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Optimization Techniques in University Timetabling Problem:Constraints,Methodologies,Benchmarks,and Open Issues

Computers, Materials & Continua 2023年第3期74卷 6461-6484页

作者： Abeer Bashab Ashraf Osman Ibrahim Ibrahim Abakar Tarigo Hashem Karan Aggarwal Fadhil Mukhlif Fuad A.Ghaleb Abdelzahir Abdelmaboud Faculty of Computer Science&Information Technology Alzaiem Alazhari University13311KhartoumSudan Faculty Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400SabahMalaysia Department of Computer Science University of SharjahSharjah27272United Arab Emirates Electronic and Communication Engineering Department Maharishi Markandeshwar Deemed to be UniversityMullanaAmbala133207HaryanaIndia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi Malaysia81310JohorMalaysia Department of Information Systems King Khaled UniversityMuhayel61913AseerSaudi Arabia

University timetabling problems are a yearly challenging task and are faced repeatedly each *** problems are considered nonpolynomial time(NP)and combinatorial optimization problems(COP),which means that they can be solved through optimization algorithms to produce the aspired optimal *** techniques have been used to solve university timetabling problems,and most of them use optimization *** paper provides a comprehensive review of the most recent studies dealing with concepts,methodologies,optimization,benchmarks,and open issues of university timetabling *** comprehensive review starts by presenting the essence of university timetabling as NP-COP,defining and clarifying the two formed classes of university timetabling:University Course Timetabling and University Examination Timetabling,illustrating the adopted algorithms for solving such a problem,elaborating the university timetabling constraints to be considered achieving the optimal timetable,and explaining how to analyze and measure the performance of the optimization algorithms by demonstrating the commonly used benchmark datasets for the *** is noted that meta-heuristic methodologies are widely used in the ***,recently,multi-objective optimization has been increasingly used in solving such a problem that can identify robust university timetabling ***,trends and future directions in university timetabling problems are *** paper provides good information for students,researchers,and specialists interested in this area of *** challenges and possibilities for future research prospects are also explored.

关键词： University timetabling timetabling approaches meta-heuristics combinatorial optimization

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

Investigation of Security Attacks in IoMT Devices and Federa...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Kiruthika, M. Poongodi, T. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Karnataka Bangalore India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients' data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research. © 2025 The Authors. Published by Elsevier B.V.

关键词： Federated learning

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Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification 14

Resolution-Adaptive Representation Learning via Eliminating ...

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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Lu, Xing Lai, Fengshan Cao, Zhixiang Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach. © 2024 IEEE.

关键词： Contrastive Learning

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Predicting Distributed Denial of Service Attacks in Machine Learning Field 2

Predicting Distributed Denial of Service Attacks in Machine ...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Umarani, S. Aruna, R. Kavitha, V. Erode Sengunthar Engineering College Department of Information Technology Perundurai India School of Computing Vel Tech Rangarajan Dr Sangunthala R &d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Nandha College of Technology Department of Information Technology Erode India

ISBN: (纸本)9781665437899

A persistent and serious danger to the Internet is a denial of service attack on a large scale (DDoS) attack using machine learning. Because they originate at the low layers, new Infections that use genuine hypertext transfer protocol requests to overload target resources are more untraceable than application layer-based cyberattacks. Using network flow traces to construct an access matrix, this research presents a method for detecting distributed denial of service attack machine learning assaults. Independent component analysis decreases the number of attributes utilized in detection because it is multidimensional. Independent component analysis can be used to translate features into high dimensions and then locate feature subsets. Furthermore, during the training and testing phase of the updated source support vector machine for classification, their performance it is possible to keep track of the detection rate and false alarms. Modified source support vector machine is popular for pattern classification because it produces good results when compared to other approaches, and it outperforms other methods in testing even when given less information about the dataset. To increase classification rate, modified source support Vector machine is used, which is optimized using BAT and the modified Cuckoo Search method. When compared to standard classifiers, the acquired findings indicate better performance. © 2022 IEEE.

关键词： Support vector machines

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A MEC Offloading Strategy Based on Improved DQN and Simulated Annealing for Internet of Behavior

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ACM Transactions on Sensor Networks 2022年第2期19卷 1-20页

作者： Yuan, Xiaoming Tian, Hansen Zhang, Zedan Zhao, Zheyu Liu, Lei Sangaiah, Arun Kumar Yu, Keping Qinghuangdao Branch Campus Northeastern University Qinhuangdao066004 China State Key Laboratory of Integrated Service Networks Xidian University Xian710071 China Xidian Guangzhou Institute of Technology Guangzhou510555 China School of Computing Science and Engineering Vellore Institute of Technology Vellore632014 India National Yunlin University of Science and Technology Douliou64002 Taiwan K. Yu Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption. © 2022 Association for computing Machinery.

