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共 1359 条记录，以下是571-580 订阅

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Cyber Security Challenges in Distributed Energy Resources for Smart Cities 9

Cyber Security Challenges in Distributed Energy Resources fo...

引用

9th international conference on computing for Sustainable Global Development, INDIACom 2022

作者： Himdi, Tarik Ishaque, Mohammed Ikram, Muhammed Jawad Jeddah International College Jeddah Saudi Arabia

ISBN: (纸本)9789380544441

With the proliferation of data in Internet-related applications, incidences of cyber security have increased manyfold. Energy management, which is one of the smart city layers, has also been experiencing cyberattacks. Furthermore, the Distributed Energy Resources (DER), which depend on different controllers to provide energy to the main physical smart grid of a smart city, is prone to cyberattacks. The increased cyber-attacks on DER systems are mainly because of its dependency on digital communication and controls as there is an increase in the number of devices owned and controlled by consumers and third parties. This paper analyzes the major cyber security and privacy challenges that might inflict, damage or compromise the DER and related controllers in smart cities. These challenges highlight that the security and privacy on the Internet of Things (IoT), big data, artificial intelligence, and smart grid, which are the building blocks of a smart city, must be addressed in the DER sector. It is observed that the security and privacy challenges in smart cities can be solved through the distributed framework, by identifying and classifying stakeholders, using appropriate model, and by incorporating fault-tolerance techniques. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Smart city

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Recognition of Traffic Police Gestures Based on Transfer Learning 3

Recognition of Traffic Police Gestures Based on Transfer Lea...

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3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Lian, Li Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University College of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350316674

At present, the auto-driving technology is becoming more and more mature, but how the auto-driving car recognizes the traffic police gesture information under special road conditions has become an urgent problem to be solved. In this paper, based on transfer learning, eight kinds of gesture signals of Chinese traffic police are recognized. The dataset is "Chinese Traffic Police Gesture dataset"(CTPGD). The total loss value is 0.2174, and good results have been achieved. © 2023 IEEE.

关键词： CTPGD traffic police gesture transfer learning

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Research on Intelligent cigarette sales forecasting model based on big data technology 2

Research on Intelligent cigarette sales forecasting model ba...

引用

2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Wei, Tai-Cheng Chen, Hao Liu, Yan-Bing Zhu, Hao-Ran China Tobacco Guangxi Industrial Co. Ltd. Nanning China

ISBN: (纸本)9783800760268

Tobacco production enterprises and retailers usually adopt the business strategy of selling according to inventory. Production enterprises need to try their best to ensure the purchase demand of retailers and avoid excessive production. Retailers should not only ensure that the commodity inventory will not be out of stock, but also prevent the economic inefficiency and waste caused by excessive inventory. Therefore, how to make purchase planning in advance through intelligent sales volume prediction is particularly important for retailers to improve their profits. For tobacco production enterprises, it is also necessary to optimize cigarette production and delivery planning through sales volume prediction in order to produce higher cost-benefit. Combined with geographic big data and the historical commodity sales data of cigarette manufacturers and retailers, this paper uses the intelligent sales prediction model to help manufacturers and retailers build a cigarette intelligent prediction model including cloud computing platform, big data analysis center, intelligent sales prediction algorithm and other core modules. Taking six stores in five cities, including Kunshan, Zhangjiaxiang, Taicang, Changshu and Wujiang district, a total of 30 tobacco retailers and one tobacco production enterprise as the experimental objects, the commodity sales in the first quarter and the second quarter is compared. The results show that the inventory turnover rate of tobacco retailers using the prediction model has increased by 30% year-on-year, and the cigarette profit has increased by 7% year-on-year, the cigarette inventory turnover rate of tobacco production enterprises increased by 21%, and the cigarette profit increased by 14% year-on-year. It is proved that the intelligent cigarette sales prediction model can help tobacco production enterprises and retailers reduce the risk of unsalable inventory, and improve the inventory turnover rate and cigarette sales. © VDE VERLAG GMBH ∙ B

关键词： Sales

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MPC-Based Privacy-Preserving Serverless Federated Learning 3

MPC-Based Privacy-Preserving Serverless Federated Learning

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3rd international conference on big data, Artificial intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Zhong, Liangyu Zhang, Lei Xu, Lin Wang, Lulu East China Normal University Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan Chengdu610041 China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Federated learning (FL) enables multiple users to collaboratively train a global model by keeping their data sets local. Since a single server is used, traditional FL faces the single point of failure problem. An available approach is to adopt a serverless architecture. However, most of existing serverless FL schemes fail to protect gradient privacy, except for a few schemes that adopt differential privacy (DP) where the global model accuracy will decrease. To address these problems, we propose a privacy-preserving serverless FL scheme based on secure multiparty computation (MPC). Combining multiple cryptographic primitives (e.g., key agreement and symmetric encryption), our scheme protects gradient privacy in FL and it is accuracy-lossless. By secret sharing, our scheme supports users to quit an FL task in each round during training. © 2022 IEEE.

