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arXiv 2017年

作者： Babaie, Morteza Kalra, Shivam Sriram, Aditya Mitcheltree, Christopher Zhu, Shujin Khatami, Amin Rahnamayan, Shahryar Tizhoosh, H.R. KIMIA Lab University of Waterloo Canada Mathematics and Computer Science Amirkabir University Tehran Iran Electrical and Computer Engineering University of Waterloo Canada School of Electronic & Optical Eng. Nanjing University of Science & Technology Jiangsu China Institute for Intelligent Systems Research and Innovation Deakin University Australia Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa Canada

In this paper, we introduce a new dataset, Kimia Path24, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000×1000 (0.5mm×0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch- and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80% for CNN. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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Modeling uncertainty and evolving self-adaptive software: A fuzzy theory based requirements engineering approach

arXiv

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arXiv 2017年

作者： Yang, Zhuoqun Jin, Zhi Li, Zhi Institute of Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Haidian Dstr. Beijing100190 China Peking University Haidian Dstr. Beijing100871 China Software Engineering Dept. College of Computer Science and Information Technology Guangxi Normal University Guilin Guangxi541004 China

By comparing the accuracy performance and time performance of these schemas, we show the availability in modeling and reasoning over uncertainties. It also shows the inconsistencies between users’ preferences and prior knowledge at RE stage, which can be diminished through learning with our approach. Besides, our approach can be used as a solution to reasoning over continuously ***-adaptive software (SAS) is capable of adjusting its behavior in response to meaningful changes in the operational context and itself. Due to the inherent volatility of the open and changeable environment in which SAS is embedded, the ability of adaptation is highly demanded by many software-intensive systems. Two concerns, i.e., the requirements uncertainty and the context uncertainty are most important among others at Requirements engineering (RE) stage. However, requirements analyzers can hardly figure out the mathematical relation between requirements, system behavior and context, especially for complex and nonlinear systems, due to the existence of above uncertainties, misunderstanding and ambiguity of prior knowledge. An essential issue to be addressed is how to model and specify these uncertainties at RE stage and how to utilize the prior knowledge to achieve *** this paper, we propose a fuzzy-based approach to modeling uncertainty and achieving evolution. The approach introduces specifications to describe fuzziness. Based on the specifications, we derive a series of reasoning rules as knowledge base for achieving adaptation and evolution. These two targets are implemented through four reasoning schemas from a control theory perspective. Specifically, forward reasoning schema is used for direct adaptation;backward reasoning schema is used for optimal adaptation. Parameter-identified schema implements learning evolution by considering SAS as the gray-box system, while system-identified reasoning schema implements learning evolution by considering SAS as the gray-

关键词： Requirements engineering

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Review on requirements modeling and analysis for self-adaptive systems: A ten-year perspective

arXiv

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arXiv 2017年

作者： Yang, Zhuoqun Lib, Zhi Jin, Zhi Zhang, He Institute of Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Haidian Dstr. Beijing100190 China Software Engineering Dept. College of Computer Science and Information Technology Guangxi Normal University Guilin Guangxi541004 China Peking University Haidian Dstr. Beijing100871 China State Key Laboratory of Novel Software Technology Software Institute Nanjing University Hankou Road Nanjing Jiangsu210093 China

Context: Over the last decade, software researchers and engineers have developed a vast body of methodologies and technologies in requirements engineering for self-adaptive systems. Although existing studies have explored various aspects of this field, no systematic study has been performed on summarizing modeling methods and corresponding requirements activities. Objective: This study summarizes the state-of-the-art research trends, details the modeling methods and corresponding requirements activities, identifies relevant quality attributes and application domains and assesses the quality of each study. Method: We perform a systematic literature review underpinned by a rigorously established and reviewed protocol. To ensure the quality of the study, we choose 21 highly regarded publication venues and 8 popular digital libraries. In addition, we apply text mining to derive search strings and use Kappa coefficient to mitigate researchers' disagreements. Results: We selected 109 papers during the period of 2003-2013 and presented the research distributions over various kinds of factors. We extracted 29 modeling methods which are classified into 8 categories and identified 14 requirements activities which are classified into 4 requirements timelines. We captured 8 concerned software quality attributes based on the ISO 9126 standard and 12 application domains. Conclusion: The frequency of application of modeling methods varies greatly. Enterprise models were more widely used while behavior models were more rigorously evaluated. Requirements-driven runtime adaptation was the most frequently studied requirements activity. Activities at runtime were conveyed with more details. Finally, we draw other conclusions by discussing how well modeling dimensions were considered in these modeling methods and how well assurance dimensions were conveyed in requirements *** Codes 68N30 Copyright © 2017, The Authors. All rights reserved.

