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A Lightweight Open Space for the Classroom -- Collaborative Learning with Whiteboards and Pen-Tablets

A Lightweight Open Space for the Classroom -- Collaborative ...

引用

International Conference on Advanced Learning Technologies (ICALT)

作者： Henning Breuer Christian Sousa Nelson Baloian Mitsuji Matsumoto Interaction Design Laboratory University of Applied Sciences Potsdam Potsdam Germany Graduate School of Global Information and Telecommunication Studies Waseda University Tokyo Japan Department of Computer Science University of Chile Santiago Chile

Software for interactive whiteboards in the classroom usually combines tools for showing prefabricated presentations with annotation features. Instead of encouraging students to participate by contributing, this design promotes a teacher-centered interaction in the classroom. Through literature review, observation, and interviews with teachers we identified pedagogical and situational limitations of this approach and derived design guidelines. We developed an application with an open space interface for interactive whiteboards. It applies a gesture-based interaction paradigm and allows teachers and students to flexibly create, structure, present and document learning materials. Within collaborative learning activities multiple students may synchronously access the whiteboard through pen-tablets.

关键词： Collaborative work Java Collaborative software Guidelines Collaboration Writing Switches computer science Software tools Prototypes

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Feature selection for co-training: A QSAR study

Feature selection for co-training: A QSAR study

引用

2007 International Conference on Artificial Intelligence, ICAI 2007

作者： Li, Guo-Zheng Li, Dan Lu, Wen-Cong Yang, Mary Q. Yang, Jack Y. School of Computer Science and Engineering Shanghai University Shanghai 200072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Department of Chemistry School of Science Shanghai University Shanghai 200444 China U.S. Department of Health and Human Services Bethesda MD 20852 United States Harvard Medical School Harvard University Cambridge MA 02140 United States

ISBN: (纸本)9781601320254

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with known activity is less than that of unknown, it is important to predict molecular activities utilizing unlabeled instances. Here, semi-supervised learning (SSL) is introduced and a SSL method, Co-Training is investigated on predicting drug activities utilizing unlabeled instances. At the same time, the numerous features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named FESCOT is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for Co-Training. Numerical experimental results on a data set of molecular activities show that Co-Training improves prediction accuracy of molecular activities with unlabeled instances, and feature selection furthermore helps to improve the prediction ability of Co-Training.

关键词： Feature Selection

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Feature selection and partial least squares based dimension reduction for tumor classification

Feature selection and partial least squares based dimension ...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Bu, Hua-Long Li, Guo-Zheng Zeng, Xue-Qiang Yang, Jack Y. Yang, Mary Qu School of Computer Engineering and Science Shanghai University Shanghai 200072 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Harvard Medical School Harvard University Cambridge MA 02140 United States U.S. Department of Health and Human Services Bethesda MD 20852 United States

ISBN: (纸本)1424415098

Partial Least Squares (PLS) is one of the widely used dimension reduction methods for analysis of gene expression microarray data, it represents the data in a low dimensional space through linear transformation, the size of the reduced space by PLS is critical to generalization performance of classifiers. The previous works always determined the top fixed number of components or the top several components by cross-validation. Here we demonstrate the usage of feature selection for PLS based dimension reduction. As a case study, PLS is combined with two feature selection methods (Genetic Algorithm and Sequential Backward Floating Selection) to get more robust and efficient dimensional space, and then the constructed data from the selected components is used as input for the Support Vector Machine (SVM) classifier. We use the method for tumor classification on gene microarray data, experimental results illustrate that our proposed framework is effective both to reduce classification error rates and get compact dimensional space. ©2007 IEEE.

关键词： Linear transformations

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Support vector regression with feature selection for the multivariate calibration of spectrofluorimetric determination of aromatic amino acids

Support vector regression with feature selection for the mul...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Li, Guo-Zheng Meng, Hao-Hua Yang, Mary Qu Yang, Jack Y. State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China U.S. Department of Health and Human Services Bethesda MD 20852 United States Harvard Medical School Harvard University Cambridge MA 02140 United States

ISBN: (纸本)1424415098

Several artificial intelligent methods, including Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Partial Least Square (PLS) are used for the multivariate calibration in the determination of the three aromatic amino acids (phenylalanine, tyrosine and tryptophan) in their mixtures by fluorescence spectroscopy. The results of the leave-one-out method show that SVR perform better than other methods, and appear to be good methods for this task. Furthermore, feature selection is performed for SVR to remove redundant features and a novel algorithm named PRIFER (Prediction RIsk based FEature selection for support vector Regression) is proposed. Results on the above multivariate calibration data set show that PRIFER is a powerful tool for solving the multivariate calibration problems. ©2007 IEEE.

