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IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

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Modeling a target-selection motion by leveraging an optimal feedback control mechanism

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science China(Information sciences) 2018年第4期61卷 209-211页

作者： Jin HUANG Xiaolan PENG Feng TIAN Hongan WANG Guozhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

Target selection has always been a popular research topic in the human-computer interaction(HCI)*** with continuous interactive spaces,such as video games,augmented reality(AR),and virtual reality(VR),are becom-

关键词： Modeling a target-selection motion by leveraging an optimal feedback control mechanism

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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Full-Coverage Mapping of Daily High-Resolution XCO2 across China from 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Yi Yan, Jining Zhong, Liheng Bao, De Sun, Leigang Li, Guangyu China University of Geosciences School of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Ant Group World Financial Center Beijing100020 China Hebei Academy of Sciences Institute of Geographical Sciences Shijiazhuang050011 China Hebei Technology Innovation Center for Geographic Information Application Shijiazhuang050011 China Environmental Development Center of the Ministry of Ecology and Environment China

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1° spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1° resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R2 of 0.86 and an RMSE of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. © 1980-2012 IEEE.

关键词： Atmospheric movements

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Generalized Cross-Entropy Benchmarking for Random Circuits with Ergodicity

arXiv

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arXiv 2025年

作者： Cheng, Bin Meng, Fei Zhang, Zhi-Jiong Yung, Man-Hong Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Technologies National University of Singapore Singapore Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Department of Physics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Cross-entropy benchmarking is a central technique used to certify a quantum chip in recent experiments. To better understand its mathematical foundation and develop new benchmarking schemes, we introduce the concept of ergodicity to random circuit sampling and find that the Haar random quantum circuit satisfies an ergodicity condition—the average of certain types of post-processing function over the output bit strings is close to the average over the unitary ensemble. For noiseless random circuits, we prove that the ergodicity holds for polynomials of degree t with positive coefficients and when the random circuits form a unitary 2t-design. For strong enough noise, the ergodicity condition is violated. This suggests that ergodicity is a property that can be exploited to certify a quantum chip. We formulate the deviation of ergodicity as a measure for quantum chip benchmarking and show that it can be used to estimate the circuit fidelity for global depolarizing noise and weakly correlated noise. For a quadratic post-processing function, our framework recovers Google’s result on estimating the circuit fidelity via linear cross-entropy benchmarking (XEB), and we give a sufficient condition on the noise model characterizing when such estimation is valid. Our results establish an interesting connection between ergodicity and noise in random circuits and provide new insights into designing quantum benchmarking schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum electronics

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A constraint-weaving approach to points-to analysis for AspectJ

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Frontiers of computer science 2014年第1期8卷 52-68页

作者： Qiang SUN Yuting CHEN Jianjun ZHAO Department of Computer Science and Engineering and School of Software Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

Points-to analysis is a static code analysis tech- nique that establishes the relationships between variables of references and allocated objects. A number of points-to analysis algorithms have been proposed for procedural and object-oriented languages like C and Java, while few of them can be used for AspectJ as we know so far. One main rea- son is that Aspect/is an aspect-oriented language which im- plements the separation of crosscutting concerns by advices, pointcuts, and inter-type declarations, while a points-to anal- ysis of AspectJ programs may be imprecise because any as- pect woven into the base code may change the points-to rela- tions in the program and thus a conservative analysis has to be taken in order to handle the aspects. In this paper, we pro- pose a context-sensitive points-to analysis technique called AJPoints for AspectJ. Similar to the weaving mechanism for AspectJ, AJPoints obtains the constraints and templates on the points-to relations for the base code and the aspects, re- spectively, but weaves and solves them in an iterative manner in order to cross the boundary between the base code and the aspects. We have implemented AJPoints on abc AspectJ compiler and evaluated it by using twelve AspectJ benchmark programs. The experimental can achieve a high precision pectJ programs. results show that our technique about points-to relations in As-

关键词： points-to analysis context-sensitive analysis,AspectJ

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STGCN-LSTM FOR OLYMPIC MEDAL PREDICTION: DYNAMIC POWER MODELING AND CAUSAL POLICY OPTIMIZATION

arXiv

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arXiv 2025年

作者： Wang, Yiquan Wang, Jiaying Huang, Tin-Yeh Yang, Jingyi Wang, Guanjie Xu, Zihao College of Mathematics and System Science Xinjiang University Xinjiang Urumqi830046 China Intelligent Software Research Center Institute of Software Chinese Academy of Sciences Beijing100190 China Shenzhen X-Institute Shenzhen518055 China College of Architecture and Environment Sichuan University Chengdu610065 China Department of Industrial and System Engineering Faculty of Engineering The Hong Kong Polytechnic University 999077 Hong Kong School of Design and Art Hunan Institute of Engineering Xiangtan411104 China School of Computer and Artificial Intelligence Huaihua University Huaihua418099 China Faculty of Science and Engeerning The University of Nottingham-Ningbo Ningbo315100 China

This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such as coaching exchange and socio-economic links-while the Long Short-Term Memory (LSTM) module models historical trends in medal counts, economic data, and demographics. To address zero-inflated outputs (i.e., the disparity between countries that consistently yield wins and those never having won medals), a Zero-Inflated Compound Poisson (ZICP) framework is incorporated to separate random zeros from structural zeros, providing a clearer view of potential breakthrough performances. Validation includes historical backtracking, policy shock simulations, and causal inference checks, confirming the robustness of the proposed method. Results shed light on the influence of coaching mobility, event specialization, and strategic investment on medal forecasts, offering a data-driven foundation for optimizing sports policies and resource allocation in diverse Olympic contexts. © 2025, CC BY-NC-SA.

