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International Conference on software engineering (ICSE)

作者： Daniel Oliveira Wesley K. G. Assunção Alessandro Garcia Ana Carla Bibiano Márcie Ribeiro Rohit Gheyi Baldoino Fonseca Informatics Department Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Institute of Software Systems Engineering - Johannes Kepler University (JKU) Linz Austria Computing Institute - Federal University of Alagoas (UFAL) Maceió Brazil Department of Computing and Systems Federal University of Campina Grande (UFCG) Campina Grande Brazil

Refactoring is a common software maintenance practice. The literature defines standard code modifications for each refactoring type and popular IDEs provide refactoring tools aiming to support these standard modifications. However, previous studies indicated that developers either frequently avoid using these tools or end up modifying and even reversing the code automatically refactored by IDEs. Thus, developers are forced to manually apply refactorings, which is cumbersome and error-prone. This means that refactoring support may not be entirely aligned with practical needs. The improvement of tooling support for refactoring in practice requires understanding in what ways developers tailor refactoring modifications. To address this issue, we conduct an analysis of 1,162 refactorings composed of more than 100k program modifications from 13 software projects. The results reveal that developers recurrently apply patterns of additional modifications along with the standard ones, from here on called patterns of customized refactorings. For instance, we found customized refactorings in 80.77% of the Move Method instances observed in the software projects. We also investigated the features of refactoring tools in popular IDEs and observed that most of the customization patterns are not fully supported by them. Additionally, to understand the relevance of these customizations, we conducted a survey with 40 developers about the most frequent customization patterns we found. Developers confirm the relevance of customization patterns and agree that improvements in IDE's refactoring support are needed. These observations highlight that refactoring guidelines must be updated to reflect typical refactoring customizations. Also, IDE builders can use our results as a basis to enable a more flexible application of automated refactorings. For example, developers should be able to choose which method must handle exceptions when extracting an exception code into a new method.

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Anytime diagnosis for reconfiguration

arXiv

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arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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University Distributed Computer Network Vulnerability Assessment

University Distributed Computer Network Vulnerability Assess...

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Valerii Lakhno Volodimir Malyukov Bakhtyzhan Akhmetov Bagdat Yagaliyeva Olena Kryvoruchko Alona Desiatko Department of Computer Systems Networks and Cybersecurity National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Informatics and Informatization of Education Abai Kazakh National Pedagogical University Almaty Kazakhstan Department of Computer Sciences Yessenov University Aktau Kazakhstan Department of Software Engineering and Cybersecurity State University of Trade and Economics Kyiv Ukraine

A methodology for evaluating information security (IS) for a distributed computer network (DCN) of a university (hereinafter referred to as UDCN) has been proposed. A mathematical model for calculating the UDCN vulnerability factor has been presented. The UDCN vulnerability coefficient makes it possible to assess its IS level at the UDCN design stage. The results of simulation modeling and virtualization of IS UDCN components have also been presented. In the course of simulation modeling, a model of a protected UDCN was designed and tested. In the experimental UDCN model, the network devices selected in the course of modeling the vulnerability coefficient were emulated on virtual machines (VMs). UDCN resources were reproduced using the Proxmox VE virtualization system. On the UDCN hosts running PVE, the Suricata Intrusion Detection System (IPS) was used. Splunk was used as SIEM.

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CASKA-CRT: Chinese Remainder Theorem Empowered Certificateless Aggregate Signcryption Scheme with Key Agreement in IoVs

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu Sun, Zhili Mumtaz, Shahid Tian, Daxin School of Cyber Science and Engineering Wuhan University Wuhan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Institute of Communication Systems University of Surrey Guildford U.K Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland State Key Laboratory of Vehicle-Infrastructure Integrated ITS the State Key Laboratory of Intelligent Transportation System and the School of Transportation Science and Engineering Beihang University Beijing China

To enhance the privacy of messages in Internet of Vehicles (IoVs), it is critical to preserve the communication security between Road Side Unit (RSU) and numerous vehicles. The primitive of signcryption is introduced to guarantee the confidentiality, integrity and unforgeability of transmitted messages. Nevertheless, the existing signcryption schemes fail to achieve a balance between security and efficiency. In this paper, we construct a Chinese remainder theorem (CRT) empowered certificateless aggregate signcryption scheme with key agreement (CASKA-CRT). As the vehicle joins and leaves dynamically, the proposed scheme can ensure the dynamic security via one modulo division operation. Besides, the certificateless aggregate signcryption and key agreement mechanisms are embedded. The former idea can both address the certificate management and key escrow problems, while the latter technology can create authentication as a premise of secure communication. Based on this construction, the hash-to-group operation and bilinear pairing are avoided, to realize a faster verification with the increased number of messages. Moreover, the security of proposed scheme is proved under the random oracle model. Finally, the performance analysis demonstrates the advantage of CASKA-CRT in terms of security and reliability over related works. IEEE

关键词： Public key cryptography

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Energy Efficiency Improvement System for AC Circuits

