检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

引用

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Minimum Spanning Tree clustering Based on Density Filtering 7th

Minimum Spanning Tree Clustering Based on Density Filtering

引用

7th CCF Academic Conference on BigData, CCF BigData 2019

作者： Wang, Ke Xie, Xia Sun, Jiayu Cao, Wenzhi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789811518980

clustering analysis is an important method in data mining. In order to recognize clusters with arbitrary shapes as well as clusters with different density, we propose a new clustering approach: minimum spanning tree clustering based on density filtering. It masks the low-density points in the density filtering step, which reduces the interference of noise and makes the gap between clusters clearer. It uses relative values of adjacent distances to find mutations of density and changes between clusters to divide data sets. It is tested on multiple synthetic data sets and real-world data sets, the results of which show that the algorithm is able to detect clusters with arbitrary shape and it is insensitive to the imbalance of density between clusters. It has achieved great results on multiple data sets. © Springer Nature Singapore Pte Ltd 2019.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

NaturalCC: A toolkit to naturalize the source code corpus

arXiv

引用

arXiv 2020年

作者： Wan, Yao He, Yang Zhang, Jian-Guo Sui, Yulei Jin, Hai Xu, Guandong Xiong, Caiming Yu, Philip S. Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science University of Technology Sydney NSW Australia Department of Computer Science University of Illinois at Chicago IL United States Salesforce Research Palo Alto United States

We present NATURALCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NATURALCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NATURALCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model’s performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is availab.e at https://***/watch?v=q4W5VSI-u3E&t=25s. © 2020, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

WiWrite: Wi-Fi Based Handwriting Recognition like Playing Lego

WiWrite: Wi-Fi Based Handwriting Recognition like Playing Le...

引用

International Conference on Computer Communications and Networks (ICCCN)

作者： Jiang Xiao Huichuwu Li Yuxiang Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Effective finger writing underpins semantic text input task for virtual reality applications. We demonstrate WiWrite, a novel finger writing system that leverages the ubiquitous wireless signals to enable character recognition and word estimation. WiWrite decomposes characters into basic strokes and distinguishes them by exploring the unique signal reflection pattern caused by finger movements. Apart from the prior arts relying on specialized sensors or wearable devices, WiWrite is device-free without any accessories in deed. Our real-world experiments in two different scenarios show that WiWrite succeeds in classifying 26 upper-case alphabets with 90.8% average accuracy and recognizing simple words with an average accuracy of 96.7%.

关键词： Writing Character recognition Wireless fidelity Hidden Markov models Sensors Filtering Noise reduction

来源：评论

学校读者我要写书评

暂无评论

Energy Saving Strategy of Power system cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Svega: Answering natural language questions over knowledge base with semantic matching 30

Svega: Answering natural language questions over knowledge b...

引用

30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Li, Gaofeng Yuan, Pingpeng Jin, Hai Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)1891706446

Nowadays, more and more large scale knowledge bases are availab.e for public access. Although these knowledge bases have their inherent access interfaces, such as SPARQL, they are generally unfriendly to end users. An intuitive way to bridge the gap between users and knowledge bases is to enable users to ask questions with natural language interface and return desired answers directly. Here the challenge is how to discover the query intention of users. Another challenge is how to obtain accurate answers from knowledge bases. In this paper, we model the query intention with a graph based on an entity-driven method. Consequently, the core problem of natural language question answering can be treated as subgraph matching over knowledge bases. For a query graph, there is a huge number of candidate mappings in a knowledge base, including ambiguities. Thus, a semantic vector is proposed to address disambiguation by evaluating the semantic similarity between edges in a query graph and paths in a knowledge base. By this way, our system can extract accurate answers directly without any offline work. Extensive experiments over the series of QALD challenges show the effectiveness of our system Svega in terms of recall and precision against other state-of-The-Art systems. © 2018 Universitat zu Koln. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Context aware 3d unet for brain tumor segmentation

arXiv

引用

arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

VAIL: A victim-aware cache policy for improving lifetime of hybrid memory 9

VAIL: A victim-aware cache policy for improving lifetime of ...

引用

9th International Workshop on Programming Models and Applications for Multicores and Manycores, PMAM 2018

作者： Jia, Youchuang Zhou, Fang Gao, Xiang Wu, Song Jin, Hai Liao, Xiaofei Yuan, Pingpeng Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450356459

Nowadays emerging Non-Volatile Memory (NVM) technologies are introduced to remedy the shortages of the current DRAM-based memory system. However, NVM has limited write endurance, which would severely restrict the performance of memory system. In order to relieve above limitation, we proposed a victim-aware cache policy (VAIL), based on DRAM and NVM hybrid memory system. VAIL took the eviction locality of victims from the DRAM cache into consideration, to reduce writebacks to NVM and improve DRAM hit ratio at the same time. Our evaluation shows that VAIL could reduce 17.2% writebacks to NVM with single-core workloads, 22% writebacks to NVM with multi-core workloads and improve the DRAM hit ratio of the hybrid memory system. © 2018 Association for computing Machinery.

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

Shattered chain of trust: understanding security risks in cross-cloud IoT access delegation 20

Shattered chain of trust: understanding security risks in cr...

引用

Proceedings of the 29th USENIX Conference on Security Symposium

作者： Bin Yuan Yan Jia Luyi Xing Dongfang Zhao XiaoFeng Wang Deqing Zou Hai Jin Yuqing Zhang School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center and Indiana University Bloomington and Shenzhen Huazhong University of Science and Technology Research Institute China School of Cyber Engineering Xidian University China and National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and Indiana University Bloomington Indiana University Bloomington School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center Huazhong Univ. of Sci. & Tech. China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and School of Cyber Engineering Xidian University China

ISBN: (纸本)9781939133175

IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by the device vendor, e.g., Philips bulbs are managed under Philips Hue cloud. Today's mainstream IoT clouds also support device access delegation across different vendors (e.g., Philips Hue, LIFX, etc.) and cloud providers (e.g., Google, IFTTT, etc.): for example, Philips Hue and SmartThings clouds support to delegate device access to another cloud such as Google Home, so a user can manage multiple devices from different vendors all through Google Home. Serving this purpose are the IoT delegation mechanisms developed and utilized by IoT clouds, which we found are heterogeneous and ad-hoc in the wild, in the absence of a standardized delegation protocol suited for IoT environments. In this paper, we report the first systematic study on real-world IoT access delegation, based upon a semi-automatic verification tool we developed. Our study brought to light the pervasiveness of security risks in these delegation mechanisms, allowing the adversary (e.g., Airbnb tenants, former employees) to gain unauthorized access to the victim's devices (e.g., smart locks) or impersonate the devices to trigger other devices. We confirmed the presence of critical security flaws in these mechanisms through end-to-end exploits on them, and further conducted a measurement study. Our research demonstrates the serious consequences of these exploits and the security implications of the practice today for building these mechanisms. We reported our findings to related parties, which acknowledged the problems. We further propose principles for developing more secure cross-cloud IoT delegation services, before a standardized solution can be widely deployed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：