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

作者： Wei Wang Tao Wang Gang Yin National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In recent years, online programming education and evaluation have developed rapidly. The traditional online education platform focuses on the teaching of knowledge, lacking a good evaluation platform to assess the learning outcomes, while the existing online evaluation platform only focuses on the evaluation of algorithms or languages. Until now, no platform can support the training of engineering-level complex technologies. In this paper, we present our solution - Educoder. Educoder is an one-stop service platform that integrates “teaching”, “learning” and “use”. It not only supports simple programming language training type, but also supports the training of complex technology such as Hadoop, Spring, Machine Learning, Docker, etc. The code environment for student learning and evaluation is managed by Gitlab, and the runtime environment is provided by Kubernetes pod, which guarantees good isolation. For complex types of training, we provide the Docker Web Terminal module for SSH connection to enter the pod for operation and use docker in docker technology to provide support for training that requires multiple services or multiple nodes. In Educoder, we use scripts to serialize the training evaluation steps, and we formed a private evaluation script template library for training teacher to reference, which greatly reduced the time cost of writing a new training course.

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Aircraft Segmentation Based On Deep Learning framework : from extreme points to remote sensing image segmentation

Aircraft Segmentation Based On Deep Learning framework : fro...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Lei Zhao Peng Qiao Yong Dou Science and Technology on Parallel and Distributed Laboratory National University of Defense Changsha China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Remote sensing image segmentation is a very important technology. Although the segmentation method based on convolutional neural networks (CNNs) has achieved promising results in natural image test set, e.g. VOC or COCO, they provide inferior performance when being transferred to remote sensing images. Due to the limits of labeled remote sensing images, finetuning pre-trained CNNs using remote sensing images do not benefit the image segmentation performance. Inspired by the recent works of interactive segmentation methods which exploit several extreme clicks that are fed into CNNs to improve the accuracy of the segmentation, we propose an effective method to improve the segmentation accuracy, which uses four extreme points (the top, bottom, left, and right) as the guide information. In terms of mIoU, our method achieves 84.4% on remote sensing image dataset, which outperforms the previous work by 23.1%. Compared with the previous interactive segmentation methods, the proposed method achieves superior performance. In addition, an improved method with an extra point is proposed based on the inaccurate part of results obtained by four extreme points. It is very feasible to be applied in an interactive segmentation toolbox.

关键词： Image segmentation Remote sensing Training Aircraft Perturbation methods Semantics Heating systems

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Quantitative analysis of collaborative-based annotation in software engineering education 8

Quantitative analysis of collaborative-based annotation in s...

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2018 8th International Workshop on Computer science and Engineering, WCSE 2018

作者： Yu, Jie Wang, RenMin Zeng, LingBin Wang, Tao Yin, Gang Fan, Qiang Yu, Yue National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9789811178610

The primary goal of software engineering education is to develop the ability of students, a common method is to let students to read excellent open source code, so that students can improve themselves through this behavior. When reading the code, we tend to add comments to the code, in the past, the labeling of students tend to be an independent behavior, in this paper we analyze the quantitative of collaborative-based code annotation method, and the result shows the collaborative-based code annotation method is working well in improving students programming skills. © Proceedings of 2018 the 8th International Workshop on Computer science and Engineering, WCSE 2018. All rights reserved.

关键词： Open source software

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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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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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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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Study on the Drill of Maritime Rescue Oriented Modeling Method of Climate Background Environment

Study on the Drill of Maritime Rescue Oriented Modeling Meth...

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作者： Sha Chang Xuyun Zhao Bin Song Yu Wang Institute of Meteorology and Oceanography National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

In order to provide an about real-world rescue environment for marine rescue simulation training, meteorological marine environment modeling becomes more and more important. In the modeling of meteorological ocean environment, the modeling method of complex environment is becoming more and more mature and the models are becoming more sophisticated, while the climate background models are often overlooked. How to provide real and accurate data-based climate background modeling for marine rescue simulation training has become an urgent problem to be solved. Based on the seasonal-trend decomposition procedure based on Loess(STL) and statistical feature modeling method, a modeling method for constructing dynamic climate background modeling using climate time series is proposed. For different climate model simulation application and the specific characteristics of climate change, our method builds the dynamic climate background model for different sea areas and different months accordingly, while considering the changing of climatic factors at different time scales. The dynamic climate background model is composed of four climate trend models with different time periods, namely, the annual variation trend of climate elements, the daily mean anomaly variation trend, the daily variation trend and the random perturbation. This modeling method of climate background model is not just a way to reflect the trends of climate change, but also to provide a dynamic and numerical climate background information for simulation training. As a result, providing this information to the commander or the rescue equipment can further improve the effectiveness of maritime rescue drill.

关键词： Maritime rescue Simulation training Dynamic climate background model STL decomposition theory

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Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising

Fast Nonlocal Diffusion by Stacking Local Filters for Image ...

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2019国际计算机前沿大会

作者： Peng Qiao Weichu Sun Yong Dou Rongchun Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology University of South China

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing highdefinition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrss was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion(TNLRD)with local filters. For image with 4096 × 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore,the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM.

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Research on accelerating CEE-DGCNN event extraction algorithm based on multi-CPU platform

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Journal of Physics: Conference Series 2021年第1期1738卷

作者： Yan Li Jinfei Jiang Jinwei Xu Zikai Gao Yong Dou Haifang Zhou Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha 410005 China Department of Computational Science National University of Defense Technology Changsha 410005 China

With the development of the Internet, the volume of information is expanding rapidly, and the complex information makes it particularly important to extract information quickly and intelligently. Event extraction algorithm is such a kind of fast and accurate information acquisition algorithm. With the development of deep learning in recent years, deep learning also shines in the field of time extraction. CEE-DGCNN network is an efficient and accurate event extraction algorithm for Chinese text. At present, the acceleration research on deep learning in the academic community mainly focuses on GPU, FPGA and other platforms. In fact, compared with these platforms, CPU is more versatile. Considering that the algorithm should have universality and universality in industrial application, this paper makes an in-depth analysis of CEE-DGCNN algorithm to study its acceleration on CPU. Experiments were carried out on x86 and ARM architectures respectively, and good acceleration effects were achieved.

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Rev Rec: A two-layer reviewer recommendation algorithm in pull-based development model

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Journal of Central South University 2018年第5期25卷 1129-1143页

作者：杨程张迅晖曾令斌范强王涛余跃尹刚王怀民 National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology

Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests （PRs） in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.

关键词： Pull-Request code reviewer recommendation GitHub open source community

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