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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1437-1448页

作者： Zhang, Yushu Zhao, Jiahao Jiang, Jiajia Zhu, Youwen Wang, Liangmin Xiang, Yong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210023 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Deakin University School of Information Technology MelbourneVIC3125 Australia

The key challenges of current blockchain, such as low throughput, high latency, and poor scalability, hinder its future development. The recent advances in artificial intelligence (AI) can well overcome these challenges, thus spurring an increasing number of organizations and individuals to equip blockchain with AI, making blockchain become more intelligent. However, AI behaviors need to be supervised for ex-post forensics in case of dispute and accountability. This motivates us to envision a model to record the AI behaviors in intelligent blockchain. In this article, we propose such a model called AI-Tracer, which leverages proxy re-encryption based on Schnorr signature as well as InterPlanetary File System (IPFS) to be integrated into intelligent blockchain. AI-Tracer generates AI-digest during the AI learning process and encrypts it with proxy re-encryption. IPFS is responsible to store the encrypted AI-digest and returns a hash pointer related to AI-digest for further verification. The authorized user can verify the validity of AI-digest by leveraging advanced features of blockchain technology and only the valid AI-digest can be uploaded to intelligent blockchain, which implements trusted tracking for AI behaviors. AI-Tracer achieves fine-grained access control of AI-digest via proxy re-encryption. A user can retrieve the plaintext of AI-digest from intelligent blockchain with specific authorization. We conduct theoretical analysis to indicate the privacy and security of AI-Tracer. Moreover, we deploy AI-Tracer on the Hyperledger Fabric and demonstrate the efficiency and effectiveness of AI-Tracer through extensive experiments. © 2020 IEEE.

关键词： Smart contract

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PPA: Preference Profiling Attack Against Federated Learning 30

PPA: Preference Profiling Attack Against Federated Learning

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30th Annual network and Distributed System Security Symposium, NDSS 2023

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Syndey Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

ISBN: (纸本)1891562835

—Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. In this work, we propose a new type of privacy inference attack, coined Preference Profiling A ttack (PPA), t hat accurately profiles t he p rivate p references o f a l ocal u ser, e .g., m ost liked (disliked) items from the client’s online shopping and most common expressions from the user’s selfies. I n g eneral, P PA can profile t op-k (i.e., k = 1, 2, 3 a nd k = 1 i n p articular) preferences contingent on the local client (user)’s characteristics. Our key insight is that the gradient variation of a local user’s model has a distinguishable sensitivity to the sample proportion of a given class, especially the majority (minority) class. By observing a user model’s gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user’s local dataset, and thus the user’s preference of the class is exposed. The inherent statistical heterogeneity of FL further facilitates PPA. We have extensively evaluated the PPA’s effectiveness using four datasets (MNIST, CIFAR10, RAF-DB and Products-10K). Our results show that PPA achieves 90% and 98% top-1 attack accuracy to the MNIST and CIFAR10, respectively. More importantly, in real-world commercial scenarios of shopping (i.e., Products-10K) and social network (i.e., RAF-DB), PPA gains a top-1 attack accuracy of 78% in the former case to infer the most ordered items (i.e., as a commercial competitor), and 88% in the latter case to infer a victim user’s most often facial expressions, e.g., disgusted. The top-3 attack accuracy and top-2 accuracy is up to 88% and 100% for the Products-10K and RAF-DB, re

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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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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Generalization analysis for multi-label learning 24

Generalization analysis for multi-label learning

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Proceedings of the 41st International Conference on Machine Learning

作者： Yi-Fan Zhang Min-Ling Zhang School of Cyber Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China and School of Computer Science and Engineering Southeast University Nanjing China

Despite great advances in algorithms for multilabel learning, research on the theoretical analysis of generalization is still in the early stage. Some recent theoretical results has investigated the generalization performance of multi-label learning under several evaluation metrics, however, how to reduce the dependency on the number of labels, explicitly introduce label correlations, and quantitatively analyze the impact of various inductive biases in the generalization analysis of multi-label learning is still a crucial and open problem. In an attempt to make up for the gap in the generalization theory of multi-label learning, we develop several novel vector-contraction inequalities, which exploit the Lipschitz continuity of loss functions, and derive generalization bounds with a weaker dependency on the number of labels than the state of the art in the case of decoupling the relationship among different components, which serves as theoretical guarantees for the generalization of multilabel learning. In addition, we derive the generalization bound for Macro-Averaged AUC and analyze its relationship with class-imbalance. The mild bounds without strong assumptions explain the good generalization ability of multi-label learning with first-order label correlations and high-order label correlations induced by norm regularizers.

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Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

arXiv

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arXiv 2025年

作者： Wang, Zhenglin Wu, Jialong Li, Pengfei Jiang, Yong Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Tongyi Lab Alibaba Group China

Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Mandarin Lombard Grid: A Noise Induced Lombard-Grid-Like Corpus of Standard Chinese 23

Mandarin Lombard Grid: A Noise Induced Lombard-Grid-Like Cor...

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23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022

作者： Yang, Yuhong Chen, Xufeng Liu, Qingmu Tu, Weiping Chen, Hongyang Cai, Linjun National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China

The Lombard effect is natural, whereby speakers automatically adjust the vocal effort to facilitate speech understanding in noise. Since real-world applications are generally involved in noisy environments, the Lombard effect of highly variable speech features due to changing background noise is one of those challenges to match these real scenarios. Existing Lombard corpora show variations in the background noise level, ranging from 35 to 96 dB sound pressure level (SPL). However, it remains unclear if we need to collect all SPLs to build a comprehensive Lombard corpus. And most existing Lombard corpora are built for English;however, Mandarin and English are different in pronunciation. This paper describes our effort to build the first open-source Lombard corpus of standard Chinese, the Mandarin Lombard Grid. The effort involves three steps: (1) Classify Mandarin Lombard styles according to different background noise levels. (2) Create the corpus containing each style. (3) Analyze Mandarin Lombard effects showing their differences from English. We found three critical Lombard styles ranging from 30 dB to 85 dB-SPL and built the corpus containing the three Lombard styles and one reference plain style. Lombard effect analyses on this corpus showed consistency and some differences with the English Lombard Grid corpus. Copyright © 2022 ISCA.

