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Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Yufeng Li Xu Yan Mingxuan Xiao Weimin Wang Fei Zhang Department of Digital Information Technology Zhejiang Technical Institute of Economics China and Department of Electronics and Computer Science University of Southampton United Kingdom Department of Computer Science Trine University USA Department of Computer Science McDonald (China) Co. Ltd. China and Department of Computer Science SouthWest JiaoTong University China Department of Computer Science Alibaba International China and School of Engineering Hong Kong University of Science and Technology China Department of Computer Science Trine University USA and Department of Geographic Information System Yangtze University China

ISBN: (纸本)9798400707964

With the fast growth of web, the web is full of diverse data. Linked Data is data on the web that gives URIs to entities and links different data from different domains together, which results in the part of the web infrastructure. Since the dramatic development of Linked Data movement, a great number of accessibility datasets have been published as open data on the web by governments or organizations so that they can be accessed and reused by anyone without any restriction. However, the majority of the accessibility datasets available on the web are in diverse formats such as HTML table, Excel and CSV, rather than Linked Data format – RDF. This paper mainly investigates the method of creating accessibility Linked Data based on the publicly existing available accessibility datasets. First of all, requested accessibility data was extracted from the accessibility dataset. Moreover, Jena RDF API is applied to create RDF documents based on the extracted data from the accessibility dataset. Compared to other approaches such as transforming XML to RDF via schema, current method of this project does not have mapping work and it shows significant convenience. Finally, a triple store was set up to sever the RDF documents. In addition, the server has query interface support for test; as a result, users could query the data they need. In the future development, relevant dataset links would be added to current RDF documents. At last the accessibility RDF dump would be published on the web as a part of the web infrastructure. Index Terms—Linked Data, Open Data, Semantic Web, Accessibility Dataset.

关键词： Accessibility Dataset. Linked Data Open Data Semantic Web

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Tactile-Based Object Pose Estimation Employing Extended Kalman Filter 8

Tactile-Based Object Pose Estimation Employing Extended Kalm...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Lin, Qiguang Yan, Chaojie Li, Qiang Ling, Yonggen Zheng, Yu Lee, Wangwei Wan, Zhaoliang Huang, Bidan Liu, Xiaofeng College of IoT Engineering Hohai University Jiangsu Key Laboratory of Special Robotic Technology Jiangsu Changzhou213022 China Zhejiang University Institute of Cyber-System and Control State Key Laboratory of Industrial Control and Technology Hangzhou China Bielefeld33619 Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350300178

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks. © 2023 IEEE.

关键词： Extended Kalman filters

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A Study of Data Augmentation for Imbalance Problem in Network Traffic

A Study of Data Augmentation for Imbalance Problem in Networ...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Xu Wang Shimin Sun Li Han School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid. This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network, we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems.

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A Scheme for Authentication and Traceability of 5G Mobile Terminal Based on IPv6 and Blockchain

A Scheme for Authentication and Traceability of 5G Mobile Te...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xinsheng Ji Ying Zhao Xingxing Liao Jie Yang Chunxiao Wang Department of Computer Science and Technology Tsinghua University Beijing China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China Purple Mountain Laboratories Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

To satisfy the service-side requirements for network layer authentication and traceability of users, this paper proposes a method for mobile terminal authentication and identity tracing based on IPv6 and blockchain. Our method introduces a new verification function (VEF) to verify the interface identification between the terminal and the 5G core network (5GC), thereby effectively enhancing the security of terminal access. Furthermore, considering the privacy protection of user identity information to prevent attackers from deducing encrypted content, it is necessary to periodically update encryption keys. Focusing on the practical deployment of VEFs on a network-wide scale and server-proximal for authentication and traceability, a time-varying key sharing scheme based on blockchain is proposed. The experimental section analyzes the communication performance and security of the proposed scheme, showing that our scheme does not significantly degrade the original network performance while providing substantial security gains.

