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4th International conference on Human Centered Computing, HCC 2018

4th International Conference on Human Centered Computing, HC...

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4th International conference on Human Centered Computing, HCC 2018

ISBN: (纸本)9783030151263

The proceedings contain 68 papers. The special focus in this conference is on Human Centered Computing. The topics include: Wi-Fi Attention network for Indoor Fingerprint Positioning;Reinforcement Learning Based Cooperation Transmission Policy for HetNets with CoMP Technology;capacity Estimation of Time-Triggered Ethernet network Based on Complex network Theory;a New Communication P System Model Based on Hypergraph;Non-Orthogonal Multiple Access (NOMA) in Providing Services for High-Speed Railway and Local Users in DownLink MIMO System;research on Multi-agent distributed Supply Chain Information Collaboration Based on Cloud Environment;mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing;Single Image Super-Resolution by Parallel CNN with Skip Connections and ResNet;incorporating Description Embeddings into Medical Knowledge Graphs Representation Learning;Research on the Classification and Channel Selection of Emotional EEG;A Piezoelectric MEMS Harvester Suitable Adopt a New Two-Degree-of-Freedom Structure;spiking neural P Systems with Time Delay;energy Efficiency MapReduce Job Scheduling of Shuffle and Reduce Phases in Data Center;research on Weibo Emotion Classification Based on Context;research on Data Visualization in Different Scenarios;model Checking for Turn-Based Probability Epistemic Game Structure;depressive Emotion Recognition Based on Behavioral Data;knowledge Graph Embedding by Translation Model on Subgraph;linguistic Signatures of Impulsive Buying Consumer Based on Microblog;Research on the Model of Anomaly Detection of FMCG Based on Time Series: Illustrated by the Case of Cosmetics;research on Real-Time Low Air Image Intelligence Image Acquisition and processing Methods;research on Font Emotion Based on Semantic Difference Method;simulation Research on Aircraft Anti-collision Algorithm.

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MapReduce-based Capsule networks

MapReduce-based Capsule Networks

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International conference on Social networks Analysis, Management and Security (SNAMS)

作者： Sun Jin Park Ho-Hyun Park LG CNS Seoul Korea School of electrical and electronics engineering Chung-Ang university Seoul Korea

Currently, artificial intelligence technology is attracting much attention, and image processing field is also making remarkable progress in recognition rate through CNN models. Furthermore, Capsule network which is flexible in changing pose of image is being studied in various fields by improving disadvantage of Pooling Layer of CNN model. We propose a method to accelerate the learning of CapsNet model, which is much slower than the existing neural network model. TensorFlow, Google's deep-running library, and the Apache Foundation's Hadoop framework are run through Python. We can confirm that the learning time is decreased in inverse proportion to the increase of the number of nodes in learning CapsNet according to the proposed method of distributed processing.

关键词： distributed processing Deep learning Computational modeling Google neural networks Big Data Libraries

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A neural Language Model for Multi-Dimensional Textual Data based on CNN-LSTM network 19

A Neural Language Model for Multi-Dimensional Textual Data b...

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19th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed Computing (SNPD)

作者： Park, Seongik Song, Jin-Hee Kim, Yanggon Towson Univ Dept Comp & Informat Sci Towson MD 21204 USA Shinhan Univ Sch IT Convergence Engn Dongducheon Gyeonggi Do South Korea

ISBN: (纸本)9781538658895

Language Modeling (LM) is a subtask in Natural Language processing (NLP), and the goal of LM is to build a statistical language model that can learn and estimate a probability distribution of natural language over sentences of terms. Recently, many recurrent neural network based LM, a type of deep neural network for dealing with sequential data, have been proposed and achieved remarkable results. However, they only rely upon the analysis on the words occurred in the sentences even though every sentence contains various useful morphological information, such as Part-of-Speech (POS) tag that is necessary for constituting a sentence and can be used for an analysis as a feature. Although morphological information can be useful for LM, using that information as the input data to neural network based LM is not straightforward because adding features between words as a one-dimensional array can cause the vanishing gradient problem by increasing the time steps of recurrent neural network. In order to solve this problem, in this paper, we propose a CNN-LSTM based language model that deals with textual data regarding a multi-dimensional data with respect to the input of the network. To train this multi- dimensional input to Long-Short Term Memory (LSTM), we use a convolutional neural network (CNN) with a 1x1 filter for dimensionality reduction of input data to avoid the vanishing gradient problem by decreasing the time step between input words. In addition, our approach that uses multi-dimension data reduced by CNN can be used as a plugin with many customized LSTM based LM. On the Penn Treebank corpus, our model has shown improvement of the perplexity with not only vanilla LSTM but customized LSTM models.

关键词： Language Modeling neural Language Modeling Deep Learning neural network Natural Language processing

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English Character Recognition Using Robust Back Propagation neural network 2nd

English Character Recognition Using Robust Back Propagation ...

