False positives leading to high recall rates, however, results in unnecessary negative consequences to patients and health care systems. In order to better aid radiologists, computer-aided tools can be utilized to improve distinction between image classifications and thus potentially reduce false recalls. The emergence of deep learning has shown promising results in the area of biomedical imaging data analysis. This study aimed to investigate deep learning and transfer learning methods that can improve digital mammography classification performance. In particular, we evaluated the effect of pre-training deep learning models with other imaging datasets in order to boost classification performance on a digital mammography dataset. Two types of datasets were used for pre-training: (1) a digitized film mammography dataset, and (2) a very large non-medical imaging dataset. By using either of these datasets to pre-train the network initially, and then fine-tuning with the digital mammography dataset, we found an increase in overall classification performance in comparison to a model without pre-training, with the very large non-medical dataset performing the best in improving the classification accuracy.

Managing Digital Learning Environments: Student Teachers' Perception on the Social Networking Services Use in Writing Courses in Teacher Education

ERIC Educational Resources Information Center

Prasojo, Lantip Diat; Habibi, Akhmad; Mukminin, Amirul; Muhaimin; Taridi, Muhammad; Ikhsan; Saudagar, Ferdiaz

2017-01-01

Limited studies have been conducted to examine how effective and what impacts dealing with students' learning experiences as well as the problems faced by the students. This study focused on English student teachers' experiences on the advantages and problems faced in using Social Networking Services (SNS) in English as Foreign Language (EFL)…

Multimedia and the Future of Distance Learning Technology.

ERIC Educational Resources Information Center

Barnard, John

1992-01-01

Describes recent innovations in distance learning technology, including the use of video technology; personal computers, including computer conferencing, computer-mediated communication, and workstations; multimedia, including hypermedia; Integrated Services Digital Networks (ISDN); and fiber optics. Research implications for multimedia and…

Massively Open Online Course for Educators (MOOC-Ed) Network Dataset

ERIC Educational Resources Information Center

Kellogg, Shaun; Edelmann, Achim

2015-01-01

This paper presents the Massively Open Online Course for Educators (MOOC-Ed) network dataset. It entails information on two online communication networks resulting from two consecutive offerings of the MOOC called "The Digital Learning Transition in K-12 Schools" in spring and fall 2013. The courses were offered to educators from the USA…

Digital implementation of a neural network for imaging

NASA Astrophysics Data System (ADS)

Wood, Richard; McGlashan, Alex; Yatulis, Jay; Mascher, Peter; Bruce, Ian

2012-10-01

This paper outlines the design and testing of a digital imaging system that utilizes an artificial neural network with unsupervised and supervised learning to convert streaming input (real time) image space into parameter space. The primary objective of this work is to investigate the effectiveness of using a neural network to significantly reduce the information density of streaming images so that objects can be readily identified by a limited set of primary parameters and act as an enhanced human machine interface (HMI). Many applications are envisioned including use in biomedical imaging, anomaly detection and as an assistive device for the visually impaired. A digital circuit was designed and tested using a Field Programmable Gate Array (FPGA) and an off the shelf digital camera. Our results indicate that the networks can be readily trained when subject to limited sets of objects such as the alphabet. We can also separate limited object sets with rotational and positional invariance. The results also show that limited visual fields form with only local connectivity.

Online Music Collaboration Project: Digitally Mediated, Deterritorialized Music Education

ERIC Educational Resources Information Center

Cremata, Radio; Powell, Bryan

2017-01-01

This article investigates and interrogates notions of student-centered music learning through collaboration in digital spaces. By harnessing the power and potential of Internet networks, one music educator in Miami, FL challenged his students to an online music collaboration project (OMCP) where students were asked to engage in deterritorialized…

1995 Joseph E. Whitley, MD, Award. A World Wide Web gateway to the radiologic learning file.

PubMed

Channin, D S

1995-12-01

Computer networks in general, and the Internet specifically, are changing the way information is manipulated in the world at large and in radiology. The goal of this project was to develop a computer system in which images from the Radiologic Learning File, available previously only via a single-user laser disc, are made available over a generic, high-availability computer network to many potential users simultaneously. Using a networked workstation in our laboratory and freely available distributed hypertext software, we established a World Wide Web (WWW) information server for radiology. Images from the Radiologic Learning File are requested through the WWW client software, digitized from a single laser disc containing the entire teaching file and then transmitted over the network to the client. The text accompanying each image is incorporated into the transmitted document. The Radiologic Learning File is now on-line, and requests to view the cases result in the delivery of the text and images. Image digitization via a frame grabber takes 1/30th of a second. Conversion of the image to a standard computer graphic format takes 45-60 sec. Text and image transmission speed on a local area network varies between 200 and 400 kilobytes (KB) per second depending on the network load. We have made images from a laser disc of the Radiologic Learning File available through an Internet-based hypertext server. The images previously available through a single-user system located in a remote section of our department are now ubiquitously available throughout our department via the department's computer network. We have thus converted a single-user, limited functionality system into a multiuser, widely available resource.

Towards Networked Knowledge: The Learning Registry, an Infrastructure for Sharing Online Learning Resources

ERIC Educational Resources Information Center

Lee, Ashley; Hobson, Joe; Bienkowski, Marie; Midgley, Steve; Currier, Sarah; Campbell, Lorna M.; Novoselova, Tatiana

2012-01-01

In this article, the authors describe an open-source, open-data digital infrastructure for sharing information about open educational resources (OERs) across disparate systems and platforms. The Learning Registry, which began as a project funded by the U.S. Departments of Education and Defense, currently has an active international community…

Use of Web 2.0 Technologies to Enhance Learning Experiences in Alternative School Settings

ERIC Educational Resources Information Center

Karahan, Engin; Roehrig, Gillian

2016-01-01

As the learning paradigms are shifting to include various forms of digital technologies such as synchronous, asynchronous, and interactive methods, social networking technologies have been introduced to the educational settings in order to increase the quality of learning environments. The literature suggests that effective application of these…

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

PubMed Central

Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

PubMed

Pang, Shan; Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

PubMed

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Public and Private Interests in Networking Educational Services for Schools, Households, Communities.

ERIC Educational Resources Information Center

Sheekey, Arthur D.

1997-01-01

Discusses the networking of educational services for schools, homes, and communities. Highlights include equal access; the development of digital technologies; visions for electronic information services; the public sector; the private sector; creating learning communities; and future possibilities, including funding strategies. (LRW)

Digital cleaning and "dirt" layer visualization of an oil painting.

PubMed

Palomero, Cherry May T; Soriano, Maricor N

2011-10-10

We demonstrate a new digital cleaning technique which uses a neural network that is trained to learn the transformation from dirty to clean segments of a painting image. The inputs and outputs of the network are pixels belonging to dirty and clean segments found in Fernando Amorsolo's Malacañang by the River. After digital cleaning we visualize the painting's discoloration by assuming it to be a transmission filter superimposed on the clean painting. Using an RGB color-to-spectrum transformation to obtain the point-per-point spectra of the clean and dirty painting images, we calculate this "dirt" filter and render it for the whole image.

Learning and optimization with cascaded VLSI neural network building-block chips

NASA Technical Reports Server (NTRS)

Duong, T.; Eberhardt, S. P.; Tran, M.; Daud, T.; Thakoor, A. P.

1992-01-01

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

End-to-end learning for digital hologram reconstruction

NASA Astrophysics Data System (ADS)

Xu, Zhimin; Zuo, Si; Lam, Edmund Y.

2018-02-01

Digital holography is a well-known method to perform three-dimensional imaging by recording the light wavefront information originating from the object. Not only the intensity, but also the phase distribution of the wavefront can then be computed from the recorded hologram in the numerical reconstruction process. However, the reconstructions via the traditional methods suffer from various artifacts caused by twin-image, zero-order term, and noise from image sensors. Here we demonstrate that an end-to-end deep neural network (DNN) can learn to perform both intensity and phase recovery directly from an intensity-only hologram. We experimentally show that the artifacts can be effectively suppressed. Meanwhile, our network doesn't need any preprocessing for initialization, and is comparably fast to train and test, in comparison with the recently published learning-based method. In addition, we validate that the performance improvement can be achieved by introducing a prior on sparsity.

Fueling a Third Paradigm of Education: The Pedagogical Implications of Digital, Social and Mobile Media

ERIC Educational Resources Information Center

Pavlik, John V.

2015-01-01

Emerging technologies are fueling a third paradigm of education. Digital, networked and mobile media are enabling a disruptive transformation of the teaching and learning process. This paradigm challenges traditional assumptions that have long characterized educational institutions and processes, including basic notions of space, time, content,…

What Learners "Know" through Digital Media Production: Learning by Design

ERIC Educational Resources Information Center

Mills, Kathy A.

2010-01-01

The power to influence others in ever expanding social networks in the new knowledge economy is tied to capabilities with digital media production that require increased technological knowledge. This article draws on research in primary classrooms to examine the repertoires of cross-disciplinary knowledge that literacy learners need to produce…

The Worldly Space: The Digital University in Network Time

ERIC Educational Resources Information Center

Hassan, Robert

2017-01-01

This article considers the effect of information technology upon teaching, learning and research in the "digital university". In less than a generation the university has become a business like any other. It does so in the determining context of neoliberal globalisation and the computer revolution. The university develops through what we…

Social Media and Networking Technologies: An Analysis of Collaborative Work and Team Communication

ERIC Educational Resources Information Center

Okoro, Ephraim A.; Hausman, Angela; Washington, Melvin C.

2012-01-01

Digital communication increases students' learning outcomes in higher education. Web 2.0 technologies encourages students' active engagement, collaboration, and participation in class activities, facilitates group work, and encourages information sharing among students. Familiarity with organizational use and sharing in social networks aids…

Dialogic e-Learning2learn: Creating Global Digital Networks and Educational Knowledge Building Architectures across Diversity

ERIC Educational Resources Information Center

Sorensen, Elsebeth Korsgaard

2007-01-01

Purpose: The purpose of this paper is to address the challenge and potential of online higher and continuing education, of fostering and promoting, in a global perspective across time and space, democratic values working for a better world. Design/methodology/approach: The paper presents a generalized dialogic learning architecture of networked…

Social Networking and Democratic Practices as Spheres for Innovative Musical Learning

ERIC Educational Resources Information Center

Thorgersen, Cecilia Ferm; Georgii-Hemming, Eva

2012-01-01

This chapter takes into account and discusses innovative learning in the 21st digital and communicative century based on life-world-phenomenology and Hannah Arendt's view of democracy. From this point of view, the authors address and discuss how democratic practices can offer innovative musical learning in relation to what is taking place in…

Neomillennial User Experience Design Strategies: Utilizing Social Networking Media to Support "Always On" Learning Styles

ERIC Educational Resources Information Center

Baird, Derek E.; Fisher, Mercedes

2006-01-01

Raised in the "always on" world of interactive media, the Internet, and digital messaging technologies, today's student has different expectations and learning styles than previous generations. This net-centric generation values their ability to use the Web to create a self-paced, customized, on-demand learning path that includes multiple forms of…

Learning in the Digital Era.

ERIC Educational Resources Information Center

Basto, Jorge

1999-01-01

Describes a training system designed and developed by the Portuguese Welding and Quality Institute of Macau for all aspects of open learning. Highlights include the technical training of teachers, using the Internet; the Asian version; and new challenges resulting from new training technologies such as shared multimedia networks. (Author/LRW)

Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.

PubMed

Campanella, Gabriele; Rajanna, Arjun R; Corsale, Lorraine; Schüffler, Peter J; Yagi, Yukako; Fuchs, Thomas J

2018-04-01

Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC). Currently, digital slides are screened manually to detected out-of-focus regions, to compensate for the limitations of scanner software. We present a solution to this problem by introducing a benchmark dataset for blur detection, an in-depth comparison of state-of-the art sharpness descriptors and their prediction performance within a random forest framework. Furthermore, we show that convolution neural networks, like residual networks, can be used to train blur detectors from scratch. We thoroughly evaluate the accuracy of feature based and deep learning based approaches for sharpness classification (99.74% accuracy) and regression (MSE 0.004) and additionally compare them to domain experts in a comprehensive human perception study. Our pipeline outputs spacial heatmaps enabling to quantify and localize blurred areas on a slide. Finally, we tested the proposed framework in the clinical setting and demonstrate superior performance over the state-of-the-art QC pipeline comprising commercial software and human expert inspection by reducing the error rate from 17% to 4.7%. Copyright © 2017. Published by Elsevier Ltd.

University Students' Perceptions of Social Networking Sites (SNSs) in Their Educational Experiences at a Regional Australian University

ERIC Educational Resources Information Center

Sadowski, Christina; Pediaditis, Mika; Townsend, Robert

2017-01-01

Higher education institutions, and the way education is delivered and supported, are being transformed by digital technologies. Internationally, institutions are increasingly incorporating online technologies into delivery frameworks and administration -- both through internal learning management systems (LMS) and external social networking sites…

Does Artificial Neural Network Support Connectivism's Assumptions?

ERIC Educational Resources Information Center

AlDahdouh, Alaa A.

2017-01-01

Connectivism was presented as a learning theory for the digital age and connectivists claim that recent developments in Artificial Intelligence (AI) and, more specifically, Artificial Neural Network (ANN) support their assumptions of knowledge connectivity. Yet, very little has been done to investigate this brave allegation. Does the advancement…

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

PubMed Central

Diehl, Peter U.; Cook, Matthew

2015-01-01

In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

PubMed

Hoseini, Farnaz; Shahbahrami, Asadollah; Bayat, Peyman

2018-02-27

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain. These complexities are the reasons to choose a high-capacity Deep Convolutional Neural Network (DCNN) containing more than one layer. The proposed DCNN contains two parts: architecture and learning algorithms. The architecture and the learning algorithms are used to design a network model and to optimize parameters for the network training phase, respectively. The architecture contains five convolutional layers, all using 3 × 3 kernels, and one fully connected layer. Due to the advantage of using small kernels with fold, it allows making the effect of larger kernels with smaller number of parameters and fewer computations. Using the Dice Similarity Coefficient metric, we report accuracy results on the BRATS 2016, brain tumor segmentation challenge dataset, for the complete, core, and enhancing regions as 0.90, 0.85, and 0.84 respectively. The learning algorithm includes the task-level parallelism. All the pixels of an MR image are classified using a patch-based approach for segmentation. We attain a good performance and the experimental results show that the proposed DCNN increases the segmentation accuracy compared to previous techniques.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Digital learning programs - competition for the classical microscope?

PubMed

Schmidt, Peter

2013-01-01

The development of digital media has been impressive in recent years which is also among the reason for their increasing use in academic teaching. This is especially true for teaching Anatomy and Histology in the first two years in medical and dental curricula. Modern digital technologies allow for efficient, affordable and easily accessible distribution of histological images in high quality. Microscopy depends almost exclusively on such images. Since 20 years numerous digital teaching systems have been developed for this purpose. Respective developments have changed the ways students acquire knowledge and prepare for exams. Teaching staff should adapt lectures, seminars and labs accordingly. As a first step, a collection of high resolution digital microscopic slides was made available for students at the Friedrich-Schiller-University in Jena. The aim of the present study was to evaluate the importance of conventional light microscopy and related technologies in current and future medical and dental education aswell. A survey was done among 172 medical and dental students at the Friedrich-Schiller-University Jena. 51% of students use now frequently new digital media for learning histology in contrast to 5% in the year 2000 [1]. Digital media including Internet, CD- based learning combined with social networks successfully compete with classical light microscopy.

Adaptive Learning and Pruning Using Periodic Packet for Fast Invariance Extraction and Recognition

NASA Astrophysics Data System (ADS)

Chang, Sheng-Jiang; Zhang, Bian-Li; Lin, Lie; Xiong, Tao; Shen, Jin-Yuan

2005-02-01

A new learning scheme using a periodic packet as the neuronal activation function is proposed for invariance extraction and recognition of handwritten digits. Simulation results show that the proposed network can extract the invariant feature effectively and improve both the convergence and the recognition rate.

Moving Edtech Forward: Upstart School Networks Are Betting on a Breakthrough

ERIC Educational Resources Information Center

Horn, Michael B.

2016-01-01

The digital revolution occurring in schools has focused predominantly on online education in its various forms--including fully online courses, learning management systems, games, and mobile applications--to personalize learning and boost the performance of all students. Companies have been experimenting with technologies for years, yet these…

Expanding Learning Opportunities with Transmedia Practices: "Inanimate Alice" as an Exemplar

ERIC Educational Resources Information Center

Fleming, Laura

2013-01-01

The proliferation of digital and networking technologies enables us to rethink, restructure, and redefine teaching and learning. Transmedia storytelling takes advantage of the rapid convergence of media and allows teachers and learners to participate in rich virtual (and physical) environments that have been shown to foster students' real…

The Role of Networked Learning in Academics' Writing

ERIC Educational Resources Information Center

McCulloch, Sharon; Tusting, Karin; Hamilton, Mary

2017-01-01

This article explores academics' writing practices, focusing on the ways in which they use digital platforms in their processes of collaborative learning. It draws on interview data from a research project that has involved working closely with academics across different disciplines and institutions to explore their writing practices,…

Ahead of the Digital Learning Curve

ERIC Educational Resources Information Center

Cook, Glenn

2013-01-01

Dwight Carter admits he was a novice at social networking when he was introduced to the 140-character world of Twitter in 2010. Now, three years later, the principal of Ohio's Gahanna Lincoln High School and one of three winners of the 2013 National Association of Secondary School Principals (NASSP) Digital Principal Award does not know what he…

Digital Divides and Social Network Sites: Which Students Participate in Social Media?

ERIC Educational Resources Information Center

Ahn, June

2011-01-01

Social network sites (SNSs) like Myspace and Facebook are now popular online communities with large teenage user populations. Teens use these technologies to interact, play, explore, and learn in significant ways. As scholars become interested in studying these new online communities, I contribute to the emerging conversation by re-examining…

Examining Digital Literacy Practices on Social Network Sites

ERIC Educational Resources Information Center

Buck, Amber

2012-01-01

Young adults represent the most avid users of social network sites, and they are also the most concerned with their online identity management, according to the Pew Internet and American Life Project. These practices represent important literate activity today, as individuals who are writing online learn to negotiate interfaces, user agreements,…

#SocialNetworks: Making Nonfiction Trend in Your Classroom

ERIC Educational Resources Information Center

Williams, Lunetta; Scott, Kelly; Simone, Danielle

2015-01-01

Students must be proficient readers of nonfiction texts to be successful in school and life. Since engaging students in this genre can be challenging, this article focuses on how students can respond digitally and socially to nonfiction through the use of free, secure social networks. Not only can students become more engaged in learning when…

Shaping Networked Theatre: Experience Architectures, Behaviours and Creative Pedagogies

ERIC Educational Resources Information Center

Sutton, Paul

2012-01-01

Since 2006 the UK based applied theatre company C&T has been using its experience and expertise in mixing drama, learning and digital media to create a new online utility for shaping collaborative educational drama experiences. C&T describes this practice as "Networked Theatre". This article describes both the motivations for…

The significance of digital citizenship in the well-being of older migrants.

PubMed

Millard, A; Baldassar, L; Wilding, R

2018-05-01

To understand the increasingly important role of digital citizenship (the ability to participate in society online) in supporting the well-being of ageing migrants. Participant observation, social network mapping, ethnographic and life-history interviews. Fifteen in-depth case studies examined the role of online participation in fostering the well-being and care of older migrants in Perth, Western Australia. Participants are members of an 'internet café' that facilitates their shared development of Internet skills. The case studies are derived from ethnographic research conducted between July and October 2016. Older peoples' maintenance of support networks and social engagement, and their access to healthcare services, can be enhanced when they are motivated to increase their digital literacy (the ability to use the Internet for information and communication) through appropriate educational, technological, infrastructure and social support. This support is likely to be more effective when developed through social learning systems that create communities of practice. Improving digital literacy has special implications for the well-being of older migrants because it can enhance their ability to exchange emotional support across distance. Digital literacy for older migrants can dramatically increase their ability to maintain and expand dispersed networks of support. Effective implementation of affordable and age-inclusive information and communication technology (ITC) infrastructure requires integrated support that connects individuals and their homes with social learning systems to ensure that participation continues as mobility declines. As health information and social engagement are increasingly delivered through online platforms, supporting the digital citizenship of older people is becoming an important equity issue. Copyright © 2018 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

Challenges to Learning and Schooling in the Digital Networked World of the 21st Century

ERIC Educational Resources Information Center

Voogt, J.; Erstad, O.; Dede, C.; Mishra, P.

2013-01-01

This article elaborates on the competencies, often referred to as 21st century competencies, that are needed to be able to live in and contribute to our current (and future) society. We begin by describing, analysing and reflecting on international frameworks describing 21st century competencies, giving special attention to digital literacy as one…

Can You Hear Us Now? Investigating the Effects of a Wireless Grid Social Radio Station on Collaboration and Communication in Fragile Populations

ERIC Educational Resources Information Center

Chauncey, Sarah A.

2012-01-01

The ability to interact with peers and coworkers in online digital networks is essential in learning and business environments. Our digital participatory culture is based on communication in response to purposeful activity and is facilitated by information and communication technologies (ICT). Students with emotional, behavioral, and learning…

The Metadata Education and Research Information Commons (MERIC): A Collaborative Teaching and Research Initiative

ERIC Educational Resources Information Center

Vellucci, Sherry L.; Hsieh-Yee, Ingrid; Moen, William E.

2007-01-01

The networked environment forced a sea change in Library and Information Science (LIS) education. Most LIS programs offer a mixed-mode of instruction that integrates online learning materials with more traditional classroom pedagogical methods and faculty are now responsible for developing content and digital learning objects. The teaching commons…

Dialogue and Connectivism: A New Approach to Understanding and Promoting Dialogue-Rich Networked Learning

ERIC Educational Resources Information Center

Ravenscroft, Andrew

2011-01-01

Connectivism offers a theory of learning for the digital age that is usually understood as contrasting with traditional behaviourist, cognitivist, and constructivist approaches. This article will provide an original and significant development of this theory through arguing and demonstrating how it can benefit from social constructivist…

Virtual Office Hours as Cyberinfrastructure: The Case Study of Instant Messaging

ERIC Educational Resources Information Center

Balayeva, Jeren; Quan-Haase, Anabel

2009-01-01

Although out-of-class communication enhances students' learning experience, students' use of office hours has been limited. As the learning infrastructures of the social sciences and humanities have undergone a range of changes since the diffusion of digital networks, new opportunities emerge to increase out-of-class communication. Hence, it is…

Learning with Portable Digital Devices in Australian Schools: 20 Years On!

ERIC Educational Resources Information Center

Newhouse, C. Paul

2014-01-01

Portable computing technologies such as laptops, tablets, smartphones, wireless networking, voice/stylus input, and plug and play peripheral devices, appear to offer the means of finally realising much of the long heralded vision for computers to support learning in schools. There is the possibility for the technology to finally become a…

Can Public Education Coexist with Participatory Culture?

ERIC Educational Resources Information Center

Losh, Elizabeth; Jenkins, Henry

2012-01-01

Participatory culture has many mechanisms to support peer-to-peer learning as young people enter interest-driven and friendship-driven networks. In this article, the authors argue that school librarians can help bridge the gap between the excitement of having students experiment with new forms of social learning and new digital-media practices,…

Online access and motivation of tutors of health professions higher education.

PubMed

Monaco, Federico; Sarli, Leopoldo; Guasconi, Massimo; Alfieri, Emanuela

2016-11-22

The case study of PUNTOZERO as an open web lab for activities, research and support to 5 Master's courses for the health professions is described. A virtual learning environment integrated in a much wider network including social networks and open resources was experimented on for five Master's Courses for the health professions at the University of Parma. A social learning approach might be applied by the engagement of motivated and skilled tutors. This is not only needed for the improvement and integration of the digital and collaborative dimension in higher education, but it aims to introduce issues and biases of emerging e-health and online networking dimensions for future healthcare professionals. Elements of e-readiness to train tutors and improve their digital skills and e-moderation approaches are evident. This emerged during an online and asynchronous interview with two tutors out of the four that were involved, by the use of a wiki where interviewer and informants could both read and add contents and comments.

Dataset on the learning performance of ECDL digital skills of undergraduate students for comparing educational gaming, gamification and social networking.

PubMed

de-Marcos, Luis; García-López, Eva; García-Cabot, Antonio

2017-04-01

This paper reports data about the learning performance of students using four different motivational tools: an educational game, a gamified plugin, a social networking website and a gamified social networking website. It also reports a control group. The data pertain to 379 students of an undergraduate course that covers basic Information and Communication Technology (ICT) skills in Spain. Data corresponds to different learning modules of the European Computer Driving License (ECDL) initiative. The data include variables of four pre-test scores, four post-test scores and a final examination. It was gathered using a quasi-experimental research design during 2014. Data reported here refers to the research paper in (de-Marcos et al., 2016) [1].

The direction of cloud computing for Malaysian education sector in 21st century

NASA Astrophysics Data System (ADS)

Jaafar, Jazurainifariza; Rahman, M. Nordin A.; Kadir, M. Fadzil A.; Shamsudin, Syadiah Nor; Saany, Syarilla Iryani A.

2017-08-01

In 21st century, technology has turned learning environment into a new way of education to make learning systems more effective and systematic. Nowadays, education institutions are faced many challenges to ensure the teaching and learning process is running smoothly and manageable. Some of challenges in the current education management are lack of integrated systems, high cost of maintenance, difficulty of configuration and deployment as well as complexity of storage provision. Digital learning is an instructional practice that use technology to make learning experience more effective, provides education process more systematic and attractive. Digital learning can be considered as one of the prominent application that implemented under cloud computing environment. Cloud computing is a type of network resources that provides on-demands services where the users can access applications inside it at any location and no time border. It also promises for minimizing the cost of maintenance and provides a flexible of data storage capacity. The aim of this article is to review the definition and types of cloud computing for improving digital learning management as required in the 21st century education. The analysis of digital learning context focused on primary school in Malaysia. Types of cloud applications and services in education sector are also discussed in the article. Finally, gap analysis and direction of cloud computing in education sector for facing the 21st century challenges are suggested.

Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware

PubMed Central

Knight, James C.; Tully, Philip J.; Kaplan, Bernhard A.; Lansner, Anders; Furber, Steve B.

2016-01-01

SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models. PMID:27092061

Digital learning programs - competition for the classical microscope?

PubMed Central

Schmidt, Peter

2013-01-01

The development of digital media has been impressive in recent years which is also among the reason for their increasing use in academic teaching. This is especially true for teaching Anatomy and Histology in the first two years in medical and dental curricula. Modern digital technologies allow for efficient, affordable and easily accessible distribution of histological images in high quality. Microscopy depends almost exclusively on such images. Since 20 years numerous digital teaching systems have been developed for this purpose. Respective developments have changed the ways students acquire knowledge and prepare for exams. Teaching staff should adapt lectures, seminars and labs accordingly. As a first step, a collection of high resolution digital microscopic slides was made available for students at the Friedrich-Schiller-University in Jena. The aim of the present study was to evaluate the importance of conventional light microscopy and related technologies in current and future medical and dental education aswell. A survey was done among 172 medical and dental students at the Friedrich-Schiller-University Jena. 51% of students use now frequently new digital media for learning histology in contrast to 5% in the year 2000 [1]. Digital media including Internet, CD- based learning combined with social networks successfully compete with classical light microscopy. PMID:23467698

Social Networking as a Tool for Lifelong Learning with Orthopedically Impaired Learners

ERIC Educational Resources Information Center

Ersoy, Metin; Güneyli, Ahmet

2016-01-01

This paper discusses how Turkish Cypriot orthopedically impaired learners who are living in North Cyprus use social networking as a tool for leisure and education, and to what extent they satisfy their personal development needs by means of these digital platforms. The case study described, conducted in North Cyprus in 2015 followed a qualitative…

Getting the Most from Google Classroom: A Pedagogical Framework for Tertiary Educators

ERIC Educational Resources Information Center

Heggart, Keith R.; Yoo, Joanne

2018-01-01

Many tertiary institutions have embraced digital learning through the use of online learning platforms and social networks. However, the research about the efficacy of such platforms is confused, as is the field itself, in part because of the rapidly evolving technology, and also because of a lack of clarity about what constitutes a learning…

Improving Students' Educational Experience by Harnessing Digital Technology: elgg in the ODL Environment

ERIC Educational Resources Information Center

Tung, Lai Cheng

2013-01-01

Given the rising popularity of both open and distance learning (ODL) and social networking tools, it seems logical to merge and harness these two popular technologies with the goal of improving student educational experience. The integration seems to hold tremendous promise for the open and distance learning mode. To reduce the gap in the…

Learning and Digital Environment of Dance--The Case of Greek Traditional Dance in Youtube

ERIC Educational Resources Information Center

Gratsiouni, Dimitra; Koutsouba, Maria; Venetsanou, Foteini; Tyrovola, Vasiliki

2016-01-01

The incorporation of Information and Communication Technologies (ICT) in education has changed the educational procedures through the creation and use of new teaching and learning environments with the use of computers and network applications that afford new dimensions to distance education. In turn, these emerging and in progress technologies,…

Building Connective Capital and Personal Learning Networks through Online Professional Development Communities for New Teachers

ERIC Educational Resources Information Center

Sciuto, David J.

2017-01-01

Increasingly, researchers concerned with the effects of digital technology have hypothesized that the millennial generation does not think or process information like its predecessors. In an age of disruptive technology changing culture and learning, new teachers continue to leave the classroom within the first five years of service. Among the…

Digital Family History Data Mining with Neural Networks: A Pilot Study.

PubMed

Hoyt, Robert; Linnville, Steven; Thaler, Stephen; Moore, Jeffrey

2016-01-01

Following the passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, electronic health records were widely adopted by eligible physicians and hospitals in the United States. Stage 2 meaningful use menu objectives include a digital family history but no stipulation as to how that information should be used. A variety of data mining techniques now exist for these data, which include artificial neural networks (ANNs) for supervised or unsupervised machine learning. In this pilot study, we applied an ANN-based simulation to a previously reported digital family history to mine the database for trends. A graphical user interface was created to display the input of multiple conditions in the parents and output as the likelihood of diabetes, hypertension, and coronary artery disease in male and female offspring. The results of this pilot study show promise in using ANNs to data mine digital family histories for clinical and research purposes.

Development of programmable artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J.

1993-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Digital Learning Network Education Events for the Desert Research and Technology Studies

NASA Technical Reports Server (NTRS)

Paul, Heather L.; Guillory, Erika R.

