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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Fan, Peiru Wang, Chonghua Li, Jun Zhao, Bo Ji, Zhaoxu China Industrial Control Systems Cyber Emergency Response Team Beijing China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering WuHan China

ISBN: (纸本)9781728195582

Plain proofs (e.g., raw logs, report, etc.) are significant and effective for integrity verification. In our analysis and comparison of existing work, we found most of them did not employ any protection mechanisms on the proofs. However, these proofs contain sensitive information, which may cause privacy leakage risks when the third party verifier (TPV) is compromised. The situation is even worse when the verification objects are cloud components. Motivated by this, we present StateConsisIV, a privacy-preserving integrity verification method based on a novel state consistency feature to address the privacy leakage problem. The core idea of our work is to enable the integrity judgment through encrypted proofs, withholding plain proofs inside the cloud only to reduce attack surface and enhance privacy. In specific, we employ random transformation algorithm on cloud nodes to encrypt proofs on their birth places. Besides, we design a novel state consistency feature based on the deployment and operation pattern of structural cloud components and perform feature analysis on TPV to guarantee an accurate integrity judgment result. We evaluate our approach on one typical dataset. The experimental results show that our method is considered more worthy with a little bit of extra computation overhead. © 2020 IEEE.

关键词： Privacy-preserving techniques

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Experiences with Postdoctoral Training in Human Factors: Mentor and Trainee Perspectives

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 265 - 269页

作者： Kelly Caine Ken Catchpole Farzan Sasangohar Karen B. Chen Sudeep Hegde Dustin Souders Anthony McDonald Elease J. McLaurin Clemson University School of Computing Clemson SC USA Medical University of South Carolina Clinical Practice and Human Factors Charleston SC USA Texas A&M University Industrial and Systems Engineering Central SC USA North Carolina State University Edward P. Fitts Department of Industrial and Systems Engineering Raleigh NC USA Clemson University Department of Industrial Engineering Central SC USA Clemson University Psychology Department Clemson SC USA University of Wisconsin-Madison Department of Industrial and Systems Engineering Madison WI USA Oak Ridge Institute for Science & Education Fellowship Program Centers for Disease Control & Prevention Philadelphia PA USA

Postdoctoral training positions are becoming more common in the human factors and ergonomics (HFE) discipline. However, conversations related to training in the HFE discipline have largely focused on undergraduate and graduate education. This panel assembles both postdoctoral mentors and former trainees who collectively have a diverse set of HFE-related postdoctoral experiences. By panelists discussing their experiences, observations, and recommendations related to postdoctoral training with the audience, the panel session will support HFE faculty and students in making more informed decisions about if and how a postdoctoral experience (either as a mentor or trainee) could be a part of their career development in HFE.

关键词： education postdoctoral training human factors mentorship

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Fusing Differentiable Rendering and Language-Image Contrastive Learning for Superior Zero-Shot Point Cloud Classification

SSRN

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SSRN 2024年

作者： Xie, Jinlong Cheng, Long Wang, Gang Yu, Zaiyang Du, Minghua Ning, Xin School of Integrated Circuits University of Chinese Academy of Sciences Beijing100029 China Chinese Academy of Sciences Institute of Semiconductors Annlab Beijing100083 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computing and Data Engineering NingboTech University Ningbo315100 China Department of Bioengineering Imperial College London LondonSW7 2AZ United Kingdom Department of Emergency The First Medical Center Chinese PLA General Hospital Beijing100853 China

Zero-shot point cloud classification involves recognizing categories not encountered during training. Current models often exhibit reduced accuracy on unseen categories without 3D pre-training, emphasizing the need for improved precision and interoperability. We propose a novel approach that integrates differentiable rendering with contrastive language–image pre-training. Initially, differentiable rendering autonomously learns representative viewpoints from the data, enablingthe transformation of point clouds into multi-view images while preserving key visual information. This transformation facilitates optimized viewpoint selection during training, refining the final feature representation. Features are extracted from the multi-view images and integrated into a global multi-view feature using a merge-attention mechanism. On the textual side, a large language model(LLM) is provided with 3D heuristic prompts to generate 3D-specific text reflecting category-specific traits, from which textual features are derived. The LLM’s extensive pre-trained knowledge enables it to capture abstract notions and categorical features relevant to distinct point cloud categories. Visual and textual features are aligned in a unified embedding space, enabling zero-shot classification. Throughout training, the Structural Similarity Index (SSIM) is integrated into the loss function to encourage the model to discern more distinctive viewpoints, reduce redundancy in multi-view imagery, and enhance computational efficiency. Experimental results on the ModelNet10, ModelNet40, and ScanObjectNN datasets demonstrate classification accuracies of 74.48%, 65.28%,and 51.15%, respectively, surpassing prevailing methods in zero-shot point cloud classification accuracy. © 2024, The Authors. All rights reserved.

