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international conference on Parallel distributed and Grid computing (PDGC)

作者： Amogh Shukla Vinit Juneja Sonakshi Singh Utpal Prajapati Ankur Gupta Dharmesh Dhabliya School of CSE Vellore Institute of Technology Vellore TL India Department of CSE Vaish College of Engineering Rohtak Haryana India Vishwakarma Institute of Information Technology Pune Maharashtra India

ISBN: (纸本)9781665454025

Researchers are employing sentiment analysis on user comments and tweets to get a feel for popular opinion. The rate at which the world is changing as a consequence of technological progress is unprecedented. In today's world, having access to the Internet is a need. To share their thoughts on current events, more and more people are turning to social networking apps. Collecting and analysing client feedback is crucial whether marketing a product or attempting to enhance a government service. Opinion mining, also known as sentiment analysis, is often done in advance of a discussion in which the attitudes behind different points of view are to be found. Sentiment analysis's popularity as a tool for understanding the public's mood has skyrocketed in recent years. In the newest research, neural networks are used to analyse the tweets of Twitter users. Researchers are increasingly interested in "tweets" (comments) and the substance of these expressions, and the use of Twitter data for survey research has increased in latest years. Purpose of this research is to assess performance of several sentiment analysis methods when applied to Twitter data. Present research has used compression mechanism to reduce image size and noise removal mechanism to improve the quality of image before applying CNN model. Thus integration of compression, noise removal and CNN results in hybrid model that is providing more accurate solution with better performance.

关键词： Training Sentiment analysis Analytical models Image coding Social networking (online) Computational modeling Blogs

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Stability and Availability Optimization of distributed ERP Systems During Cloud Migration 37th

Stability and Availability Optimization of Distributed ERP...

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37th international conference on Advanced Information networking and Applications, AINA 2023

作者： Aloysius, Gerard Christopher Bhatia, Ashutosh Tiwari, Kamlesh Schneider Electric Karnataka Bangalore India Birla Institute of Technology and Science Pilani Rajasthan Pilani India

ISBN: (纸本)9783031284502

Enterprise resource planning is a powerful software package that enables businesses to integrate a variety of disparate functions, it is a concept whereby an organization enables effective communication and data exchange within the business units and associated third parties such as vendors and banks. SAP is the de-facto ERP software for a company that manages business operations and customer relations. Many organizations have started migrating their ERP to infrastructure-as-a-service (IAAS) providers like amazon web services and Microsoft Azure. Though there is a significant increase and positive sentiment to migrate productive workloads to the cloud, the availability and reliability of applications remain a huge concern. No comprehensive study specifically examines the stability and availability of productive ERP workload in the cloud with large databases. Cloud brings specific advantages like faster time-to-value, moving out of expensive hardware, ease of spinning up virtual machines, and accessibility of the latest technologies in one place. There is a need for a study to specifically look into moving productive ERP workload to the cloud and a roadmap to achieve optimum stability and availability while leveraging all the advantages we get from cloud computing. The paper’s objective is to take in the experience of migrating several productive ERP landscapes with a database size ranging from 2 TB to 20 TB and propose a design roadmap to attain optimum Stability and 99.9% Availability. The design was implemented and measured for its effectiveness post-migration by comparing the application with the data before migration. SAP pre-go-live and post-go-live reports show a 33% reduction in dialog response time and a 40% improvement in response time during peak hours. The overall database grew from 8449 GB to 8703 GB during the analysis. When we extrapolate the analysis to 21 days post-migration, we see that the response time improves even further. We see 100% applicatio

关键词： Enterprise resource planning

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An Efficient and distributed Data Storage and Sharing Method Based on Blockchain 12th

An Efficient and Distributed Data Storage and Sharing Method...

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12th international conference on Computer Engineering and Networks, CENet 2022

作者： Sun, Yizhe Chen, Shiyou Fang, Yadong Xu, Wei Luo, Qun Rui, Lanlan National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Network Technology Research Center China Unicom Research Institute Beijing China

ISBN: (纸本)9789811969003

With the development of intelligent medical, electronic medical records (EMR) promote the information management of Internet of Medical Things (IoMT) data. As the need for sharing IoMT data between organizations grows, EMR faces challenges in terms of reliability of the third-party storage, availability of cross-domain authentication, and efficiency of access control in mobile network. In this paper, we propose a blockchain-based medical data sharing model to guarantee the security and efficiency of cross-domain sharing of medical privacy data. Firstly, heterogeneous data of IoMT are classified, and the lightweight data are mixed encrypted and stored in the blockchain full nodes. Then considering the availability and scalability, we introduce the light nodes deployed on mobile edge computing (MEC) platforms to provide distributed access control proxy service. In the CP-ABE access control algorithm, we decouple the relationship between attributes and decryption time, and further design a token mechanism in improved CP-ABE algorithm. Finally, security analysis proves that our model performs well in terms of privacy. Simulation results show that the encryption and decryption performance and key storage space of our CP-ABE access control algorithm are better than traditional algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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SemRAN: Semantic Radio Access Networks with Collective Knowledge Sharing in 6G and Beyond

SemRAN: Semantic Radio Access Networks with Collective Knowl...

