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5th International conference on Advanced Engineering Theory and Applications, AETA 2018

5th International Conference on Advanced Engineering Theory ...

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5th International conference on Advanced Engineering Theory and Applications, AETA 2018

ISBN: (纸本)9783030149062

The proceedings contain 93 papers. The special focus in this conference is on Advanced Engineering Theory and Applications. The topics include: An optimised hybrid group method in data handling (GMDH) network;a better indexing method for closest open location policy in forklift warehouse operation;on-line efficiency-optimization control of induction motor drives using particle swarm optimization algorithm;introducing the run support strategy for the bison algorithm;optimizing automated storage and retrieval algorithm in cold warehouse by combining dynamic routing and continuous cluster method;Dependency of GPA-ES algorithm efficiency on es parameters optimization strength;hybrid fuzzy neural model based dempster-shafer system for processing of diagnostic information;a modified bat algorithm to improve the search performance applying for the optimal combined heat and power generations;prediction of hourly vehicle flows by optimized evolutionary fuzzy rules;a new simple, fast and robust total least square error computation in e2: Experimental comparison;on the self-organizing migrating algorithm comparison by means of centrality measures;a brief overview of the synergy between metaheuristics and unconventional dynamics;An examination of outage performance for selected relay and fixed relay in cognitive radio-aided NOMA;throughput analysis of power beacon-aided multi-hop relaying networks employing non-orthogonal multiple access with hardware impairments;optimum selection of the reference signal for correlation receiver applied to marker localization;Comparing of transfer process data in PLC and mcu based on IoT;Protecting gateway from ABP replay attack on LoRaWAN;Development of a distributed VoIP honeypot system with advanced malicious traffic detection;waste management - weighing-machine automation;foreword.

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Proceedings of the 2019 International conference on Asian Language processing, IALP 2019

Proceedings of the 2019 International Conference on Asian La...

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23rd International conference on Asian Language processing, IALP 2019

ISBN: (纸本)9781728150147

The proceedings contain 89 papers. The topics discussed include: articulatory features based TDNN model for spoken language recognition;quantifying the use of English words in Urdu news-stories;a new method of tonal determination for Chinese dialects;duplicate question detection based on neural networks and multi-head attention;acoustic scene classification using deep convolutional neural network via transfer learning;effect of preprocessing for distributed representations: case study of Japanese radiology reports;character decomposition for Japanese-Chinese character-level neural machine translation;effective data augmentation approaches to end-to-end task-oriented dialogue;and extremely low resource text simplification with pre-trained transformer language model.

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Performance Evaluation of distributed Training in Tensorflow 2

Performance Evaluation of Distributed Training in Tensorflow...

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International conference on Advanced Computing and Applications (ACOMP)

作者： Nguyen Quang-Hung Hieu Doan Nam Thoai Vietnam National University Ho Chi Minh City Ho Chi Minh City Vietnam Hewlett Packard Enterprise Vietnam Ho Chi Minh City Vietnam

ISBN: (数字)9781728181677

ISBN: (纸本)9781728181684

Deep learning (DL) is most interested in Artificial Intelligence (AI) because it brings many interesting results in object identification, natural language processing, speech processing, application algorithms in autonomous vehicles, etc. DL also use neutron network. A useful neural architecture for an application may have tens or hundreds of millions of numbers that need to be selected appropriately during the training process. The most popular technique for training this architecture is a gradient-based iteration technique. To complete the training process, it needs to iterate over large amounts of data, which is why the training process is so slow. Without high performance computing (HPC) technology, it will take weeks or months to complete the training of network architecture. Training DL by distributed training is one of the solutions to improve the learning process faster and reduce computing time. In this paper, we study performance evaluation for distributed training strategy of the Tensorflow 2.2 on some GPUs.

关键词： Training Performance evaluation Computer architecture Neutrons Object recognition Artificial intelligence Speech processing

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Determination of Shelled Corn Damages using Colored Image Edge Detection with Convolutional neural network

Determination of Shelled Corn Damages using Colored Image Ed...

