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Modality Unified Attack for Omni-Modality Person Re-Identification

IEEE Transactions on Information Forensics and Security 2025年 20卷 5577-5587页

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Feature extraction Perturbation methods Measurement Generators Collaboration Robustness Identification of persons Data mining Closed box Training

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A Model that Learns Discriminative Features for Automatic Sleep Stage Classification Based on Raw Single-Channel EEG

SSRN

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SSRN 2023年

作者： Heng, Xia Wang, Miao Wang, Zhong-Min Zhang, Jie He, Lang Fan, Lin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

Sleep staging is the basis of sleep quality assessment. In the process of scoring each sleep stage, some automatic sleep staging models often fail to effectively capture the more accurate long-range correlation coupling between the input sleep EEG signals and the output sleep stage, which leads to the extracted features cannot effectively distinguish the different sleep stage. In order to alleviate the long-term time series problem of sleep EEG signals and learn the discriminative features of each sleep stage in sleep EEG signals, we propose an end-to-end automatic sleep stage classification method based on raw single-channel sleep EEG signals, which can improve the attention to the discriminative features of each sleep stage. In the construction of the model, in order to learn the source domain information of the key part of the sleep EEG signal, SE-Block is added to Convolutional Neural Network (CNN) to highlight the time-frequency domain features related to the sleep stage. Meanwhile, the attention mechanism is added to the decoding part of Bidirectional Recurrent Unit (Bi-GRU) to enhance the long-term memory ability of Bi-GRU and learn the conversion rules between sleep stages. To validate the accuracy and stability of the model, the Fpz-Cz channel and Pz-Oz channel EEG signals in the Sleep-EDF sleep dataset are used for 10-fold cross-validation. The classification accuracies obtained are 88.48% and 87.56%, respectively. The results show that our model has stronger ability to extract discriminative features, better representation ability, more stable performance and relatively simple structure. © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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R-calculus for post three-valued description logic 7

R-calculus for post three-valued description logic

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7th International Conference on Fuzzy Systems and Data Mining, FSDM 2021

作者： Cao, Cungen Hu, Lanxi Sui, Yuefei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9781643682143

R-calculus is a belief revision operator satisfying AGM postulates, and belief revision in ontology engineering is ontology revision, which based logic is description logics. In Post three-valued description logic, a tableau proof system Tt will be given such that Tt is sound and complete for t-satisfiability, and nonmonotonic, that is, a theory Δis t-satisfiable if and only if Δis deducible in Tt. Based on the tableau proof system, an R-calculus Rt will be given such that a configuration Î"|C(a) is reducible to C(a),Δif and only if C(a) is t-satisfiable with Î", if and only if reduction Î"|C(a) â‡' C(a),Δis deducible in Rt. © 2021 The authors and IOS Press.

关键词： Ontology

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High Transmittance Band-Pass Plasmonic Filter Using Metal-Insulator-Metal Waveguide with Double Split Circular Ring Resonator

High Transmittance Band-Pass Plasmonic Filter Using Metal-In...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Hui Wang Yi-yang Huang San-wang Ma Chun Ni Zhi-xiang Huang Xian-liang Wu Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu'an China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (纸本)9781665482271

In this paper, a band-pass plasmonic filter based on Metal-Insulator-Metal (MIM) waveguides and its modified structures are designed and investigated theoretically and numerically. The filter consists of two circular ring resonator and its modified structures which has narrow gaps in the middle of resonant rings. To study their performance in different structures, propagation modes of surface plasmon polaritons (SPPs) are investigated. In outer gapped model, we found that the transmission in different resonant modes can be effectively tuned by varying the gap width, and the transmission peak wavelength is nonlinear with the gap width. The results are obtained by high-order symplectic FDTD (SFDTD) algorithm which is non-dissipative and has low dispersion error. The structure with double gaps in both external and internal rings have potential applications in plasmonic integrated circuits, because of its characteristic of a single narrow band-pass with high transmittance. The introduced structures may provide a novel method for the design of devices in highly integrated optical circuits.

关键词： Band-pass filters Optical filters Structural rings Integrated optics Resonator filters Plasmons Optical ring resonators

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Stabilization of Uncertain Fractional Memristor Chaotic Time⁃Delay System Based on Fractional Order Sliding Mode Control

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Journal of Harbin Institute of Technology(New Series) 2020年第4期27卷 74-83页

作者： Dawei Ding Fangfang Liu Nian Wang Dong Liang School of Electronics and Information Engineering Anhui UniversityHefei 230601China Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationHefei 230601China

Based on Lyapunov theorem and sliding mode control scheme,the chaos control of fractional memristor chaotic time⁃delay system was *** order to stabilize the system,a fractional sliding mode control method for fractional time⁃delay system was *** addition,Lyapunov stability theorem was used to analyze the control scheme theoretically,which guaranteed the stability of commensurate and non⁃commensurate order systems with or without uncertainties and ***,to illustrate the feasibility of controller,the conditions for designing the controller parameters were ***,the simulation results presented the effectiveness of the designed strategy.

关键词： fractional order system time⁃delay chaos control uncertainty sliding mode control

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

DOA Estimation for RIS Assisted Multi-User Wireless Communic...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Canping Yu Li Li Yingsong Li Dazhi Sun Liping Li Zhixiang Huang Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Teddington Middlesex UK

Reconfigurable intelligent surface (RIS) can control the phase of reflected signals, and has become a hotspot in the field of wireless communication. In this paper, we propose a direction-of-arrival (DOA) estimation algorithm for RIS assisted multi-user wireless communication systems with squared-sine error criterion and Lawson norm constraint. This algorithm utilizes the sparsity of the signal and can achieve fast and accurate DOA estimation under the interference of impulsive noises. To suppress the line-of-sight (LOS) interference and thus to maximize the received signal-to-interference-plus-noise ratio (SINR), we also propose a random optimal phase selection (ROPS) technique to design the control matrix of RIS elements. The simulation results show that the proposed algorithm performs better than the existing methods.

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Identification of pseudo dissolution hysteresis coefficient of industrial aluminium electrolysis cell based on process mechanism knowledge

Identification of pseudo dissolution hysteresis coefficient ...

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第33届中国过程控制会议

作者： Zhaohui Zeng Weihua Gui Xiaofang Chen Yongfang Xie Renchao Wu School of Automation Central South University Key Laboratory of Intelligent Computing & Information Processing Ministry of EducationXiangtan University Peng Cheng Laboratory

To overcome the difficulties that are associated with the online recognition of the alumina dissolution properties in industrial aluminium electrolysis cells,a new parameter pseudo dissolution hysteresis coefficient(PDHC) is identified based on process mechanism *** process mechanism analysis and process semantic embedding,the proposed PDHC explicitly represents the process semantemes that are implied in the normalized cell voltage(NCV) and the feed *** PDHC quantifies the online dissolution performance of alumina and,thus,can overcome the inability to express the alumina online dissolution performance in industrial cells.

关键词： process mechanism analysis process semantic embedding explicit representation automatic knowledge acquisition pseudo dissolution hysteresis coefficient aluminium electrolysis

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KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation

KERM: Knowledge Enhanced Reasoning for Vision-and-Language N...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiangyang Li Zihan Wang Jiahao Yang Yaowei Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The re-trieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method. The source code is available at https://***/XiangyangLi20/KERM.

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction

DISC: Learning from Noisy Labels via Dynamic Instance-Specif...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yifan Li Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of the Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two- view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. Code is available at https://***/JackYFL/DISC.

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