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SSRN 2024年

作者： Chen, Gaige Zhong, Siqi Yang, Yahong Zhang, Xiaoshen Deng, Wei Liu, Jinsong School of Communications and Information Engineering School of Artificial Intelligence Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Shaanxi Xi’an710121 China State Key Laboratory of Space Power Sources Shanghai Institute of Space Power-sources Shanghai200245 China Xi'an Aviation Brake Technology Co. Ltd Shaanxi Xi’an710065 China Contemporary Amperex Technology Co. Ltd. Fujian Ningde352107 China

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to guarding battery and device safety. However, current prediction methods are mainly based on battery voltage, current, temperature and other data, and there is a challenge problem with neglecting the intrinsic connection between these data. To address this problem, a lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features of discharge curves is proposed. First, new slope features are proposed based on the lithium-ion battery discharge mechanism;second, slope features are utilized to construct health indicator (HI) and estimate state of health (SOH);then, unsupervised clustering of unlabeled state of health data is performed to construct a health state assessment model;and finally, the remaining useful life prediction model is constructed based on grey wolf optimizer and gated recurrent unit (GWO-GRU). The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center, and the dominant is emphasized by comparison with other important methods. The results show that the method can effectively assess the health state of lithium-ion batteries and accurately predict their remaining useful life, which can provide support for the realization of lithium-ion battery health management and intelligent operation and maintenance. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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S3 Agent: Unlocking the power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Peng Wang Yongheng Zhang Hao Fei Qiguang Chen Yukai Wang Jiasheng Si Wenpeng Lu Min Li Libo Qin School of Computer Science and Engineering Central South University China National University of Singapore Singapore Harbin Institute of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

关键词： Natural language processing Multi-modal sarcasm detection Vision Large language model

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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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Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation

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ACM Transactions on information Systems 1000年

作者： Ziang Lu Lei Guo Xu Yu Zhiyong Cheng Xiaohui Han Lei Zhu Shandong Normal University China China University of Petroleum (East China) China Hefei University of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China Tongji University China

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. 2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. 3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items’ semantic encodings from their original texts by a multi-layer semantic encoder, and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform (FFT)-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at: https://***/Sapphire-star/FFMSR.

关键词： Cross-domain Recommendation Federated Learning Semantic Fusion Semantic Filter

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Independent component analysis based on information bottleneck

Abstract and Applied Analysis

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Abstract and Applied Analysis 2015年第1期2015卷

作者： Ke, Qiao Zhang, Jiangshe Srivastava, H.M. Wei, Wei Chen, Guang-Sheng School of Mathematics and Statistics Xi'An Jiaotong University Xi'an 710049 China Department of Mathematics and Statistics University of Victoria Victoria V8W3R4 BC Canada School of Computer Science and Engineering Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an 710048 China Department of Construction and Information Engineering Guangxi Modern Vocational Technology College Hechi Guangxi 547000 China

The paper is mainly used to provide the equivalence of two algorithms of independent component analysis (ICA) based on the information bottleneck (IB). In the viewpoint of information theory, we attempt to explain the two classical algorithms of ICA by information bottleneck. Furthermore, via the numerical experiments with the synthetic data, sonic data, and image, ICA is proved to be an edificatory way to solve BSS successfully relying on the information theory. Finally, two realistic numerical experiments are conducted via FastICA in order to illustrate the efficiency and practicality of the algorithm as well as the drawbacks in the process of the recovery images the mixing images. © 2015 Qiao Ke et al.

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

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IEEE Transactions on information Forensics and security 2024年 19卷 8773-8785页

作者： Haoxin Yang Xuemiao Xu Cheng Xu Huaidong Zhang Jing Qin Yi Wang Pheng-Ann Heng Shengfeng He 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 Guangzhou China Centre for Smart Health The Hong Kong Polytechnic University Hong Kong China School of Future Technology South China University of Technology Guangzhou China School of Computer Science and Network Security Dongguan University of Technology Dongguan China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong SAR China School of Computing and Information Systems Singapore Management University Victoria St 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 .

关键词： Faces Data privacy information integrity information filtering Face recognition Generative adversarial networks Feature extraction

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Implementation Method Of Remote Assistance Instruction Based On Hardware Secure Encryption

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Journal of Physics: Conference Series 2021年第1期2136卷

作者： Birong Huang Zhansheng Hou Peng Lin Fei Zhou Zhangjiagang Power Supply Branch of State Grid Jiangsu Electric Power Co. LTD JiangsuChina Global Energy Interconnection Research Institute nanjing Jiangsu China State Grid Key Laboratory Of Information & Network Security Nanjing China

As a public service enterprise related to energy security and national economy and people's livelihood, the safe work is related to all walks of life and thousands of households. In power scenarios, the remote cooperative guidance needs to ensure the security of data communication. The implementation method of remote cooperative guidance in power scenarios based on hardware security encryption realizes the functions of terminal registration, user online and offline management, message call, control signaling negotiation, audio and video real-time call, graphic guidance and target tracking and labeling. Configuration TF card encryption at the mobile terminal chip and video server configuration high speed encryption card, WIFI / 4 g wireless networks to establish encryption channel, remote collaborative guidance terminal power scene registration messages, messages, call, and by guiding and structured data security encryption transmission, the unstructured data such as audio stream and video stream is encrypted. It not only realizes the secure encryption of message communication, but also guarantees the high-speed transmission of audio and video streams.

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Corrigendum to “A variable-length model for masquerade detection” [J. Syst. Softw. 85 (2012) 2470–2478]

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Journal of Systems and Software 2013年第3期86卷 868-868页

作者： Xi Xiao Xinguang Tian Qibin Zhai Shutao Xia Graduate School at Shenzhen Tsinghua University 518055 Shenzhen China State Key Laboratory of Information Security Graduate University of Chinese Academy of Sciences 100049 Beijing China Key Laboratory of Network Science and Technology Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

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Research on Mimic Defence Technology for Smart Unit Application Environment of power Grid

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Journal of Physics: Conference Series 2019年第1期1314卷

作者： Zesheng Xi Bo Zhang Xin Sun Xiaojuan Guan Institute of Information and Communication Global Energy Interconnection Research Institute Nanjing Jiangsu China State Grid Key Laboratory of Information & Network Security Nanjing Jiangsu China State Grid Zhejiang Electric Power Research Institute Hangzhou Zhejiang China

Aiming at the problem that the new network attack cannot be effectively defended at present, this paper proposes a framework of application-level mimic defense technology for smart grid units to solve the active attack defense problem of smart terminal units in the source grid environment. In this paper, the application security mimic defense architecture and interactive instruction level heterogeneous redundancy technology for smart grid units are studied to achieve universal and self-immune active immunity against grid application-level attacks. Furthermore, the dynamic change and heterogeneous redundancy characteristics of application level are constructed by mimetic defense technology. The risk and vulnerability of terminal application security vulnerabilities are actively hidden to actively avoid attacks, so as to enhance the active defense capability of industrial control terminal side of power grid.

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Research on Electric Micro-Meteorological Monitoring and Early Warning System

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Journal of Physics: Conference Series 2020年第1期1449卷

作者： Zehao Zhang Zhen Yu Xiyuan Xu Cheng Guan Song Zhang Global Energy Interconnection Research Institute co. Ltd Research Institute of Information Technology & Communication Beijing 102206 China. State Grid Key Laboratory of Information & Network Security State Grid FuJian Electric Power co. LTD

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.

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