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International Conference on Parallel and distributed Systems (ICPADS)

作者： Zhongyang Wang Yijie Wang Hongzuo Xu Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Mixed-type data with both categorical and numerical features are ubiquitous in network security, but the existing methods are minimal to deal with them. Existing methods usually process mixed-type data through feature conversion, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, existing methods usually superimpose domain knowledge and machine learning in which fixed thresholds are used. It cannot dynamically adjust the anomaly threshold to the actual scenario, resulting in inaccurate anomalies obtained, which results in poor performance. To address these issues, this paper proposes a novel Anomaly Detection method based on Reinforcement Learning, termed ADRL, which uses reinforcement learning to dynamically search for thresholds and accurately obtain anomaly candidate sets, fusing domain knowledge and machine learning fully and promoting each other. Specifically, ADRL uses prior domain knowledge to label known anomalies and uses entropy and deep autoencoder in the categorical and numerical feature spaces, respectively, to obtain anomaly scores combining with known anomaly information, which are integrated to get the overall anomaly scores via a dynamic integration strategy. To obtain accurate anomaly candidate sets, ADRL uses reinforcement learning to search for the best threshold. Detailedly, it initializes the anomaly threshold to get the initial anomaly candidate set and carries on the frequent rule mining to the anomaly candidate set to form the new knowledge. Then, ADRL uses the obtained knowledge to adjust the anomaly score and get the score modification rate. According to the modification rate, different threshold modification strategies are executed, and the best threshold, that is, the threshold under the maximum modification rate, is finally obtained, and the modified anomaly scores are obtained. The scores are used to re-carry out machine learning to improve the algorithm's accuracy for anomalo

关键词： Knowledge engineering Machine learning algorithms Conferences distributed databases Reinforcement learning Telecommunication traffic Network security

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Fisher Discriminant Analysis Random Forest for Online Class Incremental Learning

Fisher Discriminant Analysis Random Forest for Online Class ...

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

作者： Wang Xiong Yijie Wang Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728111421;9781728111414

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.

关键词： Vegetation Training Covariance matrices Memory Forestry Learning systems

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Using Document Embedding Techniques for Similar Bug Reports Recommendation

Using Document Embedding Techniques for Similar Bug Reports ...

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IEEE International Conference on Software Engineering and Service sciences (ICSESS)

作者： Ming Chen Dongyang Hu Tao Wang Jun Long Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

In the software development, it is common to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task that could assist developers in bug triaging and fixing. If developers can focus on fewer code files, they can fix similar bugs in less time with higher quality. Therefore, building a similar bug recommendation system is a meaningful task that can improve development efficiency. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with Word Embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on the datasets of J dt, Birt and Eclipse platform. And the results show that our approach outperforms Yang et ale `s, with 2.67-9.50% of improvement.

关键词： Computer bugs Task analysis Predictive models Measurement Context modeling Training Software

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DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

DPSS: Dynamic Parameter Selection for Outlier Detection on D...

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International Conference on Parallel and distributed Systems (ICPADS)

作者： Ruyi Zhang Yijie Wang Haifang Zhou Bin Li Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Outlier detection on data streams identifies unusual states to sense and alarm potential risks and faults of the target systems in both the cyber and physical world. As different parameter settings of machine learning algorithms can result in dramatically different performance, automatic parameter selection is also of great importance in deploying outlier detection algorithms in data streams. However, current canonical parameter selection methods suffer from two key challenges: (i) Data streams generally evolve over time, but these existing methods use a fixed training set, which fails to handle this evolving environment and often results in suboptimal parameter recommendations; (ii) The stream is infinite, and thus any parameter selection method taking the entire stream as input is infeasible. In light of these limitations, this paper introduces a Dynamic Parameter Selection method for outlier detection on data Streams (DPSS for short). DPSS uses Gaussian process regression to model the relationship between parameters and detecting performance and uses Bayesian optimization to explore the optimal parameter setting. For each new subsequence, DPSS updates the recommended parameter setting to suit the evolving characteristics. Besides, DPSS only uses historical calculations to guide the parameter setting sampling and adjust the Gaussian process regression results. DPSS can be employed as an auxiliary plug-in tool to improve the detection performance of outlier detection methods. Extensive experiments show that our method can significantly improve the F-score of outlier detectors in data streams compared to its counterparts and obtains more superior parameter selection performance than other state-of the-art parameter selection approaches. DPSS also achieves better time and memory efficiency compared to competitors.