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Future Communications and Networks (FCN), international conference on

作者： Hao Xu Weichao Chen Keji Zhu Merouane Debbah Tongji University Shanghai China Department of Electrical Engineering and Computer Science 6G Center Khalifa University of Science and Technology Abu Dhabi UAE

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

Semantic communications are considered the next stage for the communications industry, particularly the unique fusion of channel coding and source coding for wireless communications, such as the joint source channel coding (JSCC) with AL/ML trained encoders/decoders. However, the critical challenge of implementing semantic communications methodology in 6G and beyond are two folds: a) The circulation of source and channel information and b) The distribution of shared knowledge both in the public domain and between peers. This paper introduces a generic semantic-ready architecture for future networks by exchanging channel information with source information in purpose-built reasoning plane functions and service-oriented integration architecture. Besides, the paper proposes a novel distributed knowledge exchange scheme resembling social network characteristics. The proposed architecture enables collaborative semantic processing between users and networks with privacy-preserving computing and storage resource configuration using coherent social knowledge bases. With extensive simulations on three datasets, our proposed architecture demonstrates advancements in decoding accuracy, transmission latency, as well as computational efficiency.

关键词： 6G mobile communication Knowledge engineering Wireless communication Social networking (online) Source coding Computer architecture Semantic communication Cognition Channel coding Radio access networks

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Hybrid Coded MapReduce for Latency-Constrained Tasks with Straggling Servers

Hybrid Coded MapReduce for Latency-Constrained Tasks with St...

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IEEE international conference on Communications (ICC)

作者： Ruxue Mei Wei Wang Nannan Zhang Zhaoyang Zhang Zhejiang Key Lab of Information Processing College of Information Science and Electronic Engineering Communication and Networking Zhejiang University Hangzhou P.R. China

Coded distributed computing (CDC) can efficiently improve the performance of Map Reduce by mitigating the effects of straggling servers and decreasing the communication load simultaneously. In this paper, for the latency-constrained computation tasks, we use hybrid coded MapReduce and optimize the hybrid coded scheme to minimize the total latency, including the computation latency in Map phase and the communication latency in Shuffle phase. For the practical scenarios where the number of straggling servers is relatively small compared with the number of all servers, we reformulate the original optimization problem into a non-convex optimization problem. Then we use the successive convex approximation (SCA) algorithm to approximate the non-convex optimization problem into the iterations of a convex optimization problem. We prove the effectiveness and convergence of the proposed iterative SCA algorithm. Besides, the proposed SCA-based algorithm shows the effective convergence and outperforms the enumeration algorithm of the original problem with the approximated total latency and the short execution time via numerical experiments. Furthermore, the hybrid coded scheme achieves significant performance improvement in terms of decreasing the total latency compared with baseline schemes.

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DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

DSANE:A Dual Structure-Aware Network Embedding Approach for ...

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IEEE international conference on Big Data Analysis (ICBDA)

作者： Zhihao Xiong Xiaqing Xie Xu Wu Yuan Peng Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT China school of cyberspace security BUPT China Libarary of BUPT China

Increasing popularity and diversity of social media networks has encouraged people to participate on multiple online social networks. Thus, linking account across different networks belonging to the same person becomes an emerging task and is important to many downstream tasks. The mainstream approach is to regard users as nodes in networks and embed them in a low-dimensional latent space, making it convenient to input into subsequent predictive models. Existing methods are still faced up with challenges of incompleteness of networks and insufficiency performance. We propose a novel method called DSANE-Dual Structure-Aware Network Embedding for user identity linkage based on deep neural network, which firstly optimizes networks by a complete-filter method, and then embeds both local and global information into nodes’ vectors, thus greatly enriches nodes’ representation. Experiments on real-world datasets show that our method greatly outperforms baselines.

