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A Survey of Non-Volatile Main Memory File Systems

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Journal of Computer Science & Technology 2023年第2期38卷 348-372页

作者：王盈贾文庆蒋德钧熊劲 State Key laboratory of Processors Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China Research Center for Advanced Computer Systems Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store ***,we can directly build a file system on ***,traditional file systems are designed based on slow block *** use a deep and complex software stack to optimize file system *** design results in software overhead being the dominant factor affecting NVMM file ***,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file *** survey existing work on optimizing NVMM file ***,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,***,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and ***,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory ***,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.

关键词： non-volatile main memory(NVMM) file system performance scalability crash consistency data protection crossmeida

Multi-style video stylization based on texture advection

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Science China(Information Sciences) 2015年第11期58卷 90-102页

作者： TANG Ying ZHANG Yan SHI XiaoYing FAN Jing School of Computer Science and Technology Zhejiang University of Technology Key laboratory of Visual media Intelligent Process Technology of Zhejiang Province School of Software Engineering Hangzhou Dianzi University

Artistic video stylization, which is widely used in multimedia entertainment, transforms a given video into different artistic styles. Most of the existing video stylization algorithms can simulate single or limited video artistic styles. Although some algorithms can achieve multi-style video processing, these algorithms are complex and difficult to implement. To solve this problem, we propose a multi-styled video stylization algorithm based on texture advection, where different artistic styles are synthesized and transferred from user-specified texture samples of desired styles. We use the direction field-guided texture synthesis to compute the texture layer that represents the artistic style. Painterly directional video styles are simulated competently by the orientation changes in the synthesized anisotropic textures. There appeared local distorted region of the texture layer during texture advection under the optical flow field. To address this issue, we propose the texture inpaint to synthesize the limited distorted region and make the stylized video temporally coherent. We also accelerate the video stylization by using the CUDA parallel computing framework that parallelly computes the morphological operations used for video abstraction. Finally, we produce stylized videos of multiple artistic styles with satisfactory experimental results, including the styles of oil painting, watercolor painting and stylized lines drawing.

关键词： non-photorealistic rendering video stylization texture synthesis direction field CUDA

Semantic movie summarization based on string of IE-Role Nets

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Computational Visual media 2015年第2期1卷 129-141页

作者： Wen Qu Yifei Zhang Daling Wang Shi Feng Ge Yu School of Information Science and Engineering Northeastern University Key laboratory of Medical Image computing(Northeastern University) Ministry of Education

Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization *** substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.

关键词： movie summarization content analysis movie understanding

A kernel learning framework for domain adaptation learning

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Science China(Information Sciences) 2012年第9期55卷 1983-2007页

作者： TAO JianWen 1,3,CHUNG FuLai 2 & WANG ShiTong 1,2 1 School of Digital media,Jiangnan University,Wuxi 214122,China 2 Department of computing,Hong Kong Polytechnic University,Hong Kong,China 3 School of Information Engineering,Zhejiang Business Technology Institute,Ningbo 315012,China School of Digital media Jiangnan University Wuxi China School of Information Engineering Zhejiang Business Technology Institute Ningbo China Department of computing Hong Kong Polytechnic University Hong Kong China

Domain adaptation learning(DAL) methods have shown promising results by utilizing labeled samples from the source(or auxiliary) domain(s) to learn a robust classifier for the target domain which has a few or even no labeled ***,there exist several key issues which need to be addressed in the state-of-theart DAL methods such as sufficient and effective distribution discrepancy metric learning,effective kernel space learning,and multiple source domains transfer learning,*** at the mentioned-above issues,in this paper,we propose a unified kernel learning framework for domain adaptation learning and its effective extension based on multiple kernel learning(MKL) schema,regularized by the proposed new minimum distribution distance metric criterion which minimizes both the distribution mean discrepancy and the distribution scatter discrepancy between source and target domains,into which many existing kernel methods(like support vector machine(SVM),v-SVM,and least-square SVM) can be readily *** framework,referred to as kernel learning for domain adaptation learning(KLDAL),simultaneously learns an optimal kernel space and a robust classifier by minimizing both the structural risk functional and the distribution discrepancy between different ***,we extend the framework KLDAL to multiple kernel learning framework referred to as *** the KLDAL or MKLDAL framework,we also propose three effective formulations called KLDAL-SVM or MKLDAL-SVM with respect to SVM and its variant μ-KLDALSVM or μ-MKLDALSVM with respect to v-SVM,and KLDAL-LSSVM or MKLDAL-LSSVM with respect to the least-square SVM,*** experiments on real-world data sets verify the outperformed or comparable effectiveness of the proposed frameworks.