关键词： Computer vision

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Research of Virtual Simulation Experiment Course Resource Intelligent Information System Construction 2

Research of Virtual Simulation Experiment Course Resource In...

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2nd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Wang, Yangping Dang, Jianwu Yong, Jiu Wang, Wenrun Yue, Biao Jin, Fangrui Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China National Virtual Simulation Experiment Teaching Center of Railway Traffic Information and Control Lanzhou Jiaotong University College of Intelligence and Computing Tianjin University Computer Science and Technology Experimental Teaching Demonstration Center of National Lanzhou Jiaotong University Gansu Prov. Eng. Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China College of Electronic and Information Engineering Lanzhou Jiaotong University Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics and Image Processing Lanzhou730070 China

ISBN: (纸本)9781665442763

In order to enable the construction of virtual simulation experiment resources in colleges and universities to keep up with the development of professional technology, this paper proposes to build a 'diversified virtual simulation experiment teaching resource construction system' through exploration and research. With the help of virtual reality, AR, panoramic roaming, video and other technologies, it provides a unified development framework, development documents and case templates for professional teachers to independently develop virtual simulation experiment teaching resources in line with undergraduate teaching, and helps teachers quickly build virtual simulation experiment resources in accordance with unified standards. It effectively solves the problems that it is difficult for college teachers to independently develop virtual simulation courseware and the technology is not unified. © 2022 IEEE.

关键词： Virtual reality

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Kartalol: A New Deep Neural Network Framework Based on Transfer Learning for Iris Segmentation and Localization Task: New Dataset for Iris Segmentation

SSRN

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SSRN 2022年

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Malek-Ashtar University of Technology Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Iran Stanford University United States

Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature, each of which has somehow mitigated some of the mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture that has been initialized with a pre-trained MobileNetV2 model. In addition, we have collected 1000 images to study the detectors in iris recognition scenarios better. The provided dataset (KartalOl) is available free for the research community. In the proposed framework, to better generalizability, we fine-tune the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Also, data augmentation technics are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, CASIA-Iris-Africa, along the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The codes and evaluation results are publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Decentralized Strategy for Cooperative Multi-Robot Exploration and Mapping

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IFAC-PapersOnLine 2020年第2期53卷 9682-9687页

作者： Ana Batinović Juraj Oršulić Tamara Petrović Stjepan Bogdan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems (LARICS) 10000 Zagreb Croatia

This work presents a novel approach to autonomous decentralized multi-robot frontier exploration and mapping of an unknown area. A mobile robot team simultaneously explores the environment, discovers frontier points (points on the border between explored and unexplored space), and shares information in order to become dispersed throughout the environment. During the exploration, information exchanged between the mobile robots is limited to data containing mobile robot positions and current mobile robot target points. The main goal of the approach is to allocate the mobile robots to target frontier points in a way which minimizes the overall exploration time. Moreover, a mobile robot team at the same time creates a common map of the environment. The proposed strategy has been implemented in a simulation environment and compared with a state-of-the-art exploration strategy. Simulation results demonstrate the advantages of the proposed decentralized multi-robot strategy.

关键词： Mobile Robot Multi-Robot System Frontier Points Target Point Decentralized Strategy Exploration Mapping

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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

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Procedia Computer Science 2021年 194卷 45-59页

作者： Muhammad Haleem Junejo Ab Al-Hadi Ab Rahman Riaz Ahmed Shaikh Kamaludin Mohamad Yusof Dileep Kumar Imran Memon Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia State Key Laboratory of Industrial Control Technology College of Control Science & Engineering Zhejiang University Hangzhou china Department of computer science bahria university Karachi campus sindh Pakistan

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.

关键词： VANET Trust Model Machine Learning

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Double deep Q-learning for anti-saturation attack problem of warship group 1

Double deep Q-learning for anti-saturation attack problem of...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Wei Xiong, Gang Tao, Hao Han, Yunjun Wang, Fang Dong, Xisong Shen, Zhen Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan430064 China Shandong University Jinan China

ISBN: (纸本)9781665433372

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios. © 2021 IEEE.

关键词： Reinforcement learning

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Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Pei, Yingjie Ni, Wanli Xu, Jin Yue, Xinwei Tao, Xiaofeng Niyato, Dusit School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electronic Engineering Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China Key Laboratory of Information and Communication Systems Ministry of Information Industry China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS;ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity;and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

arXiv

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arXiv 2022年

作者： Zhang, Fangyu Wei, Yanjie Liu, Jin Wang, Yanlin Xi, Wenhui Pan, Yi College of Engineering Southern University of Science and Technology Shenzhen518055 China Centre for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Information Science and Engineering Central South University Changsha410083 China

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the original tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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