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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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CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge computing Networks

arXiv

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arXiv 2025年

作者： Zhang, Ye Shen, Zhishu Jiang, Dawen Liu, Xiangrui Zheng, Qiushi Jin, Jiong School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an emerging paradigm that leverages the execution results of previous tasks to enhance the utilization of computational resources. While this paradigm has been extensively studied in terrestrial networks with abundant computing and caching resources, such as named data networking (NDN), it is essential to develop a framework appropriate for resource-constrained satellite networks, which are expected to have longer task completion time. In this paper, we propose CCRSat, a collaborative computation reuse framework for satellite edge computing networks. CCRSat initially implements local computation reuse on an independent satellite, utilizing a satellite reuse status (SRS) to assess the efficiency of computation reuse. Additionally, an inter-satellite computation reuse algorithm is introduced, which utilizes the collaborative sharing of similarity in previously processed data among multiple satellites. The evaluation results tested on real-world datasets demonstrate that, compared to comparative scenarios, our proposed CCRSat can significantly reduce task completion time by up to 62.1% and computational resource consumption by up to 28.8%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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On Efficient Variants of Segment Anything Model: A Survey

arXiv

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arXiv 2024年

作者： Sun, Xiaorui Liu, Jun Shen, Heng Tao Zhu, Xiaofeng Hu, Ping School of Computer Science and Engineering UESTC China School of Computing and Communications Lancaster University United Kingdom School of Computer Science and Technology Tongji University China

The Segment Anything Model (SAM) is a foundational model for image segmentation tasks, known for its strong generalization across diverse applications. However, its impressive performance comes with significant computational and resource demands, making it challenging to deploy in resource-limited environments such as mobile devices. To address this, a variety of SAM variants have been proposed to enhance efficiency without sacrificing accuracy. This survey provides the first comprehensive review of these efficient SAM variants. We begin by exploring the motivations driving this research. We then present core techniques used in SAM and model acceleration. This is followed by an in-depth analysis of various acceleration strategies, categorized by approach. Finally, we offer a unified and extensive evaluation of these methods, assessing their efficiency and accuracy on representative benchmarks, and providing a clear comparison of their overall performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Exploration on IoT based Edge Cloud computing Techniques for Improving the Patient Information Management System

Exploration on IoT based Edge Cloud Computing Techniques for...

引用

2022 International Conference on Edge computing and Applications, ICECAA 2022

作者： Saranya, S.S. Fatima, N. Sabiyath Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattakulathur Campus Tamil Nadu Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Institute of Science and Technology Tamil Nadu Vandalur Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Clinics around the globe are adopting newer, highly advanced technologies. The Internet of Things (IoT) has emerged due to advancements in information and communication technology. Integration of the IoT with accessible medical devices, and patients and their surroundings has opened up new possibilities for data collecting and more intelligent decisions in recent years. IoT technologies benefithealthcare professionals and patients in today's medical context because they are used in several treatment domains (like real-time monitoring, patient information and healthcare management). Diverse and distributed devices will gather, analyse, and transmit real-time medical data to open clouds in the upcoming world of IoT for healthcare, allowing for the accumulation, piling, and analysis of elementary data streams in many new procedures and the activation of context-dependent intimations. Healthmonitoring and remote treatment are possible with an IoT paradigm that uses sensors (environmental, wearable, implanted) distributed throughout the local *** been critical to create new technology for storing large amounts of clinical data in recent years. Cloud storage has become enormous, and it is managed by merging IoT and Edge computing (EC) technologies. According to recent research, the integration of IoT and the cloud has a considerable impact on remote healthcare. This survey report focuses on howIoT devices with EC can enhance people's health while guaranteeing that medicines and medicine delivery are error-free. This research will act as a source and reference of knowledge regarding smarthealthcare for healthcare professionals, researchers, and others interested in learning more about IoT with EC-based healthcare applications. © 2022 IEEE.

关键词： Internet of things

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Equivalence Checking for Flow-Based computing using Iterative SAT Solving 24

Equivalence Checking for Flow-Based Computing using Iterativ...