2007-01-01

NASA s Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and webcasting. As part of NASA s Strategic Plan to reach the next generation of space explorers, the DLN develops and delivers educational programs that reinforce principles in the areas of science, technology, engineering and mathematics. The DLN has created a series of live education videoconferences connecting the Desert Research and Technology Studies (RATS) field test to students across the United States. The programs are also extended to students around the world via live webcasting. The primary focus of the events is the Vision for Space Exploration. During the programs, Desert RATS engineers and scientists inform and inspire students about the importance of exploration and share the importance of the field test as it correlates with plans to return to the Moon and explore Mars. This paper describes the events that took place in September 2006.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-05-01

Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

Do Institutional Social Networks Work? Fostering a Sense of Community and Enhancing Learning

ERIC Educational Resources Information Center

Hatzipanagos, Stylianos; John, Bernadette A.

2017-01-01

In this paper we report on the evaluation of an institutional social network (KINSHIP) whose aims were to foster an improved sense of community, enhance communication and serve as a space to model digital professionalism for students at King's College London, UK. Our evaluation focused on a pilot where students' needs with regard to the provision…

Disrupting the Implementation Gap with Digital Technology in Healthcare Distance Education: Critical Insights from an e-Mentoring Intensional Network Practitioner Research Project

ERIC Educational Resources Information Center

Singh, Gurmit

2013-01-01

Effective professional distance education is urgently needed to develop a well-trained workforce and improve impact on healthcare. However, distance education initiatives have had mixed results in improving practice. Often, successful implementation fails to leverage insights on the social and emergent nature of learning in networks. This paper…

Higher-order neural network software for distortion invariant object recognition

NASA Technical Reports Server (NTRS)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging

NASA Astrophysics Data System (ADS)

Lee, Jason Sang Hun; Park, Inkyu; Park, Sangnam

2018-06-01

Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective preprocessing step which transforms each realvalued observational quantity or input feature into a binary number with a fixed number of digits. Each binary digit represents the quantity or magnitude in different scales. We have shown that this approach improves the performance of DNNs significantly for some specific tasks without any further complication in feature engineering. We apply this multi-scale distributed binary representation to deep learning on b-jet tagging using daughter particles' momenta and vertex information.

Learning and Skills Development in a Virtual Class of Educommunication Based on Educational Proposals and Interactions

ERIC Educational Resources Information Center

Bohorquez Sotelo, Maria Cristina; Rodriguez Mendoza, Brigitte Julieth; Vega, Sandra Milena; Roja Higuera, Naydu Shirley; Barbosa Gomez, Luisa Fernanda

2016-01-01

In the present paper we describe the analysis of qualitative and quantitative data from asynchronous learning networks, the virtual forums that take place in VirtualNet 2.0, the platform of the University Manuela Beltran (UMB), inside the course of Educommunication, from the master of Digital technologies applied to education. Here, we performed a…

Military Interoperable Digital Hospital Testbed (MIDHT)

DTIC Science & Technology

2010-07-01

solutions to optimize healthcare resources for rural communities and identify lessons learned and best practices that benefit both the global MHS...providers and three CHS facilities on their business practices and process flows. Research initiatives will focus on the impact of an electronic...strategic goals and the Nationwide Health Information Network (NHIN). The MIDHT will continue to identify lessons learned/best practices that benefit

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

PubMed

Ertosun, Mehmet Günhan; Rubin, Daniel L

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.

Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

PubMed Central

Ertosun, Mehmet Günhan; Rubin, Daniel L.

2015-01-01

Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

PubMed

Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

2017-07-24

We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

Building effective learning experiences around visualizations: NASA Eyes on the Solar System and Infiniscope

NASA Astrophysics Data System (ADS)

Tamer, A. J. J.; Anbar, A. D.; Elkins-Tanton, L. T.; Klug Boonstra, S.; Mead, C.; Swann, J. L.; Hunsley, D.

2017-12-01

Advances in scientific visualization and public access to data have transformed science outreach and communication, but have yet to realize their potential impacts in the realm of education. Computer-based learning is a clear bridge between visualization and education, but creating high-quality learning experiences that leverage existing visualizations requires close partnerships among scientists, technologists, and educators. The Infiniscope project is working to foster such partnerships in order to produce exploration-driven learning experiences around NASA SMD data and images, leveraging the principles of ETX (Education Through eXploration). The visualizations inspire curiosity, while the learning design promotes improved reasoning skills and increases understanding of space science concepts. Infiniscope includes both a web portal to host these digital learning experiences, as well as a teaching network of educators using and modifying these experiences. Our initial efforts to enable student discovery through active exploration of the concepts associated with Small Worlds, Kepler's Laws, and Exoplanets led us to develop our own visualizations at Arizona State University. Other projects focused on Astrobiology and Mars geology led us to incorporate an immersive Virtual Field Trip platform into the Infiniscope portal in support of virtual exploration of scientifically significant locations. Looking to apply ETX design practices with other visualizations, our team at Arizona State partnered with the Jet Propulsion Lab to integrate the web-based version of NASA Eyes on the Eclipse within Smart Sparrow's digital learning platform in a proof-of-concept focused on the 2017 Eclipse. This goes a step beyond the standard features of "Eyes" by wrapping guided exploration, focused on a specific learning goal into standards-aligned lesson built around the visualization, as well as its distribution through Infiniscope and it's digital teaching network. Experience from this development effort has laid the groundwork to explore future integrations with JPL and other NASA partners.

Using Web Maps to Analyze the Construction of Global Scale Cognitive Maps

ERIC Educational Resources Information Center

Pingel, Thomas J.

2018-01-01

Game-based Web sites and applications are changing the ways in which students learn the world map. In this study, a Web map-based digital learning tool was used as a study aid for a university-level geography course in order to examine the way in which global scale cognitive maps are constructed. A network analysis revealed that clicks were…

A Hybrid Approach to Develop an Analytical Model for Enhancing the Service Quality of E-Learning

ERIC Educational Resources Information Center

Wu, Hung-Yi; Lin, Hsin-Yu

2012-01-01

The digital content industry is flourishing as a result of the rapid development of technology and the widespread use of computer networks. As has been reported, the market size of the global e-learning (i.e., distance education and telelearning) will reach USD 49.6 billion in 2014. However, to retain and/or increase the market share associated…

Lifelong Learning for Clinical Practice: How to Leverage Technology for Telebehavioral Health Care and Digital Continuing Medical Education.

PubMed

Hilty, Donald M; Turvey, Carolyn; Hwang, Tiffany

2018-03-12

Psychiatric practice continues to evolve and play an important role in patients' lives, the field of medicine, and health care delivery. Clinicians must learn a variety of clinical care systems and lifelong learning (LLL) is crucial to apply knowledge, develop skills, and adjust attitudes. Technology is rapidly becoming a key player-in delivery, lifelong learning, and education/training. The evidence base for telepsychiatry/telemental health via videoconferencing has been growing for three decades, but a greater array of technologies have emerged in the last decade (e.g., social media/networking, text, apps). Clinicians are combining telepsychiatry and these technologies frequently and they need to reflect on, learn more about, and develop skills for these technologies. The digital age has solidified the role of technology in continuing medical education and day-to-day practice. Other fields of medicine are also adapting to the digital age, as are graduate and undergraduate medical education and many allied mental health organizations. In the future, there will be more online training, simulation, and/or interactive electronic examinations, perhaps on a monthly cycle rather than a quasi-annual or 10-year cycle of recertification.

Numerical solution of differential equations by artificial neural networks

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1995-01-01

Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

Increasing access to learning for the adult basic education learner with learning disabilities: evidence-based accommodation research.

PubMed

Gregg, Noel

2012-01-01

Accommodating adult basic education (ABE) learners with learning disabilities (LD) is common practice across many instructional, testing, and work settings. However, the results from this literature search indicate that very few empirically based studies are available to support or reject the effectiveness of a great deal of accommodation implementation. In addition, in light of the profound changes to literacy taking place in today's digital, networked, and multimodal world, technology is redefining traditional concepts of accessibility and accommodation.

Report on Distance Learning Technologies.

DTIC Science & Technology

1995-09-01

26 cities. The CSX system includes full-motion video, animations , audio, and interactive examples and testing to teach the use of a new computer...video. The change to all-digital media now permits the use of full-motion video, animation , and audio on networks. It is possible to have independent...is possible to download entire multimedia presentations from the network. To date there is not a great deal known about teaching courses using the

Progress in the capture, manipulation, and delivery of medical media and its impact on education, clinical care, and research.

PubMed

Bernardo, Theresa M; Malinowski, Robert P

2005-01-01

In this article, advances in the application of medical media to education, clinical care, and research are explored and illustrated with examples, and their future potential is discussed. Impact is framed in terms of the Sloan Consortium's five pillars of quality education: access; student and faculty satisfaction; learning effectiveness; and cost effectiveness. (Hiltz SR, Zhang Y, Turoff M. Studies of effectiveness of learning networks. In Bourne J, Moore J, ed. Elements of Quality Online Education. Needham, MA: Sloan-Consortium, 2002:15-45). The alternatives for converting analog media (text, photos, graphics, sound, video, animations, radiographs) to digital media and direct digital capture are covered, as are options for storing, manipulating, retrieving, and sharing digital collections. Diagnostic imaging is given particular attention, clarifying the difference between computerized radiography and digital radiography and explaining the accepted standard (DICOM) and the advantages of Web PACS. Some novel research applications of medical media are presented.

Galaxy Classification using Machine Learning

NASA Astrophysics Data System (ADS)

Fowler, Lucas; Schawinski, Kevin; Brandt, Ben-Elias; widmer, Nicole

2017-01-01

We present our current research into the use of machine learning to classify galaxy imaging data with various convolutional neural network configurations in TensorFlow. We are investigating how five-band Sloan Digital Sky Survey imaging data can be used to train on physical properties such as redshift, star formation rate, mass and morphology. We also investigate the performance of artificially redshifted images in recovering physical properties as image quality degrades.

Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

NASA Astrophysics Data System (ADS)

Fang, Le-Heng; Lin, Wei; Luo, Qiang

In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

Web 2.0 and Emergent Multiliteracies

ERIC Educational Resources Information Center

Alexander, Bryan

2008-01-01

Students are, increasingly, digital content producers, and participate extensively in evolving online social networks. The emergence of the former represents subtle changes in students' experience of images, audience, copyright, ownership of learning, and technology. Experiencing the latter places students in an awkward position in terms of…

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

PubMed Central

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

PubMed

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

PubMed

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

PubMed Central

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

Classification of Encrypted Web Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2013-06-01

DPI devices to block certain websites; Yu, Cong, Chen, and Lei [52] suggest hashing the domains of pornographic and illegal websites so ISPs can...Zhenming Lei. “Blocking pornographic , illegal websites by internet host domain using FPGA and Bloom Filter”. Network Infrastructure and Digital Content

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

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

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Tactile Feedback Display with Spatial and Temporal Resolutions

PubMed Central

Vishniakou, Siarhei; Lewis, Brian W.; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-01-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications. PMID:23982053

Tactile feedback display with spatial and temporal resolutions.

PubMed

Vishniakou, Siarhei; Lewis, Brian W; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-01-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications.

Tactile Feedback Display with Spatial and Temporal Resolutions

NASA Astrophysics Data System (ADS)

Vishniakou, Siarhei; Lewis, Brian W.; Niu, Xiaofan; Kargar, Alireza; Sun, Ke; Kalajian, Michael; Park, Namseok; Yang, Muchuan; Jing, Yi; Brochu, Paul; Sun, Zhelin; Li, Chun; Nguyen, Truong; Pei, Qibing; Wang, Deli

2013-08-01

We report the electronic recording of the touch contact and pressure using an active matrix pressure sensor array made of transparent zinc oxide thin-film transistors and tactile feedback display using an array of diaphragm actuators made of an interpenetrating polymer elastomer network. Digital replay, editing and manipulation of the recorded touch events were demonstrated with both spatial and temporal resolutions. Analog reproduction of the force is also shown possible using the polymer actuators, despite of the high driving voltage. The ability to record, store, edit, and replay touch information adds an additional dimension to digital technologies and extends the capabilities of modern information exchange with the potential to revolutionize physical learning, social networking, e-commerce, robotics, gaming, medical and military applications.

Korean letter handwritten recognition using deep convolutional neural network on android platform

NASA Astrophysics Data System (ADS)

Purnamawati, S.; Rachmawati, D.; Lumanauw, G.; Rahmat, R. F.; Taqyuddin, R.

2018-03-01

Currently, popularity of Korean culture attracts many people to learn everything about Korea, particularly its language. To acquire Korean Language, every single learner needs to be able to understand Korean non-Latin character. A digital approach needs to be carried out in order to make Korean learning process easier. This study is done by using Deep Convolutional Neural Network (DCNN). DCNN performs the recognition process on the image based on the model that has been trained such as Inception-v3 Model. Subsequently, re-training process using transfer learning technique with the trained and re-trained value of model is carried though in order to develop a new model with a better performance without any specific systemic errors. The testing accuracy of this research results in 86,9%.

Examining Mendeley: Designing Learning Opportunities for Digital Scholarship

ERIC Educational Resources Information Center

Hicks, Alison; Sinkinson, Caroline

2015-01-01

Researchers have widely adopted computer programs for reference management, such as Mendeley, due to their ability to support a variety of research practices, including organization and storage of pdfs. These programs also afford participation and networking within new scholarly information landscapes. This paper uses a survey and semi-structured…

The People Unite: Learning Meaningful Civics Online

ERIC Educational Resources Information Center

Pitts, Annette Boyd; Dziuban, Charles; Cornett, Jeffrey W.

2011-01-01

Throughout the world, today's students are being characterized as digital natives, the "net generation." This twenty-first-century student cohort is adept at multi-tasking and at using a variety of tools and resources including electronic search engines, blogs, wikis, visual images, videos, gaming platforms, and social networking.…

Always-on Education and Hybrid Learning Spaces

ERIC Educational Resources Information Center

Trentin, Guglielmo

2016-01-01

The possibility of being always connected to the Internet and/or the mobile network (hence the term "always-on") is increasingly blurring the borderline between physical and digital spaces, introducing a new concept of space, known as "hybrid." Innovative forms of teaching have been developing in hybrid spaces for some time…

Web 2.0: Today's Technology, Tomorrow's Learning

ERIC Educational Resources Information Center

Groff, Jennifer; Haas, Jason

2008-01-01

When it comes to technologies like digital games, simulations, and social networking, teachers and students may find themselves at cross purposes. Often, students find that these technologies, so prevalent in their lives outside of school, are unwelcome in their classrooms. Many teachers can tell stories about the disruptive influence of video…

Giving Life to Data: University-Community Partnerships in Addressing HIV and AIDS through Building Digital Archives

ERIC Educational Resources Information Center

de Lange, Naydene; Mnisi, Thoko; Mitchell, Claudia; Park, Eun G.

2010-01-01

The partnerships, especially university-community partnerships, that sustain globally networked learning environments often face challenges in mobilizing research to empower local communities to effect change. This article examines these challenges by describing a university-community partnership involving researchers and graduate students in…

Design of a universal two-layered neural network derived from the PLI theory

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

PubMed Central

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks. PMID:28932180

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks.

PubMed

Mostafa, Hesham; Pedroni, Bruno; Sheik, Sadique; Cauwenberghs, Gert

2017-01-01

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation. Learning is performed in parallel with inference in the forward pass, removing the need for an explicit backward pass and requiring no extra weight lookup. By using binary state variables in the feedforward network and ternary errors in truncated-error backpropagation, the need for any multiplications in the forward and backward passes is removed, and memory requirements for the pipelining are drastically reduced. Further reduction in addition operations owing to the sparsity in the forward neural and backpropagating error signal paths contributes to highly efficient hardware implementation. For proof-of-concept validation, we demonstrate on-line learning of MNIST handwritten digit classification on a Spartan 6 FPGA interfacing with an external 1Gb DDR2 DRAM, that shows small degradation in test error performance compared to an equivalently sized binary ANN trained off-line using standard back-propagation and exact errors. Our results highlight an attractive synergy between pipelined backpropagation and binary-state networks in substantially reducing computation and memory requirements, making pipelined on-line learning practical in deep networks.

Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

NASA Astrophysics Data System (ADS)

Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

2015-05-01

In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

On Textual Analysis and Machine Learning for Cyberstalking Detection.

PubMed

Frommholz, Ingo; Al-Khateeb, Haider M; Potthast, Martin; Ghasem, Zinnar; Shukla, Mitul; Short, Emma

2016-01-01

Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.

Media Literacy, Social Networking, and the Web 2.0 Environment for the K-12 Educator. Minding the Media: Critical Issues for Learning and Teaching. Volume 4

ERIC Educational Resources Information Center

de Abreu, Belinha S.

2011-01-01

This book, a resource for educators, uses the theme of media literacy as a lens through which to view and discuss social networking and Web 2.0 environments. There is ongoing and positive research on the participatory culture created by youth who are heavily involved in the new digital technologies, yet schools tend to avoid these mediums for fear…

Adaptive Leadership in Times of Crisis

DTIC Science & Technology

2011-12-01

HAyASHI AnD AMEy Soo Chiemi Hayashi is Research Director of the Risk Response Network at the World Economic Forum . Amey Soo is a Researcher at the...and the resulting complexities of this world. after each crisis it must be the goal of author- ities to learn from the lessons that emerged and...digital media, which offers essential tools that anyone aspiring to lead in the 21st century must master. Learning from its past mistakes, the

Using YOLO based deep learning network for real time detection and localization of lung nodules from low dose CT scans

NASA Astrophysics Data System (ADS)

Ramachandran S., Sindhu; George, Jose; Skaria, Shibon; V. V., Varun

2018-02-01

Lung cancer is the leading cause of cancer related deaths in the world. The survival rate can be improved if the presence of lung nodules are detected early. This has also led to more focus being given to computer aided detection (CAD) and diagnosis of lung nodules. The arbitrariness of shape, size and texture of lung nodules is a challenge to be faced when developing these detection systems. In the proposed work we use convolutional neural networks to learn the features for nodule detection, replacing the traditional method of handcrafting features like geometric shape or texture. Our network uses the DetectNet architecture based on YOLO (You Only Look Once) to detect the nodules in CT scans of lung. In this architecture, object detection is treated as a regression problem with a single convolutional network simultaneously predicting multiple bounding boxes and class probabilities for those boxes. By performing training using chest CT scans from Lung Image Database Consortium (LIDC), NVIDIA DIGITS and Caffe deep learning framework, we show that nodule detection using this single neural network can result in reasonably low false positive rates with high sensitivity and precision.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

2015-01-01

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems. PMID:26262687

Place-Making in Higher Education: Co-Creating Engagement and Knowledge Practices in the Networked Age

ERIC Educational Resources Information Center

Swist, Teresa; Kuswara, Andreas

2016-01-01

The pedagogical locations, functions and possibilities of higher education continuously unfold as mobile technologies, digital content and social practices intersect at a rapid pace. There is an urgent need to understand better how student learning is situated within this complex system and interrelates with broader sociotechnical knowledge…

Facebook Tools and Digital Learning Achievements in Higher Education

ERIC Educational Resources Information Center

Davidovitch, Nitza; Belichenko, Margarita

2018-01-01

During recent years there has been a significant increase in the usage of technological tools in general, and in academic teaching in particular. Many programs have been developed, including online teaching and online courses at educational institutions. In this paper, we discuss the Facebook social network and its use at the University. The…

Learning with iLife

ERIC Educational Resources Information Center

Technology & Learning, 2007

2007-01-01

A podcast is audio or visual content that is automatically delivered over a network via free subscription. The advantage podcasts have over traditional oral reports is that students can edit and revise until what they say and how they say it is perfected. iLife applications are ideal for creating podcasts and other digital projects because of…

The Digital Agora: Interaction and Learning in Political Science.

ERIC Educational Resources Information Center

Watters, Carolyn; Conley, Marshall; Alexander, Cynthia

Acadia University is the first "laptop" university in Canada. The Acadia Advantage program has each incoming student and each faculty member equipped with a laptop computer. In addition, classrooms, library, residence rooms, and common areas are wired so that the network is accessible both in and out of classrooms. This initiative has…

The Dubious Promise of Educational Technologies: Historical Patterns and Future Challenges

ERIC Educational Resources Information Center

Cuban, Larry; Jandric, Petar

2015-01-01

In this article, Larry Cuban discusses his ideas about the topic of this Special Issue of E-learning and Digital Media "Networked Realms and Hoped-For Futures: A Trans-Generational Dialogue" with one of its co-editors, Petar Jandric. The conversation explores the historical relationships between education and information and…

Fostering Foreign Language Learning with Twitter: Reflections from English Learners

ERIC Educational Resources Information Center

Taskiran, Ayse; Gumusoglu, Eylem Koral; Aydin, Belgin

2018-01-01

Education in 21st century is dominated by the generation of digital natives who are greatly exposed to and participate in technology in their social and educational lives. There is no doubt that anything experienced in social life directly affects learners' educational experiences. Highly popular social networks are being used in almost every…

Applications of Technology, Currently Being Used in Business and Industry, to Education.

ERIC Educational Resources Information Center

Satterlee, Brian

Most educational institutions lag far behind business and industry in the adoption and use of technology. This paper explores the applications of technologies that are currently being used in business and industry, to education. The following technologies are reviewed: virtual learning, wireless networking, collaboration tools, digital video,…

Scotland's knowledge network: a progress report on Knowledge into Action.

PubMed

Wales, Ann; Boyle, Derek

2015-11-01

Launched in 2012, Knowledge into Action is the national knowledge management strategy for the health and social care workforce in Scotland. It is transforming the role of the national digital knowledge service--NHS Education for Scotlands' Knowledge Network--and the NHSS librarian role to offer more active, tailored support for translating knowledge into frontline clinical practice. This includes the development of a national evidence search and summary service, help with converting knowledge into practical and usable formats for easy use at point of care and with using digital tools to share clinicians' learning, experience and expertise. Through this practical support, Knowledge into Action is contributing to quality and safety outcomes across NHS Scotland, building clinicians' capacity and capability in applying knowledge in frontline practice and service improvement. © The Author(s) 2015.

Video on phone lines: technology and applications

NASA Astrophysics Data System (ADS)

Hsing, T. Russell

1996-03-01

Recent advances in communications signal processing and VLSI technology are fostering tremendous interest in transmitting high-speed digital data over ordinary telephone lines at bit rates substantially above the ISDN Basic Access rate (144 Kbit/s). Two new technologies, high-bit-rate digital subscriber lines and asymmetric digital subscriber lines promise transmission over most of the embedded loop plant at 1.544 Mbit/s and beyond. Stimulated by these research promises and rapid advances on video coding techniques and the standards activity, information networks around the globe are now exploring possible business opportunities of offering quality video services (such as distant learning, telemedicine, and telecommuting etc.) through this high-speed digital transport capability in the copper loop plant. Visual communications for residential customers have become more feasible than ever both technically and economically.

Exploring the story, science, and adventure of small worlds

NASA Astrophysics Data System (ADS)

Swann, J. L.; Elkins-Tanton, L. T.; Anbar, A. D.; Klug Boonstra, S.; Tamer, A. J.; Mead, C.; Hunsley, D.

2017-12-01

Small worlds are a strategic focus at NASA, reflected by missions such as Osiris Rex and Psyche among others. The Infiniscope project, with funding from NASA SMD, is building on this scientific and public interest to teach formal and informal learners about asteroids and other small worlds. The digital learning experience, "Where are the small worlds?", and future Infiniscope experiences, incorporate a design theory that we describe as "education through exploration" (ETX) which is provided through an adaptive e-learning platform. This design ensures that learners actively engage in exploration and discovery, while receiving targeted feedback to push through challenges. To ensure that this and future experiences reach and meet the needs of as many educators as possible, Infiniscope includes a digital teaching network to host the experiences and support the reuse and adaptation of digital resources in new lessons. "Where are the small worlds?" puts learners in an interactive simulation of the solar system and provides a mission structure in which they hunt for "astrocaches" on near earth objects, main belt asteroids, and Kuiper-belt objects. These activities allow the learner to discover the locations of the small worlds in the solar system and develop an intuitive understanding for the relative motion of objects at various distances from the Sun. The experience is NGSS-aligned and accompanied by a lesson plan for integration into the classroom. In testing with more than 500 middle-school students, 83% of participants said they wanted to do more experiences like "Where are the small worlds?" They also found the experience both "fun" and "interesting" while being moderately difficult. "Where are the small worlds?" is one of many visualizations and lessons that is available within the Infiniscope teaching network. The network already has hundreds of members and is expected to grow in both numbers and engagement over time. Currently, educators can search and use pre-existing experiences, but as the visualization library expands and educators learn more about exploration-learning design, they may modify existing experiences and even build entirely new experiences to meet specific needs. In parallel, we are also developing a professional development program that builds understanding of the principles of ETX design.

Exploring TechQuests Through Open Source and Tools That Inspire Digital Natives

NASA Astrophysics Data System (ADS)

Hayden, K.; Ouyang, Y.; Kilb, D.; Taylor, N.; Krey, B.

2008-12-01

"There is little doubt that K-12 students need to understand and appreciate the Earth on which they live. They can achieve this understanding only if their teachers are well prepared". Dan Barstow, Director of Center for Earth and Space Science Education at TERC. The approach of San Diego County's Cyberinfrastructure Training, Education, Advancement, and Mentoring (SD Cyber-TEAM) project is to build understandings of Earth systems for middle school teachers and students through a collaborative that has engaged the scientific community in the use of cyber-based tools and environments for learning. The SD Cyber-TEAM has used Moodle, an open source management system with social networking tools, that engage digital native students and their teachers in collaboration and sharing of ideas and research related to Earth science. Teachers participate in on-line professional dialog through chat, wikis, blogs, forums, journals and other tools and choose the tools that will best fit their classroom. The use of Moodle during the Summer Cyber Academy developed a cyber-collaboratory environment where teaching strategies were discussed, supported and actualized by participants. These experiences supported digital immigrants (teachers) in adapting teaching strategies using technologies that are most attractive and familiar to students (digital natives). A new study by the National School Boards Association and Grunwald Associates LLC indicated that "the online behaviors of U.S. teens and 'tweens shows that 96 percent of students with online access use social networking technologies, such as chatting, text messaging, blogging, and visiting online communities such as Facebook, MySpace, and Webkinz". While SD Cyber-TEAM teachers are implementing TechQuests in classrooms they use these social networking elements to capture student interest and address the needs of digital natives. Through the Moodle environment, teachers have explored a variety of learning objects called TechQuests, to support classroom instruction previously outlined through a textbook. Project classrooms have participated in videoconferences over high-speed networks and through satellite connections with experts in the field investigating scientific data found in the CA State Park of Anza Borrego. Other engaging tools include: An Interactive Epicenter Locator Tool developed through the project in collaboration with the Scripps Institution of Oceanography to engage students in the use of data to determine earthquake epicenters during hands on investigations, and a TechQuest activity where GoogleEarth allows students to explore geographic locations and scientific data.

The Role of the Human Mirror Neuron System in Supporting Communication in a Digital World.

PubMed

Dickerson, Kelly; Gerhardstein, Peter; Moser, Alecia

2017-01-01

Humans use both verbal and non-verbal communication to interact with others and their environment and increasingly these interactions are occurring in a digital medium. Whether live or digital, learning to communicate requires overcoming the correspondence problem: There is no direct mapping, or correspondence between perceived and self-produced signals. Reconciliation of the differences between perceived and produced actions, including linguistic actions, is difficult and requires integration across multiple modalities and neuro-cognitive networks. Recent work on the neural substrates of social learning suggests that there may be a common mechanism underlying the perception-production cycle for verbal and non-verbal communication. The purpose of this paper is to review evidence supporting the link between verbal and non-verbal communications, and to extend the hMNS literature by proposing that recent advances in communication technology, which at times have had deleterious effects on behavioral and perceptual performance, may disrupt the success of the hMNS in supporting social interactions because these technologies are virtual and spatiotemporal distributed nature.

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses.

PubMed

Qiao, Ning; Mostafa, Hesham; Corradi, Federico; Osswald, Marc; Stefanini, Fabio; Sumislawska, Dora; Indiveri, Giacomo

2015-01-01

Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm(2), and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.

Deep convolutional neural networks for classifying GPR B-scans

NASA Astrophysics Data System (ADS)

Besaw, Lance E.; Stimac, Philip J.

2015-05-01

Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

PubMed

Devalla, Sripad Krishna; Chin, Khai Sing; Mari, Jean-Martial; Tun, Tin A; Strouthidis, Nicholas G; Aung, Tin; Thiéry, Alexandre H; Girard, Michaël J A

2018-01-01

To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer + prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 ± 0.03, 0.92 ± 0.03, 0.99 ± 0.00, 0.89 ± 0.03, and 0.94 ± 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P < 0.001). Besides offering a good reliability, digital staining also performed well on OCT images of both glaucoma and healthy individuals. Our deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management.

Views and considerations on ICT-AT competences development within the ENTELIS project: The Case of Cyprus.

PubMed

Mavrou, Katerina; Meletiou-Mavrotheris, Maria

2015-01-01

This submission presents part of the EU funded project ENTELIS (European Network for Technology Enhanced Learning in an Inclusive Society), which aims to address issues of digital divide and digital equity for people with disabilities of all ages, and to increase participation and social inclusion. This paper presents the main activities and outcomes of the research work package of the project (WP3), from one of the partner countries, Cyprus. The aim of the conducted research was to identify the conceptions and beliefs of end-users, trainers, and service/technology providers and professionals, on the multifaceted relation between ICT/ICT-AT (Information Communication Technology - Assistive Technology) and learning of technology. Data collection involved the development and administration of three semi-structured interview protocols, one for each group of participants, in five different European countries. Results have been compiled to develop a State-of-Art Report on ICT and ICT-AT education and learning, highlighting the main trends, as well the main present barriers, emergent and future needs in terms of analysis, acquisition and reinforcing of digital competences bridging the worlds of education and work.

Legal Issues & Education Technology: A School Leader's Guide. An ITTE Technology Leadership Network Special Report. Second Edition.

ERIC Educational Resources Information Center

Darden, Edwin C., Ed.

This book is intended to help school leaders achieve a workable balance that allows schools to take advantage of the educational and administrative benefits of digital technologies while protecting the district from disruptive and expensive litigation. Chapter 1, "Student Learning and the Law of School Technology," focuses on developing policies…

Status Quo and Prospective of WeChat in Improving Chinese English Learners' Pronunciation

ERIC Educational Resources Information Center

Wang, Kanghui

2017-01-01

With the ubiquitous usage of wireless, portable, and handheld devices gaining popularity in 21st century, the revolutionary mobile technology introduces digital new media to educational settings, which has changed the way of traditional teaching and learning. WeChat is one of the most popular social networking applications in China featured by its…

Analysis of Attitude and Achievement Using the 5E Instructional Model in an Interactive Television Environment

ERIC Educational Resources Information Center

Cherry, Gamaliel R.

