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arXiv 2017年

作者： Hu, Ben Wang, Huaimin Zhang, Pengfei Ding, Bo Che, Huimin National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re)configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robots capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the"cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup. Copyright © 2017, The Authors. All rights reserved.

关键词： Quality of service

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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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Predicting potential gene ontology from cellular response data 17

Predicting potential gene ontology from cellular response da...

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5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017

作者： Hong, Hao Yin, Xiaoyao Li, Fei Guan, Naiyang Bo, Xiaochen Luo, Zhigang Department of Chemistry and Biology National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Biotechnology Beijing Institute of Radiation Medicine Beijing China

ISBN: (纸本)9781450348270

Ontologies have proven to be useful for capturing and organizing knowledge as a hierarchical set of terms and their relationships. However, curating gene ontology data by hand requires specialized knowledge of certain field, which is inefficient. Thus inferring gene ontology from the exponentially increased biological data is getting hot. Based on the Library of Integrated Network-Based Cellular Signatures (LINCS) data we came up with the hypothesis that genes participate in analogous biological processes might affect cells accordantly. By assessing cellular response after genes were knock out we built a similarity matrix with the Gene Set Enrichment Analysis (GSEA) and clustered the genes with affinity propagation algorithm. Next we mapped the cluster result to gene ontology biological process data for annotation and enrichment analysis, which confirmed our hypothesis and made it possible to predict biological processes for unannotated genes from cellular response data after genes are knock out for the first time. We further validated the rationality from the gene ontology molecular function data. © 2017 ACM.

关键词： Gene Ontology

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yan Hou Yijie Wang Xingkong Ma Li Cheng College of Computer National University of Defense Technology Changsha National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9781538637913

The application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. The real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. The global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： Support vector machines Training distributed databases distributed algorithms Real-time systems Partitioning algorithms Streaming media

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Mechanism of floating body effect mitigation via cutting off source injection in a fully-depleted silicon-on-insulator technology

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Chinese Physics B 2016年第3期25卷 283-289页

作者：黄鹏程陈书明陈建军 College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

In this paper, the effect of floating body effect （FBE） on a single event transient generation mechanism in fully depleted （FD） silicon-on-insulator （SOI） technology is investigated using three-dimensional technology computer-aided design （3D- TCAD） numerical simulation. The results indicate that the main SET generation mechanism is not carder drift/diffusion but floating body effect （FBE） whether for positive or negative channel metal oxide semiconductor （PMOS or NMOS）. Two stacking layout designs mitigating FBE are investigated as well, and the results indicate that the in-line stacking （IS） layout can mitigate FBE completely and is area penalty saving compared with the conventional stacking layout.

关键词： floating body effect in-line stacking silicon-on-insulator source injection

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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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A Scalable and Flexible Monitoring System Framework for Supercomputers

A Scalable and Flexible Monitoring System Framework for Supe...

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2017 International Conference on Computer, Electronics and Communication Engineering(CECE2017)

作者： Tong XIAO Kai LU College of Computer National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

Mankind's demand for more powerful computing capabilities is never met, which has led to the continuous improvement of supercomputers' performance. A more powerful supercomputer tends to have a larger system scale, which brings serious challenges to the system management, within which how to monitor the system's state is a critical problem. To address this problem, a scalable and flexible monitoring system framework for supercomputers is brought forward in this paper which can monitor supercomputers with tens of thousands of nodes effectively and efficiently. In this paper, we firstly give an overview of the framework and then focus on the Super Computer System Description Language(SCSDL) which is key to the framework. In the end, we explain some techniques about implementing the framework, and the client GUIs of a job monitoring system and an error monitoring system for Tianhe-2 based on this framework are given, from which we can see that the framework is well scalable and flexible to monitor Tianhe-2 which has 16,000 nodes effectively and efficiently.

关键词： Monitoring system Framework Supercomputer Scalable Flexible

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Effect of supply voltage and body-biasing on single-event transient pulse quenching in bulk fin field-effect-transistor process

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Chinese Physics B 2016年第4期25卷 495-500页

作者：于俊庭陈书明陈建军黄鹏程宋睿强 College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Charge sharing is becoming an important topic as the feature size scales down in fin field-effect-transistor （FinFET） technology. However, the studies of charge sharing induced single-event transient （SET） pulse quenching with bulk FinFET are reported seldomly. Using three-dimensional technology computer aided design （3DTCAD） mixed-mode simulations, the effects of supply voltage and body-biasing on SET pulse quenching are investigated for the first time in bulk FinFET process. Research results indicate that due to an enhanced charge sharing effect, the propagating SET pulse width decreases with reducing supply voltage. Moreover, compared with reverse body-biasing （RBB）, the circuit with forward body-biasing （FBB） is vulnerable to charge sharing and can effectively mitigate the propagating SET pulse width up to 53% at least. This can provide guidance for radiation-hardened bulk FinFET technology especially in low power and high performance applications.

关键词： body-biasing SET pulse quenching charge sharing bulk FinFET process

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Image Annotation by Object Hypotheses-oriented Deep Neural Networks

Image Annotation by Object Hypotheses-oriented Deep Neural N...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Fang MA Shao-he LV Ke-xin ZHENG Chi JIN Fei CHEN Ke YANG and Yong DOU National Laboratory for Parallel and Distributed Processing National University of Defense Technology University of South China School of Computer Science and Technology

Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label image annotation method that combines unsupervised object hypotheses generation and deep neural *** an image,object hypotheses are generated in an unsupervised *** we extract the image features for each hypothesis with a deep neural network *** combining the features of all hypotheses,we get the features of the entire ***,we calculate for each label the probability of that the label is correlated with the given *** can be trained in an end-to-end way using the standard backward propagation *** results on multiple benchmark datasets show that our method is better than the state-of-the-art ones.

关键词： Deep learning Multi-label annotation Object hypotheses

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Aircraft Detection in Remote Sensing Images via CNN Multi-scale Feature Representation

Aircraft Detection in Remote Sensing Images via CNN Multi-sc...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Jia-qi WANG Xin NIU Peng ZHANG Yong DOU Fei XIA National Laboratory for Parallel and Distributed Processing National University of Defense Technology Institute of Electronic Information Warfare Naval University of Engineering

Aircraft detection in remote sensing images is an intractable *** current aircraft detection methods have limited representative capabilities and heavy computational *** paper studies how to apply multi-scale feature representation of Convolutional Neural Networks(CNN) to aircraft detection by qualitatively and quantitatively analyzing the performance of Single Shot Detection(SSD) *** first,we find that low-level detectors are not robust enough to detect as the semantic gap *** we propose a data driven hyper-parameter selection method to alleviate this problem by determining appropriate hyper-parameters of sliding window and default box ***,we employ a multi-scale training strategy to enhance low-level predictive ***,we propose an accurate and efficient aircraft detection *** results illustrate that our method could achieve 96.84% AP at 20 FPS on NVIDIA TITAN *** with original SSD method,our proposed approach achived 2.13% AP improvement.

关键词： Aircraft detection Remote sensing Single Shot Detection(SSD) CNN

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