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9th Asia-Pacific Symposium on Internetware, Internetware 2017

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

ISBN: (纸本)1595930361

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainty. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefullydesigned algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainty successfully in robotic semantic mapping without sacrificing critical real-time constraints. © 2017 Association for computing Machinery.

关键词： Deep neural networks

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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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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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Unsupervised feature learning for human activity recognition using smartphone sensors 2

Unsupervised feature learning for human activity recognition...

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2nd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2014

作者： Li, Yongmou Shi, Dianxi Ding, B. Liu, Dongbo National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783319138169

Feature representation has a significant impact on human activity recognition. While the common used hand-crafted features rely heavily on the specific domain knowledge and may suffer from nonadaptability to the particular dataset. To alleviate the problems of handcrafted features, we present a feature extraction framework which exploits different unsupervised feature learning techniques to learning useful feature representation from accelerometer and gyroscope sensor data for human activity recognition. The unsupervised learning techniques we investigate include sparse auto-encoder, denoising auto-encoder and *** evaluate the performance on a public human activity recognition dataset and also compare our method with traditional features and another way of unsupervised feature learning. The results show that the learned features of our framework outperform the other two methods. The sparse auto-encoder achieves better results than other two techniques within our framework. © Springer International Publishing Switzerland 2014.

关键词： Signal encoding

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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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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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Exploring the use of @-mention to assist software development in GitHub 15

Exploring the use of @-mention to assist software developmen...

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7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Zhang, Yang Wang, Huaimin Yin, Gang Wang, Tao Yu, Yue Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450336413

Recently, many researches propose that social media tools can promote the collaboration among developers, which are beneficial to the software development. Nevertheless, there is little empirical evidence to confirm that using @-mention has indeed a beneficial impact on the issues in GitHub. In this paper, we analyze the data from GitHub and give some insights on how @-mention is used in the issues (general- issues and pull-requests). Our statistical results indicate that, @-mention attracts more participants and tends to be used in the difficult issues. @-mention favors the solving process of issues by enlarging the visibility of issues and facilitating the developers' collaboration. In addition to this global study, our study also build a @-network based on the @-mention database we extract. Through the @-network, we can mine the relationships and characteristics of developers in GitHub's issues. © 2015 ACM.

关键词： Social networking (online)

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Evaluating bug severity using crowd-based knowledge: An exploratory study 15

Evaluating bug severity using crowd-based knowledge: An expl...

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7th Asia-Pacific Symposium on Internetware, Internetware 2015

作者： Zhang, Yang Yin, Gang Wang, Tao Yu, Yue Wang, Huaimin Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450336413

In bug tracking system, the high volume of incoming bug reports poses a serious challenge to project managers. Triaging these bug reports manually consumes time and resources which leads to delaying the resolution of important bugs. StackOverow is the most popular crowdsourcing Q&A community with plenty of bug-related posts. In this paper, we explore the correlation between bug severity and the crowd attributes of linked posts. Two typical types of projects' bug repositories are studied here, e.g. Mozilla (user-centric project) and Eclipse (developer-centric project). Our results show that the bug severity is consistent with the crowd- based knowledge both in Mozilla and Eclipse, i.e. the linked posts of severe bugs have higher score etc. in StackOverow than non-severe bugs. This interesting phenomenon inspires us that we can optimize the existing evaluation methods of bug severity by incorporating the crowd-based knowledge from a third-party in future. © 2015 ACM.

关键词： StackOverflow crowd-based knowledge Bugzilla Bug severity

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False-positive probability and compression optimization for tree-structured bloom filters

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ACM Transactions on Modeling and Performance Evaluation of computing Systems 2016年第4期1卷 1–39页

作者： Fu, Yongquan Biersack, Ernst National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Sanyi Road Changsha Hunan Province410073 China CAIPY Valbonne06560 France

Bloom filters are frequently used to to check the membership of an item in a set. However, Bloom filters face a dilemma: the transmission bandwidth and the accuracy cannot be optimized simultaneously. This dilemma is particularly severe for transmitting Bloom filters to remote nodes when the network bandwidth is limited. We propose a novel Bloom filter called BloomTree that consists of a tree-structured organization of smaller Bloom filters, each using a set of independent hash functions. BloomTree spreads items across levels that are compressed to reduce the transmission bandwidth need. We show how to find optimal configurations for BloomTree and investigate in detail by how much BloomTree outperforms the standard Bloom filter or the compressed Bloom filter. Finally, we use the intersection of BloomTrees to predict the set intersection, decreasing the false-positive probabilities by several orders of magnitude compared to both the compressed Bloom filter and the standard Bloom filter. © 2016 ACM 2376-3639/2016/09-ART19 $15.00

关键词： Genetic algorithms

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A technical capability evaluation model based concept and prerequisite relation in computer education 33

A technical capability evaluation model based concept and pr...

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33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021

作者： Luo, Jiwen Wang, Tao Chang, Junsheng Guo, Xiaoting National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1891706527

Effectively assessing the results of users' online learning and enhancing social recognition has become a major development direction for online education platforms. For computer education, this article constructs a technical capability assessment model. This model integrates professional concepts in the field of computer science and extracts knowledge concepts from educational resources. The model first extracts candidate concepts, then uses a graph propagation algorithm to quantify candidate concepts and obtains concepts from them, and finally uses prerequisite relationships to further quantify the concepts mastered by students. The model combines the prerequisite relationship among concepts to quantify the skills that students have mastered. It can not only effectively evaluate the user's skill mastery but also lays a foundation for subsequent course recommendations and career recommendations for users. The model is tested in the real learning environment of 250 students. This model has been proved to own certain practicability and reliability by Kendall rank correlation coefficient, which is used as an evaluation index. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

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