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A Novel Hierarchical Edge Computing Solution Based on Deep Learning for distributed Image Recognition in IoT Systems 4

A Novel Hierarchical Edge Computing Solution Based on Deep L...

引用

4th International conference on Information Technology (InCIT)

作者： Monburinon, Nitis Zabir, Salahuddin Muhammad Salim Vechprasit, Natthasak Utsumi, Satoshi Shiratori, Norio Thai Nichi Inst Technol Fac Informat Technol Bangkok Thailand Natl Inst Technol Tsuruoka Coll Dept Creat Engn Tsuruoka Yamagata Japan Fukushima Univ Fac Symbiot Syst Sci Fukushima Japan Chuo Univ Res & Dev Initiat Tokyo Japan

ISBN: (纸本)9781728110196

Traditionally, IoT systems utilize cloud computing platforms for ease of deployment. However, such platforms are resource-intensive, relatively expensive, and lead to longer response time due to network latency. Hence, cloud computing may not always be suitable for unusually large scale deployment. Various edge computing solutions have been proposed to overcome the above issues. However, most of such solutions rely upon expensive edge servers, which make them unsuitable for distributed applications like image recognition in agricultural fields. In this paper, we propose a hierarchical edge computing-based image recognition system in which the major processing is carried out at low-cost gateway devices like Raspberry Pi. As an example case, we address the issue of recognition of animals intruding in agricultural fields. We implement a dynamic learning method, in which a convolutional neural network is dynamically trained to recognize potential target classes based on a specific deployment environment. The AI detection module is then loaded on to the lowest level of edge servers on gateway devices for detection of animals and providing feedback. Experiments show that our proposed recognition system can perform offline image classification tasks with up to seven times higher accuracy and more than two times faster evaluation time in comparison with general-purpose cloud recognition systems. Besides, it consumes less than 6% of the network bandwidth and only a fraction of the energy as well as other computational resources compared with existing approaches.

关键词： Edge Computing Image Recognition Internet of Things

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Machine learning based performance analysis and prediction of jobs on a HPC cluster 20

Machine learning based performance analysis and prediction o...

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20th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Hou, Zhengxiong Zhao, Shuxin Yin, Chao Wang, Yunlan Gu, Jianhua Zhou, Xingshe High-performance Computing Center School of Computer Science Northwestern Polytechnical University Xi'an China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728126166

There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms. © 2019 IEEE.

关键词： neural networks

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DDoS Attack Detection and Clustering of Attacked and Non-attacked VMs Using SOM in Cloud network 3rd

DDoS Attack Detection and Clustering of Attacked and Non-att...

引用

3rd International conference on Advances in Computing and Data Sciences, ICACDS 2019

作者： Bharot, Nitesh Suraparaju, Veenadhari Gupta, Sanjeev Rabindranath Tagore University Raisen India

ISBN: (纸本)9789811399411

Cloud computing has gained more importance in the IT service model that offers cost-effective and scalable processing. It provides virtualized and on-demand services to the user over the internet using several networking protocols with exceptional flexibility. However, with the existing technologies and the vulnerabilities, it leads to the occurrence of several attacks in the cloud environment. distributed Denial of Service (DDoS) is most dangerous among all the attacks which limit the cloud users to access service and resources. Therefore, the detection of DDoS in the network and the identification of attacked VMs is the most dominating task in the cloud environment. In this work, a novel DDoS attack detection mechanism is presented. The research is carried out as follows: (i) Initially DDoS attack is detected by identifying the maximum number of connections to the network, (ii) then the attacked virtual machine and non-attacked virtual machines will be clustered using Self-Organized Mapping (SOM) based neural network (NN). The experimental results exhibit that the presented system can efficiently detect DDoS attacks and cluster attack and non-attack VMs in an attacked cloud network. Moreover, these results demonstrate that the proposed DDoS attack prediction accuracy of 97.63% and precision of 95.4% and it is better than the existing technique. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Proceedings - 2019 IEEE International conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2019

Proceedings - 2019 IEEE International Conference on Industry...