2011-01-01

The purpose of this quasi-experimental study was to examine attitude and achievement among fifth grade students participating in inquiry and lecture-based forms of instruction through interactive television. Participants (N = 260) were drawn from registered users of NASA's Digital Learning Network[TM]. The first three levels of Bloom's Revised…

The Development of Pre-Service Science Teachers' Professional Knowledge in Utilizing ICT to Support Professional Lives

ERIC Educational Resources Information Center

Arnold, Savittree Rochanasmita; Padilla, Michael J.; Tunhikorn, Bupphachart

2009-01-01

In the rapidly developing digital world, technology is and will be a force in workplaces, communities, and everyday lives in the 21st century. Information and Communication Technology (ICT) including computer hardware/software, networking and other technologies such as audio, video, and other multimedia tools became learning tools for students in…

Towards Meaningful Learning through Digital Video Supported, Case Based Teaching

ERIC Educational Resources Information Center

Hakkarainen, Paivi; Saarelainen, Tarja; Ruokamo, Heli

2007-01-01

This paper reports an action research case study in which a traditional lecture based, face to face "Network Management" course at the University of Lapland's Faculty of Social Sciences was developed into two different course versions resorting to case based teaching: a face to face version and an online version. In the face to face…

Creating Micro-Videos to Demonstrate Technology Learning and Digital Literacy

ERIC Educational Resources Information Center

Frydenberg, Mark; Andone, Diana

2016-01-01

Purpose: Short videos, also known as micro-videos, have emerged as a platform for sharing ideas, experiences and life events via online social networks. This paper aims to share preliminary results of a study, involving students from two universities who created six-second videos using the Vine mobile app to explain or illustrate technological…

Reimagining the Role of School Libraries in STEM Education: Creating Hybrid Spaces for Exploration

ERIC Educational Resources Information Center

Subramaniam, Mega M.; Ahn, June; Fleischmann, Kenneth R.; Druin, Allison

2012-01-01

In recent years, many technological interventions have surfaced, such as virtual worlds, games, and digital labs, that aspire to link young people's interest in media technology and social networks to learning about science, technology, engineering, and math (STEM) areas. Despite the tremendous interest surrounding young people and STEM education,…

Modeling Data from Collaborative Assessments: Learning in Digital Interactive Social Networks

ERIC Educational Resources Information Center

Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen

2017-01-01

This article summarizes assessment of cognitive skills through collaborative tasks, using field test results from the Assessment and Teaching of 21st Century Skills (ATC21S) project. This project, sponsored by Cisco, Intel, and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and…

Smart Collections: Can Artificial Intelligence Tools and Techniques Assist with Discovering, Evaluating and Tagging Digital Learning Resources?

ERIC Educational Resources Information Center

Leibbrandt, Richard; Yang, Dongqiang; Pfitzner, Darius; Powers, David; Mitchell, Pru; Hayman, Sarah; Eddy, Helen

2010-01-01

This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be…

A network-based training environment: a medical image processing paradigm.

PubMed

Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

1998-01-01

The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training.

WNN 92; Proceedings of the 3rd Workshop on Neural Networks: Academic/Industrial/NASA/Defense, Auburn Univ., AL, Feb. 10-12, 1992 and South Shore Harbour, TX, Nov. 4-6, 1992

NASA Technical Reports Server (NTRS)

Padgett, Mary L. (Editor)

1993-01-01

The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.

The Significance of Kinship for Medical Education: Reflections on the Use of a Bespoke Social Network to Support Learners' Professional Identities.

PubMed

Hatzipanagos, Stylianos; John, Bernadette; Chiu, Yuan-Li Tiffany

2016-03-03

Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. In this paper we report on the evaluation of an institutional social network-King's Social Harmonisation Project (KINSHIP)-established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King's College London, United Kingdom. Our evaluation focused on a study that examined students' needs and perceptions with regard to the provision of a cross-university platform. Data were collected from students, including those in the field of health and social care, in order to recommend a practical way forward to address current needs in this area. The findings indicate that the majority of the respondents were positive about using a social networking platform to develop their professional voice and profiles. Results suggest that timely promotion of the platform, emphasis on interface and learning design, and a clear identity are required in order to gain acceptance as the institutional social networking site. Empirical findings in this study project an advantage of an institutional social network such a KINSHIP over other social networks (eg, Facebook) because access is limited to staff and students and the site is mainly being used for academic purposes.

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

DOE PAGES

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.; ...

2017-11-23

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

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

Chakma, Gangotree; Adnan, Md Musabbir; Wyer, Austin R.

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. Here in this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed systemmore » additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.« less

Telemedicine in Western Africa: lessons learned from a pilot project in Mali, perspectives and recommendations.

PubMed

Geissbuhler, Antoine; Ly, Ousmane; Lovis, Christian; L'Haire, Jean-François

2003-01-01

to evaluate the feasibility, potential and risks of an internet-based telemedicine network in developing countries of Western Africa. a project for the development of a national telemedicine network in Mali was initiated in 2001, using internet-based technologies for distance learning and teleconsultations. the telemedicine network has been in productive use for 12 months and has enabled various collaboration channels, including North-South, South-South, and South-North distance learning and teleconsultations. It also unveiled a set of potential problems: a) limited pertinence of North-South collaborations when there are major differences in available resources or socio-cultural contexts between the collaborating parties; b) risk of induced digital divide if the periphery of the health system is not involved in the development of the network, and c) need for the development of local medical contents management skills. the identified risks must be taken into account when designing large-scale telemedicine projects in developing countries and can be mitigated by the fostering of South-South collaboration channels, the use of satellite-based internet connectivity in remote areas, and the valorization of local knowledge and its publication on-line.

Stellar Atmospheric Parameterization Based on Deep Learning

NASA Astrophysics Data System (ADS)

Pan, Ru-yang; Li, Xiang-ru

2017-07-01

Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature (Teff), surface gravitational acceleration (lg g), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for Teff/K, 0.0058 for (lg Teff/K), 0.1706 for lg (g/(cm·s-2)), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for Teff/K, 0.0011 for lg (Teff/K), 0.0214 for lg(g/(cm · s-2)), and 0.0121 dex for [Fe/H], respectively.

A survey on deep learning in medical image analysis.

PubMed

Litjens, Geert; Kooi, Thijs; Bejnordi, Babak Ehteshami; Setio, Arnaud Arindra Adiyoso; Ciompi, Francesco; Ghafoorian, Mohsen; van der Laak, Jeroen A W M; van Ginneken, Bram; Sánchez, Clara I

2017-12-01

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. Copyright © 2017 Elsevier B.V. All rights reserved.

Educating Through Exploration: Emerging Evidence for Improved Learning Outcomes Using a New Theory of Digital Learning Design

NASA Astrophysics Data System (ADS)

Anbar, Ariel; Center for Education Through eXploration

2018-01-01

Advances in scientific visualization and public access to data have transformed science outreach and communication, but have yet to realize their potential impacts in the realm of education. Computer-based learning is a clear bridge between visualization and education that benefits students through adaptative personalization and enhanced access. Building this bridge requires close partnerships among scientists, technologists, and educators.The Infiniscope project fosters such partnerships to produce exploration-driven online learning experiences that teach basic science concepts using a combination of authentic space science narratives, data, and images, and a personalized guided inquiry approach. Infiniscope includes a web portal to host these digital learning experiences, as well as a teaching network of educators using and modifying these experiences. Infiniscope experiences are built around a new theory of digital learning design that we call “education through exploration” (ETX) developed during the creation of successful online, interactive science courses offered at ASU and other institutions. ETX builds on the research-based practices of active learning and guided inquiry to provide a set of design principles that aim to develop higher order thinking skills in addition to understanding of content. It is employed in these experiences by asking students to solve problems and actively discover relationships, supported by an intelligent tutoring system which provides immediate, personalized feedback and scaffolds scientific thinking and methods. The project is led by ASU’s School of Earth and Space Exploration working with learning designers in the Center for Education Through eXploration, with support from NASA’s Science Mission Directorate as part of the NASA Exploration Connection program.We will present an overview of ETX design, the Infinscope project, and emerging evidence of effectiveness.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

Audio-based deep music emotion recognition

NASA Astrophysics Data System (ADS)

Liu, Tong; Han, Li; Ma, Liangkai; Guo, Dongwei

2018-05-01

As the rapid development of multimedia networking, more and more songs are issued through the Internet and stored in large digital music libraries. However, music information retrieval on these libraries can be really hard, and the recognition of musical emotion is especially challenging. In this paper, we report a strategy to recognize the emotion contained in songs by classifying their spectrograms, which contain both the time and frequency information, with a convolutional neural network (CNN). The experiments conducted on the l000-song dataset indicate that the proposed model outperforms traditional machine learning method.

Autofocusing in digital holography using deep learning

NASA Astrophysics Data System (ADS)

Ren, Zhenbo; Xu, Zhimin; Lam, Edmund Y.

2018-02-01

In digital holography, it is critical to know the distance in order to reconstruct the multi-sectional object. This autofocusing is traditionally solved by reconstructing a stack of in-focus and out-of-focus images and using some focus metric, such as entropy or variance, to calculate the sharpness of each reconstructed image. Then the distance corresponding to the sharpest image is determined as the focal position. This method is effective but computationally demanding and time-consuming. To get an accurate estimation, one has to reconstruct many images. Sometimes after a coarse search, a refinement is needed. To overcome this problem in autofocusing, we propose to use deep learning, i.e., a convolutional neural network (CNN), to solve this problem. Autofocusing is viewed as a classification problem, in which the true distance is transferred as a label. To estimate the distance is equated to labeling a hologram correctly. To train such an algorithm, totally 1000 holograms are captured under the same environment, i.e., exposure time, incident angle, object, except the distance. There are 5 labels corresponding to 5 distances. These data are randomly split into three datasets to train, validate and test a CNN network. Experimental results show that the trained network is capable of predicting the distance without reconstructing or knowing any physical parameters about the setup. The prediction time using this method is far less than traditional autofocusing methods.

West Virginia Digital Learning: Report to the Governor, Legislature, and West Virginia Board of Education

ERIC Educational Resources Information Center

Alliance for Excellent Education, 2014

2014-01-01

Accomplishing personalized, deeper learning through anywhere, anytime digital learning requires a redesign of the K-12 education system. This report looks at readiness for digital learning at two levels in West Virginia: the district capacity building to ready the system for digital learning and school implementation of digital learning. The…

An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

PubMed

Cho, Hwasuk; Son, Hyunwoo; Seong, Kihwan; Kim, Byungsub; Park, Hong-June; Sim, Jae-Yoon

2018-02-01

This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture achieves simultaneous processing of multiplications and accumulations. In addition, a transposable memory read for both forward and backward propagations and a virtual lookup table are also proposed to perform an unsupervised learning of restricted Boltzmann machine. The IC is fabricated using 28-nm CMOS process and is verified in a three-layer network of encoder-decoder pair for training and recovery of images with two-dimensional pixels. With a dataset of 50 digits, the IC shows a normalized root mean square error of 0.078. Measured energy efficiencies are 4.46 pJ per synaptic operation for inference and 19.26 pJ per synaptic weight update for learning, respectively. The learning performance is also estimated by simulations if the proposed hardware architecture is extended to apply to a batch training of 60 000 MNIST datasets.

Learning to Write in the Digital Age: ELLs' Literacy Practices in and out of Their Western Urban High School

ERIC Educational Resources Information Center

Pu, Jiang

2013-01-01

The definition of literacy is constantly changing and expanding. A sociocultural view of Literacy considers literacy to be multiple, multimodal, and multilingual as situated in and across the social and cultural contexts. As technology, new media and social network has reformed many aspects of writing, they provide ELLs (English language learners)…

Digital Learning Network Education Events of NASA's Extreme Environments Mission Operations

NASA Technical Reports Server (NTRS)

Paul, Heather; Guillory, Erika

2007-01-01

NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and web casting. The DLN has created a series of live education videoconferences connecting NASA s Extreme Environment Missions Operations (NEEMO) team to students across the United States. The programs are also extended to students around the world live web casting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO Crewmembers including NASA astronauts, engineers and scientists inform and inspire students about the importance of exploration and share the impact of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration s underwater laboratory. With this program, NASA continues the Agency s tradition of investing in the nation's education programs. It is directly tied to the Agency's major education goal of attracting and retaining students in science, technology, and engineering disciplines. Before connecting with the aquanauts, the students conduct experiments of their own designed to coincide with mission objectives. This paper describes the events that took place in September 2006.

A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

PubMed Central

Qiao, Ning; Mostafa, Hesham; Corradi, Federico; Osswald, Marc; Stefanini, Fabio; Sumislawska, Dora; Indiveri, Giacomo

2015-01-01

Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge. In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems. The proposed architecture allows the on-chip configuration of a wide range of network connectivities, including recurrent and deep networks, with short-term and long-term plasticity. The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities. In addition to the analog circuits, the device comprises also asynchronous digital logic circuits for setting different synapse and neuron properties as well as different network configurations. This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm2, and consumes approximately 4 mW for typical experiments, for example involving attractor networks. Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential. By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities. PMID:25972778

Deep learning for medical image segmentation - using the IBM TrueNorth neurosynaptic system

NASA Astrophysics Data System (ADS)

Moran, Steven; Gaonkar, Bilwaj; Whitehead, William; Wolk, Aidan; Macyszyn, Luke; Iyer, Subramanian S.

2018-03-01

Deep convolutional neural networks have found success in semantic image segmentation tasks in computer vision and medical imaging. These algorithms are executed on conventional von Neumann processor architectures or GPUs. This is suboptimal. Neuromorphic processors that replicate the structure of the brain are better-suited to train and execute deep learning models for image segmentation by relying on massively-parallel processing. However, given that they closely emulate the human brain, on-chip hardware and digital memory limitations also constrain them. Adapting deep learning models to execute image segmentation tasks on such chips, requires specialized training and validation. In this work, we demonstrate for the first-time, spinal image segmentation performed using a deep learning network implemented on neuromorphic hardware of the IBM TrueNorth Neurosynaptic System and validate the performance of our network by comparing it to human-generated segmentations of spinal vertebrae and disks. To achieve this on neuromorphic hardware, the training model constrains the coefficients of individual neurons to {-1,0,1} using the Energy Efficient Deep Neuromorphic (EEDN)1 networks training algorithm. Given the 1 million neurons and 256 million synapses, the scale and size of the neural network implemented by the IBM TrueNorth allows us to execute the requisite mapping between segmented images and non-uniform intensity MR images >20 times faster than on a GPU-accelerated network and using <0.1 W. This speed and efficiency implies that a trained neuromorphic chip can be deployed in intra-operative environments where real-time medical image segmentation is necessary.

Beneficial effects of reading aloud and solving simple arithmetic calculations (learning therapy) on a wide range of cognitive functions in the healthy elderly: study protocol for a randomized controlled trial

PubMed Central

2012-01-01

Background Almost all cognitive functions decline with age. Results of previous studies have shown that cognitive training related to everyday life (reading aloud and solving simple arithmetic calculations), namely learning therapy, can improve two cognitive function (executive functions and processing speed) in elderly people. However, it remains unclear whether learning therapy engenders improvement of various cognitive functions or not. We investigate the impact of learning therapy on various cognitive functions (executive functions, episodic memory, short-term memory, working memory, attention, reading ability, and processing speed) in healthy older adults. Methods We use a single-blinded intervention with two parallel groups (a learning therapy group and a waiting list control group). Testers are blind to the study hypothesis and the group membership of participants. Through an advertisement in local newspaper, 64 healthy older adults are recruited. They will be assigned randomly to a learning therapy group or a waiting list control group. In the learning therapy group, participants are required to perform two cognitive tasks for 6 months: reading Japanese aloud and solving simple calculations. The waiting list group does not participate in the intervention. The primary outcome measure is the Stroop test score: a measure of executive function. Secondary outcome measures are assessments including the following: verbal fluency task, logical memory, first and second names, digit span forward, digit span backward, Japanese reading test, digit cancellation task, digit symbol coding, and symbol search. We assess these outcome measures before and after the intervention. Discussion This report is the first study which investigates the beneficial effects of learning therapy on a wide range of cognitive functions of elderly people. Our study provides sufficient evidence of learning therapy effectiveness. Most cognitive functions, which are correlated strongly with daily life activities, decrease with age. These study results can elucidate effects of cognitive training on elderly people. Trial registration This trial was registered in The University Hospital Medical Information Network Clinical Trials Registry (No. UMIN000006998). PMID:22483196

Beneficial effects of reading aloud and solving simple arithmetic calculations (learning therapy) on a wide range of cognitive functions in the healthy elderly: study protocol for a randomized controlled trial.

PubMed

Nouchi, Rui; Taki, Yasuyuki; Takeuchi, Hikaru; Hashizume, Hiroshi; Nozawa, Takayuki; Sekiguchi, Atsushi; Nouchi, Haruka; Kawashima, Ryuta

2012-04-06

Almost all cognitive functions decline with age. Results of previous studies have shown that cognitive training related to everyday life (reading aloud and solving simple arithmetic calculations), namely learning therapy, can improve two cognitive function (executive functions and processing speed) in elderly people. However, it remains unclear whether learning therapy engenders improvement of various cognitive functions or not. We investigate the impact of learning therapy on various cognitive functions (executive functions, episodic memory, short-term memory, working memory, attention, reading ability, and processing speed) in healthy older adults. We use a single-blinded intervention with two parallel groups (a learning therapy group and a waiting list control group). Testers are blind to the study hypothesis and the group membership of participants. Through an advertisement in local newspaper, 64 healthy older adults are recruited. They will be assigned randomly to a learning therapy group or a waiting list control group. In the learning therapy group, participants are required to perform two cognitive tasks for 6 months: reading Japanese aloud and solving simple calculations. The waiting list group does not participate in the intervention. The primary outcome measure is the Stroop test score: a measure of executive function. Secondary outcome measures are assessments including the following: verbal fluency task, logical memory, first and second names, digit span forward, digit span backward, Japanese reading test, digit cancellation task, digit symbol coding, and symbol search. We assess these outcome measures before and after the intervention. This report is the first study which investigates the beneficial effects of learning therapy on a wide range of cognitive functions of elderly people. Our study provides sufficient evidence of learning therapy effectiveness. Most cognitive functions, which are correlated strongly with daily life activities, decrease with age. These study results can elucidate effects of cognitive training on elderly people. This trial was registered in The University Hospital Medical Information Network Clinical Trials Registry (No. UMIN000006998).

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

NASA Astrophysics Data System (ADS)

Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

2010-02-01

In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Astronomy in the Digital Universe

NASA Astrophysics Data System (ADS)

Haisch, Bernard M.; Lindblom, J.; Terzian, Y.

2006-12-01

The Digital Universe is an Internet project whose mission is to provide free, accurate, unbiased information covering all aspects of human knowledge, and to inspire humans to learn, make use of, and expand this knowledge. It is planned to be a decades long effort, inspired by the Encyclopedia Galactica concept popularized by Carl Sagan, and is being developed by the non-profit Digital Universe Foundation. A worldwide network of experts is responsible for selecting content featured within the Digital Universe. The first publicly available content is the Encyclopedia of Earth, a Boston University project headed by Prof. Cutler Cleveland, which will be part of the Earth Portal. The second major content area will be an analogous Encyclopedia of the Cosmos to be part of the Cosmos Portal. It is anticipated that this will evolve into a major resource for astronomy education. Authors and topic editors are now being recruited for the Encyclopedia of the Cosmos.

Dynamically stable associative learning: a neurobiologically based ANN and its applications

NASA Astrophysics Data System (ADS)

Vogl, Thomas P.; Blackwell, Kim L.; Barbour, Garth; Alkon, Daniel L.

1992-07-01

Most currently popular artificial neural networks (ANN) are based on conceptions of neuronal properties that date back to the 1940s and 50s, i.e., to the ideas of McCullough, Pitts, and Hebb. Dystal is an ANN based on current knowledge of neurobiology at the cellular and subcellular level. Networks based on these neurobiological insights exhibit the following advantageous properties: (1) A theoretical storage capacity of bN non-orthogonal memories, where N is the number of output neurons sharing common inputs and b is the number of distinguishable (gray shade) levels. (2) The ability to learn, store, and recall associations among noisy, arbitrary patterns. (3) A local synaptic learning rule (learning depends neither on the output of the post-synaptic neuron nor on a global error term), some of whose consequences are: (4) Feed-forward, lateral, and feed-back connections (as well as time-sensitive connections) are possible without alteration of the learning algorithm; (5) Storage allocation (patch creation) proceeds dynamically as associations are learned (self- organizing); (6) The number of training set presentations required for learning is small (< 10) and does not change with pattern size or content; and (7) The network exhibits monotonic convergence, reaching equilibrium (fully trained) values without oscillating. The performance of Dystal on pattern completion tasks such as faces with different expressions and/or corrupted by noise, and on reading hand-written digits (98% accuracy) and hand-printed Japanese Kanji (90% accuracy) is demonstrated.

Making cytological diagnoses on digital images using the iPath network.

PubMed

Dalquen, Peter; Savic Prince, Spasenija; Spieler, Peter; Kunze, Dietmar; Neumann, Heinrich; Eppenberger-Castori, Serenella; Adams, Heiner; Glatz, Katharina; Bubendorf, Lukas

2014-01-01

The iPath telemedicine platform Basel is mainly used for histological and cytological consultations, but also serves as a valuable learning tool. To study the level of accuracy in making diagnoses based on still images achieved by experienced cytopathologists, to identify limiting factors, and to provide a cytological image series as a learning set. Images from 167 consecutive cytological specimens of different origin were uploaded on the iPath platform and evaluated by four cytopathologists. Only wet-fixed and well-stained specimens were used. The consultants made specific diagnoses and categorized each as benign, suspicious or malignant. For all consultants, specificity and sensitivity regarding categorized diagnoses were 83-92 and 85-93%, respectively; the overall accuracy was 88-90%. The interobserver agreement was substantial (κ = 0.791). The lowest rate of concordance was achieved in urine and bladder washings and in the identification of benign lesions. Using a digital image set for diagnostic purposes implies that even under optimal conditions the accuracy rate will not exceed to 80-90%, mainly because of lacking supportive immunocytochemical or molecular tests. This limitation does not disqualify digital images for teleconsulting or as a learning aid. The series of images used for the study are open to the public at http://pathorama.wordpress.com/extragenital-cytology-2013/. © 2014 S. Karger AG, Basel.

Kinship--king's social harmonisation project. Pilot phase of a social network for use in higher education (HE).

PubMed

John, B A

2013-05-08

Students entering Higher Education are increasingly information and communications technology literate. Many students (graduates and undergraduates) arrive as "digital residents", who are adept with social media and technologically fluent. The informal use of social media for learning is becoming increasingly evident, along with the potentially detrimental effects of a poor digital profile on employment prospects. This paper describes the creation of Kinship (King's Social Harmonisation Project), a university hosted, members only social network, which is currently being piloted in the Medical School at King's College London. Along with a number of other teaching and learning resources, it is intended to use Kinship to establish an informal code of conduct by modelling and moderating appropriate professional online behaviour. Kinship was developed using an open source Elgg platform, thanks to funding of £20,000 from the College Teaching Fund under the mentorship of Brighton University (1). This educational research project, led by Medicine, was proposed to select, customise and evaluate a social networking platform in order to provide functionality that would enhance new and existing e-learning resources, support group interaction, participation and sharing and meet the diverse needs of three academic schools: Medicine, the Dental Institute and two separate Departments, the Modern Languages Centre and the Department of English from Arts & Humanities, as a pilot for wider College deployment. Student involvement is central to the project, from conducting the evaluation to moulding and customising the functionality and look of Kinship, in order to ensure that the site is authentic and evolves in response to their wishes and requirements. Formal evaluation of Kinship commences summer 2012.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting.

PubMed

Alomar, Miquel L; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

PubMed Central

Alomar, Miquel L.; Canals, Vincent; Perez-Mora, Nicolas; Martínez-Moll, Víctor; Rosselló, Josep L.

2016-01-01

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities. In this work, we show a new approach to implement RC systems with digital gates. The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations. The result is the development of a highly functional system with low hardware resources. The presented methodology is applied to chaotic time-series forecasting. PMID:26880876

GPON FTTH trial: lessons learned

NASA Astrophysics Data System (ADS)

Weis, Erik; Hölzl, Rainer; Breuer, Dirk; Lange, Christoph

2009-11-01

This paper reports on a FTTH field trial with GPON (Gigabit-capable passive optical network) technology in the network of Deutsche Telekom in the region of the cities of Berlin and Potsdam. Focus of this trial was to gain practical experience regarding GPON technology, fibre installation in existing ducts with micro duct technology, fibre cabling in customer buildings and impact on operational processes. Furthermore it is reported on an initial Deutsche Telekom FTTB deployment based on GPON technology in the city of Dresden with the main targets to obtain practical deployment and operation experiences with fibre-based access networks and to provide broadband access to a part of the city formerly not servable by DSL (digital subscriber line) technology.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Temporal neural networks and transient analysis of complex engineering systems

NASA Astrophysics Data System (ADS)

Uluyol, Onder

A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

Living and Learning with New Media: Summary of Findings from the Digital Youth Project

ERIC Educational Resources Information Center

Ito, Mizuko; Horst, Heather; Bittanti, Matteo; Boyd, Danah; Herr-Stephenson, Becky; Lange, Patricia G.; Pascoe, C.J.; Robinson, Laura

2008-01-01

Social network sites, online games, video-sharing sites, and gadgets such as iPods and mobile phones are now fixtures of youth culture. They have so permeated young lives that it is hard to believe that less than a decade ago these technologies barely existed. Today's youth may be coming of age and struggling for autonomy and identity as did their…

You Can Lead Students to the Classroom, and You Can Make Them Think: Ten Brain-Based Strategies for College Teaching and Learning Success

ERIC Educational Resources Information Center

Freeman, Greta G.; Wash, Pamela D.

2013-01-01

Teaching in the digital age has become increasingly challenging for college and university faculty. Application, relevance, and active engagement rather than traditional PowerPoint slide show lectures are what our technology-savvy, socially networked students crave and need to keep their attention and interest levels high. Using a combination of…

A flexible, open, decentralized system for digital pathology networks.

PubMed

Schuler, Robert; Smith, David E; Kumaraguruparan, Gowri; Chervenak, Ann; Lewis, Anne D; Hyde, Dallas M; Kesselman, Carl

2012-01-01

High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers.

A Flexible, Open, Decentralized System for Digital Pathology Networks

PubMed Central

SMITH, David E.; KUMARAGURUPARAN, Gowri; CHERVENAK, Ann; LEWIS, Anne D.; HYDE, Dallas M.; KESSELMAN, Carl

2014-01-01

High-resolution digital imaging is enabling digital archiving and sharing of digitized microscopy slides and new methods for digital pathology. Collaborative research centers, outsourced medical services, and multi-site organizations stand to benefit from sharing pathology data in a digital pathology network. Yet significant technological challenges remain due to the large size and volume of digitized whole slide images. While information systems do exist for managing local pathology laboratories, they tend to be oriented toward narrow clinical use cases or offer closed ecosystems around proprietary formats. Few solutions exist for networking digital pathology operations. Here we present a system architecture and implementation of a digital pathology network and share results from a production system that federates major research centers. PMID:22941985

Twenty-first century learning in school systems: the case of the Metropolitan School District of Lawrence Township, Indianapolis, Indiana.

PubMed

Capuano, Marcia; Knoderer, Troy

2006-01-01

To empower students with skills such as information and technological literacy, global awareness and cultural competence, self-direction, and sound reasoning, teachers must master these skills themselves. This chapter examines how the Digital Age Literacy Initiative of the Metropolitan School District of Lawrence Township in Indianapolis, Indiana, which is funded by the Lilly Endowment, incorporated twenty-first century learning through a systemic approach involving teacher training and the use of data. The authors explain the district's content, process, and context goals toward accomplishing its mission of empowering students with the necessary twenty-first century skills to succeed in the digital age. The district places a strong emphasis on professional development for teachers. To support the necessary teacher learning and therefore sustain the work of the initiative, the district has adopted action research, self-assessment, and an online professional development network. To support teachers in implementing new strategies, master teachers serve as digital age literacy coaches. The chapter discusses the initiative's focus on evidence of progress. Through a partnership with the Metiri Group of California, the district has built a range of assessments including online inventories and twenty-first century skill rubrics. For example, the Mankato Survey collected teacher and student data around access, ability, and use of technology in the classroom in 2001 and then in 2004. This research showed significant gains in some technologies across all grade levels and consistent gains in nearly all technologies for middle and high school students. As it moves into the next phase of implementing the Digital Age Literacy Initiative, the district embraces the systemic shifts in school culture necessary to institutionalize twenty-first century learning.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Teaching and Learning in the Digital Age

ERIC Educational Resources Information Center

Starkey, Louise

2012-01-01

"Teaching and Learning in the Digital Age" is for all those interested in considering the impact of emerging digital technologies on teaching and learning. It explores the concept of a digital age and perspectives of knowledge, pedagogy and practice within a digital context. By examining teaching with digital technologies through new learning…

Digital Literacy: A Prerequisite for Effective Learning in a Blended Learning Environment?

ERIC Educational Resources Information Center

Tang, Chun Meng; Chaw, Lee Yen

2016-01-01

Blended learning has propelled into mainstream education in recent years with the help of digital technology. Commonly available digital devices and the Internet have made access to learning resources such as learning management systems, online libraries, digital media, etc. convenient and flexible for both lecturers and students. Beyond the…

Auto-programmable impulse neural circuits

NASA Technical Reports Server (NTRS)

Watula, D.; Meador, J.

1990-01-01

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

[Digital learning and teaching in medical education : Already there or still at the beginning?

PubMed

Kuhn, Sebastian; Frankenhauser, Susanne; Tolks, Daniel

2018-02-01

The current choice of digital teaching and learning formats in medicine is very heterogeneous. In addition to the widely used classical static formats, social communication tools, audio/video-based media, interactive formats, and electronic testing systems enrich the learning environment.For medical students, the private use of digital media is not necessarily linked to their meaningful use in the study. Many gain their experience of digital learning in the sense of "assessment drives learning", especially by taking online exams in a passive, consuming role. About half of all medical students can be referred to as "e-examinees" whose handling of digital learning is primarily focused on online exam preparation. Essentially, they do not actively influence their digital environment. Only a quarter can be identified as a "digital all-rounder", who compiles their individual learning portfolio from the broad range of digital media.At present, the use of digital media is not yet an integral and comprehensive component of the teaching framework of medical studies in Germany, but is rather used in the sense of a punctual teaching enrichment. Current trends in digital teaching and learning offerings are mobile, interactive, and personalized platforms as well as increasing the relevance of learning platforms. Furthermore, didactical concepts targeting the changed learning habits of the students are more successful regarding the acceptance and learning outcomes. In addition, digitalization is currently gaining importance as a component in the medical school curricula.