关键词： Wireless communication Privacy 5G mobile communication Authentication Signal processing User experience Blockchains Synchronization Noise measurement Protection

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SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

arXiv

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arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Employment of ANN for Predictive Motor Maintenance and Bearing Fault Detection Using Park's Vector Analysis 5

Employment of ANN for Predictive Motor Maintenance and Beari...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Mahesh, Abishek Aadhavan, B. Aadhi Meenaa, V. Valli Omar, Madiah Binti Ibrahim, Rosdiazli Bin Salehuddin, Nurliana Farhana Sujatha, R Vellore Institute of Technology Computer Science Tamil Nadu Vellore India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore India Institute of Autonomous System Universiti Teknologi Petronas Perak Seri Iskandar31750 Malaysia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Predictive maintenance is an emerging concept that is gaining mainstream popularity in industrial automation. It involves continuous monitoring of the machinery's health, status and performance in real-time. Hence, it allows the industry to schedule maintenance only when specific conditions are met and before the machinery breaks down. Critical machinery such as three-phase induction motors is being used widely in industrial processes. However, they are subjected to many electrical and mechanical stresses due to their long operating times. Bearing faults account for the highest faults in the induction motor. If the faults go unnoticed, they can result in motor failure, which generates economic losses. Thus, the tracking of motor health becomes ineluctable. One of the most effective ways to understand health and efficiency is through motor current signature analysis (MCSA). The proposed model employed MCSA by plotting Park's vector analysis graph to predict motor health. This graph would then be fed as the input to an artificial neural network (ANN) for automated classification of the healthy or unhealthy motor. The trained model has managed to acquire an accuracy >99% in the test set, thus triumphing over other predictive maintenance methods. It is designed so that the proposed model's computational complexity is low enough to run on edge computing devices to implement an efficient industrial automation system. © 2022 IEEE.

关键词： Neural networks

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Geometric Constellation Shaping for Wireless Optical Intensity Channels: An Information-Theoretic Approach

arXiv

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arXiv 2024年

作者： Zhou, Suhua Li, Tianqi Fang, Zhaoxi Zhou, Jing Zhang, Wenyi School of Automation Beijing Information Science and Technology University Beijing100192 China National Computer System Engineering Research Institute of China Beijing100083 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China Henan Key Laboratory of Visible Light Communications Zhengzhou450002 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All 2b levels (b ∈ N) of the obtained constellation can be represented by a basic level and b + 2 bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple 16-level LDPC coded modulation scheme show that a shaping gain of 0.65 dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution. © 2024, CC BY-NC-ND.

关键词： Digital to analog conversion

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Brain tumor classification model of ResNet-50 network based on different data enhancement algorithms 8

Brain tumor classification model of ResNet-50 network based ...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Zou, Menghan Ma, Mingze Wang, Anan Li, Yujie Toe, Teoh Teik Harbin Institute of Technology Weihai School of Computer Science and Technology WeiHai China Harbin Institute of Technology Weihai Welding Technology and Engineering WeiHai China Harbin Institute of Technology Weihai Marine Information Engineering WeiHai China Harbin Institute of Technology Weihai Information Management and Information System WeiHai China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350302455

The accuracy and stability of brain tumor MRI image classification is significant for the healthcare system, but the traditional models have the defects of difficulty in handling complex features and unstable classification. In this paper, we propose a novel brain tumor classification model based on residual neural network, and use three different data enhancement algorithms: geometric transformation, mixup, and SamplePairing to process a dataset containing four different types of brain tumor images, and finally evaluate the classification effect of our proposed model. Among them, the best results were achieved by using geometric transformations for data enhancement. © 2023 IEEE.

关键词： Image classification

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3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack

3DHacker: Spectrum-based Decision Boundary Generation for Ha...

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International Conference on computer Vision (ICCV)

作者： Yunbo Tao Daizong Liu Pan Zhou Yulai Xie Wei Du Wei Hu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality.

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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Electronics and Devices, Computational science (ICEDCS), International Conference on

作者： Wenbo Zhang Boxiao Yu Jiahui Tong Jing Li National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing Electro-Mechanical Engineering Institute Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement.

关键词： Accuracy Uncertainty systematics Scientific computing Atmospheric modeling Computational modeling Decision making Data models Bayes methods Optimization

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