Energy Efficiency Improvement System for AC Circuits

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Vladimir Kulagin Kirill Selivanov Andrey Vlasov Dmitrii Aminev Lyudmila Bunina Sergey Bikovsky Moscow technical university of communications and informatics Moscow Russian Federation Department of Design and Technology of Electronic Devices Bauman Moscow State Technical University BMSTU Moscow Russian Federation Department of Development of software solutions and system programming Russian Technological University – MIREA Moscow Russian Federation Department of Electrical Engineering Systems Russian Technological University – MIREA Moscow Russian Federation

The problem of the rational use of energy resources remains constantly relevant and requires the search for new approaches. One of them is power control. In AC circuits, the authors see the most promising method of phase AC power control. Based on it, a power control module was developed. The structural and circuit diagrams of the developed device are presented to implement the proposed solution. The authors produced its experimental prototype and conducted experimental testing at various levels of regulated power. Savings when using the proposed power control module were calculated using the example of energy consumption in various sectors of energy-intensive systems. According to the data obtained, the module allows saving energy consumption without significant discomfort to the energy user. The calculated results allow concluding that the proposed AC power control module is fully operational, and its widespread use will significantly reduce the need for electricity.

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Expert Evaluation of Main Measures Proposed for Human Resources Rational Usage for Region Competitiveness Improvement

Expert Evaluation of Main Measures Proposed for Human Resour...

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International Science and Technology Conference FarEastCon, 2021

作者： Kenzhin, Z.B. Zolkin, A.L. Aygumov, T.G. Poskryakov, I.A. West Kazakhstan Agrarian and Technical University Named After Zhangir Khan Uralsk090009 Kazakhstan Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Department of Computer Engineering Software and Automated Systems Dagestan State Technical University Makhachkala367000 Russia Department of Transport Economic and Management Federal State Autonomous Educational Institution for Higher Education Russian Transport University Moscow127994 Russia

ISBN: (纸本)9789811688287

A process of expert evaluation of actions based on complex methodology is reviewed in this article. The methods of questionnaire, expert evaluations, multi-variation problem of optimal alternative were used. Authors explained the methods of expert evaluations, e.g. Delphi method, as the good forecasting tool for region competitiveness improvement measures based on achievement of optimal balance of supply and demand of human resources. As a result of applied methodology, the main measures for rational usage of human resources in region competitiveness improvement were selected. Application of obtained results and proposed methodology allows to improve the region economic management. Authors came to the conclusion that measures for development of human resources as region competitive advantage are referred to quality education, healthcare, effective migration policy as base for labour efficiency improvement and reducing the shortage of skilled manpower. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Personnel

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Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

arXiv

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arXiv 2024年

作者： Yu, Jun Dai, Yutong Liu, Xiaokang Huang, Jin Shen, Yishan Zhang, Ke Zhou, Rong Adhikarla, Eashan Ye, Wenxuan Liu, Yixin Kong, Zhaoming Zhang, Kai Yin, Yilong Namboodiri, Vinod Davison, Brian D. Moore, Jason H. Chen, Yong Department of Computer Science and Engineering Lehigh University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Department of Industrial and Systems Engineering Lehigh University United States Department of Statistics University of Missouri United States School of Software Shandong University China Department of Computer Science University of Hong Kong Hong Kong Department of Computer Science and Engineering South China University of Technology China Department of Community and Population Health Lehigh University United States Department of Computational Biomedicine Cedars-Sinai Medical Center United States

Multi-Task Learning (MTL) is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to Single-Task Learning (STL), MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including computer vision, natural language processing, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for zero-shot learning, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://***/junfish/Awesome-Multi

关键词： Learning systems

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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Practicable Paradigms for Semi-Automated Expert-User Post-Processing of Deep-Learning Segmentations in 3D Radiology 10

Practicable Paradigms for Semi-Automated Expert-User Post-Pr...

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10th International Workshop on Innovative Simulation for Health Care, IWISH 2021

作者： Jany, Julian Zwettler, Gerald Adam Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria

ISBN: (纸本)9788885741652

With recent improvements in deep-learning architectures and availability of GPU hardware, state of the art deep learning (DL) has already manifested as powerful image processing technology in the clinical routine to provide segmentation results of high accuracy. As a drawback, it's black-box nature does not naturally feature inspection and post-processing by medical experts. We present a Graph segmentation (GS) approach that derives it's fitness function from arbitrary DL results in a generic way. To allow for efficient and effective post-processing by the medical experts, various interaction paradigms are presented and evaluated in this paper. The trade-off of GS compared to the initial DL results is marginal (delta JI= 0.196%), yet potential DL segmentation errors can be corrected in a reliable way. The intuitive approach shows a high level of both, inter and intra user reproducibility. Change propagation of corrected slices keeps the demand for user-interaction to a minimum when successfully correction potential weaknesses in the DL segmentations. Thereby, the formerly error-prone slice mini-batches get corrected in an automated way with the JI being significantly increased. © 2021 The Authors.

关键词： Deep learning

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