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Robot technological system of analysis of cybersecurity information systems and communication networks

Robot technological system of analysis of cybersecurity info...

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2020 International conference on Information Technology in Business and Industry, ITBI 2020

作者： Maksimova, E.A. Sadovnikova, N.P. Baranov, V.V. Gromov, Yu Yu Lauta, O.S. Tret'yakova, L.V. Department of Information Security Volgograd State University 100 Universitetskiyave Volgograd400062 Russia Department of Information Security South Russian State Polytechnic University Npi Named after M. i Platov Enlightenment 132 Novocherkassk346428 Russia Institute of Automation and Information Technology Tambov State Technical University 1 Leningradskayastr Tambov392036 Russia Department of Information Security Military Academy on Communications N.a.Budeniy 3 Tikhoreckiyave St Petersburg194064 Russia Department of Information Security Sevastopol State University 33 Universitetskaya str Sevastopol299053 Russia

The article presents the authors' views on the organization of the modern system of cybersecurity. We considered the tasks, assigned to the monitoring subsystem and reconnaissance of cyberspace as a source of preventive hardware and software effects on the opposing side. The shortcomings of the existing structures of such subsystems are analyzed and the requirements to the intelligent automated system of cybersecurity are specified. The article offers hardware and software solutions, based on the development of a mobile robotic system for audit the stability of the network infrastructure and applications to existing and future cyber threats. A variant of the functional scheme of a robotic system with a universal housing is presented and with a set of mechanical drives, depending on the characteristics of the problems to be solved. For providing high functional flexibility and speed of the robotic system it is proposed to use fuzzy neural networks. The union of two independent theories-neural networks and fuzzy logic-Allowed creating neural-fuzzy systems, having a high level of intelligence and independence in decision-making. The advantage of hybrid neural-fuzzy technology of modeling and information processing is the ability to view the generated rules and giving them a meaningful linguistic interpretation. A reasonable conclusion is made, that is the main meaning of that is the main meaning of the shift of creation concepts and using of artificial intelligence-Transition from individual systems to distributed information processing and the development of multi-Agent intelligent systems. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy neural networks

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IREHI 2019 - Proceedings of the International conference on Rural and Elderly Health Informatics

IREHI 2019 - Proceedings of the International Conference on ...

引用

2019 International conference on Rural and Elderly Health Informatics, IREHI 2019

The proceedings contain 13 papers. The topics discussed include: computer platform for remote monitoring of distributed installations in rural areas using GISs;e-commerce and e-health strategies and implementation activities in the united kingdom: review study;design and deployment of a low-cost communication solution in rural areas: case of the central region in Mali;tracking, safety of the small pirogue and monitoring of ocean natural resource in West Africa coast;the impact of mHealth apps on student success in professional activity and economic effect assessment;reconfiguration durations optimization for high-availability distributed systems: the case of ICT rural and elderly infrastructures for development;predicting elderly patient behavior in rural healthcare using machine learning;architecture of the platform for big data preprocessing and processing in medical sector;convolutional neural network and decision support in medical imaging: case study of the recognition of blood cell subtypes;online branding of scientific medical conference and its economic expediency;an intelligent framework of swine flu status prediction by rainforest algorithm;and prospects for the development of e-health in Africa through the integration of optical networks.

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Efficient GPU Memory Management for Nonlinear DNNs 20

Efficient GPU Memory Management for Nonlinear DNNs

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29th International conference on High-Performance Parallel and distributed Computing (HPDC)

作者： Yang, Donglin Cheng, Dazhao Univ N Carolina Charlotte NC 28223 USA

ISBN: (纸本)9781450370523

Deep neural networks (DNNs) have been widely applied in the field of artificial intelligence, e.g., natural language processing, computer vision, etc. Researchers and industry practitioners typically use GPU to train complex hundred-layers deep networks. However, as the networks going wider and deeper, the limited GPU memory becomes a significant bottleneck, restricting the size of networks to be trained. In the training of DNNs, the intermediate layer outputs are the major contributors to the memory footprint. Offloading and prefetching feature maps is one of the crucial techniques to overcome the GPU memory shortage by utilizing the CPU DRAM as an external buffer for the GPU. However, we find that the layer-by-layer asynchronous approach cannot be effectively applied to the overlap between communication and computation, particularly for nonlinear networks. Furthermore, the default memory management policy could cause high GPU memory fragmentation for the networks with complex nonlinearities. Based on these observations, we adopt an efficient graph analysis and exploit the layered dependency structures to improve the overlap ratio. To achieve minimal memory fragmentation, we design a Group Tensors By Mobility (GTBM) placement policy to allocate tensors on the proposed unified memory pool for data structures with varied data sizes and dynamic dependencies. We implement and evaluate our system, Dymem, on several linear and nonlinear networks. Compared with vDNN and SuperNeurons, our proposed approach can achieve memory cost reduction by up to 31%. The dependency-aware strategy can improve the end-to-end throughput for nonlinear networks by up to 42%.

