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AP3: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model 23

AP3: Adaptive Power Prediction Framework based on Spatial Pa...

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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： Chen, Juan Ou, Zhixin Guo, Yifei Qi, Xinxin Sun, Yuyang Deng, Lin Chen, Hongyu Lin, Zihan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

The accuracy of processor power modeling is an important foundation for power management and optimization on parallel computing system. It is difficult to build a high-accuracy instantaneous CPU/DRAM power prediction model. One of the main reasons for the low accuracy is that the processor architecture sometimes influence greatly on the accuracy of power prediction. For example, the accuracy of processor power model is affected by inaccurate modeling of the uncore part of processor. Another reason comes from the limitation of static models for instantaneous power prediction. Despite the existence of various optional linear/nonlinear models, the fixed training set and model coefficients are insufficient to make high-accuracy instantaneous predictions for various target programs. Aiming at the above two issues, this paper proposes an Adaptive Power Prediction framework based on spatial Partition multi-phase model (AP^{3}). Spatial partition mainly solves the impact of uncore power on the prediction accuracy, and adaptability solves the limitation of the static model on the power prediction accuracy. According to the experimental results on both ARM-based and x86-based processor platforms, AP^{3} greatly increases the CPU and DRAM power instantaneous prediction accuracy. Spatial partition reduce the prediction error (MRE) by 0.3%-8.2% compared to previous single model, while adaptive update further reduces the error (MRE) by 1.7%-7.1% compared to previous static model. © 2021 ieee.

关键词： Training Adaptation models Smart cities Multicore processing Computational modeling Power system management Random access memory

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Secure Skyline Groups Queries on Encrypted Data on Cloud Platform 23

Secure Skyline Groups Queries on Encrypted Data on Cloud Pla...

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作者： Teng, Yiping Sun, Yue Shi, Zhan Jiang, Dongyue Zhao, Liang Fan, Chunlong School of Computer Shenyang Aerospace University Liaoning Large-scale Distributed System Laboratory Shenyang China

ISBN: (纸本)9781665494571

As an important category of skyline queries, the skyline groups query has drawn growing interest from the academic communities. For taking the benefit of cost savings, data owners are motivated to outsource the data storage and query processing to the public cloud to offload the massive data management. However, direct outsourcing may raise severe privacy issues. In this paper, we define and study the secure skyline groups query problem, and propose the secure skyline groups query methods. To address this problem, we first propose the Basic Secure Skyline Groups Query (BSSGQ) method to find the skyline groups on encrypted data. In BSSGQ method, candidate groups and their aggregate tuples are generated under encryption. To calculate group dominance relations, we propose a secure group dominance protocol to facilitate the dominance computation between groups. With the dominance relations, the skyline groups w.r.t the query group can be obtained, and finally the query results of the skyline groups can be securely returned without being learnt by the cloud servers. To address the efficiency problem in BSSGQ method, we further propose a Dynamic-programming-based Secure Skyline Groups Query (DSSGQ) method to effectively avoid generating the candidate groups. In DSSGQ method, the encrypted data are equivalently mapped based on the query group as the preprocessing to achieve a dynamic skyline groups query. Then, DSSGQ method dynamically constructs and calculates the skyline sub-groups in ciphertext, and the final skyline groups can be combined with the obtained skyline sub-groups. Thorough analysis shows the security and complexity of the proposed methods, and extensive experimental results on real datasets and synthetic datasets illustrate the performance of our proposed methods. © 2021 ieee.

关键词： Cloud computing Privacy Protocols Smart cities Query processing Memory Complexity theory

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The Role of Blockchain Technology in Supply chain Management for Data Security 3

The Role of Blockchain Technology in Supply chain Management...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Birari, Dipika R. Rathod, Vijay U. Dandavate, Aarti Shelke, Nilesh Kumar, Ranjit Kulkarni, Masira M. S Army Institute of Technology Department of Information Technology Pune India G H Raisoni College of Engineering and Management Department of Artificial Intelligent and Machine Learning Wagholi Pune India Dhole Patil College of Engineering Department of Computer Engineering Pune India Symbiosis International Deemed University Symbiosis Institute of Technology Department of Computer Science and Engineering Nagpur Campus Pune India Ajeenkya D Y Patil University Department of Computer Engineering Pune India G H Raisoni College of Engineering and Management Department of Cyber Security and Data Science Wagholi Pune India

ISBN: (纸本)9798350354348

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. Data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well. © 2024 ieee.

关键词： Smart contract

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Effects of Electric Vehicles Charging on the distribution system 3

Effects of Electric Vehicles Charging on the distribution sy...

