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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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computer Modeling in engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks

Energy-Efficient Client Sampling for Federated Learning in H...

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IEEE International Conference on Communications (ICC)

作者： Jian Tang Xiuhua Li Hui Li Min Xiong Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Training Energy consumption Multi-access edge computing Federated learning Computational modeling Simulation Sampling methods

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ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks 24

ReForm-Eval: Evaluating Large Vision Language Models via Uni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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Federated privacy computing based on oblivious transfer in financial applications: A case study in credit risk management 3

Federated privacy computing based on oblivious transfer in f...

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3rd International Symposium on computer Applications and Information Systems, ISCAIS 2024

作者： Liang, Jian Quan, Fan Yang, Suyi Tao, Ping Ge, Fei Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China School of Information and Communication Studies University College Dublin Dublin Ireland Department of Statistics and Data Science University of California Los Angeles United States

ISBN: (纸本)9781510681316

Invoice data is critical and highly sensitive for enterprises, yet it stands as one of the most extensively used data categories in the financial credit sector. Participants in the credit market, engaged in corporate lending activities, face the dual challenge of ensuring the security of enterprise invoice data and safeguarding the integrity of institutional rules. Traditional data computation and encryption present a dilemma, as two encrypted datasets cannot directly engage in calculations, and two datasets participating in calculations must be in plaintext. This necessitates the convergence of enterprise data and institutional rules for computation. Consequently, two sets of data must be exposed to one of the parties or a mutually trusted third party. In practice, identifying a genuinely trusted third party proves to be a formidable task. This paper introduces a federated privacy computing method based on an oblivious transfer algorithm in the context of risk control admission rules within the credit risk management field. This approach enables both parties holding data to determine admission rules without disclosing their own data. © 2024 SPIE.

关键词： Risk management

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2 18

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Murugan, S. Palani Peter, M. Chandrakumar Raj, P. Anand Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Tamil Nadu Nagapattinam India Periyar Maniammai Institute of Science & Technology Department of Software Engineering Tamil Nadu Thanjavur India E.G.S. Pillay Engineering College Department of Computer Science and Business Systems Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AOMDV Protocol Intelligent Gen Algorithm MANET Network Simulator NS-2

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An empirical IoT and cloud-based customizable healthcare surveillance system

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5317-5323页

作者： Meti, Subhash Razauddin, S. Nallakumar, R. Mansingh, P. M. Benson Sameen, Aws Zuhair Pandey, Sakshi Bhatt, Sanjeev Kumar Jayabalan, Bhuvana Department of Electronics and Communication Engineering SKSVMACET Lakshmeshwar Karnataka Gadag India College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India College of Medical Techniques Al-Farahidi University Baghdad Iraq Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Software Architect and Research Scholar Digital Innovation College PDM University Bahadurgarh India Department of Computer Science and Information Technology JAIN (Deemed-to-Be University) Karnataka Bengaluru India

In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： BPNN Cloud computing Healthcare surveillance IoT Particle swarm intelligence (PSI)

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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A Survey on Evolutionary Feature Selection in Multi-label Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world problems, MLC has become a popular area of research. These datasets frequently contain irrelevant, redundant, or noisy features, highlighting the importance of feature selection in MLC tasks. As a result, numerous multi-label feature selection (MLFS) approaches have been introduced in the literature. Given their effective search capabilities, evolutionary computation (EC) techniques have been adopted to develop MLFS approaches. However, there is a notable absence of a comprehensive survey dedicated to EC-based MLFS approaches to MLC tasks. Although few attempts have been made to address this gap, they do not provide detailed discussions on EC-based approaches. They also tend to overlook the strengths and weaknesses of existing approaches, especially regarding search, optimization, and evaluation processes. This paper aims to fill this gap by presenting a comprehensive survey of EC-based MLFS approaches, focusing on the latest advancements, current challenges, and future directions. To be specific, we categorize EC-based MLFS approaches based on different criteria and provide detailed descriptions for each category. © 1997-2012 IEEE.

关键词： Evolutionary Computation Multi-label Classification Multi-label Feature Selection Multi-Objective Optimization

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Multiobjective Many-Tasking Evolutionary Optimization using Diversified Gaussian-Based Knowledge Transfer

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IEEE Transactions on Evolutionary Computation 2024年

作者： Lin, Qiuzhen Wang, Qianhui Chen, Baihao Ye, Yulong Ma, Lijia Tan, Kay Chen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong

Multiobjective multitasking evolutionary algorithms have shown promising performance for tackling a set of multiobjective optimization tasks simultaneously, as the optimization experience gained within one task can be transferred to accelerate the solving of others. However, most studies only select similar transfer tasks based on their designed metrics, which become less efficient when tackling a large number of optimization tasks, as their transferred knowledge may be insufficiently diversified. To alleviate this issue, this article proposes a Multiobjective Many-Tasking Evolutionary Algorithm using Diversified Gaussian-based knowledge Transfer, named MMaTEA-DGT. In this algorithm, a diversified transfer selection strategy is presented to choose a number of similar and complementary source tasks for knowledge transfer. Then, based on the above diversified source tasks, a Gaussian-based transfer strategy is designed to transfer their various optimization knowledge. In this way, MMaTEA-DGT is more effective in transferring optimization knowledge to speed up the solving of many tasks. Experimental studies on both the benchmark suites and a real-world dynamic vaccine prioritization problem have indicated the superiority of MMaTEA-DGT over some recently proposed multiobjective many-tasking evolutionary algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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