关键词： Resource allocation

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MCGIM-Based Model Streaming for Realtime Progressive Rendering

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Journal of computer science & Technology 2011年第1期26卷 166-175页

作者：盛斌孟维亮孙汉秋吴恩华 Department of Computer Science and Engineering the Chinese University of Hong Kong State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences Faculty of Science and Technology University of Macao

While most mesh streaming techniques focus on optimizing the transmission order of the polygon data, few approaches have addressed the streaming problems by using geometry images （GIM）. In this paper, we present a new approach which firstly partitions a mesh into several surface patches, then converts these patches into multi-chart geometry images （MCGIM）. After resampling the MCGIM and normal map atlas are obtained, we hierarchically construct the regular geometry image representation by adopting the quadtree structure. In this way, the encoded nodes can be transmitted in arbitrary order with high transmission flexibility. Also, the rendering quality of the partially transmitted models can be greatly improved by using the normal texture atlas. Meanwhile only the geometry on the silhouette to the current viewpoint are required to be refined and transmitted, therefore the amount of data is minimized for transferring each frame. In particular, our approach also allows users to encode and transmit the mesh data via JPEG2000 technique. Therefore, our mesh streaming method is suitable for transmitting 3D animation models with use of Motion JPEG2000 videos. Experimental results have demonstrated the effectiveness of our approach, which enables one server to stream the MCGIM texture atlas to the clients. Also, the transmitted model can be rendered in a multiresolution manner by GPU acceleration on the client side, due to the regular geometry structure of MCGIM.

关键词： mesh streaming geometry images JPEG2000 multiresolution rendering

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MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching

arXiv

引用

arXiv 2025年

作者： Liu, Yepeng Sun, Zhichao Yu, Baosheng Zhao, Yitian Du, Bo Xu, Yongchao Cheng, Jun National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430070 China Lee Kong Chian School of Medicine Nanyang Technological University 308232 Singapore Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315211 China 1 Fusionpolis Way #21-01 Connexis South Tower Singapore138632 Singapore

Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness against the non-linear variations present in multimodal data. Extending such methods to multimodal image matching often requires well-aligned multimodal data to learn modality-invariant descriptors. However, acquiring such data is often costly and impractical in many real-world scenarios. To address this challenge, we propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching using only single-modality training data. Specifically, we propose a novel latent feature aggregation module and a cumulative hybrid aggregation module to enhance the base keypoint descriptors trained on single-modality data by leveraging pretrained features from Stable Diffusion models. We validate our method with recent keypoint detection and description methods in three multimodal retinal image datasets (CF-FA, CF-OCT, EMA-OCTA) and two remote sensing datasets (Optical-SAR and Optical-NIR). Extensive experiments demonstrate that the proposed MIFNet is able to learn modality-invariant feature for multimodal image matching without accessing the targeted modality and has good zero-shot generalization ability. The source code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Multi-domain Lightweight Asymmetric Group Key Agreement

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Chinese Journal of Electronics 2018年第5期27卷 1085-1091页

作者： ZHENG Jiamin TAN Yu'an ZHANG Xiaosong ZHANG Qikun ZHANG Quanxin ZHANG Changyou School of Computer Science and Technology Beijing Institute of Technology Department of Computer Science and Technology Tangshan University Institute of Computer and Communication Engineering Zhengzhou University of Light Industry Institute of Software Chinese Academy of Science

The terminals in mobile cloud computing have themselves the special characteristics, such as mobile flexibility, distributed in different domains, resourceconstraint and easily to be captured et al. The terminals in mobile cloud computing participated the collaborative computing, information exchange and sharing secrets may come from different domains, different networks or different clouds. For this complex network environment the paper proposes a Multi-domain lightweight Asymmetric group key agreement(ML-AGKA). It adopts the bilinear mapping and blind key technology to achieve an asymmetric group key agreement protocol among mobile terminals distributed in different domains, proposes a computation and communication migration technologies to ensure that the mobile terminals are lightweight computing and communication consumption. The protocol can also achieve anonymity and authentication. The protocol is proven secure under the Computational Diffe-Hellman(CDH) problem assumption and the performance analysis shows that the proposed protocol is highly efficient.

关键词： Asymmetric group key agreement(AGKA) Lightweight computing Bilinear mapping Multi-domain

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