关键词： Privacy-preserving techniques

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CH-Go: Online Go System Based on Chunk data Storage 24

CH-Go: Online Go System Based on Chunk Data Storage

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Hui Li, Chuan Yang, Yiming Li, Cheng Islam, Ashraful Computer School Sichuan University Chengdu China School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9798350319934

The training and running of an online Go system require the support of effective data management systems to deal with vast data, such as the initial Go game records, the feature data set obtained by representation learning, the experience data set of self-play, the randomly sampled Monte Carlo tree, and so on. Previous work has rarely mentioned this problem, but the ability and efficiency of data management systems determine the accuracy and speed of the Go system. To tackle this issue, we propose an online Go game system based on the chunk data storage method (CH-Go), which processes the format of 160k Go game data released by Kiseido Go Server (KGS) and designs a Go encoder with 11 planes, a parallel processor and generator for better memory performance. Specifically, we store the data in chunks, take the chunk size of 1024 as a batch, and save the features and labels of each chunk as binary files. Then a small set of data is randomly sampled each time for the neural network training, which is accessed by batch through yield method. The training part of the prototype includes three modules: supervised learning module, reinforcement learning module, and an online module. Firstly, we apply Zobrist-guided hash coding to speed up the Go board construction. Then we train a supervised learning policy network to initialize the self-play for generation of experience data with 160k Go game data released by KGS. Finally, we conduct reinforcement learning based on REINFORCE algorithm. Experiments show that the training accuracy of CH-Go in the sampled 150 games is 99.14%, and the accuracy in the test set is as high as 98.82 %. Under the condition of limited local computing power and time, we have achieved a better level of intelligence. Given the current situation that classical systems such as GOLAXY are not free and open, CH-Go has realized and maintained complete Internet openness. © 2022 IEEE.

关键词： Reinforcement learning

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Analysis and Application of Power Equipment Production Quality Based on data Hierarchical Processing 2

Analysis and Application of Power Equipment Production Quali...

引用

2nd IEEE international conference on data Science and Computer Application, ICDSCA 2022

作者： Xiang, Wang Jianfeng, Zhao Chunxiao, Liu Southeast University Department of Electrical Engineering Nanjing China Manufacturing Center Nanjing Nr Electric Co. Ltd. Nanjing China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

The Internet of things (IoT), cloud computing, big data technology, and artificial intelligence algorithms have developed rapidly. However, the data generated by the above process, due to its characteristics of multiple sources, large volume, rapid growth, heterogeneity, and complicated correlation, pose challenges to orthodox data processing methods. This research proposes a data layering model based on the weight of data and access frequency. Storing data with different functions and adjusting the data level according to various conditions enhances the query, reading efficiency, and data reliability of massive data. Furthermore, this research provided a solid foundation for subsequent data analysis. Specifically, we analyzed he processed data according to the technique benchmark of electrical devices and exhibited it graphically in the validation part. © 2022 IEEE.

关键词： Internet of things

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QHSL image encryption scheme based on a logistic chaotic system 23

QHSL image encryption scheme based on a logistic chaotic sys...

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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： Yan, Fei Li, Mao Li, Nianqiao Iliyasu, Abdullah M. School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9781665494571

On digital computers, the hue, saturation, and lightness (HSL) image model is especially useful in feature detection, image segmentation, and other operations in robot vision, object recognition, etc. On the quantum computing paradigm, such a model was only recently formulated and many new inroads with it are just beginning. Our study proposes a scheme to secure quantum images encoded using the quantum HSL (QHSL) image model. The proposed QHSL encryption scheme (or simply QHES) utilises logistic mapping to induce the chaotic system at the heart of the QHES strategy. The scheme consists of a quantum logistic chaos unit composed of quantum arithmetic modules to generate chaotic sequences. This is followed by a quantum logistic key generation that is subsequently used to encrypt the HSL components of QHSL images. The proposed scheme exploits quantum parallelism to execute its encryption procedure which leads to significant reduction in computational resources. Results of simulation-based experiments reported suggest potentials of the proposed QHES as an efficient cryptosystem on quantum computers. © 2021 IEEE.

关键词： Heart Image segmentation Quantum computing Computational modeling Feature detection Robot vision systems Parallel processing

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QoE-Guaranteed Heterogeneous Task Offloading with Deep Reinforcement Learning in Edge computing 8

QoE-Guaranteed Heterogeneous Task Offloading with Deep Reinf...

