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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 802 条记录，以下是231-240 订阅

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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A Spatiotemporal Analysis of Age of Information at V2X-enabled Intersection 23

A Spatiotemporal Analysis of Age of Information at V2X-enabl...

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作者： Wang, Yaying Tan, Guoping Zhou, Siyuan Yang, Dengsong Ni, Baili Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China College of Computer and Information Hohai University Nanjing China Jiangsu Intelligent Transportation and Intelligent Driving Research Institute Nanjing China Jiangsu Expressway Company Limited China

ISBN: (纸本)9781665494571

Ensuring the freshness of information is of importance to autonomous vehicles at vehicle-to-everything (V2X)-enabled intersection. In this paper we theoretically investigate the Age of information (AoI) at V2X-enabled suburban intersections and urban intersections. Specifically, we model the locations of connected vehicles by using stochastic geometry and consider a last-come first-serve discipline with preemption. Then we analyze the moments and distribution of the conditional successful probability and the conditional average AoI. Finally, we obtain the moments and the distribution of conditional average AoI. The accuracy of theoretical derivation is verified via simulations. Our numerical results show the influence of different parameters on the average AoI and the theoretical analysis provides a useful guideline for further system design. © 2021 IEEE.

关键词： Transmitters Smart cities Roads Stochastic processes Receivers Information age Spatiotemporal phenomena

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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EEAI: An End-edge Architecture for Accelerating Deep Neural Network Inference 23

EEAI: An End-edge Architecture for Accelerating Deep Neural ...

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作者： Liu, Guozhi Dai, Fei Huang, Bi Qiang, Zhenping Li, LeCheng Wang, Shuai School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (纸本)9781665494571

Deep Neural Networks (DNNs), as a key technology for Artificial Intelligence (AI) applications in the 5G era, have been widely used in the field of mobile intelligence. However, it is challenging to run computation-intensive DNN-based applications on mobile devices due to the limited resources. What's worse, traditional cloud-assisted approach requires significant amounts of data to be uploaded to the cloud through wireless network, leading to poor real-time performance and low quality of user experience. To address these challenges, in this paper, we propose EEAI, a framework that end-edge collaborative DNN inference. Firstly, we analyze the characteristics of DNN-based applications. Then, we introduce edge computing, and design EEAI, a light-weight scheduler to automatically partition DNN computation between mobile devices and edge servers at the granularity of DNN layers. Finally, we verify the effectiveness of the EEAI under different network environments (4G, 5G, and Wi-Fi). Experiments show that the proposed method is effective and has good performance. © 2021 IEEE.

关键词： Deep learning Performance evaluation Energy consumption 5G mobile communication Neural networks Collaboration computer architecture

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Fast Distributed Stochastic Gradient Descent for Big Data Classification 23

Fast Distributed Stochastic Gradient Descent for Big Data Cl...

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作者： Xiao, Mingfei Wang, Huihui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the needs of big data applications. When a single machine is trained, it will face the problem of insufficient memory and limited computing capacity. Therefore, distributed algorithms have been proposed to solve big data classification, and have received much attention. Particularly, distributed gradient descent (GD) is a widely-used algorithm for solving big data problem. However, distributed algorithms based on GD always converge slowly and suffer from expensive time in the training process. To alleviate this problem, we propose a novel fast distributed stochastic gradient descent algorithm for big data classification in this paper. By formulating the original optimization problem as a series of subproblems for classification in each machine. In particular, we exploit a fast stochastic method combined with the Nesterov acceleration method for sub-problem optimization in parallel to improve the convergence speed, and thus reduce the training time. The experimental results on some public and benchmark data sets show that our proposed distributed algorithm can converge faster with the competitive accuracy performance compared with distributed GD-based methods, which is suitable to big data classification. © 2021 IEEE.

关键词： Training Machine learning algorithms Smart cities Big Data Benchmark testing Classification algorithms Distributed algorithms

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An Improved Slime Mould Algorithm with Quasi Reflection-based Learning for Global Optimization Problems 23

An Improved Slime Mould Algorithm with Quasi Reflection-base...

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作者： Liu, Qingxin Qi, Qi Jia, Heming Li, Ni School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Intelligence Education Ministry of Education Haikou China

ISBN: (纸本)9781665494571

The Slime Mould Algorithm (SMA) is a swarm-based method inspired by hunting behaviors of slime mould. However, SMA often suffers from slow convergence and local minima. In this paper, we propose an improved variant of SMA, coined as QRSMA, which is enhanced with the quasi reflection-based learning approach. The new algorithm involves two major components. First, the tent map is used to increase the population diversity in the initial phase. Second, the quasi reflection-based learning is introduced to increase the ability of exploitation. Finally, the performance of QRSMA is evaluated through assorted tests including thirteen benchmark functions and one engineering design problem against other four well-known optimization algorithms. The experimental results demonstrate better performances of the proposed QRSMA in exploring capability and convergence quality than other algorithms. © 2021 IEEE.