The Significance of Kinship for Medical Education: Reflections on the Use of a Bespoke Social Network to Support Learners’ Professional Identities

PubMed Central

2016-01-01

Background Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. Objective In this paper we report on the evaluation of an institutional social network—King's Social Harmonisation Project (KINSHIP)—established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King’s College London, United Kingdom. Methods Our evaluation focused on a study that examined students’ needs and perceptions with regard to the provision of a cross-university platform. Data were collected from students, including those in the field of health and social care, in order to recommend a practical way forward to address current needs in this area. Results The findings indicate that the majority of the respondents were positive about using a social networking platform to develop their professional voice and profiles. Results suggest that timely promotion of the platform, emphasis on interface and learning design, and a clear identity are required in order to gain acceptance as the institutional social networking site. Conclusions Empirical findings in this study project an advantage of an institutional social network such a KINSHIP over other social networks (eg, Facebook) because access is limited to staff and students and the site is mainly being used for academic purposes. PMID:27731848

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system.

PubMed

Al-Masni, Mohammed A; Al-Antari, Mugahed A; Park, Jeong-Min; Gi, Geon; Kim, Tae-Yeon; Rivera, Patricio; Valarezo, Edwin; Choi, Mun-Taek; Han, Seung-Moo; Kim, Tae-Seong

2018-04-01

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework. The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant. Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%. Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. Copyright © 2018 Elsevier B.V. All rights reserved.

Digital intelligent booster for DCC miniature train networks

NASA Astrophysics Data System (ADS)

Ursu, M. P.; Condruz, D. A.

2017-08-01

Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

A similarity learning approach to content-based image retrieval: application to digital mammography.

PubMed

El-Naqa, Issam; Yang, Yongyi; Galatsanos, Nikolas P; Nishikawa, Robert M; Wernick, Miles N

2004-10-01

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.

The Indonesian Digital Library Network Is Born To Struggle with the Digital Divide.

ERIC Educational Resources Information Center

Fahmi, Ismail

2002-01-01

Describes the Indonesian Digital Library Network that is designed to develop Indonesia as a knowledge-based society. Highlights include the digital divide; problems in a developing country, including Internet accessibility, bandwidth capacity, and network delays; gathering information about national assets; information infrastructure; data…

Twelve tips for using social media as a medical educator.

PubMed

Kind, Terry; Patel, Pradip D; Lie, Désirée; Chretien, Katherine C

2014-04-01

We now live, learn, teach and practice medicine in the digital era. Social networking sites are used by at least half of all adults. Engagement with social media can be personal, professional, or both, for health-related and educational purposes. Use is often public. Lapses in professionalism can have devastating consequences, but when used well social media can enhance the lives of and learning by health professionals and trainees, ultimately for public good. Both risks and opportunities abound for individuals who participate, and health professionals need tips to enhance use and avoid pitfalls in their use of social media and to uphold their professional values. This article draws upon current evidence, policies, and the authors' experiences to present best practice tips for health professions educators, trainees, and students to build a framework for navigating the digital world in a way that maintains and promotes professionalism. These practical tips help the newcomer to social media get started by identifying goals, establishing comfort, and connecting. Furthermore, users can ultimately successfully contribute, engage, learn, and teach, and model professional behaviors while navigating social media.

Characterizing the Networks of Digital Information that Support Collaborative Adaptive Forest Management in Sierra Nevada Forests.

PubMed

Lei, Shufei; Iles, Alastair; Kelly, Maggi

2015-07-01

Some of the factors that can contribute to the success of collaborative adaptive management--such as social learning, open communication, and trust--are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study--the Sierra Nevada Adaptive Management Project--using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks.

Characterizing the Networks of Digital Information that Support Collaborative Adaptive Forest Management in Sierra Nevada Forests

NASA Astrophysics Data System (ADS)

Lei, Shufei; Iles, Alastair; Kelly, Maggi

2015-07-01

Some of the factors that can contribute to the success of collaborative adaptive management—such as social learning, open communication, and trust—are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study—the Sierra Nevada Adaptive Management Project—using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks.

A robust sound perception model suitable for neuromorphic implementation.

PubMed

Coath, Martin; Sheik, Sadique; Chicca, Elisabetta; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas

2013-01-01

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to "noisy" stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.

Lights, Camera, Action Research: The Effects of Didactic Digital Movie Making on Students' Twenty-First Century Learning Skills and Science Content in the Middle School Classroom

ERIC Educational Resources Information Center

Ochsner, Karl

2010-01-01

Students are moving away from content consumption to content production. Short movies are uploaded onto video social networking sites and shared around the world. Unfortunately they usually contain little to no educational value, lack a narrative and are rarely created in the science classroom. According to new Arizona Technology standards and…

The Combined Enterprise Regional Information Exchange System -- The Way Ahead

DTIC Science & Technology

2007-09-01

the more complex, difficult functions have been centralized to areas under less stress that are staffed with support personnel with a higher level of...ABBREVIATIONS AND ACRONYMS AAP Accelerated Acquisition Plan ACAT Acquisition Category ADNS Automated Digital Network System ALT Actual Learning...a half, while we worked on this project was invaluable. We would especially like to thank LtCol Karl Pfeiffer and Mr. Buddy Barreto for their

Digital Systems Supporting Cognition and Exploratory Learning in Twenty-First Century: Guest Editorial

ERIC Educational Resources Information Center

Spector, J. Michael; Ifenthaler, Dirk; Sampson, Demetrios G.

2016-01-01

Digital systems and digital technologies are globally investigated for their potential to transform learning, teaching and assessment towards offering unique learning experiences to the twenty-first century learners. This Special Issue on "Digital systems supporting cognition and exploratory learning in twenty-first century" aims to…

Construction of a Digital Learning Environment Based on Cloud Computing

ERIC Educational Resources Information Center

Ding, Jihong; Xiong, Caiping; Liu, Huazhong

2015-01-01

Constructing the digital learning environment for ubiquitous learning and asynchronous distributed learning has opened up immense amounts of concrete research. However, current digital learning environments do not fully fulfill the expectations on supporting interactive group learning, shared understanding and social construction of knowledge.…

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Clinical Neuropathology Views - 2/2016: Digital networking in European neuropathology: An initiative to facilitate truly interactive consultations.

PubMed

Idoate, Miguel A; García-Rojo, Marcial

2016-01-01

Digital technology is progressively changing our vision of the practice of neuropathology. There are a number of facts that support the introduction of digital neuropathology. With the development of wholeslide imaging (WSI) systems the difficulties involved in implementing a neuropathology network have been solved. A relevant difficulty has been image standardization, but an open digital image communication protocol defined by the Digital Imaging and Communications in Medicine (DICOM) standard is already a reality. The neuropathology network should be established in Europe because it is the expected geographic context for relationships among European neuropathologists. There are several limitations in the implementation of a digital neuropathology consultancy network such as financial support, operational costs, legal issues, and technical assistance of clients. All of these items have been considered and should be solved before implementing the proposal. Finally, the authors conclude that a European digital neuropathology network should be created for patients' benefit.

Gaming, texting, learning? Teaching engineering ethics through students' lived experiences with technology.

PubMed

Voss, Georgina

2013-09-01

This paper examines how young peoples' lived experiences with personal technologies can be used to teach engineering ethics in a way which facilitates greater engagement with the subject. Engineering ethics can be challenging to teach: as a form of practical ethics, it is framed around future workplace experience in a professional setting which students are assumed to have no prior experience of. Yet the current generations of engineering students, who have been described as 'digital natives', do however have immersive personal experience with digital technologies; and experiential learning theory describes how students learn ethics more successfully when they can draw on personal experience which give context and meaning to abstract theories. This paper reviews current teaching practices in engineering ethics; and examines young people's engagement with technologies including cell phones, social networking sites, digital music and computer games to identify social and ethical elements of these practices which have relevance for the engineering ethics curricula. From this analysis three case studies are developed to illustrate how facets of the use of these technologies can be drawn on to teach topics including group work and communication; risk and safety; and engineering as social experimentation. Means for bridging personal experience and professional ethics when teaching these cases are discussed. The paper contributes to research and curriculum development in engineering ethics education, and to wider education research about methods of teaching 'the net generation'.

Learning to Teach in the Digital Age: New Materialities and Maker Paradigms in Schools. New Literacies and Digital Epistemologies

ERIC Educational Resources Information Center

Justice, Sean

2016-01-01

"Learning to Teach in the Digital Age" tells the story of a group of K-12 teachers as they began to connect with digital making and learning pedagogies. Guiding questions at the heart of this qualitative case study asked how teaching practices engaged with and responded to the maker movement and digital making and learning tools and…

Digital Networked Information Society and Public Health: Problems and Promises of Networked Health Communication of Lay Publics.

PubMed

Kim, Jeong-Nam

2018-01-01

This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.

Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers

ERIC Educational Resources Information Center

Gracious, F. L. Antony; Shyla, F. L. Jasmine Anne

2012-01-01

The present study Multiple Intelligence and Digital Learning Awareness of prospective B.Ed teachers was probed to find the relationship between Multiple Intelligence and Digital Learning Awareness of Prospective B.Ed Teachers. Data for the study were collected using self made Multiple Intelligence Inventory and Digital Learning Awareness Scale.…

Online Activities, Digital Media Literacy, and Networked Individualism of Korean Youth

ERIC Educational Resources Information Center

Park, Sora; Kim, Eun-mee; Na, Eun-Yeong

2015-01-01

Networked individualism enables Internet users to connect and socialize via their loose and transient multiple networks, whereas digital media literacy is a precondition of effective Internet use. In this study, an attempt has been made to find the link between networked individualism, digital media literacy, and young people's perception of their…

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

2018-02-01

Deep-learning models are highly parameterized, causing difficulty in inference and transfer learning. We propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in DBT while maintaining the classification accuracy. Two-stage transfer learning was used to adapt the ImageNet-trained DCNN to mammography and then to DBT. In the first-stage transfer learning, transfer learning from ImageNet trained DCNN was performed using mammography data. In the second-stage transfer learning, the mammography-trained DCNN was trained on the DBT data using feature extraction from fully connected layer, recursive feature elimination and random forest classification. The layered pathway evolution encapsulates the feature extraction to the classification stages to compress the DCNN. Genetic algorithm was used in an iterative approach with tournament selection driven by count-preserving crossover and mutation to identify the necessary nodes in each convolution layer while eliminating the redundant nodes. The DCNN was reduced by 99% in the number of parameters and 95% in mathematical operations in the convolutional layers. The lesion-based area under the receiver operating characteristic curve on an independent DBT test set from the original and the compressed network resulted in 0.88+/-0.05 and 0.90+/-0.04, respectively. The difference did not reach statistical significance. We demonstrated a DCNN compression approach without additional fine-tuning or loss of performance for classification of masses in DBT. The approach can be extended to other DCNNs and transfer learning tasks. An ensemble of these smaller and focused DCNNs has the potential to be used in multi-target transfer learning.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

PubMed Central

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

PubMed

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Interactive Panel and Audience Discussion: The Future is Here: Can EPO Navigate the Digital Age?

NASA Astrophysics Data System (ADS)

Shipp, S. S.; Dribin, N.; Gay, P. L.; Stockman, S.

2010-08-01

The digital divide refers to the gap between individuals with access to digital technology and those with limited or no access. In the EPO profession there is another digital divide: the divide between EPO practitioners who believe Twitter and other forms of social networking are the downfall of literacy—and perhaps of American society, and those who see boundless potential for engaging a global audience in Earth and space science. One thing is certain: we're not in our parent's world anymore—our's is a world increasingly run by electrons and hand-held devices that inform, entertain, connect, and fragment our audiences into an infinite number of special-interest groups with shortened attention spans that form and reform in nonlinear ways. How does EPO evolve to match the new media and electronic realities? Is there still a place for storytelling, for laddered learning experiences, for traditional methods? How do we adapt? How do we rise to the new challenges of the new media age?

Digital Signal Processing and Control for the Study of Gene Networks

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun

2016-04-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks.

PubMed

Shin, Yong-Jun

2016-04-22

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks.

Digital Signal Processing and Control for the Study of Gene Networks

PubMed Central

Shin, Yong-Jun

2016-01-01

Thanks to the digital revolution, digital signal processing and control has been widely used in many areas of science and engineering today. It provides practical and powerful tools to model, simulate, analyze, design, measure, and control complex and dynamic systems such as robots and aircrafts. Gene networks are also complex dynamic systems which can be studied via digital signal processing and control. Unlike conventional computational methods, this approach is capable of not only modeling but also controlling gene networks since the experimental environment is mostly digital today. The overall aim of this article is to introduce digital signal processing and control as a useful tool for the study of gene networks. PMID:27102828

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

Literacy Learning in a Digitally Rich Humanities Classroom: Embracing Multiple, Collaborative, and Simultaneous Texts

ERIC Educational Resources Information Center

Buckley-Marudas, Mary Frances

2016-01-01

Understanding what happens when teachers embrace digital media for literacy learning is critical to realizing the potential of learning in the digital era. This article examines some of the ways that a high school teacher and his students leverage digital technologies for literacy learning in their humanities classrooms. The author introduces the…

Video data compression using artificial neural network differential vector quantization

NASA Technical Reports Server (NTRS)

Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

1991-01-01

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Connecting Land-Based Networks to Ships

DTIC Science & Technology

2013-06-01

multipoint wireless broadband systems, and WiMAX networks were initially deployed for fixed and nomadic (portable) applications. These standards...CAPABILITIES OF SHIP-TO-SHORE COMMUNICATIONS A. US Navy Automated Digital Network System (ADNS) The U.S. Navy’s Automated Digital Network System (ADNS...submit digitally any necessary documents to the terminal operators, contact their logistics providers, access tidal information and receive

Learning Behavior and Achievement Analysis of a Digital Game-Based Learning Approach Integrating Mastery Learning Theory and Different Feedback Models

ERIC Educational Resources Information Center

Yang, Kai-Hsiang

2017-01-01

It is widely accepted that the digital game-based learning approach has the advantage of stimulating students' learning motivation, but simply using digital games in the classroom does not guarantee satisfactory learning achievement, especially in the case of the absence of a teacher. Integrating appropriate learning strategies into a game can…

Anthropomorphic robot for recognition of objects

NASA Astrophysics Data System (ADS)

Ginzburg, Vera M.

1999-08-01

Heated debates were taking place a few decades ago between the proponents of digital and analog methods in information. Technology have resulted in unequivocal triumph of the former. However, some serious technological problems confronting the world civilization on the threshold of the new millennium, such as Y2K and computer network vulnerability, probably spring from this one-sided approach. Dire consequences of problems of this kind can be alleviated through learning from the nature.

Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017.

PubMed

Clunie, David; Hosseinzadeh, Dan; Wintell, Mikael; De Mena, David; Lajara, Nieves; Garcia-Rojo, Marcial; Bueno, Gloria; Saligrama, Kiran; Stearrett, Aaron; Toomey, David; Abels, Esther; Apeldoorn, Frank Van; Langevin, Stephane; Nichols, Sean; Schmid, Joachim; Horchner, Uwe; Beckwith, Bruce; Parwani, Anil; Pantanowitz, Liron

2018-01-01

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products.

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

PubMed

Janowczyk, Andrew; Doyle, Scott; Gilmore, Hannah; Madabhushi, Anant

2018-01-01

Deep learning (DL) has recently been successfully applied to a number of image analysis problems. However, DL approaches tend to be inefficient for segmentation on large image data, such as high-resolution digital pathology slide images. For example, typical breast biopsy images scanned at 40× magnification contain billions of pixels, of which usually only a small percentage belong to the class of interest. For a typical naïve deep learning scheme, parsing through and interrogating all the image pixels would represent hundreds if not thousands of hours of compute time using high performance computing environments. In this paper, we present a resolution adaptive deep hierarchical (RADHicaL) learning scheme wherein DL networks at lower resolutions are leveraged to determine if higher levels of magnification, and thus computation, are necessary to provide precise results. We evaluate our approach on a nuclear segmentation task with a cohort of 141 ER+ breast cancer images and show we can reduce computation time on average by about 85%. Expert annotations of 12,000 nuclei across these 141 images were employed for quantitative evaluation of RADHicaL. A head-to-head comparison with a naïve DL approach, operating solely at the highest magnification, yielded the following performance metrics: .9407 vs .9854 Detection Rate, .8218 vs .8489 F -score, .8061 vs .8364 true positive rate and .8822 vs 0.8932 positive predictive value. Our performance indices compare favourably with state of the art nuclear segmentation approaches for digital pathology images.

Digital interactive learning of oral radiographic anatomy.

PubMed

Vuchkova, J; Maybury, T; Farah, C S

2012-02-01

Studies reporting high number of diagnostic errors made from radiographs suggest the need to improve the learning of radiographic interpretation in the dental curriculum. Given studies that show student preference for computer-assisted or digital technologies, the purpose of this study was to develop an interactive digital tool and to determine whether it was more successful than a conventional radiology textbook in assisting dental students with the learning of radiographic anatomy. Eighty-eight dental students underwent a learning phase of radiographic anatomy using an interactive digital tool alongside a conventional radiology textbook. The success of the digital tool, when compared to the textbook, was assessed by quantitative means using a radiographic interpretation test and by qualitative means using a structured Likert scale survey, asking students to evaluate their own learning outcomes from the digital tool. Student evaluations of the digital tool showed that almost all participants (95%) indicated that the tool positively enhanced their learning of radiographic anatomy and interpretation. The success of the digital tool in assisting the learning of radiographic interpretation is discussed in the broader context of learning and teaching curricula, and preference (by students) for the use of this digital form when compared to the conventional literate form of the textbook. Whilst traditional textbooks are still valued in the dental curriculum, it is evident that the preference for computer-assisted learning of oral radiographic anatomy enhances the learning experience by enabling students to interact and better engage with the course material. © 2011 John Wiley & Sons A/S.

Performance evaluation of a distance learning program.

PubMed

Dailey, D J; Eno, K R; Brinkley, J F

1994-01-01

This paper presents a performance metric which uses a single number to characterize the response time for a non-deterministic client-server application operating over the Internet. When applied to a Macintosh-based distance learning application called the Digital Anatomist Browser, the metric allowed us to observe that "A typical student doing a typical mix of Browser commands on a typical data set will experience the same delay if they use a slow Macintosh on a local network or a fast Macintosh on the other side of the country accessing the data over the Internet." The methodology presented is applicable to other client-server applications that are rapidly appearing on the Internet.

Photometric redshift estimation based on data mining with PhotoRApToR

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Brescia, M.; De Stefano, V.; Longo, G.

2015-03-01

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C ++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multi-layer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and post-processing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.

One hundred case studies of Asia-Pacific telemedicine using a digital video transport system over a research and education network.

PubMed

Shimizu, Shuji; Nakashima, Naoki; Okamura, Koji; Tanaka, Masao

2009-01-01

Although the use of video in telemedicine is most helpful, the transmission of high-quality moving images is difficult in conventional systems due to the limitation of network bandwidth and the quality of service. We have established a new system via the academic broadband network that can preserve the original quality and assure smooth movement of the image. Here we report on 100 case studies and discuss the lessons we have learned. Kyushu University Hospital in Fukuoka, Japan, was linked to 53 medical institutions and meeting venues in 13 countries and regions over the Asia-Pacific Advanced Network, an international research and education consortium. The digital video transport system (DVTS), free software that transforms digital video signals directly into Internet Protocol, was installed on a personal computer (PC) with a network bandwidth of 30 Mbps per channel. Between February 2003 and June 2007, 100 telecommunication sessions were held, 94 of which were international and 6 domestic. Furthermore, 47 involved real-time demonstrations and 53 interactive teleconferences using video or PC presentations. Multiple stations were connected in 37 events, and the number of connected stations in total reached 269. The time delay was restricted to 0.3-1.0 seconds between the stations. Participants provided feedback via questionnaires, and with respect to image quality, 509 (68.3%) participants reported "very good," 206 (27.7%) reported "good," 19 (2.6%) reported "poor," and 11 (1.5%) reported "very poor." DVTS is both economical, with a minimal initial investment, and simple to set up, and this is the first time that this advanced system has been used so widely in the Asia-Pacific region. Because the high-speed academic network for research and education is available worldwide, we believe our cutting-edge technology will facilitate medical standardization beyond geographic borders in the world.

Digital and traditional slides for teaching cellular morphology: a comparative analysis of learning outcomes.

PubMed

Solberg, Brooke L

2012-01-01

Recent advances in technology have brought forth an intriguing new tool for teaching hematopoietic cellular identification skills: the digital slide. Although digitized slides offer a number of appealing options for educators, little research has been done to examine how their utilization would impact learning outcomes. To fill that void, this study was designed to examine student performance, skill retention and transferability, and self-efficacy beliefs amongst undergraduate MLS students learning cellular morphology with digital versus traditional slides. Results showed that students learning with digital slides performed better on assessments containing only traditional slide specimens than students learning with traditional slides, both immediately following the learning activity and after a considerable duration of time. Students learning with digital slides also reported slightly higher levels of self-efficacy related to cellular identification. The findings of this study suggest that students learning cellular identification skills with digital slides are able to transfer that skill directly to traditional slides, and that their ability to identify cells is not negatively affected in present or future settings.

Medical student use of digital learning resources.

PubMed

Scott, Karen; Morris, Anne; Marais, Ben

2018-02-01

University students expect to use technology as part of their studies, yet health professional teachers can struggle with the change in student learning habits fuelled by technology. Our research aimed to document the learning habits of contemporary medical students during a clinical rotation by exploring the use of locally and externally developed digital and print self-directed learning resources, and study groups. We investigated the learning habits of final-stage medical students during their clinical paediatric rotation using mixed methods, involving learning analytics and a student questionnaire. Learning analytics tracked aggregate student usage statistics of locally produced e-learning resources on two learning management systems and mobile learning resources. The questionnaire recorded student-reported use of digital and print learning resources and study groups. The students made extensive use of digital self-directed learning resources, especially in the 2 weeks before the examination, which peaked the day before the written examination. All students used locally produced digital formative assessment, and most (74/98; 76%) also used digital resources developed by other institutions. Most reported finding locally produced e-learning resources beneficial for learning. In terms of traditional forms of self-directed learning, one-third (28/94; 30%) indicated that they never read the course textbook, and few students used face-to-face 39/98 (40%) or online 6/98 (6%) study groups. Learning analytics and student questionnaire data confirmed the extensive use of digital resources for self-directed learning. Through clarification of learning habits and experiences, we think teachers can help students to optimise effective learning strategies; however, the impact of contemporary learning habits on learning efficacy requires further evaluation. Health professional teachers can struggle with the change in student learning habits fuelled by technology. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Predicting Digital Informal Learning: An Empirical Study among Chinese University Students

ERIC Educational Resources Information Center

He, Tao; Zhu, Chang; Questier, Frederik

2018-01-01

Although the adoption of digital technology has gained considerable attention in higher education, currently research mainly focuses on implementation in formal learning contexts. Investigating what factors influence students' digital informal learning is still unclear and limited. To understand better university students' digital informal…

Behavior-based network management: a unique model-based approach to implementing cyber superiority

NASA Astrophysics Data System (ADS)

Seng, Jocelyn M.

2016-05-01

Behavior-Based Network Management (BBNM) is a technological and strategic approach to mastering the identification and assessment of network behavior, whether human-driven or machine-generated. Recognizing that all five U.S. Air Force (USAF) mission areas rely on the cyber domain to support, enhance and execute their tasks, BBNM is designed to elevate awareness and improve the ability to better understand the degree of reliance placed upon a digital capability and the operational risk.2 Thus, the objective of BBNM is to provide a holistic view of the digital battle space to better assess the effects of security, monitoring, provisioning, utilization management, allocation to support mission sustainment and change control. Leveraging advances in conceptual modeling made possible by a novel advancement in software design and implementation known as Vector Relational Data Modeling (VRDM™), the BBNM approach entails creating a network simulation in which meaning can be inferred and used to manage network behavior according to policy, such as quickly detecting and countering malicious behavior. Initial research configurations have yielded executable BBNM models as combinations of conceptualized behavior within a network management simulation that includes only concepts of threats and definitions of "good" behavior. A proof of concept assessment called "Lab Rat," was designed to demonstrate the simplicity of network modeling and the ability to perform adaptation. The model was tested on real world threat data and demonstrated adaptive and inferential learning behavior. Preliminary results indicate this is a viable approach towards achieving cyber superiority in today's volatile, uncertain, complex and ambiguous (VUCA) environment.

Information technology and its role in anaesthesia training and continuing medical education.

PubMed

Chu, Larry F; Erlendson, Matthew J; Sun, John S; Clemenson, Anna M; Martin, Paul; Eng, Reuben L

2012-03-01

Today's educators are faced with substantial challenges in the use of information technology for anaesthesia training and continuing medical education. Millennial learners have uniquely different learning styles than previous generations of students. These preferences distinctly incorporate the use of digital information technologies and social technologies to support learning. To be effective teachers, modern educators must be familiar with these new information technologies and understand how to use them for medical education. Examples of new information technologies include learning management systems, lecture capture, social media (YouTube, Flickr), social networking (Facebook), Web 2.0, multimedia (video learning triggers and point-of-view video) and mobile computing applications. The information technology challenges for educators in the twenty-first century include: (a) understanding how technology shapes the learning preferences of today's anaesthesia residents, (b) distinguishing between the function and properties of new learning technologies and (c) properly using these learning technologies to enhance the anaesthesia curriculum. Copyright © 2012 Elsevier Ltd. All rights reserved.

Motivated Learning with Digital Learning Tasks: What about Autonomy and Structure?

ERIC Educational Resources Information Center

van Loon, Anne-Marieke; Ros, Anje; Martens, Rob

2012-01-01

In the present study, the ways in which digital learning tasks contribute to students' intrinsic motivation and learning outcomes were examined. In particular, this study explored the relative contributions of autonomy support and the provision of structure in digital learning tasks. Participants were 320 fifth- and sixth-grade students from eight…

Empirical Study on the Effect of Digital Game-Based Instruction on Students' Learning Motivation and Achievement

ERIC Educational Resources Information Center

Chen, Yen-Chun

2017-01-01

As pupils are largely increased the opportunities to contact digital games, the effect of digital games has been broadly discussed and studied. Digital games no longer play the function of entertainment, but could assist students in more active learning and deeper and broader learning, when being applied to instruction. It is limited to learn in…

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor)

2005-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Ellerbrock, Philip J. (Inventor); Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor); Grant, Robert L. (Inventor)

2004-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.

PubMed

Hatipoglu, Nuh; Bilgin, Gokhan

2017-10-01

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

Stress Prevention@Work: a study protocol for the evaluation of a multifaceted integral stress prevention strategy to prevent employee stress in a healthcare organization: a cluster controlled trial.

PubMed

Hoek, Rianne J A; Havermans, Bo M; Houtman, Irene L D; Brouwers, Evelien P M; Heerkens, Yvonne F; Zijlstra-Vlasveld, Moniek C; Anema, Johannes R; van der Beek, Allard J; Boot, Cécile R L

2017-07-17

Adequate implementation of work-related stress management interventions can reduce or prevent work-related stress and sick leave in organizations. We developed a multifaceted integral stress-prevention strategy for organizations from several sectors that includes a digital platform and collaborative learning network. The digital platform contains a stepwise protocol to implement work-related stress-management interventions. It includes stress screeners, interventions and intervention providers to facilitate access to and the selection of matching work-related stress-management interventions. The collaborative learning network, including stakeholders from various organizations, plans meetings focussing on an exchange of experiences and good practices among organizations for the implementation of stress prevention measures. This paper describes the design of an integral stress-prevention strategy, Stress Prevention@Work, and the protocol for the evaluation of: 1) the effects of the strategy on perceived stress and work-related outcomes, and 2) the barriers and facilitators for implementation of the strategy. The effectiveness of Stress Prevention@Work will be evaluated in a cluster controlled trial, in a large healthcare organization in the Netherlands, at six and 12 months. An independent researcher will match teams on working conditions and size and allocate the teams to the intervention or control group. Teams in the intervention group will be offered Stress Prevention@Work. For each intervention team, one employee is responsible for applying the strategy within his/her team using the digital platform and visiting the collaborative learning network. Using a waiting list design, the control group will be given access to the strategy after 12 months. The primary outcome is the employees' perceived stress measured by the stress subscale of the Depression, Anxiety, and Stress Scale (DASS-21). Secondary outcome measures are job demands, job resources and the number of preventive stress measures implemented at the team level. Alongside the trial, a process evaluation, including barriers and facilitators of the implementation of Stress Prevention@Work, will be conducted in one healthcare organisation. If Stress Prevention@Work is found to be effective in one healthcare organisation, further implementation on a broader scale might lead to increased productivity and decreased stress and sick leave in other organizations. Results are expected in 2018. NTR5527 . Registered 7 Dec 2015.

Time concurrency/phase-time synchronization in digital communications networks

NASA Technical Reports Server (NTRS)

Kihara, Masami; Imaoka, Atsushi

1990-01-01

Digital communications networks have the intrinsic capability of time synchronization which makes it possible for networks to supply time signals to some applications and services. A practical estimation method for the time concurrency on terrestrial networks is presented. By using this method, time concurrency capability of the Nippon Telegraph and Telephone Corporation (NTT) digital communications network is estimated to be better than 300 ns rms at an advanced level, and 20 ns rms at final level.

Can New Digital Technologies Support Parasitology Teaching and Learning?

PubMed

Jabbar, Abdul; Gasser, Robin B; Lodge, Jason

2016-07-01

Traditionally, parasitology courses have mostly been taught face-to-face on campus, but now digital technologies offer opportunities for teaching and learning. Here, we give a perspective on how new technologies might be used through student-centred teaching approaches. First, a snapshot of recent trends in the higher education is provided; then, a brief account is given of how digital technologies [e.g., massive open online courses (MOOCs), flipped classroom (FC), games, quizzes, dedicated Facebook, and digital badges] might promote parasitology teaching and learning in digital learning environments. In our opinion, some of these digital technologies might be useful for competency-based, self-regulated, learner-centred teaching and learning in an online or blended teaching environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Telelearning standards and their application in medical education.

PubMed

Duplaga, Mariusz; Juszkiewicz, Krzysztof; Leszczuk, Mikolaj

2004-01-01

Medial education, both on the graduate and postgraduate levels, has become a real challenge nowadays. The volume of information in medical sciences grows so rapidly that many health professionals experience essential problems in keeping track of the state of the art in this domain. e-learning offers important advantages to medical education continuation due to its universal availability and opportunity for implementation of flexible patterns of training. An important trace of medical education is developing practical skills. Some examples of standardization efforts include: the CEN/ISSS Workshop on Learning Technology (WSLT), the Advanced Learning Infrastructure Consortium (ALIC), Education Network Australia (EdNA) and PROmoting Multimedia access to Education and Training in European Society (PROMETEUS). Sun Microsystems' support (Sun ONE, iPlanetTM ) for many of the above-mentioned standards is described as well. Development of a medical digital video library with recordings of invasive procedures incorporating additional information and commentary may improve the efficiency of the training process in interventional medicine. A digital video library enabling access to videos of interventional procedures performed in the area of thoracic medicine may be a valuable element for developing practical skills. The library has been filled with video resources recorded at the Department of Interventional Pulmonology; it enhances training options for pulmonologists and thoracic surgeons. The main focus was put on demonstration of bronchofiberoscopic and videothoracoscopic procedures. The opportunity to browse video recordings of procedures performed in the specific field also considerably enhances the options for training in other medical specialties. In the era of growing health consumer awareness, patients are also perceived as the target audience for medical digital libraries. As a case study of Computer-Based Training systems, the Medical Digital Video Library is presented.