关键词： Amino acids

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Mobile Computing to Seamlessly Integrate Formal and Informal Learning

Mobile Computing to Seamlessly Integrate Formal and Informal...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Henning Breuer Roberto Konow Nelson Baloian Gustavo Zurita Interaction Design Laboratory University of Applied Sciences Potsdam Potsdam Germany Universidad Diego Portales Santiago Chile Department of Computer Science University of Chile Santiago Chile Department of Management and Information Systems Business School University of Chile Santiago Chile

Even though handhelds have been discussed as an ideal means to support "learning in the wild", a seamless integration with classroom technologies is usually missing. This paper discusses a system for students called MCPresenter that facilitates both performing and documenting learning experiences outside the classroom and bringing them into more formal settings for discussion and further development. It supports collaborative learning and applies the same gesture-based interaction principles as software for interactive whiteboards, enabling seamless transition between inside and outside as well as facilitating individual work, peer-to-peer interaction and group discussions.

关键词： Mobile computing Collaborative work Collaboration Handheld computers Education Cities and towns computer science Information management Technology management Management information systems

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Discovery of genetic networks through abduction and qualitative simulation

Discovery of genetic networks through abduction and qualitat...

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作者： Zupan, Blaž Bratko, Ivan Demšar, Janez Juvan, Peter Kuspa, Adam Halter, John A. Shaulsky, Gad Artificial Inteligence Laboratory Faculty of Computer and Information Science University of Ljubljana Slovenia Department of Intelligent Systems Jožef Stefan Institute Ljubljana Slovenia Department of Biochemistry and Molecular Biology Baylor College of Medicine Houston TX United States Department of Molecular and Human Genetcs Baylor College of Medicine Houston TX United States PM and R Division of Neuroscience Baylor College of Medicine Houston TX United States

ISBN: (纸本)9783540739197

GenePath is an automated system for reasoning about genetic networks, wherein a set of genes have various influences on one another and on a biological outcome. It acts on a set of experiments in which genes are knocked-out or overexpressed, and the outcome of interest is evaluated. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Two uses of the system are demonstrated: synthesis of a consistent network from abduced constraints, and qualitative reasoning-based approach that generates a set of networks consistent with the data. In practice, as illustrated by an example on aggregation of a soil amoeba Dictyostelium discoideum, a combination of constraint satisfaction and qualitative reasoning produces a small set of plausible networks. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Genetic algorithms

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Finite Difference Time Domain (FDTD) Simulations Using Graphics Processors

Finite Difference Time Domain (FDTD) Simulations Using Graph...

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DoD High Performance Computing Modernization Program Users Group

作者： S. Adams J. Payne R. Boppana Human Effectiveness Directorate U.S. Air Force Research Laboratory Brooks TX USA Department of Computer Science University of Texas San Antonio San Antonio TX USA

This paper presents a graphics processor based implementation of the Finite Difference Time Domain (FDTD), which uses a central finite differencing scheme for solving Maxwell's equations for electromagnetics. FDTD simulations can be very computationally expensive and require thousands of CPU hours to solve on traditional general purpose processors. Modern Graphics Processing Units (GPUs) found in desktop computers are programmable and are capable of much higher vector floating-point performance than general purpose CPUs. This paper shows how GPUs can be used to greatly speedup FDTD simulations. The main objective is to leverage GPU processing power for FDTD update calculations and complete computationally expensive simulations in reasonable time. This allows researchers to simulate much longer pulse lengths and larger models than was possible in the past. A new FDTD code was developed to leverage graphics processors using Linux, C, OpenGL, Cg, and commodity GeForce 7 series GPUs. The graphics hardware was accessed through standard OpenGL. The FDTD model space was then transferred to the GPU device memory through OpenGL textures and host readable via frame buffer objects exposed by the OpenGL 2.0 application programming interface (API). GPU fragment processors were utilized for the FDTD update computations via Cg fragment programs. For models that were sufficiently large, greater than (140) 3 cells, the GPU performed FDTD update calculations at least 12 times faster than the execution of the same simulation on a contemporary multicore CPU from Intel or AMD. The use of GPUs shows great promise for high performance computing applications like FDTD that have high arithmetic intensity and limited or no data dependencies in computation streams. Until recently, to use GPUs as a co-processor, the normalCPU-based code needed to be rewritten extensively using special graphics programming language Cg and OpenGL APIs, which is difficult for non-graphics programmers. However, n

关键词： Finite difference methods Time domain analysis Graphics Computational modeling High performance computing Parallel programming Central Processing Unit Hardware Maxwell equations Electromagnetics

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Young researchers' views on the current and future state of HRI 07

Young researchers' views on the current and future state of ...

