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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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Cross-chain Transaction Tracking Protocol based on Multi-dimensional Digital Watermarking Fingerprints

Cross-chain Transaction Tracking Protocol based on Multi-dim...

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Frontiers technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Duanzhen Li Shujiang Xu Haochen He Hongrui Xue Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Cross-chain technology plays a critical role in the process of interconnecting blockchains. However, all attention has been focused on how to achieve efficient cross-chain interoperability protocols, thus neglecting the most important security issues in cross-chain data interaction. Frequent network attacks and data privacy leaks have sounded the alarm for the safety of cross-chain data transactions. How to solve the problem of data ownership tracking in cross-chain transactions has become the top priority. This paper designs a cross-chain transaction tracking protocol based on multi-dimensional digital watermark fingerprints, using robust watermark, fragile watermark, and user identity key to achieve multi-dimensional tracking and supervision of cross-chain data transactions. Finally, this paper analyzes the feasibility and efficiency of the proposed protocol through experiments.

关键词： Data privacy Protocols Watermarking Fingerprint recognition Blockchains Safety Discrete wavelet transforms Interoperability

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Multi-Relational Variational Contrastive Learning for Next POI Recommendation

Multi-Relational Variational Contrastive Learning for Next P...

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

作者： Lin Chen Peipei Wang Xiaohui Han Lijuan Xu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Next point-of-interest (POI) recommendation aims to predict the next interested POI to the user based on their historical check-in data in location-based social services. Most existing studies have attempted to model user visiting behaviors via sequence-based and graph-based models, and have achieved impressive performance. However, there is still room to explore the implicit transition preferences and contextual multiple semantic relationships among various POIs. To this end, we propose a novel graph-based Multi-Relational Variational Contrastive Learning (MRVCL) method for next POI recommendation, which captures local similarity associations and global contextual dependencies among POIs. Specifically, an order-free local-relational adaptive weighting module is designed to alleviate the problem of insufficient utilization of multi-hop neighbor information caused by the limit of neighbor order of nodes. We then develop a contextaware global-relational encoding module to capture implicit semantic sequential relationships. Finally, generative variational-contrastive learning is employed to construct a continuous representation of the latent features and reinforce the quality of representation learning. Extensive experiments on two real-world datasets validate that our MRVCL outperforms existing state-of-the-art methods on various evaluation metrics. To facilitate future research, our code and data are open-sourced at https://***/LinCH-en/MRVCL.

关键词： Representation learning Measurement Correlation Semantics Neural networks Contrastive learning Signal processing Transformers Encoding Speech processing

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Fault-tolerant identity-based encryption from SM9

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Science China(Information Sciences) 2024年第2期67卷 104-117页

作者： Xiaohong LIU Xinyi HUANG Zhaohui CHENG Wei WU Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal University Artificial Intelligence Thrust Information HubThe Hong Kong University of Science and Technology (Guangzhou) Olym Information Security Technology Ltd. Center for Applied Mathematics of Fujian Province School of Mathematics and StatisticsFujian Normal University

This paper initiates the formal study of attribute-based encryption within the framework of SM9,the Chinese National Cryptography Standard for Identity-Based Cryptography, by presenting two new faulttolerant identity-based encryption(FIBE) schemes. Our first scheme uses the same private-key/ciphertext structure as the original SM9 algorithm and operates in a small attribute universe. As a result, it can be effectively and smoothly integrated into the information systems using SM9. In the random oracle model,we prove that our scheme is ciphertext-indistinguishable against fuzzy selective-identity and chosen-plaintext attacks under the(k + 3)-DBDHI assumption. Our second design is a large universe FIBE scheme based on SM9 that is ciphertext-indistinguishable against chosen-plaintext attacks in the random oracle model under the(f, g)-GDDHE assumption. Finally, we compare the communication and computing costs of our schemes to those of other classical ones. The comparison shows that our schemes have comparable performance as others. We believe that our findings will accelerate the applications of SM9 in modern information systems such as cloud computing and blockchain.

关键词： attribute-based encryption identity-based encryption fault-tolerant SM9

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An Improved Q-RRT^(*) Algorithm Based on Virtual Light

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computer Systems Science & Engineering 2021年第10期39卷 107-119页

作者： Chengchen Zhuge Qun Wang Jiayin Liu Lingxiang Yao Department of Computer Information and Cyber Security Jiangsu Police InsituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InsituteNanjing210031China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Jiangsu Police InsituteNanjing210031China University of Technology Sydney Sydney2007Australia

The Rapidly-exploring Random Tree(RRT)algorithm is an efficient path-planning algorithm based on random *** RRT^(*)algorithm is a variant of the RRT algorithm that can achieve convergence to the optimal ***,it has been proven to take an infinite time to do *** improved Quick-RRT^(*)(Q-RRT^(*))algorithm based on a virtual light source is proposed in this paper to overcome this *** virtual light-based Q-RRT^(*)(LQRRT^(*))takes advantage of the heuristic information generated by the virtual light on the *** this way,the tree can find the initial solution ***,the LQRRT^(*)algorithm combines the heuristic information with the optimization capability of the Q-RRT^(*)algorithm to find the approximate optimal ***^(*)further optimizes the sampling space compared with the Q-RRT^(*)algorithm and improves the sampling *** efficiency of the algorithm is verified by comparison experiments in different simulation *** results show that the proposed algorithm can converge to the approximate optimal solution in less time and with lower memory consumption.

关键词： Path planning RRT^(*) Q-RRT^(*) LQ-RRT^(*) virtual light

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Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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A Secure Lightweight RFID Mutual Authentication Protocol Without Explicit Challenge-Response Pairs 2nd

A Secure Lightweight RFID Mutual Authentication Protocol Wit...

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2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Huang, Keke Lin, Changlu Liu, Yali College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Lab of Network Security and Cryptology Fujian Fuzhou350007 China School of Mathematics and Statistics Fujian Normal University Fujian Fuzhou350117 China College of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China

ISBN: (纸本)9783031170805

Radio Frequency Identification (RFID) has been widely deployed to various scenarios, but its security and privacy issues need to be concerned due to the tag’s limited computing and storage resources. While benefiting from the great convenience and advantages of RFID systems, security is still a considerable threat to their applications, such as desynchronization attacks and cloning attacks. In this paper, we propose a secure lightweight mutual authentication protocol based on the configurable tristate physical unclonable functions and the cryptographic primitive of verifiable secret sharing to solve these issues. More specifically, the tag equipped with the configurable tristate physical unclonable functions structure can enhance the tag’s security and effectively resist machine learning modeling attacks. Verifiable secret sharing plays the role of decentralized storage of secrets, and ensures the verifiability of the correctness of the each shares. The verifiability provided by verifiable secret sharing is effective against tag impersonation attacks, and the validity of the each share provided by the tag has to be verified before it is adopted. Finally, the correctness of our protocol is analyzed formally using BAN-logic and the its security is verified informally by the Scyther. In addition, we analyze security properties including data integrity, data confidentiality, anonymity, mutual authentication, forward security and resistance to various malicious attacks. The results show that the proposed protocol satisfies various security properties and resistance to diverse malicious attacks. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Digital storage

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