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International Conference on Information and Communication systems (ICICS)

作者： Jamal Alsakran Nouh Alhindawi Loai Alnemer Computer Science Department The University of Jordan Amman Jordan Software Engineering Department Jadara University Irbid Jordan Computer Information Systems Department The University of Jordan Amman Jordan

ISBN: (纸本)9781467386159

The high dimensionality of data presents a major issue in understanding and interpreting the results of classification learning. Among the various approaches that address this issue, parallel coordinates visualization has proven its capabilities to enhance investigation and comprehension of data dimension features especially when the number of dimensions is high and there are numerous output classes. We propose several parallel coordinates metrics, namely entropy, class ordering, and edge crossing, to further facilitate inspection of data features and their relevance to output class. Experiments on real world datasets are presented to show the effectiveness of the proposed approach.

关键词： Data visualization Entropy Measurement Visualization Clutter Training Layout

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Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction

Tensor Canonical Correlation Analysis for Multi-view Dimensi...

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IEEE International Conference on Data engineering

作者： Yong Luo Dacheng Tao Kotagiri Ramamohanarao Chao Xu Yonggang Wen School of Computer Engineering Nanyang Technological University Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Department of Computer Science and Software Engineering The University of Melbourne School of Electronics Engineering and Computer Science Peking University

ISBN: (纸本)9781509020218

Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.

关键词： Correlation Tensile stress Electronic mail Feature extraction Approximation algorithms computers Australia

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Enhancing service quality through Service Level Agreement (SLA) full implementation

Enhancing service quality through Service Level Agreement (S...

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International Conference on science in Information Technology (ICSITech)

作者： Rodziah binti Atan Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 Serdang Selangor MALAYSIA

ISBN: (纸本)9781509017225

Various SLA monitoring systems are proposed by different features and abilities to evaluate the agreed SLA. The current SLA monitoring systems in cloud computing for its structural, behavioral characteristics and situation are also in place. The systematic reviews of a well-known methods and approaches shows a significant numbers of researches been done in this area. Based on the number of effort and researches, the quality of services should proportionately increase alongside them. We look this matter from the perspectives of enforcement, that evident the stand of quality of services. Service Level Agreement (SLA) enforcement impact measures is a potential research area to be explored. Assumptions that this study is making are, SLA management will become better by a firm enforcement, where every customers are responsible to launch report of bugs or mischief of services such as unsatisfactory quality or service unavailability to a collection pool, and the provider will react immediately to the complaints so that the total downtime not exceeding the SLA value, with efficient enforcement. This study establishes fundamental theory to measure enforcement impact to SLA monitoring and management. We proposed eight activity phases from formulating until analyzing and decision formation. Descriptive statistics is utilized to analyze the extracted data. The SLA validation detection is the most frequent purpose of SLA monitoring systems in cloud by 58% and throughput is checked as an attribute target by 28%. The self-monitoring SLA, self-healing system, hierarchical structure are recognized points of SLA monitoring systems which need improvement before the enforcement could be based upon.

关键词： quality of service Service Level Agreement enforcement monitoring cloud computing

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Data collection in open ended learning environment for learning analytics

Data collection in open ended learning environment for learn...

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24th International Conference on computers in Education - Workshop, ICCE 2016

作者： Tscholl, Michael Rajendran, Ramkumar Biswas, Gautam Goldberg, Benjamin S. Sottilare, Robert A. Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems Vanderbilt University 1025 16th Avenue South NashvilleTN37212 United States U.S. Army Research Laboratory - Human Research and Engineering Simulation and Training Technology Center OrlandoFL32826 United States

ISBN: (纸本)9789869401210

As the development and use of Open-Ended Learning Environments (OELEs) continues to increase, supporting students' learning in these environments with Intelligent Tutoring is rapidly becoming an important area of research. Many existing learning environments guide students in step-by-step processes to reach their learning goal;consequently, data preprocessing is well defined. In OELEs, in contrast, students may achieve task goals through multiple pathways, and there exist multiple ways to assess performance. We present a simulation OELE designed to teach students decision-making in a complex problem solving task. To provide Intelligent Tutoring Support, we are required to track performance along several dimensions. We present our approach to extract data for performance assessments that can be leveraged to provide Intelligent Tutoring Support. We generalize our approach and present guidelines applicable for similar OELEs. Copyright 2016 Asia-Pacific Society for computers in Education. All rights reserved.

