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PSGraph: How Tencent trains extremely large-scale graphs with Spark?

PSGraph: How Tencent trains extremely large-scale graphs wit...

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International conference on Data Engineering

作者： Jiawei Jiang Pin Xiao Lele Yu Xiaosen Li Jiefeng Cheng Xupeng Miao Zhipeng Zhang Bin Cui Department of Computer Science ETH Zürich Zürich Swizterland Data Platform TEG Tencent Inc Shenzhen China School of EECS & MOE Peking University Beijing China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Spark has extensively used in many applications of Tencent, due to its easy deployment, pipeline capability, and close integration with the Hadoop ecosystem. As the graph computing engine of Spark, GraphX is also widely deployed to process large-scale graph data in Tencent. However, when the size of the graph data is up to billion-scale, GraphX encounters serious performance degradation. Worse, Graphx cannot support the rising advancement of graph embedding (GE) and graph neural network (GNN) algorithms. To address these challenges, we develop a new graph processing system, called PSGraph, which uses Spark executor and PyTorch to perform calculation, and develops a distributed parameter server to store frequently accessed models. PSGraph can train extremely large-scale graph data in Tencent with the parameter server architecture, and enable the training of GE and GNN algorithms. Moreover, PSGraph still benefits from the advantages of Spark via staying inside the Spark ecosystem, and can directly replace GraphX without modification to the existing application framework. Our experiments show that PSGraph outperforms GraphX significantly.

关键词： Sparks Servers Partitioning algorithms Engines Biological system modeling neural networks Synchronization

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An Artificial neural network for distributed Constrained Optimization 25th

An Artificial Neural Network for Distributed Constrained Opt...

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25th International conference on neural Information processing (ICONIP)

作者： Liu, Na Jia, Wenwen Qin, Sitian Li, Guocheng Harbin Inst Technol Dept Math Weihai Peoples R China Beijing Informat Sci & Technol Univ Dept Math Beijing Peoples R China

ISBN: (纸本)9783030041793;9783030041786

This paper studies the distributed convex optimization problems, where the objective function can be expressed as the sum of nonsmooth local convex objective functions. By the virtue of KKT conditions, an artificial neural network is presented to solve the distributed convex optimization problems with inequality and equality constraints. And it is shown that the state solution of the artificial neural network converges to the optimal solution to the original optimization problem. Compared with the existing continuous time algorithms, the provided algorithm has the advantages of lower model complexity and easy implementation. Finally, a numerical example displays the practicality of the algorithm.

关键词： distributed optimization Artificial neural network Consensus Lyapunov function Global convergence

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A Comparative Study of Soft Computing Paradigms for Automatic Humour Detection in Tweets 6

A Comparative Study of Soft Computing Paradigms for Automati...

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6th International conference on Computing for Sustainable Global Development (INDIACom)

作者： Mathur, Vaibhav Mathur, Anshu Kumar, Sunil Birla Inst Technol & Sci BITS Dept Elect & Elect Engn Pilani Rajasthan India Indira Gandhi Delhi Tech Univ Women Dept Comp Sci & Engn Delhi India Maharaja Agrasen Inst Technol Dept Elect & Elect Engn Delhi India

ISBN: (纸本)9789380544342

Humour has been an integral entity of human life with its lingual, social and psychological aspects. Due to its vast applications and increasing popularity on social media platforms, enabling computers to process humour has become very crucial. Therefore, we intent to aid task of computational analysis of humour by employing deep learning and machine learning techniques for humour detection. We use micro-blogging website twitter, being an abundant source of humorous content, as the focus of this study. We created a symmetrically distributed dataset containing 4000 funny and plain tweets and applied seven supervised classification algorithms to perform the task of detecting tweets, which contain humour. Accuracy, recall and precision have been used as the metrics of performance.

关键词： humor twitter machine learning deep learning the recurrent neural network random forest natural language processing

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On Machine Learning-based Stage-aware Performance Prediction of Spark Applications

On Machine Learning-based Stage-aware Performance Prediction...

