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2nd International Workshop on High Performance Computing for Advanced modeling and simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth in cloud-based services, big data analytics, and artificial intelligence related services provisioning leads to the rapid growth of construction of large scale Internet data centers (IDCs). Modern IDCs are equipped with various sensors to monitor its healthy operation and maintenance states, such as temperature, thermal distribution, air flow, etc. However, fine-grained, single-node level and even mainboard level monitoring including server resource consumption and power consumption is still needed for more progressive resource multiplexing and job scheduling in IDCs. In this paper, we propose an edge computing platform for intelligent Internet data center operational monitoring, which integrates wireless sensors and on-board built-in sensors to sense and collect the data of data center operation and maintenance. The edge computing based solution reduces the latency for data transportation to central clouds and reduces the amount of data and enhances the real time resource capping decisions in data centers. © Springer Nature Singapore Pte Ltd 2019.

关键词： Energy efficiency

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Towards computation offloading in edge computing: A survey 2nd

Towards computation offloading in edge computing: A survey

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作者： Cheng, Xiaolan Zhou, Xin Jiang, Congfeng Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase in cloud data centers for data analytics. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conduct to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services (QoS), and Quality of Experiences (QoE) are surveyed. Resource scheduling approaches, gaming and tradeoffing among system performance and system overheads for offloading decision making are also reviewed. © Springer Nature Singapore Pte Ltd 2019.

关键词： computation offloading

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Agent-Based Model Characterization Using Natural Language Processing

Agent-Based Model Characterization Using Natural Language Pr...

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simulation Winter Conference

作者： Jose J Padilla David Shuttleworth Kevin O’Brien Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Comp. Modeling and Simulation Eng. Dept. Old Dominion University Norfolk VA USA Computer Science Department Old Dominion University Norfolk VA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

This paper reports on Natural Language Processing (NLP) as a technique to analyze phenomena towards specifying agent-based models (ABM). The objective of the ABM NLP Analyzer is to facilitate non-simulationists to actively engage in the learning and collaborative designing of ABMs. The NLP model identifies candidate agents, candidate agent attributes, and candidate rules all of which non-simulationists can later evaluate for feasibility. IBM's Watson Natural Language Understanding (NLU) and Knowledge Studio were used in order to annotate, evaluate, extract agents, agent attributes, and agent rules from unstructured descriptions of phenomena. The software, and related agent-attribute-rule characterization, provides insight into a simple but useful means of conceptualizing and specifying baseline ABMs. Further, it emphasizes on how to approach the design of ABMs without the use of NLP by focusing on the identification of agent, attributes and rules.

关键词： Unified modeling language Object oriented modeling Natural language processing Biological system modeling Computational modeling Adaptation models

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Query modeling for click data based image recognition using graph based propagation and sparse coding

Query modeling for click data based image recognition using ...

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9th International Conference on Internet Multimedia Computing and Service, ICIMCS 2017

作者： Wu, Weichen Tan, Min Zheng, Guangjian Yu, Jun Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9789811085291

We address the fine-grained image recognition problem using user click data, wherein each image is represented as a semantical query-click feature vector. Usually, the query set obtained from search engines is large-scale and redundant. We propose a novel query modeling approach to merge semantically similar queries and construct a compact click feature. We represent each query as a click feature, and design a graph based propagation approach to predict the zero-clicks, ensuring similar images have similar clicks. Afterwards, using this feature, we formulate the problem as a sparse coding based recognition task, wherein the dictionary is discriminatively trained. We evaluate our method for fine-grained image recognition on the public Clickture-Dog dataset. It is shown that, the propagated click feature performs much better than the original one. Also, sparse coding performs better than K-means in query merging. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Image recognition

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Reachability as a Unifying Framework for Computing Helicopter Safe Operating Conditions and Autonomous Emergency Landing ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9282-9287页

作者： Matthew R. Kirchner Eddie Ball Jacques Hoffler Don Gaublomme Image and Signal Processing Branch Code D5J1000 Naval Air Warfare Center Weapons Division China Lake CA USA Electrical and Computer Engineering Department University of California Santa Barbara CA 93106 USA Flight Vehicle Modeling and Simulation Branch Naval Air Warfare Center Aircraft Division Patuxent River MD USA

We present a numeric method to compute the safe operating flight conditions for a helicopter such that we can ensure a safe landing in the event of a partial or total engine failure. The unsafe operating region is the complement of the backwards reachable tube, which can be found as the sub-zero level set of the viscosity solution of a Hamilton-Jacobi (HJ) equation. Traditionally, numerical methods used to solve the HJ equation rely on a discrete grid of the solution space and exhibit exponential scaling with dimension, which is problematic for the high-fidelity dynamics models required for accurate helicopter modeling. We avoid the use of spatial grids by formulating a trajectory optimization problem, where the solution at each initial condition can be computed in a computationally efficient manner. The proposed method is shown to compute an autonomous landing trajectory from any operating condition, even in non-cruise flight conditions.

