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TOWARDS CONTINUOUS REUSE OF GRAPH MODELS VIA HOLISTIC MEMORY DIVERSIFICATION

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arXiv 2024年

作者： Qiao, Ziyue Xiao, Junren Sun, Qingqiang Xiao, Meng Luo, Xiao Xiong, Hui School of Computing and Information Technology Great Bay University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China School of Engineering Great Bay University China Computer Network Information Center Chinese Academy of Sciences China Department of Computer Science University of California Los Angeles United States

This paper addresses the challenge of incremental learning in growing graphs with increasingly complex tasks. The goal is to continuously train a graph model to handle new tasks while retaining proficiency in previous tasks via memory replay. Existing methods usually overlook the importance of memory diversity, limiting in selecting high-quality memory from previous tasks and remembering broad previous knowledge within the scarce memory on graphs. To address that, we introduce a novel holistic Diversified Memory Selection and Generation (DMSG) framework for incremental learning in graphs, which first introduces a buffer selection strategy that considers both intra-class and inter-class diversities, employing an efficient greedy algorithm for sampling representative training nodes from graphs into memory buffers after learning each new task. Then, to adequately rememorize the knowledge preserved in the memory buffer when learning new tasks, a diversified memory generation replay method is introduced. This method utilizes a variational layer to generate the distribution of buffer node embeddings and sample synthesized ones for replaying. Furthermore, an adversarial variational embedding learning method and a reconstruction-based decoder are proposed to maintain the integrity and consolidate the generalization of the synthesized node embeddings, respectively. Extensive experimental results on publicly accessible datasets demonstrate the superiority of DMSG over state-of-the-art methods. The code and data can be found in https://***/joe817/DMSG. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

Condition assessment of 150 kV outdoor composite insulators following a vegetation fire event

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Condition assessment of 150 kV outdoor composite insulators ...

23rd International Symposium on High Voltage engineering (ISH 2023)

作者： Nikolaos C. Mavrikakis Kiriakos Siderakis Kiriakos Mouratis Emmanouel Koudoumas Section of Crete-Rhodes Transmission System Islands Network Operation Department Hellenic Electricity Distribution Network Operator S.A Heraklion Greece Center of Materials Technology and Photonics Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece

ISBN: (数字)9781839539923

In electric power transmission and distribution systems, the use of overhead lines instead of underground cables is preferred by power utilities mainly due to the lower construction cost and the easier maintenance access. On the other hand the equipment used in overhead lines is stressed by several factors, such as weather conditions, lightning, vegetation fires, etc. In particular, vegetation fires under the overhead transmission lines could have catastrophic results for the installed equipment and can cause long-lasting electricity interruptions. In this study, condition assessment results are presented for eighteen 150 kV composite insulators exposed to a forest fire event, occurred in the August 2021, in the island of Rhodes, Greece. The evaluation of the fire effects on composite insulators was performed by employing several macroscopic and microscopic diagnostic techniques including visual inspection, hydrophobicity classification, Fourier Transform Infrared spectroscopy (FTIR) and optical microscopy analysis.

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Privacy-Preserving Quantum Two-Party Geometric Intersection

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, James C.N. Yu, Wen-Bin Chi, Lian-Hua Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan Department of Computer Science and Information Technology La Trobe University VIC3086 Australia

Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB ) as the set of numbered grids SA (SB ), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB ) is firstly utilized to encode the private elements of SA (a0, a1,, aM1) (SB (b0,b1,bN1)) into the quantum states, and then the oracle operation Of is applied to obtain a new quantum state which includes the XOR results between each element of SA and SB . Finally, the quantum counting is introduced to get the amount (t ) of the states ai bj equaling to 0, and the intersection result can be obtained by judging t 0 or not. Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity. © 2023, CC BY.

关键词： Computational geometry

HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

Pioneering Deterministic Scheduling and network Structure Optimization for Time-Critical Computing Tasks in Industrial IoT

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arXiv 2024年

作者： Hu, Yujiao Zhu, Yining Zhang, Huayu Pan, Yan Jia, Qingmin Xie, Renchao Yang, Gang Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi’an710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

The Industrial Internet of Things (IIoT) has become a critical technology to accelerate the process of digital and intelligent transformation of industries. As the cooperative relationship between smart devices in IIoT becomes more complex, getting deterministic responses of IIoT periodic time-critical computing tasks becomes a crucial and nontrivial problem. However, few current works in cloud/edge/fog computing focus on this problem. This paper is a pioneer to explore the deterministic scheduling and network structural optimization problems for IIoT periodic time-critical computing tasks. We first formulate the two problems and derive theorems to help quickly identify computation and network resource sharing conflicts. Based on this, we propose a deterministic scheduling algorithm, IIoTBroker, which realizes deterministic response for each IIoT task by optimizing the fine-grained computation and network resources allocations, and a network optimization algorithm, IIoTDeployer, providing a cost-effective structural upgrade solution for existing IIoT networks. Our methods are illustrated to be cost-friendly, scalable, and deterministic response guaranteed with low computation cost from our simulation results. © 2024, CC BY-NC-ND.

关键词： Internet of things

FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation

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arXiv 2024年

作者： Pan, Quyang Sun, Sheng Wu, Zhiyuan Wang, Yuwei Liu, Min Gao, Bo Wang, Jingyuan The State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The School of Computer and Information Technology The Engineering Research Center of Network Management Technology for High-Speed Railway Ministry of Education Beijing Jiaotong University Beijing China The School of Computer Science and Engineering Beihang Unversity Beijing China

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges stemming from device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication conditions. Knowledge Cache-driven Federated Learning (FedCache) is a promising architecture that enables communication-efficient and heterogeneous-aware collaborative training in edge computing scenarios. However, previous work is limited by the intrinsic nature of logits-based interactions, leading to performance bottlenecks due to the poor richness of exchanged information for on-device model optimization. To tackle this issue, we introduce FedCache 2.0, a novel personalized FEL architecture that enhances the exchange of optimization insights while delivering state-of-the-art performance with efficient communication. FedCache 2.0 incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache, allowing devices to periodically download and utilize personalized knowledge for local model optimization. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) FedCache 2.0 significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) FedCache 2.0 can train splendid personalized on-device models with at least ×28.6 improvement in communication efficiency. Our code is available at https://***/poppanda/FedCache2. 0. Copyright © 2

关键词： Adversarial machine learning

Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

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Comprehensive Feature Processing Based on Attention Mechanis...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

CoRaiS: Lightweight Real-Time Scheduler for Multi-Edge Cooperative Computing

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arXiv 2024年

作者： Hu, Yujiao Jia, Qingmin Chen, Jinchao Yao, Yuan Pan, Yan Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China The School of Computer Science Northwestern Polytechnical University Xi'An710029 China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology China The State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Multi-edge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multi-edge computing system particularly complicated. This paper first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Secondly, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS, is proposed. CoRaiS embeds the real-time states of multi-edge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that CoRaiS can make a high-quality scheduling decision in real time, and can be generalized to other multi-edge computing system, regardless of system scales. Characteristic validation also demonstrates that CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling. © 2024, CC BY-NC-ND.

关键词： Edge computing

G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

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arXiv 2024年

作者： Yang, Haoxin Xu, Xuemiao Xu, Cheng Zhang, Huaidong Qin, Jing Wang, Yi Heng, Pheng-Ann He, Shengfeng The School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The School of Future Technology South China University of Technology Guangzhou China The School of Computer Science and Network Security Dongguan University of Technology Dongguan China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Computing and Information Systems Singapore Management University Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face. Copyright © 2024, The Authors. All rights reserved.

关键词： Anonymity