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

作者： Guangjun Ye Wuji Liu Chase Q. Wu Wei Shen Xukang Lyu School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou China New Jersey Institute of Technology Newark NJ USA School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

The data volume of large-scale applications in various science, engineering, and business domains has experienced an explosive growth over the past decade, and has gone far beyond the computing capability and storage capacity of any single server. As a viable solution, such data is oftentimes stored in distributed file systems and processed by parallel computing engines, as exemplified by Spark, which has gained increasing popularity over the traditional MapReduce framework due to its fast in-memory processing of streaming data. Spark engines are generally deployed in cloud environments such as Amazon EC2 and Alibaba Cloud. However, storage and computing resources in these cloud environments are typically provisioned on a pay-as-you-go basis and thus an accurate estimate of the execution time of Spark workloads is critical to making full utilization of cloud resources and meeting performance requirements of end users. Our insight is that the execution pattern of many Spark workloads is qualitatively similar, which makes it possible to leverage historical performance data to predict the execution time of a given Spark application. We use the execution information extracted from Spark History Server as training data and develop a stage-aware hierarchical neural network model for performance prediction. Experimental results show that the proposed hierarchical model achieves higher accuracy than a holistic prediction model at the end-to-end level, and also outperforms other existing regression-based prediction methods.

关键词： neural networks Predictive models Parallel processing Data models Sparks Servers Engines

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RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

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20th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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11th International conference on Computational Collective Intelligence (ICCCI)

作者： Mahmoud, Adnen Zrigui, Mounir Univ Monastir Algebra Number Theory & Nonlinear Anal Lab Monastir Tunisia Univ Sousse Higher Inst Comp Sci & Commun Tech Hammam Sousse Sousse Tunisia

ISBN: (纸本)9783030283773;9783030283766

The progressive advancement in technology has become easy to present the language expression of someone else as one's own with similar words semantically. This phenomenon increased the potential source of plagiarism. Its detection is a challenge especially in the case of Arabic paraphrase because of the semantic ambiguity of this language. In recent decades, researches have been hindered by the very limited availability of well-structured datasets. In this context, our main objectives are focused on constructing a corpus for Arabic and presenting thereafter its impact for identifying paraphrase. Indeed, we generated the suspect documents from the Open Source Arabic Corpora (OSAC). distributed word representation (word2vec) and part-of-speech methods were useful for replacing each original word by its most similar one that had the same grammatical class. Moreover, we captured the structure of Arabic sentences with different window sizes and vector dimensions. Then, we studied how this corpus could be used efficiently in the evaluation of Natural Language processing (NLP) methods (i.e. Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), word2vec, Global Vector Representation (GloVe), and Convolutional neural network (CNN)) for paraphrase detection. Experiments revealed which one could outperformed significantly for preserving semantic properties of Arabic words with various linear regularities, alleviating data sparseness and increasing the degree of semantic similarity, in terms of precision and recall.

关键词： Arabic language Paraphrase Semantic similarity Corpus development Word embeddings Convolutional neural network

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Attentional Residual Dense Factorized network for Real-Time Semantic Segmentation 28th

Attentional Residual Dense Factorized Network for Real-Time ...

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28th International conference on Artificial neural networks (ICANN)

作者： Yang, Lulu Lan, Long Zhang, Xiang Huang, Xuhui Luo, Zhigang NUDT Sci & Technol Parallel & Distributed Lab Changsha 410073 Hunan Peoples R China NUDT Coll Comp Changsha 410073 Hunan Peoples R China NUDT State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China NUDT Dept Comp Sci Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783030305086;9783030305079

Semantic segmentation is a pixel-level image dense labeling task and plays a core role in autonomous driving. In this regard, how to balance between precision and speed is a frequently-studied issue. In this paper, we propose an alternative attentional residual dense factorized network (AttRDFNet) to address this issue. Specifically, we design a residual dense factorized convolution block (RDFB), which reaps the benefits of low-level and high-level layer-wise features through dense connection to boost segmentation precision whilst enjoying efficient computation by factorizing large convolution kernel into the product of two smaller kernels. This reduces computational burdens and makes real time become possible. To further leverage layer-wise features, we explore the graininess-aware channel and spatial attention modules to model different levels of salient features of interest. As a result, AttRDFNet can run with the inputs of the resolution 512 x 1024 at the speed of 55.6 frames per second on a single Titan X GPU with solid 68.5% Mean IOU on the test set of Cityscapes. Experiments on the Cityscapes dataset show that AttRDFNet has real-time inference whilst achieving competitive precision against well-behaved counterparts.

关键词： Real-time semantic segmentation Attention module Residual dense factorized convolution block

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Human Chemosignals Modulate Interactions Between Social and Emotional Brain Areas

Human Chemosignals Modulate Interactions Between Social and ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Saideh Ferdowsi Dimitri Ognibene Tom Foulsham Vahid Abolghasemi Wen Li Luca Citi School of Computer Science and Electronic Engineering (CSEE) University of Essex UK University of Essex UK Florida State University FL USA