关键词： Requirements engineering

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Virtual Control Labs Experimentation: The Water Tank System

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IFAC-PapersOnLine 2016年第6期49卷 87-92页

作者： Galan, Daniel Heradio, Ruben Torre, Luis de la Dormido, Sebastian Esquembre, Francisco Dept. of Computer Science and Automatic Control Universidad Nacional de Educación a Distancia Juan del Rosal 16 MadridE-28040 Spain Dept. of Software Engineering and Computer Systems Universidad Nacional de Educación a Distancia Juan del Rosal 16 MadridE-28040 Spain Dept. of Mathematics Universidad de Murcia Campus de Espinardo MurciaE-30071 Spain

This paper describes a new way to perform automated experiments using virtual laboratories. Experiments are developed and executed using a new software tool: the Experiment Editor. This tool uses virtual laboratories applications designed with educational purposes. The main features/advantages of the Experiment Editor are: 1) the possibility of modifying the initial functionality of the laboratories (i.e. adding new controllers to the plant), 2) analyzing the obtained results during the experiment and performing complex or repetitive tasks in a simple way, 3) and any others explained in the paper. To illustrate the utility of the Experiment Editor a very well-known system is used: the water tank system. © 2016

关键词： Distance education computer aided software engineering computer software Experiments Laboratories Water tanks Easy java(script) Simulations Experimentation Language Repetitive task Simulations Tank system Tool use Virtual control Virtual laboratories

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Support Vector Regression for Predicting the Enhancement Duration of software Projects

Support Vector Regression for Predicting the Enhancement Dur...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Cuauhtemoc Lopez-Martin Shadi Banitaan Andres Garcia-Floriano Cornelio Yanez-Marquez Department of Information Systems Universidad de Guadalajara México Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy Detroit MI USA Unidad de Estudios Superiores Tepotzotlan Universidad Mexiquense del Bicentenario México State México Laboratorio de Computo Inteligente Centro de Investigacion en Computacion México City México

software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed ε-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the ε-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a ε-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation.

关键词： software Support vector machines Data models Predictive models Maintenance engineering Training Testing

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High Dimensional Electromagnetic Interference Signal Clustering Based On SOM Neural Network

Electronics

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Electronics 2016年第1期20卷 27-31页

作者： Li, Hongyi Zhao, Di Xu, Shaofeng Wang, Pidong Chen, Jiaxin LMIB School of Mathematics and Systems Science Beihang University Beijing100191 China School of Mathematics and Systems Science Beihang University Beijing100191 China School of Software Beihang University China School of Reliability and Systems Engineering Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

In this paper, we study the spectral characteristics and global representations of strongly nonlinear, non-stationary electromagnetic interferences (EMI), which is of great significance in analysing the mathematical modelling of electromagnetic capability (EMC) for a large scale integrated system. We firstly propose to use Self-Organizing Feature Map Neural Network (SOM) to cluster EMI signals. To tackle with the high dimensionality of EMI signals, we combine the dimension reduction and clustering approaches, and find out the global features of different interference factors, in order to finally provide precise mathematical simulation models for EMC design, analysis, forecasting and evaluation. Experimental results have demonstrated the validity and effectiveness of the proposed method. © (2016) All rights reserved.

