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Sentiment Analysis for Twitter Data in the Hindi Language

Sentiment Analysis for Twitter Data in the Hindi Language

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Anjum Madan Udayan Ghose University School of Information Communication & Technology GGSIPU Delhi India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

In the past decade, mining the opinions for information extraction and retrieval has been one of the major fields of study. Sentiment Analysis (SA) can be explained as the process of identifying the polarity of a given content for gaining an insight into the hidden information stored in a text or a piece of document. Today, various social networking sites serve as a platform for expression. Twitter, is one such microblogging application that is tremendously used by various users for expressing the true intent about a particular entity in the form of tweets. the ultimate goal of analyzing the sentiments is to mine the useful content from these opinion sources. the Natural Language Processing (NLP) technique has been used for the initial processing of tweets. there are broadly two methods available for further analyzing the opinionated data: Lexicon Based Approach (LBA) and Machine Learning Approach (MLA) based on supervised learning. the Supervised method has a disadvantage of its dependency on the quality and amount of training data. the LBA technique uses an enhanced dictionary i.e. Hindi SentiWordNet as a resource and Hybrid Based Approach (HBA) which combines the LBA and MLA for classifying movie tweets as either positive or negative. the English language has majorly dominated in this area of research. We, on the other hand, have gathered movie tweets in the Hindi language for Sentiment Analysis of Twitter data and led a comparative evaluation for both the techniques: LBA and HBA.

关键词： Training Sentiment analysis Dictionaries Social networking (online) Blogs Training data Motion pictures

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A data-centric approach to distributed tracing 11

A data-centric approach to distributed tracing

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11th IEEE Int Conf on Cloud Comp Technol and Sci (CloudCom) / 19th IEEE Int Conf Comp and Informat Technol (CIT) / 2019 Int Workshop on Resource Brokering with Blockchain (RBchain) / Asia-Pacific Serv Comp Conf (APSCC)

作者： Popa, Nicolae Marian Oprescu, Ana Media Distillery Amsterdam Netherlands Univ Amsterdam Informat Dept Amsterdam Netherlands

ISBN: (纸本)9781728150116

Modern applications are often implemented as distributed systems consisting of multiple application layers and spread across many machines. Monitoring and diagnosing are fundamental challenges of such systems, and many solutions have been proposed. they all aim to abstract the distributed nature and offer a concise overview of the system. distributed logging, monitoring and tracing solutions help with root cause analysis or assessing the performance of the system. In data processing situations, data is the main driver and it should be treated accordingly. Microservices are often used to implement highly distributed data processing systems, so troubleshooting from a data point of view is also difficult. Data should be monitored and traced across machines and applications in order to get reliable insights into how the data is stored and processed. However, existing approaches to distributed tracing are based on tracing requests that are propagated throughout the system instead on their content. In this work we take a data-centric perspective to tracing and data processing to investigate how content can be traced in a distributed system. We evaluate existing tracing approaches to see if they can be extended to the content itself, then we take into consideration new ones. We implement three different approaches to data-centric distributed tracing in a highly distributed data processing system built using microservices and discuss their advantages and disadvantages.

关键词： microservices distributed tracing observability instrumentation

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11th international conference on Internet and distributed computing Systems, IDCS 2018

11th International Conference on Internet and Distributed Co...

引用

11th international conference on Internet and distributed computing Systems, IDCS 2018

ISBN: (纸本)9783030027377

the proceedings contain 28 papers. the special focus in this conference is on Internet and distributed computing Systems. the topics include: Towards Island Networks: SDN-Enabled Virtual Private Networks with Peer-to-Peer Overlay Links for Edge computing;almost-Fully Secured Fully Dynamic Group Signatures with Efficient Verifier-Local Revocation and Time-Bound Keys;path Planning for Multi-robot Systems in Intelligent Warehouse;dynamic Framework for Reconfiguring computing Resources in the Inter-cloud and Its Application to Genome Analysis Workflows;game-theoretic Approach to Self-stabilizing Minimal Independent Dominating Sets;towards Social Signal Separation Based on Reconstruction Independent Component Analysis;Performance, Resilience, and Security in Moving Data from the Fog to the Cloud: the DYNAMO Transfer Framework Approach;development of a Support System to Resolve Network Troubles by Mobile Robots;a Benchmark Model for the Creation of Compute Instance Performance Footprints;towards the Succinct Representation of m Out of n;developing Agent-Based Smart Objects for IoT Edge computing: Mobile Crowdsensing Use Case;path Planning of Robotic Fish in Unknown Environment with Improved Reinforcement Learning Algorithm;review of Swarm Intelligence Algorithms for Multi-objective Flowshop Scheduling;exploiting Long Distance Connections to Strengthen Network Robustness;an Online Adaptive Sampling Rate Learning Framework for Sensor-Based Human Activity Recognition;a Secure Video-Based Robust and Aesthetic 2D Barcode;a Migratable Container-Based Replication Management for Inter-cloud;Dilated Deep Residual Network for Post-processing in TPG Based Image Coding;underground Intelligent Logistic System Integrated with Internet of things;extending the Advisor Concept to Deal with Known-Ahead Transportation Tasks;a Framework for Task-Guided Virtual Machine Live Migration.

