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Generation and Detection of Artificial Facial Images

Generation and Detection of Artificial Facial Images

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Suvarna Pawar Sudarshan Vetal Sumit Sharma Avishkar Pagare Chetan Patil Department of Computer Engineering Sinhgad College of Engineering Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Artificial Intelligence (AI), machine learning, and deep learning have revolutionized various real-world applications. this paper presents a technique to generate artificial facial images and proposes deep-learning approaches to distinguish between real and artificial facial images. the objective is to develop an effective method for discerning genuine faces from manipulated ones. the proposed models utilize a dataset comprising both real and artificial images and employ neural networks-based deep learning algorithms. Social media platforms have become predominant sources of information, yet not all shared content is genuine; photographs can be altered, leading to deepfakes as a significant threat for spreading misinformation. therefore, there is an urgent need to develop models capable of detecting deepfakes. this paper introduces a deepfake detection framework utilizing convolutional neural networks and the publicly available 140K Real and Fake Faces dataset to accurately identify deepfakes in images.

关键词： Deep learning Deepfakes Adaptation models Sensitivity Image synthesis Social networking (online) Computational modeling Predictive models Reliability Faces

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Smart Cloud Monitoring: Optimising Server Loads for Reducing Energy Consumption

Smart Cloud Monitoring: Optimising Server Loads for Reducing...

引用

international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R Vignesh S. thenmalar Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

the recent proliferation of cloud computing services brings forth the focal issues of energy consumption in data centers from an environmental and financial standpoint. this paper describes a monitoring system focused on energy consumption reduction in the cloud by dynamically regulating loads in servers through constrained optimization techniques. the system employs the Gated Recurrent Unit algorithm in order to analyze the patterns of server usage over time and predict future demand, thereby allowing for proactive adjustments in workload distribution. the system maintains an optimal balance between performance and energy efficiency by continuously monitoring server utilization and power consumption, taking into consideration others like actual computation abilities, temperature limits, and SLAs. Our approach intensely reduces the power consumption while keeping a similar service quality level. Our testing results have proven to work well in real-world cloud environments, thus forming a basis that will help enhance sustainable practices in the cloud.

关键词： Cloud computing Energy consumption Data centers Energy conservation Real-time systems Servers Resource management Sustainable development Monitoring Optimization

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A Yet Powerful Routing Protocol for Vehicular Ad-hoc Networks (VANETs) Utilizing Software Defined Network (SDN) and Fog computing

A Yet Powerful Routing Protocol for Vehicular Ad-hoc Network...

引用

international conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Khalid A. Darabkh Mamoun F. Al-Mistarihi Mera Ismail Al-Maaitah Computer Engineering Department School of Engineering The University of Jordan Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

In this article, we propose a novel protocol that employs different technologies together, such as Software Defined Network (SDN) for the purpose of offering scalability, programmability, and global network information, and Fog computing for the reason of providing location services that meet V ANET standards. Additionally, we propose a novel cluster head selection algorithm for the purpose of enhancing the efficiency of the protocol by considering three different dimensions, namely, lifetime, average distance, and signal-to-interference noise ratio. the novelty of this proposed algorithm comes from reducing the number of control overheads, reducing long-distance communications, and reducing packet loss due to collisions and contentions inside each cluster, respectively. the proposed protocol has been assessed using N s3 simulator whereas its results are so promising and worth publishing.

关键词： Simulation Software algorithms Clustering algorithms Packet loss Computer architecture throughput Routing protocols Software defined networking Standards Edge computing

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX

Simplifying Graph-Parallel Computation in Apache Spark with ...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Lasya Vyakaranam Ramakrishnan Raman Harshal Shah Nilamadhab Mishra R. Meenakshi S. Murugan Symbiosis Law School (SLS) Pune Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. the goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. the eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX’s performance, expanding its features, and integrating it with Spark’s ecosystem are all part of this process. the expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. this will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. the graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. the degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores.

关键词： Histograms Technological innovation Social networking (online) Scalability Data visualization Cluster computing Distance measurement Sparks Recommender systems Convergence

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Proceedings - 8th international conference on Applied computing and Information Technology, ACIT 2021

Proceedings - 8th International Conference on Applied Comput...

引用

8th international conference on Applied computing and Information Technology, ACIT 2021

ISBN: (纸本)9781450384933

the proceedings contain 24 papers. the topics discussed include: estimation of emotional scenes from lifelog videos with moving average of facial expression intensity;facial expression intensity estimation using deep convolutional neural network;fuzzy database and interface to analyze management system operations;materialized view driven architecture over lattice of cuboids in data warehouse;identification of corresponding numerical attributes in heterogeneous databases based on instances;reducing the repairing penalty on misspeculation in thread-level speculation;GPU parallelization of all-pairs-shortest-path algorithm in low-degree unweighted regular graph;an adaptive tiering scheme for an autonomous distributed storage system;and redundancy of routing information on the distributed key-value store based on order preserving linear hashing and skip graph with the load balancing method.

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Knowledge Base Learning Portal For Organization

Knowledge Base Learning Portal For Organization

引用

international conference on Communication and Electronics Systems (ICCES)

作者： Chitra Devi D L. Kiran Kumar Reddy B. Yamini M. Nalini M. Anuradha K. Gokulnath Dept of CSE S. A. Engineering College Chennai India Dept of CSE(AIML) Geethanjali College of Engineering and Technology Hydreabad India Dept of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology (SRMIST) Kattankulathur India Inspiration ManPower Consultancy Bangalore India

the knowledge base learning site's objective is to arrange the training materials and provide the trainee with a "Role-Based learning" path. Students can access resources to meet various learning needs by starting at the knowledge base learning portal. Giving access to a cutting-edge learning environment and courses is the main objective of this initiative. Any course taken only requires an internet connection; there is no need to download any additional software. the course can be accessed at any time and go at the user’s own pace. In addition to maintaining digital records free of duplicate entries, the company can monitor the trainee's actions and timely performance.

