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arXiv 2024年

作者： Yuan, Haonan Sun, Qingyun Fu, Xingcheng Ji, Cheng Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Guangxi Guilin China

Dynamic Graphs widely exist in the real world, which carry complicated spatial and temporal feature patterns, challenging their representation learning. Dynamic Graph Neural Networks (DGNNs) have shown impressive predictive abilities by exploiting the intrinsic dynamics. However, DGNNs exhibit limited robustness, prone to adversarial attacks. This paper presents the novel Dynamic Graph Information Bottleneck (DGIB) framework to learn robust and discriminative representations. Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition. To compress redundant as well as conserve meritorious information into latent representation, DGIB iteratively directs and refines the structural and feature information flow passing through graph snapshots. To meet the MSC Condition, we decompose the overall IB objectives into DGIBMS and DGIBC, in which the DGIBMS channel aims to learn the minimal and sufficient representations, with the DGIBC channel guarantees the predictive consensus. Extensive experiments on real-world and synthetic dynamic graph datasets demonstrate the superior robustness of DGIB against adversarial attacks compared with state-of-the-art baselines in the link prediction task. To the best of our knowledge, DGIB is the first work to learn robust representations of dynamic graphs grounded in the information-theoretic IB principle. © 2024, CC BY-NC-SA.

关键词： Information theory

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HiCoCS: High Concurrency Cross-Sharding on Permissioned blockchains

arXiv

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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

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Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios

arXiv

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arXiv 2024年

作者： Zhang, Yuan Tao, Xiaomei Ai, Hanxu Chen, Tao Gan, Yanling Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security China School of Computer Science and Engineering Guangxi Normal University China School of Computer Science University of Birmingham United Kingdom

In the Massive Open Online Courses (MOOC) learning scenario, the semantic information of instructional videos has a crucial impact on learners' emotional state. Learners mainly acquire knowledge by watching instructional videos, and the semantic information in the videos directly affects learners' emotional states. However, few studies have paid attention to the potential influence of the semantic information of instructional videos on learners' emotional states. To deeply explore the impact of video semantic information on learners' emotions, this paper innovatively proposes a multimodal emotion recognition method by fusing video semantic information and physiological signals. We generate video descriptions through a pre-trained large language model (LLM) to obtain high-level semantic information about instructional videos. Using the cross-attention mechanism for modal interaction, the semantic information is fused with the eye movement and PhotoPlethysmoGraphy (PPG) signals to obtain the features containing the critical information of the three modes. The accurate recognition of learners' emotional states is realized through the emotion classifier. The experimental results show that our method has significantly improved emotion recognition performance, providing a new perspective and efficient method for emotion recognition research in MOOC learning scenarios. The method proposed in this paper not only contributes to a deeper understanding of the impact of instructional videos on learners' emotional states but also provides a beneficial reference for future research on emotion recognition in MOOC learning scenarios. © 2024, CC BY.

关键词： Emotion Recognition

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A Privacy-Preserving and Efficient Data Sharing Scheme Based on blockchain in IIoT

A Privacy-Preserving and Efficient Data Sharing Scheme Based...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Hongyan Peng Yipeng Yang Dongcheng Li Peng Wang Peng Liu Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China School of Computer Science and Engineering Guangxi Normal University China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the industrial Internet of Things (IIoT) scenarios, data sharing can promote mutual collaboration among production parties to improve productivity and optimise resource allocation, but data sharing in industrial scenarios faces the risk of privacy leakage due to open networks. Attribute-based encryption (ABE) can be used to solve the problem of data sharing privacy leakage. However, there is still no effective privacy protection solution for data sharing and access control policies in blockchain, which may expose sensitive information of data owners. Moreover, existing schemes only focus on protecting data privacy and ignore the protection of users’ query privacy. In addition, most of these schemes are based on cloud servers, which may lead to data tampering and a single point of failure. To address these issues, we propose a blockchain-based data security sharing scheme (BAPIR) that combines attribute-based encryption and private information retrieval (PIR). In this paper, we implement fine-grained access control using ABE based on the inner product and protecting the access policy. Additionally, PIR technology is introduced to protect users’ query privacy. To achieve efficient blockchain data storage, IPFS is used for on-chain and off-chain collaboration, and complex computational tasks are outsourced to edge servers, ensuring secure and efficient data sharing. Finally, we demonstrate the security and efficiency of BAPIR through security analysis and performance evaluation.