关键词： Image segmentation Sociology Benchmark testing Behavioral sciences Statistics Optimization Convergence

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Edge Intelligence-Based UAV Human Target Recognition with Improved YOLOV5 Algorithm 23

Edge Intelligence-Based UAV Human Target Recognition with Im...

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作者： Cai, Zeyu Qu, Yuben Dong, Chao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665494571

Edge intelligent computing is often combined with the tasks of Unmanned Aerial Vehicle (UAV). Most applications of UAV require the location and identity information of human targets. Traditional human target detection and face recognition algorithms require a lot of time in edge intelligent computing. In addition, the final recognition result is not accurate enough. To this end, this paper proposes an improved YOLOV5-Cozy target detection algorithm to detect human targets and faces in a fast and accurate way. The algorithm uses MobileNetV3, depthwise separable convolution and shuffleconv to improve the original YOLOV5 (You Only Look Once version 5) algorithm. Compared with the original algorithm, the improved algorithm has an average detection accuracy drop of less than 5%, and its calculation time has been reduced by 20%. In terms of face recognition, we use the FaceNet algorithm with the addition of a face augmentation module. The module enhances the face recognition ability of the FaceNet algorithm in different environments, and the accuracy of face recognition has increased by 20%. We implement the proposed algorithm on the embedded computer Jetson Agx Xavier for UAV edge intelligent computing, and the average FPS achieves at about 23.6. © 2021 IEEE.

关键词： Target recognition Smart cities Convolution Face recognition Image edge detection Computational modeling Object detection

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HotFed: Hot Start through Self-Supervised Learning in Federated Learning 23

HotFed: Hot Start through Self-Supervised Learning in Federa...

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作者： Zhao, Juan Li, Ruixuan Wang, Haozhao Xu, Zijun School of Computer Science and Technology Huazhong University of Science and Technology China Intel Asia-Pacific Research & Development Ltd.

ISBN: (纸本)9781665494571

Federated Learning learns a shared model for prediction from the rich and privacy-sensitive training data distributed on the clients which are resource-constrained edge devices or isolated cloud clusters with high-security protection. The training data collected by separated clients faces the non-IID (not Independent and Identically Distributed) challenge, which requires more communication rounds to achieve an acceptable precision and hence costs considerable computing and network resources. The bias of the dataset requires more communication rounds to achieve an acceptable precision which costs a lot of computing and network resources. Existing works propose sharing a small amount of dataset among clients to mitigate the non-IID issue, bringing a new communication burden for shared data transition and the new distribution statistical challenge that the model will inevitably over-fit the shared data. Inspired by the hot start-up in system design, this paper designs a novel HotFed method in which each client starts its training with a representation model for better precision and communication efficiency. This paper analyzes the value of HotFed and different methods to warm up the client's Federated Learning. The conclusion turns out that HotFed through Self-Supervised Learning (SSL) can efficiently solve the non-IID issue and receive the training efficiency improvement. © 2021 IEEE.

关键词： Training Costs Computational modeling Training data Predictive models Collaborative work Data models

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Zone Based Writing Optimization in User Space 23

Zone Based Writing Optimization in User Space

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作者： Shen, Runnan Liu, Yuting Zhang, Rui Zhu, Jinbin Liu, Zhonglin Xiao, Limin School of Computer Science and Engineering Beihang University Beijing100191 China North China Institute of Computing Technology Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665494571

Write operation of the latest storage media such as NAND SSDs has certain restrictions. Data can only be written in an additional write mode. When erasing, some valid data still needs to be written again, which will cause write amplification. Researches about write amplification focused on conventional NVMe protocol and did not consider some forward features. In response to this problem, this paper designs a user-mode write operation optimization algorithm based on the dynamic adjustment of zone space. When the block is erased, the impact caused by write amplification can be significantly resolved, and operations of multiple tenants can be effectively isolated. The Zone Based Algorithm will implement a mapping algorithm based on zone space, and select a suitable GC to maximize the performance. We also designed and implemented a variety of mapping algorithms and GC algorithms to improve the processing efficiency of IO requests. Experiments based on FEMU show at least 25% improvement when increasing data of different life span. © 2021 IEEE.

关键词： Protocols Smart cities Heuristic algorithms Clustering algorithms Writing Media Jitter

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Assessment of Machine Learning Techniques used for EEG based Pain Monitoring 23

Assessment of Machine Learning Techniques used for EEG based...

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Mirela, Riciu Ionela Hossu, Daniela Hossu, Andrei Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

The purpose of this article is to provide an overview of research on the analysis and interpretation of electroencephalographic (EEG) signals, using automated artificial intelligence (AI) applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing neurological disorders. In this study, we assessed the sensitivity of different algorithms and datasets classifying pain syndromes. Based on this evaluation, the SVM classification algorithm, considered the most suitable for EEG analysis, was applied in an experimental study that certifies its performance. © 2021 IEEE.

关键词： Data mining

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