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An Insight into the Impact of Dockerfile Evolutionary Trajectories on Quality and Latency 42

An Insight into the Impact of Dockerfile Evolutionary Trajec...

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42nd Annual IEEE-Computer-Society Computers, Software and applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Zhang, Yang Yin, Gang Wang, Tao Yu, Yue Wang, Huaimin Natl Univ Def Technol Coll Comp Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (纸本)9781538626672

Containerization is a software development approach aimed at packaging an application together with all its dependencies and execution environment in a light-weight, self-contained unit, of which Docker has become the de-facto industry standard. By defining the specific Docker image architecture and building orders, dockerfile plays an important role in the Dockerbased containerization process. Understanding the evolution of dockerfile and which dockerfile architecture attributes enhance dockerfile quality and reduce image build latency can benefit the efficient processing of containerization. In this paper, we perform an empirical study on a large dataset of 2,840 projects to shed light on the impact of dockerfile evolutionary trajectories on quality and latency in the Docker-based containerization. Based on the six categories of dockerfile evolutionary trajectories we discovered, we build two regression models to explore the impact of dockerfile evolutionary trajectories and specific architecture attributes on dockerfile quality and image build latency, which derives a number of suggestions for practitioners.

关键词： Docker dockerfile evolutionary trajectory containerization project maintenance

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parallel Primitives for Vendor-Agnostic Implementation of Big Data Mining Algorithms 32

Parallel Primitives for Vendor-Agnostic Implementation of Bi...

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32nd IEEE international Conference on Advanced Information Networking and applications (IEEE AINA)

作者： Bandirali, Cesare Lodi, Stefano Moro, Gianluca Pagliarani, Andrea Sartori, Claudio D'Angelo, Salvatore Di Martino, Beniamino Esposito, Antonio Univ Bologna Dipartimento Informat Sci & Ingn DISI Viale Risorgimento 2 I-40136 Bologna Italy Univ Campania Luigi Vanvitelli Dipartimento Ingn Via Roma 29 I-81031 Aversa Italy

ISBN: (纸本)9781538653951

In the age of Big Data, scalable algorithm implementations as well as powerful computational resources are required. For data mining and data analytics the support of big data platforms is becoming increasingly important, since they provide algorithm implementations with all the resources needed for their execution. However, choosing the best platform might depend on several constraints, including but not limited to computational resources, storage resources, target tasks, service costs. Sometimes it may be necessary to switch from one platform to another depending on the constraints. As a consequence, it is desirable to reuse as much algorithm code as possible, so as to simplify the setup in new target platforms. Unfortunately each big data platform has its own peculiarity, especially to deal with parallelism. This impacts on algorithm implementation, which generally needs to be modified before being executed. This work introduces functional parallel primitives to define the parallelizable parts of algorithms in a uniform way, independent of the target platform. Primitives are then transformed by a compiler into skeletons, which are finally deployed on vendor-dependent frameworks. The procedure proposed aids not only in terms of code reuse but also in terms of parallelization, because programmer's expertise is not demanded. Indeed, it is the compiler that entirely manages and optimizes algorithm parallelization. The experiments performed show that the transformation process does not negatively affect algorithm performance.

关键词： Big Data Skeleton Data mining Task analysis Sparks Machine learning algorithms Data analysis

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Large Scale Language Modeling: Converging on 40GB of Text in Four Hours 30

Large Scale Language Modeling: Converging on 40GB of Text in...

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30th international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Puri, Raul Kirby, Robert Yakovenko, Nikolai Catanzaro, Bryan NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781538677698

Recent work has shown how to train Convolutional Neural Networks (CNNs) rapidly on large image datasets [1], then transfer the knowledge gained from these models to a variety of tasks [2]. Following [3], in this work, we demonstrate similar scalability and transfer for Recurrent Neural Networks (RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to train a character-level 4096-dimension multiplicative LSTM (mLSTM) [4] for unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews dataset [5] in four hours. This runtime compares favorably with previous work taking one month to train the same size and configuration for one epoch over the same dataset [3]. Converging large batch RNN models can be challenging. Recent work has suggested scaling the learning rate as a function of batch size, but we find that simply scaling the learning rate as a function of batch size leads either to significantly worse convergence or immediate divergence for this problem. We provide a learning rate schedule that allows our model to converge with a 32k batch size. Since our model converges over the Amazon Reviews dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while substantial, is commercially available, this work opens up large scale unsupervised NLP training to most commercial applications and deep learning researchers1. A model can be trained over most public or private text datasets overnight.