Learning to Read in the Digital Age

ERIC Educational Resources Information Center

Rose, David; Dalton, Bridget

2009-01-01

The digital age offers transformative opportunities for individualization of learning. First, modern imaging technologies have changed our understanding of learning and the sources and ranges of its diversity. Second, digital technologies make it possible to design learning environments that are responsive to individual differences. We draw on…

TH-A-12A-01: Medical Physicist's Role in Digital Information Security: Threats, Vulnerabilities and Best Practices

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

McDonald, K; Curran, B

I. Information Security Background (Speaker = Kevin McDonald) Evolution of Medical Devices Living and Working in a Hostile Environment Attack Motivations Attack Vectors Simple Safety Strategies Medical Device Security in the News Medical Devices and Vendors Summary II. Keeping Radiation Oncology IT Systems Secure (Speaker = Bruce Curran) Hardware Security Double-lock Requirements “Foreign” computer systems Portable Device Encryption Patient Data Storage System Requirements Network Configuration Isolating Critical Devices Isolating Clinical Networks Remote Access Considerations Software Applications / Configuration Passwords / Screen Savers Restricted Services / access Software Configuration Restriction Use of DNS to restrict accesse. Patches / Upgrades Awareness Intrusionmore » Prevention Intrusion Detection Threat Risk Analysis Conclusion Learning Objectives: Understanding how Hospital IT Requirements affect Radiation Oncology IT Systems. Illustrating sample practices for hardware, network, and software security. Discussing implementation of good IT security practices in radiation oncology. Understand overall risk and threats scenario in a networked environment.« less

Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Sheth, Deepa; Giger, Maryellen L.

2018-02-01

With growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening protocols, it is important to compare the performance of computer-aided diagnosis (CAD) in the diagnosis of breast lesions on DBT images compared to conventional full-field digital mammography (FFDM). In this study, we retrospectively collected FFDM and DBT images of 78 lesions from 76 patients, each containing lesions that were biopsy-proven as either malignant or benign. A square region of interest (ROI) was placed to fully cover the lesion on each FFDM, DBT synthesized 2D images, and DBT key slice images in the cranial-caudal (CC) and mediolateral-oblique (MLO) views. Features were extracted on each ROI using a pre-trained convolutional neural network (CNN). These features were then input to a support vector machine (SVM) classifier, and area under the ROC curve (AUC) was used as the figure of merit. We found that in both the CC view and MLO view, the synthesized 2D image performed best (AUC = 0.814, AUC = 0.881 respectively) in the task of lesion characterization. Small database size was a key limitation in this study, and could lead to overfitting in the application of the SVM classifier. In future work, we plan to expand this dataset and to explore more robust deep learning methodology such as fine-tuning.

Examining the Characteristics of Digital Learning Games Designed by In-Service Teachers

ERIC Educational Resources Information Center

An, Yun-Jo; Cao, Li

2017-01-01

In order to better understand teachers' perspectives on the design and development of digital game-based learning environments, this study examined the characteristics of digital learning games designed by teachers. In addition, this study explored how game design and peer critique activities influenced their perceptions of digital game-based…

Demonstrating DREAM: A Digital Resource Exchange about Music

ERIC Educational Resources Information Center

Upitis, Rena; Boese, Karen; Abrami, Philip C.

2015-01-01

The Digital Resource Exchange About Music (DREAM) is an online tool for exchanging information about digital learning tools for music education. DREAM was designed by our team to encourage music teachers to learn about digital resources related to learning to play a musical instrument, both in classroom and independent music studio settings. In…

Digital Media and Learning

ERIC Educational Resources Information Center

John D. and Catherine T. MacArthur Foundation, 2012

2012-01-01

MacArthur launched the digital media and learning initiative in 2006 to explore how digital media are changing the way young people learn, socialize, communicate, and play. Since 2006, the Foundation has awarded grants totaling more than $100 million for research, development of innovative new technologies, new learning environments for youth,…

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Ellerbrock, Philip J. (Inventor); Winkelmann, Joseph P. (Inventor); Grant, Robert L. (Inventor); Konz, Daniel W. (Inventor)

2006-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

Building Virtual Watersheds: A Global Opportunity to Strengthen Resource Management and Conservation.

PubMed

Benda, Lee; Miller, Daniel; Barquin, Jose; McCleary, Richard; Cai, TiJiu; Ji, Y

2016-03-01

Modern land-use planning and conservation strategies at landscape to country scales worldwide require complete and accurate digital representations of river networks, encompassing all channels including the smallest headwaters. The digital river networks, integrated with widely available digital elevation models, also need to have analytical capabilities to support resource management and conservation, including attributing river segments with key stream and watershed data, characterizing topography to identify landforms, discretizing land uses at scales necessary to identify human-environment interactions, and connecting channels downstream and upstream, and to terrestrial environments. We investigate the completeness and analytical capabilities of national to regional scale digital river networks that are available in five countries: Canada, China, Russia, Spain, and United States using actual resource management and conservation projects involving 12 university, agency, and NGO organizations. In addition, we review one pan-European and one global digital river network. Based on our analysis, we conclude that the majority of the regional, national, and global scale digital river networks in our sample lack in network completeness, analytical capabilities or both. To address this limitation, we outline a general framework to build as complete as possible digital river networks and to integrate them with available digital elevation models to create robust analytical capabilities (e.g., virtual watersheds). We believe this presents a global opportunity for in-country agencies, or international players, to support creation of virtual watersheds to increase environmental problem solving, broaden access to the watershed sciences, and strengthen resource management and conservation in countries worldwide.

Building Virtual Watersheds: A Global Opportunity to Strengthen Resource Management and Conservation

NASA Astrophysics Data System (ADS)

Benda, Lee; Miller, Daniel; Barquin, Jose; McCleary, Richard; Cai, TiJiu; Ji, Y.

2016-03-01

Modern land-use planning and conservation strategies at landscape to country scales worldwide require complete and accurate digital representations of river networks, encompassing all channels including the smallest headwaters. The digital river networks, integrated with widely available digital elevation models, also need to have analytical capabilities to support resource management and conservation, including attributing river segments with key stream and watershed data, characterizing topography to identify landforms, discretizing land uses at scales necessary to identify human-environment interactions, and connecting channels downstream and upstream, and to terrestrial environments. We investigate the completeness and analytical capabilities of national to regional scale digital river networks that are available in five countries: Canada, China, Russia, Spain, and United States using actual resource management and conservation projects involving 12 university, agency, and NGO organizations. In addition, we review one pan-European and one global digital river network. Based on our analysis, we conclude that the majority of the regional, national, and global scale digital river networks in our sample lack in network completeness, analytical capabilities or both. To address this limitation, we outline a general framework to build as complete as possible digital river networks and to integrate them with available digital elevation models to create robust analytical capabilities (e.g., virtual watersheds). We believe this presents a global opportunity for in-country agencies, or international players, to support creation of virtual watersheds to increase environmental problem solving, broaden access to the watershed sciences, and strengthen resource management and conservation in countries worldwide.

When a Classroom Is Not Just a Classroom: Building Digital Playgrounds in the Classroom

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Chuang, Chi-Kuo; Nurkhamid; Liu, Tzu-Chien

2012-01-01

In the context of classroom, it is possible to create a playground with digital technology beneficial for learning in spite of rising enthusiasm in incorporating educational games in classroom. This paper is an essay to describe a learning playground called Digital Learning Playground (DLP). It is essentially an application of digital technology…

Digital Leisure-Time Activities, Cognition, Learning Behaviour and Information Literacy: What Are Our Children Learning?

ERIC Educational Resources Information Center

Grimley, Mick

2012-01-01

Recent developments in digital technology have resulted in the unprecedented uptake of digital technology engagement as a leisure-time pursuit across the age span. This has resulted in the speculation that such use of digital technology is responsible for changes in cognition and learning behaviour. This study investigated two groups of…

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

PubMed Central

Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

PubMed

Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

Automated Categorization Scheme for Digital Libraries in Distance Learning: A Pattern Recognition Approach

ERIC Educational Resources Information Center

Gunal, Serkan

2008-01-01

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is…

Learning in the Digital Age: Control or Connection?

ERIC Educational Resources Information Center

Van Galen, Jane

2013-01-01

In October 2011, 200 state school officers and legislators gathered at a hotel in San Francisco to learn how to "revolutionize" learning by "personalizing" instruction. The occasion was former Florida Gov. Jeb Bush's second annual National Summit on Education Reform. The topic was digital learning. The vision of digitally managed curriculum and…

A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP

PubMed Central

Balduzzi, David; Tononi, Giulio

2012-01-01

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of object recognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Stellar Atmospheric Parameterization Based on Deep Learning

NASA Astrophysics Data System (ADS)

Pan, R. Y.; Li, X. R.

2016-07-01

Deep learning is a typical learning method widely studied in machine learning, pattern recognition, and artificial intelligence. This work investigates the stellar atmospheric parameterization problem by constructing a deep neural network with five layers. The proposed scheme is evaluated on both real spectra from Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with Kurucz's New Opacity Distribution Function (NEWODF) model. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for the effective temperature (T_{eff}/K), 0.0058 for lg (T_{eff}/K), 0.1706 for surface gravity (lg (g/(cm\\cdot s^{-2}))), and 0.1294 dex for metallicity ([Fe/H]), respectively; On the theoretic spectra, the MAEs are 15.34 for T_{eff}/K, 0.0011 for lg (T_{eff}/K), 0.0214 for lg (g/(cm\\cdot s^{-2})), and 0.0121 dex for [Fe/H], respectively.

Agriscience Teachers' Implementation of Digital Game-based Learning in an Introductory Animal Science Course

NASA Astrophysics Data System (ADS)

Webb, Angela W.; Bunch, J. C.; Wallace, Maria F. G.

2015-12-01

In today's technological age, visions for technology integration in the classroom continue to be explored and examined. Digital game-based learning is one way to purposefully integrate technology while maintaining a focus on learning objectives. This case study sought to understand agriscience teachers' experiences implementing digital game-based learning in an introductory animal science course. From interviews with agriscience teachers on their experiences with the game, three themes emerged: (1) the constraints of inadequate and inappropriate technologies, and time to game implementation; (2) the shift in teacher and student roles necessitated by implementing the game; and (3) the inherent competitive nature of learning through the game. Based on these findings, we recommend that pre-service and in-service professional development opportunities be developed for teachers to learn how to implement digital game-based learning effectively. Additionally, with the potential for simulations that address cross-cutting concepts in the next generation science standards, digital game-based learning should be explored in various science teaching and learning contexts.

Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

NASA Astrophysics Data System (ADS)

Liyanagedera, Chamika M.; Sengupta, Abhronil; Jaiswal, Akhilesh; Roy, Kaushik

2017-12-01

Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

Textbooks vs. techbooks: Effectiveness of digital textbooks on elementary student motivation for learning

NASA Astrophysics Data System (ADS)

Oman, Auna

This action research project investigated fourth grade students¡¦ motivation to learn science using a digital science techbook. Participants in the study included 29 fourth grade students in two different classrooms. One classroom of 16 students used a digital science techbook to learn science while the other classroom of 13 students used a traditional paper science textbook to learn science. Students in both classrooms answered five sets of questions regarding their experience using a digital science techbook and a paper science techbook to understand science, find science information, solve science problems, learn science, and assess learning science was fun. Results were compiled and coded based on positive and negative responses to conditions. A chi-square was used to analyze the ordinal data. Overall differences between techbooks vs. textbook were significant, X2 (1, N = 29) = 23.84, p = .000, justifying further examination of individual survey items. Three items had statistically significant difference for finding science information, solving science problems, and learning science. A gender difference was also found in one item. Females preferred to use paper science textbooks to understand science, while males preferred digital techbooks to learn science. The fourth graders in this study indicated that digital techbooks were a powerful learning tool for increasing interest, excitement and learning science. Even though students reported paper science textbooks as easy to use, they found using digital science techbooks a far more appealing way to learn science.

Design mobile satellite system architecture as an integral part of the cellular access digital network

NASA Technical Reports Server (NTRS)

Chien, E. S. K.; Marinho, J. A.; Russell, J. E., Sr.

1988-01-01

The Cellular Access Digital Network (CADN) is the access vehicle through which cellular technology is brought into the mainstream of the evolving integrated telecommunications network. Beyond the integrated end-to-end digital access and per call network services provisioning of the Integrated Services Digital Network (ISDN), the CADN engenders the added capability of mobility freedom via wireless access. One key element of the CADN network architecture is the standard user to network interface that is independent of RF transmission technology. Since the Mobile Satellite System (MSS) is envisioned to not only complement but also enhance the capabilities of the terrestrial cellular telecommunications network, compatibility and interoperability between terrestrial cellular and mobile satellite systems are vitally important to provide an integrated moving telecommunications network of the future. From a network standpoint, there exist very strong commonalities between the terrestrial cellular system and the mobile satellite system. Therefore, the MSS architecture should be designed as an integral part of the CADN. This paper describes the concept of the CADN, the functional architecture of the MSS, and the user-network interface signaling protocols.

Digital Technology Use by the Students and English Teachers and Self-Directed Language Learning

ERIC Educational Resources Information Center

Sert, Nehir; Boynuegri, Ebru

2017-01-01

The digital era is a new challenge for teachers. While children get acquainted with the digital technology before the age of six, teachers, who have encountered with the digital world at a later time in their lives, struggle with it. Self-directed learning, which is crucial for lifelong learning, can be enhanced by the use technology particularly…

ICC '86; Proceedings of the International Conference on Communications, Toronto, Canada, June 22-25, 1986, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

Papers are presented on ISDN, mobile radio systems and techniques for digital connectivity, centralized and distributed algorithms in computer networks, communications networks, quality assurance and impact on cost, adaptive filters in communications, the spread spectrum, signal processing, video communication techniques, and digital satellite services. Topics discussed include performance evaluation issues for integrated protocols, packet network operations, the computer network theory and multiple-access, microwave single sideband systems, switching architectures, fiber optic systems, wireless local communications, modulation, coding, and synchronization, remote switching, software quality, transmission, and expert systems in network operations. Consideration is given to wide area networks, image and speech processing, office communications application protocols, multimedia systems, customer-controlled network operations, digital radio systems, channel modeling and signal processing in digital communications, earth station/on-board modems, computer communications system performance evaluation, source encoding, compression, and quantization, and adaptive communications systems.

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Konz, Daniel W. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor)

2006-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted into digital signals and transmitted back to the controller. In one embodiment, the bus controller sends commands and data a defined bit rate, and the network device interface senses this bit rate and sends data back to the bus controller using the defined bit rate.

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Konz, Daniel W. (Inventor); Winkelmann, Joseph P. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor)

2007-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. In some embodiments, network device interfaces associated with different data channels coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

Organisational aspects and benchmarking of e-learning initiatives: a case study with South African community health workers.

PubMed

Reisach, Ulrike; Weilemann, Mitja

2016-06-01

South Africa desperately needs a comprehensive approach to fight HIV/AIDS. Education is crucial to reach this goal and Internet and e-learning could offer huge opportunities to broaden and deepen the knowledge basis. But due to the huge societal and digital divide between rich and poor areas, e-learning is difficult to realize in the townships. Community health workers often act as mediators and coaches for people seeking medical and personal help. They could give good advice regarding hygiene, nutrition, protection of family members in case of HIV/AIDS and finding legal ways to earn one's living if they were trained to do so. Therefore they need to have a broader general knowledge. Since learning opportunities in the townships are scarce, a system for e-learning has to be created in order to overcome the lack of experience with computers or the Internet and to enable them to implement a network of expertise. The article describes how the best international resources on basic medical knowledge, HIV/AIDS as well as on basic economic and entrepreneurial skills were benchmarked to be integrated into an e-learning system. After tests with community health workers, researchers developed recommendations on building a self-sustaining system for learning, including a network of expertise and best practice sharing. The article explains the opportunities and challenges for community health workers, which could provide information for other parts of the world with similar preconditions of rural poverty. © The Author(s) 2015.

Developing and deploying a community healthcare worker-driven, digitally- enabled integrated care system for municipalities in rural Nepal.

PubMed

Citrin, David; Thapa, Poshan; Nirola, Isha; Pandey, Sachit; Kunwar, Lal Bahadur; Tenpa, Jasmine; Acharya, Bibhav; Rayamazi, Hari; Thapa, Aradhana; Maru, Sheela; Raut, Anant; Poudel, Sanjaya; Timilsina, Diwash; Dhungana, Santosh Kumar; Adhikari, Mukesh; Khanal, Mukti Nath; Pratap Kc, Naresh; Acharya, Bhim; Karki, Khem Bahadur; Singh, Dipendra Raman; Bangura, Alex Harsha; Wacksman, Jeremy; Storisteanu, Daniel; Halliday, Scott; Schwarz, Ryan; Schwarz, Dan; Choudhury, Nandini; Kumar, Anirudh; Wu, Wan-Ju; Kalaunee, S P; Chaudhari, Pushpa; Maru, Duncan

2018-06-04

Integrating care at the home and facility level is a critical yet neglected function of healthcare delivery systems. There are few examples in practice or in the academic literature of affordable, digitally-enabled integrated care approaches embedded within healthcare delivery systems in low- and middle-income countries. Simultaneous advances in affordable digital technologies and community healthcare workers offer an opportunity to address this challenge. We describe the development of an integrated care system involving community healthcare worker networks that utilize a home-to-facility electronic health record platform for rural municipalities in Nepal. Key aspects of our approach of relevance to a global audience include: community healthcare workers continuously engaging with populations through household visits every three months; community healthcare workers using digital tools during the routine course of clinical care; individual and population-level data generated routinely being utilized for program improvement; and being responsive to privacy, security, and human rights concerns. We discuss implementation, lessons learned, challenges, and opportunities for future directions in integrated care delivery systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Teaching in a Digital Age: How Educators Use Technology to Improve Student Learning

ERIC Educational Resources Information Center

McKnight, Katherine; O'Malley, Kimberly; Ruzic, Roxanne; Horsley, Maria Kelly; Franey, John J.; Bassett, Katherine

2016-01-01

A successful digital conversion for classrooms, districts, and states is not determined by the technology, but by how technology enables teaching and learning. The purpose of our multisite case study was to document digital instructional strategies teachers use to enhance and transform student learning, and align that use with learning research.…

Adult Learning in the Digital Age: Perspectives on Online Technologies and Outcomes

ERIC Educational Resources Information Center

Kidd, Terry T., Ed.; Keengwe, Jared, Ed.

2010-01-01

As instructors move further into the incorporation of 21st century technologies in adult education, a new paradigm of digitally-enriched mediated learning has emerged. This book provides a comprehensive framework of trends and issues related to adult learning for the facilitation of authentic learning in the age of digital technology. This…

The Digital Learning Imperative: How Technology and Teaching Meet Today's Education Challenges. Digital Learning Series

ERIC Educational Resources Information Center

Schwartzbeck, Terri Duggan; Wolf, Mary Ann

2012-01-01

This report outlines how digital learning can connect middle and high school students with better teaching and learning experiences while also addressing three major challenges facing the nation's education system--access to good teaching, tight budgets, and boosting student achievement. But simply slapping a netbook on top of a textbook will not…

Word Learning and Story Comprehension from Digital Storybooks: Does Interaction Make a Difference?

ERIC Educational Resources Information Center

Kelley, Elizabeth S.; Kinney, Kara

2017-01-01

An emerging body of research examines language learning of young children from experiences with digital storybooks, but little is known about the ways in which specific components of digital storybooks, including interactive elements, may influence language learning. The purpose of the study was to examine the incidental word learning and story…

Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017

PubMed Central

Clunie, David; Hosseinzadeh, Dan; Wintell, Mikael; De Mena, David; Lajara, Nieves; Garcia-Rojo, Marcial; Bueno, Gloria; Saligrama, Kiran; Stearrett, Aaron; Toomey, David; Abels, Esther; Apeldoorn, Frank Van; Langevin, Stephane; Nichols, Sean; Schmid, Joachim; Horchner, Uwe; Beckwith, Bruce; Parwani, Anil; Pantanowitz, Liron

2018-01-01

As digital pathology systems for clinical diagnostic work applications become mainstream, interoperability between these systems from different vendors becomes critical. For the first time, multiple digital pathology vendors have publicly revealed the use of the digital imaging and communications in medicine (DICOM) standard file format and network protocol to communicate between separate whole slide acquisition, storage, and viewing components. Note the use of DICOM for clinical diagnostic applications is still to be validated in the United States. The successful demonstration shows that the DICOM standard is fundamentally sound, though many lessons were learned. These lessons will be incorporated as incremental improvements in the standard, provide more detailed profiles to constrain variation for specific use cases, and offer educational material for implementers. Future Connectathon events will expand the scope to include more devices and vendors, as well as more ambitious use cases including laboratory information system integration and annotation for image analysis, as well as more geographic diversity. Users should request DICOM features in all purchases and contracts. It is anticipated that the growth of DICOM-compliant manufacturers will likely also ease DICOM for pathology becoming a recognized standard and as such the regulatory pathway for digital pathology products. PMID:29619278

Digital Learning in the Wild: Re-Imagining New Ruralism, Digital Equity, and Deficit Discourses through the Thirdspace

ERIC Educational Resources Information Center

Cirell, Anna Montana

2017-01-01

Digital media is becoming increasingly important to learning in today's changing times. At the same time, digital technologies and related digital skills are unevenly distributed. Further, deficit-based notions of this digital divide define the public's educational paradigm. Against this backdrop, I forayed into the social reality of one rural…

Does Not Compute: The High Cost of Low Technology Skills in the U.S.--and What We Can Do about It. Vital Signs: Reports on the Condition of STEM Learning in the U.S.

ERIC Educational Resources Information Center

Change the Equation, 2015

2015-01-01

Although American millennials are the first generation of "digital natives"--that is, people who grew up with computers and the internet--they are not very tech savvy. Using technology for social networking, surfing the web, or taking selfies is a far cry from using it to solve complex problems at work or at home. Truly tech savvy people…

Growing a National Learning Environments and Resources Network for Science, Mathematics, Engineering, and Technology Education: Current Issues and Opportunities for the NSDL Program; Open Linking in the Scholarly Information Environment Using the OpenURL Framework; The HeadLine Personal Information Environment: Evaluation Phase One.

ERIC Educational Resources Information Center

Zia, Lee L.; Van de Sompel, Herbert; Beit-Arie, Oren; Gambles, Anne

2001-01-01

Includes three articles that discuss the National Science Foundation's National Science, Mathematics, Engineering, and Technology Education Digital Library (NSDL) program; the OpenURL framework for open reference linking in the Web-based scholarly information environment; and HeadLine (Hybrid Electronic Access and Delivery in the Library Networked…

News

NASA Astrophysics Data System (ADS)

2005-01-01

Einstein year: Einstein is brought back to life for a year of educational events Workshop: Students reach out for the Moon Event: Masterclasses go with a bang Workshop: Students search for asteroids on Einstein's birthday Scotland: Curriculum for Excellence takes holistic approach Conference: Reporting from a mattress in Nachod Conference: 'Change' is key objective at ICPE conference 2005 Lecture: Institute of Physics Schools Lecture series Conference: Experience showcase science in Warwick National network: Science Learning Centre opens Meeting: 30th Stirling Physics Meeting breaks records Competition: Win a digital camera! Forthcoming Events

High-speed digital wireless battlefield network

NASA Astrophysics Data System (ADS)

Dao, Son K.; Zhang, Yongguang; Shek, Eddie C.; van Buer, Darrel

1999-07-01

In the past two years, the Digital Wireless Battlefield Network consortium that consists of HRL Laboratories, Hughes Network Systems, Raytheon, and Stanford University has participated in the DARPA TRP program to leverage the efforts in the development of commercial digital wireless products for use in the 21st century battlefield. The consortium has developed an infrastructure and application testbed to support the digitized battlefield. The consortium has implemented and demonstrated this network system. Each member is currently utilizing many of the technology developed in this program in commercial products and offerings. These new communication hardware/software and the demonstrated networking features will benefit military systems and will be applicable to the commercial communication marketplace for high speed voice/data multimedia distribution services.

Modeling digits. Digit patterning is controlled by a Bmp-Sox9-Wnt Turing network modulated by morphogen gradients.

PubMed

Raspopovic, J; Marcon, L; Russo, L; Sharpe, J

2014-08-01

During limb development, digits emerge from the undifferentiated mesenchymal tissue that constitutes the limb bud. It has been proposed that this process is controlled by a self-organizing Turing mechanism, whereby diffusible molecules interact to produce a periodic pattern of digital and interdigital fates. However, the identities of the molecules remain unknown. By combining experiments and modeling, we reveal evidence that a Turing network implemented by Bmp, Sox9, and Wnt drives digit specification. We develop a realistic two-dimensional simulation of digit patterning and show that this network, when modulated by morphogen gradients, recapitulates the expression patterns of Sox9 in the wild type and in perturbation experiments. Our systems biology approach reveals how a combination of growth, morphogen gradients, and a self-organizing Turing network can achieve robust and reproducible pattern formation. Copyright © 2014, American Association for the Advancement of Science.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

PubMed

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection

PubMed Central

Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant; González, Fabio

2018-01-01

Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200 × 200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500 × 500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (∼6 million of samples in 24 hours) with far fewer samples (∼2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%. PMID:29795581

Learning fuzzy logic control system

NASA Technical Reports Server (NTRS)

Lung, Leung Kam

1994-01-01

The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore, the LFLC has better performance in rise time, settling time and steady state error than to the conventional PI controller. This abstract accurately represents the content of the candidate's thesis. I recommend its publication.

E-Learning Environments for Digitally-Minded Students

ERIC Educational Resources Information Center

Andone, Diana; Dron, Jon; Pemberton, Lyn; Boyne, Chris

2007-01-01

While most existing online learning environments cater for needs identified during the 1990s, a new generation of digital students has emerged in the developed world. Digital students are young adults who have grown up with digital technologies integrated as an everyday feature of their lives. Digital students use technology differently, fluidly…

Wirelessly Networked Digital Phased Array: Analysis and Development of a Phase Synchronization Concept

DTIC Science & Technology

2007-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited WIRELESSLY NETWORKED...DIGITAL PHASED ARRAY: ANALYSIS AND DEVELOPMENT OF A PHASE SYNCHRONIZATION CONCEPT by Micael Grahn September 2007 Thesis Advisor...September 2007 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Wirelessly Networked Digital Phased Array: Analysis and

A Review of Digital, Social, and Mobile Technologies in Health Professional Education.

PubMed

Curran, Vernon; Matthews, Lauren; Fleet, Lisa; Simmons, Karla; Gustafson, Diana L; Wetsch, Lyle

2017-01-01

Digital, social, and mobile technologies (DSMTs) can support a wide range of self-directed learning activities, providing learners with diverse resources, information, and ways to network that support their learning needs. DSMTs are increasingly used to facilitate learning across the continuum of health professional education (HPE). Given the diverse characteristics of DSMTs and the formal, informal, and nonformal nature of health professional learning, a review of the literature on DSMTs and HPE could inform more effective adoption and usage by regulatory organizations, educators, and learners. A scoping review of the literature was performed to explore the effectiveness and implications of adopting and using DSMTs across the educational continuum in HPE. A data extraction tool was used to review and analyze 125 peer-reviewed articles. Common themes were identified by thematic analysis. Most articles (56.0%) related to undergraduate education; 31.2% to continuing professional development, and 52.8% to graduate/postgraduate education. The main DSMTs described include mobile phones, apps, tablets, Facebook, Twitter, and YouTube. Approximately half of the articles (49.6%) reported evaluative outcomes at a satisfaction/reaction level; 45.6% were commentaries, reporting no evaluative outcomes. Most studies reporting evaluative outcomes suggest that learners across all levels are typically satisfied with the use of DSMTs in their learning. Thematic analysis revealed three main themes: use of DSMTs across the HPE continuum; key benefits and barriers; and best practices. Despite the positive commentary on the potential benefits and opportunities for enhancing teaching and learning in HPE with DSMTs, there is limited evidence at this time that demonstrates effectiveness of DSMTs at higher evaluative outcome levels. Further exploration of the learning benefits and effectiveness of DSMTs for teaching and learning in HPE is warranted.

Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.

PubMed

Larson, David B; Chen, Matthew C; Lungren, Matthew P; Halabi, Safwan S; Stence, Nicholas V; Langlotz, Curtis P

2018-04-01

Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models. © RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on January 19, 2018.

Mobile learning: a workforce development strategy for nurse supervisors.

PubMed

Mather, Carey; Cummings, Elizabeth

2014-01-01

Digital technology provides opportunities for using mobile learning strategies in healthcare environments. To realise the vision of the National Workforce Development Strategy there needs to be innovation of health professionals to further develop knowledge and skills of clinical supervisors to access and gain an understanding of the value of mobile learning at the workplace. The use of digital technology by clinical supervisors was explored in 2012 as part of a teaching development grant to evaluate the use of Web 2.0 technology to develop a community of practice about clinical supervision. Prior to developing the virtual network of clinical supervisors, feedback about the use of Web 2.0 technology by clinicians was sought via an online survey. Over 90% of respondents used social media, 85% understood what a blog and wiki were and approximately half of the respondents used smart phones. More than one-third indicated they would participate in a virtual community of practice and would like to receive information about clinical facilitation at least once per week. Findings indicate both inhibitors and opportunities for workforce development within healthcare environments that need to be addressed. Support of graduate-ready nurses can be achieved through an integrated outlook that enables health professionals within organisations to undertake mobile learning in situ. A flexible and collaborative approach to continuing professional development within organisations could enhance practice development and could positively impact on workforce development.

System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Danshi; Zhang, Min; Li, Ze; Song, Chuang; Fu, Meixia; Li, Jin; Chen, Xue

2017-09-01

A bio-inspired detector based on the artificial neural network (ANN) and genetic algorithm is proposed in the context of a coherent optical transmission system. The ANN is designed to mitigate 16-quadrature amplitude modulation system impairments, including linear impairment: Gaussian white noise, laser phase noise, in-phase/quadrature component imbalance, and nonlinear impairment: nonlinear phase. Without prior information or heuristic assumptions, the ANN, functioning as a machine learning algorithm, can learn and capture the characteristics of impairments from observed data. Numerical simulations were performed, and dispersion-shifted, dispersion-managed, and dispersion-unmanaged fiber links were investigated. The launch power dynamic range and maximum transmission distance for the bio-inspired method were 2.7 dBm and 240 km greater, respectively, than those of the maximum likelihood estimation algorithm. Moreover, the linewidth tolerance of the bio-inspired technique was 170 kHz greater than that of the k-means method, demonstrating its usability for digital signal processing in coherent systems.

