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Reveal the Collaboration in a Open learning Environment

Reveal the Collaboration in a Open Learning Environment

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3rd international Work-conference on the Interplay Between Natural and Artificial Computation

作者： Anaya, Antonio R. Boticario, Jesus G. ETSII UNED Madrid 28040 Spain

ISBN: (纸本)9783642022630

The management and characterization of collaboration to improve students' learning is still an open issue, which needs standardized models and inferring methods for effective collaboration indicators, especially when online courses are based on open approaches where students are not following CSCL scripts. We have supplied our students with a scrutable (manageable and understandable) web application that shows an ontology, which includes collaborative features. The ontology structures collaboration context information, which has been obtained form explicit (based on questionnaires) and implicit methods (supported by several machine learning techniques). From two consecutive years of experiences with hundreds of students we researched students' interactions to find implicit methods to identify and characterize students' collaboration. Based on the outcomes of our experiments we claim that showing useful and structured information to students and tutors about students' collaborative features can have a twofold beneficial impact on students learning and on the management of their collaboration.

关键词： Students

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Multi-agent Reinforcement learning in Network Management

Multi-agent Reinforcement Learning in Network Management

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3rd international conference on Autonomous Infrastructure, Management and Security

作者： Bagnasco, Ricardo Serrat, Joan Univ Politecn Cataluna Network Management Grp Dept Teoria Senyal & Comunicac E-08028 Barcelona Spain

ISBN: (纸本)9783642026263

This paper outlines research in progress intended to contribute to the autonomous management of networks, allowing policies to be dynamically adjusted and aligned to application directives according to the available resources. Many existing management approaches require static a priori policy deployment but our proposal goes one step further modifying initially deployed policies by learning from the system behaviour. We use a hierarchical policy model to show the connection of high level goals with network level configurations. We also intend to solve two important and mostly forgotten issues: the system has multiple goals some of them contradictory and we will show how to overcome it;and, some current works optimize one network element but being unaware of other participants;instead, our proposed scheme takes into account various social behaviours, such as cooperation and competition among different elements.

关键词： machine learning policy-based management adaptive policies autonomic communication system

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3rd IFIP WG 5.4 Working conference on Growth and Development of computer-Aided Innovation, CAI 2009

3rd IFIP WG 5.4 Working Conference on Growth and Development...

引用

3rd IFIP WG 5.4 Working conference on computer-Aided Innovation, CAI 2009

ISBN: (纸本)9783642033452

The proceedings contain 35 papers. The special focus in this conference is on Growth and Development of computer-Aided Innovation. The topics include: Monitoring the impact of solution concepts within a given problematic;predicting innovation acceptance by simulation in virtual environments (theoretical foundations);computer-aided TRIZ ideality and level of invention estimation using natural language processing and machine learning;a novel paradigm for computer-aided design;product integrated innovation based on function;towards an automatic extraction of generalized system of contradictions out of solutionless design of experiments;TRIZ tool for optimization of airport runway;the research of platform-based product configuration model;the research of improving the particleboard glue dosing process based on TRIZ analysis;the prompt design of CNC grinder based on flexible product platform;a planning approach of engineering characteristics based on QFD-TRIZ integrated;effective new product ideation;study of 3d model function-coded genome in evolutionary design;research on computer aided innovation model of weapon equipment requirement demonstration;study on product innovative design process driven by ideal solution;application of TRIZ theory in patternless casting manufacturing technique;a study on the application of the extended matrices based on TRIZ in constructing a collaborative model of enterprise network;the evolvement of automobile steering system based on TRIZ;research on product conceptual design based on integrated of TRIZ and HOQ and innovating method of existing mechanical product based on TRIZ theory.

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Pattern Recognition and machine Intelligence - Third international conference, PReMI 2009, Proceedings

Pattern Recognition and Machine Intelligence - Third Interna...

