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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Towards Privacy-Preserving and Practical Vaccine Supply Chain based on Blockchain 18

Towards Privacy-Preserving and Practical Vaccine Supply Chai...

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18th Asia Joint Conference on information security, AsiaJCIS 2023

作者： Wen, Baodong Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Luo, Decun Hangzhou Innovation Institute of Beihang University Hangzhou China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China

ISBN: (纸本)9798350341638

Vaccines play a vital role in safeguarding people's health and improving their well-being. With the increase in the variety and number of infectious diseases, the importance of vaccination cannot be underestimated. Despite being responsible for overseeing the entire lifecycle of vaccines, the current vaccine supply chain is encountering certain issues. There is no effective mechanism for sharing vaccine data, while the privacy of recipient data cannot be guaranteed. Also, different entities involved in the supply chain manage their data separately, leading to data barriers that hinder the effective supervision and tracking of vaccine data. This paper designs a vaccine whole process management system that employs a consortium blockchain to facilitate trustworthy collaboration among various entities involved in the vaccine supply chain. The system leverages data encryption to preserve data privacy and promote seamless data sharing, thereby overcoming data silos among participants. Additionally, smart contracts are utilized to establish uniform data operation protocols, while a regulator-friendly vaccine management mechanism is proposed to enable comprehensive supervision of the entire vaccine supply process. Experiments demonstrate the effectiveness of the proposed system. © 2023 IEEE.

关键词： Vaccines

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Post-quantum Dropout-Resilient Aggregation for Federated Learning via Lattice-Based PRF 1st

Post-quantum Dropout-Resilient Aggregation for Federated Le...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Zuo, Ruozhou Tian, Haibo Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9789819997848

Machine learning has greatly improved the convenience of modern life. As the deployment scale of machine learning grows larger, the corresponding data scale also increases, leading to a large number of small and medium-sized organizations wishing to use their respective data to train models together, even though this may bring risks of violating data privacy regulations and privacy leakage. To meet this demand, federated learning was proposed, which can satisfy the needs of various organizations to expand the training data scale without directly sharing data, while avoiding violations of data privacy regulations and privacy leakage. General federated learning usually allows clients to train local models independently, and then aggregate them on a central server to build a global model in a privacy-preserving manner. There are various ways to protect privacy, such as homomorphic encryption, differential privacy, etc. Among these methods, one type of federated learning scheme is based on homomorphic pseudorandom functions. This type of scheme is relatively simple to construct, has a smaller communication scale, is more resilient to disconnections, and has high scalability. However, the security aggregation with cryptographic primitives based on classic assumptions such as DDH cannot resist quantum attacks, and since the protected gradient vectors are usually tens of thousands of dimensions, obtaining the aggregation results requires solving tens of thousands of discrete logarithms, which leads to some loss of efficiency. In this paper, we proposed a secure aggregation scheme based on HPRG over lattice, which has practical efficiency and resilience to dropout and can resist quantum attacks due to the hardness of the RLWE assumption. Moreover, our scheme only requires polynomial multiplication and addition (usually treated as vectors in implements), thus significantly improving computational efficiency. © The Author(s), under exclusive license to Springer Nature Singapo

关键词： Computational efficiency

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Research on Interpretable Fake News Detection Technology Based on Co-Attention Mechanism 23

Research on Interpretable Fake News Detection Technology Bas...

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2023 International Conference on computer, Vision and Intelligent Technology, ICCVIT 2023

作者： Jiang, Wen Zhang, Mingshu Wei, Bin Ren, Kelan Yan, Facheng Zhang, Xiong Armed Police Engineering University Shaanxi Province Xi'an China Key Laboratory of Network and Information Security of Armed Police Force Xi'an China

ISBN: (纸本)9798400708701

The wide dissemination of false news is increasingly threatening both individuals and society. Aiming at the problem of insufficient interpretability in false news detection task, the model TCIN based on collaborative attention mechanism is proposed. The model integrates the advantages of the existing dEFEND model and GCAN model. It captures more detailed semantic features through three attention layers (sentence-level cooperative attention layer, word-level cooperative attention layer and sentence-comment cooperative attention layer). The experimental results show that, compared with the model before combination, the accuracy of this model is 9.7% higher on the data set of Weibo-20 and on Twitter15 data set the accuracy is increased by 7.43%. © 2023 ACM.

关键词： Semantics

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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models 31

Chain-of-Specificity: Enhancing Task-Specific Constraint Adh...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Kaiwen Zhong, Jiang Zhang, Hongzhi Zhang, Di Jin, Li Yu, Yue Zhang, Jingyuan College of Computer Science Chongqing University China Kuaishou Inc. China Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing, rewriting input instructions, or reflecting on prior failings, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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LogESP: Enhancing Log Semantic Representation With Word Position for Anomaly Detection 22

LogESP: Enhancing Log Semantic Representation With Word Posi...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Ni, Zhengping Di, Xiaoqiang Liu, Xu Chang, Lianjie Li, Jinqing Tang, Qiyue Changchun University of Science and Technology School of Computer Science and Technology Changchun130022 China Changchun University of Science and Technology Jilin Province Key Laboratory of Network and Information Security Changchun130022 China Changchun University of Science and Technology Information Center Changchun130022 China

ISBN: (纸本)9798331509712

Logs are valuable data for detecting anomalous network behavior. Accurate feature extraction from logs is essential for anomaly detection. However, statistical-based feature extraction methods consider the statistical features of logs over a period of time, while ignoring the semantic information in each log. Most semantic-based feature extraction methods only embed the token semantics into high-dimensional vectors for semantic representation, which may lead to the lack of positional information between tokens, so that the uniqueness of the log template semantics cannot be maintained. At the same time, it also causes time-consuming problems due to high-dimensional data. To address the above challenges, we propose a novel log anomaly detection method called LogESP, which enhances the semantic representation of logs with positional information. Lo-gESP aims to effectively capture the semantic differences between normal and abnormal logs, thereby providing a new perspective for accurate detection of abnormal network behavior. First, we select representative words to summarize the semantics of log templates, then use structured tuples to enhance the semantic representation of the templates, and finally, construct LSTM model to extract the temporal features of the log sequences. Experimental results on two public log datasets (BGL and HDFS) show better performance compared to other state-of-the-art methods. © 2024 IEEE.

关键词： Spatio-temporal data

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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