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3rd international conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Saonerkar, Ameya K. Kadu, Akshay D. Padole, Nivedita S. Mohamed, Osman M. Alnabulsi, Walid N. Khot, Purva P. Bagde, B.Y. Fulzele, Punit Yeshwantrao Chavan College of Engineering Dept. of Electrical Engineering Nagpur India g2energy Dept. of Electrical Design Milton Keynes United Kingdom Shifamed Llc Dept. of Research & Development CA United States Yeshwantrao Chavan College of Engineering Dept. of Mathematics and Humanities Nagpur India Datta Meghe Institute of Medical Sciences Jawaharlal Nehru Medical College Dept. of Research and Development Wardha India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

This paper presents effects of charging electric vehicles (EV) on distribution system. Precisely, effects on bus voltage profile and power losses, due to charging on radial distribution system (rdS) to ring main distribution system (RMDS) and advance RMDS networks with the assistance of distributed Generation (DG) as well as capacitor banks. Framework assimilate DG which replicates current inceptions due to solar photo voltaic (PV), wind, biogas and other upcoming renewable power sources along with traditional power factor correction measures like capacitor banks in the first place, so that system is pragmatic in design to replicate the real distribution system. The work will be conducted on steady state system and implemented on ieee 33-Bus test system using the MATLAB programming environment on manifold case studies. © 2022 ieee.

关键词： distributed power generation

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Data-driven parallel optimization method for sensor placement and state estimation based on generative adversarial principle in unobservable systems 3

Data-driven parallel optimization method for sensor placemen...

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2024 3rd international conference on Power System and Energy Technology, ICPSET 2024

作者： Wei, Chengzhi Li, Ming Liu, Yang Huang, Runhong Chen, Yizhe Gao, Yifan Gan, Kai Zhao, Ruifeng Lu, Jiangang Che, Liang Electric Power Research Institute China Southern Power Grid Co. Ltd. Guangzhou510663 China Zhaoqing Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhaoqing526040 China Power Dispatching and Control Center Guangdong Power Grid Co. Ltd. Guangzhou510600 China College of Electrical and Information Engineering Hunan University Changsha410082 China

Data-Driven State Estimation (DDSE) is characterized by low measurement requirements and high estimation accuracy, which provides the possibility of stable and efficient estimation for unobservable distribution systems with low measurement quantities. However, the lack of an efficient and flexible sensor placement method with DDSE at this stage hinders the development and application of DDSE in distribution networks. To address this issue, a data-driven parallel optimization method for sensor placement and state estimation based on the generative adversarial principle in unobservable systems is proposed in this paper. This method transforms sensor placement and state estimation into an adversarial learning model, solvable through gradient descent. The sensor placement optimization strategy generates a cost-effective placement scheme, while DDSE evaluates the sensor scheme, provides improvement suggestions, and enhances its estimation accuracy. Through adversarial training, the proposed scheme yields a significantly reduced number of sensor placement schemes compared to traditional methods, while maintaining high precision of DDSE. Simulation results on the ieee-33 node system validate the effectiveness of the proposed approach. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Generative adversarial networks

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SA_3AM: A Novel Sentiment Analysis Approach Integrating Three-Level Attention Mechanism 23

SA_3AM: A Novel Sentiment Analysis Approach Integrating Thre...

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作者： Wang, Qingren Sun, Liang Cui, Jie Zhang, Jing School of Computer Science and Technology Anhui University Hefei China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

Nowadays, social networks have become the primary ways for people to communicate in their daily lives. Due to the great value of short texts generated by social networks, sentiment analysis on them has been attracting more attention from academics and business. However, emojis, which have taken the world by storm, have been challenging sentiment analysis because of their flexible ability to convey emotions. Therefore, this paper proposes a novel sentiment analysis approach by integrating three-level attention mechanism, namely SA_3AM. Particularly, SA_3AM can be divided into three phases: i) attention-oriented word sentiment semantics learning, where SA_3AM utilizes Bi-LSTM to capture bidirectional semantic dependencies within various dimensions conveyed in pure words;ii) attention-oriented emoji sentiment seantics mining, where SA_3AM employs column max-pooling to obtain rich sentiment semantics of emojis by fusing the sentiment dependencies of words;and iii) attention-oriented microblog sentiment semantics calculating, where SA_3AM uses attention mechanism to mine implicit intentions conveyed by microblogs. A real-world dataset Weibo_SL containing 6,436 Chinese microblogs from January 2017 to July 2020 was built to evaluate our SA_3AM. Our experimental results not only show that SA_3AM outperforms the peers, but also demonstrate the feasibility of the dataset Weibo SL. © 2021 ieee.

关键词： Training Deep learning Sentiment analysis Social networking (online) Storms Smart cities Semantics

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Design & development of transmitted & encrypted datas using SDN and energy self-healing concepts used in RF energy harvesting wireless sensor nets 3

Design & development of transmitted & encrypted datas using ...

