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6th International Conference on Intelligent Computing, Communication, and Devices, ICCD 2023

作者： Li, Hui Yin, Zhenyu Fan, Chao Wang, Xiaohui School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Liaoning Key Lab. of Domestic Industrial Control Platform Technology on Basic Hardware and Software Shenyang110168 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

YOLOv3, as a multi-scale object detection algorithm, has a simple structure and can be detected quickly. However, during the training process, as the bottom convolutional layer contains more object detail, and the information in the underlying layer will be gradually faded out through continuous convolution. To address this, this paper adds a normalized attention mechanism behind the feature layers at different scales to further suppress insignificant features and enhance the discriminability and robustness of the features. Moreover, an improved pyramid structure is leveraged to fuse the detail of the shallow feature maps with the semantic feature information of the higher layers, aiming to improve the model performance. In particular, a YOLOv3 object detection algorithm named YOLO-MFE based on improved multi-scale feature extraction is proposed. Experimental results on the Pascal VOC dataset demonstrate that our algorithm outperforms the original YOLOv3 algorithm in terms of object detection accuracy, with a mAP on the Pascal VOC 2007 test set that is 1.66% higher. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Object detection

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Differentially Private Online Federated Learning with Correlated Noise 63

Differentially Private Online Federated Learning with Correl...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (, δ) DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Federated learning

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Secure Synchronization of Heterogeneous Pulse-Coupled Oscillators

arXiv

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arXiv 2024年

作者： Yan, Jiaqi Ishii, Hideaki The Automatic Control Laboratory ETH Zurich Switzerland The Department of Computer Science Tokyo Institute of Technology Japan

In this paper, we consider the synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some of the oscillators might be faulty or malicious. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Despite the presence of misbehaviors, benign oscillators aim to reach synchronization. To achieve this objective, two resilient synchronization protocols are developed in this paper by adapting the real-valued mean-subsequence reduced (MSR) algorithm to pulse-based interactions. The first protocol relies on packet-based communication to transmit absolute frequencies, while the second protocol operates purely with pulses to calculate relative frequencies. In both protocols, each normal oscillator periodically counts the received pulses to detect possible malicious behaviors. By disregarding suspicious pulses from its neighbors, the oscillator updates both its phases and frequencies. The paper establishes sufficient conditions on the initial states and graph structure under which resilient synchronization is achieved in the PCO network. Specifically, the normal oscillators can either detect the presence of malicious nodes or synchronize in both phases and frequencies. Additionally, a comparison between the two algorithms reveals a trade-off between relaxed initial conditions and reduced communication burden. © 2024, CC BY.

关键词： Synchronization

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A Ray-based Asynchronous Distributed Parallel K-SVD Method 12

A Ray-based Asynchronous Distributed Parallel K-SVD Method

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Li, Yongkang Chen, Xiaodong Wan, Xiaohui Sun, Jin Zheng, Peng Wang, Yunchang Wu, Zebin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Quzhou Transportation Bureau Department of Industry Development Quzhou China Chang shan Inspection and Testing institute Department of Quality Control and Testing Quzhou China

ISBN: (纸本)9798331511074

K-SVD (K-Singular Value Decomposition)is a com-monly used dictionary learning method that progressively opti-mizes dictionaries to better represent data by iterating sparse coding and dictionary update steps. However, with the increasing data size, the traditional K -SVD method faces significant challenges in terms of computational efficiency. Therefore, this paper proposes an asynchronous distributed parallel K-SVD method based on Ray framework to cope with the computational efficiency problem in large-scale data processing. This method achieves efficient parallel computation by decomposing the sparse coding and dictionary updating steps of the K-SVD method into multiple independent tasks and utilizing the distributed scheduling capability of the Ray framework. Since the zero elements of the coefficient matrix are unevenly distributed, we introduce a stride partitioning approach when performing the decomposition. In addition, in order to avoid the method falling into local optima and failing to find global optima due to parallelism, this paper synchronizes with the global at the same time as the atomic update of the dictionary. Meanwhile, for the purpose of avoiding the overhead caused by data synchronization, a parameter server is introduced to realize asynchronous updating of dictionary and coefficient matrix to reduce the overhead caused by communication. The experimental results show that, compared with the traditional K-SVD method, the method proposed in this paper has a significant advantage in computational efficiency and is able to efficiently process hyperspectral image datasets under the premise of guaranteeing the sparse representation accuracy. Compared with the synchronous parallel K-SVD method, the proposed method has faster error convergence and does not fall into local optima. © 2024 IEEE.

关键词： Network security

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Studying Negative Rationality in Quantal Response Equilibrium 4

Studying Negative Rationality in Quantal Response Equilibriu...

