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

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

Mixed-type data containing categorical and numerical features 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. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. This paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Telecommunication traffic Machine learning Detectors Network security Feature extraction Entropy Data models

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Multi-Discussing across Issues in GitHub: A Preliminary Study

Multi-Discussing across Issues in GitHub: A Preliminary Stud...

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Asia-Pacific Conference on Software Engineering

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

ISBN: (纸本)9781728119717;9781728119700

Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously may enhance the work efficiency of developers. However, multi-discussing also relies on developers' rationally allocating their time and focus, which may bring different influence to the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question which can help developers understand the benefits and limitations when they switch their discussion between issues. In this paper, we present a preliminary study of the impact of multi-discussing on issue resolution in GitHub projects, by using quantitative methods. First, we collect and analyzed data from 631 GitHub projects to explore how multi-discussing affects the average resolution latency of project issues. Further, we develop method for measuring the rate and breadth of a developers' discussion switching behavior, and we use regression modeling to study how discussion-switching affects the single issue resolution latency. We find that multi-discussing is a common behavior of developers in GitHub projects. Also, multi-discussing is associated with shorter average issue resolution latency of project. However, during a single issue resolution, more participants' discussion-switching tend to bring longer issue resolution latency. Our study motivates the need for further research on the multi-discussing.

关键词： Switches Multitasking Collaboration Task analysis Encoding Rails Tools

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Hardware design of ML algorithm in MIMO-OFDM system

Hardware design of ML algorithm in MIMO-OFDM system

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International Conference on Systems and Informatics (ICSAI)

作者： Suncheng Xiang Minxuan Zhang Zuocheng Xing Cang Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781509055227

With the development of maritime information technology and computer technology, wireless communication technology in underwater networks and systems has made great progress and achieved wide range of application, which greatly promoted the development of ocean communication. Due to the presence of severe frequency selective fading, fast fading and slow fading, including all kinds of Noise interference and ISI problem under multipath propagation, especially ISI. In the meantime, in order to improve efficiency in underwater communications, a kind of signal detection algorithm is proposed in the case of interference in this article to deal with this problems-Maximum likelihood detection, this algorithm has a good complexity and relatively a high degree of precision in MIMO-OFDM system. So we do a circuit design based on the ML algorithm. The test result shows that this kind of circuit meets the requirements successfully.

关键词： Algorithm design and analysis OFDM Hardware Receiving antennas Integrated circuit modeling Maximum likelihood detection MIMO

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Accelerating embarrassingly parallel algorithm on Intel MIC

Accelerating embarrassingly parallel algorithm on Intel MIC

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Qinglin Wang Jie Liu Xiantuo Tang Feng Wang Guitao Fu Zuocheng Xing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781479920327

The Embarrassingly Parallel (EP) algorithm which is typical of many Monte Carlo applications provides an estimate of the upper achievable limits for double precision performance of parallel supercomputers. Recently, Intel released Many Integrated Core (MIC) architecture as a many-core co-processor. MIC often offers more than 50 cores each of which can run four hardware threads as well as 512-bit vector instructions. In this paper, we describe how the EP algorithm is accelerated effectively on the platforms containing MIC using the offload execution model. The result shows that the efficient implementation of EP algorithm on MIC can take full advantage of MIC's computational resources and achieves a speedup of 3.06 compared with that on Intel Xeon E5-2670 CPU. Based on the EP algorithm on MIC and an effective task distribution model, the implementation of EP algorithm on a CPU-MIC heterogeneous platform achieves the performance of up to 2134.86 Mop/s and 4.04 times speedup compared with that on Intel Xeon E5-2670 CPU.

关键词： Microwave integrated circuits Computer architecture Vectors Algorithm design and analysis Graphics processing units Clustering algorithms Load modeling

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Crowd intelligence in AI 2.0 era

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Frontiers of Information technology & Electronic Engineering 2017年第1期18卷 15-43页

作者： Wei LI Wen-jun WU Huai-min WANG Xue-qi CHENG Hua-jun CHEN Zhi-hua ZHOU Rong DING State Key Laboratory of Software Development Beihang University National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences College of Computer Science and Technology Zhejiang University National Key Laboratory for Novel Software Technology Nanjing University

The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.

