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Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models

IEEE Transactions on Big data 2025年第3期11卷 1013-1026页

作者： Chen, Weijie Wang, Haoyu Yang, Shicai Zhang, Lei Wei, Wei Zhang, Yanning Lin, Luojun Xie, Di Zhuang, Yueting Zhejiang University College of Computer Science and Technology Hangzhou310027 China Hikvision Research Institute Hangzhou310052 China Northwestern Polytechnical University Xi’an710072 China Fuzhou University College of Computer and Data Science Fuzhou350025 China

We study a novel problem in this paper, that is, if a modern text-to-image diffusion model can tailor any image classifier across domains and categories. Existing domain adaption works exploit both source and target data for domain alignment so as to transfer the knowledge from the labeled source data to the unlabeled target data. However, as the development of text-to-image diffusion models, we wonder if the high-fidelity synthetic data can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each image classification task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with labels derived from text prompts, and then leverage them as a bridge to dig out the knowledge from the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as any unlabeled target data. Extensive experiments validate the feasibility of this idea, which even surprisingly surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world. © 2015 IEEE.

关键词： Spatio-temporal data

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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DkvSSO: Delegatable Keyed-Verification Credentials for Efficient Anonymous Single Sign-On

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4196-4211页

作者： Xue, Wenyi Yang, Yang Huang, Minming Li, Yingjiu Pang, Hwee Hwa Deng, Robert H. Fuzhou University College of Computer and Data Science Fuzhou350116 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore University of Oregon Department of Computer and Information Science EugeneOR97403 United States

Anonymous single sign-on (ASSO) is an anonymous multi-service authentication method for end users. However, existing ASSO schemes suffer from heavy ticket requesting and verifying overheads, limiting their applications in large-scale settings. To address this problem, we propose a novel concept called keyed-verification anonymous credentials with disposable delegation (KVAC-DD) in the multi-verifier setting. Next, we extend KVAC-DD to build an efficient ASSO system, dubbed DkvSSO. The construction of DkvSSO can be instantiated in efficient prime-order groups, avoiding costly operations required in previous ASSO systems. We formally prove the security of our proposed constructions. Extensive experiments show that DkvSSO is significantly more efficient than existing ASSO schemes, making it suitable to be deployed in large-scale settings. © 2005-2012 IEEE.

关键词： Anonymity

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A Novel Community Detection Algorithm Based on Node Propagation and Attraction

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IEEE Access 2025年 13卷 69161-69174页

作者： Fu, Lidong Xie, Xinxing Dou, Zengfa Bai, Yun Zhao, Xi Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710600 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Xi'an University School of Information Engineering Xi'an710065 China Xi'an Big Data Service Center Xi'an710007 China

High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms. © 2013 IEEE.

关键词： Optimization algorithms

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Leveraging BERT, DistilBERT, and TinyBERT for Rumor Detection

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IEEE Access 2025年 13卷 72918-72929页

作者： Qazi, Aijazahamed Goudar, R.H. Patil, Rudragoud Hukkeri, Geetabai S. Kulkarni, Dhanashree Angadi Institute of Technology and Management Department of Artificial Intelligence and Data Science Belagavi591108 India Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi590018 India KLS Gogte Institute of Technology Department of Computer Science and Engineering Belagavi590008 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal576104 India Angadi Institute of Technology and Management Department of Computer Science and Engineering Belagavi591108 India

The rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT + DT and TinyBERT + SVM provided significant results, with BERT + RF and DistilBERT + NB demonstrating better classification capabilities across various events and split ratios on the dataset. © 2013 IEEE.

关键词： Tweets

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Graph-Based Cooperation Multi-Agent Reinforcement Learning for Intelligent Traffic Signal Control

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IEEE Internet of Things Journal 2025年第10期12卷 14362-14374页

作者： Shang, Jing Meng, Shunmei Hou, Jun Zhao, Xiaoran Zhou, Xiaokang Jiang, Rong Qi, Lianyong Li, Qianmu Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210049 China Nanjing Vocational University of Industry Technology School of Social Science Nanjing210046 China Qufu Normal University School of Computer Science Qufu273165 China Kansai University Faculty of Business and Data Science Osaka Prefecture 564-8680 Japan Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650399 China College of Computer Science and Technology Qingdao266580 China

In the trend of continuously advancing urban intelligent transport construction, traditional traffic signal control (TSC) struggles to make effective decisions with complex traffic conditions. Although multi-agent deep reinforcement learning (MARL) shows promise in optimizing traffic flow, most existing studies ignore the complex relationships between signal lights and fail to communicate with neighbors effectively. Moreover, the deterministic strategies generated by Q-learning-based methods struggle to be extended to large-scale urban road networks. Therefore, this paper proposes a multi-agent graph-based soft actor-critic (MAGSAC) approach for TSC, which combines graph neural networks with the Soft Actor-Critic (SAC) algorithm and extends it to multi-agent environments to address the TSC problem. Specifically, we employ graph-based networks and attention mechanism to expand the receptive domain of agents, enable environmental information to be shared among agents, and utilize the attention mechanism to filter out unimportant information. The algorithm adheres to the Centralized Training Decentralized Execution (CTDE) paradigm to minimize the non-stationarity of MARL. Finally, a rigorous experimental evaluation was conducted using the CityFlow simulator on both synthetic traffic grids and real-world urban road networks. Experimental results show that MAGSAC outperforms other TSC methods in performance metrics, including average queue length and waiting time, and achieves excellent performance under complex urban traffic conditions. © 2025 IEEE.

关键词： Deep reinforcement learning

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Implicit Bonded Discrete Element Method with Manifold Optimization

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ACM Transactions on Graphics 2025年第1期44卷 1-17页

作者： Lu, Jia-Ming Cao, Geng-Chen Li, Chenfeng Hu, Shi-Min BNRist Department of Computer Science and Technology Tsinghua University Beijing Beijing China Zienkiewicz Institute for Modelling Data and AI Swansea University Bay Campus Swansea United Kingdom BNRist Department of Computer Science and Technology Tsinghua University Beijing China

This article proposes a novel simulation approach that combines implicit integration with the Bonded Discrete Element Method (BDEM) to achieve faster, more stable, and more accurate fracture simulation. The new method leverages the efficiency of implicit schemes in dynamic simulation and the versatility of BDEM in fracture modeling. Specifically, an optimization-based integrator for BDEM is introduced and combined with a manifold optimization approach to accelerate the solution process of the quaternion-constrained system. Our comparative experiments indicate that our method offers better scale consistency and more realistic collision effects than finite element method and material point method fragmentation approaches. Additionally, our method achieves a computational speedup of 2.1 to 9.8 times over explicit BDEM methods. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Discrete element method fracture simulation optimization-based integrator manifold optimization

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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-Hop Wireless Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Future Network of Intelligence Institute School of Science and Engineering Shenzhen518172 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen518060 China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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