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RISC-V: Redefining the Future of computing, Architecture, Innovations, and Beyond

RISC-V: Redefining the Future of Computing, Architecture, In...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Ankit P. Navik Sachin Kumar Tiwari Vipin Anand Brijesh Yadav Jonglae Park Ha Jun Sung System LSI Samsung Semiconductor India Research Bengaluru India System LSI Samsung Electronics Gyeonggi-do Republic of Korea

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

In recent years, RISC-V has gained significant popularity and RISC-V computing device is going to increase by 2030 due to open-source and royalty-free instruction set architecture (ISA). RISC-V is widely adopted as an ISA for embedded processors globally. India has launched the Digital India RISC-V (DIR-V) program for next generation microprocessors in 2022, and many organizations have started working on the RISC-VISA. the RISC-V standards organization consistently introduces new ISA extensions to cater to the evolving requirements of advanced computing. the widespread of use of Android/Wear OS is attributed to technological advancements and the availability of cost-effective solutions for mobile and wearable devices. there are many operating systems are available for embedded devices such as iOS, Android, Tizen and FireFox. Among all of these OS Android is one of the most popular OS for wide variety of devices such as smartphones, wearable, automotive, smart TV, etc. In this paper, we present the study of RISC vs RISC-V. Further, the details of challenges to run operating system especially linux kernel based OS on RISC-V is covered.

关键词： Technological innovation Reduced instruction set computing Smart TV Operating systems Standards organizations Organizations Wearable devices Smart phones Assembly Next generation networking

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A Comprehensive Survey on Energy Efficiency in Federated Learning: Strategies and Challenges

A Comprehensive Survey on Energy Efficiency in Federated Lea...

引用

IEEE international Energy conference and Exhibition (EnergyCon)

作者： Ala Gouissem Zina Chkirbene Ridha Hamila College of Computing and Information Technology University of Doha for Science and Technology Qatar Electrical Engineering Qatar University Qatar

Federated Learning (FL), a burgeoning approach in machine learning, facilitates collaborative model training across distributed devices while maintaining data privacy. Although gaining traction, FL faces a critical challenge in energy efficiency, which is vital for its scalability and practicality, especially in resource-limited settings like IoT networks and mobile devices. this paper provides a comprehensive survey of current methods and techniques aimed at enhancing energy efficiency in FL systems. We delve into various resource allocation techniques and algorithm optimization strategies. Additionally, we examine the role of cutting-edge technologies such as Blockchain and 6G networks, which play a crucial role in minimizing the energy footprint of FL systems. Our survey pinpoints the principal challenges and identifies prospective areas for future research, intending to spur further advancements in energy-efficient FL. We discuss the intricate interplay between energy efficiency, model accuracy, and system scalability in FL. Furthermore, the paper emphasizes the real-world implications of these strategies, highlighting their practical relevance in various technological applications.

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A Grouping Design for Cascaded Coded distributed computing

A Grouping Design for Cascaded Coded Distributed Computing

引用

international conference on Computer and Communications (ICCC)

作者： Hongyan Dong Jing Jiang Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

In order to reduce the communication load in general distributed computing frameworks such as MapReduce Li et al. introduced coded distributed computing (CDC). the authors in [1] proposed a scheme for cascaded coded distributed computing on general networks. Although the numbers of input files and output functions in [1] are much less than those in [2], large numbers of input files and output functions are still required. In this paper, we present a new CDC scheme which 1) designs the assignments of input file and output function; 2) requires less number of input files and output functions compared with those in the scheme derived by [1]. Meanwhile, the ratio of communication load to that of the scheme in [1] is less than $\mathbf{the}\fracdistributedcomputing$ .

关键词： distributed computing

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A distributed Privacy-Preserving Framework for Deep Learning with Edge-Cloud computing 15

A Distributed Privacy-Preserving Framework for Deep Learning...

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2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of things, ithings 2022, 18th IEEE international conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE international conference on Smart Data, SmartData 2022

作者： Dai, Fei Liu, Guozhi Huang, Bi Xu, Xiaolong Chen, Chaochao Zhou, Zhangbing Zhou, Xiaokang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Zhejiang University College of Computer Science and Technology Zhejiang China China University of Geosciences School of Information Engineering Beijing China Shiga University Faculty of Data Science Shiga Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Industrial Internet of things (IIoT) systems can leverage deep learning (DL) models to provide intelligent applications. However, the training process of such DL models tends to leak privacy when the training data contain sensitive operational and management information. Most studies about privacy-preserving DL require a trusted data collector and thus are not suitable in an untrusted environment. In this paper, we propose a distributed privacy-preserving framework for DL with edge-cloud computing, where direct privacy leakage and indirect privacy leakage of training data are both considered. First, a pre-trained convolutional neural network (CNN) is partitioned into two parts for limiting direct privacy leakage of raw data, namely the feature module and the classification module. the feature module consisting of the lower layers of the CNN is deployed on an edge server, which is used to attract the feature of raw data. the feature data is fed to the classification module consisting of the higher layers of the CNN, which is deployed on the cloud server to compute image classification tasks. Second, a perturbation module between the feature module and the classification module is designed for limiting indirect privacy leakage during feature data transmission. the perturbation module uses local differential privacy (LDP) to produce noise in the feature data. third, to further improve the accuracy, we retrain the classification module with the randomized feature data from edge servers. Experimental results show that the proposed framework achieves high accuracy under low privacy budgets. © 2022 IEEE.

关键词： Classification (of information)

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