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8th IEEE international conference on Cloud computing and intelligence Systems, CCIS 2022

作者： Zhou, Zhiwen Wu, Yingbo Hou, Jiaxin Chongqing University School of Big Data and Software Chongqing China

ISBN: (纸本)9781665477352

In edge-enabled Internet-of-Things (IoT), various IoT applications commonly generate heterogeneous tasks. Existing heterogeneous task offloading methods are developed to guarantee the Quality-of-Service (QoS), instead of the Quality-of-Experience (QoE) from the user's perspective. However, QoE-guaranteed heterogeneous task offloading can significantly improve the actual user experiences of IoT applications. In this paper, the heterogeneity of offloading tasks is categorized as delay-aware, energy-aware, and privacy-aware. We define a novel QoE metric and adopt a statistical policy to relax the deterministic constraints of heterogeneous tasks to improve the offloading success rate. Furthermore, we formulate the QoE-guaranteed heterogeneous tasks offloading problem as a Mixed-integer Nonlinear Programming (MINLP) problem. Conventional numerical optimization methods are inefficient in solving such problems, therefore, we apply a Deep Reinforcement Learning (DRL) algorithm to make the optimal offloading decision that satisfies the offloading intention of the task. Experiment results show that our algorithm effectively guarantees the global QoE performance and improves the offloading success rate of heterogeneous tasks. © 2022 IEEE.

关键词： Internet of things

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7th international conference on Smart computing and Communication, SmartCom 2022

7th International Conference on Smart Computing and Communic...

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7th international conference on Smart computing and Communication, SmartCom 2022

ISBN: (纸本)9783031281235

The proceedings contain 64 papers. The special focus in this conference is on Smart computing and Communication. The topics include: Equipment Health Assessment Based on Node Embedding;Improvement of ERP Cost Accounting System with big data;OpenVenus: An Open Service Interface for HPC Environment Based on SLURM;design and Analysis of Two Efficient Socialist Millionaires’ Protocols for Privacy Protection;ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter data;Confidentially computing DNA Matching Against Malicious Adversaries;research on Action Recognition Based on Zero-shot Learning;DPSD: Dynamic Private Spatial Decomposition Based on Spatial and Temporal Correlations;Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS;GenGLAD: A Generated Graph Based Log Anomaly Detection Framework;research of Duplicate Requirement Detection Method;bayesian Causal Mediation Analysis with Longitudinal data;Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX;Research on Sharding Strategy of Blockchain Based on TOPSIS;parallel Pileup Correction for Nuclear Spectrometric data on Many-Core Accelerators;component Extraction for Deep Learning Through Progressive Method;lowFreqAttack: An Frequency Attack Method in Time Series Prediction;a Novel Machine Learning-Based Model for Reentrant Vulnerabilities Detection;deep Learning Object Detection;a Decision-Making Method for Blockchain Platforms Using Axiomatic Design;a New Digital-Currency Model Based on Certificates;An Auxiliary Classifier GAN-Based DDoS Defense Solution in Blockchain-Based Software Defined Industrial Network;blockchain-Based Fairness-Enhanced Federated Learning Scheme Against data Poisoning Attack;cryptography of Blockchain;an Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities;using Convolutional Neural Network to Redress Outliers in Clustering Based Side-Channel A

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Vehicle-mounted Early Warning System Based on Edge intelligence 9

Vehicle-mounted Early Warning System Based on Edge Intellige...

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9th international conference on Advanced Cloud and big data, CBD 2021

作者： Gu, Yifeng Zhang, Zhenghua Hong, Qingqing Yangzhou University Information Engineering College Yangzhou City China

ISBN: (纸本)9781665407458

With the steady development of the social economy, the number of motor vehicles has increased rapidly. The number of traffic accidents continues to rise, and fatigue driving is one of the key factors leading to frequent traffic accidents. The Internet of Things (IoT) technology based on cloud computing is receiving more and more attention, and the requirement of device for edge of IoT also has been increased. The amount of data generated by devices has exploded, which has brought a higher demand for data transmission bandwidth. Therefore, this paper proposes an edge intelligent vehicle-mounted early warning system based on Gradient Boosting Decision Tree (GBDT), YOLOv4, and Deep Simple Online And Realtime Tracking (Deep SORT) algorithms. The system does not need to transmit data to the cloud center for processing, and realizes dual monitoring inside and outside the vehicle locally, effectively alleviating the pressure on transmission bandwidth. The result performed that the edge system can detect driver fatigue status and object categories outside the vehicle in real time, which has a positive effect on reducing traffic accidents and developing intelligent transportation. © 2022 IEEE.

关键词： Object detection

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