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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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MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data

MIX: A Joint Learning Framework for Detecting Both Clustered...

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IEEE International Conference on Data Mining (ICDM)

作者： Hongzuo Xu Yijie Wang Yongjun Wang Zhiyue Wu National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Another kind of approaches separately evaluates outlierness in numerical and categorical features. However, they fail to adequately consider the behaviours of data objects in different feature spaces, often leading to suboptimal results. As for outlier form, both clustered outliers and scattered outliers are contained in many real-world data, but a number of outlier detectors are inherently restricted by their outlier definitions to simultaneously detect both of them. To address these issues, an unsupervised outlier detection method MIX is proposed. MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space. Specifically, MIX iteratively performs outlier scoring in numerical and categorical space. Each outlier scoring phase can be iteratively and cooperatively enhanced by the prior knowledge given by another feature space. To target both clustered and scattered outliers, the outlier scoring phases capture the essential characteristic of outliers, i.e., evaluating outlierness via the deviation from the normal model. We show that MIX significantly outperforms eight state-of-the-art outlier detectors on twelve real-world datasets and obtains good scalability.

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Geng, Mingyang Li, Yiying Ding, Bo Wang, And Huaimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)9781509060146

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system. © 2018 IEEE.

关键词： Multipurpose robots

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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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Untrusted Hardware Causes Double-Fetch Problems in the I/O Memory

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Journal of Computer science & technology 2018年第3期33卷 587-602页

作者： Kai Lu Peng-Fei Wang Gen Li Xu Zhou College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High-Performance Computing National University of Defense Technology Changsha 410073 China

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices （aka. the hardware double fetch）. Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O （input and output） memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

关键词： hardware double fetch double-fetch bug I/O memory peripheral device double-fetch vulnerability

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RLLL: Accurate Relative Localization of RFID Tags with Low Latency

RLLL: Accurate Relative Localization of RFID Tags with Low L...

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International Workshop on Quality of Service

作者： Xuan Liu Quan Yang Shigeng Zhang Bin Xiao The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) P. R. China College of Computer Science and Electronic Engineering Hunan University P. R. China School of Computer Science and Engineering Central South University P. R. China The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applications that require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with commercial RFID devices show that RLLL achieves an ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm from adjacent tags, in which case the state-of-the-art solutions only achieve ordering accuracy of lower than 0.8 with localization latency larger than 3 seconds.

关键词： RFID tags Manufacturing Antenna arrays Quality of service Libraries

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Modeling complex relationship paths for knowledge graph completion

Modeling complex relationship paths for knowledge graph comp...

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

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translationmodels obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Knowledge graph

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Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach

arXiv

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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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An efficient SIMD compression format for sparse matrix-vector multiplication

An efficient SIMD compression format for sparse matrix-vecto...

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作者： Chen, Xinhai Xie, Peizhen Chi, Lihua Liu, Jie Gong, Chunye Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Institute of Advanced Science and Technology Hunan Institute of Traffic Engineering Hengyang China

Sparse matrix-vector multiplication (SpMV) is an essential kernel in sparse linear algebra and has been studied extensively on all modern processor and accelerator architectures. Compressed Sparse Row (CSR) is a frequently used format for sparse matrices storage. However, CSR-based SpMV has poor performance on processors with vector units. In order to take full advantage of SIMD acceleration technology in SpMV, we proposed a new matrix storage format called CSR-SIMD. The new storage format compresses the non-zero elements into many variable-length data fragments with consecutive memory access addresses. Thus, the data locality of sparse matrix A and dense vector x expands and the floating-point operations for each fragment can be completely calculated by vectorized implementation on wide SIMD units. Our experimental results indicate that CSR-SIMD has better storage efficiency and low-overhead for format conversion. Besides, the new format achieves high scalability on wide SIMD units. In comparison with the CSR-based and BCSR-based SpMV, CSR-SIMD obtains better performance on FT1500A, Intel Xeon, and Intel Xeon Phi. © 2018 John Wiley & Sons, Ltd.

关键词： Storage efficiency

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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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