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Compressibility of distributed Document Representations 21

Compressibility of Distributed Document Representations

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21st IEEE International conference on Data Mining (IEEE ICDM)

作者： Skrlj, Blaz Petkovic, Matej Jozef Stefan Inst Ljubljana Slovenia Jozef Stefan Int Postgrad Sch Ljubljana Slovenia

ISBN: (纸本)9781665423984

Contemporary natural language processing (NLP) revolves around learning from latent document representations, generated either implicitly by neural language models or explicitly by methods such as doc2vec or similar. One of the key properties of the obtained representations is their dimension. Whilst the commonly adopted dimensions of 256 and 768 offer sufficient performance on many tasks, it is many times unclear whether the default dimension is the most suitable choice for the subsequent downstream learning tasks. Furthermore, representation dimensions are seldom subject to hyperparameter tunning due to computational constraints. The purpose of this paper is to demonstrate that a surprisingly simple and efficient recursive compression procedure can be sufficient to both significantly compress the initial representation, but also potentially improve its performance when considering the task of text classification. Having smaller and less noisy representations is the desired property during deployment, as orders of magnitude smaller models can significantly reduce the computational overload and with it the deployment costs. We propose CORE, a straightforward, compression-agnostic framework suitable for representation compression. The CoRE's performance is showcased and studied on a collection of 17 real-life corpora from biomedical, news, social media, and literary domains. We explored CoRE's behavior when considering contextual and non-contextual document representations, different compression levels, and 9 different compression algorithms. Current results based on more than 100,000 compression experiments indicate that recursive Singular Value Decomposition offers a very good trade-off between the compression efficiency and performance, making CORE useful in many existing, representation-dependent NLP pipelines.

关键词： document classification document embedding machine learning text mining

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HPSGD: Hierarchical Parallel SGD with Stale Gradients Featuring 27th

HPSGD: Hierarchical Parallel SGD with Stale Gradients Featur...

引用

27th International conference on neural Information processing

作者： Zhou, Yuhao Ye, Qing Mang, Hailun Lv, Jiancheng Sichuan Univ Coll Comp Sci Chengdu Peoples R China

ISBN: (纸本)9783030638320;9783030638337

While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers. To alleviate this problem, a novel Hierarchical Parallel SGD (HPSGD) strategy is proposed based on the observation that the data synchronization phase can be paralleled with the local training phase (i.e., Feed-forward and back-propagation). Furthermore, an improved model updating method is unitized to remedy the introduced stale gradients problem, which commits updates to the replica (i.e., a temporary model that has the same parameters as the global model) and then merges the average changes to the global model. Extensive experiments are conducted to demonstrate that the proposed HPSGD approach substantially boosts the distributed DNN training, reduces the disturbance of the stale gradients and achieves better accuracy in given fixed wall-time.

关键词： distributed training Parallel SGD Hierarchical computation Large scale Optimization

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Industrial Vision Optimization distributed Strategy based on Edge Intelligence Collaboration

Industrial Vision Optimization Distributed Strategy based on...

引用

International Wireless Communications and Mobile Computing conference, IWCMC

作者： Yaqi Song Yun Shen Peng Ding Xuezhi Zhang Yuying Xue China Telecom Corporation Limited Research Institute Beijing China

In this paper, it is proposed that a novel strategy of based hierarchical data distribution and deep neural networks distribution over edge and end devices. In the Industrial Internet of Things environment, deep learning tasks such as smoke and fire classification based on convolutional neural network usually need to be performed on edge servers and end devices, which have limited computing resources, while edge servers have abundant computing resources. While being able to accommodate inference of a deep neural network (DNN) at the edge server, a distributed deep neural network (DDNN) also allows localized inference using a portion of the neural network at the end sensing devices. Therefore, this article proposed the distributed strategy can dynamically adjust network layers and data allocation proportion of end devices and edge servers according to different tasks to shorten the data processing time. A joint optimization problem is proposed to minimize the total delay, which is affected by the complexity of the DL model, the inference error rate, the computing power of the end devices and the edge servers. An analytical solution of a closed solution is derived and an optimal distributed data allocation and neural network allocation algorithm is proposed

关键词： Deep learning Wireless communication Performance evaluation neural networks distributed databases Sensors Servers

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A Dual-stage Attention Based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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8th International conference on Future Data and Security Engineering , FDSE 2021

8th International Conference on Future Data and Security Eng...