关键词： Training Computational modeling Task analysis parallel processing Numerical models distributed databases Data models

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Improving Accuracy of Key Information Acquisition for Social Media Text Summarization

Improving Accuracy of Key Information Acquisition for Social...

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IEEE international symposium on parallel and distributed processing with applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Wanwan Miao Guiping Zhang Yu Bai Dongfeng Cai Human-Computer Intelligence Research Center Shenyang Aerospace University Shenyang Liaoning China Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

The attention-based sequence-to-sequence (Seq2Seq) model for abstractive summarization has achieved good performance, but it still suffers from generating inaccurate information and repetitive content when applied to noisy social media text. To address these issues, we propose a novel model that includes a feature extractor and a semantic enhancer. In our model, the feature extractor produces feature vectors that contain key information, filtering out noise in the source text. The semantic enhancer takes the concatenation of the feature vectors and the encoder hidden states as input to learn long-range dependencies, further improving semantic relevance and reducing repetition. Experimental results on a Chinese social media dataset show that our model can capture the key information from noisy text and achieve improvement as compared to state-of-the-art baseline models.

关键词： feature extraction social networking (online) text analysis text processing information gathering Feature extraction Social Media texts Eigenvector Noise Semantics extraction device

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Single-Pixel Estimation for Enhanced Object Detection and Recognition with Kinect for Windows V2

Single-Pixel Estimation for Enhanced Object Detection and Re...

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IEEE international symposium on parallel and distributed processing with applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Cosmas Obiora ndubuisi Hua Li Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781728152103

Microsoft Kinect sensor has been used for all sort of computer vision projects since inception, as it provides us with both RGB and depth data. With a high range of depth information provided by the Microsoft Kinect v2 Infrared (IR) depth sensors, there has been increased attention in utilizing this depth information for detection and tracking. In this paper, we proposed a Single-pixel-grid based method for calculating the pixel of an object that is the closest or highest within a particular threshold. After establishing the record holding pixel object, we developed an algorithm for detecting and tracking the location of the object based on the pixel. At the end of the experiment, results show that using this algorithm, the Kinect v2 was able to detect the pixel that is the closest or highest in two tested thresholds and as well tracked accurately the object with the record pixel point. Analysis and comparison of results shows improved accuracy in object location detection using our algorithm.

关键词： estimation theory image enhancement image recognition image sensors object detection object tracking Object detection game player estimation theory Sensors (image) Image recognition image enhancement pixel tracking objects Recognition (Psychology)

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VANET Channel Allocation Scheme Based on Hungarian Algorithm

VANET Channel Allocation Scheme Based on Hungarian Algorithm

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IEEE international symposium on parallel and distributed processing with applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Tengyuan Liu Ruifang Li School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781728152103

With the development of vehicle network services and related software, the dedicated short-range communication (DSRC) frequency band is not sufficient to carry the increased wireless service demand in the in-vehicle network. The large television spectrum (i.e., the TV white space spectrum) issued by the US Federal Communications Commission for cognitive access will be used to provide additional bandwidth to the in-vehicle network, and the effective channel allocation scheme for TV White Space (TVWS) has become our goal. In this paper, we design a channel allocation scheme based on the Hungarian algorithm. Our main goal is to increase network throughput while minimizing network latency. We present simulation schemes and numerical evaluations to illustrate the desired performance of the proposed channel allocation scheme.

