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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 393-404页

作者： Lin, Wenmin Zhou, Xinyi Sun, Lu Qi, Lianyong Tsai, Sang-Bing Yang, Yihong Liu, Hanwen Kou, Huaizhen Kong, Lingzhen Hangzhou Normal University Alibaba Business School Hangzhou311121 China College of Computer Science and Technology Qingdao266580 China International Engineering and Technology Institute Hong Kong999077 Hong Kong China University of Geosciences School of Information Engineering Beijing100083 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

As one of the most representative recommendation solutions, traditional collaborative filtering (CF) models typically have limitations in dealing with large-scale, sparse data to capture complex relationships between users and items. The rise of artificial intelligence (AI) provides powerful tools such as deep neural networks to overcome the data sparsity issue with typical CF models. Existing works on AI-driven recommender systems are focusing on improving recommendation accuracy by extracting rich user/item features from multisource data with deep learning tools to build more accurate user preference model. How to ensure the responsibility of AI-driven recommender systems is still a big challenge. On one hand, AI-driven recommender systems need to access raw data such as user profiles to train recommendation model, which face the risk of leaking user privacy information. On the other hand, the ever-increasing volume of user/item interaction records raises the efficiency challenge to provide recommendation results in a real-time manner. In view of those observations, we propose a collaborative recommendation method by adopting the locality-sensitive-hashing (LSH) technique (i.e., DisRecLSH), which aims to achieve the goal of privacy protection and calculation efficiency for AI-driven recommender systems. More specifically, we split DisRecLSH into three phases: 1) offline feature extraction: deep neural networks are applied to extract user/item features from multisource user/item data on each local dataset;2) offline user index building: LSH technique is adopted to map user features to hash codes to quickly recall correlated similar users for a given target user;and 3) online top-N items calculation: with similar users selected by phase 2, top-N items are calculated based on the ranking of predicted user-item rating score. Finally, extensive experiments are conducted on three public datasets to evaluate the efficiency of our proposal. © 2020 IEEE.

关键词： Recommender systems

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De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

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Journal of Electronic science and technology 2024年第3期22卷 57-68页

作者： Yan Li Tai-Kang Tian Meng-Yu Zhuang Yu-Ting Sun School of Economics and Management University of Electronic Science and Technology of ChinaChengdu611731China School of Economics and Management Beijing University of Posts and TelecommunicationBeijing100876China School of Electrical Engineering and Computer Science The University of QueenslandBrisbane4072Australia

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various ***,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still *** address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification *** the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing *** on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student *** approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student *** conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

关键词： De-biasing Deep learning Knowledge distillation Model compression

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Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles

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Digital communications and Networks 2024年第6期10卷 1698-1707页

作者： Haoye Chai Supeng Leng Jianhua He Ke Zhang School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu611731China Shenzhen Institute for Advanced Study UESTCShenzhen518000China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

The Internet of Vehicles(Io V)has great potential for Intelligent Transportation Systems(ITS),enabling interactive vehicle applications,such as advanced driving and *** is crucial to ensure the reliability during the vehicle-to-vehicle interaction *** the emerging blockchain has superiority in handling security-related issues,existing blockchain-based schemes show weakness in highly dynamic Io *** the transaction broadcast and consensus process require multiple rounds of communication throughout the whole network,while the high relative speed between vehicles and dynamic topology resulting in the intermittent connections will degrade the efficiency of *** this paper,we propose a Digital Twin(DT)-enabled blockchain framework for dynamic Io V,which aims to reduce both the communication cost and the operational latency of *** address the dynamic context,we propose a DT construction strategy that jointly considers the DT migration and blockchain computing ***,a communication-efficient Local Perceptual Multi-Agent Deep Deterministic Policy Gradient(LPMA-DDPG)algorithm is designed to execute the DT construction strategy among edge servers in a decentralized *** simulation results show that the proposed framework can greatly reduce the communication cost,while achieving good security *** dynamic DT construction strategy shows superiority in operation latency compared with benchmark *** decentralized LPMA-DDPG algorithm is helpful for implementing the optimal DT construction strategy in practical ITS.

关键词： Digital twin Blockchain Internet of vehicles Multi-agent DDPG

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DUFuse: Deep U-Net for visual and infrared images fusion

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12549-12561页

作者： Pan, Yue Pi, Dechang Khan, Izhar Ahmed Meng, Han School of Information Science and Engineering University of Jinan Jinan250022 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210000 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100000 China

The vision sensor is capable of capturing image detail features suitable for human observation, while the infrared sensor is capable of capturing the thermal characteristics of the target object. Therefore, the vision and infrared image fusion aim to retain both the rich detail features in the visual image and the thermal characteristics of the infrared image. This study proposes a novel deep U-Net network model to solve the fusion task. First, an improved deep model is proposed for better feature extraction by borrowing the process of decomposition, fusion and reconstruction in the multiscale decomposition process. Second, structural similarity is introduced into the loss function, which enables the network to enhance the quality of the detailed features of the generated images. Third, we propose a new hierarchical fusion strategy as well as average fusion and weighted fusion rules. Extensive experiments demonstrate that the proposed algorithm is superior to state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image fusion

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A General Linguistic Steganalysis Framework Using Multi-Task Learning

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computer Systems science & engineering 2023年第8期46卷 2383-2399页

作者： Lingyun Xiang Rong Wang Yuhang Liu Yangfan Liu Lina Tan School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK School of Computer Science Hunan University of Technology and BusinessChangsha410205China

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary *** it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts *** this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic ***-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed *** the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis ***,a shared CNN is built to capture potential interaction information and share linguistic features among all ***,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or *** results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,*** ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.