A deep semantic mobile application for thyroid cytopathology

NASA Astrophysics Data System (ADS)

Kim, Edward; Corte-Real, Miguel; Baloch, Zubair

2016-03-01

Cytopathology is the study of disease at the cellular level and often used as a screening tool for cancer. Thyroid cytopathology is a branch of pathology that studies the diagnosis of thyroid lesions and diseases. A pathologist views cell images that may have high visual variance due to different anatomical structures and pathological characteristics. To assist the physician with identifying and searching through images, we propose a deep semantic mobile application. Our work augments recent advances in the digitization of pathology and machine learning techniques, where there are transformative opportunities for computers to assist pathologists. Our system uses a custom thyroid ontology that can be augmented with multimedia metadata extracted from images using deep machine learning techniques. We describe the utilization of a particular methodology, deep convolutional neural networks, to the application of cytopathology classification. Our method is able to leverage networks that have been trained on millions of generic images, to medical scenarios where only hundreds or thousands of images exist. We demonstrate the benefits of our framework through both quantitative and qualitative results.

Differentiation of arterioles from venules in mouse histology images using machine learning

NASA Astrophysics Data System (ADS)

Elkerton, J. S.; Xu, Yiwen; Pickering, J. G.; Ward, Aaron D.

2016-03-01

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

Effects of Character Voice-Over on Players' Engagement in a Digital Role-Playing Game Environment

ERIC Educational Resources Information Center

Byun, JaeHwan

2012-01-01

Learner engagement has been considered one of the keys that can lead learners to successful learning in a multimedia learning environment such as digital game-based learning. Regarding this point, game-based learning advocates (e.g., Gee, 2003; Prensky, 2001) have asserted that digital games have great potential to engage learners. Nonetheless,…

The Costs of Online Learning. Creating Sound Policy for Digital Learning: A Working Paper Series from the Thomas B. Fordham Institute

ERIC Educational Resources Information Center

Battaglino, Tamara Butler; Haldeman, Matt; Laurans, Eleanor

2012-01-01

The latest installment of the Fordham Institute's "Creating Sound Policy for Digital Learning" series investigates one of the more controversial aspects of digital learning: How much does it cost? In this paper, the Parthenon Group uses interviews with more than fifty vendors and online-schooling experts to estimate today's average…

Digital Tools and Challenges to Institutional Traditions of Learning: Technologies, Social Memory and the Performative Nature of Learning

ERIC Educational Resources Information Center

Saljo, R.

2010-01-01

The purpose of this article is to offer some reflections on the relationships between digital technologies and learning. It is argued that activities of learning, as they have been practised within institutionalized schooling, are coming under increasing pressure from the developments of digital technologies and the capacities to store, access and…

Digital Game-Based Learning: It's Not Just the Digital Natives Who Are Restless

ERIC Educational Resources Information Center

Van Eck, Richard

2006-01-01

With the widespread public interest in games as learning tools, digital game-based learning (DGBL) proponents now need to explain why games are engaging and effective and how those principles can be leveraged to best integrate games into the learning process. In this article, Richard Van Eck outlines why DGBL is effective and engaging, how those…

Creating the Future of Games and Learning

ERIC Educational Resources Information Center

Squire, Kurt

2015-01-01

Games for learning are poised to enter mainstream education. Several factors driving this movement are the following: (1) Digital distribution through cloud computing services and ubiquitous connectivity which will make digital learning tools--such as games--affordable and easily accessible; (2) The proliferation of digital devices; (3) Digital…

78 FR 43882 - Sunshine Act Meeting; Open Commission Meeting; Friday, July 19, 2013

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-22

... the delivery of video programming. 2 TITLE: Presentation on LEAD Recommendations and Digital Learning... Five Point Blueprint recommending a national initiative to expand digital learning in K-12 education... teachers at Kenmore are using digital technologies and broadband connectivity to expand learning...

Digital case-based learning system in school.

PubMed

Gu, Peipei; Guo, Jiayang

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Digital case-based learning system in school

PubMed Central

Gu, Peipei

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework. PMID:29107965

K-12 Professional Development at the Harvard Forest LTER

NASA Astrophysics Data System (ADS)

Bennett, K.

2012-12-01

As part of the Long Term Ecological Research (LTER) program, the Harvard Forest in Petersham, Massachusetts seeks to train the next generation of researchers, by involving K-12 grade students and their teachers in hands-on, field-based, ecological research in their own schoolyard and community. Students learn to collect data on important long-term ecological issues and processes. Student data are then shared on the Harvard Forest website. To prepare teachers for project protocols, teachers are given direct access to Harvard ecologists with professional development workshops and on-line resources. With the Harvard Forest Schoolyard LTER program, students can participate in three different research projects focusing on phenology, invasive insects, and vernal pools. Teachers attend the Summer Institute for Teachers to learn project content and methods. They return in fall to participate in one of three levels of data workshops to learn how to input, manage, and analyze project data. In the spring, teachers again meet with the Harvard ecologists about project protocols, and to share, through a series of teacher presentations, the ways these project themes are being integrated into class curricula. These professional development opportunities result in long term collaborative partnerships with local schools and the Harvard Forest LTER. In addition to the LTER Schoolyard Ecology Program, the Harvard Forest has supported a successful Research Experience for Teachers (RET) program for the last six years. Throughout the summer, teachers work on research projects alongside Harvard Forest and affiliated scientists, post-docs, graduate students, and REU's (Research Experience for Undergraduates). The RET program provides teachers with the opportunity to build scientific knowledge, develop an understanding of research methods, and translate their new knowledge and experiences into cutting edge classroom lessons. The past two summers I have worked with Dr. Andrew Richardson's Phenocam project, a network of near remote sensing digital phenology cameras that send images of forest, shrub, and grassland vegetation cover at more than 130 diverse sites in North America to the digital archives at the University of New Hampshire. Our school district is now part of this network providing a digital image every half hour of the mixed deciduous/ coniferous forest canopy due north from Overlook Middle School in Ashburnham, Massachusetts. As a part of the Phenocam network, students at the K-12 level have expanded the scope of phenological monitoring that is part of the Harvard Forest LTER Schoolyard Ecology Program protocol, Buds, Leaves, and Global Warming. I have developed a series of lessons comparing student data to phenology data derived from Phenocam network images and Modis satellites. The Phenocam Project and the RET program is supported by NASA.

Advances in optical information processing IV; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

NASA Astrophysics Data System (ADS)

Pape, Dennis R.

1990-09-01

The present conference discusses topics in optical image processing, optical signal processing, acoustooptic spectrum analyzer systems and components, and optical computing. Attention is given to tradeoffs in nonlinearly recorded matched filters, miniature spatial light modulators, detection and classification using higher-order statistics of optical matched filters, rapid traversal of an image data base using binary synthetic discriminant filters, wideband signal processing for emitter location, an acoustooptic processor for autonomous SAR guidance, and sampling of Fresnel transforms. Also discussed are an acoustooptic RF signal-acquisition system, scanning acoustooptic spectrum analyzers, the effects of aberrations on acoustooptic systems, fast optical digital arithmetic processors, information utilization in analog and digital processing, optical processors for smart structures, and a self-organizing neural network for unsupervised learning.

Neural classification of the selected family of butterflies

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Mueller, W.; Górna, K.; Okoń, P.

2017-07-01

There have been noticed growing explorers' interest in drawing conclusions based on information of data coded in a graphic form. The neuronal identification of pictorial data, with special emphasis on both quantitative and qualitative analysis, is more frequently utilized to gain and deepen the empirical data knowledge. Extraction and then classification of selected picture features, such as color or surface structure, enables one to create computer tools in order to identify these objects presented as, for example, digital pictures. The work presents original computer system "Processing the image v.1.0" designed to digitalize pictures on the basis of color criterion. The system has been applied to generate a reference learning file for generating the Artificial Neural Network (ANN) to identify selected kinds of butterflies from the Papilionidae family.

Libraries in Today's Digital Age: The Copyright Controversy. ERIC Digest.

ERIC Educational Resources Information Center

Russell, Carrie

This digest focuses on the continuing ambiguities libraries and their users face in dealing with copyright in the digital environment. In the networked digital world, the basic principles of copyright are more difficult to apply. Digital copies are easy to create, modify, and manipulate, they are extremely easy to distribute widely over networks,…

Digital Badges for STEM Learning in Secondary Contexts: A Mixed Methods Study

NASA Astrophysics Data System (ADS)

Elkordy, Angela

The deficit in STEM skills is a matter of concern for national economies and a major focus for educational policy makers. The development of Information and Communications Technologies (ICT) has resulted in a rapidly changing workforce of global scale. In addition, ICT have fostered the growth of digital and mobile technologies which have been the learning context, formal and informal, for a generation of youth. The purpose of this study was to design an intervention based upon a competency-based, digitally-mediated, learning intervention: digital badges for learning STEM habits of mind and practices. Designed purposefully, digital badge learning trajectories and criteria can be flexible tools for scaffolding, measuring, and communicating the acquisition of knowledge, skills, or competencies. One of the most often discussed attributes of digital badges, is the ability of badges to motivate learners. However, the research base to support this claim is in its infancy; there is little empirical evidence. A skills-based digital badge intervention was designed to demonstrate mastery learning in key, age-appropriate, STEM competencies aligned with Next Generation Science Standards (NGSS) and other educational standards. A mixed methods approach was used to study the impact of a digital badge intervention in the sample middle and high school population. Among the findings were statistically significant measures which substantiate that in this student population, the digital badges increased perceived competence and motivated learners to persist at task.

Collaborative learning model inquiring based on digital game

NASA Astrophysics Data System (ADS)

Yuan, Jiugen; Xing, Ruonan

2012-04-01

With the development of computer education software, digital educational game has become an important part in our life, entertainment and education. Therefore how to make full use of digital game's teaching functions and educate through entertainment has become the focus of current research. The thesis make a connection between educational game and collaborative learning, the current popular teaching model, and concludes digital game-based collaborative learning model combined with teaching practice.

Quality Control in K-12 Digital Learning: Three (Imperfect) Approaches. Creating Healthy Policy for Digital Learning. A Working Paper Series from the Thomas B. Fordham Institute

ERIC Educational Resources Information Center

Hess, Frederick M.

2011-01-01

Digital learning makes possible the "unbundling" of school provisions--that is, it allows children to be served by providers from almost anywhere, in new and more customized ways. At the same time, because it destandardizes and decentralizes educational delivery, digital education is far harder to bring under the yoke of the…

Application of the ANNA neural network chip to high-speed character recognition.

PubMed

Sackinger, E; Boser, B E; Bromley, J; Lecun, Y; Jackel, L D

1992-01-01

A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision.

Digital Social Network Mining for Topic Discovery

NASA Astrophysics Data System (ADS)

Moradianzadeh, Pooya; Mohi, Maryam; Sadighi Moshkenani, Mohsen

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.

Exploring Digital Badges in University Courses: Relationships between Quantity, Engagement, and Performance

ERIC Educational Resources Information Center

Fanfarelli, Joseph R.; McDaniel, Rudy

2017-01-01

Digital badging research is gaining momentum as instructors and administrators consider new models for assessing learning in nontraditional contexts (e.g., informal science learning programs, flexible online courses, adaptive learning systems). While many studies are examining the effectiveness of digital badges for pedagogical functions, such as…

Enhancing Digital Literacy and Learning among Adults with Blogs

ERIC Educational Resources Information Center

Sharp, Laurie A.

2017-01-01

Digital literacy and learning among adults has been identified as an area requiring research. The purpose of the present study was to explore technology acceptance and digital collaborative learning experiences with blogs among adult learners. This analysis employed a quasi-experimental mixed-methods approach guided by a sociocultural theoretical…

Digital Tools and Solutions for Inquiry-Based STEM Learning

ERIC Educational Resources Information Center

Levin, Ilya, Ed.; Tsybulsky, Dina, Ed.

2017-01-01

In the digital age, the integration of technology has become a ubiquitous aspect of modern society. These advancements have significantly enhanced the field of education, allowing students to receive a better learning experience. "Digital Tools and Solutions for Inquiry-Based STEM Learning" is a comprehensive source of scholarly material…

Engaging Adolescents Through Participatory and Qualitative Research Methods to Develop a Digital Communication Intervention to Reduce Adolescent Obesity.

PubMed

Livingood, William C; Monticalvo, David; Bernhardt, Jay M; Wells, Kelli T; Harris, Todd; Kee, Kadra; Hayes, Johnathan; George, Donald; Woodhouse, Lynn D

2017-08-01

The complexity of the childhood obesity epidemic requires the application of community-based participatory research (CBPR) in a manner that can transcend multiple communities of stakeholders, including youth, the broader community, and the community of health care providers. To (a) describe participatory processes for engaging youth within context of CBPR and broader community, (b) share youth-engaged research findings related to the use of digital communication and implications for adolescent obesity intervention research, and (c) describe and discuss lessons learned from participatory approaches. CBPR principles and qualitative methods were synergistically applied in a predominantly African American part of the city that experiences major obesity-related issues. A Youth Research Advisory Board was developed to deeply engage youth in research that was integrated with other community-based efforts, including an academic-community partnership, a city-wide obesity coalition, and a primary care practice research network. Volunteers from the youth board were trained to apply qualitative methods, including facilitating focus group interviews and analyzing and interpreting data with the goal of informing a primary care provider-based obesity reduction intervention. The primary results of these efforts were the development of critical insights about adolescent use of digital communication and the potential importance of messaging, mobile and computer apps, gaming, wearable technology, and rapid changes in youth communication and use of digital technology in developing adolescent nutrition and physical activity health promotion. The youth led work helped identify key elements for a digital communication intervention that was sensitive and responsive to urban youth. Many valuable lessons were also learned from 3 years of partnerships and collaborations, providing important insights on applying CBPR with minority youth populations.

Digital education and dynamic assessment of tongue diagnosis based on Mashup technique.

PubMed

Tsai, Chin-Chuan; Lo, Yen-Cheng; Chiang, John Y; Sainbuyan, Natsagdorj

2017-01-24

To assess the digital education and dynamic assessment of tongue diagnosis based on Mashup technique (DEDATD) according to specifific user's answering pattern, and provide pertinent information tailored to user's specifific needs supplemented by the teaching materials constantly updated through the Mashup technique. Fifty-four undergraduate students were tested with DEDATD developed. The effificacy of the DEDATD was evaluated based on the pre- and post-test performance, with interleaving training sessions targeting on the weakness of the student under test. The t-test demonstrated that signifificant difference was reached in scores gained during pre- and post-test sessions, and positive correlation between scores gained and length of time spent on learning, while no signifificant differences between the gender and post-test score, and the years of students in school and the progress in score gained. DEDATD, coupled with Mashup technique, could provide updated materials fifiltered through diverse sources located across the network. The dynamic assessment could tailor each individual learner's needs to offer custom-made learning materials. DEDATD poses as a great improvement over the traditional teaching methods.

Network device interface for digitally interfacing data channels to a controller via a network

NASA Technical Reports Server (NTRS)

Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor); Konz, Daniel W. (Inventor)

2009-01-01

A communications system and method are provided for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is converted into digital signals and transmitted to the controller. Network device interfaces associated with different data channels can coordinate communications with the other interfaces based on either a transition in a command message sent by the bus controller or a synchronous clock signal.

A compound memristive synapse model for statistical learning through STDP in spiking neural networks

PubMed Central

Bill, Johannes; Legenstein, Robert

2014-01-01

Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. The fabrication of reliable nanoscale memristive synapses, that feature continuous conductance changes based on the timing of pre- and postsynaptic spikes, has however turned out to be challenging. In this article, we propose an alternative approach, the compound memristive synapse, that circumvents this problem by the use of memristors with binary memristive states. A compound memristive synapse employs multiple bistable memristors in parallel to jointly form one synapse, thereby providing a spectrum of synaptic efficacies. We investigate the computational implications of synaptic plasticity in the compound synapse by integrating the recently observed phenomenon of stochastic filament formation into an abstract model of stochastic switching. Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP) with a stabilizing weight dependence in compound synapses. In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture. Our theoretical analysis reveals that compound-synapse STDP implements generalized Expectation-Maximization in the spiking network. Specifically, the emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model. Furthermore, the network's spike response to spiking input streams approximates a well-defined Bayesian posterior distribution. We show in computer simulations how such networks learn to represent high-dimensional distributions over images of handwritten digits with high fidelity even in presence of substantial device variations and under severe noise conditions. Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures. PMID:25565943

The iPad and EFL Digital Literacy

NASA Astrophysics Data System (ADS)

Meurant, Robert C.

In future, the uses of English by non-native speakers will predominantly be online, using English language digital resources, and in computer-mediated communication with other non-native speakers of English. Thus for Korea to be competitive in the global economy, its EFL should develop L2 Digital Literacy in English. With its fast Internet connections, Korea is the most wired nation on Earth; but ICT facilities in educational institutions need reorganization. Opportunities for computer-mediated second language learning need to be increased, providing multimedia-capable, mobile web solutions that put the Internet into the hands of all students and teachers. Wi-Fi networked campuses allow any campus space to act as a wireless classroom. Every classroom should have a teacher's computer console. All students should be provided with adequate computing facilities, that are available anywhere, anytime. Ubiquitous computing has now become feasible by providing every student on enrollment with a tablet: a Wi-Fi+3G enabled Apple iPad.

GLOBECOM '89 - IEEE Global Telecommunications Conference and Exhibition, Dallas, TX, Nov. 27-30, 1989, Conference Record. Volumes 1, 2, & 3

NASA Astrophysics Data System (ADS)

The present conference discusses topics in multiwavelength network technology and its applications, advanced digital radio systems in their propagation environment, mobile radio communications, switching programmability, advancements in computer communications, integrated-network management and security, HDTV and image processing in communications, basic exchange communications radio advancements in digital switching, intelligent network evolution, speech coding for telecommunications, and multiple access communications. Also discussed are network designs for quality assurance, recent progress in coherent optical systems, digital radio applications, advanced communications technologies for mobile users, communication software for switching systems, AI and expert systems in network management, intelligent multiplexing nodes, video and image coding, network protocols and performance, system methods in quality and reliability, the design and simulation of lightwave systems, local radio networks, mobile satellite communications systems, fiber networks restoration, packet video networks, human interfaces for future networks, and lightwave networking.

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

2015-12-01

Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

Recognition of Telugu characters using neural networks.

PubMed

Sukhaswami, M B; Seetharamulu, P; Pujari, A K

1995-09-01

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

NASA Astrophysics Data System (ADS)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning.

PubMed

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-13

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

An application of digital network technology to medical image management.

PubMed

Chu, W K; Smith, C L; Wobig, R K; Hahn, F A

1997-01-01

With the advent of network technology, there is considerable interest within the medical community to manage the storage and distribution of medical images by digital means. Higher workflow efficiency leading to better patient care is one of the commonly cited outcomes [1,2]. However, due to the size of medical image files and the unique requirements in detail and resolution, medical image management poses special challenges. Storage requirements are usually large, which implies expenses or investment costs make digital networking projects financially out of reach for many clinical institutions. New advances in network technology and telecommunication, in conjunction with the decreasing cost in computer devices, have made digital image management achievable. In our institution, we have recently completed a pilot project to distribute medical images both within the physical confines of the clinical enterprise as well as outside the medical center campus. The design concept and the configuration of a comprehensive digital image network is described in this report.

Network design for telemedicine--e-health using satellite technology.

PubMed

Graschew, Georgi; Roelofs, Theo A; Rakowsky, Stefan; Schlag, Peter M

2008-01-01

Over the last decade various international Information and Communications Technology networks have been created for a global access to high-level medical care. OP 2000 has designed and validated the high-end interactive video communication system WinVicos especially for telemedical applications, training of the physician in a distributed environment, teleconsultation and second opinion. WinVicos is operated on a workstation (WoTeSa) using standard hardware components and offers a superior image quality at a moderate transmission bandwidth of up to 2 Mbps. WoTeSa / WinVicos have been applied for IP-based communication in different satellite-based telemedical networks. In the DELTASS-project a disaster scenario was analysed and an appropriate telecommunication system for effective rescue measures for the victims was set up and evaluated. In the MEDASHIP project an integrated system for telemedical services (teleconsultation, teleelectro-cardiography, telesonography) on board of cruise ships and ferries has been set up. EMISPHER offers an equal access for most of the countries of the Euro-Mediterranean area to on-line services for health care in the required quality of service. E-learning applications, real-time telemedicine and shared management of medical assistance have been realized. The innovative developments in ICT with the aim of realizing a ubiquitous access to medical resources for everyone at any time and anywhere (u-Health) bear the risk of creating and amplifying a digital divide in the world. Therefore we have analyzed how the objective needs of the heterogeneous partners can be joined with the result that there is a need for real integration of the various platforms and services. A virtual combination of applications serves as the basic idea for the Virtual Hospital. The development of virtual hospitals and digital medicine helps to bridge the digital divide between different regions of the world and enables equal access to high-level medical care. Pre-operative planning, intra-operative navigation and minimally-invasive surgery require a digital and virtual environment supporting the perception of the physician. As data and computing resources in a virtual hospital are distributed over many sites the concept of the Grid should be integrated with other communication networks and platforms.

Inspiring Climate Education Excellence (ICEE): Developing self-directed professional development modules for secondary science teachers

NASA Astrophysics Data System (ADS)

Buhr, S. M.; Lynds, S. E.; McCaffrey, M. S.; Morton, E.

2010-12-01

Inspiring Climate Education Excellence (ICEE) is a NASA-funded project to develop online course modules and self-directed learning resources aligned with the Essential Principles of Climate Science. Following a national needs assessment survey and a face to face workshop to pilot test topics, a suite of online modules is being developed suitable for self-directed learning by secondary science teachers. Modules are designed around concepts and topics in which teachers express the most interest and need for instruction. Module design also includes attention to effective teaching strategies, such as awareness of student misconceptions, strategies for forestalling controversy and advice from master teachers on implementation and curriculum development. The resources are being developed in partnership with GLOBE, and the National Science Digital Library (NSDL) and is informed by the work of the Climate Literacy and Energy Awareness Network (CLEAN) project. ICEE will help to meet the professional development needs of teachers, including those participating in the GLOBE Student Climate Research Campaign. Modules and self-directed learning resources will be developed and disseminated in partnership with the National Science Digital Library (NSDL). This presentation introduces the needs assessment and pilot workshop data upon which the modules are based, and describes the modules that are available and in development.

A mobile clinical e-portfolio for nursing and medical students, using wireless personal digital assistants (PDAs).

PubMed

Garrett, Bernard Mark; Jackson, Cathryn

2006-12-01

This paper outlines the development and evaluation of a wireless personal digital assistant (PDA) based clinical learning tool designed to promote professional reflection for health professionals. The "Clinical e-portfolio" was developed at the University of British Columbia School of Nursing to enable students immediately to access clinical expertise and resources remotely, and record their clinical experiences in a variety of media (text, audio and images). The PDA e-portfolio tool was developed to demonstrate the potential use of mobile networked technologies to support and improve clinical learning; promote reflective learning in practice; engage students in the process of knowledge translation; help contextualize and embed clinical knowledge whilst in the workplace; and to help prevent the isolation of students whilst engaged in supervised clinical practice. The mobile e-portfolio was developed to synchronise wirelessly with a user's personal Web based portfolio from any remote location where a cellular telephone signal or wireless (Wi-Fi) connection could be obtained. An evaluation of the tool was undertaken with nurse practitioner and medical students, revealing positive attitudes to the use of PDA based tools and portfolios, but limits to the use of the PDA portfolio due to the inherent interface restrictions of the PDA.

ARCH: Adaptive recurrent-convolutional hybrid networks for long-term action recognition

PubMed Central

Xin, Miao; Zhang, Hong; Wang, Helong; Sun, Mingui; Yuan, Ding

2017-01-01

Recognition of human actions from digital video is a challenging task due to complex interfering factors in uncontrolled realistic environments. In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities. Utilizing a cognitive-based data reduction method and a hybrid “network upon networks” architecture, we extract human action representations which are robust against spatial and temporal interferences and adaptive to variations in both action speed and duration. We evaluated our method on the UCF101 and other three challenging datasets. Our results demonstrated a superior performance of the proposed algorithm in human action recognition. PMID:29290647

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

NASA Astrophysics Data System (ADS)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Storytelling in the digital world: achieving higher-level learning objectives.

PubMed

Schwartz, Melissa R

2012-01-01

Nursing students are not passive media consumers but instead live in a technology ecosystem where digital is the language they speak. To prepare the next generation of nurses, educators must incorporate multiple technologies to improve higher-order learning. The author discusses the evolution and use of storytelling as part of the digital world and how digital stories can be aligned with Bloom's Taxonomy so that students achieve higher-level learning objectives.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

CYBERWAR-2012/13: Siegel 2011 Predicted Cyberwar Via ACHILLES-HEEL DIGITS BEQS BEC ZERO-DIGIT BEC of/in ACHILLES-HEEL DIGITS Log-Law Algebraic-Inversion to ONLY BEQS BEC Digit-Physics U Barabasi Network/Graph-Physics BEQS BEC JAMMING Denial-of-Access(DOA) Attacks 2012-Instantiations

NASA Astrophysics Data System (ADS)

Huffmann, Master; Siegel, Edward Carl-Ludwig

2013-03-01

Newcomb-Benford(NeWBe)-Siegel log-law BEC Digit-Physics Network/Graph-Physics Barabasi et.al. evolving-``complex''-networks/graphs BEC JAMMING DOA attacks: Amazon(weekends: Microsoft I.E.-7/8(vs. Firefox): Memorial-day, Labor-day,...), MANY U.S.-Banks:WF,BoA,UB,UBS,...instantiations AGAIN militate for MANDATORY CONVERSION to PARALLEL ANALOG FAULT-TOLERANT but slow(er) SECURITY-ASSURANCE networks/graphs in parallel with faster ``sexy'' DIGITAL-Networks/graphs:``Cloud'', telecomm: n-G,..., because of common ACHILLES-HEEL VULNERABILITY: DIGITS!!! ``In fast-hare versus slow-tortoise race, Slow-But-Steady ALWAYS WINS!!!'' (Zeno). {Euler [#s(1732)] ∑- ∏()-Riemann[Monats. Akad. Berlin (1859)] ∑- ∏()- Kummer-Bernoulli (#s)}-Newcomb [Am.J.Math.4(1),39 (81) discovery of the QUANTUM!!!]-{Planck (01)]}-{Einstein (05)]-Poincar e [Calcul Probabilités,313(12)]-Weyl[Goett. Nach.(14); Math.Ann.77,313(16)]-(Bose (24)-Einstein(25)]-VS. -Fermi (27)-Dirac(27))-Menger [Dimensiontheorie(29)]-Benford [J.Am. Phil.Soc.78,115(38)]-Kac[Maths Stats.-Reason. (55)]- Raimi [Sci.Am.221,109(69)]-Jech-Hill [Proc.AMS,123,3,887(95)] log-function

Digital Games and Learning: Identifying Pathways of Influence

ERIC Educational Resources Information Center

Subrahmanyam, Kaveri; Renukarya, Bhavya

2015-01-01

Digital games and gamelike contexts have become an integral part of young people's lives, and scholars have speculated about their potential to engage and enhance children's learning. Given that digital games are complex systems, we propose that different aspects of game features and game play might influence learning in different ways. Drawing on…

A Study on Exploiting Commercial Digital Games into School Context

ERIC Educational Resources Information Center

Panoutsopoulos, Hercules; Sampson, Demetrios G.

2012-01-01

Digital game-based learning is a research field within the context of technology-enhanced learning that has attracted significant research interest. Commercial off-the-shelf digital games have the potential to provide concrete learning experiences and allow for drawing links between abstract concepts and real-world situations. The aim of this…

Using Digital Photography to Supplement Learning of Biotechnology

ERIC Educational Resources Information Center

Norflus, Fran

2012-01-01

The author used digital photography to supplement learning of biotechnology by students with a variety of learning styles and educational backgrounds. Because one approach would not be sufficient to reach all the students, digital photography was used to explain the techniques and results to the class instead of having to teach each student…

Information Activities and Appropriation in Teacher Trainees' Digital, Group-Based Learning

ERIC Educational Resources Information Center

Hanell, Fredrik

2016-01-01

Introduction: This paper reports results from an ethnographic study of teacher trainees' information activities in digital, group-based learning and their relation to the interplay between use and appropriation of digital tools and the learning environment. Method: The participants in the present study are 249 pre-school teacher trainees in…

Digital Competence Model of Distance Learning Students

ERIC Educational Resources Information Center

da Silva, Ketia Kellen A.; Behar, Patricia A.

2017-01-01

This article presents the development of a digital competency model of Distance Learning (DL) students in Brazil called CompDigAl_EAD. The following topics were addressed in this study: Educational Competences, Digital Competences, and Distance Learning students. The model was developed between 2015 and 2016 and is being validated in 2017. It was…

Technical Guidelines for Digital Learning Content: Development, Evaluation, Selection, Acquisition and Use

ERIC Educational Resources Information Center

Southern Regional Education Board (SREB), 2005

2005-01-01

The Educational Technology Cooperative of the Southern Regional Education Board (SREB) established the Digital Learning Content initiative to identify guidelines and develop recommendations to assist those who develop, evaluate, select, acquire and use digital learning content to create products that are easy to access and use in order to ensure…

Digital Learning in California's K-12 Schools. Just the Facts

ERIC Educational Resources Information Center

Gao, Niu

2015-01-01

This fact page briefly discusses the following facts on digital learning in California's K-12: (1) As California implements new tests in its K-12 schools, technology infrastructure is a key concern; (2) Many districts are confident that they had enough bandwidth for online field tests; (3) Digital learning will require significantly greater…

Educating for Digital Futures: What the Learning Strategies of Digital Media Professionals Can Teach Higher Education

ERIC Educational Resources Information Center

Bridgstock, Ruth

2016-01-01

This article explores how universities might engage more effectively with the imperative to develop students' twenty-first century skills for the information society, by examining learning challenges and professional learning strategies of successful digital media professionals. The findings of qualitative interviews with professionals from…

Agriscience Teachers' Implementation of Digital Game-Based Learning in an Introductory Animal Science Course

ERIC Educational Resources Information Center

Webb, Angela W.; Bunch, J. C.; Wallace, Maria F.