关键词： channel allocation cognitive radio radio spectrum management vehicular ad hoc networks wireless channels Channel allocation Radio spectrum management wireless channels Vehicle Network Cognitive radio V2V Dedicated short range communications Federal Communications Commission (FCC) network delay Hungarian

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HCW 2018 Keynote Talk 2

HCW 2018 Keynote Talk 2

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Umit Catalyurek Georgia Institute of Technology Atlanta GA US

ISBN: (纸本)9781538655566;9781538655559

Heterogeneity in computing has been with us for a long time, and it is here to stay for a foreseeable future too. In this talk, with some diverse set of application examples, ranging from histopathology image analysis to graph analytics, we will discuss the challenges of application developers for developing efficient, scalable and maintainable applications for heterogeneous systems, and associated opportunities for systems researchers in this field.

关键词： Scientific computing parallel processing distributed processing Conferences Heterogeneous networks Distance measurement Image analysis

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M^2 S^2 F^2 : Multiscale Multistage Spectral-Spatial Features Fusion Framework for Hyperspectral Image Classification

M^2 S^2 F^2 : Multiscale Multistage Spectral-Spatial Feature...

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IEEE international symposium on parallel and distributed processing with applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xiangbin Shi Kuo Song Zhaokui Li Jing Bi Deyuan Zhang School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Hyperspectral image classification has been widely applied in many fields, but it also faces challenges because of small number of labeled samples. In this paper, we propose the Multiscale Multistage Spectral-Spatial Feature Fusion Framework (M^2 S^2 F^2 ) for hyperspectral image classification using small training samples. The Framework is the combination of two deep convolutional neural networks, which can extract more representative and discriminative features by combining the following operations. Firstly, two different scale 3-D cubes are the inputs for the spectral and spatial feature extraction respectively. Secondly, by fusing strong complementary information between different layers, we form multistage spectral and spatial features by fusion primary, intermediate and advanced features of the spectral and spatial features respectively. Spectral and spatial features are extracted by spectral and spatial skipped residual blocks, which can effectively alleviate the problems of gradient degradation. Thirdly, the fusion of complementary multistage spectral and spatial features can improve the classification accuracy. Experimental results on the IN, UP and KSC datasets show the effectiveness of the proposed method using small training samples.

关键词： convolutional neural nets feature extraction hyperspectral imaging image classification image fusion Image classification hyperspectral imagery Feature extraction image fusion MULTISTAGE Fusion reactions Fusion, biological FUSION fusion (melting) Taxonomy

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An Identity Privacy Protection Strategy in Vehicle Named Data Network

An Identity Privacy Protection Strategy in Vehicle Named Dat...

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IEEE international symposium on parallel and distributed processing with applications and IEEE international Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xuejie Liu Qi Bing Xinhua Lu Lihong Zhong Da Wei Guannan Qu College of Computer Science and Technology Jilin University Changchun China Jilin University Changchun China

ISBN: (纸本)9781728152103

Although Vehicle Named Data Network (VndN) possess the communication benefits of Named Data Network and Vehicle Opportunity Network, it also introduces some new privacy problems, including the identity security of Data Requesters and Data Providers. Data providers in VndN need to sign data packets directly, which will leak the identity information of the providers, while the vicinity malicious nodes can access the sensitive information of Data Requesters by analyzing the relationship between Data Requesters and the data names in Interest Packages that are sent directly in plaintext. In order to solve the above privacy problems, this paper presents an identity privacy protection strategy for Data Requesters and Data Providers in VndN. A ring signature scheme is used to hide the correlation between the signature and the data provider and the anonymous proxy idea is used to protect the real identity of the data requester in the proposed strategy. Security Analysis and experiments in the ONE-based VndN platform indicate that the proposed strategy is effective and practical.

关键词： computer network security data privacy digital signatures Internet vehicular ad hoc networks DADANET Network security digital signatures Data privacy vehicle V2V Internets Privacy personal identity cleartext Sensitive data

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Simple, distributed, and accelerated probabilistic programming 18

Simple, distributed, and accelerated probabilistic programmi...

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Proceedings of the 32nd international Conference on Neural Information processing Systems

作者： Dustin Tran Matthew D. Hoffman Dave Moore Christopher Suter Srinivas Vasudevan Alexey Radul Matthew Johnson Rif A. Saurous Google Brain Google

We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction—the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.

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