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International Conference on Automatic Face and Gesture Recognition

作者： Ruochen Tang Xun Gong School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Re-identification (ReID) of individuals across different cameras is a challenging task due to the high-quality large-scale datasets required for the model. Intra-camera supervised (ICS) person re-identification has been proposed to address the high cost of annotating large-scale datasets, but reducing the gap between camera domains remains a major challenge. Current approaches use intra- and inter-camera learning with contrastive learning performed separately in both phases. However, the effect of features from the same person under different cameras on the model during inter-camera learning, and the fine-grained characteristic where the same person can be classified into multiple classes based on the camera labels, still require further research. To address this issue, we propose a Camera-Based Contrastive Learning (CBCL) method that moves features away from their respective cameras and closer to other cameras to reduce domain gaps. We also introduce an Intra-Person Camera Adversarial (IPCA) loss that effectively utilizes fine-grained characteristics of person re-identification and improve IPCA by introducing camera labels to obtain IPCA_2 which achieves better model recognition performance than IPCA alone. Extensive experiments on multiple datasets demonstrate that our method outperforms existing methods and is comparable to fully-supervised methods.

关键词： Costs Scalability Face recognition Contrastive learning Gesture recognition Cameras Robustness

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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2021国际计算机前沿大会

作者： Junchao Wang Jianmin Pang Jin Wei State Key Laboratory of Mathematical Engineering and Advanced Computing School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application's security. Cyberspace mimic defense(CMD) technologies can help to enhance the application's security despite the existence of the vulnerability. In this paper,the concept of SECurity-as-a-Service(SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application's security is greatly improved to meet the user's security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements(monetary costs,required security level, etc.) and automate the process of application orchestration.

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Modeling Method for Sampling Zeros of Sampled-data System with Time Delay in Generalized Sample Hold

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IFAC-PapersOnLine 2023年第2期56卷 9799-9805页

作者： Minghui Ou Shan Liang Zhiyong Yang Zhenjie Yan Zhiyong Li School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing 402260 PR China College of Automation Chongqing University Chongqing 400044 PR China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing 400044 PR China. School of Electrical Engineering Southwest Jiaotong University Chengdu 611756 PR China

Unstable zeros are widely known to dramatically curb the performance of controllers and the direct applications of several control algorithms. Time delay is a crucial research topic due to the prevalence of communication and computational delays in the control process. In particular, time delay exacerbates the possibility of continuous-time systems with stable zeros transforming into discrete-time systems with unstable zeros. For continuous-time systems with a relative degree greater than or equal to two, at least one zero of the corresponding discrete-time system converges to either marginally stable or unstable locations as the sampling time approaches zero. This study investigates the sampling zeros of discrete-time systems in the case of a generalized sample hold function (GSHF) with time delay. Moreover, it presents a novel modeling method and stability conditions of the limiting zeros for the discrete-time system. The results demonstrate that the stable properties of zeros for the time delay systems can be preserved in the discretization process when GSHF is applied for signal reconstruction when zero-order hold fails to do so. Finally, numerical examples are provided to illustrate the effectiveness of the proposed method in this study.

关键词： Sampling zeros stability generalized sample hold function (GSHF) time delay. sampled-data system

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Visible Light Positioning Based on a Single Luminaire: A Novel visual Odometry Assisted Algorithm

Visible Light Positioning Based on a Single Luminaire: A Nov...

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IEEE International Conference on Communications (ICC)

作者： Zhiyu Zhu Yang Yang Mingzhe Chen Caili Guo Yipeng Bai Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Visible light positioning (VLP) is a promising positioning technique, which, however, typically requires multiple luminaires to achieve accurate positioning. This paper proposes a novel visual odometry (VO) assisted visible light positioning algorithm (VO-VLP) in achieving positioning with only a single luminaire. In the considered model, a user equipped with a camera jointly uses geometric features in the captured images and coordinates information obtained via visible light communication (VLC) for positioning. The proposed VLP algorithm does not rely on any extra inertial measurement unit and relaxes the tilted angle limitation at the user. In particular, VO-VLP first uses the circle feature of a luminaire to obtain dual normal vectors of the luminaire. Then, the basic principle of VO is used to eliminate the wrong normal vector by exploiting the geometric features in two consecutive images captured when the user moves. Finally, the pose and location of the user are obtained by using an artificially marked point on the luminaire's contour. VO-VLP can achieve accurate positioning with only a single luminaire and a camera. Simulation results show that the proposed indoor positioning algorithm can achieve a 97th-percentile positioning accuracy of around 10 cm.