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International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Analyn Yumang Glenn Magwili Sev Kyle C. Montoya Corleone Jorel G. Zaldarriaga School of EECE Mapúa University Intramuros Manila Philippines

ISBN: (纸本)9781665429979

In the Philippines corn is one of the top agricultural products produced in the country, specifically yellow corn. It is distributed in various cities and provinces to consumers. It is important that the corn kernels to undergo quality assurance before releasing them to the consumers. The methods for evaluating and qualifying corn kernels that are employed by most farms in the country are only done by manual human inspection and these methods are inconsistent which results to inaccurate findings. This is more prevalent when dealing with large amounts of kernels that need to be qualified. This study offers to reduce those inconsistencies by implementing a neural network-assisted method of inspection. The damages to corn kernels can be determined by its physical attributes and as such, the neural network will easily detect the type of damage within a given sample. Aside from the healthy kernels, the types of damage that was included in this study are the following: drier damage, heat damage, heat damage (drier phase), OCOL (Other Color) Type A and OCOL Type B. The neural network that will be used will be a Convolutional neural network wherein the images of the samples are subjected to layers of processing. This study also uses Colored Image Edge Detection. The detection method used in this study has obtained an accuracy rating of 96.66%.

关键词： Training Heating systems Image edge detection neural networks Inspection Convolutional neural networks Kernel

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A Fully-integrated Gesture and Gait processing SoC for Rehabilitation with ADC-less Mixed-signal Feature Extraction and Deep neural network for Classification and Online Training

A Fully-integrated Gesture and Gait Processing SoC for Rehab...

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Custom Integrated Circuits conference (CICC)

作者： Yijie Wei Qiankai Cao Jie Gu Kofi Otseidu Levi Hargrove Northwestern University Evanston IL Intel Corporation Hudson MA Shirley Ryan AbilityLab Chicago IL

ISBN: (数字)9781728160313

ISBN: (纸本)9781728160320

An ultra-low-power gesture and gait classification SoC is presented for rehabilitation application featuring (1) mixed-signal feature extraction and integrated low-noise amplifier eliminating expensive ADC and digital feature extraction, (2) an integrated distributed deep neural network (DNN) ASIC supporting a scalable multi-chip neural network for sensor fusion with distortion resiliency for low-cost front end modules, (3) onchip learning of DNN engine allowing in-situ training of user specific operations. A 12-channel 65nm CMOS test chip was fabricated with 1μW power per channel, less than 3ms computation latency, on-chip training for user-specific DNN model and multi-chip networking capability.

关键词： Feature extraction Biological neural networks Voltage-controlled oscillators System-on-chip Training Nonlinear distortion

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Relaxation Method of Convolutional neural networks for Natural Language processing 15th

Relaxation Method of Convolutional Neural Networks for Natur...

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

作者： Iwasaki, Ryo Hasegawa, Taku Mori, Naoki Matsumoto, Keinosuke Osaka Prefecture Univ Naka Ku 1-1 Gakuen Cho Sakai Osaka Japan

ISBN: (纸本)9783319946498;9783319946481

Deep learning has developed into one of the most powerful methods in the machine learning field. In particular, convolutional neural networks (CNNs) have been applied not only to image recognition tasks but also to natural language processing (NLP). To reuse older deep learning models, transfer learning techniques have been widely used in the image recognition field. However, there has been little research on transfer learning in NLP. In this paper, we propose a novel transfer learning model based on a relaxation method of CNNs for NLP. The effectiveness of the proposed method is verified using computer simulations, taking a film review score recognition task as an example.

关键词： Deep learning Convolutional neural network Relaxation Natural language processings

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NARPCA: neural Accumulate-Retract PCA for Low-Latency High-Throughput processing on Datastreams 28th

NARPCA: Neural Accumulate-Retract PCA for Low-Latency High-T...

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28th International conference on Artificial neural networks (ICANN)

作者： Axenie, Cristian Tudoran, Radu Bortoli, Stefano Hassan, Mohamad Al Hajj Brasche, Goetz Huawei German Res Ctr Riesstr 25 D-80992 Munich Germany

ISBN: (纸本)9783030304874;9783030304867

The increasingly interconnected and instrumented world, provides a deluge of data generated by multiple sensors in the form of continuous streams. Efficient stream processing needs control over the number of useful variables. This is because maintaining data structure in reduced sub-spaces, given that data is generated at high frequencies and is typically follows non-stationary distributions, brings new challenges for dimensionality reduction algorithms. In this work we introduce NARPCA, a neural network streaming PCA algorithm capable to explain the variance-covariance structure of a set of variables in a stream through linear combinations. The essentially neural-based algorithm is leveraged by a novel incremental computation method and system operating on data streams and capable of achieving low-latency and high-throughput when learning from data streams, while maintaining resource usage guarantees. We evaluate NARPCA in real-world data experiments and demonstrate low-latency (millisecond level) and high-throughput (thousands events/second) for simultaneous eigenvalues and eigenvectors estimation in a multi-class classification task.

