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arXiv 2019年

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Zhang, Lei National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology China National Key Laboratory of Integrated Automation of Process Industry Northeastern University China

Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the RGB-D camera maintains a limited range for working and is hard to accurately measure the depth information in a far distance. Besides, the RGB-D camera will easily be influenced by strong lighting and other external factors, which will lead to a poor accuracy on the acquired environmental depth information. Recently, deep learning technologies have achieved great success in the visual SLAM area, which can directly learn high-level features from the visual inputs and improve the estimation accuracy of the depth information. Therefore, deep learning technologies maintain the potential to extend the source of the depth information and improve the performance of the SLAM system. However, the existing deep learning-based methods are mainly supervised and require a large amount of ground-truth depth data, which is hard to acquire because of the realistic constraints. In this paper, we first present an unsupervised learning framework, which not only uses image reconstruction for supervising but also exploits the pose estimation method to enhance the supervised signal and add training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task comparably to the supervised methods and outperforms the previous state-of-the-art approach by 13.5% on KITTI dataset. Besides, our unsupervised learning framework could significantly accelerate the initialization process of ORB-SLAM system and effectively improve the accuracy on environme

关键词： Unsupervised learning

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Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

Succinct Representations in Collaborative Filtering: A Case ...

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IEEE International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Xiangjun Peng Qingfeng Wang Xu Sun Chunye Gong Yaohua Wang User-Centric Computing Group University of Nottingham Ningbo China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results.

关键词： Vegetation Collaboration Filtering Data structures Runtime Indexes Time complexity

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TMDA: Task-Specific Multi-source Domain Adaptation via Clustering Embedded Adversarial Training

TMDA: Task-Specific Multi-source Domain Adaptation via Clust...

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IEEE International Conference on Data Mining (ICDM)

作者： Haotian Wang Wenjing Yang Zhipeng Lin Yue Yu State Key Laboratory of High Performance Computing (HPCL) National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Peng Cheng Laboratory Shenzhen China

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.

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Locality-sensitive sketching for resilient network flow monitoring

arXiv

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arXiv 2019年

作者： Fu, Yongquan Li, Dongsheng Shen, Siqi Zhang, Yiming Chen, Kai Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

Network monitoring is vital in modern clouds and data center networks for traffic engineering, network diagnosis, network intrusion detection, which need diverse traffic statistics ranging fromflow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions. In addition, deploying the sketch involves fine-grained performance control and instrumentation. This paper presents a locality-sensitive sketch (LSS) to be resilient to hash collisions. LSS proactively minimizes the estimation error due to hash collisions with an autoencoder based optimization model, and reduces the estimation variance by keeping similar network flows to the same bucket array. To illustrate the feasibility of the sketch, we develop a disaggregatedmonitoring application that supports non-intrusive sketching deployment and native network-wide analysis. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance underwide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 103 to 105 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%. Copyright © 2019, The Authors. All rights reserved.

关键词： Intrusion detection

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Attention-based fault-tolerant approach for multi-agent reinforcement learning systems

arXiv

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arXiv 2019年

作者： Geng, Mingyang Xu, Kele Li, Yiying Liu, Shuqi Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory of Big Data Management and Analysis Northeastern University Shenyang110000 China

The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents. In many real-world applications, the agents can only acquire a partial view of the world. However, in realistic settings, one or more agents that show arbitrarily faulty or malicious behavior may suffice to let the current coordination mechanisms fail. In this paper, we study a practical scenario considering the security issues in the presence of agents with arbitrarily faulty or malicious behavior. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we present an Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and relevant information for each agent at every time-step. The multi-head attention mechanism enables the agents to learn effective communication policies through experience concurrently to the action policies. Empirical results have shown that FT-Attn beats previous state-of-the-art methods in some complex environments and can adapt to various kinds of noisy environments without tuning the complexity of the algorithm. Furthermore, FT-Attn can effectively deal with the complex situation where an agent needs to reach multiple agents' correct observation at the same time. Copyright © 2019, The Authors. All rights reserved.