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3rd international conference on Pattern Recognition and machine Intelligence, PReMI 2009

ISBN: (纸本)3642111637

The proceedings contain 101 papers. The topics discussed include: new approaches to design and control of time limited search algorithms;feature selection using non linear feature relation index;a geometric algorithm for learning oblique decision trees;effect of subsampling rate on subbagging and related ensembles of stable classifiers;constructive semi-supervised classification algorithm and its implement in data mining;a fast supervised method of feature ranking and selection for pattern classification;clustering in concept association networks;application of neural networks in preform design of aluminium upsetting process considering different interfacial frictional conditions;incorporating fuzziness to CLARANS;development of a neuro-fuzzy MR image segmentation approach using fuzzy c-means and recurrent neural network;and identification of N-glycosylation sites with sequence and structural features employing random forests.

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Using GA and KMP algorithm to implement an approach to learning through intelligent framework documentation

Using GA and KMP algorithm to implement an approach to learn...

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3rd international United Information Systems conference, UNISCON 2009

作者： Mat Jani, Hajar Lee, Sai Peck College of Information Technology Universiti Tenaga Nasional Km. 7 Jalan Kajang-Puchong Kajang Selangor 43009 Malaysia Faculty of Computer Science and Information Technology University Malaya Kuala Lumpur 50603 Malaysia

ISBN: (纸本)9783642011115

Object-oriented application framework is one of the most important implementations of object-oriented software engineering. Normally, a user takes several months of learning in order to become highly productive in using a specific object-oriented application framework. Without proper documentation, frameworks are not very usable to framework users. Currently available framework documentation approaches are not very effective for new framework users, and this scenario tends to discourage new users in using frameworks. The main objective of this paper is to propose and implement an intelligent framework documentation approach that integrates case-based learning (CBL) with genetic algorithm (GA) and Knuth-Morris-Pratt (KMP) pattern matching algorithm with the intention of making learning a framework more effective. GA assists in optimizing the search process and performs machine learning. Within the GA, nearest neighbor algorithm is used in determining the most similar recorded case that can be used in solving the new case. A new case is retained in the case base for future retrievals. A framework user is allowed to select from a list of features provided by the framework that he or she is interested in learning, and the system will give an example of application related to the selected features. This paper concludes with a prototype that implements the intelligent framework documentation approach. © 2009 Springer Berlin Heidelberg.

关键词： Genetic algorithms

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Online learning in biometrics: A case study in face classifier update

Online learning in biometrics: A case study in face classifi...

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IEEE international conference on Biometrics: Theory, applications, and Systems (BTAS)

作者： Richa Singh Mayank Vatsa Arun Ross Afzel Noore Indraprastha Institute of Information Technology Delhi New Delhi Delhi IN Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown USA

In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application in classifier update algorithms to re-train classifier decision boundaries. Specifically, the algorithm employs online learning technique in a 2nu-granular soft support vector machine for rapidly training and updating face recognition systems. The proposed online classifier is used in a face recognition application for classifying genuine and impostor match scores impacted by different covariates. Experiments on a heterogeneous face database of 1,194 subjects show that the proposed online classifier not only improves the verification accuracy but also significantly reduces the computational cost.

关键词： Biometrics machine learning Support vector machines Support vector machine classification Face recognition Databases Large-scale systems Image sensors Aging Biosensors

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Enzyme Function Classification using Protein Sequence Features and Random Forest

Enzyme Function Classification using Protein Sequence Featur...

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The 3rd international conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

作者： Chetan Kumar Gang Li Alok Choudhary Department of Electrical Engineering and Computer Science Northwestern University EvanstonILUSA

Enzymes are proteins that catalyze bio-chemical reactions in different ways and play important roles in metabolic pathways. The exponential rise in sequences of new enzymes has necessitated developing methods that accurately predict their function. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been applied, but are known to fail for dissimilar proteins that perform the same function. In this paper, we present a machine learning approach to accurately predict the main function class of enzymes based on a unique set of 73 sequence-derived features. Our features can be extracted using freely available online tools. We used different multi-class classifiers to categorize enzyme protein sequences into one of the NC-IUBMB defined six main function classes. Amongst the classifiers, Random Forest reported the best results with an overall accuracy of 88％ and precision and recall in the range of 84％ to 93％ and 82％ to 93％ respectively. Our results compare favorably with existing methods, and in some cases report better performance. Random Forest has been proven to be a very efficient data mining algorithm. This paper is first in exploring their application to enzyme function prediction. The datasets can be accessed online at the location: .

