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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Qi Zhang Yan Liu Yuheng Cai Zhong Yuan Network Security Research Institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781665447300

The network group detection has a large number of application researches and has achieved certain results in network security, IP entity location and load balancing. However, due to the large scale of the network topology graph and the large number of terminal nodes, most of the existing overlapping group detection algorithms are relatively complex, and the data size that can be processed is limited, and cannot be directly applied to the network topology. This paper proposes a network topology overlapping group detection algorithm based on h-core pruning. This algorithm decomposes the network into smaller strong connected components, reduces the interference of noisy nodes to overlapping group discovery, and more efficiently detect the largest k-plex group on the target network. A large number of experiments show that the method in this paper is effective. Compared with the full-enum algorithm, our method increases the network size that can be handled by the largest k-plex group by two orders of magnitude, and can detect overlapping groups in the network topology more efficiently.

关键词： network topology Signal processing algorithms Interference Signal processing Load management Topology Noise measurement

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Research on data security sharing mechanism of power internet of things based on blockchain 9

Research on data security sharing mechanism of power interne...

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9th IEEE Joint International information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Yang, Huiting Bai, Yunxiao Zou, Zhenwan Zhang, Qiang Wang, Bin Yang, Ruxia State Grid Xinjiang Electric Power Research Institute Urumqi China Global Energy Interconnection Research Institute Co. Ltd Nanjing China State Grid Key Laboratory of Information and Network Security Nanjing China

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods. © 2020 IEEE.

关键词： Internet of things

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Uncertainty-Aware Relational Graph Neural network for Few-Shot Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Li, Qian Guo, Shu Chen, Yingjia Ji, Cheng Sheng, Jiawei Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The State Key Laboratory of Software Development Environment Beihang University Beijing100083 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100083 China School of Mathematics Edinburgh University Edinburgh United Kingdom Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China

Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better understanding of the limited data by learning representations under Gaussian distribution. Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution. Further, to better integrate the neighbors with uncertainty characteristics for entity features, we design an uncertainty-aware relational graph neural network (UR-GNN) to conduct convolution operations between the Gaussian distributions. Then, multiple random samplings are conducted for reference triples within the Gaussian distribution to generate smooth reference representations during the optimization. The final completion score for each query instance is measured by the designed uncertainty optimization to make our approach more robust to the noises in few-shot scenarios. Experimental results show that our approach achieves excellent performance on two benchmark datasets compared to its competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert Systems with Applications 2022年 202卷 1页

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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Enhanced competitive swarm optimizer for multi-task optimization 9

Enhanced competitive swarm optimizer for multi-task optimiza...

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9th International Conference on computing and Pattern Recognition, ICCPR 2020

作者： Li, Wei Yuan, Junqing Luo, Haonan Lei, Zhou Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450387835

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms. © 2020 ACM.

关键词： Evolutionary algorithms

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Disentangled Representation Learning with Transmitted information Bottleneck

arXiv

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arXiv 2023年

作者： Dang, Zhuohang Luo, Minnan Jia, Chengyou Dai, Guang Wang, Jihong Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The SGIT AI Laboratory Xi’an710048 China The State Grid Shaanxi Electric Power Company Ltd. State Grid Corporation of China Xi’an710048 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc Beijing100085 China

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop;2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： information theory

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Research on Electric Micro-Meteorological Monitoring and Early Warning System 2

Research on Electric Micro-Meteorological Monitoring and Ear...

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2019 2nd International Symposium on power Electronics and Control Engineering, ISPECE 2019

作者： Zhang, Zehao Yu, Zhen Xu, Xiyuan Guan, Cheng Zhang, Song Global Energy Interconnection Research Institute Co. Ltd Research Institute of Information Technology and Communication Beijing102206 China State Grid Key Laboratory of Information and Network Security China State Grid FuJian Electric Power Co. LTD Switzerland

This paper studies and analyzes the influence of micro-meteorological disasters on power system, points out the main problems in the establishment of power micro-meteorological monitoring and early warning system, puts forward the system architecture of power micro-meteorological monitoring and early warning system, which provides a reference for the establishment of power micro-meteorological monitoring and early warning system. © Published under licence by IOP Publishing Ltd.