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Research on Security Assessment of Intelligent Electronic Devices in Smart Grids Considering Cyber-Attacks and Physical Faults

SSRN

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SSRN 2023年

作者： Chen, Yang-Rong Li, Jun-E Xia, Yu Zhang, Rui-Wen Li, Ling-Ling Li, Xiao-Yu Ge, Lin School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450046 China Henan Data Security Engineering Technology Research Center Zhengzhou450046 China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Zhengzhou University of Aeronautics China

The smart grid contains lots of intelligent electronic devices (IEDs). IEDs are considered high-priority targets of cyber-attacks. Assessing the security of IEDs effectively can help operators take timely measures to improve the ability of the smart grid (SG) to resist cyber-attacks. Therefore, it is a new challenge how to evaluate the security of IEDs to support the decision-making of grid control policies in the condition of cyber-attacks probably occurring. However, there is a lack of research on the security assessment of IEDs considering cyber-attacks. Hence, this paper takes cyber-attacks and physical faults into consideration, and presents security assessment indicators and assessment methods for IEDs. Firstly, the security monitoring technology of IEDs is presented from the perspective of data sources required for the security assessment of IEDs. The classification of IEDs is carried out, and the security monitoring index system considering the running states of IEDs, network traffic, and abnormal behaviours is presented. Then, the quantitative methods of various security monitoring indicators are expounded. Furthermore, a security assessment method (i.e., GRCW-hybrid method) for IEDs based on grey relational analysis (GRA) and combination weight method (CWM) is proposed. Finally, to verify the effectiveness of the proposed index system and assessment method, 15 indicator exception scenarios, which are established based on monitoring indicators and the actual situation of IEDs, are evaluated. © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

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Brain Informatics 2020年第1期7卷 16页

作者： Li, Rui Zhang, Junsong National Engineering Laboratory for Educational Big Data Central China Normal University WuhanHubei China Fujian Key Laboratory of Brain-Inspired Computing Technique and Applications Department of Artificial Intelligence School of Informatics Xiamen University Xiamen China

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration. © 2020, The Author(s).

关键词： Brain

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Performance Optimization for Multicast Mm Wave MIMO Networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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LF-PGVIO: A visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

arXiv

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arXiv 2023年

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

In this paper, we propose LF-PGVIO, a visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation

arXiv

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arXiv 2023年

作者： Li, Xiaofan Peng, Bo Hu, Jie Ma, Changyou Yang, Daipeng Xie, Zhuyang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University Sichuan Neijiang641100 China

Unsupervised skin lesion segmentation offers several benefits, such as conserving expert human resources, reducing discrepancies caused by subjective human labeling, and adapting to novel environments. However, segmenting dermoscopic images without manual labeling guidance is a challenging task due to image artifacts such as hair noise, blister noise, and subtle edge differences. In this paper, we introduce an innovative Uncertainty Self-Learning Network (USL-Net) to eliminate the need for manual labeling guidance for the segmentation. Initially, features are extracted using contrastive learning, followed by the generation of Class Activation Maps (CAMs) as saliency maps. High-saliency regions in the map serve as pseudo-labels for lesion regions while low-saliency regions represent the background. Besides, intermediate regions can be hard to classify, often due to their proximity to lesion edges or interference from hair or blisters. Rather than risking potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, they are taken as uncertainty regions by exempted from pseudo-labeling and allowing the network to self-learning. Further, we employ connectivity detection and centrality detection to refine foreground pseudo-labels and reduce noise-induced errors. The performance is further enhanced by the iterated refinement process. The experimental validation on ISIC-2017, ISIC-2018, and PH2 datasets demonstrates that its performance is comparable to supervised methods, and exceeds that of other existing unsupervised methods. On the typical ISIC-2017 dataset, our method outperforms state-of-the-art unsupervised methods by 1.7% in accuracy, 6.6% in Dice coefficient, 4.0% in Jaccard index, and 10.6% in sensitivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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