引用

43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Thijssen, Sven Rashed, Muhammad Rashedul Haq Ahmed, Md Rubel Singireddy, Suraj Jha, Sumit Kumar Ewetz, Rickard Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9798400710773

Processing in-memory is projected to shatter the von Neumann bottleneck and enable acceleration of data-intensive applications. Flow-based computing is an efficient in-memory computing paradigm for accelerating the execution of Boolean logic. While recent synthesis algorithms can map complex functions into flow-based computing circuits, the functional correctness cannot be verified using state-of-the-art equivalence checking techniques. The challenge is that non-volatile memory devices are intrinsically bi-directional, which introduces cycles in the computational graph. These cycles break traditional equivalence checking methods that are based on SAT formulations. In this paper, we propose a framework for equivalence checking of flow-based computing circuits that is called FlowSAT. The framework captures each circuit using an undirected computational graph. The key idea of FlowSAT is to introduce helper variables, in the form of arrows, that dynamically convert the undirected graph into a directed graph. This facilitates equivalence checking to be performed using traditional SAT formulations. However, it is prohibitively expensive to ban all possible cycles using arrow variables. Therefore, we propose to eliminate cycles by iteratively adding constraints to the SAT formulation. Our experimental evaluation demonstrates that FlowSAT is up to an order of magnitude faster than state-of-the-art methods. The framework is capable of verifying all 20/20 benchmark circuits, while the previous state-of-the-art technique is only capable of verifying 12/20 circuits within a time limit of one hour. © 2024 Copyright is held by the owner/author(s).

关键词： Data flow graphs

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Researches Advanced in Data-To-Text Generation Based on Neural Networks 2

Researches Advanced in Data-To-Text Generation Based on Neur...

引用

2nd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2022

作者： Wang, Shengyu Wang, Yanping School of Computer Science Southwest Petroleum University Chengdu China School of Computing North China Institute of Aerospace Engineering Langfang China

ISBN: (纸本)9781510657717

Data-to-text generation has always been a research hotspot in multimodal machine learning. Given the structured data such as a list of characteristics of a person or statistical data from an exam, the data-to-text generation aims to automatically generate smooth, true and accurate text which can describe the input data. Thanks to the rapid development of convolutional neural networks, the text generation models based on deep learning have made great breakthroughs in performance, but still face many challenges which mainly include confusion of the output location of different information, generation of non-existent content, duplication of informationand data sparseness. In this paper, based on in-depth literature survey, we introduce the representative algorithms from the aspects of improving text evaluation quality, combining deep learning and knowledge, adapting to various expressions and aligning text and structured data. We also summarize the existing problems and prospect the possible future development direction in the data-to-text generation research field. © 2022 SPIE. All rights reserved.

关键词： Deep learning

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Combinatorial Bat Optimization for WSN with Hoover Index Clustering

Combinatorial Bat Optimization for WSN with Hoover Index Clu...

引用

2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Gurupriya, M. Vasavi, C.S. Ramasamy, Gayathri Karthikeyan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru India School of Electronics Engineering Vellore Institute of Technology Department of Embedded Technology Vellore India

ISBN: (纸本)9798350319088

The rise in popularity of wireless sensor networks (WSNs) has garnered a lot of attention and offers a variety of manufacturing industries and residential day to day applications that acquire data from sensors mounted within homogeneous as well as heterogeneous networks. Battery-operated -nodes have limited energy and resources. Since WSNs are more vulnerable to assaults because they are dynamic. Energy utilization of a sensor node is crucial for effective clustering and data transmission. Nodes are clustered using the Linde-Buzo-Gray Algorithm (LBGA), and the cluster head (CH) is chosen using the LEACH protocol. Route identification and secondary CH is selected using Combinatorial Bat Optimization Algorithm (CBOA) thereby, data loss is minimized during CH failure. To validate the performance measures of energy consumption, throughput and packet delivery ratio (PDR), various experiments were carried out. The innovative approach cuts energy consumption by 11 % and elevates packet delivery ratio by 5% when benchmarked against existing approaches. © 2023 IEEE.