2015-01-01

In today's technological age, visions for technology integration in the classroom continue to be explored and examined. Digital game-based learning is one way to purposefully integrate technology while maintaining a focus on learning objectives. This case study sought to understand agriscience teachers' experiences implementing digital game-based…

The Digital Learning Transition MOOC for Educators: Exploring a Scalable Approach to Professional Development

ERIC Educational Resources Information Center

Kleiman, Glenn M.; Wolf, Mary Ann; Frye, David

2013-01-01

In conjunction with the relaunch of the Digital Learning Transition (DLT) Massive Open Online Course for Educatos (MOOC-Ed) in September 2013, the Alliance and the Friday Institute released "The Digital Learning Transition MOOC for Educators: Exploring a Scalable Approach to Professional Development", a new paper that describes the…

A Professional Learning Model Supporting Teachers to Integrate Digital Technologies

ERIC Educational Resources Information Center

Sheffield, Rachel; Blackley, Susan; Moro, Paul

2018-01-01

Contemporary teachers have an obligation to support and scaffold students' learning in digital technologies and to do this in authentic contexts. In order for teachers to be successful in this, their own competency in digital technologies needs to be high, and their own 21st century learning skills of communication, collaboration, creativity and…

Developing Competencies by Playing Digital Sports-Games

ERIC Educational Resources Information Center

Kretschmann, Rolf

2010-01-01

The idea of digital game-based learning (DGBL) is that students (or players) learn something by playing a computer or video game and that an educator can employ digital games to assist and boost both formal and informal learning. There is game software that is not specifically produced for educational use but which is nonetheless regularly…

Supporting Digital Natives to Learn Effectively with Technology Tools

ERIC Educational Resources Information Center

Keengwe, Jared; Georgina, David

2013-01-01

Majority of learners in our classrooms are digital natives or Millennials--a category of learners who tend toward independence and autonomy in their learning styles. The primary challenges then facing instructors include: How do digital natives learn and how do you teach them? The answers to these questions will help instructors to: (a) identify…

Theoretical Perspectives of How Digital Natives Learn

ERIC Educational Resources Information Center

Kivunja, Charles

2014-01-01

Marck Prensky, an authority on teaching and learning especially with the aid of Information and Communication Technologies, has referred to 21st century children born after 1980 as "Digital Natives". This paper reviews literature of leaders in the field to shed some light on theoretical perspectives of how Digital Natives learn and how…

A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

ERIC Educational Resources Information Center

Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

2015-01-01

This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

New Generation of Broadcasting Satellite Systems: New Markets and Business Developments

NASA Astrophysics Data System (ADS)

Perrot, Bruno; Michel, Cyril; Villaret, Stéfanie

2002-01-01

Since the deployment of the first Digital Broadcasting Satellite Systems, European satellite operators and service providers have been faced with the continuously increasing demand for Digital Broadcasting Services. Their success is built on the availability of the MPEG and DVB standards. Undoubtedly, conventional digital television broadcasting is today the `Killer' application. Various service providers already offer multimedia applications through DVB-S systems based upon the `Push' technology. Although these services do not currently represent the core business for broadcasting satellite operators, their percentage is increasing. `Push' technology services include Data Carousel, Webcasting, Turbo Internet, File casting and so on. Such technology can support the implementation of different emerging multimedia services scenarios from Newsgroups, Network collaborative learning, and tele-medicine, to others that may be invented in the near future. The penetration rate of multi-channel television reception is still increasing. Broadcasting satellites benefit both from the development of new, more segmented and sophisticated offers and from the development of Internet services. Satellite is likely to enter these new markets at different levels of the value chain: Even if the satellite has demonstrated its capacity to fully serve the television, combinations with other networks may be necessary to address the new markets: at the consumer premises, Internet-related services will require a return path; at the backbone level, satellite becomes a component of a full telecommunications solution. This article focuses on the European market and proposes:

Image texture segmentation using a neural network

NASA Astrophysics Data System (ADS)

Sayeh, Mohammed R.; Athinarayanan, Ragu; Dhali, Pushpuak

1992-09-01

In this paper we use a neural network called the Lyapunov associative memory (LYAM) system to segment image texture into different categories or clusters. The LYAM system is constructed by a set of ordinary differential equations which are simulated on a digital computer. The clustering can be achieved by using a single tuning parameter in the simplest model. Pattern classes are represented by the stable equilibrium states of the system. Design of the system is based on synthesizing two local energy functions, namely, the learning and recall energy functions. Before the implementation of the segmentation process, a Gauss-Markov random field (GMRF) model is applied to the raw image. This application suitably reduces the image data and prepares the texture information for the neural network process. We give a simple image example illustrating the capability of the technique. The GMRF-generated features are also used for a clustering, based on the Euclidean distance.

Two algorithms for neural-network design and training with application to channel equalization.

PubMed

Sweatman, C Z; Mulgrew, B; Gibson, G J

1998-01-01

We describe two algorithms for designing and training neural-network classifiers. The first, the linear programming slab algorithm (LPSA), is motivated by the problem of reconstructing digital signals corrupted by passage through a dispersive channel and by additive noise. It constructs a multilayer perceptron (MLP) to separate two disjoint sets by using linear programming methods to identify network parameters. The second, the perceptron learning slab algorithm (PLSA), avoids the computational costs of linear programming by using an error-correction approach to identify parameters. Both algorithms operate in highly constrained parameter spaces and are able to exploit symmetry in the classification problem. Using these algorithms, we develop a number of procedures for the adaptive equalization of a complex linear 4-quadrature amplitude modulation (QAM) channel, and compare their performance in a simulation study. Results are given for both stationary and time-varying channels, the latter based on the COST 207 GSM propagation model.

Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

NASA Technical Reports Server (NTRS)

Zee, Frank

1995-01-01

The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

Lights, camera, action research: The effects of didactic digital movie making on students' twenty-first century learning skills and science content in the middle school classroom

NASA Astrophysics Data System (ADS)

Ochsner, Karl

Students are moving away from content consumption to content production. Short movies are uploaded onto video social networking sites and shared around the world. Unfortunately they usually contain little to no educational value, lack a narrative and are rarely created in the science classroom. According to new Arizona Technology standards and ISTE NET*S, along with the framework from the Partnership for 21st Century Learning Standards, our society demands students not only to learn curriculum, but to think critically, problem solve effectively, and become adept at communicating and collaborating. Didactic digital movie making in the science classroom may be one way that these twenty-first century learning skills may be implemented. An action research study using a mixed-methods approach to collect data was used to investigate if didactic moviemaking can help eighth grade students learn physical science content while incorporating 21st century learning skills of collaboration, communication, problem solving and critical thinking skills through their group production. Over a five week period, students researched lessons, wrote scripts, acted, video recorded and edited a didactic movie that contained a narrative plot to teach a science strand from the Arizona State Standards in physical science. A pretest/posttest science content test and KWL chart was given before and after the innovation to measure content learned by the students. Students then took a 21st Century Learning Skills Student Survey to measure how much they perceived that communication, collaboration, problem solving and critical thinking were taking place during the production. An open ended survey and a focus group of four students were used for qualitative analysis. Three science teachers used a project evaluation rubric to measure science content and production values from the movies. Triangulating the science content test, KWL chart, open ended questions and the project evaluation rubric, it appeared that science content was gained from this project. Students felt motivated to learn and had positive experience. Students also felt that the repetition of production and watching their movies helped them remember science. Students also perceived that creating the didactic digital movie helped them use collaboration, communication, problem solving and critical thinking skills throughout their production.

Student perceptions of digital versus traditional slide use in undergraduate education.

PubMed

Solberg, Brooke L

2012-01-01

Digitized slides provide a number of intriguing benefits for educators. Before their implementation, however, educators should consider student opinion related to their use. This mixed-methods study directly compared Medical Laboratory Science (MLS) student perceptions of learning experiences in both digital and traditional slide laboratory settings. Results suggested that the majority of students preferred learning with digital slides, and numerous reasons for this preference were identified. Survey responses indicated that students using digital slides tended to view their performances, instructor feedback, and their learning environment more positively than students using traditional slides. Apprehensions about digital slide use were also detected from students preferring traditional slides. These findings provide a guide on how best to exploit both digital and traditional slides in an educational setting.

From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers

PubMed Central

Purkayastha, S.; Biswas, R.; Jai Ganesh, A.U.; Otero, P.

2015-01-01

Summary Objective To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care. Introduction This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting. Methods We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence. Conclusion UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all. PMID:26123908

From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers. Contribution of the Health Informatics Education Working Group.

PubMed

Purkayastha, S; Price, A; Biswas, R; Jai Ganesh, A U; Otero, P

2015-08-13

To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care. This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting. We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence. UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Outcasts on the Inside: Academics Reinventing Themselves Online

ERIC Educational Resources Information Center

Costa, Cristina

2015-01-01

Recent developments in digital scholarship point out that academic practices supported by technologies may not only be transformed through the obvious process of digitization, but also renovated through distributed knowledge networks that digital technologies enable, and the practices of openness that such networks develop. Yet, this apparent…

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

Science 2.0: When Students Become Digital Citizens

ERIC Educational Resources Information Center

Smith, Ben; Mader, Jared

2016-01-01

Modern science learning requires the use of digital tools and a shift in teaching philosophy and pedagogy. The backbone to this shift rests in a yet unaddressed skill: digital citizenship. The authors discuss the Digital Citizen standard where "students (will) recognize the rights, responsibilities, and opportunities of living, learning, and…

"Scratch"ing below the Surface: Mathematics through an Alternative Digital Lens?

ERIC Educational Resources Information Center

Calder, Nigel; Taylor, Merilyn

2010-01-01

A key element in the examination of how students process mathematics through digital technologies is considering the ways that digital pedagogical media might influence the learning process. How might students' understanding emerge through engagement in a digital-learning environment? Interactive software that has cross-curricula implications and…

Sustainable Innovations: Bringing Digital Media and Emerging Technologies to the Classroom

ERIC Educational Resources Information Center

Herro, Danielle

2015-01-01

Because traditional schools struggle to effectively understand, implement, and sustain digital learning initiatives, innovating with digital media in classrooms is a difficult endeavor. Practitioners need examples to better understand conditions necessary to move forward with digital media and learning (DML) in schools. This article provides…

Questioning and metacognitive thinking: On-line and off-line assessments in understanding the role of prompting/questioning and metacognitive thinking in a digital learning environment

NASA Astrophysics Data System (ADS)

Schroeder, Mubina Khan

In science education, the use of digital technology-based learning can help students struggling with difficult concepts such as the movement of molecules. While digital learning tools hold much promise for science education, the question arises as to whether or not such technology can serve as an adequate surrogate for the teacher-student interactions that theorists like Lev Vygotsky (1978) underscored as being critical to learning. In response to such concerns, designers of digital curricula often utilize scaffolds to help students as they learn from such programs. Using a simulation designed to teach students about the concept of diffusion as an example, I examine the effect of including prompting language in the learning sequence of the simulation. The use of prompting language in digital curriculum appears to be successful because it elicits science students to reflect and metacognise about their learning, lending support to Vygotsky's (1978) ideas of teaching and learning involving outer and inner dialog. However, findings from think aloud data continue to underscore the importance of human linguistic exchange as a preferable learning paradigm.

Learning "in" or "with" Games? Quality Criteria for Digital Learning Games from the Perspectives of Learning, Emotion, and Motivation Theory

ERIC Educational Resources Information Center

Hense, Jan; Mandl, Heinz

2012-01-01

This conceptual paper aims to clarify the theoretical underpinnings of game based learning (GBL) and learning with digital learning games (DLGs). To do so, it analyses learning of game related skills and contents, which occurs constantly during playing conventional entertainment games, from three perspectives: learning theory, emotion theory, and…

Social Phenomenon of Community on Online Learning: Digital Interaction and Collaborative Learning Experience

ERIC Educational Resources Information Center

Aleksic-Maslac, Karmela; Magzan, Masha; Juric, Visnja

2009-01-01

Digital interaction in e-learning offers great opportunities for education quality improvement in both--the classical teaching combined with e-learning, and distance learning. Zagreb School of Economics & Management (ZSEM) is one of the few higher education institutions in Croatia that systematically uses e-learning in teaching. Systematically…

Connected Learning: Harnessing the Information Age to Make Learning More Powerful

ERIC Educational Resources Information Center

Roc, Martens

2014-01-01

This report introduces connected learning, a promising educational approach supported by the MacArthur Foundation and the Digital Learning Media (DLM) initiative that schools and out-of-school sites are adopting to enhance student learning and outcomes by connecting their education to their interests. Connected learning uses digital media to…

Six Strategies for Digital Learning Success. White Paper

ERIC Educational Resources Information Center

Mehta, Samir; Downs, Holly

2016-01-01

Technology has revolutionized corporate learning and leadership development. The number of organizations that use learning management systems is higher than ever before, and thanks to massive open online courses (MOOCs), small private online courses (SPOCS), microlearning, nanolearning, and other new media learning platforms, digital learning and…

Designing learning apparatus to promote twelfth grade students’ understanding of digital technology concept: A preliminary studies

NASA Astrophysics Data System (ADS)

Marlius; Kaniawati, I.; Feranie, S.

2018-05-01

A preliminary learning design using relay to promote twelfth grade student’s understanding of logic gates concept is implemented to see how well it’s to adopted by six high school students, three male students and three female students of twelfth grade. This learning design is considered for next learning of digital technology concept i.e. data digital transmition and analog. This work is a preliminary study to design the learning for large class. So far just a few researches designing learning design related to digital technology with relay. It may due to this concept inserted in Indonesian twelfth grade curriculum recently. This analysis is focus on student difficulties trough video analysis to learn the concept. Based on our analysis, the recommended thing for redesigning learning is: students understand first about symbols and electrical circuits; the Student Worksheet is made in more detail on the assembly steps to the project board; mark with symbols at points in certain places in the circuit for easy assembly; assembly using relays by students is enough until is the NOT’s logic gates and the others that have been assembled so that effective time. The design of learning using relays can make the relay a liaison between the abstract on the digital with the real thing of it, especially in the circuit of symbols and real circuits. Besides it is expected to also enrich the ability of teachers in classroom learning about digital technology.

Enhancing Writing Achievement through a Digital Learning Environment: Case Studies of Three Struggling Adolescent Male Writers

ERIC Educational Resources Information Center

Pruden, Manning; Kerkhoff, Shea N.; Spires, Hiller A.; Lester, James

2017-01-01

The aim of this study was to explore how "Narrative Theatre," a narrative-centered digital learning environment, supported the writing processes of 3 struggling adolescent male writers. We utilized a multicase study approach to capture 3 sixth-grade participants' experiences with the digital learning environment before, during, and after…

How Much Is Teaching and Learning in Higher Education Digitized? Insights from Teacher Education

ERIC Educational Resources Information Center

Riehemann, Jens; Jucks, Regina

2017-01-01

The digital age has changed how we communicate, inform ourselves, and even how we teach and learn. This study systematically analyses and compares the perspectives of university academics (N = 75) and university students (N = 206) from the field of teacher education on digitized teaching and learning. In a between-subjects design, participants of…

Utilizing Mobile Devices to Enrich the Learning Style of Students

ERIC Educational Resources Information Center

McGovern, Enda F.; Luna-Nevarez, Cuauhtemoc; Baruca, Arne

2017-01-01

As digital technologies evolve in education, business faculty have increased access to an extensive range of mobile devices and online applications to help them inspire students' passion for learning. Adopting new digital approaches to teaching can also enhance the learning style of students who are immersed in the use of digital devices. How can…

Digital Game-Based Learning Once Removed: Teaching Teachers

ERIC Educational Resources Information Center

Becker, Katrin

2007-01-01

In the spring of 2005, the author designed and taught a graduate-level course on digital game-based learning primarily for teachers. Teachers cannot be expected to embrace digital games as a tool for learning unless they have a sound understanding of the potential as well as the limitations, and are confident in their ability to use games…

A Computer-Assisted Learning Model Based on the Digital Game Exponential Reward System

ERIC Educational Resources Information Center

Moon, Man-Ki; Jahng, Surng-Gahb; Kim, Tae-Yong

2011-01-01

The aim of this research was to construct a motivational model which would stimulate voluntary and proactive learning using digital game methods offering players more freedom and control. The theoretical framework of this research lays the foundation for a pedagogical learning model based on digital games. We analyzed the game reward system, which…

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N. C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

2017-04-01

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.

PubMed

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N C; Tomaszewski, John; González, Fabio A; Madabhushi, Anant

2017-04-18

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

PubMed Central

Cruz-Roa, Angel; Gilmore, Hannah; Basavanhally, Ajay; Feldman, Michael; Ganesan, Shridar; Shih, Natalie N.C.; Tomaszewski, John; González, Fabio A.; Madabhushi, Anant

2017-01-01

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma. PMID:28418027

DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope

PubMed Central

Schaumberg, Andrew J.; Sirintrapun, S. Joseph; Al-Ahmadie, Hikmat A.; Schüffler, Peter J.; Fuchs, Thomas J.

2018-01-01

Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration’s ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist’s routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observation time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.40% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues. When training on one patient but testing on another, AUROC in bladder is 0.79±0.11 and in prostate is 0.96±0.04. Our tool is available at https://bitbucket.org/aschaumberg/deepscope PMID:29601065

Governing Software: Networks, Databases and Algorithmic Power in the Digital Governance of Public Education

ERIC Educational Resources Information Center

Williamson, Ben

2015-01-01

This article examines the emergence of "digital governance" in public education in England. Drawing on and combining concepts from software studies, policy and political studies, it identifies some specific approaches to digital governance facilitated by network-based communications and database-driven information processing software…

Metacognitive components in smart learning environment

NASA Astrophysics Data System (ADS)

Sumadyo, M.; Santoso, H. B.; Sensuse, D. I.

2018-03-01

Metacognitive ability in digital-based learning process helps students in achieving learning goals. So that digital-based learning environment should make the metacognitive component as a facility that must be equipped. Smart Learning Environment is the concept of a learning environment that certainly has more advanced components than just a digital learning environment. This study examines the metacognitive component of the smart learning environment to support the learning process. A review of the metacognitive literature was conducted to examine the components involved in metacognitive learning strategies. Review is also conducted on the results of study smart learning environment, ranging from design to context in building smart learning. Metacognitive learning strategies certainly require the support of adaptable, responsive and personalize learning environments in accordance with the principles of smart learning. The current study proposed the role of metacognitive component in smart learning environment, which is useful as the basis of research in building environment in smart learning.

"I know your name, but not your number"--Patients with verbal short-term memory deficits are impaired in learning sequences of digits.

PubMed

Bormann, Tobias; Seyboth, Margret; Umarova, Roza; Weiller, Cornelius

2015-06-01

Studies on verbal learning in patients with impaired verbal short-term memory (vSTM) have revealed dissociations among types of verbal information. Patients with impaired vSTM are able to learn lists of known words but fail to acquire new word forms. This suggests that vSTM is involved in new word learning. The present study assessed both new word learning and the learning of digit sequences in two patients with impaired vSTM. In two experiments, participants were required to learn people's names, ages and professions, or their four digit 'phone numbers'. The STM patients were impaired on learning unknown family names and phone numbers, but managed to acquire other verbal information. In contrast, a patient with a severe verbal episodic memory impairment was impaired across information types. These results indicate verbal STM involvement in the learning of digit sequences. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using blended learning and out-of-school visits: pedagogies for effective science teaching in the twenty-first century

NASA Astrophysics Data System (ADS)

Coll, Sandhya Devi; Coll, Richard Kevin

2018-04-01

Background: Recent research and curriculum reforms have indicated the need for diversifying teaching approaches by drawing upon student interest and engagement in ways which makes learning science meaningful. Purpose: This study examines the integration of informal/free choice learning which occurred during learning experiences outside school (LEOS) with classroom learning using digital technologies. Specifically, the digital technologies comprised a learning management system (LMS), Moodle, which fits well with students' lived experiences and their digital world. Design and Method: This study examines three out-of-school visits to Informal Science Institutes (ISI) using a digitally integrated fieldtrip inventory (DIFI) Model. Research questions were analysed using thematic approach emerging along with semi-structured interviews, before, during and after the visit, and assessing students' learning experiences. Data comprised photographs, field notes, and unobtrusive observations of the classroom, wiki postings, student work books and teacher planning diaries. Results: We argue, that pre- and post-visit planning using the DIFI Model is more likely to engage learners, and the use of a digital learning platform was even more likely to encourage collaborative learning. The conclusion can also be drawn that students' level of motivation for collaborative learning positively correlates with their improvement in academic achievement.

E-Learning Research and Development: On Evaluation, Learning Performance, and Visual Attention

ERIC Educational Resources Information Center

Rüth, Marco

2017-01-01

Digital learning is becoming a prevalent everyday human behavior. Effective digital learning services are integral for educational innovation and constitute competitive advantages for education businesses. Quality management in e-learning research and development is thus of utmost importance and needs both strong conceptual and empirical…

Design and clinical evaluation of a high-capacity digital image archival library and high-speed network for the replacement of cinefilm in the cardiac angiography environment

NASA Astrophysics Data System (ADS)

Cusma, Jack T.; Spero, Laurence A.; Groshong, Bennett R.; Cho, Teddy; Bashore, Thomas M.

1993-09-01

An economical and practical digital solution for the replacement of 35 mm cine film as the archive media in the cardiac x-ray imaging environment has remained lacking to date due to the demanding requirements of high capacity, high acquisition rate, high transfer rate, and a need for application in a distributed environment. A clinical digital image library and network based on the D2 digital video format has been installed in the Duke University Cardiac Catheterization Laboratory. The system architecture includes a central image library with digital video recorders and robotic tape retrieval, three acquisition stations, and remote review stations connected via a serial image network. The library has a capacity for over 20,000 Gigabytes of uncompressed image data, equivalent to records for approximately 20,000 patients. Image acquisition in the clinical laboratories is via a real-time digital interface between the digital angiography system and a local digital recorder. Images are transferred to the library over the serial network at a rate of 14.3 Mbytes/sec and permanently stored for later review. The image library and network are currently undergoing a clinical comparison with cine film for visual and quantitative assessment of coronary artery disease. At the conclusion of the evaluation, the configuration will be expanded to include four additional catheterization laboratories and remote review stations throughout the hospital.

Student perceptions of digital badges in a drug information and literature evaluation course.

PubMed

Fajiculay, Jay R; Parikh, Bhavini T; Wright, Casey V; Sheehan, Amy Heck

2017-09-01

The purpose of this article is to describe student perceptions of implementation of digital badges in a drug information and literature evaluation course. Two digital badges were developed as voluntary learning opportunities. Student perceptions were obtained through pre- and post-survey instruments consisting of selected questions from the Motivated Strategies for Learning Questionnaire. The response rate was 69% (106/153). At baseline, 53% of respondents agreed that digital badges could help them better understand course material. More students agreed they would share earned digital badges on LinkedIn (68%) than Facebook (19%). Most students who earned digital badges agreed that badges helped increase their confidence in course material (73%), focus on specific learning objectives (55%), look deeper into course competencies (64%), and were a useful adjunct to the traditional teaching method (82%). Digital badges were perceived by students as a positive adjunct to learning and may provide a novel mechanism for development of an electronic skills-based portfolio. Copyright © 2017 Elsevier Inc. All rights reserved.

Pre-Service Teachers Designing and Constructing "Good Digital Games"

ERIC Educational Resources Information Center

Artym, Corbett; Carbonaro, Mike; Boechler, Patricia

2016-01-01

There is a growing interest in the application of digital games to enhance learning across many educational levels. This paper investigates pre-service teachers' ability to operationalize the learning principles that are considered part of a good digital game (Gee, 2007) by designing digital games in Scratch. Forty pre-service teachers, enrolled…

Using a Digital Game as an Advance Organizer

ERIC Educational Resources Information Center

Denham, André R.

2018-01-01

The use of digital games as an instructional tool has garnered increasing attention in the education community. Empirical work supported by theory on the learning affordances of digital games allowed the game-based learning community to arrive at the consensus that digital games provide an excellent medium for the acquisition of skills and the…

Generative Learning Strategy Use and Self-Regulatory Prompting in Digital Text

ERIC Educational Resources Information Center

Reid, Alan J.; Morrison, Gary M.

2014-01-01

The digital revolution is shifting print-based textbooks to digital text, and it has afforded the opportunity to incorporate meaningful learning strategies and otherwise separate metacognitive activities directly into these texts as embedded support. A sample of 89 undergraduates read a digital, expository text on the basics of photography. The…

E-inclusion Process and Societal Digital Skill Development

ERIC Educational Resources Information Center

Vitolina, Ieva

2015-01-01

Nowadays, the focus shifts from information and communication technology access to skills and knowledge. Moreover, lack of digital skills is an obstacle in the process of learning new digital competences using technologies and e-learning. The objective of this research is to investigate how to facilitate students to use the acquired digital skills…

Anticipatory planning and control of grasp positions and forces for dexterous two-digit manipulation.

PubMed

Fu, Qiushi; Zhang, Wei; Santello, Marco

2010-07-07

Dexterous object manipulation requires anticipatory control of digit positions and forces. Despite extensive studies on sensorimotor learning of digit forces, how humans learn to coordinate digit positions and forces has never been addressed. Furthermore, the functional role of anticipatory modulation of digit placement to object properties remains to be investigated. We addressed these questions by asking human subjects (12 females, 12 males) to grasp and lift an inverted T-shaped object using precision grip at constrained or self-chosen locations. The task requirement was to minimize object roll during lift. When digit position was not constrained, subjects could have implemented many equally valid digit position-force coordination patterns. However, choice of digit placement might also have resulted in large trial-to-trial variability of digit position, hence challenging the extent to which the CNS could have relied on sensorimotor memories for anticipatory control of digit forces. We hypothesized that subjects would modulate digit placement for optimal force distribution and digit forces as a function of variable digit positions. All subjects learned to minimize object roll within the first three trials, and the unconstrained device was associated with significantly smaller grip forces but larger variability of digit positions. Importantly, however, digit load force modulation compensated for position variability, thus ensuring consistent object roll minimization on each trial. This indicates that subjects learned object manipulation by integrating sensorimotor memories with sensory feedback about digit positions. These results are discussed in the context of motor equivalence and sensorimotor integration of grasp kinematics and kinetics.

Digital communication constraints in prior space missions

NASA Technical Reports Server (NTRS)

Yassine, Nathan K.

2004-01-01

Digital communication is crucial for space endeavors. Jt transmits scientific and command data between earth stations and the spacecraft crew. It facilitates communications between astronauts, and provides live coverage during all phases of the mission. Digital communications provide ground stations and spacecraft crew precise data on the spacecraft position throughout the entire mission. Lessons learned from prior space missions are valuable for our new lunar and Mars missions set by our president s speech. These data will save our agency time and money, and set course our current developing technologies. Limitations on digital communications equipment pertaining mass, volume, data rate, frequency, antenna type and size, modulation, format, and power in the passed space missions are of particular interest. This activity is in support of ongoing communication architectural studies pertaining to robotic and human lunar exploration. The design capabilities and functionalities will depend on the space and power allocated for digital communication equipment. My contribution will be gathering these data, write a report, and present it to Communications Technology Division Staff. Antenna design is very carefully studied for each mission scenario. Currently, Phased array antennas are being developed for the lunar mission. Phased array antennas use little power, and electronically steer a beam instead of DC motors. There are 615 patches in the phased array antenna. These patches have to be modified to have high yield. 50 patches were created for testing. My part is to assist in the characterization of these patch antennas, and determine whether or not certain modifications to quartz micro-strip patch radiators result in a significant yield to warrant proceeding with repairs to the prototype 19 GHz ferroelectric reflect-array antenna. This work requires learning how to calibrate an automatic network, and mounting and testing antennas in coaxial fixtures. The purpose of this activity is to assist in the set-up of phase noise instrumentation, assist in the process of automated wire bonding, assist in the design and optimization of tunable microwave components, especially phase shifters, based on thin ferroelectric films, and learn how to use commercial electromagnetic simulation software.

Educating science editors: is there a comprehensive strategy?

PubMed

Gasparyan, Armen Yuri; Yessirkepov, Marlen; Gorin, Sergey V; Kitas, George D

2014-12-01

The article considers available options to educate science editors in the fast-transforming digital environment. There is no single course or resource that can cover their constantly changing and diversifying educational needs. The involvement in research, writing, and reviewing is important for gaining editing skills, but that is not all. Membership in editorial associations and access to updated scholarly information in the field are mandatory for maintaining editorial credentials. Learned associations offer access to a few widely-recognized periodicals. There are also formal training courses covering issues in science writing and ethical editing, but no high-level evidence data exist to promote any of these. Networking with like-minded specialists within the global and regional editorial associations seems a useful strategy to upgrade editorial skills and resolve problems with the quality control and digitization of scholarly periodicals.

Educating science editors: is there a comprehensive strategy?

PubMed Central

Gasparyan, Armen Yuri; Yessirkepov, Marlen; Gorin, Sergey V.; Kitas, George D.

2014-01-01

The article considers available options to educate science editors in the fast-transforming digital environment. There is no single course or resource that can cover their constantly changing and diversifying educational needs. The involvement in research, writing, and reviewing is important for gaining editing skills, but that is not all. Membership in editorial associations and access to updated scholarly information in the field are mandatory for maintaining editorial credentials. Learned associations offer access to a few widely-recognized periodicals. There are also formal training courses covering issues in science writing and ethical editing, but no high-level evidence data exist to promote any of these. Networking with like-minded specialists within the global and regional editorial associations seems a useful strategy to upgrade editorial skills and resolve problems with the quality control and digitization of scholarly periodicals. PMID:25559840

An agile high-capacity FDMA digital satellite network

NASA Astrophysics Data System (ADS)

Hawkins, R. B.; Johannes, V. I.; Lowell, R.

A centrally controlled digital transmission satellite network has been designed for High Speed Switched Digital Service (HSSDS), which uses both satellite and earth transmission facilities to provide point-to-point digital trunks on a reservation basis. HSSDS customers connect via 1.544 Mb/s loops to the nodes where switches are located, and the FDMA system employed offers 24 one-way 1.544 Mb/s trunks per satellite transponder.

