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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

Journal of computer science & Technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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Digital Twin, Metaverse and Smart Cities in a Race to the Future 24

Digital Twin, Metaverse and Smart Cities in a Race to the Fu...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zainab, Hijab E Bawanay, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In the dynamic landscape of urban development, the synergy of the Metaverse, Digital Twins, and Smart Cities emerges as a potent catalyst for transformation. The Metaverse, a vibrant virtual space, collaborates with cutting-edge technologies, including Virtual Reality (VR), Augmented Reality (AR), the Internet of Things (IoT), and Artificial Intelligence (AI), crafting immersive experiences. Simultaneously, Digital Twins, serving as virtual replicas of physical entities, instigating an industrial revolution, while Smart Cities harness advanced technologies for sustainable urban living. This paper explores the applications of converging these technologies across diverse industries, showcasing how Digital Twins reshape conventional practices and amplify the concept of smart cities through the immersive realm of the Metaverse. However, the exploration unveils challenges, particularly in data privacy, interoperability, and ethical considerations. The study advocates for a balanced and informed approach, emphasizing that the symbiotic relationship between Digital Twins and the Metaverse should steer the evolution of Smart Cities toward security, innovation, and interconnected urban spaces. © 2023 IEEE.

关键词： Digital Twins Immersive Experiences Metaverse Smart Cities Sustainable Urban Living Urban Development

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DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection 24

DP-RAE: A Dual-Phase Merging Reversible Adversarial Example ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Jiajie Du, Xia Zhou, Jizhe Pun, Chi-Man Xu, Qizhen Liu, Xiaoyuan School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Computer Science Engineering Research Center of Machine Learning and Industry Intelligence Sichuan University Chengdu China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798400706868

In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable perturbations and limiting applications to white-box scenarios. This paper proposes a novel Dual-Phase merging Reversible Adversarial Example (DP-RAE) generation framework, combining a heuristic black-box attack and RDH with Grayscale Invariance (RDH-GI) technology. This dual strategy not only evaluates and harnesses the adversarial potential of past perturbations more effectively but also guarantees flawless embedding of perturbation information and complete recovery of the original image. Experimental validation reveals our method's superiority, secured an impressive 96.9% success rate and 100% recovery rate in compromising black-box models. In particular, it achieved a 90% misdirection rate against commercial models under a constrained number of queries. This marks the first successful attempt at targeted black-box reversible adversarial attacks for commercial recognition models. This achievement highlights our framework's capability to enhance security measures without sacrificing attack performance. Moreover, our attack framework is flexible, allowing the interchangeable use of different attack and RDH modules to meet advanced technological requirements. © 2024 ACM.

关键词： Differential privacy

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Applying Competition-Based Learning to Stimulate Students' Practical and Competitive AI Ability in a Machine Learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models 24

Deep Learning for Violence Detection in Surveillance: The Ro...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Ilyas, Anum Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Jinah University for Women Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities. © 2023 IEEE.

关键词： computer Vision Deep Learning Pre-trained Models Transfer Learning Violence Categorization

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A Blockchain based Framework for Verifying Drug Authenticity 24

A Blockchain based Framework for Verifying Drug Authenticity

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Fatima, Kiran Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented. © 2023 IEEE.

关键词： Blockchain Technology Counterfeit Drugs decentralized framework Drug Supply Chain Traceability of Drugs Traditional Pharmaceutical Supply Chain

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On the Use of Multi-agent Reinforcement Learning in Cyber-physical and Internet of Thing Systems 12

On the Use of Multi-agent Reinforcement Learning in Cyber-ph...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Yarahmadi, Hossein Shiri, Mohammad Ebrahim Challenger, Moharram Navidi, Hamidreza Sharifi, Arash University of Antwerp and Flanders Make Strategic Research Center Department of Computer Science Belgium Islamic Azad University Science and Research Branch Department of Computer Engineering Tehran Iran Shahed University Department of Mathematics and Computer Science Tehran Iran

ISBN: (纸本)9798350322910

In this paper, we provide a review of cyber-physical systems (CPSs) and explore the applications of Multi-Agent Systems (MAS), Multi-Agent Reinforcement Learning (MARL), and Multi-Agent Credit Assignment Problem (MCA) in CPSs. Our primary focus is on mapping specific domains, including job scheduling, energy management, and smart transport systems, to MAS and applying MARL and MCA techniques to solve the problems. To evaluate the effectiveness of our proposed method, we applied it to the job scheduling problem, using two parameters, CPU and bandwidth, and tested its performance for four different tasks: Face Detection and Window Blind Control (FDWC), Finger Touch and Gate Control (FTGC), Weather Check and Thermostat Control (WCTC), and Temperature Check and Start Fan (TCSF). The results indicate that prioritizing tasks significantly improves the performance of the proposed method. We conclude that MAS, MARL, and MCA are powerful tools for solving problems in CPSs and IoT. Mapping these problems to MAS can help overcome the challenges associated with CPSs and IoT, and improve system performance. © 2023 IEEE.

关键词： Multi agent systems

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural Network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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Energy-Efficient Cloud Computing Through Reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced computer science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini R Paramasivam, M.E. Gulyamova, Dilfuza Saravanan, Aanandha Ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, Refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. Data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (DRL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes Deep Q-Networks (DQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as Round Robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep reinforcement learning

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Comprehensive analysis of two-dimensional charge transport mechanism in thin-film transistors based on random networks of single-wall carbon nanotubes using transient measurements

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Nano research 2022年第2期15卷 1524-1531页

作者： Hyeonwoo Shin Sang-Joon Park Byeong-Cheol Kang Tae-Jun Ha Department of Electrical Engineering and Computer Science Inter-university Semiconductor Research CenterSeoul National UniversitySeoul 08826Korea Department of Electronic Materials Engineering Kwangwoon UniversitySeoul 01897Korea

Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectronic ***,a comprehensive investigation of the two-dimensional(2D)charge transport mechanism in SWCNT-TFTs is reported by analyzing the temperature-dependent electrical characteristics determined from the direct-current and non-quasi-static transient measurements at 80-300 *** elucidate the time-domain charge transport characteristics of the random networks in the SWCNTs,an empirical equation was derived from a theoretical trapping model,and a carrier velocity distribution was determined from the differentiation of the transient ***,charge trapping and de-trapping in shallow-and deep-traps in SWCNT-TFTs were analyzed by investigating charge transport based on their trapping/de-trapping *** comprehensive analysis of this study provides fundamental insights into the 2D charge transport mechanism in TFTs based on random networks of nanomaterial channels.

关键词： single-wall carbon nanotube random networks two-dimensional(2D)charge transport time-domain transient measurements charge trapping/de-trapping shallow-/deep-traps

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