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IEEE International Conference on Systems, Man and Cybernetics

作者： Song Yu Ning Hao Jun Long Limin Liu School of Computer Science and Engineering Central South University Changsha china School of Computer Science at Central South University Changsha china The Big Data Institute Central South University Changsha china

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Addressing the issue of inadequate Quality of Service (QoS) in inter-satellite communication under high load conditions due to limited network resources and uneven user distribution in a double-layer satellite constellation, this paper proposes a routing algorithm that satisfies multiple QoS constraints based on an enhanced NSGA-II algorithm. By utilizing the NSGA-II genetic algorithm, a chromosome path encoding scheme is constructed, treating the multi-QoS constraints of paths as a minimization multi-objective optimization within the NSGA-II algorithm. Moreover, elite solution preservation strategies are fine-tuned to procure a broader and more efficacious optimal Pareto front solution ensemble. This ensures the derivation of optimal pathways that meet multiple Quality of Service (QoS) constraints effectively. Simulation results demonstrate that the proposed algorithm effectively achieves load balancing in satellite network communication. Furthermore, significant improvements are observed in meeting various QoS requirements such as end-to-end latency, latency jitter, packet loss rate, and remaining bandwidth.

关键词： Satellites Simulation Packet loss Quality of service Jitter Routing Minimization Encoding Optimization Genetic algorithms

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Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning 25

Generalizing Personalized Federated Graph Augmentation via M...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Liang Zhang Tao Long Yang Liu Lei Zhang Laizhong Cui Qingjiang Shi Shenzhen Research Institute of Big Data Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China Data Science Research Center Dukekunshan University Kunshan Jiangsu China School of Software Engineering Tongji University Yangpu Shanghai China

ISBN: (纸本)9798400712456

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

关键词： federated learning

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Network Security Situation Assessment Method Based on Absorbing Markov Chain

Network Security Situation Assessment Method Based on Absorb...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Gao, Hongbin Wang, Shangxing Zhang, Hongbin Liu, Bin Zhao, Dongmei Liu, Zhen Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation. © 2022 IEEE.

关键词： Network security

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Large Model for Small data: Foundation Model for Cross-Modal RF Human Activity Recognition

arXiv

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arXiv 2024年

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi’s generalizability across various environments. © 2024, CC BY.

关键词： Zero-shot learning

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How graph neural networks learn: lessons from training dynamics 24

How graph neural networks learn: lessons from training dynam...

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Proceedings of the 41st International Conference on Machine Learning

作者： Chenxiao Yang Qitian Wu David Wipf Ruoyu Sun Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Amazon Web Services School of Data Science The Chinese University of Hong Kong Shenzhen and Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster.

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Explainable Artificial Intelligence in Malignant Lymphoma Classification: Optimized DenseNet121 Deep Learning Approach with Particle Swarm Optimization and Genetic Algorithm.

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IEEE Access 2025年 13卷 98639-98655页

作者： Elwahsh, Haitham Bakhiet, Ali Alirr, Omar Ibrahim Khalifa, Tarek Alsabaan, Maazen Ibrahem, Mohamed I. El-Shafeiy, Engy Applied Science Private University Faculty of Information Technology Amman 11931 Jordan Kafrelsheikh University Faculty of Computers and Information Department of Computer Science Kafrelsheikh 33516 Egypt Badya University Faculty of Computing and Data Science Egypt American University of the Middle East College of Engineering and Technology Egaila 54200 Kuwait King Saud University College of Computer and Information Sciences Department of Computer Engineering P.O.Box 51178 Riyadh 11543 Saudi Arabia Augusta University School of Computer and Cyber Sciences Augusta 30912 GA United States University of Sadat City Faculty of Computers and Artificial Intelligence Department of Computer Science Sadat City 32897 Egypt

One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence. © 2013 IEEE.

关键词： Artificial intelligent deep learning genetic algorithm malignant lymphoma optimized DenseNet121 particle swarm optimization

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Sample Complexity of Posted Pricing for a Single Item

arXiv

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arXiv 2024年

作者： Jin, Billy Kesselheim, Thomas Ma, Will Singla, Sahil University of Chicago Booth School of Business United States University of Bonn Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence Germany Columbia University Graduate School of Business Data Science Institute United States Georgia Tech School of Computer Science United States

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers’ value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Sales

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Regulatory Compliance Verification: A Privacy Preserving Approach

Regulatory Compliance Verification: A Privacy Preserving App...

