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international Symposium on Computer Science, Digital Economy and Intelligent Systems, CSDEIS 2019

International Symposium on Computer Science, Digital Economy...

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international Symposium on Computer Science, Digital Economy and Intelligent Systems, CSDEIS 2019

ISBN: (纸本)9783030392154

The proceedings contain 42 papers. The special focus in this conference is on Computer Science, Digital Economy and Intelligent Systems. The topics include: A Model of Cognitive Disorders upon the Algebra of Fourier-Dual Operations;Intelligent OFDM Telecommunication Systems Based on Many-Parameter Complex or Quaternion Fourier Transforms;vibration Monitoring Systems for Power Equipment as an Analogue of an Artificial Neural Network;Integrated Computer Analysis of Genomic Sequencing Data Based on ICGenomics Tool;statistical and Linguistic Decision-Making Techniques Based on Fuzzy Set Theory;studying the Crack Growth Rate Variability by Applying the Willenborg’s Model to the Markov’s Simulated Trials;creating Spaces of Temporary Features for the Task of Diagnosing Complex Pathologies of Vision;a Modified Particle Swarm Algorithm for Solving Group Robotics Problem;Mathematical Modeling of DC Motors for the Construction of Prostheses;analysis of Diagnostic Signs of Defective States of Mechatronic Mechanisms of Cyclic Action;development and Performance Evaluation of a Software System for Multi-objective Design of Strain Gauge Force Sensors;optimization of the Structure of the Intelligent Active System as a Necessary Condition for the Harmonization of Creative Solutions;parallel Hybrid Genetic Algorithm for Solving Design and Optimization Problems;optimal Real-Time Image Processing with Imperfect Information on Convolution-Type Distortion;scalability and parallelization of Sequential Processing: Big Data Demands and Information Algebras;aggregate Estimates for Probability of Social Engineering Attack Success: Sustainability of the Structure of Access Policies;a Machine Learning Approach to the Vector Prediction of Moments of Finite Normal Mixtures;complex Risks Control for Processes in Heat Technological Systems.

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Workshop 16: SNACS Scalable Networks for Advanced computing Systems

Workshop 16: SNACS Scalable Networks for Advanced Computing ...

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IEEE international Symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： İlkay Altintas Dorian Arnold Martin Schulz Matthew G. F. Dosanjh Ryan E. Grant Taylor Groves San Diego Supercomputing Center Emory University Technical University of Munich Sandia National Laboratories Lawrence Berkeley National Laboratory

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The Workshop on Scalable Networks for Advanced computing Systems is a venue for discussions in the networks community that encompasses the full networks stack, ranging from high-level tools and interfaces, down to low-level hardware. The goal of this workshop is to bring together researchers to present research on the next-generation of large scale interconnects for scientific applications. In particular, our aim is to provide a venue for discussion of the full network stack from user level interfaces (i.e., MPI, PGAS) to the hardware level (i.e., network offload, hardware support for active messages, routing protocols). Workshop topics include: networks for exascale systems, networks for multi-facility workflows, new communication models and architectures for large scale systems, and network interface and hardware co-design.

关键词： Hardware distributed processing Distance measurement Tools Next generation networking

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Automatic Humour Detection in Tweets using Soft computing Paradigms

Automatic Humour Detection in Tweets using Soft Computing Pa...

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2019 international conference on Machine Learning, Big Data, Cloud and parallel computing, COMITCon 2019

作者： Jaiswal, Arunima Monika, M. Mathur, Anshu Prachi, P. Mattu, Sheena Department of Computer Science IGDTUW New Delhi India

ISBN: (纸本)9781728102115

Humour has been an integral entity of human life with its lingual, social and psychological aspects. Due to its vast applications and increasing popularity on social media platforms, enabling computers to process humour has become very crucial. Therefore, we intent to aid task of computational analysis of humour by employing deep learning and machine learning techniques for humour detection. Social media websites such as Facebook, Twitter, Reddit etc are an abundant source of humorous content. Hence, we use micro-blogging website Twitter as the focus of this study. We created a symmetrically distributed dataset containing 4000 funny and plain tweets and applied seven supervised classification algorithms to perform the task of detecting which tweets contain humour. Accuracy, recall and precision have been used as the metrics of performance. © 2019 IEEE.

关键词： Recurrent neural networks

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3rd international conference on advances in computing and Data Sciences, ICACDS 2019

3rd International Conference on Advances in Computing and Da...