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

Chemosensory communication is known as an effective way to influence the human emotion system. Phenomena like food selection or motivation, based on chemical signals, present a unique pathway between chemosensory and emotion systems. Human chemosignals (i.e. sweat) which are produced during different emotional states contain associated distinctive odors and are able to induce same emotions in other people. For instance, sweat is known as a social chemosignal participating in social interaction. Chemosignal perception engages a distributed neural network which has not been well characterized yet. In this paper, we use functional magnetic resonance imaging (fMRI) to investigate the neural circuits underlying social emotional chemosignal processing. Chemosignals associated with disgust and neutral conditions were used to induce specific emotional states in fMRI participants during a healthy food judgement. We performed fMRI analysis with the aim of detecting active areas in the brain, followed by a dynamic causal modeling (DCM) analysis. fMRI analysis revealed functional activity in the fusiform face area (FFA), amygdala (AMG) and orbitofrontal cortex (OFC). In order to determine the effective connectivity among these regions as a result of emotional chemosignal processing, a set of dynamic causal models is proposed. Estimating parameters of the proposed models shows that social chemosignals modulate the connections between FFA, AMG and OFC. The results indicate that social chemosignals of disgust converge on orbitofrontal cortex - an area which is a critical region for object appraisal and valuation - after first influencing fusiform face area and amygdala.

关键词： neural circuits Functional magnetic resonance imaging Brain modeling Orbits Integrated circuit modeling Faces Cost accounting

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2nd international conference on data engineering and communication technology, ICDECT 2017

2nd international conference on data engineering and communi...

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2nd International conference on Data Engineering and Communication Technology, ICDECT 2017

ISBN: (纸本)9789811316098

The proceedings contain 71 papers. The special focus in this conference is on Data Engineering and Communication Technology. The topics include: S-LSTM-GAN: Shared Recurrent neural networks with Adversarial Training;hybrid Approach for Recommendation System;discussion on Problems and Solutions in Hardware Implementation of Algorithms for a Car-type Autonomous Vehicle;software Test Case Allocation;seamless Vertical Handover for Efficient Mobility Management in Cooperative Heterogeneous networks;sentence Similarity Estimation for Text Summarization Using Deep Learning;minimization of Clearance Variation of a Radial Selective Assembly Using Cohort Intelligence Algorithm;m-Wallet Technology Acceptance by Street Vendors in India;explore-Exploit-Explore in Ant Colony Optimization;application of Blowfish Algorithm for Secure Transactions in Decentralized Disruption-Tolerant networks;an Attention-Based Approach to Text Summarization;enhancement of Security for Cloud Data Using Partition-Based Steganography;large Scale P2P Cloud of Underutilized Computing Resources for Providing MapReduce as a Service;topic Modelling for Aspect-Level Sentiment Analysis;an Image Deblocking Approach Based on Non-subsampled Shearlet Transform;an Effective Video Surveillance Framework for Ragging/Violence Recognition;DoT: A New Ultra-lightweight SP network Encryption Design for Resource-Constrained Environment;a distributed Application to Maximize the Rate of File Sharing in and Across Local networks;obstacle Detection for Auto-Driving Using Convolutional neural network;Leakage Power Improvement in SRAM Cell with Clamping Diode Using Reverse Body Bias Technique;a Rig-Based Formulation and a League Championship Algorithm for Helicopter Routing in Offshore Transportation;an Investigation of Burr Formation and Cutting Parameter Optimization in Micro-drilling of Brass C-360 Using Image processing;emotion Recognition from Sensory and Bio-Signals: A Survey.

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distributed distillation for on-device learning 20

Distributed distillation for on-device learning

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Proceedings of the 34th International conference on neural Information processing Systems

作者： Ilai Bistritz Ariana J. Mann Nicholas Bambos Stanford University

ISBN: (纸本)9781713829546

On-device learning promises collaborative training of machine learning models across edge devices without the sharing of user data. In state-of-the-art on-device learning algorithms, devices communicate their model weights over a decentralized communication network. Transmitting model weights requires huge communication overhead and means only devices with identical model architectures can be included. To overcome these limitations, we introduce a distributed distillation algorithm where devices communicate and learn from soft-decision (softmax) outputs, which are inherently architecture-agnostic and scale only with the number of classes. The communicated soft-decisions are each model's outputs on a public, unlabeled reference dataset, which serves as a common vocabulary between devices. We prove that the gradients of the distillation regularized loss functions of all devices converge to zero with probability 1. Hence, all devices distill the entire knowledge of all other devices on the reference data, regardless of their local connections. Our analysis assumes smooth loss functions, which can be non-convex. Simulations support our theoretical findings and show that even a naive implementation of our algorithm significantly reduces the communication overhead while achieving an overall comparable accuracy to the state-of-the-art. By requiring little communication overhead and allowing for cross-architecture training, we remove two main obstacles to scaling on-device learning.

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