关键词： Access control Performance evaluation Data privacy Privacy Memory Encryption blockchains Servers Protection Industrial Internet of Things

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Using Problem Frames Approach for Key Information Extraction from Natural Language Requirements 23

Using Problem Frames Approach for Key Information Extraction...

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Tang, Shangzhi Chen, Xuan Xiao, Hongbin Wei, Jiahao Li, Zhi School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Normal University Key Lab of Education Blockchain Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Laboratory of Multi-source Information Mining and Security Guilin China

ISBN: (纸本)9798350359398

The correct capturing and understanding of requirements play a crucial role in the software development process. However, the ambiguity in requirements described using natural language (NL) poses a challenge. Existing methods often require the use of specific requirements templates to rewrite system requirements, which increases efforts in analyzing requirements. Additionally, frequent requirement changes during the development process can lead to extra costs. In this paper, we propose a visualization method that allows user to extract key information from NL requirements, aiming to mitigate the difficulty and efforts in analyzing NL requirements. We also argue that more attention should be paid to the problems that need to be solved in the development process, especially the security problems. Application of our method to practical requirements sentences demonstrates its effectiveness in extracting the problem concerns from the requirements, leading to the stepwise derivation of solutions. We envision that our approach may have the potential to support iterative updates and analysis of NL requirements automatically. © 2023 IEEE.

关键词： Requirements engineering

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ScanFormer: Referring Expression Comprehension by Iteratively Scanning

ScanFormer: Referring Expression Comprehension by Iterativel...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wei Su Peihan Miao Huanzhang Dou Xi Li College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Zhejiang-Singapore Innovation and Al Joint Research Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely Ref COCO, Ref COCO+, Ref COCO g, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

关键词： Visualization computer vision Accuracy Computational modeling Linguistics Pattern recognition Iterative methods

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Joint Optimization of Model Deployment for Freshness-Sensitive Task Assignment in Edge Intelligence

Joint Optimization of Model Deployment for Freshness-Sensiti...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Haolin Liu Sirui Liu Saiqin Long Qingyong Deng Zhetao Li School of Computer Science Xiangtan University Xiangtan China College of Information Science and Technology Jinan University Guangzhou China Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Edge Intelligence aims to push deep learning (DL) services to network edge to reduce response time and protect privacy. In implementations, proximity deployment of DL models and timely updates can improve the quality of experience (QoE) for users, but increase the operation cost as well as pose a challenge for task assignment. To address the challenge, a joint online optimization problem for DL model deployment (including placement and update) and freshness-sensitive task assignment is formulated to improve QoE and application service provider (ASP) profit. In the problem, we introduce the age of information (AOI) to quantify the freshness of the DL model and represent user QoE as an AOI based utility function. To solve the problem, an online model placement, update, and task assignment (MPUTA) algorithm is proposed. It first converts the time-slot coupled problem into a single time-slot problem using the regularization technique, and decomposes the single time-slot problem into model deployment and task assignment subproblems. Then, using the randomized round technique to deal with the model deployment subproblem and the graph matching technique to solve the task assignment subproblem. In simulation experiments, MPUTA is shown to outperform other benchmark algorithms in terms of both user QoE and ASP profit.

关键词： Measurement Deep learning Privacy Computational modeling Heuristic algorithms Information age Quality of experience

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China The School of Computing Kent Interdisciplinary Research Centre in Cyber Security University of Kent CanterburyCT2 7NF United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China The Intelligent Transportation Systems Lab Innopolis University Innopolis Russia The School of Computer Science and Technology Xidian University China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. © 2004-2012 IEEE. All rights reserved.

关键词： Graph neural networks

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Enhancing Scalability: A Complete Tree Sharding Architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. This paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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