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International Conference on Ubiquitous and Future Networks, ICUFN

作者： YuSheng Xia YaPing Liu JinShu Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467399920

In content-centric networking, the schemes of innetwork caching can affect the performance of the whole network. Existing schemes lack of the global view, which results in inefficient caches. In this paper, we aim to analyze the real-time distribution of contents among caches from multiple perspectives. This paper proposes TCBRP, a scheme that analyzes caching tendency of various contents in reverse path, based on centrality of nodes, popularity of contents and replacement rate of nodes, to cache in-network contents. This scheme also has decent scalability and can be expended conveniently. The experimental results reflect that TCBRP report savings in average hops and balance cache hit rate, compared with BetwRep and LCE.

关键词： Probability Internet Network architecture Mathematical model Statistical analysis Correlation Fading channels

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Low-latency last-level cache structure based on grouped cores in Chip Multi-Processor

Low-latency last-level cache structure based on grouped core...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Jinbo Xu Weixia Xu Kefei Wang Zhengbin Pang School of Computer National University of Defense Technology Changsha China National Lab. for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781479975761

Last-Level Cache (LLC) plays an important role in Chip Multi-Processor (CMP). The objective of this work is to optimize the structure and management strategy of LLC. Based on 8-core CMP, a LLC structure based on grouped cores is proposed, where 8 cores are divided into 4 groups. All LLC resources are classified into three types, which are fixed private cache, dynamic private cache and dynamic shared cache. The layout of the LLC structure and the corresponding dynamic partitioning strategy are designed to achieve low access latency and high efficiency. Experimental results on full-system simulator suggest that the proposed structure and method are able to reduce the access latency by 2% to 12% compared with previous works, such as tiled structure, cache-centered structure and core-centered structure. Consequently, performance measured by IPC is improved up to 7%. The contribution of this paper is useful for CMP performance, and applies to not only 8-core CMP but also all small-scale CMPs.

关键词： Dynamic scheduling Multicore processing Educational institutions Organizations Delays Semiconductor device measurement Research and development

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Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach

arXiv

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arXiv 2019年

作者： Geng, Mingyang Shang, Suning Ding, Bo Wang, Huaimin Zhang, Pengfei Zhang, Lei National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology China National Key Laboratory of Integrated Automation of Process Industry Northeastern University China

Existing visual-based SLAM systems mainly utilize the three-dimensional environmental depth information from RGB-D cameras to complete the robotic synchronization localization and map construction task. However, the RGB-D camera maintains a limited range for working and is hard to accurately measure the depth information in a far distance. Besides, the RGB-D camera will easily be influenced by strong lighting and other external factors, which will lead to a poor accuracy on the acquired environmental depth information. Recently, deep learning technologies have achieved great success in the visual SLAM area, which can directly learn high-level features from the visual inputs and improve the estimation accuracy of the depth information. Therefore, deep learning technologies maintain the potential to extend the source of the depth information and improve the performance of the SLAM system. However, the existing deep learning-based methods are mainly supervised and require a large amount of ground-truth depth data, which is hard to acquire because of the realistic constraints. In this paper, we first present an unsupervised learning framework, which not only uses image reconstruction for supervising but also exploits the pose estimation method to enhance the supervised signal and add training constraints for the task of monocular depth and camera motion estimation. Furthermore, we successfully exploit our unsupervised learning framework to assist the traditional ORB-SLAM system when the initialization module of ORB-SLAM method could not match enough features. Qualitative and quantitative experiments have shown that our unsupervised learning framework performs the depth estimation task comparably to the supervised methods and outperforms the previous state-of-the-art approach by 13.5% on KITTI dataset. Besides, our unsupervised learning framework could significantly accelerate the initialization process of ORB-SLAM system and effectively improve the accuracy on environme

关键词： Unsupervised learning

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Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling

Raw Ultrasound-Based Phonetic Segments Classification Via Ma...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kang You Bo Liu Kele Xu Yunsheng Xiong Qisheng Xu Ming Feng Tamás Gábor Csapó Boqing Zhu Tongji University Shanghai China National University of Defense Technology Changsha China National Key Lab of Parallel and Distributed Processing Changsha China Budapest University of Technology and Economics Budapest Hungary

Ultrasound tongue imaging is widely used in clinical linguistics and phonetics. Recently, deep neural networks, especially convolutional neural networks, have been widely used in the interpretation and analysis of ultrasound tongue images (UTI). Despite achieving satisfactory performance, deep models rely on a large amount of manually labeled data, which is often difficult to obtain in practical settings. To address this issue, this paper focuses on how to utilize a large amount of unlabeled UTI data to improve the performance of UTI classification task. Specifically, we explore self-supervised learning with masking modeling strategy. By predicting the masked part, our pre-trained model enables the neural network to infer contextual information. Then, we fine-tune the pre-trained model with a small amount of labeled data. Compared with the previous competing algorithms, our method can improve the classification accuracy by an average of 13.33% in four different scenarios.