关键词： Energy utilization

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Fast Image matting based on matting pyramids and surrogate models 2

Fast Image matting based on matting pyramids and surrogate m...

引用

2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control, RAIIC 2023

作者： Liang, Yihui Gou, Hongshan Liu, Guisong Huang, Sibo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Zhongshan China Guiyang Institute of Humanities and Technology College of Big Data and Information Engineering Guiyang China Southwestern University of Finance and Economics School of Computing and Artificial Intelligence Chengdu China Huizhou University Network and Information Centre Huizhou China

ISBN: (纸本)9798350328004

Image matting is a foundation task in the field of computer vision. Pixel-pair-optimization-based image matting methods have attracted lots of attention due to their distinct advantages on parallelization and handling spatially disconnected foregrounds. However, these methods struggle when the computing time is limited. To address this issue, this study presents the image matting method based on matting pyramid and surrogate model (IMMPSM). IMMPSM speeds up the process of pixel pair optimization by replaces the evolutionary algorithm in the pyramid matting framework, which consumes a lot of computing time, with the efficient surrogate-model-based method. The constructed surrogate model is reused by the presented surrogate model share strategy to save computing time. Specifically, the constructed surrogate model is shared with the pixel pair optimization subproblem adjacent to current subproblem avoiding repeated computation on the surrogate model construction. Extensive experimental results show that IMMPSM can provide acceptable alpha mattes even under limited computing time. © 2023 IEEE.

关键词： Pixels

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Deep Reinforcement Learning for Low-Cost Humanoid Robot Soccer Players: Dynamic Skills and Efficient Transfer 7

Deep Reinforcement Learning for Low-Cost Humanoid Robot Socc...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Nagaraju, Animoni Kumar, M. Guru Vimal Devi, Y.Rama Basi Reddy, A. Kumar, Marrapu Aswini Kiran, Ajmeera Malla Reddy Institute of Technology And Science Department of Computer Science And Engineering Maisammaguda Telangana Hyderabad500100 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India Chaitanya Bharathi Institute of Technology Department of Aiml Gandipet Telangana Hyderabad500075 India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Centurion University of Technology And Management Department of Computer Science And Engineering Andhra Pradesh Vizianagaram India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

This research focused on whether Deep Reinforcement Learning (Deep RL) can enable a cheap, small humanoid robot to develop complex and secure movement skills, which can then be integrated into intricate behavioral strategies in dynamic environments. The humanoid robot, equipped with 20 actuated joints, was trained using Deep RL to participate in a skewed one-versus-one (1v1) soccer match. Initially, we taught the robot to separate skills before combining them in a self-play scenario. The final policy demonstrated robust and dynamic movement characteristics such as rapid fall recovery, walking, turning, kicking, and more, exceeding the logical expectations for the robot. The robot seamlessly transitioned between these skills in a stable and effective manner. Moreover, the agents developed an understanding of the game's strategy, including predicting ball trajectories and countering opponent moves. Surprisingly, a limited number of straightforward rewards led to a wide range of behaviors. The agents underwent simulated training before being transferred to real robots. Despite unmodeled effects and differences between robot instances, successful transfer was assisted by using a combination of high-voltage control, concentrated dynamics selection, and training with disturbances in simulation. By introducing small hardware modifications and applying basic behavior regularization during training, the robots achieved secure maneuvers while maintaining dynamic and agile performance. Notably, the agents in the experiments demonstrated significant improvements over a programmed baseline, walking 156% faster, standing up 63% faster, and kicking 24% quicker, all while effectively combining their skills to achieve long-term goals. © 2023 IEEE.

关键词： Anthropomorphic robots

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Automated Detection and Classification of Cotton Leaf Diseases: A computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

引用

Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Swastick Ganguly Jathin S M Mohak M Sanjay BM Somashekhar N Shobharani Department of Computer Science Amrita School of Computing Mysuru Campus Dept of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru Campus

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability

关键词： Deep learning Training Plant diseases Pathogens Accuracy Computational modeling Crops

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