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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： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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Learning-Based Directional Improvement Prediction for Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Ye, Yulong Liu, Songbai Zhou, Junwei Lin, Qiuzhen Jiang, Min Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Informatics Xiamen University Fujian China Department of Computing The Hong Kong Polytechnic University Hong Kong China

In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the prediction strategy have shown promising performance for solving dynamic multiobjective optimization problems (DMOPs), as they can predict environmental changing trends in advance. However, most of them follow a regular change pattern and thus their performance is compromised when solving DMOPs with irregular change patterns (e.g., nonlinear correlations). To alleviate this challenge, this article proposes a DMOEA with a learnable prediction for tackling DMOPs. Specifically, a neural network is designed to effectively capture diverse change patterns of the environment. Based on the change patterns learned, a directional improvement prediction (DIP) is developed to guide the evolutionary search toward promising directions in the decision space. In this way, a superior initial population with good convergence and diversity is predicted by DIP, which can be more effective for solving various DMOPs. Comprehensive empirical studies show that the proposed DIP is effective and the proposed algorithm has some advantages over five competitive DMOEAs when solving three commonly used benchmarks and one real-world problem. IEEE

关键词： Neural networks

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Mixture of Knowledge Minigraph Agents for Literature Review Generation 39

Mixture of Knowledge Minigraph Agents for Literature Review ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

ISBN: (纸本)157735897X

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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Correction to: A comprehensive performance evaluation of container migration strategies

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computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, Iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

arXiv

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arXiv 2022年

作者： Alghamdi, Wael Hsu, Hsiang Jeong, Haewon Wang, Hao Winston Michalak, P. Asoodeh, Shahab Calmon, Flavio P. John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Computing and Software McMaster University Canada

We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples. © 2022, CC BY.

关键词： Large dataset

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud computing Diagnosis Digital Health Edge computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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A Robust Anonymous Authentication Scheme for Securing IoT-Enabled Healthcare Services

A Robust Anonymous Authentication Scheme for Securing IoT-En...

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Cyber Security in Networking Conference (CSNet)

作者： Sana Ullah Shanshan Tu Akhtar Badshah Muhammad Waqas Department of Enterprise and Technology Atlantic Technological University Galway Ireland College of Computer Science Beijing University of Technology Beijing China Department of Software Engineering Faculty of Engineering University of Malakand Dir Lower Pakistan Faculty of Engineering and Science School of Computing and Mathematical Sciences University of Greenwich U.K. School of Engineering Edith Cowan University Perth WA Australia

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

Smart wearables and body implanted IoT devices continuously track and transmit health metrics wirelessly to a central controller, such as a personal server, enabling real-time monitoring, proactive treatment and timely assistance. However, the security of IoT-enabled healthcare systems remains a concern, particularly regarding the protection of sensitive patient data. To address the aforementioned issue and enhance security in IoT-enabled healthcare services, we propose a robust anonymous authentication scheme in this work. By leveraging lightweight cryptographic primitives including hash functions and XOR operations coupled with physical unclonable functions (PUFs) and fuzzy extractors, the proposed scheme introduces a novel authentication and key agreement mechanism for secure communication in IoT-enabled healthcare systems. The integration of PUF technology ensure the physical security of resource constrained implantable medical devices (IMDs) against various attacks including device physical capture and impersonation attacks that compromise the IoT-enabled healthcare operations. A thorough security analysis demonstrated the robustness and resilience of the proposed scheme against various active and pas-sive security attacks, thus ensure the integrity and confidentiality of sensitive healthcare data. Finally, comparative analysis of the proposed scheme with other state-of-the-art highlighted that our scheme outperformed other approaches in terms of providing security and additional features, positioning it as a comprehensive solution for authentication in IoT-enabled healthcare services.

关键词： Hash functions Medical devices Authentication Medical services Physical unclonable function Security Internet of Things Servers Wearable devices Resilience

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Optimizing Cloud Load Balancing with a Hybrid Bio-Inspired Approach: Enhancing Performance and Scalability

Optimizing Cloud Load Balancing with a Hybrid Bio-Inspired A...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Software Engineering Department College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Managing resources efficiently is critical in modern cloud computing environments due to the growing complexity and increasing demand for cloud services. Load balancing, a key aspect of cloud resource management, ensures optimal distribution of workloads across servers to improve system performance, minimize response times, and increase resource utilization. However, many existing load-balancing algorithms suffer from challenges such as premature convergence, high computational complexity, and inefficiency in handling dynamic and heterogeneous workloads. To address these challenges, this research proposes a Hybrid Bio-Inspired Approach that combines the Salp Swarm Algorithm (SSA) and Elephant Herding Optimization (EHO). The motivation behind this research is to develop a scalable and adaptable load-balancing mechanism capable of improving performance and handling fluctuating workloads effectively. The main objective of this research is to enhance cloud load balancing by leveraging the unique strengths of SSA and EHO, aiming for better exploration and exploitation balance, improved resource utilization, and minimized response times. The proposed hybrid approach integrates SSA's efficient search capabilities with EHO's natural clustering and resource optimization features, creating a robust framework for load balancing in cloud systems. The methodology is implemented using the CloudSim simulator, a widely used platform for modeling and simulating cloud environments. The performance of the proposed hybrid algorithm is evaluated against traditional and other bio-inspired techniques. Results from the simulations demonstrate that the hybrid SSA-EHO approach outperforms existing algorithms in terms of load distribution, response time, scalability, and resource utilization, highlighting its potential as an efficient solution for cloud resource management.

关键词： Cloud computing Energy consumption Heuristic algorithms Scalability Clustering algorithms Load management Resource management Time factors Optimization Load modeling

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design

Application of Explicit Group Sparse Projection in Optimizin...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodanda Rami Reddy Manukonda Hemanth Swamy R. Sankar Chidambaranathan C. M. Haribabu.k Saranya. Department of Consulting IBM Senior Software Engineer Motorola Solutions Department of EEE Chennai Institute of Technology Chennai Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE MLR Institute of Technology Hyderabad Department of AI & DS Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The growing adoption of 5G networks in diverse domains like IoT, smart cities, healthcare, and industrial automation necessitates advanced optimization techniques. While existing approaches have shown promise, they are often limited by computational intensity, lack of adaptability, and specific focus on geometric constraints. This paper presents EGSP-5G, a novel methodology employing Explicit Group Sparse Projection (EGSP) for 5G network resource allocation and topology design. Its capability to achieve targeted sparsity levels allows for efficient utilization of network resources, thereby making it a versatile and powerful tool for various optimization tasks. Remarkably, EGSP-5G achieved a resource utilization rate of 90.02% and a throughput of 110.32 in networks comprising 1000 nodes. It stands as a robust and versatile solution for optimizing 5G networks across diverse application domains.

关键词： Technological innovation Automation 5G mobile communication Network topology Smart cities Medical services Throughput Topology Resource management Optimization

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