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2019 IEEE International conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2019

ISBN: (纸本)9781728137452

The proceedings contain 26 papers. The topics discussed include: development of hyperplane-based adaptive T-S fuzzy controller for micro aerial robots;ultra wideband (UWB) microstrip patch antenna with adjustable notch frequencies;disaster victims detection system using convolutional neural network (CNN) method;geometrical parameterization of bowtie terahertz receiver antenna connected with conductor bar as bolometer;performance analysis of audio data transmission on FBMC - offset QAM system;a deep learning approach to predict malnutrition status of 0-59 month’s older children in Bangladesh;classification system vocal cords disease using digital image processing;automated testing framework for geographical distributed testing environment;and floods prediction using radial basis function (RBF) based on Internet of things (IoT).

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14th International conference on Hybrid Artificial Intelligence Systems, HAIS 2019

14th International Conference on Hybrid Artificial Intellige...

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14th International conference on Hybrid Artificial Intelligence Systems, HAIS 2019

ISBN: (纸本)9783030298586

The proceedings contain 64 papers. The special focus in this conference is on Hybrid Artificial Intelligence Systems. The topics include: Particle Swarm Optimization-Based CNN-LSTM networks for Anomalous Query Access Control in RBAC-Administered Model;d(Tree)-by-dx: Automatic and Exact Differentiation of Genetic Programming Trees;genetic Algorithm-Based Deep Learning Ensemble for Detecting Database Intrusion via Insider Attack;an Efficient Hybrid Genetic Algorithm for Solving a Particular Two-Stage Fixed-Charge Transportation Problem;Analysis of MOEA/D Approaches for Inferring Ancestral Relationships;parsimonious Modeling for Estimating Hospital Cooling Demand to Reduce Maintenance Costs and Power Consumption;haploid Versus Diploid Genetic Algorithms. A Comparative Study;entropy and Organizational Performance;a Semi-supervised Method to Classify Educational Videos;generating a Question Answering System from Text Causal Relations;new Approach for the Aesthetic Improvement of Images Through the Combination of Convolutional neural networks and Evolutionary Algorithms;evaluating Strategies for Selecting Test Datasets in Recommender Systems;use of Natural Language processing to Identify Inappropriate Content in Text;classification of Human Driving Behaviour Images Using Convolutional neural network Architecture;building a Classification Model Using Affinity Propagation;clustering-Based Ensemble Pruning and Multistage Organization Using Diversity;towards a Custom Designed Mechanism for Indexing and Retrieving Video Transcripts;active Image Data Augmentation;a Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data;Convolutional CARMEN: Tomographic Reconstruction for Night Observation;Creation of a distributed NoSQL Database with distributed Hash Tables;A Proof of Concept in Multivariate Time Series Clustering Using Recurrent neural networks and SP-Lines;On the Influence of Admissible Orders in IVOVO.

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Dynamic Multichannel Access for 5G and Beyond with Fast Time-Varying Channel

Dynamic Multichannel Access for 5G and Beyond with Fast Time...

引用

IEEE International conference on Communications (ICC)

作者： Shaoyang Wang Tiejun Lv Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

关键词： Delays Quality of service Time-varying channels 5G mobile communication Predictive models Wireless communication Optimization

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2nd International conference of the series Soft Computing: Theories and Applications, SoCTA 2017

2nd International conference of the series Soft Computing: T...

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2nd International conference of the series Soft Computing: Theories and Applications, SoCTA 2017

ISBN: (纸本)9789811305887

The proceedings contain 68 papers. The special focus in this conference is on Soft Computing: Theories and Applications. The topics include: Modified integral type weak contraction and common fixed point theorem with an auxiliary function;crop monitoring using IoT: A neural network approach;secure image steganography through pre-processing;understandable huffman coding: A case study;performance investigations of multi-resonance microstrip patch antenna for wearable applications;fuzzy counter propagation network for freehand sketches-based image retrieval;an efficient contrast enhancement technique based on firefly optimization;a review of computational swarm intelligence techniques for solving crypto problems;defense-in-depth approach for early detection of high-potential advanced persistent attacks;complexity metrics for component-based software system;An efficient algorithm to minimize EVM over ZigBee;performance measure of the proposed cost estimation model: Advance use case point method;fractal image compression and its techniques: A review;web services classification across cloud-based applications;performance evaluation and analysis of advanced symmetric key cryptographic algorithms for cloud computing security;A survey of CAD methods for tuberculosis detection in chest radiographs;static image shadow detection texture analysis by entropy-based method;A PSO algorithm-based task scheduling in cloud computing;a novel approach for target coverage in wireless sensor networks based on network coding;natural vibration of square plate with circular variation in thickness;Identification of relevant stochastic input variables for prediction of daily PM10 using artificial neural networks;majority-based classification in distributed environment;detection of advanced malware by machine learning techniques;Inter- and intra-scale dependencies-based CT image denoising in curvelet domain.