关键词： Signal interference

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Integrated active learning tools for enhanced pedagogy in a software engineering course

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computers in Education Journal 2016年第2期16卷 17-28页

作者： Acharya, Sushil Manohar, Priyadarshan Wu, Peter Schilling, Walter Ansari, Ali School of Engineering Mathematics and Science United States School of Communications and Information Systems Robert Morris University United States Software Engineering Milwaukee School of Engineering United States Computer Engineering Virginia State University United States

Effective teaching requires effective teaching tools. This pedagogical requirement is especially important for software engineering education, where graduates are expected to develop software that meets rigorous quality standards in functional and application domains. To enhance students' understanding of the needs of the professional software industry, lecture notes are supplanted by additional pedagogical tools being developed at the author's institution for a software verification and validation (V&V) course. These active learning teaching tools, consisting of class exercises, case studies, and case study videos, are being developed in partnership with industry. The basic objective of the project is to improve software education so that it is aligned with both academic research and industry best practices. This project is being funded through a NSF-TUES grant. Through this project in addition to enhancing twenty (20) delivery hours of lecture slides, eighteen (18) delivery hours of case studies, sixteen (16) delivery hours of exercises, and six (6) delivery hours of case study videos totaling sixty (60) delivery hours of software V&V course modules are being created. In the spring of 2014, diverse case studies were delivered to software engineering juniors at the author's institution and their learning outcomes were tracked using a survey instrument. The results showed that the students were able to better comprehend V&V topics such as requirements engineering, reviews and inspections, configuration management, and testing. The knowledge disseminated through lectures seemed to be reinforced by the case studies. In the spring of 2015, additional course materials that include exercises and role-play videos are expected to be created and delivered. The results of this research work will be shared during the poster presentation at ASEE. As part of the project dissemination plan, the teaching materials will be made available to interested institutions and professional

关键词： Students

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Algorithms for determining semantic relations of formal concepts by cognitive machine learning based on concept algebra

Algorithms for determining semantic relations of formal conc...

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IEEE International Conference on Cognitive Informatics

作者： Mehrdad Valipour Yingxu Wang Omar A. Zatarain Marina L. Gavrilova Denotational Mathematics Software Science and Cognitive Systems Dept. of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada Information Systems Lab Packard Dept. of Electrical Engineering Stanford University Stanford CA USA

ISBN: (纸本)9781509038473

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

关键词： Writing Instruments Decision support systems Ink Animals Handheld computers Lead

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Online Machine Learning for Big Data Analytics by Cognitive Robots

Online Machine Learning for Big Data Analytics by Cognitive ...

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2016 IEEE International Conference of Online Analysis and Computing science(ICOACS 2016)

作者： Yingxu Wang FICIC FWIF Information Systems Lab Stanford University Laboratory for Computational Intelligence Denotational Mathematicsand Software Science Dept.of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of Calgary

It is recognized that both sources of and solutions to the big data challenges are human collective *** are an abstract representation of the quantity of realistic entities and perceived *** data play an indispensable role not only in a wide range of engineering applications,but also in the cognitive mechanisms of both humans and cognitive robots such as sensation,quantification,qualification,estimation,memory,and *** keynote lecture presents online big data analytic theories by machine learning as well as knowledge extraction by cognitive ***,information,knowledge,and intelligence are the four hierarchical layers of cognitive objects in the brain and cognitive systems from the bottom *** is discovered by the author that,although the cognitive unit of data is bit,that of knowledge is bir,i.e.,a binary relation.A resent finding towards big data science is that big data systems in nature are a recursive ndimensional typed hyperstructure(RNDTHS).This topological property of big data reveals that the mathematical foundation of big data science is underpinned by big data algebra(BDA),which is a denotational mathematical structure for efficiently dealing with the inherited complexities and unprecedented challenges in big data *** leads to a coherent theory for big data modeling,analyses,mining,information elicitation,knowledge extraction,and intelligent generation for cognitive *** experiments demonstrate that cognitive robots may autonomously transform big data in vast linguistic databases(corpuses) into sophistic knowledge bases by cognitive machine learning.

关键词： Big data big data science denotational mathematics mathematical models recursively hierarchical hyperstructures big data algebra cogniitve machine learning formal concepts knowledge extraction knowledge generation cognitive informatics cognitive computing computational intelligence applications

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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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