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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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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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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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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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Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers

Modeling and Simulation of QoS-Aware Power Budgeting in Clou...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Jakub Krzywda Vinícius Meyer Miguel G. Xavier Ahmed Ali-Eldin Per-Olov Östberg César A. F. De Rose Erik Elmroth Department of Computing Science Umeå University Sweden Polytechnic School Pontiﬁcal Catholic University of Rio Grande do Sul Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

关键词： Data centers Power demand Servers Computational modeling Quality of service Data models Cloud computing

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Contextual-Bandit Anomaly Detection for IoT Data in distributed Hierarchical Edge computing

Contextual-Bandit Anomaly Detection for IoT Data in Distribu...

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international conference on distributed computing Systems

作者： Mao V. Ngo Tie Luo Hakima Chaouchi Tony Q.S. Quek Singapore University of Technology and Design Singapore Institute for Infocomm Research A*STAR Singapore Missouri University of Science and Technology USA CNRS SAMOVAR Telecom Sud Paris Institut Mines Telecom Paris-Saclay University France

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity, and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one of these models on the fly, based on the contextual information extracted from each input data. The model selection is formulated as a contextual bandit problem characterized by a single-step Markov decision process, and is solved using a reinforcement learning policy network. We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets. The demo shows that our proposed approach significantly reduces detection delay (e.g., by 71.4% for univariate dataset) without sacrificing accuracy, as compared to offloading detection tasks to the cloud. We also compare it with other baseline schemes and demonstrate that it achieves the best accuracy-delay tradeoff 1 .

关键词： Adaptation models Computational modeling distributed databases Delays Task analysis Anomaly detection Context modeling

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international conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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A Preliminary Model for Optimal Load Distribution in Heterogeneous Smart Environments 20

A Preliminary Model for Optimal Load Distribution in Heterog...

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IEEE 20th international conference on High Performance Switching and Routing (HPSR)

作者： Garcia, Jordi Aguilo, Francesc Simo, Ester Zaragoza, Marisa Masip-Bruin, Xavi Lu, Yunsong Prieto-Gonzalez, Andres Molinas-Ramon, Joan UPC BarcelonaTech Adv Network Architectures Lab CRAAX Vilanova I La Geltru 08800 Spain Huawei Technol Santa Clara CA 95050 USA

ISBN: (纸本)9781728116860

Smart cities are becoming popular and, with them, a plethora of resources is emerging turning into a massive concentration of computing devices. In such environments, the deployment of a smart resources management system is envisioned as an extraordinary opportunity to provide almost unlimited computing and networking capabilities obtained by putting together a vast set of nearby devices, hence offering high performance computing while benefiting from low latency networking. In this scenario, where millions of highly heterogeneous devices are available, a key challenge is to select the set of devices suited the most to carry out some specific tasks. In this paper, we present a performance estimation model intended to find an optimal solution for load distribution over massive heterogeneous systems, considering the effects of both, distributed computing and network overhead. The current implementation is in a preliminary stage, considering only fully parallel applications. Trials conducted considering several hardware configurations, with either homogeneous or heterogeneous systems, show the fact that the predicted performance matches the one obtained by the experimental executions with an almost negligible deviation. These initial results encourage us to continue developing the model for more complex and realistic applications.

关键词： Computational modeling Load modeling Task analysis Urban areas Performance evaluation Cloud computing Optimization

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