关键词： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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Fine-grained checkpoint based on non-volatile memory

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Frontiers of Information technology & Electronic Engineering 2017年第2期18卷 220-234页

作者： Wen-zhe ZHANG Kai LU Mikel LUJAN Xiao-ping WANG Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha 410072 China School of Computer The University of Manchester Manchester M13 9PL UK

New non-volatile memory （e.g., phase-change memory） provides fast access, large capacity, byteaddressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault tolerance. We propose a fine-grained checkpoint based on non-volatile memory. We extend the current virtual memory manager to manage non-volatile memory, and design a persistent heap with support for fast allocation and checkpointing of persistent objects. To achieve a fine-grained checkpoint, we scatter objects across virtual pages and rely on hardware page-protection to monitor the modifications. In our system, two objects in different virtual pages may reside on the same physical page. Modifying one object would not interfere with the other object. This allows us to monitor and checkpoint objects smaller than 4096 bytes in a fine-grained way. Compared with previous page-grained based checkpoint mechanisms, our new checkpoint method can greatly reduce the data copied at checkpoint time and better leverage the limited bandwidth of non-volatile memory.

关键词： Non-volatile memory Byte-persistency Persistent heap Fine-grained checkpoint

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Parallelization of Fast Monte Carlo Dose Calculation for Radiotherapy Treatment Planning on the ARMv8 Architecture

Parallelization of Fast Monte Carlo Dose Calculation for Rad...

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International Conference on Information science and technology (ICIST)

作者： Chi Jin Qinglin Wang Yang Zhao Yong Dou Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Monte Carlo (MC) simulation plays a key role in radiotherapy. Since the simulation time of the MC program cannot fully meet the clinical requirements, we use the ARM-based FT-2000+ multi-core processor for parallelization, which provides an effective solution for accelerating MC dose calculation. In this paper, we implement and verify FT-DPM, which is an OpenMP-based MC Dose Planning Method on FT-2000+. FT-DPM utilizes the parallelism of MC simulation and the advantage of ARM architecture to achieve the parallelization on ARM architecture. Meanwhile, we optimize the original DPM program in terms of memory allocation, data structure and data type. The experiments show that, compared with the original DPM code, FT-DPM obtains very accurate results and reaches the maximum speedups of 155.94 times for the electron case. The parallel program based on FT-2000+ only takes 44.1 seconds to simulate the particle transport of 100 million times, showing good clinical application potential. In addition, the speedup and efficiency of FT-DPM running on different core counts are also discussed.

关键词： Monte Carlo methods Multicore processing Simulation Scalability Parallel processing Data structures Load management

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Attention-based Encoder-Decoder Recurrent Neural Networks for HTTP Payload Anomaly Detection

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

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

作者： Shang Wu Yijie Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Attack payloads are often short segments hidden in HTTP requests; thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. Recurrent Neural Network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. The paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based Encoder-Decoder Recurrent Neural Networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. Through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. The experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： Deep learning Training Recurrent neural networks Protocols Focusing Manuals Feature extraction

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A causality based dynamic message ordering for DVEs on Wide Area Network

A causality based dynamic message ordering for DVEs on Wide ...

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IEEE International Conference on Communication Software and Networks

作者： Zhou, Hangjun Fu, Sha Department of Information Management Hunan University of Finance and Economics Chang Sha Hu Nan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Chang Sha Hu Nan China

ISBN: (纸本)9781612844855

Due to the large message transmission latency in distributed Virtual Environments(DVEs) on Wide Area Net-work(WAN), the effectiveness of causality consistency control of message ordering is determined by not only causal order of messages but also the real-timeness. If merely causal order is considered, the real-time property of DVEs may not be ensured because of the unlimited waiting time for the delayed messages. While if only real-timeness is emphasized, there may be too many delayed messages, which have to be discarded, to maintain the quality of causal message ordering. Therefore, a trade-off between the quality of causal order delivery and real-timeness is necessary for DVEs. In this article, a novel causality based message ordering approach is presented. In general, this new approach dynamically balances the demands of causal order delivery and real-timeness. Experiment results demonstrate the approach can enhance the quality of causality, while simultaneously keep the real-time property of DVEs. © 2011 IEEE.

关键词： Wide area networks

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An effective control scheme to ensure real-time causal order in large-scale distributed interactive simulations

An effective control scheme to ensure real-time causal order...

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2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011

ISBN: (纸本)9781612844855

When a large-scale distributed interactive simulation system is running on WAN, the sites usually disperse over a wide area in geography, which results in the simulation clock of each site is hardly to be accurately synchronized with that of other sites. The asynchronous clocks and large transmission latency on WAN bring on a problem for the large-scale simulations to preserve the real-time causal order delivery of received events at each site. In this article, we analyze the indirect way to compare the values of asynchronous simulation clocks at first, and then propose a novel scheme which can select the reconstructible causal control information for each message so as to ensure the causal ordering of events in real time. Experiments demonstrate that the scheme can weaken the effect of network latency, reduce the overhead of the transmission amount of control information and improve the causal order consistency in asynchronous distributed simulations. © 2011 IEEE.

关键词： Real time control

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