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Decentranet - An Ethereum, Proxy Re-Encryption and IPFS Based Decentralized Internet

Decentranet - An Ethereum, Proxy Re-Encryption and IPFS Base...

引用

international conference on computing and networking Technology (ICCNT)

作者： Koushik Bhargav Muthe thiru Srinivasa Teja Vemuru Khushboo Sharma Nilofer Sultana Mohammad SRM University AP Amaravati India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

the global daily data generation is estimated to be more than 2.5 quintillion bytes, and more than 90 percent of the total data produced is generated in the last two years. Most of this data is being generated and controlled by very few centralized agencies in the Web 2.0 internet architecture. this causes issues, including data manipulation, lack of privacy, and data leaks historically and is not just limited to the fields mentioned. this paper proposes a Web 3.0 based architecture which eliminates the centralized agencies and to promote a fully decentralized, secure, and transparent internet. It leverages IPFS, a peer to peer distributed hypermedia transfer protocol. Ethereum and smart contracts create a secure decentralized mechanism for initiating data-based payments. Furthermore, the proposed architecture uses zero-knowledge proofs and proxy re-encryption mechanisms to enhance the privacy of the nodes in the network.

关键词： Blockchain Internet Cloud computing Protocols Cryptography Computer architecture Service-oriented architecture

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Heralding the Future of Federated Learning Framework: Architecture, Tools and Future Directions

Heralding the Future of Federated Learning Framework: Archit...

引用

international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Saneev Kumar Das Sujit Bebortta College of Engineering and Technology Bhubaneswar India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

In today's era, the exponential growth of data and its management is a matter of concern. Machine learning has shown its efficacy in multiple application areas. But machine learning on decentralized data was a hectic task since last decade. A novel technology has gained much importance in recent days i.e., federated learning which deals with training on decentralized and distributed data along with preservation of its privacy. Smartphone data being privacy-sensitive is used for locally training a global model which further is aggregated to generate an updated global model which again is distributed among multiple clients. this paper focuses on presenting the efficacy of federated learning by epitomizing an architecture showing the working mechanism of the technology. Further, this paper exhibits an intersection of on-device machine learning, privacy preservation technology and edge computing i.e., federated learning. Also, we have used TensorFlow Federated, an open source platform to simulate federated learning tasks for MNIST and extended MNIST (E-MNIST) datasets. Further, the results contain the loss and accuracy parameters for ten iterations repeated for six optimizer states (Opt st ) for each dataset. the peak accuracy that we achieved for MNIST and E-MNIST datasets are 0.843 and 0.853 respectively by using federated averaging algorithm. Further, the minimum loss value that we obtained for MNIST and E-MNIST datasets are 0.652 and 0.646 respectively. the execution time for implementing the algorithm for each dataset is presented in a graphical manner. Finally, certain application areas where federated learning technology has aided are scrutinized.

关键词： Training distributed databases Machine learning Computer architecture Collaborative work Data models Task analysis

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Enhanced Particle Swarm Optimisation and Multi Objective Optimization for the Orchestration of Edge Cloud Clusters 11

Enhanced Particle Swarm Optimisation and Multi Objective Opt...

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11th international Joint conference on Computational Intelligence (IJCCI)

作者： Shahid, Hafiz Faheem Pahl, Claus Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9789897583841

Load balancing and workload distribution cause challenges for the management of IoT and distributed systems in the edge computing environment. Swarm intelligence is a technology suitable for the management of distributed systems, networks, communication and routing protocols. Swarm intelligence-based PSO algorithms (particle swarm optimization) can be applied for load balancing and task scheduling in cloud computing environments operating through a broker agent. In distributed cloud environments, data is collected and then processed at the center of the cloud, rather than making decision at edge nodes closer to IoT infrastructures. Here, we develop an automated orchestration technique for clustered cloud architectures. An Autonomous Particle Swarm Optimization, called the A-PSO algorithm, is implemented that enables an edge node, such as a remote storage, to work as part of a decentralized, self-adaptive intelligent task scheduling and load balancing agant between resources in distributed systems. Using Multi Objective Optimization (MOO), complementing the A-PSO algorithm, we also include metrics such as Actual Round-Trip Time (ARTT) of tasks assignments to the remote storage to reduce the execution cost. Our A-PSO algorithm call orchestrate the distribution of large volumes of data to remote storage and back in cluster. i.e.. coordinated distributed cloud environments.