关键词： Ultrasonic imaging Tongue Neural networks Signal processing algorithms Imaging Self-supervised learning Phonetics

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CHIME: A Cache-Efficient and High-Performance Hybrid Index on Disaggregated Memory 24

CHIME: A Cache-Efficient and High-Performance Hybrid Index o...

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30th ACM Symposium on Operating Systems Principles, SOSP 2024

作者： Luo, Xuchuan Shen, Jiacheng Zuo, Pengfei Wang, Xin Lyu, Michael R. Zhou, Yangfan National Key Laboratory of Parallel and Distributed Computing Changsha China School of Computer Science Fudan University Shanghai China Duke Kunshan University Kunshan China Huawei Cloud Shenzhen China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400712517

Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range indexes are widely adopted by storage systems on DM to efficiently locate and query remote data. However, existing range indexes on DM suffer from either high computing-side cache consumption or high memory-side read amplifications. In this paper, we propose CHIME, a hybrid index combining B+ trees with hopscotch hashing, to achieve low cache consumption and low read amplifications simultaneously. There are three challenges in constructing CHIME on DM, i.e., the complicated optimistic synchronization, the extra metadata access, and the read amplifications introduced by hopscotch hashing. CHIME leverages 1) a three-level optimistic synchronization scheme to synchronize read and write operations with various granularities, 2) an access-aggregated metadata management technique to eliminate extra metadata accesses by piggybacking and replicating metadata, and 3) an effective hotness-aware speculative read mechanism to mitigate the read amplifications of hopscotch hashing. Experimental results show that CHIME outperforms the state-of-the-art range indexes on DM by up to 5.1× with the same cache size and achieves similar performance with up to 8.7× lower cache consumption. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory architecture

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A compact model for multi-island single electron transistors

A compact model for multi-island single electron transistors

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IEEE International Nanoelectronics Conference (INEC)

作者： Yaqing Chi Haiqin Zhong Chao Zhang Liang Fang National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Hunan China National University of Defense Technology Changsha Hunan CN

Multi-island single electron transistor (MISET) is a kind of single electron transistor (SET), which has advantages of the room temperature operating. A novel semi-empirical compact model for MISET is proposed. The new approach combines the orthodox theory of single electron tunneling for single Coulomb island and a novel empirical analysis for a chain of Coulomb islands. The model is verified by the Monte-Carlo method in SIMON simulator, and is much faster than the traditional multi-island SET simulator, which has the advantages for the large scale multi-island SET circuit simulation.

关键词： Single electron transistors Circuit simulation Tunneling Large-scale systems Voltage Temperature Character generation Electrodes distributed processing Concurrent computing

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

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21st Annual Conference of the Special Interest Group in Information Technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China 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

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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Controllable Template Generation for Document-level Event Extraction

Controllable Template Generation for Document-level Event Ex...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Quntian Fang Feng Liu Zhen Huang Zhenliang Guo Changjian Wang Dongsheng Li Minghao Hu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China Information Research Center Academy of Military Sciences Beijing China

Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed.

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

Research on Integrated Detection of SQL Injection Behavior B...

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作者： Ming Li Bo Liu Guangsheng Xing Xiaodong Wang Zhihui Wang College of Intelligence Science and Technology National University of Defence Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users' data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile,the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.

关键词： SQL injection Integrated method SQL-LSTM Traffic features

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A distributed paging RAM grid system for wide-area memory sharing 06

A distributed paging RAM grid system for wide-area memory sh...

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International Symposium on parallel and distributed processing (IPDPS)

作者： Rui Chu Nong Xiao Yongzhen Zhuang Yunhao Liu Xicheng Lu Distributed Processing National Key Laboratory for Parallel Changsha Hunan China Science and Technology Hong Kong University Kowloon Hong Kong China

ISBN: (纸本)9781424400546

Memory-intensive applications often suffer from the poor performance of disk swapping when memory is inadequate. Remote memory sharing schemes, which provide a remote memory that is faster than the local hard disk, are able to improve the performance of such applications. Due to the limitation of being applicable within single clusters only, however, most of the previous remote memory mechanisms, such as the network memory scheme, fail to be extendable into a large scale, distributed, heterogeneous, and dynamic environment. In this work, we propose a service-oriented grid memory sharing scheme, distributed paging RAM grid (DPRG). We study the properties and criteria of large scale memory sharing, and then design major operations and optimizations to fit the usage of grid systems. We collect trace from our grid environment, and evaluate DPRG through comprehensive trace-driven simulations. Results show that DPRG significantly outperforms existing remote memory sharing schemes and supports grid computing applications effectively

关键词： Read-write memory Random access memory Hard disks Meteorology Large-scale systems Grid computing Scientific computing Computer networks distributed computing laboratories

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