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2nd International conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Zanwar, Shrinivas R. Narote, Abbhilasha S. Narote, Sandipan P. CSMSS Chh. Shahu College of Engineering Aurangabad India S.K.N.ś College of Engineering Pune India Governmentś Residence Women’s Polytechnic Tasgaon Sangli India

ISBN: (纸本)9789811391866

OCR deals with the handwritten or printed character recognition with the help of digital computers and soft computing. The scanned images of characters and numbers are used as input for system which is analyzed and transformed it into character codes, normally in ASCII format, which is taken for the data processing. Presently, there are lots of issues in recognition of characters and numbers, which can degrade the performance of the system in various ways. Mainly, the rate of recognition is not improved due to distributed neighborhood pixels of an image. Also, there are some techniques used for the OCR are having lack of contrast levels which is well known by fading of the image. So in this paper, the most important concern is to take such measures to enhance the performance of the system for automatic recognition of characters. Here, the operations are performed on the handwritten English alphabets. The dataset is selected from the Chars74K with different shapes and preprocessed it which deals with filtering and edge detection. Then, in the feature extraction process, features are extracted by using independent component analysis and swarm intelligence is used for feature vector selection. Classification of images are done with the back propagation neural network which gives an effective learning approach. The precise contribution which is evaluated in this research work is the uniqueness of classifications using a combination of the feature extraction and feature optimization (instance selection) using extraction of feature vectors. The performance of the developed system is measured in terms of recognition rate, sensitivity and specificity compared with the benchmark. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Extracting Features from Both the INput and the Output of a Convolutional neural network to Detect distributed Denial of Service Attacks 17

Extracting Features from Both the INput and the Output of a ...

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17th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Ghanbari, Maryam Kinsner, Witold Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781538633601

distributed Denial-of-Service (DDoS) attacks are serious threats to a smart grid infrastructure services' availability, and can cause massive blackouts. This study describes an anomaly detection method for improving the detection rate of a DDoS attack in a smart grid. This improvement was achieved by increasing the classification of the training and testing phases in a convolutional neural network (CNN). An improved version of the variance fractal dimension trajectory (VFDTv2) was used to extract inherent features from the non-pure fractal input data. A discrete wavelet transform (DWT) was applied to the input data and the VFDTv2 to extract distinguishing features during data pre-processing. A support vector machine (SVM) was used for post data-processing. The implementation detected the DDoS attack with 87.35% accuracy.

关键词： Cyber and physical network security anomaly detection method distributed denial of service attack discrete wavelet transform variance fractal dimension convolutional neural network

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A Framework for distributed Deep neural network Training with Heterogeneous Computing Platforms

A Framework for Distributed Deep Neural Network Training wit...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Bontak Gu Joonho Kong Arslan Munir Young Geun Kim School of Electronics Engineering Kyungpook National University Daegu South Korea Dept. of Computer Science Kansas State University Manhattan KS Dept. of Computer Science Korea University Seoul South Korea

ISBN: (纸本)9781728125848

Deep neural network (DNN) training is generally performed by cloud computing platforms. However, cloud-based training has several problems such as network bottleneck, server management cost, and privacy. To overcome these problems, one of the most promising solutions is distributed DNN model training which trains the model with not only high-performance servers but also low-end power-efficient mobile edge or user devices. However, due to the lack of a framework which can provide an optimal cluster configuration (i.e., determining which computing devices participate in DNN training tasks), it is difficult to perform efficient DNN model training considering DNN service providers' preferences such as training time or energy efficiency. In this paper, we introduce a novel framework for distributed DNN training that determines the best training cluster configuration with available heterogeneous computing resources. Our proposed framework utilizes pre-training with a small number of training steps and estimates training time, power, energy, and energy-delay product (EDP) for each possible training cluster configuration. Based on the estimated metrics, our framework performs DNN training for the remaining steps with the chosen best cluster configurations depending on DNN service providers' preferences. Our framework is implemented in TensorFlow and evaluated with three heterogeneous computing platforms and five widely used DNN models. According to our experimental results, in 76.67% of the cases, our framework chooses the best cluster configuration depending on DNN service providers' preferences with only a small training time overhead.

关键词： distributed processing Deep neural network Training time Energy efficiency Edge computing distributed processing Training Functional training Energy efficiency Train timetable Frameworks Platform

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Performance Optimization of Wildlife Recognition with distributed Learning in Ecological Surveillance

Performance Optimization of Wildlife Recognition with Distri...

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IEEE International conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Wan Dai Hongpeng Wang Yulin Song Yunwei Xin Tianjin Key Laboratory of Intelligent Robotics Tianjin Research Laboratory of Machine Learning and Pervasive Computing Nankai University Tianjin

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Recently, deep learning technology has been widely used in ecological environment monitoring, but there are still huge challenges in specific tasks. For example, data sets have increased over the past few years, and in a CPU environment, training is quite time consuming. In order to solve these problems, we bring up a novel and effective method to improve training speed. In this paper, we make use of multi-GPUs to achieve distributed parallel acceleration. Small and shallow networks are not suitable for distributed training, because the computation of each parameter in this network is much higher than that of multi-layer perception or automatic encoder architecture, we use convolutional neural network with parameter sharing as the training network. In this paper, the main adopted method is to compare the performance of training wildlife recognition in single GPU and multi-GPUs environments by using distributed deep learning framework TensorFlow. The experimental results show that multi-GPUs which adopt distributed architecture can significantly accelerate training time consumption than single GPU. The results of this experiment also provides strong support for our follow-up work.