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4th International Conference on control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Kozitsina, Tatiana Kozitsin, Ivan Moscow Institute of Physics and Technology National Research University Federal Research Center Computer Science and Control Russian Academy of Sciences Moscow Russia Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Institute of Physics and Technology National Research University Moscow Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

In this paper, we present an investigation of the negative parameter of rationality in a symmetrical quantal response equilibrium (QRE) in the Prisoner's Dilemma game (PD) constructed in Markov strategies (tolerance to defection and mutual cooperation). We characterize the behavior of a new branch of equilibrium through the considered strategies. Also, we compare the theoretically found equilibrium in the area of negative rationality with the experimental data: we observe that this part of equilibrium corresponds with a high level of cooperation close to 100%. Thus, we propose that the negative rationality in QRE could be considered as a new theoretical approximation to unconditional cooperation. © 2022 IEEE.

关键词： Game theory

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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Analyses of cell-to-cell communication combining a heterogeneous deep ensemble framework and scoring approaches from single-cell RNA sequencing data

Analyses of cell-to-cell communication combining a heterogen...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Lihong Yuan, Ruya Han, Chendi Tan, Jingwei Wang, Zhao Chen, Min Chen, Xing Hunan University of Technology School of Computer Science Zhuzhou China China University of Mining and Technology School of Information and Control Engineering Xuzhou China Hunan Institute of Technology School of Computer Science Hengyang China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (纸本)9781665468190

Cell-to-cell communication (CCC) plays essential roles in multicellular organisms. the identification of CCC between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment contributes to the understanding of carcinogenesis, cancer development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed an LRI-mediated CCC estimation framework (LRI-EnABCLG) by incorporating LRI collection, prediction and filtering, CCC inference and visualization. First, four LRI datasets were collected. Second, LRIs were predicted by a heterogeneous deep ensemble model. Third, LRIs were filtered by combining single-cell sequencing (scRNA-seq) data. Fourth, CCC was inferred by combining the filtered LRIs and scRNA-seq data. Finally, the proposed CCC prediction framework was applied to CCC analysis in colorectal tumor tissues. Our proposed LRI-EnABCLG model obtained better LRI prediction performance. Case study demonstrated that fibroblasts was more likely to communicate with colorectal cancer cells, which was in accord with the results from iTALK (a classical CCC analysis pipeline). We anticipate that this work can contribute to diagnosis and treatment of cancers. © 2022 IEEE.

关键词： Cancer cells

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Switching control for Identification Deception in Cyber-Physical Systems

Switching Control for Identification Deception in Cyber-Phys...

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IEEE Conference on Decision and control

作者： Christos Mavridis Aris Kanellopoulos Henrik Sandberg Karl Henrik Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching.

关键词： Linear systems Asymptotic stability Switching systems Statistical analysis Simulation Switches Cyber-physical systems control systems Stability analysis Security

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Concentration Diagnosis in Soft Sensing Based on Bayesian T-Distribution Mixture Regression 9

Concentration Diagnosis in Soft Sensing Based on Bayesian T-...

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Xia, Changhai Peng, Zhiping Cui, Delong Li, Qirui Sun, Lihui School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China Jiangmen Polytechnic College Jiangmen City529090 China School of Computer Science Guangdong University of Petrochemical Technology Maoming City525000 China

ISBN: (纸本)9781643684703

This study presents a cutting-edge soft sensing approach for coke-making diagnostics, aimed at tackling the challenges posed by multifaceted, nonlinear, non-Gaussian, and noisy operational data prevalent in coke-making ovens. Our proposed method leverages a Bayesian t-distributed mixed regression model, effectively capturing the intricate nature of multivariate, nonlinear, and non-Gaussian data. The utilization of the t-distribution ensures the model's resilience to interference, with model parameter estimation achieved within a Bayesian framework. Conducting simulation experiments and real industrial experiments, as well as comparative analysis with PLSR, GMR, and GPR models, we demonstrate the model's good robustness, excellent prediction accuracy, and robustness, further confirming its potential application in coking diagnosis. © 2023 The authors and IOS Press.

关键词： Coke

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Application of Deep Learning Models Based on Chaos Modeling in Power Internet of Things Forecasting Tasks 10th

Application of Deep Learning Models Based on Chaos Modeling ...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Miao, Jiahao Li, Zhixin Long, Yang School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9789819787425

Deep learning has provided a reliable technological foundation for time series prediction tasks. In the field of time series prediction, deep learning models are widely used in the Power Internet of Things due to their powerful fitting capabilities. However, efficiently capturing the relationships between time data remains a challenge. To overcome this challenge, this paper proposes a time series prediction model called the CTN-former based on chaos theory and a self-attention mechanism. CTN-former can detect chaos in time series through a carefully designed chaotic theory network (CTN), derive the relationships between time data, and perform phase space reconstruction. Additionally, to improve efficiency, the model includes a master-slave self-attention module to reduce quadratic complexity. Finally, experiments were conducted on four large-scale public datasets from three different domains. The results demonstrate that compared to traditional time series prediction models, the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks, and the CTN-former achieves a relative improvement rate of 12.7% in time series prediction tasks and provides a novel solution for time series prediction by recovering the motion trajectory between chaotic time series, thereby enhancing the data utilization efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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