关键词： domain adaptation learning support vector machine multiple kernel learning maximum mean discrepancy maximum scatter discrepancy

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

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Computational Visual media 2015年第1期1卷 59-68页

作者： Yongxia Zhang Yi Liu Xuemei Li Caiming Zhang School of Computer Science and Technology Shandong University Shandong University of Finance and Economics Shandong Provincial Key Laboratory of Digital Media Technology

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

关键词： multimedia computing noise cancellation signal denoising sparse matrices video signal processing video surveillance

Robust and efficient edge-based visual odometry

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Computational Visual media 2022年第3期8卷 467-481页

作者： Feihu Yan Zhaoxin Li Zhong Zhou State Key laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Institute of computing Technology Chinese Academy ofSciencesBeijing 100190China

Visual odometry,which aims to estimate relative camera motion between sequential video frames,has been widely used in the fields of augmented reality,virtual reality,and autonomous ***,it is still quite challenging for stateof-the-art approaches to handle low-texture *** this paper,we propose a robust and efficient visual odometry algorithm that directly utilizes edge pixels to track camera *** contrast to direct methods,we choose reprojection error to construct the optimization energy,which can effectively cope with illumination *** distance transform map built upon edge detection for each frame is used to improve tracking efficiency.A novel weighted edge alignment method together with sliding window optimization is proposed to further improve the *** on public datasets show that the method is comparable to stateof-the-art methods in terms of tracking accuracy,while being faster and more robust.

关键词： visual odometry(VO) edge structure distance transform low-texture

SMART: Speedup Job Completion Time by Scheduling Reduce Tasks

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Journal of Computer Science & Technology 2022年第4期37卷 763-778页

作者： Jia-Qing Dong Ze-Hao He Yuan-Yuan Gong Pei-Wen Yu Chen Tian Wan-Chun Dou Gui-Hai Chen Nai Xia Hao-Ran Guan State Key laboratory of media Convergence and Communication Communication University of China Beijing 100024China State Key laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Computer Science The University of SydneySydney NSW 2006Australia

Distributed computing systems have been widely used as the amount of data grows exponentially in the era of information explosion. Job completion time (JCT) is a major metric for assessing their effectiveness. How to reduce the JCT for these systems through reasonable scheduling has become a hot issue in both industry and academia. Data skew is a common phenomenon that can compromise the performance of such distributed computing systems. This paper proposes SMART, which can effectively reduce the JCT through handling the data skew during the reducing phase. SMART predicts the size of reduce tasks based on part of the completed map tasks and then enforces largest-first scheduling in the reducing phase according to the predicted reduce task size. SMART makes minimal modifications to the original Hadoop with only 20 additional lines of code and is readily deployable. The robustness and the effectiveness of SMART have been evaluated with a real-world cluster against a large number of datasets. Experiments show that SMART reduces JCT by up to 6.47%, 9.26%, and 13.66% for Terasort, WordCount and InvertedIndex respectively with the Purdue MapReduce benchmarks suite (PUMA) dataset.

关键词： job scheduling job completion time MapReduce Hadoop

Systematic review of digital twin technology and applications

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Visual computing for Industry,Biomedicine,and Art 2023年第1期6卷 119-138页

作者： Jun-Feng Yao Yong Yang Xue-Cheng Wang Xiao-Peng Zhang Center for Digital media computing School of FilmXiamen UniversityXiamen 361005China School of Informatics Xiamen UniversityXiamen 361005China Key laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and TourismXiamen 361005China State Key laboratory of Multimodal Artificial Intelligence Systems the Institute of AutomationChinese Academy of SciencesBeijing 101408China

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application *** phenomenon has been widely studied in academia and *** this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are *** internal association between DT and related technologies is *** four stages of DT development history are *** fundamentals of the technology,evaluation indexes,and model frameworks are ***,a conceptual ternary model of DT based on time,space,and logic is *** technology and application status of typical DT systems are ***,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

关键词： Digital twin Digitalization Virtual modeling Information technology Simulation

Sentence segmentation for classical Chinese based on LSTM with radical embedding

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 1-8页

作者： Han Xu Wang Hongsu Zhang Sanqian Fu Qunchao Liu Jun School of software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key laboratory of Trustworthy Distributed computing and Service (BUPT)Ministry of EducationBeijing 100876China The Key laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of ChinaBeijing 100038China Insitute of Quantitative Social Science Harvard UniversityCambridgeMAUSA Department of statistics Harvard UniversityCambridgeMAUSA

A low-than character feature embedding called radical embedding is proposed,and applied on a long-short term memory(LSTM) model for sentence segmentation of pre-modern Chinese *** dataset includes over 150 classical Chinese books from 3 different dynasties and contains different literary ***-conditional random fields(LSTM-CRF) model is a state-of-the-art method for the sequence labeling *** model adds a component of radical embedding,which leads to improved *** results based on the aforementioned Chinese books demonstrate better accuracy than earlier methods on sentence segmentation,especial in Tang’s epitaph texts(achieving an F1-score of 81.34%).

关键词： LSTM radical embedding sentence segmentation