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International Conference on software Maintenance (ICSM)

作者： M. Satpathy N.T. Siebel D. Rodriguez Applied Software Engineering Group Department of Computer Science University of Reading Reading UK COGNITIVE Systems Group Institute of Computer Science and Applied Mathematics Christian-Albrechts University Kiel Germany

Assertions had their origin in program verification. For the systems developed in industry, construction of assertions and their use in showing program correctness is a near-impossible task. However, they can be used to show that some key properties are satisfied during program execution. We first present a survey of the special roles that assertions can play in object oriented software construction. We then analyse such assertions by relating them to the case study of an automatic surveillance system. In particular, we address the following two issues: What types of assertions can be used most effectively in the context of object oriented software? How can you discover them and where should they be placed? During maintenance, both the design and the software are continuously changed. These changes can mean that the original assertions, if present, are no longer valid for the new software. Can we automatically derive assertions for the changed software?.

关键词： software maintenance computer aided software engineering

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Small sets of genuinely nonlocal Greenberger-Horne-Zeilinger states in multipartite systems

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Physical Review A 2024年第2期109卷 022428-022428页

作者： Zong-Xing Xiong Yongli Zhang Mao-Sheng Li Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou 510006 China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou 510665 China School of Mathematics South China University of Technology Guangzhou 510641 China

A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the problem of constructing small genuinely nonlocal sets consisting of generalized Greenberger-Horne-Zeilinger (GHZ) states in multipartite systems. For system (C2)⊗N where N is large, using the language of group theory, we show that a tiny proportion Θ(1/2N) of the states among the N-qubit GHZ basis suffice to exhibit genuine nonlocality. Similar arguments also hold for the canonical generalized GHZ bases in systems (Cd)⊗N, wherever d is even and N is large. What is more, moving to the condition that any fixed N is given, we show that d+1 genuinely nonlocal generalized GHZ states exist in (Cd)⊗N, provided the local dimension d is sufficiently large. As an additional merit, within and beyond an asymptotic sense, the latter result also indicates some evident limitations of the “trivial othogonality-preserving local measurements” (TOPLM) technique that has been utilized frequently for detecting genuine nonlocality.

关键词： Nonlocality Quantum correlations in quantum information

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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N-Most Interesting Closed Itemset Mining

N-Most Interesting Closed Itemset Mining

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International Conference on Convergence Information Technology

作者： Panida Songram Veera Boonjing Department of Mathematics and Computer Science Faculty of Science King Mongkuts Institute of Technology Ladkrabang Software Systems Engineering Laboratory Bangkok Thailand Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

In this paper, we propose an efficient algorithm, NCLOSED, for mining the N k-closed itemsets with the highest supports for 1 up to a certain k max value. The algorithm adopts best-first search strategy to generate closed itemsets with highest remaining supports. It does not keep closed itemsets mined in main memory to ensure that they are really closed. This is because this algorithm can directly generate closed itemsets. Moreover, duplicated closed itemsets are detected and discarded from this algorithm.

关键词： Itemsets Data mining computer science Information technology software systems systems engineering and theory Laboratories mathematics Informatics software algorithms

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Generic Environment for Full Automation of Benchmarking 1

Generic Environment for Full Automation of Benchmarking

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Testing of Component-Based systems and software Quality, 1st International Workshop on software Quality, SOQUA 2004 and Workshop Testing Component-Based systems, TECOS 2004

作者： Kalibera, Tomáš Bulej, Lubomír Tůma, Petr Distributed Systems Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Malostranské nám. 25 Prague118 00 Czech Republic Institute of Computer Science Czech Academy of Sciences Pod Vodárenskou věží 2 Prague182 07 Czech Republic

ISBN: (纸本)3885793873

Regression testing is an important part of software quality assurance. We work to extend regression testing to include regression benchmarking, which applies benchmarking to detect regressions in performance. Given the specific requirements of regression benchmarking, many contemporary benchmarks are not directly usable in regression benchmarking. To overcome this, we present a case for designing a generic benchmarking environment that will facilitate the use of contemporary benchmarks in regression benchmarking, analyze the requirements and propose architecture of such an environment. © 2004 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Benchmarking

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Application of Data science and Machine Learning in the Prediction of College Dropout: A Data-Driven Predictive Approach

Application of Data Science and Machine Learning in the Pred...

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International Conference on software Process Improvement (CIMPS)

作者： Axel Frederick Félix Jiménez Vania Stephany Sanchéz Lee Isaul Ibarra Belmonte Ezra Federico Parra González Engineering in Computer Systems National Polytechnic Institute Zacatecas México Software Engineer Center for Research in Mathematics Zacatecas México Computer Science Department Center for Research in Mathematics Zacatecas México

ISBN: (数字)9798350358568

ISBN: (纸本)9798350358575

This paper presents a study on the prediction of student graduation or failure using two predictive models: K-Nearest Neighbors (KNN) and a forward sequential Artificial Neural Network (ANN). The models, built with a well-chosen set of independent variables, were assessed using metrics like precision and accuracy. The results obtained revealed that both the KNN model and the sequential forward ANN model achieved high efficiency in predicting student graduation or failure, achieving accuracies of 0.9133% (K=3) and 0.9312% (after 50 epochs), respectively. Providing a valuable tool to identify early on students at risk of not graduating and to take preventive measures to improve their academic performance. Comparisons with related research showed consistent outcomes, underscoring the credibility and importance of the employed predictive models.

关键词： Measurement Artificial neural networks Machine learning Predictive models Data science software

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On the Convergence of Least Squares Estimator for Nonlinear Autoregressive Models

On the Convergence of Least Squares Estimator for Nonlinear ...

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第40届中国控制会议

作者： Zhaobo Liu Chanying Li College of Computer Science and Software Engineering of Shenzhen University Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

This paper is concerned with the least squares estimator for a basic class of nonlinear autoregressive models, whose outputs are not necessarily to be ergodic. Several asymptotic properties of the least squares estimator have been established under mild conditions. These properties suggest the strong consistency of the least squares estimates in nonlinear autoregressive models which are not divergent.

关键词： Nonlinear Autoregressive Models Strong Consistency Least Squares Harris Recurrent

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

A Time Effect based Collaborative Filtering Approach for Use...

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2019 2nd International Conference on computer Information science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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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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Efficient Algorithms for Mining Closed Multidimensional Sequential Patterns

Efficient Algorithms for Mining Closed Multidimensional Sequ...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Veera Boonjing Panida Songram Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

A combination of closed sequential pattern mining and closed itemset pattern mining was proposed to mine closed multidimensional sequential patterns. There are two ways for this combination; (1) mining closed itemset patterns from multidimensional information followed by mining closed sequential patterns from sequences associated with closed itemset patterns, and (2) mining closed sequential patterns from sequences followed by mining closed itemset patterns from multidimensional information associated with closed sequential patterns. In the first way the major cost is mining all sequences associated with closed itemset patterns. A similar problem occurs with the second way, the major cost is mining all multidimensional information associated with closed sequential patterns. Therefore, this paper proposes two new combinations that don't need to mine all sequences in the first combination, and all multidimensional information in the second combination. Both combinations can be effected by exploiting two concepts. In the first combination, any closed sequential patterns associated with a closed itemset patten I can be found from a set of closed sequential patterns associated with a closed itemset pattern I' if I sup I'. In the second combination, any closed itemset patterns associated with a closed sequential pattern s can be found from a set of closed sequential patterns associated with a closed itemset pattern s' if s sup s'.

关键词： Multidimensional systems Data mining Itemsets Costs computer science software algorithms software systems systems engineering and theory Laboratories mathematics

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