关键词： neural networks GPU Memory Management Resource Management

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Dynamic Heterogeneous Voltage Regulation for Systolic Array-Based DNN Accelerators 38

Dynamic Heterogeneous Voltage Regulation for Systolic Array-...

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38th IEEE International conference on Computer Design (ICCD)

作者： Chen, Jianhao Riad, Joseph Sanchez-Sinencio, Edgar Li, Peng Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9781728197104

With the growing performance and wide application of deep neural networks (DNNs), recent years have seen enormous efforts on DNN accelerator hardware design for platforms from mobile devices to data centers. The systolic array has been a popular architectural choice for many proposed DNN accelerators with hundreds to thousands of processing elements (PEs) for parallel computing. Systolic array-based DNN accelerators for datacenter applications have high power consumption and non-uniform workload distribution, which makes power delivery network (PDN) design challenging. Server-class multicore processors have benefited from distributed on-chip voltage regulation and heterogeneous voltage regulation (HVR) for improving energy efficiency while guaranteeing power delivery integrity. This paper presents the first work on HVR-based PDN architecture and control for systolic array-based DNN accelerators. We propose to employ a PDN architecture comprising heterogeneous on-chip and off-chip voltage regulators and multiple power domains. By analyzing patterns of typical DNN workloads via a modeling framework, we propose a DNN workload-aware dynamic PDN control policy to maximize system energy efficiency while ensuring power integrity. We demonstrate significant energy efficiency improvements brought by the proposed PDN architecture, dynamic control, and power gating, which lead to a more than five-fold reduction of leakage energy and PDN energy overhead for systolic array DNN accelerators.

关键词： Deep neural networks

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Evaluation of distributed DNA representations on the classification of conserved non-coding elements 2020

Evaluation of distributed DNA representations on the classif...

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11th Hellenic conference on Artificial Intelligence

作者： Gialitsis, Nikolaos Giannakopoulos, George Athanasouli, Marina NKUA Athens Greece NCSR D ITT Aghia Paraskevi Greece MPI EB Berlin Germany

ISBN: (纸本)9781450388788

The representation of DNA sequences has been an interesting topic of discussion for many years. Presently, given the usefulness of representations built upon embeddings for Natural Language processing (NLP), there have been efforts to transfer such paradigms to the DNA world and related problems. In this paper, we study different DNA representations on the well-studied problem of Conserved Non-coding Elements (CNEs), trying to understand how well existing representations utilize the value of context, both in terms of local, near context, but also of long-distance interactions in genomic sequences. To this end, we apply a number of methods, including probabilistic models (LDA) and hybrid probabilistic-neural models (lda2vec) on appropriate datasets, compare the results to pre-existing methods and discuss the findings to better understand the value and challenges of different representations in the given domain.

关键词： DNA

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A Novel Interactive Recurrent Attention network for Emotion-Cause Pair Extraction 3

A Novel Interactive Recurrent Attention Network for Emotion-...

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3rd International conference on Algorithms, Computing and Artificial Intelligence, ACAI 2020

作者： Jia, Xiangyu Chen, Xinhai Wan, Qian Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781450388115

Unlike Emotion Cause Extraction (ECE) task which consists of pre-annotate emotions and passage, emotion-cause pair extraction (ECPE) aims at extracting potential emotions and corresponding causes in the document without the need for pre-annotations. Traditional ECPE solutions divide the extracting emotions and causes operation into two separate parts. However, separating the bidirectional dependence between emotion and cause may lose a lot of potentially useful information. In this paper, we propose a novel interactive recurrent attention network (IRAN). Our approach focuses on the bidirectional impact between emotions and causes, and extracts emotions and causes simultaneously. The information in the document can be fully exploited through multiple modeling and information extraction. Our emotion-specific transformation and distance fusion correlation can adaptively focus on the emotions and the distance, gracefully incorporate them into a distinguishable neural network attention framework. The experimental results show that our proposed model achieves better performance than other widely-used models on the ECPE corpus. © 2020 ACM.