关键词： Training Fault diagnosis Machine learning algorithms Memory management Gaussian processes Detectors Bayes methods

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A low-cost fully pipelined architecture for fingerprint matching

A low-cost fully pipelined architecture for fingerprint matc...

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International Conference on Signal processing Proceedings (ICSP)

作者： Jinwei Xu Jingfei Jiang Yong Dou Xiaolong Shen Science and Technology on Parallel and distributed Processing Laboratory National University of Defense Technology Changsha China

Fingerprint matching is a key procedure in fingerprint identification applications. The fingerprint-matching algorithm based on minutiae is one of the most typical algorithms that can achieve a reasonably correct recognition rate. Performance and cost are two critical factors when implementing minutia-based matching algorithms in most embedded applications. A low-cost, fully pipelined architecture for minutia-based fingerprint matching is proposed in this paper. A regular matching unit with a pipeline of 13 stages is designed as the core of the architecture, interfacing with a two-port RAM and a DDR3 controller. We implemented the whole architecture on a Xilinx FPGA board with the Virtex VII XC7VX485T chip. The matching unit can run with a frequency of 330 MHz on the chip, which leads the system to achieve a throughput of about 430000 fingerprints per second when processing typical datasets. The unit only occupies 568 slices, which is less than 1% of the available chip resources. The board only consumes 16 W of power when run. The architecture can gain about twice the throughput of the 2.93 GHz Intel Xeon5670 CPU at a low logic cost and power.

关键词： Fingerprint recognition Throughput Random access memory Pipelines Computer architecture Field programmable gate arrays Algorithm design and analysis

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Optimizing Winograd-Based Fast Convolution Algorithm on Phytium Multi-Core CPUs

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2020年第6期57卷 1140-1151页

作者： Wang, Qinglin Li, Dongsheng Mei, Songzhu Lai, Zhiquan Dou, Yong Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China

Convolutional neural networks (CNNs) have been extensively used in artificial intelligence fields such as computer vision and natural language processing. Winograd-based fast convolution algorithms can effectively reduce the computational complexity of convolution operations in CNNs so that they have attracted great attention. With the application of Phytium multi-core CPUs independently developed by the National University of Defense technology in artificial intelligence fields, there is strong demand of high-performance convolution primitives for Phytium multi-core CPUs. This paper proposes a new high-performance parallel Winograd-based fast convolution algorithm after studying architecture characteristics of Phytium multi-core CPUs and computing characteristics of Winograd-based fast convolution algorithms. The new parallel algorithm does not rely on general matrix multiplication routines, and consists of four stages: kernels transformation, input feature maps transformation, element-wise multiplication, and output feature maps inverse transformation. The data movements in all four stages have been collaboratively optimized to improve memory access performance of the algorithm. The custom data layouts, multi-level parallel data transformation algorithms and multi-level parallel matrix multiplication algorithm have also been proposed to support the optimization above efficiently. The algorithm is tested on two Phytium multi-core CPUs. Compared with Winograd-based fast convolution implementations in ARM Computer Library (ACL) and NNPACK, the algorithm can achieve speedup of 1.05~16.11 times and 1.66~16.90 times, respectively. The application of the algorithm in the open source framework Mxnet improves the forward-propagation performance of the VGG16 network by 3.01~6.79 times. © 2020, science Press. All right reserved.

关键词： Program processors

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Asia-Pacific Conference on Software Engineering

作者： Dongyang Hu Ming Chen Tao Wang Junsheng Chang Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728119717;9781728119700

In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers in bug triaging and fixing. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with word embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on 13,090 bug reports from the Eclipse platform and the results show that our approach outperforms Yang et al.'s, with 7.89-8.96% of improvement.