关键词： Computation offloading

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Retraction Note: Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system

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Optical and Quantum Electronics 2024年第12期56卷 1-2页

作者： Saad Alqahtani, Abdulrahman Mubarakali, Azath Saravanan, M. Babu Changalasetty, Suresh Saroja Thota, Lalitha Parthasarathy, P. Sivakumar, B. College of Computing and Information Technology Department of Computer Science University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science King Khalid University Abha Kingdom of Saudi Arabia Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India King Khalid University Abha Kingdom of Saudi Arabia Department of CSE Shadan Womens College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

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A Machine Learning-Based Elastic Strategy for Operator Parallelism in a Big Data Stream computing System 1

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12th International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2021

作者： Li, Wei Sun, Dawei Gao, Shang Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University MelbourneVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

Elastic scaling in/out of operator parallelism degree is needed for processing real time dynamic data streams under low latency and high stability requirements. Usually the operator parallelism degree is set when a streaming application is submitted to a stream computing system and kept intact during runtime. This may substantially affect the performance of the system due to the fluctuation of input streams and availability of system resources. To address the problems brought by the static parallelism setting, we propose and implement a machine learning based elastic strategy for operator parallelism (named Me-Stream) in big data stream computing systems. The architecture of Me-Stream and its key models are introduced, including parallel bottleneck identification, parameter plan generation, parameter migration and conversion, and instances scheduling. Metrics of execution latency and process latency of the proposed scheduling strategy are evaluated on the widely used big data stream computing system Apache Storm. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Machine learning

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Secure computing: NGSCB's Journey and Windows Security Impact

Secure Computing: NGSCB's Journey and Windows Security Impac...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Kumar, Surendra Mishra, Umang Deep Tiwari, Sudha Singh, Teekam Vikash Graphic Era Hill University School of Computing Dehradun India Glbitm Departnment of Computer Applications Greater Noida India Unique Institute of Management & Technology Muradnagar India Government Engineering College Department of Information Technology Chhattisgarh Bilaspur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Appventurez Mobitech Pvt Ltd Greater Noida201310 India

ISBN: (纸本)9798350382723

The Next-Generation Secure computing Base (NGSCB) aimed to improve privacy, boost security, and reinforce system integrity for users of the Windows operating system. The NGSCB's approach relied on hardware that was specifically developed and constructed to meet the criteria set by the Trusted computing Group, a consortium focused on creating and advocating for secure computing standards. In addition, the programme aimed to include sophisticated functionalities such as hardware-based system isolation, which creates a safe setting for handling sensitive processes, and encrypted routes for user authentication, guaranteeing the secure management and transmission of user credentials. The NGSCB's dedication to developing a better secure and trustworthy computing experience was emphasised via its diverse approach. It discusses the scepticism and criticism that the NGSCB encountered on the possibility of restrictions being placed on user rights and the eventual reduction in the scope of its goals. The abstract digs deeper into related study fields, such as the management of unstructured data, the creation of safe computing solutions, and the growth of cloud computing. These areas of research are highlighted in order to bring attention to the continuing difficulties of data security, privacy, and effective data management. Furthermore, it presents the possibility of NGSCB capabilities being included into later Microsoft products, which is an indication of the company's impact on succeeding technology. © 2024 IEEE.

关键词： Information management

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A Study of Search User Interface Design based on Hofstede's Six Cultural Dimensions 6

A Study of Search User Interface Design based on Hofstede's ...

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6th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2022

作者： Chessum, Karen Liu, Haiming Frommholz, Ingo School of Computer Science and Technology University of Bedfordshire Luton United Kingdom School of Electronics and Computer Science University of Southampton United Kingdom School of Engineering Computing and Mathematical Sciences University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9789897586095

An information seeker's cultural background could influence their preference for search user interface (UI) design. To study cultural influences Geert Hofstede's cultural dimensions have been applied to website design for a number of years. In this paper, we examine if Hofstede's six cultural dimension can be applied to inform the design of search engine user interfaces. The culturally designed search user interfaces have been evaluated in a study with 148 participants of different cultural backgrounds. The results have been analysed to determine if Hofstede's cultural dimensions are appropriate for understanding users' preferences on search user interface design. Whilst the key findings from the study suggest Hofstede cross-cultural dimensions can be used to model users' preferences on search interface design, further work is still needed for particular cultural dimensions to reinforce the conclusions. Copyright © 2022 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Web Design

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Improved YOLOv5 Algorithm for Intensive Pedestrian Detection 29th

Improved YOLOv5 Algorithm for Intensive Pedestrian Detection

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zhao, Siqi Tian, Ye Hao, Ning Zhou, Jianbo Zhang, Xian College of Computer Science and Technology Harbin Engineering University Harbin150001 China Modeling and Emulation in E-Government National Engineering Laboratory Harbin150001 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China Beijing Institute of Spacecraft Environment Engineering Haidian District Space City Beijing100048 China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou310051 China School of Physics Mathematics and Computing University of Western Australia PerthWA6009 Australia

ISBN: (纸本)9783031449468

Pedestrian detection is an important basic research topic in the field of target detection, which can provide effective information support for public places with large flow density such as shopping malls and scenic spots as well as intelligent security fields. To solve the problem of tracking target loss and low detection and recognition rate caused by pedestrian occlusion or portrait over-lap in pedestrian target detection in a crowded area or scene, an improved YOLOv5 algorithm integrating the attention mechanism was proposed. The relationship between feature channels and spatial information of the feature map was deeply mined by introducing an attention mechanism to further enhance feature extraction of pedestrian target visual area. To improve the feature fusion ability, Bidirectional Feature Pyramid Network (BiFPN) feature pyramid was used to enrich the cross-scale connection mode, preserve the shallow layer characteristics, and improve the detection accuracy. To improve the convergence ability of the model, EIoU was used to replace the original loss function of YOLOv5 to optimize the regression prediction of the anchor, which reduces the training difficulty of the network and improves the detection rate under occlusion. Compared with the general YOLOv5 algorithm, the improved algorithm proposed in this paper has higher accuracy and a lower missing rate in pedestrian detection in crowded areas or scenes, while the real-time performance of the original algorithm is still maintained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Feature extraction

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