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HRI 2007: 2007 ACM/IEEE Conference on human-Robot interaction - Robot as Team Member

作者： Gold, Kevin Fasel, Ian Freier, Nathan G. Torrey, Cristen Department of Computer Science Yale University 51 Prospect St. New Haven CT 06511 United States Machine Perception Laboratory Institute for Neural Computation University of California San Diego 9500 Gilman Dr. San Diego CA 92093 United States Information School University of Washington Box 354985 Seattle WA 98195 United States HumanComputer Interaction Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA United States

ISBN: (纸本)1595936173

This paper presents the results of a panel discussion titled "The Future of HRI," held during an NSF workshop for graduate students on human-robot interaction in August 2006. The panel divided the workshop into groups tasked with inventing models of the field, and then asked these groups their opinions on the future of the field. In general, the workshop participants shared the belief that HRI can and should be seen as a single scientific discipline, despite the fact that it encompasses a variety of beliefs, methods, and philosophies drawn from several "core" disciplines in traditional areas of study. HRI researchers share many interrelated goals, participants felt, and enhancing the lines of communication between different areas would help speed up progress in the field. Common concerns included the unavailability of common robust platforms, the emphasis on human perception over robot perception, and the paucity of longitudinal real-world studies. The authors point to the current lack of consensus on research paradigms and platforms to argue that the field is not yet in the phase that philosopher Thomas Kuhn would call "normal science," but believe the field shows signs of approaching that phase. Copyright 2007 ACM.

关键词： User interfaces

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Walking Phase Recognition for People with Lower Limb Disability

Walking Phase Recognition for People with Lower Limb Disabil...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Sang Wan Lee Taeyoub Yi Jeong-Su Han Hyoyoung Jang Heon-Hui Kim Jin-Woo Jung Zeungnam Bien Electrical Engineering and Computer Science Department Korean Advanced Institute of Science and Technology Daejeon South Korea Core Technology Laboratory Digital Appliances Research and Development Center Samsung Electronics Company Limited Suwon South Korea Human-friendly Welfare Robot System Engineering Research Center Korean Advanced Institute of Science and Technology Daejeon South Korea Computer Engineering Department Dongguk University Seoul South Korea

This paper presents a total solution on EMG signal-based walking phase recognition for people with lower limb disability. Various environmental factors such as sensed location, walking speed, and ground inclination are taken into consideration in all the phases of signal sensing, feature extraction, feature selection, and classification. Based on analysis on fourteen well-known feature extraction methods in varying environmental situation, this paper proposes a methodology for selecting a good feature set, and then demonstrates effectiveness of the proposed approach with the classification results.

关键词： Legged locomotion Electromyography Muscles Feature extraction Environmental factors humans Rehabilitation robotics Robots Systems engineering and theory Working environment noise

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A Proposed Statistical Protocol for the Analysis of Metabolic Toxicological Data Derived from NMR Spectroscopy

A Proposed Statistical Protocol for the Analysis of Metaboli...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Benjamin J. Kelly Paul E. Anderson Nicholas V. Reo Nicholas J. DelRaso Travis E. Doom Michael L. Raymer Department of Computer Science and Engineering Wright State University Dayton OH USA Department of Biochemistry and Molecular Biology Boonshoft School of Medicine Wright State University Kettering OH USA Human Effectiveness Directorate Air Force Research Laboratory OH USA

Nuclear magnetic resonance (NMR) spectroscopy is a non-invasive method of acquiring a metabolic profile from biofluids. This metabolic information may provide keys to the early detection of exposure to a toxin. A typical NMR toxicology data set has low sample size and high dimensionality. Thus, traditional pattern recognition techniques are not always feasible. In this paper, we evaluate several common alternatives for isolating these biomarkers. The fold test, unpaired t-test, and paired t-test were performed on an NMR-derived toxicological data set and results were compared. The paired t-test method was preferred, due to its ability to attribute statistical significance, to take into consideration consistency of a single subject over a time course, and to mitigate the low sample, high dimensionality problem. We then grouped the resulting statistically salient potential biomarkers based on their significance patterns and compared results to several known metabolites affected by the tested toxin. Based on these results, we present a statistical protocol of sequential t-tests and clustering techniques for identifying putative biomarkers. We then present the results of this protocol applied to a specific real world toxicological data set.

关键词： Protocols Toxicology Nuclear magnetic resonance Spectroscopy Principal component analysis Biomarkers Metabolomics Testing Pattern recognition Performance evaluation

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