引用

8th International conference on Future Data and Security Engineering , FDSE 2021

ISBN: (纸本)9783030913861

The proceedings contain 26 papers. The special focus in this conference is on Future Data and Security Engineering. The topics include: Feature Learning and Data Generative Models for Facial Expression Recognition;authorization Strategies and Classification of Access Control Models;motorbike Counting in Heavily Crowded Scenes;pesticide Label Detection Using Bounding Prediction-Based Deep Convolutional networks;intelligent Urban Transportation System to Control Road Traffic with Air Pollution Orientation;a Data Union Method Using Hierarchical Clustering and Set Unionability;a Consensus-Based Load-Balancing Algorithm for Sharded Blockchains;neighboring Information Exploitation for Anomaly Detection in Intelligent IoT;feature Representation of AutoEncoders for Unsupervised IoT Malware Detection;Potential Threat of Face Swapping to eKYC with Face Registration and Augmented Solution with Deepfake Detection;time in Data Models;spliced Image Forgery Detection Based on the Combination of Image Pre-processing and Inception V3;comprehensive Analysis of Privacy in Black-Box and White-Box Inference Attacks Against Generative Adversarial network;face Recognition in the Wild for Secure Authentication with Open Set Approach;intrusion Detection in Software-Defined networks;clustering Analyses of Two-Dimensional Space-Filling Curves;Attendance Monitoring Using Adjustable Power UHF RFID and Web-Based Real-Time Automated Information System;integrating Deep Learning Architecture into Matrix Factorization for Student Performance Prediction;distributed Scalable Association Rule Mining over Covid-19 Data;threshold Benefit for Groups Influence in Online Social networks;application Based Cigarette Detection on Social Media Platforms Using Machine Learning Algorithms;Efficient Brain Hemorrhage Detection on 3D CT Scans with Deep neural network;multi-class Bagged Proximal Support Vector Machines for the ImageNet Challenging Problem;selective Combination and Management of distributed Machine Le

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Optimal GPU-CPU Offloading Strategies for Deep neural network Training 26th

Optimal GPU-CPU Offloading Strategies for Deep Neural Networ...

引用

26th International conference on Parallel and distributed Computing (Euro-Par)

作者： Beaumont, Olivier Eyraud-Dubois, Lionel Shilova, Alena Inria Bordeaux Sud Ouest Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (纸本)9783030576752;9783030576745

Training Deep neural networks is known to be an expensive operation, both in terms of computational cost and memory load. Indeed, during training, all intermediate layer outputs (called activations) computed during the forward phase must be stored until the corresponding gradient has been computed in the backward phase. These memory requirements sometimes prevent to consider larger batch sizes and deeper networks, so that they can limit both convergence speed and accuracy. Recent works have proposed to offload some of the computed forward activations from the device memory to the main memory and requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.

关键词： Memory management Deep neural network Dynamic programming Scheduling

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Automatic Construction of Tensor Product Variable Binding neural networks for neural-Symbolic Intelligent Systems 2

Automatic Construction of Tensor Product Variable Binding Ne...

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2nd International conference on Electrical, Communication and Computer Engineering, ICECCE 2020

作者： Demidovskij, Alexander Higher School of Economics Computer Science Department Nizhny Novgorod Russia

ISBN: (纸本)9781728171166

One of the main challenges in building hybrid systems that integrate symbolic and sub-symbolic levels of computations is elaboration of methods that would allow expressing symbolic manipulations in a form of neural networks dynamics. In particular, it is extremely important to be able to process symbolic structures in a distributed format and perform analysis over this tensor representation that would be equivalent to a sequence of symbolic operations over the original structure. In this paper, the novel method is proposed for processing and analysis of semantics tree in a form of generated neural network. This method is built upon the Tensor Product Representations (TPRs) framework, scales well for an arbitrary structure and allows expressing simple symbolic operations in a form of neural network dynamics. © 2020 IEEE.