关键词： Linguistic steganalysis multi-task learning convolutional neural network(CNN) feature extraction detection performance

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USRD: User-Searchable and Revocable Data Sharing Scheme in Multi-Fog-IoVs 6

USRD: User-Searchable and Revocable Data Sharing Scheme in M...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Jiang, LuYao Li, Yongkang Chen, DongDong Gu, Ke Zuo, YiMing School of Computer and Communication Engineering Changsha University of Science & Technology ChangSha China

ISBN: (纸本)9798350388374

Aiming at the security of data sharing in the Internet of vehicles environment, we propose a user-searchable and revocable data-sharing scheme in the Multi-Fog-IoVs. The scheme utilizes a fog node to alleviate the computing burden on users and enhance the computing efficiency of the system. The Garbled Bloom filter and inverted index are used to create a bidirectional index for efficient multi-keyword search and document retrieval. Furthermore, a flexible and efficient user revocation mechanism is constructed while realizing the dynamic updates of data security. Finally, the safety analysis and performance evaluation of the proposed scheme are conducted, and the results indicate that the scheme meets both forward and backward safety requirements while demonstrating outstanding retrieval efficiency. © 2024 IEEE.

关键词： Fog computing

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Soft transmission of 3D video for low power and low complexity scenario

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Digital communications and Networks 2023年第3期9卷 769-778页

作者： Ao Li Taihai Yang Wenxin Wu Lei Luo School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology East China Normal UniversityShanghai200062China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thanks to the rapid development of naked-eye 3D and wireless communication technology,3D video related applications on mobile devices have attracted a lot of ***,the time-varying characteristics of the wireless channel is very challenging for conventional source-channel coding based transmission ***,the high complexity of source-channel coding based transmission scheme is undesired for low power mobile *** advanced transmission scheme named Softcast was proposed to achieve efficient transmission performance for 2D image/***,it cannot be directly applied to wireless 3D video transmission with high *** paper proposes a more efficient soft transmission scheme for 3D video with a graceful quality adaptation within a wide range of channel Signal-to-Noise Ratio(SNR).The proposed method first extends the linear transform to 4 dimensions with additional view dimension to eliminate the view redundancy,and then metadata optimization and chunk interleaving are designed to further improve the transmission ***,a synthesis distortion based chunk discard strategy is developed to improve the overall 3D video quality under the condition of limited *** experimental results demonstrate that the proposed method significantly improves the 3D video transmission performance over the wireless channel for low power and low complexity scenarios.

关键词： Softcast Low power 3D video Wireless

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Emotion-Aware Synthetic Speech Detection with Multi-level Fusion Features 2nd

Emotion-Aware Synthetic Speech Detection with Multi-level Fu...

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2nd International Conference on the Frontiers of Robotics and Software engineering, FRSE 2024

作者： Xiang, Lingyun Zhou, Zhiguo Liu, Yangfan School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819631605

Current synthetic speech detection methods often overlook the emotional distinctions between synthetic and real speech. To comprehensively leverage emotional information and deep features to further enhance the accuracy of distinguishing synthetic speeches, this paper proposes an emotion-aware synthetic speech detection method by fusing multi-level different features. Initially, a multi-scale emotional feature extraction model is devised to extract emotional features from 3-D log-Mels. Considering that the emotional features learned by a single model may not be comprehensive enough, the emotion2vec model is utilized to automatically learn additional emotional features from the temporal domain of the original speech. Subsequently, by employing a co-attention network, the extracted two kinds of emotional features are both incorporated into the deep features learned by the end-to-end model Rawnet2, thereby obtaining highly sensitive and weighted deep features. Finally, the extracted emotional features and the weighted deep features are fused to classify synthetic speech from real speech. Experimental results on several datasets demonstrate that the proposed method outperforms baseline methods, with a detection accuracy exceeding 0.97. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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MeshWGAN: Mesh-to-Mesh Wasserstein GAN with Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation

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IEEE Transactions on Visualization and computer Graphics 2024年第8期30卷 4927-4940页

作者： Zhang, Jie Zhou, Kangneng Luximon, Yan Lee, Tong-Yee Li, Ping The Hong Kong Polytechnic University Department of Computing The School of Design Hong Kong University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China The Hong Kong Polytechnic University School of Design Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications. © 1995-2012 IEEE.

关键词： Face recognition

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Channel Estimation for Double Intelligent Reflecting Surface Assisted MISO System in Sub-6G 3

Channel Estimation for Double Intelligent Reflecting Surface...

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3rd International Conference on Computing, communication, Perception and Quantum technology, CCPQT 2024

作者： Wang, Liang Zhang, Jun Shi, Wenzheng Wang, Shiguo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9798331528386

In contrast to the single intelligent reflecting surface (S-IRS) systems, improved performance is attainable by employing double-IRS (D-IRS) in a wireless-communication system. Nevertheless, precise channel estimation is crucial in D-IRS assisted wireless communication systems for achieving significant performance enhancements. In some specialized communication scenarios, obstacles may block the communication links between IRS1 and IRS2, thereby diminishing the potential gains of the D-IRS system. To tackle these challenges, this article introduces a novel D-IRS method with maximal a posterior probability rule (DMAP) for a multi-input single-output (MISO) uplink communication where the line-of-sight (LOS) link between the base station (BS) and the users are ignored. Furthermore, the BS-IRS links are harnessed as a common-link for decomposing the complex cascaded channels and IRSs are kept always "ON" in the channel estimation protocol. Finally, optimization for the phase configuration of IRSs is constructed to ensure better estimation accuracy. Experimental results show that DMAP significantly reduces pilot overhead and exceeds baseline performance levels. © 2024 IEEE.

关键词： channel estimation DMAP double-IRS (D-IRS) multiple-input single-output (MISO) single intelligent reflecting surface (S-IRS)

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