关键词： Speech communication

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Screen-printed graphite electrode on polyvinyl chloride and parchment strips integrated with genetic programming for in situ nitrate sensing of aquaponic pond water

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Information Processing in Agriculture 2024年第2期11卷 187-201页

作者： Ronnie ConcepcionⅡ Bernardo Duarte Maria Gemel Palconit Jonah Jahara BaunArgel Bandala Ryan Rhay Vicerra Elmer Dadios Department of Manufacturing Engineering and Management De La Salle UniversityManila 1004 Philippines MARE-Marine and Environmental Sciences Centre.&ARNET-Aquatic Research Infrastructure Network Associated Laboratory Faculdade de Ciencias da Universidade de LisboaCampo Grande1749-016 LisboaPortugal Departamento de Biologia Vegetal da Faculdade de Ciencias da Universidade de Lisboa Campo Grande 1749-016 LisboaPortugal Department of Electronics and Computer Engineering De La Salle UniversityManila 1004 Philppines

Nitrate is the primary water-soluble macronutrient essential for plant growth that is converted from excess fish feeds,fish effluents,and degrading biomaterials on the aquaponic pond floor,and when aquacultural malpractices occur,large amounts of it retain in the water system causing increase rate in eutrophication and toxifies fish and aquaculture *** nitrate sensor prototypes still require performing the additional steps of water sample deionization and dilution and were constructed with expensive *** response to the challenge of sensor enhancement and aquaponic water quality monitoring,this study developed sensitive,repeatable,and reproducible screen-printed graphite electrodes on polyvinyl chloride and parchment paper substrates with silver as electrode material and 60:40 graphite powder:nail polish formulated conductive ink for electrical traces,integrated with 9-gene genetic expression model as a function of peak anodic current and electrochemical test time for nitrate concentration prediction that is embedded into low-power Arduino ESP32 for in situ nitrate sensing in aquaponic pond *** SPE electrical traces were designed on the two types of *** electron microscopy with energy dispersive X-ray confirmed the electrode surface *** cyclic voltammetry using 10 to 100 mg/L KNO3 and water from three-depth regions of the actual pond established the electrochemical test time(10.5 s)and electrode potential(0.135 V)protocol necessary to produce peak current that corresponds to the strength of nitrate ions during *** findings from in situ testing revealed that the proposed sensors have strong linear predictions(R2=0.968 MSE=1.659 for nSPEv and R2=0.966 MSE=4.697 for nSPEp)in the range of 10 to 100 mg/L and best detection limit of 3.15μg/L,which are comparable to other sensors of more complex *** developed three-electrode electrochemical nitrate sensor confirms that it is reliable for bot

关键词： Aquaponic water quality Electrochemical technology Genetic programming Graphite electrode Nitrate sensor Precision agriculture Printed electronics Scanning electron microscopy Screen-printed electrode Voltammetry

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Fast Multi-Instance Partial-Label Learning 39

Fast Multi-Instance Partial-Label Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yin-Fang Tang, Wei Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)157735897X

Multi-instance partial-label learning (MIPL) is a paradigm where each training example is encapsulated as a multi-instance bag associated with the candidate label set, which includes one true label and several false positives. Current MIPL algorithms typically assume that all instances are independent, thereby neglecting the dependencies and heterogeneity inherent in MIPL data. Moreover, these algorithms often prove to be excessively time-consuming when dealing with complex datasets, significantly limiting the practical application of MIPL. In this paper, we propose FASTMIPL, a framework that employs mixed-effects model to explicitly capture the dependencies and heterogeneity among instances and bags. FASTMIPL is able to learn from MIPL data both effectively and efficiently by utilizing the predefined dependencies modeling module and leveraging the posterior predictive probability disambiguation strategy. Experiments show that the performance of FASTMIPL is highly competitive to state-of-the-art methods, while significantly reducing computational time in benchmark and the real-world datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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HIAWSC: An Immune Algorithm Based Heuristic Web Service Composition Framework

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Chinese Journal of Electronics 2025年第3期23卷 579-585页

作者： Jiuyun Xu Reiff-Marganiec Stephan School of Computer and Communication Engineering China University of Petroleum Qingdao China Network and Switch Technology Key Laboratory Beijing University of Posts and Telecommunication Beijing China Department of Computer Science University of Leicester Leicester UK

The introduction of of web services has led to web service composition being a focus of many researchers. Composing web services using workflows is seen as the most realistic method from an industrial view-point. Amongst other method, the use of natural computing methods has been proposed previously to automate web service composition. The need for a fast response when computing the most suitable sequence of services is addressed in this paper. In particular, we propose a novel heuristic immune algorithm with an efficient encoding and mutation method. The algorithm involves two steps: an immune selection operation, which is maintaining antibody population diversity and the clonal selection. The use of a vaccine during the evolution provides heuristic information that accelerates the convergence. Our experimental results illustrate that the proposed heuristic immune algorithm is very effective in improving the convergence speed. We also provide a schema analysis for this method.

关键词： Web services Heuristic algorithms Antibodies Encoding Vaccines Convergence

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