关键词： enzyme function classificaiton machine learning multi-class random forest protein sequence features

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Large-Scale Knowledge Resources: Construction and application - Third international conference on Large-Scale Knowledge Resources, LKR 2008, Proceedings

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3rd international conference on Large-Scale Knowledge Resources, LKR 2008

ISBN: (纸本)3540781587

The proceedings contain 32 papers. The topics discussed include: comparing LDA with pLSI as a dimensionality reduction method in document clustering;extracting concepts from religious knowledge resources and constructing classic analysis systems;extracting prehistories of software refactorings from version archives;filling the gap between a large-scale database and multimodal interactions;design and prototype of a large-scale and fully sense-tagged corpus;supervised learning of similarity measures for content-based 3D model retrieval;using singular value decomposition to compute answer similarity in a language independent approach to question answering;a computation model of risk-context-dependent inductive reasoning based on a support vector machine;and evaluation of logical thinking ability through contributions in a learning community.

关键词： Information retrieval systems

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A New Approach using machine learning and Data Fusion Techniques for Solving Hard Combinatorial Optimization Problems

A New Approach using Machine Learning and Data Fusion Techni...

引用

3rd international conference on Information and Communication Technologies, vol.4

作者： Mahmoud Zennaki Ahmed Ech-cherif Computer Science Department Science Faculty USTO-MB Oran Algeria

We investigate the possibility of using kernel clustering and data fusion techniques for solving hard combinatorial optimization problems. The proposed general paradigm aims at incorporating unsupervised kernel methods into population-based heuristics, which rely on spatial fusion of solutions, in order to learn the solution clusters from the search history. This form of extracted knowledge guides the heuristic to detect automatically promising regions of solutions. The proposed algorithm derived from this paradigm is an extension of the classical Scatter Search and can automatically learn during the search process by exploiting the history of solutions found. Preliminary results, with an application to the well-known vehicle routing problem (VRP) show the great interest of such paradigm and can effectively find near-optimal solutions for large problem instances.

关键词： machine learning Kernel clustering Reactive Search Data fusion Unsupervised Support Vector machine Scatter search Vocabulary building

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Border Feature Detection and Adaptation Algorithm and Its application in Remote Sensing

Border Feature Detection and Adaptation Algorithm and Its Ap...

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international conference on Recent Advances in Space Technologies (RAST)

作者： N. Gokhan Kasapoglu Okan K. Ersoy Department of Electronics and Communication Eng Istanbul Technical University Istanbul Turkey School of Electrical and Computer Eng Purdue University West Lafayette IN USA

Defining decision region borders properly is a major task of classification algorithms. In this paper, the border feature detection and adaptation (BFDA) algorithm is introduced for this purpose. The BFDA is a novel classification scheme, especially useful for the classification of remote sensing images. The method exploits the powerful discrimination capability of the 1-nearest neighborhood (1-NN) method with the border feature vectors. The first part of the algorithm consists of generating border feature vectors using class centers and misclassified training vectors. With this approach, a manageable number of border feature vectors are obtained. The second part of the algorithm involves the adaptation of the border feature vectors with a technique similar to the learning vector quantization (LVQ) algorithm. The performance of the BFDA was compared with other classification algorithms including support vector machines (SVMs) and several statistical classification techniques.

关键词： computer vision Remote sensing Classification algorithms Support vector machines Support vector machine classification Hyperspectral imaging Management training Vector quantization Image classification Hyperspectral sensors

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