关键词： Reachability Autorotation Optimal Control

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On Exploring Undetermined Relationships for Visual Relationship Detection

On Exploring Undetermined Relationships for Visual Relations...

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IEEE/CVF Conference on computer Vision and Pattern Recognition

作者： Yibing Zhan Jun Yu Ting Yu Dacheng Tao Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney

ISBN: (纸本)9781728132945

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

关键词： Visualization Fuses Dataset

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modeling Interface Charge Traps in Junctionless FETs, Including Temperature Effects

arXiv

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arXiv 2019年

作者： Rassekh, Amin Jazaeri, Farzan Fathipour, Morteza Sallese, Jean-Michel Ecole Polytechnique Fédérale de Lausanne Switzerland device simulation and modeling Laboratory department of electrical and computer engineering University of Tehran Iran

In this paper, an analytical predictive model of interface charge traps in symmetric long channel double-gate junctionless transistors is proposed based on a charge-based model. Interface charge traps arising from the exposure to chemicals, high-energy ionizing radiation or aging mechanism could degrade the charge-voltage characteristics. The model is predictive in a range of temperature from 77K to 400K. The validity of the approach is confirmed by extensive comparisons with numerical TCAD simulations in all regions of operation from deep depletion to accumulation and linear to saturation. Copyright © 2019, The Authors. All rights reserved.

关键词： Ionizing radiation

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Robustness of Bayesian neural networks to gradient-based attacks 20

Robustness of Bayesian neural networks to gradient-based att...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Ginevra Carbone Matthew Wicker Luca Laurenti Andrea Patane Luca Bortolussi Guido Sanguinetti Department of Mathematics and Geosciences University of Trieste Trieste Italy Departement of Computer Science University of Oxford Oxford United Kingdom Department of Mathematics and Geosciences University of Trieste Trieste Italy and Modeling and Simulation Group Saarland University Saarland Germany School of Informatics University of Edinburgh Edinburgh United Kingdom and SISSA Trieste Italy

ISBN: (纸本)9781713829546

Vulnerability to adversarial attacks is one of the principal hurdles to the adoption of deep learning in safety-critical applications. Despite significant efforts, both practical and theoretical, the problem remains open. In this paper, we analyse the geometry of adversarial attacks in the large-data, overparametrized limit for Bayesian Neural Networks (BNNs). We show that, in the limit, vulnerability to gradient-based attacks arises as a result of degeneracy in the data distribution, i.e., when the data lies on a lower-dimensional submanifold of the ambient space. As a direct consequence, we demonstrate that in the limit BNN posteriors are robust to gradient-based adversarial attacks. Experimental results on the MNIST and Fashion MNIST datasets, representing the finite data regime, with BNNs trained with Hamiltonian Monte Carlo and Variational Inference support this line of argument, showing that BNNs can display both high accuracy and robustness to gradient based adversarial attacks.

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Analyzing Emergency Evacuation Strategies For Large Buildings Using Crowd simulation Framework

Analyzing Emergency Evacuation Strategies For Large Building...

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simulation Winter Conference

作者： Imran Mahmood Talha Nadeem Faiza Bibi Xiaolin Hu Center for Research in Modeling Simulation (Crimson) School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad PAKISTAN Department of Computer Science Georgia State University Atlanta GA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

The occurrence of natural or man-made emergencies can be quite complex and demand flawless preparedness, through tested strategies, in order to ensure the safety of the individuals. For large-scale infrastructures, whether commercial or residential, having a reliable evacuation strategy is crucial. Formulation and evaluation of these evacuation strategies is however a daunting challenge. In this paper, we propose a Crowd simulation and Analysis framework for the formulation and evaluation of effective evacuation strategies in large buildings, using real-scale building structures and agent based approach. We further propose an algorithm to devise an evacuation strategy. We first demonstrate the functionality of our algorithm using a simplistic example and then apply the algorithm in a campus evacuation case study using three scenarios. The main goal of this research is to assist regulatory authorities in developing effective disaster management plans through the use of M&S methods and tools.

关键词： Buildings Analytical models Solid modeling Safety Computational modeling Tools Vehicle dynamics

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Generative Adversarial Network for Improving Deep Learning Based Malware Classification

Generative Adversarial Network for Improving Deep Learning B...

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simulation Winter Conference

作者： Yan Lu Jiang Li Department of Modeling Simulation and Visualization Engineering Old Dominion University Norfolk VA USA Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

The generative adversarial network (GAN) had been successfully applied in many domains in the past, the GAN network provides a new approach for solving computer vision, object detection and classification problems by learning, mimicking and generating any distribution of data. One of the difficulties in deep learning-based malware detection and classification tasks is lacking of training malware samples. With insufficient training data the classification performance of the deep model could be compromised significantly. To solve this issue, in this paper, we propose a method which uses the Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic malware samples. Our experiment results show that by using the DCGAN generated adversarial synthetic malware samples, the classification accuracy of the classifier - a 18-layer deep residual network is significantly improved by approximately 6%.

关键词： Generative adversarial networks Machine learning Data models Computational modeling Trojan horses Gallium nitride

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