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3rd international conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Shobha, S.A. Manjunath, T.C. Vtu Research Centre Ece Dept. Dayananda Sagar College of Engg. Karnataka Bangalore India Ece Dept. Dayananda Sagar College of Engg. Karnataka Bangalore India Dayananda Sagar College of Engineering Electronics & Communication Engg. Bangalore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Self Healing Network (SHN) is reconfigurable, which means that the topology of the network can be changed by the nodes in the network like a mobile ad-hoc network. Software Defined Network (SDN) has gained more popularity as a network architecture platform for SHN in recent years. SDN performs an efficient centralized network management. SDN architecture can adapt quickly to new network changes and also makes an intelligent network configuration. In this paper, an efficient algorithm for SHN is proposed to allocate energies to the sensor nodes and to heal the energies of the sensor nodes after every data transmission with very less time. Also, an effective AES encryption algorithm is used for secured data transmission. Simulation results obtained by using Netbeans IDE shows time taken for energy self-healing and AES encrypted data for the original data transmitted. © 2022 ieee.

关键词： sensor nodes

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TADA: A Toolkit for Approximate distributed Agreement 23rd

TADA: A Toolkit for Approximate Distributed Agreement

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23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： da Conceicao, Eduardo Lourenco Alonso, Ana Nunes Oliveira, Rui Carlos Pereira, Jose Orlando Univ Minho Braga Portugal INESC TEC Braga Portugal

ISBN: (纸本)9783031352591;9783031352607

Approximate agreement has long been relegated to the sidelines compared to exact consensus, with its most notable application being clock synchronisation. Other proposed applications stemming from control theory target multi-agent consensus, namely for sensor stabilisation, coordination in robotics, and trust estimation. Several proposals for approximate agreement follow the Mean Subsequence Reduce approach, simply applying different functions at each phase. However, taking clock synchronisation as an example, applications do not fit neatly into the MSR model: Instead they require adapting the algorithms' internals. Our contribution is two-fold. First, we identify additional configuration points, establishing a more general template of MSR approximate agreement algorithms. We then show how this allows us to implement not only generic algorithms but also those tailored for specific purposes (clock synchronisation). Second, we propose a toolkit for making approximate agreement practical, providing classical implementations as well as allow these to be configured for specific purposes. We validate the implementation with classical algorithms and clock synchronisation.

关键词： approximate agreement distributed systems primitives practical toolkit clock synchronisation

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Analysis of Wireless sensor Network Security Models: A Salient Approach for Deeper Inspection Using Deep Neural Networks 3

Analysis of Wireless Sensor Network Security Models: A Salie...

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3rd international conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Albusaidi, Arwa Mohideen, Faizal Haja University of Technology and Applied Sciences Sohar Oman

ISBN: (纸本)9798350300604

Wireless sensor networks are systems containing sensing, computing, and communication components that are designed to enable their operators to detect, collect, and respond to malicious activity in the monitored environment. In recent years, there has been growing interest in creating networks of sensors that are low-cost, low-power, and versatile due to advancements in wireless communications and electronics. To mitigate network vulnerabilities, intrusion detection systems (IDS) have been developed. Given that the rapid development of technology has led to an increase in the frequency of cyber-Attacks, extensive research has been conducted to improve the effectiveness of WSN-IDS. The focus of this study is to evaluate the security paradigm of IDS in wireless sensor networks. The research proposes methods to improve the protection of Wireless sensor Networks (WSN s) if any security vulnerabilities are found. These methods involve utilizing signature-based IDS or anomaly-based IDS to identify and react to any harmful or invasive actions within the network infrastructure. Since WSN s are often deployed in remote areas, security measures are necessary to safeguard them against potential attacks. Due to the dynamic nature of the environment in which WSNs operate, machine learning plays a crucial role in their applications. This paper presents an analysis of security attacks on both IDS and WSN models and proposes security solutions to address them. Additionally, the paper tests one of these security solutions through an experimental setup, using Google Collaborator as the analysis tool and the NSL-KDD dataset with a CNN classifier. The experiment demonstrates that the NSL-KDD dataset is useful for comparing different intrusion detection models and supports the classifier of the dataset. Furthermore, it reveals the effectiveness and accuracy of the Random Forest model in the IDS security framework. © 2023 ieee.

关键词： Wireless sensor networks

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2022 3rd international conference on Information Science, Parallel and distributed systems, ISPDS 2022

2022 3rd International Conference on Information Science, Pa...

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3rd international conference on Information Science, Parallel and distributed systems, ISPDS 2022

ISBN: (纸本)9781665487474

The proceedings contain 87 papers. The topics discussed include: research on the layout of shared electric vehicle charging stations based on AHP-MOP method;structure-preserving video super resolution with multi-scale convolution;research on underwater measurement-device-independent quantum key distribution;an assessment and evaluation framework for highway construction management based on data analysis of the project management platform;multi-station joint long-term water level prediction model of hongze lake based on rf-informer;similarity-guided and multi-layer fusion network for few-shot semantic segmentation;an improved HRNET and its application in crowd counting;cervical cell classification based on attention mechanism and multi-resolution feature fusion;research hotspot and trend analysis of anonymous communication based on citespace;optoelectronic servo tracking technology based on particle swarm optimization compound control of feedforward coefficients;and robust control of aero-engine networked control system with long time delay.

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