关键词： distributed stream processing PCA neural networks

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Recurrent neural network Based Intrusion Detection System

Recurrent Neural Network Based Intrusion Detection System

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International conference on Communications and Signal processing

作者： Sanchit Nayyar Sneha Arora Maninder Singh Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

An increase in connectivity through the internet and increased freedom of data access has led to numerous attempts to attack network servers. These attacks have become sophisticated over time and hence difficult to detect using the existing Intrusion Detection Systems. Existing research in the field of IDS has not been able to categorize DDoS attacks with a fair degree of accuracy till date. It has been extremely difficult to strike the balance between accuracy and prediction time. This paper aims at using a LSTM based Machine Learning model to detect anomalies in the network traffic and redirecting all malicious requests to a honeypot based black hole server. This algorithm was trained and tested on the CICIDS2017 data set consisting of numerous attacks such as Patator based attacks, Web based Brute Force, DoS sloworis, DoS slowhttptest, DoS Hulk, DoS GoldenEye, DDoS LOIT and some other categorical attacks. The results exhibit a high degree of accuracy in detection of these attacks, and the model is suitable for use in existing distributed servers.

关键词： Intrusion detection Computer crime Prediction algorithms Training Machine learning IP networks Recurrent neural networks

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Performance and Consistency Analysis for distributed Deep Learning Applications

Performance and Consistency Analysis for Distributed Deep Le...

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IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Danlin Jia Manoj Pravakar Saha Janki Bhimani Ningfang Mi Northeastern University Boston USA Florida International University Miami USA

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

Accelerating the training of Deep neural network (DNN) models is very important for successfully using deep learning techniques in fields like computer vision and speech recognition. distributed frameworks help to speed up the training process for large DNN models and datasets. Plenty of works have been done to improve model accuracy and training efficiency, based on mathematical analysis of computations in the Con-volutional neural networks (CNN). However, to run distributed deep learning applications in the real world, users and developers need to consider the impacts of system resource distribution. In this work, we deploy a real distributed deep learning cluster with multiple virtual machines. We conduct an in-depth analysis to understand the impacts of system configurations, distribution typologies, and application parameters, on the latency and correctness of the distributed deep learning applications. We analyze the performance diversity under different model consistency and data parallelism by profiling run-time system utilization and tracking application activities. Based on our observations and analysis, we develop design guidelines for accelerating distributed deep-learning training on virtualized environments.

关键词： Deep learning Training Computational modeling neural networks Parallel processing Resource management Load modeling

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Performance Optimizations and Analysis of distributed Deep Learning with Approximated Second-Order Optimization Method 19

Performance Optimizations and Analysis of Distributed Deep L...

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48th International conference on Parallel processing (ICPP)

作者： Tsuji, Yohei Osawa, Kazuki Ueno, Yuichiro Naruse, Akira Yokota, Rio Matsuoka, Satoshi Tokyo Inst Technol Tokyo Japan NVIDIA Tokyo Japan AIST AIST Tokyo Tech RWBC OIL Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Tokyo Inst Technol RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9781450371964

Faster training of deep neural networks is desired to speed up the research and development cycle in deep learning. distributed deep learning and second-order optimization methods are two different techniques to accelerate the training of deep neural networks. In the previous work, researchers show that an approximated second-order optimization method, called K-FAC, can mitigate each other drawbacks of the two techniques. However, there was no detailed discussion on the performance, which is critical for the usage in practice. In this work, we propose several performance optimization techniques to reduce the overheads of K-FAC and to accelerate the overall training. Applying all performance optimizations, we are able to speed up the training 1.64 times per iteration compared to a baseline. Additional to the performance optimizations, we construct a simple performance model to predict model training performance to help the users to determine whether distributed K-FAC is appropriate or not for their training in terms of wall-time.

关键词： deep learning second-order optimization neural networks

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