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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

Control Theory and technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

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IEEE Sensors Journal 2025年第11期25卷 20547-20560页

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

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Retrieval-augmented prompts for text-only image captioning 4

Retrieval-augmented prompts for text-only image captioning

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4th International Conference on Computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Huang, Jinbo Xie, Gang Cao, Li School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China

ISBN: (纸本)9781510683242

Supervised image captioning methods have made significant progress, but high-quality human-annotated image-text paired datasets are costly to collect. Recently, pre-trained vision-language models, such as CLIP, have shown exceptional performance in cross-modal associations, offering novel solutions for image captioning, such as zero-shot captioning through purely textual training. However, the modality gap exists between image and text presents an obstacle to cross-modal alignment within text-only captioning. Moreover, insufficient visual understanding and over-reliance on textual data in training lead to hallucinations (e.g., object misidentification and inaccurate object counts), resulting in irrational captioning. To tackle these issues, this paper presents RAPCap, a text-only method with retrieval-augmented prompts for text-only image captioning. Specifically, RAPCap enhances the language model's understanding of images by incorporating similar captions obtained through retrieval augmentation, thereby alleviating hallucinations. During inference, RAPCap translates the image to textual space to bridge the modality gap. Experimental results demonstrate that RAPCap achieves a new state-of-the-art performance on the Flickr30k and performs competitively on the MS-COCO compared to previous zero-shot captioning. © 2024 SPIE.

关键词： Visual languages

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Collaborative graph contrastive learning for recommendation

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 33-46页

作者： Wei, Tao Yang, Changchun Zheng, Yanqi Zhang, Jingxue School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou China School of Microelectronics and Control Engineering Changzhou University Jiangsu Changzhou China

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. © 2025 – IOS Press. All rights reserved.

关键词： Self-supervised learning

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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Process capability monitoring and change-point analysis for S-type quality characteristic

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Quality technology and Quantitative Management 2024年第2期21卷 237-256页

作者： Liao, Mou-Yuan Wu, Chien-Wei School of Economics and Management Huzhou College Huzhou China Department of Data Science and Big Data Analytics Providence University Taichung Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan

Among various quality assurance activities, process capability indices (PCIs) are recognized as the most effective tools to quantify and evaluate process performance. The one-sided capability index (Formula presented.) can adequately measure the process capability for processes with a smaller-is-better (S-type) quality characteristic. While focusing on the index (Formula presented.), this study constructs a capability control chart to monitor the short-term capability based on the exponentially weighted moving average (EWMA). Simulations have shown that the EWMA capability control chart is adaptive regarding average run length. Further, this study applies change-point analysis to determine whether and when the short-term capability-level changes. Using these two proposed methods, engineers can realize a variety of short-term process capabilities in good time and implement control measures efficiently against a poor long-term process capability. © 2023 International Chinese Association of Quantitative Management.

关键词： Control charts

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EFFICIENT BACKDOOR ATTACKS FOR DEEP NEURAL NETWORKS IN REAL-WORLD SCENARIOS 12

EFFICIENT BACKDOOR ATTACKS FOR DEEP NEURAL NETWORKS IN REAL-...

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12th International Conference on Learning Representations, ICLR 2024

作者： Li, Ziqiang Sun, Hong Xia, Pengfei Li, Heng Xia, Beihao Wu, Yi Li, Bin Big Data and Decision Lab University of Science and Technology of China China Huazhong University of Science and Technology China

Recent deep neural networks (DNNs) have came to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry out backdoor attacks. However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data. In this paper, we introduce a more realistic attack scenario where victims collect data from multiple sources, and attackers cannot access the complete training data. We refer to this scenario as data-constrained backdoor attacks. In such cases, previous attack methods suffer from severe efficiency degradation due to the entanglement between benign and poisoning features during the backdoor injection process. To tackle this problem, we introduce three CLIP-based technologies from two distinct streams: Clean Feature Suppression and Poisoning Feature Augmentation The results demonstrate remarkable improvements, with some settings achieving over 100% improvement compared to existing attacks in data-constrained scenarios. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Individual Word Length Patterns for Fractional Factorial(Split-Plot)Designs

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Journal of Systems science & Complexity 2023年第5期36卷 2082-2099页

作者： HAN Xiaoxue CHEN Jianbin YANG Jianfeng LIU Minqian School of Statistics and Data Science Qufu Normal UniversityQufu 273165China School of Mathematics and Statistics Beijing Institute of TechnologyBeijing 100081China School of Statistics and Data Science LPMC&KLMDASRNankai UniversityTianjin 300071China

Fractional factorial(FF)designs are commonly used for factorial experiments in many *** some prior knowledge has shown that some factors are more likely to be significant than others,Li,et al.(2015)proposed a new pattern,called the individual word length pattern(IWLP),which,defined on a column of the design matrix,measures the aliasing of the effect assigned to this column and effects involving other *** this paper,the authors first investigate the relationships between the IWLP and other popular criteria for regular FF *** we know,fractional factorial split-plot(FFSP)designs are important both in theory and *** another contribution of this paper is extending the IWLP criterion from FF designs to FFSP *** authors propose the IWLP of a factor from the whole-plot(WP),or sub-plot(SP),denoted by the I_w WLP and Is WLP respectively,in the FFSP *** authors further propose combined word length patterns C_(w) WLP and Cs WLP,in order to select good designs for different *** new criteria C_(w) WLP and Cs WLP apply to the situations that the potential important factors are in WP or SP,*** examples are presented to illustrate the selected designs based on the criteria established here.

关键词： Effect hierarchy fractional factorial split-plot prior information regular design

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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An Improved Apriori Algorithm Based on Goal Orientation and Double Pruning

An Improved Apriori Algorithm Based on Goal Orientation and ...

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2023 International Conference on Networks, Communications and Intelligent Computing, NCIC 2023

作者： Deng, Shiyu Li, Li Xiong, Yichen Zhang, Jian School of Computer Science Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China School of Economics and Management Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing Information Science and Technology University Laboratory of Big Data Decision Making for Green Development Beijing China

ISBN: (纸本)9798350371949

Aiming at solving the problem of high time complexity of association rule mining among item-sets in Apriori algorithm and obtaining the set of goal-association relations of single-to-single point type, this paper improves the Apriori algorithm based on Goal Orientation and Double Pruning (GODP-Apriori). Oriented by the type of goal, the GODP-Apriori uses double pruning to remove the redundant item set during the calculation of the frequency of item set support, which greatly improves the efficiency of obtaining the frequent item set;in addition to this, by sacrificing space for time, it also records the set of transaction items corresponding to the item when calculating the support degree, which avoids the repetitive scanning in the subsequent calculations and then effectively increases the computation efficiency of traditional Apriori algorithm. The effectiveness of GODP-Apriori algorithm in improving the calculation time efficiency of traditional Apriori algorithm was verified by using a real household electricity consumption data at the end of this research. © 2023 IEEE.

关键词： Association rules

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