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3rd international conference on advances in computing and Data Sciences, ICACDS 2019

ISBN: (纸本)9789811399411

The proceedings contain 112 papers. The special focus in this conference is on advances in computing and Data Sciences. The topics include: Lexical Text Simplification Using WordNet;analysis and Impact of Trust and Recommendation in Social Network Based Algorithm for Delay Tolerant Network;formation of Hierarchies in the System of Organization of State Construction Supervision in Case of Reorientation of Urban Areas;customized Visualization of Email Using Sentimental and Impact Analysis in R;Role of Lexical and Syntactic Fixedness in Acquisition of Hindi MWEs;Five Input Multilayer Full Adder by QCA Designer;effect of Vaccination in the Computer Network for distributed Attacks – A Dynamic Model;ransomware Analysis Using Reverse Engineering;semantic Textual Similarity and Factorization Machine Model for Retrieval of Question-Answering;empirical Study of Test Driven Development with Scrum;krisha: An Interactive Mobile Application for Autism Children;advanced Spatial Reutilization for Finding Ideal Path in Wireless Sensor Networks;an Efficient Preprocessing Step for Retinal Vessel Segmentation via Optic Nerve Head Exclusion;An Upper Bound for Sorting Rn with LE;image Filtering with Iterative Wavelet Transform Based Compression;Multiresolution Satellite Fusion Method for INSAT Images;a Combinatorial Fair Economical Double Auction Resource Allocation Model;issues in Training a Convolutional Neural Network Model for Image Classification;an Approach to Find Proper Execution Parameters of n-Gram Encoding Method Based on Protein Sequence Classification;hyperglycemia Prediction Using Machine Learning: A Probabilistic Approach;microprocessor Based Edge computing for an Internet of Things (IoT) Enabled distributed Motion Control;rough-Set Based Hotspot Detection in Spatial Data.

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A Review of Mobility Prediction Models Applied in Cloud/Fog Environments 24th

A Review of Mobility Prediction Models Applied in Cloud/Fog ...

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international European conference on parallel and distributed computing (Euro-Par)

作者： Lima, David H. S. Aquino, Andre L. L. Curado, Marilia Univ Coimbra DEI FCTUC Ctr Informat & Syst CISUC Coimbra Portugal Fed Univ Alagoas UFAL Comp Inst LaCCAN CPMAT Maceio Alagoas Brazil Fed Inst Alagoas IFAL Rio Largo Brazil

ISBN: (纸本)9783030105495;9783030105488

Cloud and Fog computing are two emerging technologies that have being used in various fields of application. On one hand, Cloud computing has the problem of big latency, being especially problematic when the application requires a rapid response in the edge network. On the other hand, Fog computing distributes the computational data processing tasks to the edge network to reduce the latency, but it still faces challenges especially when dealing with support for mobile users. This work aims to present a review of the works in Cloud/Fog computing that use mobility prediction techniques in their favor in order to deal with users mobility problem. Additionally we present the potential of applying the techniques in Cloud/Fog environments.

关键词： Cloud computing Fog computing Mobility prediction

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Load Balance Awared Data Sharing Systems In Heterogeneous Edge Environment

Load Balance Awared Data Sharing Systems In Heterogeneous Ed...

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international conference on parallel and distributed Systems (ICPADS)

作者： Sheng Chen Zheng Chen Siyuan Gu Baochao Chen Junjie Xie Deke Guo Colledge of Intelligence and Computing Tianjin University Tianjin P.R. China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha Hunan P.R. China AMS PLA Institute of Systems Engineering Beijing P.R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Edge computing has become the de facto method for delay-sensitive applications, in which the computation and storage resources are placed at the edge of network. The main responsibility of edge computing is to carry data from the Cloud downlinks and terminal uplinks, and organize these data well on the edge side. This is the basis for subsequent analysis and processing of data. Therefore, this brings about a question as to how those data should be organized on the edge and how to store and retrieve them. In response to this demand, some methods have been proposed to solve the related problems of how to build data storage and retrieval services on the edge side. Those methods propose three different solutions: structured, unstructured, and hybrid schemes. However, the data storage and retrieval services for the heterogeneous edge environment is still lack of research. It is still not considered an important design test load balancing when the data is stored on the edge side. In this paper, we design and implement w-strategy, a load balance approach that implements the appropriate load balance among the heterogeneous edge nodes by using the weighted Voronoi diagram. Our solution utilizes the software defined networking paradigm to support a virtual-space based distributed hash tables (DHTs) to distribute data. Evaluation results show that w-strategy achieves better load balancing among the heterogeneous edge nodes compared to the existing methods, GRED and Chord. And, the w-strategy improves the average underutilization of the resources by 20%.

关键词： Simulation Memory Load management Resource management Uplink Software defined networking Edge computing

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Multi-user Edge-assisted Video Analytics Task Offloading Game based on Deep Reinforcement Learning

Multi-user Edge-assisted Video Analytics Task Offloading Gam...