关键词： Reinforcement learning

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Optimization and parallelization Single Particle Cryo-EM Software RELION with GPU

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2018年第2期55卷 409-417页

作者： Su, Huayou Wen, Wen Li, Dongsheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Single particle cryo-electron microscopy (cryo-EM) is one of the most important methods of macromolecular structure. RELION (regularized likelihood optimization) is an open-source computer program for the refinement of macromolecular structures by single-particle analysis of cryo-EM data. Due to its easy usage and high quality results, RELION has attracted a lot of attentions from researchers. However, the computation requirement of this program is too huge to solve some large molecular structures with CPU, which harpers the popularization of RELION. In this paper, we characterize the algorithm of RELION and parallelize it with GPU. Firstly, the mathematical theory, computer patterns and performance bottlenecks of RELION are analyzed comprehensively. Then, we optimize the program targeting at fine-grained many-core architecture processor, such as GPU. We propose an efficient multi-level parallel model to utilize the powerful computation capacity of many-core processor. In order to achieve high performance, we reconstruct the data structure for GPU continues memory access. To avoid the limitation of GPU memory size, we implement an adaptive framework. The experimental results show that the proposed GPU based algorithm can achieve good performance. When compared with the CPU implementation, the speedup ratio of the application is more than 36 times, while the speedup ratio of compute-intensive algorithm is about 75X. Moreover, the testing results on multi GPUs show that the GPU based implementation has good scalability. © 2018, Science Press. All right reserved.

关键词： Graphics processing unit

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International Conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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Character feature learning for named entity recognition

Character feature learning for named entity recognition

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作者： Zeng, Ping Tan, Qingping Zhang, Haoyu Meng, Xiankai Zhang, Zhuo Xu, Jianjun Lei, Yan College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China School of Software Engineering Chongqing University Chongqing China

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Extraction

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Surveying concurrency bug detectors based on types of detected bugs

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Science China(Information Sciences) 2017年第3期60卷 5-31页

作者： Zhendong WU Kai LU Xiaoping WANG Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Computer National University of Defense Technology

Concurrency bugs widely exist in concurrent programs and have caused severe failures in the real world. Researchers have made significant progress in detecting concurrency bugs, which improves software reliability. In this paper, we survey the most up-to-date and well-known concurrency bug detectors. We categorize the existing detectors based on the types of concurrency bugs. Consequently, we analyze data race detectors, atomicity violation detectors, order violation detectors, and deadlock detectors, respectively. We also discuss some other techniques which are mostly related to concurrency bug detection, including schedule bounding techniques, interleaving optimizing techniques, path expanding techniques, and deterministic replay techniques. Additionally, we statistically analyze the reviewed detectors and get some interesting findings, for instance, nearly 86% of previous detectors focus on data races and atomicity violations, and dynamic approaches are popular(74%). We also discuss the limitations of previous detectors, finding that 91% of previous detectors suffer from false negatives and 64% of previous detectors suffer from runtime overhead. Based on the reviewed detectors and statistical analysis, we conclude some future research directions, including accuracy, performance,applicability, and integrality.

关键词： concurrency bug detection data race atomicity violation order violation deadlock

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Building a Cloud-Ready Program:A highly scalable Implementation based on Kubernetes

Building a Cloud-Ready Program:A highly scalable Implementat...

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作者： Qiankun Li Gang Yin Tao Wang Yue Yu National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Build system,which can convert source codes into applications,is essential for the development of *** general build systems that relying on single physical or cloud host to run bring problems such as system security,resource shortage,overload,and low availability in the face of massive build *** modularizing and streamlining the steps during a build process,this paper proposes a system that introduces container technology and then builds a large-scale,real-time,and huge-concurrency supported build system based on Kubernetes[1].The system provides a highly scalable and feature-stable cloud architecture that supports huge concurrency with lower resource ***,the system controls programs' behaviors very well to avoid potential security and resource issues and shows excellent performance in concurrency,scalability,security,and load balance even when handling a large number of build tasks.

关键词： Build system Container Kubernetes Concurrent build

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