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Cyber Security in Networking Conference (CSNet)

作者： Massimo Morello Petri Sainio Mohammed Alshawki Department of Computer Algebra Eotvos Lorand University Budapest Hungary European Central Bank Frankfurt Germany Department of Computing University of Turku Finland Institute for Data Science Cloud Computing and IT Security Furtwangen Germany

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

During the regulatory compliance verification, the verifier may need to gain access to private information that can present risks to the privacy of the entities being verified. Therefore, while ensuring that entities are compliant with the reg-ulations, such as GDPR, the regulatory compliance verification process need to safeguard the privacy of those entities. This paper proposes a privacy preserving regulatory compliance verification protocol, which has been integrated and implemented in a use case to verify the compliance with the article 32 of the GDPR. It provides a regulatory verification protocol, based on the attribute verification protocol, that reveals no private information of the entity being verified, other than the fact that it is compliant. Our results showed that the proposed protocol can efficiently verify the regulatory compliance of an entity by an external verifier.

关键词： Privacy data privacy Protocols NIST Regulation Security Computer crime Standards

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A Preliminary Study on Education and Teaching Based on the Concept of Metaverse-Take "Information Technology" as an Example 8

A Preliminary Study on Education and Teaching Based on the C...

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8th International Conference on Fuzzy Systems and data Mining, FSDM 2022

作者： Liu, Xiaoli Fan, Zuzhi Gu, Shiqian Peng, Shujuan Wang, Su College of Information Science and Technology Jinan University Guangzhou China School of Public Administration Jinan University Guangzhou China Education Research Institute Xiamen University Xiamen China School of Computer Science and Technology Huaqiao University China School of Data Science and Engineering East China Normal University China

ISBN: (纸本)9781643683461

Based on the concept of the educational Metaverse, this paper combs the theoretical basis, development process and application scenario of the Metaverse. For the course of information technology, the blended teaching practice based on the concept of Metaverse is carried out, including the design of teaching process, the integration of teaching platform, the construction of resources and the setting of evaluation method. We have designed a teaching scheme based on the concept of Metaverse, including the integration of online teaching platform, resource construction and deployment, assessment design and so on. The teaching has been implemented in the advertising major from 2019 to 2021, and it is found that the teaching effect has been significantly improved. © 2022 The authors and IOS Press.

关键词： Teaching

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Advancing Healthcare IoT: Blockchain and Federated Learning Integration for Enhanced Security and Insights

Advancing Healthcare IoT: Blockchain and Federated Learning ...

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2024 International Conference on Communication, Computer sciences and Engineering, IC3SE 2024

作者： Malik, Rida Ur-Rehaman, Atta Razzaq, Hamza Bhatt, Chandradeep Kaushik, Keshav Khan, Inam Ullah Riphah Institute Of System Engineering Department Of Information Security Islamabad Pakistan Riphah Institute Of System Engineering Department Of Data Science Islamabad Pakistan Graphic Era Hill University Department Of Cse Uttarakhand India Graphic Era Deemed To Be University Uttarakhand Dehradun India School Of Computer Science University Of Petroleum And Energy Studies Dehradun India Seas Isra University Department Of Electronic Engineering Islamabad Campus Pakistan

ISBN: (纸本)9798350366846

Fast-growing IoT devices in healthcare have ushered in a new era of data-driven patient care and treatment. These gadgets capture too much sensitive medical data, a major issue. We need to boost data analysis, security, and privacy. This research uses cutting-edge concepts like federated learning and blockchain technology to improve Healthcare IoT analytics and security. This study examines blockchain and federated learning's numerous integration aspects. It begins with data security, employing blockchain's immutability and decentralization to prevent manipulation. Federation learning was utilized to solve privacy concerns while adhering to tight healthcare data security regulations, providing strong data analysis without releasing raw, patient-specific information. This study examines how federated learning and blockchain technologies enhance healthcare IoT systems' analytics. This connection allows safe, collaborative analysis among healthcare professionals and devices, offering more insights for research, diagnostics, and tailored patient care. How to integrate these cutting-edge technologies into healthcare IoT networks is also explored. © 2024 IEEE.

关键词： Diagnosis

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