关键词： Training Parallel processing Graphics processing units distributed databases Computational modeling Data models neural networks

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Research on Field Programmable Neuron Array (FPNA) Based on Reusable ANN Neurons 14

Research on Field Programmable Neuron Array (FPNA) Based on ...

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14th IEEE International conference on Solid-State and Integrated Circuit Technology (ICSICT)

作者： Guo, Jia Yan, Zijin Hou, Ligang Lv, Ang Jiang, Nan Beijing Univ Technol VLSI & Syst Lab Beijing 100124 Peoples R China

ISBN: (纸本)9781538644416

An artificial neural network (ANN) is a parallel, distributed processing system consisting of a large number of interconnected neurons. At present, artificial neural networks have been widely used in signal processing, automation, control systems, image recognition and many other fields. Usually, the realization of neural networks is based on software. However, because the software implementation method cannot achieve real-time calculation in many cases, the hardware implementation method can reflect the inherent parallel processing characteristics of neural networks. This paper proposes an ANN neural network field programmable neuron array based on the design of reusable ANN artificial neural network IP core. It not only can save a lot of hardware resources and improve processing speed, but also has a rapid development cycle and good reconfigurability and other advantages.

关键词： Biological neural networks Artificial neural networks Neurons Hardware Field programmable gate arrays IP networks Adaptation models

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A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks 10

A case study on homogeneous and heterogeneous reservoir poro...

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10th International conference on Applied Energy (ICAE)

作者： Liu, Siyan Zhong, Zhi Takbiri-Borujeni, Ali Kazemi, Mohammad Fu, Qinwen Yang, Yuhao Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA Univ Texas Austin Bur Econ Geol Austin TX 78713 USA West Virginia Univ Dept Petr & Nat Gas Engn Morgantown WV 26506 USA Slippery Rock Univ Dept Petr & Nat Gas Engn Slippery Rock PA 16057 USA

The advancing of modern X-ray computer tomography technology provides a powerful tool for us to illustrate the details inside the reservoir rock in three-dimensional space. Pore-scale rock characterization, modeling, and related fluid flow simulation can be challenging due to the high complexity of various rock samples. Conventional pore scale structure modeling methods such as various stochastic methods were developed for reservoir rock 3D microscopic structure reconstruction in order to generate representative realizations for numerical simulations and property upscaling approaches. In this work, generative adversarial networks (GANs) is used for generating the synthetic micro representations of porous rock by acquiring non-linear statistical information from the real 3D rock images in an unsupervised learning scheme. The related 3D image pre-processing, network training and adjusting as well as data post-processing procedures are addressed. The network prediction results from a homogeneous Berea sandstone and a heterogeneous Estaillades carbonate demonstrated the capability of GANs for high resolution porous rock image representations reconstruction, generated and real images are compared via various visualizations and inspections. The study also illustrated the importance of the training image preprocessing, which indicating the data augmentation techniques can be one of the promising improvements in terms of capturing the sparsely distributed features from heterogenous 3D images and reconstructing the synthetic realizations, meanwhile, the robustness of the model during training process is enhanced when limited real data is available. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： porous media image reconstruction generative adversarial networks neural networks

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Fire Detection Using DCNN for Assisting Visually Impaired People in IoT Service Environment 15th

Fire Detection Using DCNN for Assisting Visually Impaired Pe...

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15th International conference on distributed Computing and Artificial Intelligence (DCAI)

作者： Kong, Borasy Lim, Kuoysuong Kwon, Jangwoo Inha Univ Dept Comp Engn Incheon 22201 South Korea

ISBN: (纸本)9783319946498;9783319946481

In an emergency, such as fire in a building, visually impaired people are prone to danger more than non-impaired people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable. But by using vision sensor instead, fire can be proven to be detected much faster as shown in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't generalize well because those techniques use hand-crafted features. With the recent advancements in the field of deep learning, this research can be conducted to help solve the problem by using deep learning-based object detector to detect fire. Such approach can learn features automatically, so they can usually generalize well to various scenes. We introduced two object detection models (R1 and R2) with slightly different model's complexity. R1 can detect fire at 90% average precision and 85% recall at 33 FPS, while R2 has 90% average precision and 61% recall at 50 FPS. The reason why we introduced two models is because we want to have a benchmark comparison as no other research on fire detection with similar techniques exists. We also want to give two model choices when we wish to integrate the model into an IoT platform.

关键词： Object detection Deep convolutional neural network Tensorflow Darkflow

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