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GENIE: QoS-guided Dynamic Scheduling for CNN-based Tasks on SME Clusters 22

GENIE: QoS-guided Dynamic Scheduling for CNN-based Tasks on ...

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22nd Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Chen, Zhaoyun Luo, Lei Yang, Haoduo Yu, Jie Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9783981926323

Convolutional neural network (CNN) has achieved dramatic developments in emerging Machine Learning (ML) services. Compared to online ML services, offline ML services that are full of diverse CNN workloads are common in small and medium-sized enterprises (SMEs), research institutes and universities. Efficient scheduling and processing of multiple CNN-based tasks on SME clusters is both significant and challenging. Existing schedulers cannot predict the resource requirements of CNN-based tasks. In this paper, we propose GENIE, a QoS-guided dynamic scheduling framework for SME clusters that achieves users' QoS guarantee and high system utilization. Based on a prediction model derived from lightweight profiling, a QoS-guided scheduling strategy is proposed to identify the best placements for CNN-based tasks. We implement GENIE as a plugin of Tensorflow and experiment with real SME clusters and large-scale simulations. The results of the experiments demonstrate that the QoS-guided strategy outperforms other baseline schedulers by up to 67.4% and 28.2% in terms of QoS-guarantee percentage and makespan.

关键词： QoS-guided SME Cluster Scheduling CNN Multi-task

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22nd International conference on distributed and Computer and Communication networks, DCCN 2019

22nd International Conference on Distributed and Computer an...

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22nd International conference on distributed and Computer and Communication networks, DCCN 2019

ISBN: (纸本)9783030366247

The proceedings contain 52 papers. The special focus in this conference is on International conference on distributed and Computer and Communication networks. The topics include: Energy Estimation for VANET Performance Based Robust neural networks Learning;mathematical Model of Four-Dimensional Parametric Systems Based on Block Diagonal Matrix with 2 x 2 Blocks;PD-NOMA Power Coefficients Calculation While Using QAM Signals;distributed Data Compression Algorithm for Low-Power Wide-Area networks;PPPXoE - Adaptive Data Link Protocole for High-Speed Packet networks;model of Optical Non-blocking Information processing System for Next-Generation Telecommunication networks;accurate Mathematical Model of Two-Dimensional Parametric Systems Based on 2 x 2 Matrix;on a Queueing-Inventory Problem in Passenger Transport System;queueing Inventory Model for Crowdsourcing;accurate and Interval Estimates of the Probability of network Service Availability for Communication networks;An MMAP/M/∞ Queueing System with an Offer Zone Working in a Random Environment;transient Analysis of a Repairable Single Server Queue with Working Vacations and System Disasters;a Queueing Inventory System with Search and Match - An Organ Transplantation Model;Mathematical Models of the Queuing Systems with MMPP Flow and Instantaneous Feedback;on Estimation of Weibull-Tail and Log-Weibull-tail Distributions for Modeling End-to-end Delay;asymptotic Analysis of the Output Process in Retrial Queue with Markov-Modulated Poisson Input Under Low Rate of Retrials Condition;an Upper Bound of the Large Deviation Probability in Multi-server Constant Retrial Rate System;method of Moments for the Estimation of the Probability Density Parameters in Correlated Semi-synchronous Event Flow of the Second Order;on Physical Web for Social networks.

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Predicting the Price of Bitcoin Using Machine Learning 26

Predicting the Price of Bitcoin Using Machine Learning

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26th Euromicro International conference on Parallel, distributed, and network-Based processing (PDP)

作者： McNally, Sean Roche, Jason Caton, Simon Natl Coll Ireland Sch Comp Dublin 1 Ireland Dublin Business Sch Dublin 2 Ireland

ISBN: (纸本)9781538649756

The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.

关键词： Bitcoin Deep Learning GPU Recurrent neural network Long Short Term Memory ARIMA

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