关键词： Computer bugs Software engineering Task analysis Measurement Software Information retrieval Natural language processing

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Personalized reputation model in cooperative distributed systems

Personalized reputation model in cooperative distributed sys...

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16th IEEE International Conference on Parallel and distributed Systems, ICPADS 2010

作者： Liu, Wei Tang, Yang-Bin Wang, Huai-Min School of Computer National Univ. of Defense Technology Changsha Hunan China Institute of Science National Univ. of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China

ISBN: (纸本)9780769543079

Reputation systems provide a promising way to build trust relationships between users in distributed cooperation systems, such as file sharing, streaming, distributed computing and social network, through which a user can distinguish good services or users from malicious ones and cooperate with them. However, most reputation models mainly focus on evaluating the quality of services in one dimension, but care less about the preferences of different users. This paper proposes a personalized reputation model which provides each user a personalized trust view on others according to his preferences. In our approach, we aggregate the users' preferences with collaborative filtering method and qualify it with user similarity which is integrated into the computing of reputation value. The experimental results suggest that our model can resist possible kinds of malicious behaviors efficiently. © 2010 IEEE.

关键词： distributed computer systems

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A Heterogeneous KBA Parallel Algorithm for the Cartesian Discrete Ordinates for Multizone Heterogeneous System

A Heterogeneous KBA Parallel Algorithm for the Cartesian Dis...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Runhua Li Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Innovations in powerful high-performance computing (HPC) architecture are enabling high-fidelity whole-core neutron transport simulations at reasonable time. Especially, the currently fashionable heterogeneous architectures make the cost of such simulations at very low level. Neutron distribution of a reactor core is governed by the Boltzmann neutron transport equation (BTE), first viable solutions of which need tremendous computer resources. Among of the high-fidelity numerical methods, the discrete ordinates method (SN) is becoming popular in the reaction design community by taking a good balance between computational cost and accuracy. Recently, MT-3000, which is a multizone heterogeneous architecture with a peak double precision performance of 11.6 TFLOPS, is proposed. In this work, the BTE is solved by the SN with heterogenous Koch-Baker-Alcouffe (KBA) parallel algorithms based on the MT-3000 architecture. A communication mechanism has been established to efficiently transmit data among the acceleration cores and the CPU cores. The kernel computation procedure is largely accelerated by the vectorization and instruction pipelining techniques. Numerical experiments show that our formulation could achieve 1.37 TFLOPs with single MT-3000, that is 11.8% of its peak performance.

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Accelerating Monte Carlo Simulation of Neutron Transport on the Intel MIC Architecture

Accelerating Monte Carlo Simulation of Neutron Transport on ...

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International Conference on Information science and Control Engineering (ICISCE)

作者： Xiantao Cui Jie Liu Lihua Chi Qinglin Wang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467368513

Simulation of particle transport is critical for a great many of scientific and engineering domains. The Monte Carlo (MC) method is one of the most important numerical methods for the simulation of particle transport, and can simulate many complex types of particle transport. But the computation requirement of the MC simulation is very large. In 2010, Intel announced the Intel Many Integrated Core (MIC) architecture, which consists of many simple general-purpose cores and supports the well-known shared-memory execution model that is the base of most nodes in HPC machines. On account of the independence of simulation of each particle in the MC method, it is well-suited to accelerate the MC simulation on MIC. In this paper, an algorithm named MCNP-MIC based MIC is presented for MC simulation of neutron transport in the context of deep penetration problem, which includes the development of parallel random generator, the assignment of particle number based thread number and the design of high efficiency data structures for parallelism. Eventually, we get the results as follows: with the same problem scale and computational accuracy, the MCNPMIC algorithm has achieved roughly 5.6-fold speedup running on a 57-core MIC chip in comparison with the serial MCNP algorithm on an Intel Xeon E5-2670 CPU.

关键词： Microwave integrated circuits Neutrons Algorithm design and analysis Monte Carlo methods Data structures Graphics processing units

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