关键词： Couplings Deep learning Social networking (online) Neural networks Diversity reception Predictive models Feature extraction

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NOVN: A named-object based virtual network architecture to support advanced mobile edge computing services

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PERVASIVE AND MOBILE computing 2020年 69卷 101261-101261页

作者： Bronzino, Francesco Maheshwari, Sumit Seskar, Ivan Raychaudhuri, Dipankar Univ Savoie Mt Blanc Chambery France Rutgers State Univ WINLAB New Brunswick NJ 08901 USA

Achieving advanced Mobile Edge computing (MEC) services such as dynamic resource assignment and slicing, maintaining Quality of Service (QoS), and enabling heterogeneous virtual functions are some of the technical challenges associated with edge-cloud enhanced 5G architectures now under consideration. This paper proposes a named-object based virtual network (NOVN) architecture to support low-latency applications in the MEC. Software router implementation running on the ORBIT testbed validates the named-object approach, showing low VN processing and control overhead, and making it possible to achieve low latency. A latency performance improvement of 30% is achieved as compared to the baseline implementation without NOVN. The results also validate feasibility of using the advanced MEC services for an example latency constrained edge cloud scenario. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Virtual networks Name-object networking Mobile edge computing Network slicing

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Shared-Dining: Broadcasting Secret Shares Using Dining-Cryptographers Groups 21st

Shared-Dining: Broadcasting Secret Shares Using Dining-Crypt...

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21st IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 16th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Moedinger, David Dispan, Juri Hauck, Franz J. Ulm Univ Inst Distributed Syst D-89081 Ulm Germany

ISBN: (纸本)9783030781972;9783030781989

We introduce a combination of Shamir's secret sharing and dining-cryptographers networks, which provides (n - vertical bar attackers vertical bar)-anonymity for up to k - 1 attackers and has manageable performance impact on dissemination. A k-1 anonymous broadcast can be implemented using a small group of dining cryptographers to first share the message, followed by a flooding phase started by group members. Members have little incentive to forward the message in a timely manner, as forwarding incurs costs, or they may even profit from keeping the message. In worst case, this leaves the true originator as the only sender, rendering the dining-cryptographers phase useless and compromising their privacy. We present a novel approach using a modified dining-cryptographers protocol to distributed shares of an (n, k)-Shamir's secret sharing scheme. All group members broadcast their received share through the network, allowing any recipient of k shares to reconstruct the message, enforcing anonymity. If less than k group members broadcast their shares, the message cannot be decoded thus preventing privacy breaches for the originator. We demonstrate the privacy and performance results in a security analysis and performance evaluation based on a proof-of-concept prototype. Throughput rates between 10 and 100 kB/s are enough for many real applications with high privacy requirements, e.g., financial blockchain system.

关键词： Network protocol Privacy protocol Dining cryptographers Secret sharing Peer-to-Peer networking

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Multi-Agent Systems for Autonomous IoT Network Management Using distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international Symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Strengthening Healthcare Cybersecurity by Optimizing Adversarial Attack Defences in Deep Learning Models using GPU and Parallel Processing Technologies

Strengthening Healthcare Cybersecurity by Optimizing Adversa...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Hridya Venugopal Mary Subaja Christo Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu Tamilnadu India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

In healthcare, safeguarding patient data integrity is paramount, necessitating robust deep learning models. This research focuses on bolstering the cyber security defenses of these models, particularly against adversarial attacks, through the application of the Localized Adversarial Feature Attack (LAFEAT) technique. Leveraging Graphics Processing Units (GPU) and the TensorFlow framework, the study explores algorithmic enhancements like the Fast Gradient Sign Method (FGSM) and Model-Agnostic Meta-Learning (MAML). These techniques perturb input data gradients or adapt model parameters, enhancing resilience against adversarial perturbations. Additionally, Particle Swarm Optimization (PSO), a metaheuristic optimization technique, is examined to fortify defense mechanisms further. Parallel processing techniques, utilizing parallel GPU or distributed clusters, are implemented to expedite the optimization process, reducing computational burdens and enhancing scalability. Despite advancements, gaps remain in understanding how these models can be optimized for real-world healthcare applications, particularly in terms of balancing computational efficiency and robustness. The research demonstrates tangible benefits, with a significant 10% increase in success rates against adversarial attacks compared to baseline methods. Moreover, parallel processing implementation results in a 30% reduction in optimization time, improving the efficiency of cyber security defenses for deep learning models in healthcare. These numerical enhancements underscore the research’s value in fortifying healthcare systems against adversarial threats, ensuring the security and integrity of patient data while addressing existing gaps in the optimization of adversarial defenses in healthcare applications. The strengths of this research lie in its robust adversarial attack optimization techniques, integration of parallel processing for efficiency, and application to critical healthcare tasks. Comprehensive

关键词： Deep learning Adaptation models Computational modeling Graphics processing units Medical services Parallel processing Data models

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