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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Weiyi Li, Peng Li, Bo Liu, Jing Nie, Lei Bao, Haizhou Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350346558

For the application of task assignment in crowdsourcing, previous research has focused on offline version or one-sided online version. Some of them draw extra attention to stability in task assignment. Our work extends a one-sided task assignment and its stable generalization. In particular, we present a two-sided online stable task assignment (TS-OSTA) problem in which any vertex from two sides can maintain an incomplete neighbor list and ties. We first give the fundamental definitions of task assignment in spatial crowdsourcing and stable matching. Then, we analyze and present the matching condition and formulate the TS-OSTA problem. As a warm-up, we investigate and analyze two classical baseline algorithms Greedy and Random Threshold. Then we present a bipartite ranking algorithm that maintains the efficiency of Greedy and effectiveness of Random Threshold. Through extensive experiments, we examine the performance of our algorithm in size, memory, and time. The results show that our algorithm performs better than the baselines. © 2022 IEEE.

关键词： Crowdsourcing

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

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20th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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Exams Item Analysis, Online Examination, and Education Evaluation system Via Internet 20

Exams Item Analysis, Online Examination, and Education Evalu...

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20th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2023

作者： Chaidee, Kamonlak Boontasri, Kanchana Chaidee, Ekkachai Punthura, Kittichai Kumtip, Narong Pankham, Yotsanan Rajamangala University of Technology Lanna Chiangrai Faculty of Business Administration and Liberal Arts Department of Computer Information System Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Science and Agricultural Technology Department of Information Technology Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Electrical Engineering Department of Electrical Engineering Chiang Rai Thailand

ISBN: (纸本)9798350310467

The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system's accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity. © 2023 IEEE.

关键词： Decision making

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of Engineering & technology 2023.

关键词： Machine learning

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Unraveling Academic Failure: Examining the Influence of Course Correlations on Student Performance Through Association Rules Algorithms

Unraveling Academic Failure: Examining the Influence of Cour...

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International Conference on computer and Applications (ICCA)

作者： Hana Safour Mansour Essgaer Almahdi Alshareef Computer Sciences Department Faculty of Sciences Sebha University Sebha Libya Information System Department Faculty of Information Technology Sebha University Sebha Libya Computer Sciences Department Faculty of Information Technology Sebha University Sebha Libya

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The field of information technology has witnessed rapid developments in data mining applications in education. In this context, association rule mining, one of the key aspects of data mining, has emerged as a specialized technique for extracting meaningful insights from sets of frequent items. Academic institutions face persistent challenges, including high failure rates and suboptimal academic achievement among students. This study examined the use of the Apriori algorithm to analyze data within an academic setting, focusing on students enrolled in the Department of Zoology at Sebha University. The primary objective was to study the effect of interconnectedness between academic courses on the overall academic performance of students in the Department of Zoology. The results revealed that high failure rates were observed consistently during the first two academic years. When the Arabic language course was linked to the botany course for second-semester students, an alarming failure rate of 80% occurred. Similarly, linking Arabic with Chemistry in the same semester resulted in a high failure rate of 75%. The English course during the same period showed a failure rate of 52%, which dropped significantly to 51% when linked to the zoology and chemistry courses. Conversely, linking other zoology courses to chemistry increased the failure rate by 59%, highlighting the subtle effects of course linkage on academic outcomes. These findings for the Department of Zoology are of fundamental importance to educators, providing valuable insights into improving student performance and identifying those who need additional support.

关键词： Schedules Chemistry Education Neural networks Zoology Focusing Association rule learning information technology Random forests Faces

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Improving Performance Fuzzy Methods in Stroke Disease Diagnosis systems with Certainty Factor

Improving Performance Fuzzy Methods in Stroke Disease Diagno...

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International Conference on Informatics and Computing (ICIC)

作者： Rini Sovia Mutiara Salsabila Musli Yanto Information System Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Stroke is a disease caused by damage to brain cells due to impaired blood supply. Stroke has causes such as hypertension, cholesterol, arteriosclerosis, heart disease, diabetes, family history of stroke, and migraine. The purpose of this study is to develop a stroke diagnosis system by improving the performance of the fuzzy method with the performance of the Certainty Factor (CF) method. The resulting diagnosis system based on the performance of the fuzzy method enhanced with CF can present an effective and efficient diagnosis system in decision-making. The research dataset refers to the knowledge processed about stroke symptoms obtained from an expert. The results of testing the performance of the fuzzy method enhanced with CF in the diagnosis process have been able to provide the knowledge needed by patients in preventing stroke. The performance of fuzzy in the diagnosis system has also made predictions or early diagnoses so that solutions or ways to treat stroke are obtained optimally. Improving the performance of the fuzzy method with CF can present an effective and efficient system for diagnosing stroke. The accuracy of the diagnosis system produced by the fuzzy method based on testing presents results of $\mathbf{8 9 \%}$ in diagnosing stroke. The results of improving the performance of the fuzzy method with CF have also provided innovation in the Knowledge-Based system in the stroke diagnosis process. Based on these results, this study can contribute to helping medical personnel in providing alternative solutions for early prevention of increasing stroke incidence.

关键词： Technological innovation Accuracy Prevention and mitigation Knowledge based systems Decision making Migraine Stroke (medical condition) Medical diagnosis Personnel Testing

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Min Northeastern University School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system *** first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multiper-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Displacement detection method of QR code reference object based on feature points 4

Displacement detection method of QR code reference object ba...

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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Yang, Qiuhui Zhang, Baoxiang Ji, Xiaoyu Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Wuzhou China Faculty of Computer and Electronic Information Guangxi University Nanning China Faculty of Information Technology Macau University of Science and Technology China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the continuous development of construction technology and computer technology, in order to meet the needs of concrete quality management, a real-time monitoring information system based on computer vision is established. The identification label is added to the concrete specimen to prevent the exchange of concrete specimens. According to the needs of the application scene, this paper proposes a new feature point extraction algorithm based on Harris. By improving the positioning accuracy of feature points, it can avoid the interference of background noise points or feature points in unclear focus area on feature matching and the calculation of projection matrix;and a few feature points are used to calibrate the camera, which overcomes the problem that the traditional camera calibration method needs to make a calibration board. Experiments show that the algorithm proposed in this paper has good stability to illumination, camera angle and distractors, and is flexible in application. © 2022 IEEE.

关键词： Quality management

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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computer systems Science & Engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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