关键词： neural networks

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Audio Public opinion Analysis Model based on heterogeneous neural network

Audio Public opinion Analysis Model based on heterogeneous N...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Haikun Jiang Xu Wu Xiaqing Xie Jingchen Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Library Beijing China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

With the rapid development of the information age, the main body of public opinion information is no longer just structured data such as text and numbers, but the proportion of unstructured data such as audio and video has increased significantly. Therefore, the analysis and processing of unstructured public opinion data has become the key to deal with the problem of public opinion. Based on this, this paper designs an audio public opinion early warning model based on heterogeneous neural network. Heterogeneous neural network refers to a variety of different types of neural networks to participate in the completion of specified tasks under the premise of data sharing, while retaining the autonomy of each neural network. In this paper, the audio data is converted into a spectrum as an input feature. The convolution neural network can obtain the feature representation of the audio in the spectrum graph, which is used as the input of the recurrent neural network, and then the features related to the text representation are obtained. In this paper, Bert is used to modify the semantic results combined with public opinion domain knowledge, and finally the results are introduced into the shallow neural network Fasttext for public opinion early warning processing. In this paper, experiments are carried out based on the open source voice data set ST-CMDS-20170001-1 Chinese mandarin corpus and Pytorch platform. The results show that this model can effectively analyze and process public opinion of unstructured audio and video data.

关键词： Knowledge engineering Analytical models Recurrent neural networks Computational modeling neural networks Semantics Task analysis

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A Ransomware Classification Method Based on Entropy Map

A Ransomware Classification Method Based on Entropy Map

引用

IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： XiZhen Deng M. Jiang MingCan Cen College of Electronics and Engineering Guangxi Normal University China

With the rapid development and popularization of internet technologies such as AI and 5G, more and more organizations and individuals have suffered a dramatic increase in the number of ransomware attacks, which has brought substantial economic losses to them. Ransomware is an illegal act that blackmails victims into paying a ransom by locking their devices or encrypting files. Achieving fast and effective ransomware classification, attack intent and pattern analysis can improve the efficiency of security analysts and discover ransomware variants earlier. Therefore, we propose a new ransomware classification method, which uses the entropy map extracted from the ransomware binary file for classification. The entropy map retains more fine-grained features in the ransomware family, which can improve the classification result. Aiming at the problem of data imbalance among ransomware families, we propose a data augmentation method based on the Cycle-GAN network, which combines the fine-tuning technology in transfer learning to improve the classification result of the framework further. At the same time, the attention mechanism is introduced into VGG-16. It is used to enhance the ability of feature extraction of the network. The experimental results show that the proposed method achieves the best performance on 14 ransomware families, and the accuracy rate can reach 97.16%, which is better than other traditional ransomware visualization classification methods. Our proposed method still has the best classification performance compared with other neural networks.

关键词： Visualization Transfer learning neural networks Organizations network security Feature extraction Ubiquitous computing

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AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International conference on Service-Oriented Computing, ICSOC 2020

AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events h...

引用

AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International conference on Service-Oriented Computing, ICSOC 2020

ISBN: (纸本)9783030763510

The proceedings contain 55 papers. The special focus in this conference is on Service-Oriented Computing. The topics include: AuraEN: Autonomous Resource Allocation for Cloud-Hosted Data processing Pipelines;performance Diagnosis in Cloud Microservices Using Deep Learning;anomaly Detection at Scale: The Case for Deep Distributional Time Series Models;A Systematic Mapping Study in AIOps;an Influence-Based Approach for Root Cause Alarm Discovery in Telecom networks;localization of Operational Faults in Cloud Applications by Mining Causal Dependencies in Logs Using Golden Signals;using Language Models to Pre-train Features for Optimizing Information Technology Operations Management Tasks;towards Runtime Verification via Event Stream processing in Cloud Computing Infrastructures;hybrid Context-Aware Method for Quality Assessment of Data Streams;decentralized Federated Learning Preserves Model and Data Privacy;online Memory Leak Detection in the Cloud-Based Infrastructures;Multi-source Anomaly Detection in distributed IT Systems;TELESTO: A Graph neural network Model for Anomaly Classification in Cloud Services;discovering Alarm Correlation Rules for network Fault Management;resource Sharing in Public Cloud System with Evolutionary Multi-agent Artificial Swarm Intelligence;SLMAD: Statistical Learning-Based Metric Anomaly Detection;software Reliability Engineering for Resilient Cloud Operations;The Future of Robotic Process Automation (RPA);adaptive Summaries: A Personalized Concept-Based Summarization Approach by Learning from Users’ Feedback;container-Based network Architecture for Mobility, Energy and Security Management as a Service in IoT Environments;TAP: A Two-Level Trust and Personality-Aware Recommender System;am I Rare? an Intelligent Summarization Approach for Identifying Hidden Anomalies;on How Cognitive Computing Will Plan Your Next Systematic Review;stage-Based Process Performance Analysis;peer-to-Peer Application Threat Investigation.

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