关键词： Swarm Intelligence Particle Swarm Optimization distributed Systems Load Balancing Edge Cloud Cluster

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Ubiquitous distributed deep reinforcement learning at the edge: Analyzing Byzantine agents in discrete action spaces 11

Ubiquitous distributed deep reinforcement learning at the ed...

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11th international conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Zhao, Wenshuai Queralta, Jorge Peña Qingqing, Li Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab University of Turku Finland

the integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems. this will further boost the increasing intelligence that is being embedded at the edge in various types of autonomous systems, where collaborative machine learning has the potential to play a significant role. this paper discusses some of the challenges in multi-agent distributed deep reinforcement learning that can occur in the presence of byzantine or malfunctioning agents. As the simulation-to-reality gap gets bridged, the probability of malfunctions or errors must be taken into account. We show how wrong discrete actions can significantly affect the collaborative learning effort. In particular, we analyze the effect of having a fraction of agents that might perform the wrong action with a given probability. We study the ability of the system to converge towards a common working policy through the collaborative learning process based on the number of experiences from each of the agents to be aggregated for each policy update, together with the fraction of wrong actions from agents experiencing malfunctions. Our experiments are carried out in a simulation environment using the Atari testbed for the discrete action spaces, and advantage actor-critic (A2C) for the distributed multi-agent training. © 2020 the Authors. Published by Elsevier B.V.

关键词： Reinforcement learning

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Beamforming in distributed Antenna Systems Based on Successive Convex Approximation and Dual Decomposition 11

Beamforming in Distributed Antenna Systems Based on Successi...

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11th IEEE international conference on Wireless Communications and Signal Processing (WCSP)

作者： Li, Zhiyuan Chen, Li Chen, Xiaohui Wang, Weidong Univ Sci & Technol China CAS Key Lab Wireless Opt Commun Hefei Peoples R China

ISBN: (纸本)9781728135557

In distributed antenna systems (DASs), the main process units (MPUs) undertake all of the computing tasks in the centralized beamforming algorithm, which causes high computation complexity at the MPU. this paper proposes a distributed beamforming algorithm in DASs to reduce the computation complexity. However, the original problem is non-convex and inseparable, which hinders solving the original problem in a distributed manner. To overcome these difficulties, we first construct a sequence of separable convex approximations of the original problem based on the successive convex approximation (SCA) technique and then decompose their dual to yield the distributed beamforming algorithm. the convergence and signalling overhead of the proposed algorithm are also provided. the simulation results show that the proposed algorithm has higher energy efficiency than the traditional algorithms and lower computation complexity than the centralized SCA algorithm.

关键词： distributed antenna systems distributed beamforming algorithm Successive convex approximation Dual decomposition

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Kaa: Evaluating Elasticity of Cloud-hosted DBMS 11

Kaa: Evaluating Elasticity of Cloud-hosted DBMS

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作者： Seybold, Daniel Volpert, Simon Wesner, Stefan Bauer, Andre Herbst, Nikolas Domaschka, Joerg Ulm Univ Inst Informat Resource Management Ulm Germany Univ Wurzburg Informat 2 Wurzburg Germany Ulm Univ Inst Inf Resource Management Ulm Germany

ISBN: (纸本)9781728150116

Auto-scaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an auto-scaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many large-scale cloud applications, it is currently an open question to what extent they support scaling at run-time. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. this paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for write-heavy and read-heavy workloads of different intensities. the results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at run-time may even decrease the experienced performance.

关键词： elasticity cloud NoSQL scalability distributed DBMS

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Peer Consonance in Blockchain based Healthcare Application using AI-based Consensus Mechanism

Peer Consonance in Blockchain based Healthcare Application u...

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international conference on computing and networking Technology (ICCNT)

作者： Nishant Kumar C Parangjothi Sankarshan Guru M Kiran National Institue of Technology Surathkal Karnataka India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

the term `Blockchain', commonly referred to as the brain behind the Bitcoin network, works on the simple principle of the presence of a distributed and decentralized ledger in a public or private network. Since blockchain is decentralized, it is the duty of the Consensus Algorithm to substantiate the details in the blockchain. Traditional consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS), although widely used, are a matter of concern due to computationally expensive operations and convergence towards a monopolized system respectively. though optimizations of PoW and PoS algorithms were subsequently introduced, their features precincts. this paper aims to provide a solution by presenting a consensus algorithm based on Artificial Intelligence (AI) technology while maintaining the fairness of the system. A Healthcare based system was set up on top of the blockchain network to generate the dataset about the miners in order to train our neural network. On the whole, it incorporated the advantages of the state of the art consensus models which can increase the efficiency of the healthcare industry while diminishing their demerits.

关键词： Blockchain Consensus algorithm Artificial intelligence Medical services Peer-to-peer computing Machine learning algorithms Data models

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