关键词： Students

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Filling fuzzy ontologies with people knowledge using fuzzy ontologies and group decision making methods

Filling fuzzy ontologies with people knowledge using fuzzy o...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Juan Antonio Morente-Molinera Ignacio Javier Pérez Francisco Javier Cabrerizo Sergio Alonso Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Information Science University of Cádiz Cádiz Spain Department of Software Engineering and Computer Systems National Distance Education University (UNED) Madrid Spain Department of Languages and Computer Systems University of Granada Granada Spain

Internet has experienced a deep change. Now, people can communicate among themselves and share information and experiences thanks to Web 2.0 technologies. One way of storing all these information in a sorted way implies the use of Fuzzy Ontologies. In this paper, a novel and automatic method that is capable of retrieving and storing information coming from Internet users has been designed. Thanks to our method, it is possible to create knowledge databases that store knowledge coming from the consensus of a set of users. Thanks to group decision making methods, it is possible for all these users to debate and reach an agreement. Fuzzy Ontologies provide us with a trustful environment that we can use to store all the retrieved information. They also allow other users to consult and get advantage of the stored knowledge. Multi-granular fuzzy linguistic modelling methods are used in order to create an user-friendly interface for the users to debate and perform queries.

关键词： Ontologies Decision making Pragmatics Internet Electronic mail Data mining computer science

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Iterative unified clustering in big data

Iterative unified clustering in big data

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IEEE International Conference on Big Data

作者： Vasundhara Misal Vandana P. Janeja Sai.C. Pallaprolu Yelena Yesha Raghu Chintalapati Information Systems Department University of Maryland Baltimore County (UMBC) Department of Computer Science and Electrical Engineering University of Maryland Baltimore County (UMBC) Ekagra Software Technologies USA

ISBN: (纸本)9781467390064

We propose a novel iterative unified clustering algorithm for data with both continuous and categorical variables, in the big data environment. Clustering is a well-studied problem and finds several applications. However, none of the big data clustering works discuss the challenge of mixed attribute datasets, with both categorical and continuous attributes. We study an application in the health care domain namely Case Based Reasoning (CBR), which refers to solving new problems based on solutions to similar past problems. This is particularly useful when there is a large set of clinical records with several types of attributes, from which similar patients need to be identified. We go one step further and include the genomic components of patient records to enhance the CBR discovery. Thus, our contributions in this paper spans across the big data algorithmic research and a key contribution to the domain of heath care information technology research. First, our clustering algorithm deals with both continuous and categorical variables in the data; second, our clustering algorithm is iterative where it finds the clusters which are not well formed and iteratively drills down to form well defined clusters at the end of the process; third we provide a novel approach to CBR across clinical and genomic data. Our research has implications for clinical trials and facilitating precision diagnostics in large and heterogeneous patient records. We present extensive experimental results to show the efficacy or our approach.

关键词： Big data Clustering algorithms Cognition Diabetes Genomics Bioinformatics Diseases

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Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy

Deep convolutional neural networks for detecting secondary s...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rongjian Li Dong Si Tao Zeng Shuiwang Ji Jing He Department of Computer Science Old Dominion University Norfolk Virginia Division of Computing and Software Systems University of Washington Bothell Bothell WA School of Electrical Engineering and Computer Science Washington State University Pullman WA

ISBN: (纸本)9781509016129

The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.

关键词： Proteins Convolution Image resolution Three-dimensional displays Deconvolution Protein engineering Computational modeling

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It Takes Two to Tango: Deleted Stack Overflow Question Prediction with Text and Meta Features

It Takes Two to Tango: Deleted Stack Overflow Question Predi...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Xin Xia David Lo Denzil Correa Ashish Sureka Emad Shihab College of Computer Science and Technology Zhejiang University Hangzhou China Singapore Management University Singapore Singapore SG Max Planck Institute for Software Systems (MPI-SWS) Germany ABB Corporate Research India Department of Computer Science and Software Engineering Concordia University Montreal Canada

Stack Overflow is a popular community-based Q&A website that caters to technical needs of software developers. As of February 2015 - Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach - DelPredictor - which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008-2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.

关键词： Feature extraction software Programming Text mining Text processing software engineering Maintenance engineering

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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