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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

The Stackelberg prediction game (SPG) is a popular model for characterizing strategic interactions between a learner and an adversarial data provider. Although optimization problems in SPGs are often NP-hard, a notable special case involving the least squares loss (SPG-LS) has gained significant research attention recently [1, 2, 3]. The latest state-of-the-art method for solving the SPG-LS problem is the spherically constrained least squares reformulation (SCLS) method proposed in the work of [3]. However, the paper [3] lacks theoretical analysis on the error of the SCLS method, which limits its large-scale applications. In this paper, we investigate the estimation error between the learner obtained by the SCLS method and the actual learner. Specifically, we reframe the estimation error of the SCLS method as a Primary Optimization (PO) problem and utilize the Convex Gaussian min-max theorem (CGMT) to transform the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for the SCLS method based on this simplified AO problem. This analysis not only strengthens the theoretical framework of the SCLS method but also confirms the reliability of the learner produced by it. We further conduct experiments to validate our theorems, and the results are in excellent agreement with our theoretical predictions.

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The reliability of OKRidge method in solving sparse ridge regression problems 24

The reliability of OKRidge method in solving sparse ridge re...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Youjun Wang Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

Sparse ridge regression problems play a significant role across various domains. To solve sparse ridge regression, [1] recently proposes an advanced algorithm, Scalable Optimal K-Sparse Ridge Regression (OKRidge), which is both faster and more accurate than existing approaches. However, the absence of theoretical analysis on the error of OKRidge impedes its large-scale applications. In this paper, we reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and employ the Convex Gaussian min-max theorem (CGMT) to simplify the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for OKRidge based on the AO problem. This error analysis improves the theoretical reliability of OKRidge. We also conduct experiments to verify our theorems and the results are in excellent agreement with our theoretical findings.

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SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime Statistics Sensing 9

SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime St...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Yin Fu, Anmin Zhang, Yuqing University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350319804

The ICS protocols undertake critical functions in industrial control systems, by enabling real-time data exchange, data acquisition, parameter configuration, status monitoring, fault diagnosis, command issuance and execution. It serves as the foundation for the collaborative operations of all components in industrial control systems. With the development of internet technology, industrial control systems, which were originally physically isolated, have become new targets for hackers. However, due to the high level of privatization of ICS protocols and their complex workflow, existing methods are insufficient in detecting vulnerabilities in these protocols. This paper proposes a black-box fuzzing test method based on sensing the program runtime feedback. By collecting run-time statistics, including CPU utilization, memory utilization, disk I/O, and message interaction, our method is able to determine whether the program has triggered a new execution path, thus improving the efficiency of the fuzzing procedure. We evaluated our prototype on five open-source ICS protocol programs and discovered six reported vulnerabilities, among which five could cause the industrial control programs to become unresponsive or crash. © 2023 IEEE.

关键词： Fuzzing ICS Protocols

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Perception-Enhanced Generative Transformer for Key Information Extraction from Documents 27th

Perception-Enhanced Generative Transformer for Key Informat...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Runbo Ou Yang, Jun Jie Gao, Chen Qin, Xugong Zeng, Gangyan Hu, Xiaoxu Zhang, Peng School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Viterbi School of Engineering University of Southern California Los Angeles United States National Computer Network Response Technical Team/Coordination Center Technology Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)9783031781186

Key information extraction (KIE) from scanned documents has attracted significant attention due to practical real-world applications. Despite impressive results achieved by incorporating multimodal information within the generative framework, existing methods fail to understand complex layouts and fuzzy semantics in document images. To settle these issues, we propose a perception-enhanced generative transformer (PEGT), which improves the model through fine-grained multimodal modeling and pre-training tasks tailored for the generative framework. Firstly, we introduce a pre-trained vision-language model to provide transferable knowledge for visual text perceptron. Then two auxiliary pre-training tasks including absolute position prediction (APP) and semantic relationship reasoning (SRR) are designed for the generative framework. APP learns to predict which grids the texts fall into, improving the model on utilization of the position information. SRR exploits prior information of semantic relationships, injecting the ability for better semantic discrimination into PEGT. Finally, well-designed prompts are leveraged to unleash the potential of PEGT for extracting key information from documents. Extensive experiments on several public datasets show that PEGT effectively generalizes over different types of documents. Especially, PEGT achieves state-of-the-art results in terms of F-measure, i.e., 97.47%, 98.04%, and 84.32% on the SROIE, CORD, and FUNSD datasets, demonstrating the superiority of the proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Research on Lightweight Sensing technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年第12期12卷 20678-20694页

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Science and Engineering Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering State Key Laboratory of Mobile Communication Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China

Channel state information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This article concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) the development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multiantenna techniques for single-antenna settings and 2) the enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver-based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multiantenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and position and environment independent features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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Task Offloading Strategy with Emergency Handling and Blockchain Security in SDN-Empowered and Fog-Assisted Healthcare IoT

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Tsinghua Science and technology 2022年第4期27卷 760-776页

作者： Junyu Ren Jinze Li Huaxing Liu Tuanfa Qin School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510640China Guangxi University Xingjian College of Science and Liberal Arts Nanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Faculty of Earth Sciences&Geography Trinity CollegeUniversity of CambridgeCambridgeCB21TNUK

With the rapid advancement of the Internet of Things(IoT),the typical application of wireless body area networks(WBANs)based smart healthcare has drawn wide attention from all sectors of *** alleviate the pressing challenges,such as resource limitations,low-latency service provision,mass data processing,rigid security demands,and the lack of a central entity,the advanced solutions of fog computing,software-defined networking(SDN)and blockchain are leveraged in this *** the basis of these solutions,a task offloading strategy with a centralized low-latency,secure and reliable decision-making algorithm having powerful emergency handling capacity(LSRDM-EH)is designed to facilitate the resource-constrained edge devices for task ***,to well ensure the security of the entire network,a comprehensive blockchain-based two-layer and multidimensional security strategy is ***,to tackle the inherent time-inefficiency problem of blockchain,we propose a blockchain sharding scheme to reduce system time *** simulation has been conducted to validate the performance of the proposed measures,and numerical results verify the superiority of our methods with lower time-latency,higher reliability and security.

关键词： wireless body area networks(WBANs) healthcare IoT software-defined networking(SDN) fog computing blockchain task offloading blockchain sharding

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

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Frontiers of computer Science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

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Dual-focus: person search from Coarse-Grained Focus to Fine-Grained Focus

Dual-focus: person search from Coarse-Grained Focus to Fine-...

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作者： Hu, Wenyi Wang, Xiao Wang, Zheng Xu, Xin Hu, Ruimin National Engineering Research Center for Multimedia Software Wuhan University Hubei Wuhan430079 China School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China

Person search, which aims to find the target person in the whole scene images, has become a crucial application in security surveillance with various types of noise. Existing methods solve this problem by addressing two subtasks simultaneously: detection, distinguishing people from background noise;and identification, separating target from others noise. However, these two tasks are fundamentally contradictory, as detection is designed to be robust to intra-class variations to capture general human features, while identification is designed to be sensitive to inter-class variations to focus only on the target. Inspired by the observation that the police first narrowed the scope to a small group of people similar to the suspect, and then located the target, we propose a Dual-Focus framework consisting of Coarse-Grained Focus (search for similar people) and Fine-Grained Focus (find the target). Extensive experiments on datasets such as CUHK-SYSU and PRW are conducted to evaluate the effectiveness of our proposed methods. The evaluations demonstrate that our method delivers superior results compared with the state-of-art methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Coarse-Grained Focus Fine-Grained Focus Person search

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