Teaching 2.0: Teams Keep Teachers and Students Plugged into Technology

ERIC Educational Resources Information Center

Bourgeois, Michelle; Hunt, Bud

2011-01-01

A Colorado district develops a two-year program that gives teacher teams an opportunity to learn how to use digital tools in the classroom. Called the Digital Learning Collaborative, it is built on three things about professional learning: (1) Learning takes time; (2) Learning is a social process; and (3) Learning about technology should be…

Organisational Learning as an Emerging Process: The Generative Role of Digital Tools in Informal Learning Practices

ERIC Educational Resources Information Center

Za, Stefano; Spagnoletti, Paolo; North-Samardzic, Andrea

2014-01-01

Increasing attention is paid to organisational learning, with the success of contemporary organisations strongly contingent on their ability to learn and grow. Importantly, informal learning is argued to be even more significant than formal learning initiatives. Given the widespread use of digital technologies in the workplace, what requires…

Learning random networks for compression of still and moving images

NASA Technical Reports Server (NTRS)

Gelenbe, Erol; Sungur, Mert; Cramer, Christopher

1994-01-01

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

The Interaction Effects of Working Memory Capacity, Gaming Expertise, and Scaffolding Design on Attention and Comprehension in Digital Game Based Learning

ERIC Educational Resources Information Center

Lee, Yu-Hao

2013-01-01

Educational digital games are often complex problem-solving experiences that can facilitate systematic comprehension. However, empirical studies of digital game based learning (DGBL) have found mixed results regarding DGBL's effect in improving comprehension. While learners generally enjoyed the DGBL learning experience, they often failed to…

There Is More to Digital Learning than Counting on Your Fingers: Transforming Learning and Teaching with Digital Pedagogy

ERIC Educational Resources Information Center

Smirnova, Lyudmila; Lazarevic , Bojan; Malloy, Veronica

2018-01-01

This paper explores how pedagogy is being influenced by fast developing digital technologies. Results are presented from exploratory research conducted in 2016. The findings are addressed in terms of the transformation of learning and education, including the move from the measured to the engaged classroom. Emerging technology creates a natural…

High-Performance Satellite/Terrestrial-Network Gateway

NASA Technical Reports Server (NTRS)

Beering, David R.

2005-01-01

A gateway has been developed to enable digital communication between (1) the high-rate receiving equipment at NASA's White Sands complex and (2) a standard terrestrial digital communication network at data rates up to 622 Mb/s. The design of this gateway can also be adapted for use in commercial Earth/satellite and digital communication networks, and in terrestrial digital communication networks that include wireless subnetworks. Gateway as used here signifies an electronic circuit that serves as an interface between two electronic communication networks so that a computer (or other terminal) on one network can communicate with a terminal on the other network. The connection between this gateway and the high-rate receiving equipment is made via a synchronous serial data interface at the emitter-coupled-logic (ECL) level. The connection between this gateway and a standard asynchronous transfer mode (ATM) terrestrial communication network is made via a standard user network interface with a synchronous optical network (SONET) connector. The gateway contains circuitry that performs the conversion between the ECL and SONET interfaces. The data rate of the SONET interface can be either 155.52 or 622.08 Mb/s. The gateway derives its clock signal from a satellite modem in the high-rate receiving equipment and, hence, is agile in the sense that it adapts to the data rate of the serial interface.

Adolescents' Social Networks: Exploring Different Patterns of Socio-Digital Participation

ERIC Educational Resources Information Center

Li, Shupin; Hietajärvi, Lauri; Palonen, Tuire; Salmela-Aro, Katariina; Hakkarainen, Kai

2017-01-01

The purpose of the study was to assess adolescents' participation in various socio-digital activities by using a self-report questionnaire, a social networking questionnaire, and interviews. The participants (n = 253) were grade 6-9 students from a multicultural lower-secondary school in Finland. Three profiles of socio-digital participation were…

Strategies for synchronisation in an evolving telecommunications network

NASA Astrophysics Data System (ADS)

Avery, Rob

1992-06-01

The achievement of precise synchronization in the telecommunications environment is addressed. Transmitting the timing from node to node has been the inherent problem for all digital networks. Traditional network equipment used to transfer synchronization, such as digital switching ststems, adds impairments to the once traceable signal. As the synchronization signals are passed from node to node, they lose stability by passing through intervening clocks. Timing would be an integrated part of all new network and service deployments. New transmission methods, such as the Synchronous Digital Hierarchy (SDH), survivable network topologies and the issues that arise from them, necessitate a review of current network synchronization strategies. Challenges that face the network are itemized. A demonstration of why localized Primary Reference Clocks (PRC) in key nodes and the Synchronization Supply Unit (SSU) clock architecture of transit and local node clocks is a technically and economically viable solution to the issues facing network planners today is given.

Network device interface for digitally interfacing data channels to a controller a via network

NASA Technical Reports Server (NTRS)

Konz, Daniel W. (Inventor); Ellerbrock, Philip J. (Inventor); Grant, Robert L. (Inventor); Winkelmann, Joseph P. (Inventor)

2006-01-01

The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.

Virtual Learning Ecosystems: A Proposed Framework for Integrating Educational Games, E-Learning Methods, and Virtual Community Platforms

ERIC Educational Resources Information Center

Washington, Christopher

2015-01-01

Digitally delivered learning shows the promise of enhancing learner motivation and engagement, advancing critical thinking skills, encouraging reflection and knowledge sharing, and improving professional self-efficacy. Digital learning objects take many forms including interactive media, apps and games, video and other e-learning activities and…

Remembering Math: The Design of Digital Learning Objects to Spark Professional Learning

ERIC Educational Resources Information Center

Halverson, Richard; Wolfenstein, Moses; Williams, Caroline C.; Rockman, Charles

2009-01-01

This article describes how the design of digital learning objects can spark professional learning. The challenge was to build learning objects that would help experienced special education teachers, who had been teaching in math classes, to demonstrate their proficiency in middle and secondary school mathematics on the PRAXIS examination. While…

Development of an E-Learning Platform for Vocational Education Systems in Germany

ERIC Educational Resources Information Center

Schober, Andreas; Müller, Frederik; Linden, Sabine; Klois, Martha; Künne, Bernd

2014-01-01

This paper describes an existing web-based learning platform named "Third Place of Learning" (TPL)--"Dritter Lernort". This project's aim is to connect the system of vocational education with digital media by a web-based learning platform. TPL supports student's digital learning by means of interactive examples and exercises.…

FODEM: Developing Digital Learning Environments in Widely Dispersed Learning Communities

ERIC Educational Resources Information Center

Suhonen, Jarkko; Sutinen, Erkki

2006-01-01

FODEM (FOrmative DEvelopment Method) is a design method for developing digital learning environments for widely dispersed learning communities. These are communities in which the geographical distribution and density of learners is low when compared to the kind of learning communities in which there is a high distribution and density of learners…

Digital Media Production to Support Literacy for Secondary Students with Diverse Learning Abilities

ERIC Educational Resources Information Center

Leach, April Marie

2017-01-01

Producing digital media is a hands-on, inquiry-based mindful process that naturally embeds Universal Design for Learning (UDL) principles into literacy instruction, providing options for learning and assessment for a wide array of students with diverse learning abilities. Video production learning experiences acknowledge the cognitive talents of…

A Study of the Effects of Digital Learning on Learning Motivation and Learning Outcome

ERIC Educational Resources Information Center

Lin, Ming-Hung; Chen, Huang-Cheng; Liu, Kuang-Sheng

2017-01-01

In the modern society when intelligent mobile devices become popular, the Internet breaks through the restrictions on time and space and becomes a ubiquitous learning tool. Designing teaching activity for digital learning and flexibly applying technology tools are the key issues for current information technology integrated education. In this…

Design of Ontology-Based Sharing Mechanism for Web Services Recommendation Learning Environment

NASA Astrophysics Data System (ADS)

Chen, Hong-Ren

The number of digital learning websites is growing as a result of advances in computer technology and new techniques in web page creation. These sites contain a wide variety of information but may be a source of confusion to learners who fail to find the information they are seeking. This has led to the concept of recommendation services to help learners acquire information and learning resources that suit their requirements. Learning content like this cannot be reused by other digital learning websites. A successful recommendation service that satisfies a certain learner must cooperate with many other digital learning objects so that it can achieve the required relevance. The study proposes using the theory of knowledge construction in ontology to make the sharing and reuse of digital learning resources possible. The learning recommendation system is accompanied by the recommendation of appropriate teaching materials to help learners enhance their learning abilities. A variety of diverse learning components scattered across the Internet can be organized through an ontological process so that learners can use information by storing, sharing, and reusing it.

Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

NASA Astrophysics Data System (ADS)

Swastika, Windra

2017-03-01

A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

The 3D Digital Story-telling Media on Batik Learning in Vocational High Schools

NASA Astrophysics Data System (ADS)

Widiaty, I.; Achdiani, Y.; Kuntadi, I.; Mubaroq, S. R.; Zakaria, D.

2018-02-01

The aim of this research is to make 3D digital Story-telling Media on Batik Learning in Vocational High School. The digital story-telling developed in this research is focused on 3D-based story-telling. In contrast to the digital story-telling that has been developed in existing learning, this research is expected to be able to improve understanding of vocational students about the value of local wisdom batik more meaningful and “live”. The process of making 3D digital story-telling media consists of two processes, namely the creation of 3D objects and the creation of 3D object viewer.

Digital Histories for the Digital Age: Collaborative Writing in Large Lecture Courses

ERIC Educational Resources Information Center

Soh, Leen-Kiat; Khandaker, Nobel; Thomas, William G.

2013-01-01

The digital environment has had an immense effect on American society, learning, and education: we have more sources available at our fingertips than any previous generation. Teaching and learning with these new sources, however, has been a challenging transition. Students are confronted with an ocean of digital objects and need skills to navigate…

Digital Media's Transformative Role in Education: Beyond Potential to Essential

ERIC Educational Resources Information Center

Chien, Ming-tso

2012-01-01

Achieving effective learning via digital media continues to be a major concern in contemporary education. The daily use of all forms of digital media is part of our lives and therefore becomes a key component of education. Educators must consider the process of digital media curriculum as a learning model and form of experience adapted to…

Visual Design Guidelines for Improving Learning from Dynamic and Interactive Digital Text

ERIC Educational Resources Information Center

Jin, Sung-Hee

2013-01-01

Despite the dynamic and interactive features of digital text, the visual design guidelines for digital text are similar to those for printed text. The purpose of this study was to develop visual design guidelines for improving learning from dynamic and interactive digital text and to validate them by controlled testing. Two structure design…

Exploring the Use of Interactive Digital Storytelling Video: Promoting Student Engagement and Learning in a University Hybrid Course

ERIC Educational Resources Information Center

Shelton, Catharyn C.; Warren, Annie E.; Archambault, Leanna M.

2016-01-01

This study explores interactive digital storytelling in a university hybrid course. Digital stories leverage imagery and narrative-based content to explore concepts, while appealing to millennials. When digital storytelling is used as the main source of course content, tensions arise regarding how to engage and support student learning while…

Status of the Desert Fireball Network

NASA Astrophysics Data System (ADS)

Devillepoix, H. A. R.; Bland, P. A.; Towner, M. C.; Cupák, M.; Sansom, E. K.; Jansen-Sturgeon, T.; Howie, R. M.; Paxman, J.; Hartig, B. A. D.

2016-01-01

A meteorite fall precisely observed from multiple locations allows us to track the object back to the region of the Solar System it came from, and sometimes link it with a parent body, providing context information that helps trace the history of the Solar System. The Desert Fireball Network (DFN) is built in arid areas of Australia: its observatories get favorable observing conditions, and meteorite recovery is eased thanks to the mostly featureless terrain. After the successful recovery of two meteorites with 4 film cameras, the DFN has now switched to a digital network, operating 51 cameras, covering 2.5 million km2 of double station triangulable area. Mostly made of off-the-shelf components, the new observatories are cost effective while maintaining high imaging performance. To process the data (~70TB/month), a significant effort has been put to writing an automated reduction pipeline so that all events are reduced with little human intervention. Innovative techniques have been implemented for this purpose: machine learning algorithms for event detection, blind astrometric calibration, and particle filter simulations to estimate both physical properties and state vector of the meteoroid. On 31 December 2015, the first meteorite from the digital systems was recovered: Murrili (the 1.68 kg H5 ordinary chondrite was observed to fall on 27 November 2015). Another 11 events have been flagged as potential meteorites droppers, and are to be searched in the coming months.

SHD digital cinema distribution over a long distance network of Internet2

NASA Astrophysics Data System (ADS)

Yamaguchi, Takahiro; Shirai, Daisuke; Fujii, Tatsuya; Nomura, Mitsuru; Fujii, Tetsuro; Ono, Sadayasu

2003-06-01

We have developed a prototype SHD (Super High Definition) digital cinema distribution system that can store, transmit and display eight-million-pixel motion pictures that have the image quality of a 35-mm film movie. The system contains a video server, a real-time decoder, and a D-ILA projector. Using a gigabit Ethernet link and TCP/IP, the server transmits JPEG2000 compressed motion picture data streams to the decoder at transmission speeds as high as 300 Mbps. The received data streams are decompressed by the decoder, and then projected onto a screen via the projector. With this system, digital cinema contents can be distributed over a wide-area optical gigabit IP network. However, when digital cinema contents are delivered over long distances by using a gigabit IP network and TCP, the round-trip time increases and network throughput either stops rising or diminishes. In a long-distance SHD digital cinema transmission experiment performed on the Internet2 network in October 2002, we adopted enlargement of the TCP window, multiple TCP connections, and shaping function to control the data transmission quantity. As a result, we succeeded in transmitting the SHD digital cinema content data at about 300 Mbps between Chicago and Los Angeles, a distance of more than 3000 km.

Digital echocardiography 2002: now is the time

NASA Technical Reports Server (NTRS)

Thomas, James D.; Greenberg, Neil L.; Garcia, Mario J.

2002-01-01

The ability to acquire echocardiographic images digitally, store and transfer these data using the DICOM standard, and routinely analyze examinations exists today and allows the implementation of a digital echocardiography laboratory. The purpose of this review article is to outline the critical components of a digital echocardiography laboratory, discuss general strategies for implementation, and put forth some of the pitfalls that we have encountered in our own implementation. The major components of the digital laboratory include (1) digital echocardiography machines with network output, (2) a switched high-speed network, (3) a high throughput server with abundant local storage, (4) a reliable low-cost archive, (5) software to manage information, and (6) support mechanisms for software and hardware. Implementation strategies can vary from a complete vendor solution providing all components (hardware, software, support), to a strategy similar to our own where standard computer and networking hardware are used with specialized software for management of image and measurement information.

Learning Behaviors and Interaction Patterns among Students in Virtual Learning Worlds

ERIC Educational Resources Information Center

Lin, Chi-Syan; Ma, Jung Tsan; Chen, Yi-Lung; Kuo, Ming-Shiou

2010-01-01

The goal of this study is to investigate how students behave themselves in the virtual learning worlds. The study creates a 3D virtual learning world, entitled the Best Digital Village, and implements a learning program on it. The learning program, the Expo, takes place at the Exhibition Center in the Best Digital Village. The space in the Expo is…

Effects of Anxiety Levels on Learning Performance and Gaming Performance in Digital Game-Based Learning

ERIC Educational Resources Information Center

Yang, J. C.; Lin, M. Y. D.; Chen, S. Y.

2018-01-01

Anxiety plays an influential role in foreign language learning. However, a lack of attention was paid to examining the effects of anxiety levels on learning performance and gaming performance in digital game-based learning. To this end, this study developed a game-based English learning system and investigated how different levels of anxiety…

Use of Social Media in Radiology Education.

PubMed

Ranginwala, Saad; Towbin, Alexander J

2018-01-01

Social media has become the dominant method of mass digital communication over the past decade. Public figures and corporations have learned how to use this new approach to deliver their messages directly to their followers. Recently, medical educators have begun to use social media as a means to deliver educational content directly to learners. The purpose of this article is to describe the benefits of using social media for medical education. Because each social media platform has different platform-specific constraints, several different popular social media networks are discussed. For each network, the authors discuss the basics of the platform and its benefits and disadvantages for users and provide examples of how they have used each platform to target a unique audience. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

CADC and CANFAR: Extending the role of the data centre

NASA Astrophysics Data System (ADS)

Gaudet, Severin

2015-12-01

Over the past six years, the CADC has moved beyond the astronomy archive data centre to a multi-service system for the community. This evolution is based on two major initiatives. The first is the adoption of International Virtual Observatory Alliance (IVOA) standards in both the system and data architecture of the CADC, including a common characterization data model. The second is the Canadian Advanced Network for Astronomical Research (CANFAR), a digital infrastructure combining the Canadian national research network (CANARIE), cloud processing and storage resources (Compute Canada) and a data centre (Canadian Astronomy Data Centre) into a unified ecosystem for storage and processing for the astronomy community. This talk will describe the architecture and integration of IVOA and CANFAR services into CADC operations, the operational experiences, the lessons learned and future directions

Noise reduction and image enhancement using a hardware implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

David, Robert; Williams, Erin; de Tremiolles, Ghislain; Tannhof, Pascal

1999-03-01

In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

Digital Image Compression Using Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Serra-Ricart, M.; Garrido, L.; Gaitan, V.; Aloy, A.

1993-01-01

The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

Use of learning media by undergraduate medical students in pharmacology: a prospective cohort study.

PubMed

Gutmann, Joanna; Kühbeck, Felizian; Berberat, Pascal O; Fischer, Martin R; Engelhardt, Stefan; Sarikas, Antonio

2015-01-01

The ubiquity of the internet and computer-based technologies has an increasing impact on higher education and the way students access information for learning. Moreover, there is a paucity of information about the quantitative and qualitative use of learning media by the current student generation. In this study we systematically analyzed the use of digital and non-digital learning resources by undergraduate medical students. Daily online surveys and semi-structured interviews were conducted with a cohort of 338 third year medical students enrolled in a general pharmacology course. Our data demonstrate a predominant use of digital over non-digital learning resources (69 ± 7% vs. 31 ± 7%; p < 0.01) by students. Most used media for learning were lecture slides (26.8 ± 3.0%), apps (22.0 ± 3.7%) and personal notes (15.5 ± 2.7%), followed by textbooks (> 300 pages) (10.6 ± 3.3%), internet search (7.9 ± 1.6%) and e-learning cases (7.6 ± 3.0%). When comparing learning media use of teaching vs. pre-exam self-study periods, textbooks were used significantly less during self-study (-55%; p < 0.01), while exam questions (+334%; p < 0.01) and e-learning cases (+176%; p < 0.01) were utilized more. Taken together, our study revealed a high prevalence and acceptance of digital learning resources by undergraduate medical students, in particular mobile applications.

Use of Learning Media by Undergraduate Medical Students in Pharmacology: A Prospective Cohort Study

PubMed Central

Gutmann, Joanna; Kühbeck, Felizian; Berberat, Pascal O.; Fischer, Martin R.; Engelhardt, Stefan; Sarikas, Antonio

2015-01-01

The ubiquity of the internet and computer-based technologies has an increasing impact on higher education and the way students access information for learning. Moreover, there is a paucity of information about the quantitative and qualitative use of learning media by the current student generation. In this study we systematically analyzed the use of digital and non-digital learning resources by undergraduate medical students. Daily online surveys and semi-structured interviews were conducted with a cohort of 338 third year medical students enrolled in a general pharmacology course. Our data demonstrate a predominant use of digital over non-digital learning resources (69 ± 7% vs. 31 ± 7%; p < 0.01) by students. Most used media for learning were lecture slides (26.8 ± 3.0%), apps (22.0 ± 3.7%) and personal notes (15.5 ± 2.7%), followed by textbooks (> 300 pages) (10.6 ± 3.3%), internet search (7.9 ± 1.6%) and e-learning cases (7.6 ± 3.0%). When comparing learning media use of teaching vs. pre-exam self-study periods, textbooks were used significantly less during self-study (-55%; p < 0.01), while exam questions (+334%; p < 0.01) and e-learning cases (+176%; p < 0.01) were utilized more. Taken together, our study revealed a high prevalence and acceptance of digital learning resources by undergraduate medical students, in particular mobile applications. PMID:25849565

Identifying and describing patients' learning experiences towards self-management of bipolar disorders: a phenomenological study.

PubMed

Van den Heuvel, S C G H; Goossens, P J J; Terlouw, C; Van Achterberg, T; Schoonhoven, L

2015-12-01

Existing evidence suggest that patient education in promoting self-management strategies of bipolar disorder (BD) is effective. However, results across the full range of service users with BD vary. Learning experiences of service users look to be a crucial factor to take into account when designing, delivering, and evaluating effective interventions that promote self-management in chronic illness. What learning activities service users actually undertake themselves when self-managing BD that might explain varying success rates, and guide future self-management educational programmes has not been examined. Unlike previous studies that suggest that outcomes in self-management depend on individual learning activities, the current study found that learning to self-manage BD takes place in a social network that functions as a learning environment in which it is saved for service users to make mistakes and to learn from these mistakes. Especially, coping with the dormant fear of a recurrent episode and acknowledging the limitations of an individual approach are important factors that facilitate this learning process. Practitioners who provide patient education in order to promote self-management of BD should tailor future interventions that facilitate learning by reflecting on the own experiences of service users. Community psychiatric nurses should keep an open discussion with service users and caregivers, facilitate the use of a network, and re-label problems into learning situations where both play an active role in building mutual trust, thereby enhancing self-management of BD. Existing evidence suggest that self-management education of bipolar disorder (BD) is effective. However, why outcomes differ across the full range of service users has not been examined. This study describes learning experiences of service users in self-managing BD that provide a possible explanation for this varying effectiveness. We have conducted a phenomenological study via face-to-face, in-depth interviews, guided by a topic list, along service users with BD I or II (n = 16) in three specialised community care clinics across the Netherlands. Interviews were digitally recorded and transcribed verbatim prior to analysis in Atlas.ti 7. Unlike existing studies, which suggest that individual abilities of service users determine outcomes in self-management of BD, the current study found that self-management of BD is a learning process that takes place in a collaborative network. We identified five categories: acknowledgment of having BD, processing the information load, illness management, reflecting on living with BD, and self-management of BD. The success of self-management depends on the acknowledgment of individual limitations in learning to cope with BD and willingness to use a social network as a back-up instead. Especially, the dormant fear of a recurrent episode is a hampering factor in this learning process. © 2015 John Wiley & Sons Ltd.

Multiple-Ring Digital Communication Network

NASA Technical Reports Server (NTRS)

Kirkham, Harold

1992-01-01

Optical-fiber digital communication network to support data-acquisition and control functions of electric-power-distribution networks. Optical-fiber links of communication network follow power-distribution routes. Since fiber crosses open power switches, communication network includes multiple interconnected loops with occasional spurs. At each intersection node is needed. Nodes of communication network include power-distribution substations and power-controlling units. In addition to serving data acquisition and control functions, each node acts as repeater, passing on messages to next node(s). Multiple-ring communication network operates on new AbNET protocol and features fiber-optic communication.

3D interactive augmented reality-enhanced digital learning systems for mobile devices

NASA Astrophysics Data System (ADS)

Feng, Kai-Ten; Tseng, Po-Hsuan; Chiu, Pei-Shuan; Yang, Jia-Lin; Chiu, Chun-Jie

2013-03-01

With enhanced processing capability of mobile platforms, augmented reality (AR) has been considered a promising technology for achieving enhanced user experiences (UX). Augmented reality is to impose virtual information, e.g., videos and images, onto a live-view digital display. UX on real-world environment via the display can be e ectively enhanced with the adoption of interactive AR technology. Enhancement on UX can be bene cial for digital learning systems. There are existing research works based on AR targeting for the design of e-learning systems. However, none of these work focuses on providing three-dimensional (3-D) object modeling for en- hanced UX based on interactive AR techniques. In this paper, the 3-D interactive augmented reality-enhanced learning (IARL) systems will be proposed to provide enhanced UX for digital learning. The proposed IARL systems consist of two major components, including the markerless pattern recognition (MPR) for 3-D models and velocity-based object tracking (VOT) algorithms. Realistic implementation of proposed IARL system is conducted on Android-based mobile platforms. UX on digital learning can be greatly improved with the adoption of proposed IARL systems.

Approaches to Learning Design: Past the Head and the Hands to the HEART of the Matter

ERIC Educational Resources Information Center

Donald, Claire; Blake, Adam; Girault, Isabelle; Datt, Ashwini; Ramsay, Elizabeth

2009-01-01

Digital technologies have been used increasingly in open, distance, and flexible learning to both facilitate learning and depict learning designs. While the portable nature of a learning design once captured in digital form appears to offer limitless possibilities for sharing and reuse, dissemination initiatives have failed to thrive. This may be…

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

USGS Publications Warehouse

Brakebill, John W.; Wolock, David M.; Terziotti, Silvia

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based ⁄ statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

Psychology Teaching Resources in the MERLOT Digital Learning Objects Catalog

ERIC Educational Resources Information Center

Brinthaupt, Thomas M.; Pilati, Michelle L.; King, Beverly R.

2008-01-01

MERLOT (Multimedia Educational Resource for Learning and Online Teaching) is a free multidisciplinary catalog of digital learning materials, peer reviews, learning assignments, and member comments designed to facilitate faculty instruction. The catalog's goal is to expand the quantity and quality of peer-reviewed online teaching materials. We…

Digital Game-Based Learning Supports Student Motivation, Cognitive Success, and Performance Outcomes

ERIC Educational Resources Information Center

Woo, Jeng-Chung

2014-01-01

Traditional multimedia learning is primarily based on the cognitive load concept of information processing theory. Recent digital game-based learning (DGBL) studies have focused on exploring content support for learning motivation and related game characteristics. Motivation, volition, and performance (MVP) theory indicates that cognitive load and…

Profiling Language Learners in Hybrid Learning Contexts: Learners' Perceptions

ERIC Educational Resources Information Center

Lintunen, Pekka; Mutta, Maarit; Pelttari, Sanna

2017-01-01

This article discusses formal and informal foreign language learning before university level. The focus is on beginning university students' perceptions of their earlier learning experiences, especially in digital contexts. Language learners' digital competence is a part of their everyday lives, but its relationship to learning in and outside…

Critically Evaluating Prensky in a Language Learning Context: The "Digital Natives/Immigrants Debate" and Its Implications for CALL

ERIC Educational Resources Information Center

Benini, Silvia; Murray, Liam

2013-01-01

More than 10 years have passed since the first introduction of the term "digital natives" in Prensky's (2001a, 2001b) two seminal articles. Prensky argues that students today, having grown up in the Digital Age, learn differently from their predecessors, or "digital immigrants". As such, the pedagogical tools and methods used…

The Digital Skills Paradox: How Do Digitally Excluded Youth Develop Skills to Use the Internet?

ERIC Educational Resources Information Center

Eynon, Rebecca; Geniets, Anne

2016-01-01

Digital skills are an important aspect of ensuring that all young people are digitally included. Yet, there tends to be an assumption in popular discourse that young people can simply learn these skills by themselves. While experience of technologies forms an important part of the learning process, other resources (i.e., access to technology and…

Method and system for conserving power in a telecommunications network during emergency situations

DOEpatents

Conrad, Stephen H [Algodones, NM; O'Reilly, Gerard P [Manalapan, NJ

2011-10-11

Disclosed is a method and apparatus for conserving power in a telecommunications network during emergency situations. A permissible number list of emergency and/or priority numbers is stored in the telecommunications network. In the event of an emergency or power failure, input digits of a call to the telecommunications network are compared to the permissible number list. The call is processed in the telecommunications network and routed to its destination if the input digits match an entry in the permissible number list. The call is dropped without any further processing if the input digits do not match an entry in the permissible number list. Thus, power can be conserved in emergency situations by only allowing emergency and/or priority calls.

Storytelling and professional learning: a phenomenographic study of students' experience of patient digital stories in nurse education.

PubMed

Christiansen, Angela

2011-04-01

This paper reports the findings of a phenomenographic study which sought to identify the different ways in which patient digital stories influence students' professional learning. Patient digital stories are short multimedia presentations that combine personal narratives, images and music to create a unique and often emotional story of a patients' experience of health care. While these are increasingly used in professional education little is known about how and what students learn through engagement with patient digital stories. Drawing upon interviews with 20 students within a pre-registration nursing programme in the UK, the study identifies four qualitatively different ways in which students approach and make sense of patient digital stories with implications for learning and professional identity development. Through an identification of the critical aspects of this variation valuable insights are generated into the pedagogic principles likely to engender transformational learning and patient centred practice. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

PubMed

Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J

2015-01-01

We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

The impact of specially designed digital games-based learning in undergraduate pathology and medical education.

PubMed

Kanthan, Rani; Senger, Jenna-Lynn

2011-01-01

The rapid advances of computer technologies have created a new e-learner generation of "Homo-zappien" students that think and learn differently. Digital gaming is an effective, fun, active, and encouraging way of learning, providing immediate feedback and measurable process. Within the context of ongoing reforms in medical education, specially designed digital games, a form of active learning, are effective, complementary e-teaching/learning resources. To examine the effectiveness of the use of specially designed digital games for student satisfaction and for measurable academic improvement. One hundred fourteen students registered in first-year pathology Medicine 102 had 8 of 16 lecture sessions reviewed in specially designed content-relevant digital games. Performance scores to relevant content sessions were analyzed at midterm and final examinations. Seventy-one students who registered in second-year pathology Medicine 202 were exposed to the games only during the final examination, with the midterm examination serving as an internal matched-control group. Outcome measures included performance at midterm and final examinations. Paired 2-tailed t test statistics compared means. A satisfaction survey questionnaire of yes or no responses analyzed student engagement and their perceptions to digital game-based learning. Questions relevant to the game-play sessions had the highest success rate in both examinations among 114 first-year students. In the 71 second-year students, the examination scores at the end of the final examination were significantly higher than the scores on the midterm examination. Positive satisfaction survey noted increased student engagement, enhanced personal learning, and reduced student stress. Specially constructed digital games-based learning in undergraduate pathology courses showed improved academic performance as measured by examination test scores with increased student satisfaction and engagement.

Digital Learning As Enhanced Learning Processing? Cognitive Evidence for New insight of Smart Learning.

PubMed

Di Giacomo, Dina; Ranieri, Jessica; Lacasa, Pilar

2017-01-01

Large use of technology improved quality of life across aging and favoring the development of digital skills. Digital skills can be considered an enhancing to human cognitive activities. New research trend is about the impact of the technology in the elaboration information processing of the children. We wanted to analyze the influence of technology in early age evaluating the impact on cognition. We investigated the performance of a sample composed of n. 191 children in school age distributed in two groups as users: high digital users and low digital users. We measured the verbal and visuoperceptual cognitive performance of children by n. 8 standardized psychological tests and ad hoc self-report questionnaire. Results have evidenced the influence of digital exposition on cognitive development: the cognitive performance is looked enhanced and better developed: high digital users performed better in naming, semantic, visual memory and logical reasoning tasks. Our finding confirms the data present in literature and suggests the strong impact of the technology using not only in the social, educational and quality of life of the people, but also it outlines the functionality and the effect of the digital exposition in early age; increased cognitive abilities of the children tailor digital skilled generation with enhanced cognitive processing toward to smart learning.

Twelve tips for using digital storytelling to promote reflective learning by medical students.

PubMed

Sandars, John; Murray, Christopher; Pellow, Andy

2008-01-01

Digital storytelling has potential to motivate students to engage in reflective learning since it uses a range of new technologies and multimedia that are more familiar to young people. The use of visual and audio media offers creative opportunities that can motivate students to develop deeper learning. A structured approach to creating a digital story is essential so that its potential is achieved.