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international conference on parallel and distributed Systems (ICPADS)

作者： Yu Chen Sheng Zhang Mingjun Xiao Zhuzhong Qian Jie Wu Sanglu Lu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer Science and Technology / Suzhou Institute for Advanced Study University of Science and Technology of China P.R. China Center for Networked Computing Temple University

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

With the development of deep learning, artificial intelligence applications and services have boomed in the recent years, including recommendation systems, personal assistant and video analytics. Similar to other services in the edge computing environment, artificial intelligence computing tasks are pushed to the network edge. In this paper, we consider the multi-user edge-assisted video analytics task offloading (MEVAO) problem, where users have video analytics tasks with various accuracy requirements. All users independently choose their accuracy decisions, satisfying the accuracy requirement, and offload the video data to the edge server. With the utility function designed based on the features of video analytics, we model MEVAO as a game theory problem and achieve the Nash equilibrium. For the flexibility of making accuracy decisions under different circumstances, a deep reinforcement learning approach is applied to our problem. Our proposed design has much better performance compared with some other approaches in the extensive simulations.

关键词： Visual analytics Reinforcement learning Games Nash equilibrium Servers Task analysis Artificial intelligence

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Self-driving through a Time-distributed Convolutional Recurrent Neural Network

Self-driving through a Time-distributed Convolutional Recurr...

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international conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Antonio Luna-Álvarez Dante Mújica-Vargas Manuel Matuz-Cruz Jean Marie Vianney Kinani Eduardo Ramos-Díaz Tecnológico Nacional de México-CENIDET Cuernavaca-Morelos México Tecnologico Nacional de México-Tapachula Tapachula-Chiapas Mexico Instituto Politecnico Nacional-UPIIH San Agustín Tlaxiaca-Hidalgo Mexico

ISBN: (数字)9781728189871

ISBN: (纸本)9781728189888

This paper proposes an approach based on the use of time dimension within a Convolutional and Recurrent Hybrid Neural Network to carry out the driving of a simulated vehicle in real time. Convolutional layers are transformed into time-distributed layers that process a series of images in parallel and are related in time and space by recurrent layers. The experimentation showed an approximate 10% improvement over other autonomous driving approaches.

关键词： Neurons Convolution Roads Feature extraction Data models Training Tensors

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Safe and Optimal Path Planning for Autonomous UAV Using a Decomposition-Coordination Method 3rd

Safe and Optimal Path Planning for Autonomous UAV Using a De...

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3rd international conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems

作者： Nizar, Imane Illoussamen, Youssef Illoussamen, El Hossein Mestari, Mohammed ENSET Mohammedia Lab Signals Distributed Syst & Artificial Intelli Av Hassan II Mohammadia 28830 Morocco Univ Hassan II Casablanca Mohammedia Casablanca Morocco

ISBN: (纸本)9783030451820;9783030451837

In this paper, we propose a new Decomposition-Coordination Method (DCM) to solve the nonlinear problem of optimal path planning, for autonomous Unmanned Aerial Vehicle (UAV) in a dynamic environment. The main objective of this work is to enable safe autonomous navigation to the UAV. Our algorithm of decomposition-coordination computes initially an optimal path leading to the desired position, and according to the information supplied by a deciding unit, the UAV can predict the potential collisions, and avoid them by computing new collision-free paths if needed, allowing more reactivity to the UAV. This approach consists of first, choosing a suitable mathematical model that depicts the dynamics of the UAV, to which we associate the objective functions to end up with a multi-objective optimization problem. We proceed then to the resolution of the nonlinear system, by subdividing it into a set of smaller interconnected subsystems. The DCM is then used to achieve a local treatment of the non-linearity and reduce the processing time, where each subsystem is split between two levels for parallel processing, and the coordination is ensured after the resolution of the system using the method of Lagrange multipliers. We study as well the convergence and the stability of the algorithm, then we present the results of our simulation on Matlab to corroborate the potential of this theoretical method.

关键词： Decomposition-Coordination Method Optimal control Non-linear system Autonomous navigation Unmanned Aerial Vehicle

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distributed-Memory Vertex-Centric Network Embedding for Large-Scale Graphs 5

Distributed-Memory Vertex-Centric Network Embedding for Larg...

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5th IEEE international conference on Big Data Intelligence and computing, DataCom 2019

作者： Riazi, Sara Norris, Boyana University of Oregon Department of Computer and Information Science EugeneOR97403 United States

ISBN: (纸本)9781728141176

Network embedding is an important step in many different computations based on graph data. However, existing approaches are limited to small or middle size graphs with fewer than a million edges. In practice, web or social network graphs are orders of magnitude larger, thus making most current methods impractical for very large graphs. To address this problem, we introduce a new distributed-memory parallel network embedding method based on Apache Spark and GraphX. We demonstrate the scalability of our method as well as its ability to generate meaningful embeddings for vertex classification and link prediction on both real-world and synthetic graphs. © 2019 IEEE.

关键词： Graph embeddings

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