关键词： Monitoring

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High-Ni Low-Co High-Capacity Li-Rich Cathode Materials Via Adjusting Li2mno3 Content and Li/Ni Mixing Structures

SSRN

引用

SSRN 2023年

作者： Wu, Zhen Zou, Kunyang Dai, Xin Zhang, Yu-Han Zhang, Xudong Wang, Hao Hou, Zhufeng Ma, Lijing Liu, Yan Chen, Yuanzhen Guo, Shengwu Liu, Yongning State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an710049 China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of Sciences Qingdao266101 China Center for High Performance Computing Network Information Center Xi’an Jiaotong University Xi’an710049 China Hefei Advanced Computing Center Operation Management Corp Ltd Hefei230088 China State Key Laboratory of Structural Chemistry Fujian Institute of Research the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an710049 China

Li-rich layered oxides (LLOs) are considered as the promising cathode materials for next-generation high energy density lithium-ion batteries (LIBs). However, severe voltage fade and capacity decay hinder their commercial applications. Increasing the Ni content in LLOs effectively improves the stability and discharge voltage, but shows reduced capacities, typically © 2023, The Authors. All rights reserved.

关键词： Lithium compounds

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AirDIV: Over-the-Air Cloud-Fog Data Integrity Verification Scheme for Industrial Cyber-Physical Systems

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IEEE Journal on Selected Areas in Communications 2025年

作者： Zhao, Yao Xiang, Yong Uddin, Md Palash Zhang, Yushu Liu, Lu Liu, Yansong Gao, Longxiang Deakin University School of Information Technology VIC Australia Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China University of Exeter Department of Computer Science Exeter United Kingdom RWTH Aachen University Department of Computer Science Aachen Germany Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Industrial Cyber-Physical Systems (ICPSs) have been motivating various Industry 4.0 endeavours, particularly with the integration of fog computing. Cloud-fog data caching paradigms, as supportive elements of ICPSs, have been adopted to cache user data, catering to diverse ICPS requirements such as data sensitivity and reduced access latency. In this hierarchical caching context, ensuring Cloud-Fog Data Integrity (CFDI) is crucial for maintaining the consistent functionality of ICPSs. Existing solutions primarily focus on examining the integrity of data cached solely on either cloud or fog nodes. However, cloud-cached data and fog-cached data are tightly coupled and should be considered simultaneously when checking data integrity. In this work, we introduce an over-the-air CFDI verification scheme, namely AirDIV, with a high accuracy and security guarantee. Instead of aggregating integrity proofs after proof transmission, AirDIV completes proof aggregation and transmission over the air for efficiency improvement. To enhance practicability, we derive adjustable parameters and formulate an optimization problem to minimize over-the-air aggregation errors. Furthermore, with an effective proof generation method, AirDIV can defend against two common attacks, i.e., replay and forge attacks. We provide a theoretical analysis of AirDIV's correctness, accuracy and security, while conducting extensive experiments on both simulated and real platforms to validate its efficiency. © 1983-2012 IEEE.

关键词： Cache memory

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Towards practical anonymous quantum communication: A measurement-device-independent approach

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Physical Review A 2021年第5期104卷 052415-052415页

作者： Yu-Guang Yang Xiao-Xiao Liu Shang Gao Yi-Hua Zhou Wei-Min Shi Jian Li Dan Li Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China College of Network Security Beijing University of Posts and Telecommunications Beijing 100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

The multipartite Greenberger-Horne-Zeilinger (GHZ) entangled state is an important resource for anonymous quantum communication tasks. But its low intensity and fragility limit the practical applications of these anonymous communication tasks under current conditions. Here, we present a feasible anonymous communication protocol in the measurement-device-independent framework. We analyze its performance in a quantum network under certain types of noise. Then we compare the performance of our protocol with the GHZ-state-based anonymous protocol. We demonstrate that our protocol has a unique advantage, i.e., it can tolerate an arbitrary number of nonresponsive nodes. We prove that the security of our protocol in an active adversary scenario in which active malicious agents and an untrusted ancillary node are taken into account. Utilizing the measurement-device-independent technology, our proposal suggests an important avenue for practical anonymous quantum communication.

关键词： Quantum communication Quantum cryptography

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