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Future Communications and Networks (FCN), International Conference on

作者： Yu Zhang Yang Yang Shuangrui Zhao Teng Li Yulong Shen Xiaohong Jiang Jianfeng Ma School of Computer Science and Technology Xidian University Xi’an China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Cyber Engineering Xidian University Xi’an China School of Systems Information Science Future University Hakodate Japan

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

This paper focuses on a reconfigurable intelligent surface (RIS) enabled integrated multibeam-satellite terrestrial network, which consists of a satellite, a base station (BS), an RIS, multiple earth stations (ESs), an eavesdropper (Eve) and a terrestrial user (TU). In such a network, the satellite provides broadband services to multiple ESs, while the BS transmits friendly interference to the Eve for crippling confidential information interception with the help of the RIS. To enhance the security performance, we provide a theoretical modelling secrecy rate (SR) and formulate the SR maximization problem subject to the signal-to-interference-plus-noise ratio (SINR) requirements, the total transmit power limit and the RIS unitmodulus constraints. By combining approaches like alternative optimization (AO), bisection search, semi-definite programming (SDP), eigenvalue decomposition, and Gaussian randomization, we then propose a joint secure beamforming scheme to solve the non-convex optimization problem and thus identify the optimal beamforming designs for the satellite, the BS, and the RIS. Simulation results indicate the effectiveness of security enhancement via the proposed joint secure beamforming scheme.

关键词： Satellites Array signal processing Simulation Interference Reconfigurable intelligent surfaces Space-air-ground integrated networks Search problems

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Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation

RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

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International Conference on Data engineering

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations; therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands" problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA.

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VSCODE- Code With Voice Using Natural Language Processing(NLP) 1

VSCODE- Code With Voice Using Natural Language Processing(NL...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Prakash, M. Gnana Ponmalar, A. Deeba, S. Akilandeswari, A. Rasool, Saira Banu Mohammed Lavanya, P. Sri Sai Ram Institute of Technology Department of Information Technology Tamil Nadu Chennai India Sri Manakula Vinayagar Engineering College Department of Computer Science and Engineering Tamil Nadu Puducherry India Saveetha School of Engineering Simats Tamil Nadu Chennai India Applied College Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350397840

Physically Challenged people who are interested in coding, in the current scenario cannot code and exhibit their knowledge in programming skills. Our project enables them to code without using a keyboard. They can code entirely by their voice and execute their code. We are converting the user's speech to text by Natural Language Processing (NLP). With the use of text obtained from NLP model we generate code snippets using our custom code generator API. The code snippets are generated in the Visual Studio Code text editor using our own VSCode extension. © 2022 IEEE.

关键词： Speech recognition

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An Evolutionary Task Scheduling Algorithm Using Fuzzy Fitness Evaluation Method for Communication Satellite Network

arXiv

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arXiv 2024年

作者： Jiang, Xuemei Guo, Yangyang Zhang, Yue Song, Yanjie Pedrycz, Witold Xing, Lining School of Electronic Engineering Xidian University Xi’an China School of Systems Science Beijing Jiaotong University Beijing China School of Reliability and Systems Engineering Beihang University Beijing China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays

Range-angle Imaging Based on the Prior of Target Scattering ...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Yongjie Mei Guangbin Zhang Tianyao Huang Yan Wang School of Information and Electronics Beijng Institute of Technology Beijing China Radar System and Intelligent Signal Processing Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method.

关键词： Array signal processing Simulation Scattering Imaging Estimation Particle measurements Arrays Data mining Sparse matrices Signal resolution

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Mobile Malaysian Sign Language Application 4

Mobile Malaysian Sign Language Application

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4th International Conference Advancement in Data science, E-Learning and Information systems, ICADEIS 2022

作者： Hao, Lye Chee Nugraha, Ryan Adhitya Md Fudzee, Mohd Farhan Ismail, Mohd Norasri Syamsuar, Dedy Dalim, Che Samihah Witarsyah, Deden Senan, Norhalina Nurhayati, Iis Kurnia Sugiat, Maria Apsari Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and IT Parit Raja86400 Malaysia School of Industrial Engineering Telkom University Bandung Indonesia School of Information System Bina Nusantara University Jakarta Indonesia School of Communication and Business Telkom University Indonesia Bandung Indonesia Telkom University Indonesia Faculty of Economics and Business Bandung Indonesia

