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IEEE International Conference on Systems, Man and Cybernetics

作者： Sevil Zanjani Miyandoab Shahryar Rahnamayan Azam Asilian Bidgoli Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multi-objective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail.

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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

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Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication Process control Memory

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A Systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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A STUDY ON INTERIOR Γ-HYPERFILTERS IN ORDERED Γ-SEMIHYPERGROUPS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第1期84卷 71-80页

作者： Chen, C. Kosari, S. Omidi, S. Davvaz, B. Akhoundi, M. Institute of Software School of Electronics Engineering and Computer Sciences Peking University Beijing100871 China Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Ministry of Education Iran Department of Education in Tehran Tehran Iran Department of Mathematics Yazd University Yazd Iran Clinical Research Development Unit of Rouhani Hospital Babol University of Medical Sciences Babol Iran

In this paper, interior Γ-hyperfilters of ordered Γ-semihypergroups are de- fined and various types of them are studied. Results related to productional ordered Γ- semihypergroups were investigated. Moreover, we investigate some properties of the in- verse images of strong interior Γ-hyperfilters in ordered Γ-semihypergroups. Finally, we discuss the relationship between two fundamental notions of ordered Γ-semihypergroup, the several types of interior Γ-hyperfilters and the (completely) prime interior Γ-hyperideals. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： (completely) prime I-Γ-hyperfilter Ordered Γ-semihypergroup Strong I-Γ-hyperfilter Weak I-Γ-hyperfilter

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Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze

arXiv

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arXiv 2022年

作者： Pourmemar, Majid Joshi, Yashas Poullis, Charalambos Immersive and Creative Technologies Lab Department of Computer Science and Software Engineering Concordia University Montreal Canada

There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users' performance. In this work, we introduce a predictive model for determining users' performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants' responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users' non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications. © 2022, CC BY-NC-ND.

关键词： User interfaces

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Linear time algorithms for k-means with multi-swap local search 23

Linear time algorithms for k-means with multi-swap local sea...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Junyu Huang Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Department of Computer Science and Engineering State University of New York at Buffalo NY School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China and The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

The local search methods have been widely used to solve the clustering problems. In practice, local search algorithms for clustering problems mainly adapt the singleswap strategy, which enables them to handle large-scale datasets and achieve linear running time in the data size. However, compared with multi-swap local search algorithms, there is a considerable gap on the approximation ratios of the single-swap local search algorithms. Although the current multi-swap local search algorithms provide small constant approximation, the proposed algorithms tend to have large polynomial running time, which cannot be used to handle large-scale datasets. In this paper, we propose a multi-swap local search algorithm for the k-means problem with linear running time in the data size. Given a swap size t, our proposed algorithm can achieve a (50(1 + 1/t) + ε)-approximation, which improves the current best result 509 (ICML 2019) with linear running time in the data size. Our proposed method, compared with previous multi-swap local search algorithms, is the first one to achieve linear running time in the data size. To obtain a more practical algorithm for the problem with better clustering quality and running time, we propose a sampling-based method which accelerates the process of clustering cost update during swaps. Besides, a recombination mechanism is proposed to find potentially better solutions. Empirical experiments show that our proposed algorithms achieve better performances compared with branch and bound solver (NeurIPS 2022) and other existing state-of-the-art local search algorithms on both small and large datasets.

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The Sophisticated Prognostication of Migraine Aura Using Machine Learning

The Sophisticated Prognostication of Migraine Aura Using Mac...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Samiullah Abdul Rehman Attique Ur Rehman Sabeen Javaid Tahir Mohammad Ali Azka Mir Yadaiah Nirsanametla Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait Division of Research and Development Lovely Professional University Phagwara Punjab KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. The symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen.

关键词： Training Visualization Machine learning algorithms software algorithms Migraine Data models software Classification algorithms Internet Random forests

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Impact of Adopting Robots as Teachers: A Review Study

Impact of Adopting Robots as Teachers: A Review Study

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Emerging Technologies in Electronics, Computing and Communication (ICETECC), International Conference on

作者： Muhammad Ali Sehrish Munawar Cheema Nasir Ayub Ammerha Naz Zaheer Aslam Department of Software Engineering The Superior University Lahore Lahore Pakistan Department of Computer Science University of Management and Technology Sialkot Pakistan Department of Computer Science University of Sialkot Sialkot

Robotic technologies have opened up hundreds of new limitless perspectives for educational reform. We have conducted research to study the impact and outcomes of Robotic teaching in systematic studies, experimental studies, and surveys. The conclusions of this study reveal that educational robots have an impact on children, teachers, and students. We looked into how robots affect children’s behavior, learning outcomes, perceptions, and human interactions. The learning efficacy of educational Robot teachers is determined by a variety of factors, including learning outcome, student behavior and mood during class sessions, student reaction, and student involvement during workshops/class participation/quizzes/Q&A sessions. The majority of the research articles we chose were experimental studies, and they all met their objectives, which included teaching mathematical problems, unit conversion problems, teaching English/secondary language, developing analytical, and computational skills, behavior, and attitude development, critical thinking, and improving communication skills.

关键词： Systematics Educational robots Mood Education Humanoid robots Behavioral sciences Robots

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The ABLoTS Approach for Bug Localization: is it replicable and generalizable?

The ABLoTS Approach for Bug Localization: is it replicable a...

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International Mining software Repositories, MSR

作者： Feifei Niu Christoph Mayr-Dorn Wesley K. G. Assunção LiGuo Huang Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.

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Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Niyato, Dusit Kang, Jiawen Jamalipour, Abbas Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Automation Guangdong University of Technology Guangzhou510006 China The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that variously balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we perform a multi-objective optimization analysis and formulate a long-term optimization problem. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal multi-objective Markov decision process. Then, we propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies, in which the low-value actions are masked to speed up the training process. As such, the applicability of a onetime trained model can cover more changing terminal-satellite uplink scenarios. Simulation results show that the proposed algorithm outmatches various baselines, and draw some useful insights. Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications. Moreover, it reveals that the proposed algorithm achieves multiple policies favoring different objectives and achieving near-optimal uplink achievable rates with low switching frequency. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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