关键词： Sentiment analysis

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Mask neural network for Predicting Flight Ticket Price 6th

Mask Neural Network for Predicting Flight Ticket Price

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6th conference on Signal and Information processing, networking and Computers, ICSINC 2019

作者： Zhang, Qianqian He, Yuan Jing, Xiaojun Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811541629

Recently, the global air traffic has increased rapidly, most passengers choose to buy tickets in their own experiences. So accurately predicting flight ticket price is of great significance. Recently there exist some works on flight ticket price prediction. However, these works pay attention to solve a single flight price prediction. However, it’s unrealistic to build a lot of models for different flights. In this paper, we consider a lot of factors, including additional service data, flight data and time data. Firstly, we analyze data and extract flight information and time features. Furthermore, we propose a model: mask neural network, to solve flight ticket price prediction, which divides the features into two parts: individual information and general information. General information represents the portrait of a certain flight and individual information represents features of a certain flight. Since every flight has its own distribution, we cannot put individual information into the network directly. So we introduce mask concept and use general information as a mask to filter individual information. Experiments on a competition dataset with 3 months datasets introduce the effectiveness of our approaches, that can achieve the state-of-the-art in ticket price prediction. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Proceedings - 2020 IEEE International conference on Artificial Intelligence Circuits and Systems, AICAS 2020

Proceedings - 2020 IEEE International Conference on Artifici...

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2020 IEEE International conference on Artificial Intelligence Circuits and Systems, AICAS 2020

ISBN: (纸本)9781728149226

The proceedings contain 72 papers. The topics discussed include: wearable DPM system with intelligent imager and GPU;event-based attention and tracking on neuromorphic hardware;live demonstration: video-to-spike conversion using a real-time retina cell network simulator;fault-tolerant-driven clustering for large scale neuromorphic computing systems;XBAROPT - enabling ultra-pipelined, novel STT MRAM based processing-in-memory DNN accelerator;real-time slam based on image stitching for autonomous navigation of UAVs in GNSS-denied regions;distributed clique-based neural networks for data fusion at the edge;and online extreme learning machine design for the application of federated learning.

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Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection 6th

Deep Learning Based Hyperspectral Images Analysis for Shrimp...

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6th EAI International conference on Industrial networks and Intelligent Systems, INISCOM 2020

作者： Nguyen, Minh-Hieu Nguyen-Thi, Xuan-Huyen Pham, Cong-Nguyen Lê, Ngoc C. Han, Huy-Dung Electronics and Computer Engineering Department School of Electronics and Telecommunications Hanoi University of Science and Technology Hanoi Viet Nam School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783030630829

In this paper, a deep learning based hyperspectral image analysis for detecting contaminated shrimp is proposed. The ability of distinguishing shrimps into two classes: clean and contaminated shrimps is visualized by t-distributed Stochastic Neighbor Embedding (t-SNE) using spectral feature data. Using only some small data set of hyperspectral images of shrimps, a simple processing technique is applied to generate enough data for training a deep neural network (DNN) with high reliability. Our results attain the accuracy of 98% and F1-score over 94%. This works confirms that with only few data samples, Hyperspectral Imaging processing technique together with DNN can be used to classify abnormality in agricultural productions like shrimp. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep neural networks

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A Methodology for Characterizing Voltage Disturbances in Distribution Systems With DG Based on neural networks

A Methodology for Characterizing Voltage Disturbances in Dis...

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conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Sergio Turizo Gustavo Ramos Universidad de los Andes Bogotá Colombia

ISBN: (纸本)9781728164021

Voltage sag characteristics are subject to change owing to system short-circuit rating in situations where DG’s contribution to power injection during fault events is considered. Therefore, the fast growth in the deployment of DG leads to an analysis of its impact on system operation and reliability. A methodology for characterization and identification of voltage sags and swells caused by faults in a distribution system with distributed generation (DG) is presented in this paper. The proposed methodology, involving Wavelet transform processing technique and a neural network classifier, yields a computational tool applied to IEEE 13-Node Test Feeder. This methodology provides a way to handle high data stream to obtain a power system response generalization to voltage disturbances.

关键词： Wavelet transforms Fault diagnosis Systems operation Power quality neural networks Industry applications Power system reliability

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