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DISVMs: Fast SVMs Training on Large-scale Data Sets

DISVMs: Fast SVMs Training on Large-scale Data Sets

引用

International Conference on Tools with Artificial Intelligence

作者： Lijuan Cui Changjian Wang Ziyang Li Yuxing Peng Science and Technology on Parallel and distributed Processing Laboratory National University of Defence Technology

ISBN: (纸本)9781509044603

Support Vector Machines (SVMs) are powerful classification tools. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to screening out non-support vectors (non-SVs) to accelerate the training. But their processes rely on prior knowledge of other classifiers with different parameters to screen out non-SVs. In this paper, we propose Directional Indicator Support Vector Machines (DISVMs) to efficiently identify non-SVs. DISVMs employs a directional indicator, which points to the approximately orthogonal direction of the separating hyperplane, to qualitatively define the location of different samples and thus identify non-SVs. Furthermore, DISVMs leverages a two-stage algorithm: the first stage is to compute the directional indicator. The second stage is to identify non-SVs using the indicator. To avoid misjudgement, we propose CnSV method for non-SVs based on the majority rule. DISVMs screens out non-SVs with light computation and little accuracy loss. Experiments show that our approach significantly reduces the total computation cost.

关键词： Support vector machines Non-support vector Directional indicator Computation efficiency Data preprocessing

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DIPP——An LLC Replacement Policy for On-chip Dynamic Heterogeneous Multi-core Architecture

DIPP——An LLC Replacement Policy for On-chip Dynamic Hetero...

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Zhang Yang Xing Zuocheng Ma Xiao Science and technology on Parallel and distributed processing laboratory National University of Defense Technology

As the big data era is coming,it brings new challenges to the massive data processing.A combination of GPU and CPU on chip is the trend to release the pressure of large scale *** found that there are different memory access characteristics between GPU and *** most important one is that the programs of GPU include a large number of threads,which lead to higher access frequency in cache than the CPU *** the LRU policy favors the programs with high memory access frequency,the programs of GPU can't get the corresponding performance boost even more cache resources are *** LRU policy is not suitable for heterogeneous multi-core *** on the different characteristics of GPU and CPU programs on memory access,this paper proposes an LLC dynamic replacement policy--DIPP(Dynamic Insertion/Promotion Policy) for heterogeneous multi-core *** core idea of the replacement policy is to reduce the miss rate of the program and enhance the overall system performance by limiting the cache resources that GPU can acquire and reducing the thread interferences between *** compare the DIPP replacement policy with LRU and we conduct a classified discussion according to the program results of *** programs enhance 23.29% on the average performance(using arithmetic mean).Large working sets programs can improve 13.95%,compute-intensive programs enhance 9.66% and stream class programs improve 3.8%.

关键词： Big data Heterogeneous Multicore Replacement Policy DIPP

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Failure-divergence semantics and refinement of long running transactions

Failure-divergence semantics and refinement of long running ...

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作者： Chen, Zhenbang Liu, Zhiming Wang, Ji National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China UNU-IIST P.O. Box 3058 China

Compensating CSP (cCSP) models long-running transactions. It can be used to specify service orchestrations written in programming languages like WS-BPEL. However, the original cCSP does not allow to model internal (non-deterministic) choice, synchronized parallel composition, hiding or recursion. In this paper, we introduce these operators and define for the extended language a failure-divergence (FD) semantics to allow reasoning about non-determinism, deadlock and livelock. Furthermore, we develop a refinement calculus that allows us to compare the level of non-determinism between long running transactions, and transform specifications for design and analysis. © 2012 Elsevier B.V. All rights reserved.

关键词： Calculations

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A secure light-weight public auditing scheme in cloud computing with potentially malicious third party auditor

A secure light-weight public auditing scheme in cloud comput...

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作者： Wu, Yilun Lin, Xinye Lu, Xicheng Su, Jinshu Chen, Peixin College of Computer National University of Defense Technology China School of Computer Science McGill University Montreal Canada National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient. © 2016 The Institute of Electronics, Information and Communication Engineers.

关键词： Cloud security

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Design and Implementation of distributed Stage DB:A High Performance distributed Key-Value Database

Design and Implementation of Distributed Stage DB:A High Per...