ISBN: (纸本)9781665463874

Due to the Covid-19 plague, children with hearing misfortune have experienced challenges in learning and practicing sign language due to need of educational services and learning assets. The next considerations for creating an educational mobile learning application to help deaf kids learn Malaysian Sign Language (MSL). The investigation was conducted through gatherings based on inquire about and interviews with subject matter experts. Instructors recommend that within the current circumstance, versatile learning application are the foremost reasonable stage for understudies to memorize or as an extra learning strategy in arrange to educate them well. This research was conducted through research-based and interview sessions with subject matter experts and followed the Multimedia Mobile Content Development (MMCD) methodology as it was shown to help accelerate development activities and ensure that the application would work and execute as planned. Once the application is developed, students from Sekolah Kebangsaan Silabukan in Lahad Datu, Sabah, will participate in beta testing. Based on the results of user acceptance tests, the application has received positive reviews from its target users. As such, the project's goals are said to have been achieved. At the conclusion of the study, the advantages, limitations and future work of Pocket Sign Language were also identified. © 2022 IEEE.

关键词： Acceptance tests

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An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM systems

An Efficient Deep Network for Modulation Classification in I...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Thien Huynh-The Quoc-Viet Pham Toan-Van Nguyen Daniel Benevides Da Costa Van-Phuc Hoang Department of Computer and Communication Engineering HCMC University of Technology and Education Vietnam Institute of System Integration Le Quy Don Technical University Vietnam School of Computer Science and Statistics Trinity College Dublin Ireland School of Computer Science and Engineering International University - VNU HCM City Vienam Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates

In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.

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A Dynamic Load Balancing Algorithm for Stable Pushing of Docker Images in Nginx

A Dynamic Load Balancing Algorithm for Stable Pushing of Doc...

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International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Hajie Xi Song Hu He Zhang Shuang Zhang Jingnong Weng Jian Huang School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Transportation System Research Institute of Highway Ministry of Transport Beijing China Transportation Comprehensive Enforcement Corps Beijing China School of Software Beihang University Beijing China

ISBN: (数字)9798331541798

ISBN: (纸本)9798331505615

With the rapid development of artificial intelligence, a wide variety of deep learning models have emerged across various fields. Nevertheless, the quality of these models varies significantly, making the effective evaluation of model performance a pressing issue. In some open data competitions, remote evaluation based on Docker images has increasingly become a novel and reliable approach, providing a consistent environment to ensure the fairness of model evaluations. However, it imposes high demands on hardware resources, typically requiring dedicated resources for each participant to serve as Docker Registries for the pushing and management of Docker images. In this paper, we extend the load balancing module of the Nginx server by integrating Lua scripts and propose a Cache-based Dynamic Smooth Weighted Round-robin (CDSWRR) algorithm to optimize the pushing process of Docker images. By constructing a unified load balancing entry, the proposed approach simplifies the deployment of Docker Registry clusters. Experiments show that compared to weighted round-robin and IP hash algorithms of Nginx, the CDSWRR algorithm is able to ensure stable pushing of Docker images and reduce both response error rate and response time.

关键词： Machine learning algorithms Error analysis Heuristic algorithms Clustering algorithms Pressing Load management Time factors Servers Reliability Open data

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Time series generator adversarial network with stochastic process for the degradation generation and prediction

Time series generator adversarial network with stochastic pr...

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Data Driven Control and Learning systems (DDCLS)

作者： Anqi Shangguan Nan Feng Lingxia Mu Rong Fei Xinhong Hei Guo Xie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China School of computer science and Engineering Xi'an University of Technology Xi'an China

Since the cumbersome collection process and high cost, the collected degradation of the product is basically small samples, which will affect the accuracy of reliability evaluation. It is necessary to expand the degradation to improve the accuracy of later reliability assessment. Therefore, a degradation generation and prediction method is proposed combining the time series generator adversarial network (TimeGAN) and stochastic process. Firstly, the input degradation is expanded by the sliding window to improve the later training accuracy; Then, the construction of the generator in TimeGAN is linked with the stochastic process to make the generation data more realistic. Finally, the results of degradation prediction by the Gated Recurrent Unit (GRU) can be obtained. Two datasets and different generation methods are adopted to evaluate the effectiveness of the proposed method. The results shows that the Kullback-Leibler(KL) divergence is the smallest, and the prediction error is the smallest compared with the other methods. So, the proposed method is proved that it is valid in the degradation generation and prediction, and can be used for the further reliability assessment of the product in the industrial system.

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