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arXiv 2017年

作者： Min, Wenwen Liu, Juan Zhang, Shihua State Key Laboratory of Software Engineering School of Computer Wuhan University National Center for Mathematics and Interdisciplinary Sciences Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics Sciences University of Chinese Academy of Sciences

Given two data matrices X and Y, Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. To this end, we propose a novel Sparse weighted canonical correlation analysis (SWCCA), where weights are used for regularizing different samples. We solve the L0-regularized SWCCA (L0-SWCCA) using an alternating iterative algorithm. We apply L0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. Lastly, we consider also SWCCA with different penalties like LASSO (Least absolute shrinkage and selection operator) and Group LASSO, and extend it for integrating more than three data matrices. Copyright © 2017, The Authors. All rights reserved.

关键词： Iterative methods

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A Proof-of-Concept Model Demonstration of System Level Cyber Attacks on Availability

A Proof-of-Concept Model Demonstration of System Level Cyber...

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IEEE SoutheastCon

作者： Uma Kannan Rajendran Swamidurai David Umphress Department of Computer Information Systems Alabama State University Montgomery AL USA Department of Mathematics and Computer Science Alabama State University Montgomery AL USA Department of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (纸本)9781538661345

In this study we have modeled a cyber security situation and demonstrated the system level effects using system dynamics PoC (Proof-of-Concept) model. We modeled a hypothetical Company's Information Technology infrastructure and simulated a Distributed Denial-of-Service attack on one part of the system in an effort to see how other parts of the system were affected. To validate our simulation model, we developed a cybersecurity testbed and conducted a cyber-attack on one application and observed its impact on the whole system. The results indicate that by using known vulnerabilities and the current knowledge about infrastructure and security controls, the system dynamic cyber security simulation modeling allows an organization to imitate the attacker activities and helps to assess and mitigate the system's risk exposure.

关键词： Cybersecurity system dynamics denial-of-service attack continuous simulation cybersecurity simulation

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Testing and Certification of Trustworthy systems introduction to Minitrack

Testing and Certification of Trustworthy Systems introductio...

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38th Annual Hawaii International Conference on System sciences, HICSS 2005

作者： Hevner, Alan R. Linger, Richard C. Walton, Gwendolyn H. Information Systems and Decision Sciences Dept. Univ. of South Florida Tampa FL 33620 United States CERT Research Center Software Engineering Institute Carnegie Mellon University Pittsburgh PA 15213 United States Dept. of Mathematics and Computer Science Florida Southern College Lakeland FL 33801 United States

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ν-SVR polynomial kernel for predicting the defect density in new software projects

arXiv

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arXiv 2018年

作者： López-Martín, Cuauhtémoc Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi Department of Information Systems Universidad de Guadalajara Mexico Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy DetroitMI United States

An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new software projects obtained from the International software Benchmarking Standards Group (ISBSG) Release 2018 data set. Two types of SVR (i.e., Ε-SVR and ν-SVR) were applied to train and test these projects. Each SVR used four types of kernels. The prediction accuracy of each SVR was compared to that of a statistical regression (i.e., a simple linear regression, SLR). Statistical significance test showed that ν-SVR with polynomial kernel was better than that of SLR when new software projects were developed on mainframes and coded in programming languages of third generation. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Generation and application of constrained interaction test suites using base forbidden tuples with mixed neighborhood tabu search

arXiv

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arXiv 2019年

作者： Hasan, Imad H. Ahmed, Bestoun S. Potrus, Moayad Y. Zamli, Kamal Z. Erbil44002 Iraq Department of Mathematics and Computer Science Karlstad University Karlstad651 88 Sweden Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Pahang26300 Malaysia

To ensure the quality of current highly configurable software systems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern software systems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient search-based strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible t − tuple combinations;then, it filters out the set by removing the forbidden t − tuples using the base forbidden tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also

关键词： software testing

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural Computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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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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Scenario-Assisted Deep Reinforcement Learning

arXiv

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arXiv 2022年

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Dynamical Sampling with Additive Random Noise

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Sampling Theory, Signal Processing, and Data Analysis 2018年第2期17卷 153-182页

作者： Akram Aldroubi Longxiu Huang Ilya Krishtal Akos Ledeczi Roy R. Lederman Peter Volgyesi Department of Mathematics Vanderbilt University Nashville USA Department of Mathematics Northern Illinois University DeKalb USA Department of Electrical Engineering and Computer Science Vanderbilt University Nashville USA Department of Statistics and Data Science Yale University New Haven USA Institute for Software Integrated Systems Vanderbilt University Nashville USA

Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.

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Towards socially sophisticated BDI agents

Towards socially sophisticated BDI agents

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International Conference on MultiAgent systems Proceedings

作者： F. Dignum D. Morley E.A. Sonenberg L. Cavedon Department of Mathematics and Computer Science Eindhovan University of Technology Eindhoven Netherlands Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia Department of Information Systems University of Melbourne Parkville VIC Australia Department of Computer Science RMIT University Melbourne VIC Australia

We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reasoning, and their explicit representation supports reasoning about a wide range of behaviour types in a single framework. We propose a modified BDI interpreter loop that takes norms and obligations into account in an agent's deliberation.

关键词： computer science Multiagent systems mathematics software engineering Information systems Collaborative work Humans Stability

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