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2014 International Conference on Industrial Engineering and Information technology

作者： Hui-jun Wu Kai Lu Gen Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

With the development of high performance computing and Web 2.0 applications,unstructured data storage becomes more and more *** RDBMS isn't efficient for big data ***,RDBMS's scalability is ***' expansion often leads to a large scale of data *** paper designs and implements a high performance distributed key-value database,which is distributed Stage *** servers are organized by a consistent hashing ring and distributed with the support of Zookeeper,a distributed service *** has a high single-node read/write *** route information is calculated by clients,which reduces the expense of expansion.

关键词： distributed system,database,key-value,Zookeeper

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An efficient packing algorithm for spatial keyword queries

An efficient packing algorithm for spatial keyword queries

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6th International Conference on Convergence and Hybrid Information technology, ICHIT 2012

作者： Pan, Jinkun Li, Dongsheng Li, Liming National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology 410073 Changsha China Shanghai Garrision 200040 Shanghai China

ISBN: (纸本)9783642326448

With the increase of location-based services, Web contents are being geo-tagged, and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. Unfortunately, the state-of-the-art packing algorithms only focus on spatial relationship, and they are not fit for the spatial keyword queries. In this paper, we propose a new packing algorithm named KBS which takes both location and keyword information into consideration, thus optimizing R-tree for spatial keyword queries. Experimental results on both real and synthetic datasets show that our method achieves high performance and space utilization. © 2012 Springer-Verlag.

关键词： Location based services

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A Novel Interactive Recurrent Attention Network for Emotion-Cause Pair Extraction

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

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

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.

关键词： Natural language processing Deep learning Sentiment analysis Text mining Emotion-cause pair extraction

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Serialization/Deserialization-free State Transfer in Serverless Workflows 24

Serialization/Deserialization-free State Transfer in Serverl...

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19th European Conference on Computer Systems, EuroSys 2024

作者： Lu, Fangming Wei, Xingda Huang, Zhuobin Chen, Rong Wu, Minyu Chen, Haibo Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China University of Electronic Science and Technology of China China Institute of Parallel and Distributed Systems Germany

ISBN: (纸本)9798400704376

Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack of ability to efficiently access the (remote) memory of another function. We propose RMMap, an OS primitive for remote memory map. It allows a serverless function to directly access the memory of another function, even if it is located remotely. RMMap is the first to completely eliminates serialization and deserialization when transferring states between any pairs of functions in (unmodified) serverless workflows. To make remote memory map efficient and feasible, we co-design it with fast networking (RDMA), OS, language runtime, and serverless platform. Evaluations using real-world serverless workloads show that integrating RMMap with Knative reduces the serverless workflow execution time on Knative by up to 2.6 × and improves resource utilizations by 86.3%. © 2024 ACM.

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FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction 12th

FedEAE: Federated Learning Based Privacy-Preserving Event Ar...

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12th National CCF Conference on Natural Language processing and Chinese Computing, NLPCC 2023

作者： Hu, Fei Dong, Shenpo Chang, Tao Zhou, Jie Li, Haili Wang, Jingnan Chen, Rui Liu, Haijiao Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031446955

Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Extraction

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Learning from Wireless: a Prospective Approach to Human-Centered Computing

Learning from Wireless: a Prospective Approach to Human-Cent...

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International Joint Conference on Neural Networks

作者： Yong Lu Shaohe Lv Xue Yang Xiaodong Wang Minne Li Xingming Zhou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (纸本)9781509006212

One of the most important topics in Human-Centered Computing (HCC) is to recognise human's activities. In this paper, the technology of wireless-based activity recognition is introduced. By using wireless signals, one can achieve Non-Line-Of-Sight (NLOS) recognition without carrying any devices. Also, it is easy to deploy a wireless-based recognition system due to the ubiquity of wireless communication systems. The basic idea is to detect different characteristics of signal propagation that correspond to the distinct human behaviors. As a result, action recognition is performed by analyzing the distinguishable features of signal propagation. This paper introduces the basic principles and applications of wireless-based activity recognition, and discusses the challenges and related performance metrics. Finally, open problems are discussed to point out the future research trends.

关键词： Human-centered computing wireless Wireless communication systems Activity recognition recognition system Performance metrics signals Learning Paper technology Human behavior

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