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IEEE International Conference on Computational Science and Engineering, CSE

作者： Kai Lei Yining Ma Zhi Tan Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University SHENZHEN P.R. CHINA GuangDong Poya Information & Technology Co. LTD GUANGZHOU P.R. CHINA

ISBN: (纸本)9781479979820

Large scale, high concurrency, and vast amount of data are important trends for the new generation of website. *** becomes popular and successful to build data-intensive web applications. To study and compare the performance of ***, Python-Web and PHP, we used benchmark tests and scenario tests. The experimental results yield some valuable performance data, showing that PHP and Python-Web handle much less requests than that of *** in a certain time. In conclusion, our results clearly demonstrate that *** is quite lightweight and efficient, which is an idea fit for I/O intensive websites among the three, while PHP is only suitable for small and middle scale applications, and Python-Web is developer friendly and good for large web architectures. To the best of our knowledge, this is the first paper to evaluate these Web programming technologies with both objective systematic tests (benchmark) and realistic user behavior tests (scenario), especially taking *** as the main topic to discuss.

关键词： Benchmark testing Web servers Concurrent computing Java Educational institutions

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General Self-repairing Codes for Distributed Storage Systems

General Self-repairing Codes for Distributed Storage Systems

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IEEE International Conference on Communications

作者： Hanxu Hou Hui Li Kenneth W. Shum Shenzhen Eng. Lab of Converged Networks Technology Shenzhen Key Lab of Cloud Computing Tech. and App. Shenzhen Graduate School Peking University Shenzhen China Institute of Network Coding The Chinese University of Hong Kong Shatin Hong Kong

In distributed storage systems, a data file is encoded and distributed to storage nodes, such that the data file can be recovered from some subsets of the nodes. Upon the failure of a storage node, we want to repair it efficiently by contacting and downloading some encoded bits from a small number of surviving nodes. Using projective-geometric self-repairing codes (PSRC), proposed by Oggier and Datta, one can repair a failed node by contacting only two nodes. However, in their construction, the number of storage nodes in the storage system is a large number, and thus the storage efficiency is low. In this paper, we investigate how to be more flexible in the number of storage nodes. The proposed code in this paper is called general projective geometric self-repairing codes (GPSRC). GPSRC reduces high redundancy of PSRC, while retains the basic property of PSRC. We present some methods for repairing a failed node, in which the number of contacted surviving nodes is flexible. These repairing methods provide tradeoff between repair-degree and repair-bandwidth.

关键词： Maintenance engineering Vectors Encoding Finite element analysis Bandwidth Galois fields Educational institutions

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Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation

arXiv

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arXiv 2024年

作者： Wang, Zhuoyuan Sun, Dong Zeng, Xiangyun Wu, Ruodai Wang, Yi National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen China Lab Shenzhen China Huawei Cloud Computing Technologies Company Limited China Department of Radiology Shenzhen University General Hospital Shenzhen University Shenzhen China

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually suffer from occupying excessive memory and computation nevertheless. In this study we aim to enhance the 2D networks with contextual information for better volumetric image segmentation. Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly. Our approach leverages the learned embedding and the slice-wisely neighboring matching as a soft cue to guide the network. In such a way, the contextual information can be transfered slice-by-slice thus boosting the volumetric representation of the network. Experiments on challenging prostate MRI dataset (PROMISE12) and abdominal CT dataset (CHAOS) show that our contextual embedding learning can effectively leverage the inter-slice context and improve segmentation performance. The proposed approach is a plug-and-play, and memory-efficient solution to enhance the 2D networks for volumetric segmentation. Our code is publicly available at https://***/JuliusWang-7/CE_Block. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Design and implementation of E-government oriented rapid BPM OA platform

Design and implementation of E-government oriented rapid BPM...

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International Conference on Systems and Informatics (ICSAI)

作者： Yukun Wang Kai Lei Jincheng Wu Xiaohui Huang Yuming Lu Institute of Big Data Technologies Peking University Shenzhen P.R. China Shenzhen Cloud Computing Center National Supercomputing Center in Shenzhen Shenzhen P.R. China Shenzhen Key Lab for Cognitive Services Engineering Shenzhen Institute of Information Technology Shenzhen P.R. China

The e-government system plays a prominent part in government's office work, however, in the development process, there exist some problems currently, such as repeated development of common functions but hard to satisfy various personalized needs, independence of different systems which makes information difficult to share. These problems have seriously hindered development progress, increased development cost, and may result in information isolated islands. To solve these problems, this paper proposes an e-government oriented BPM OA platform, and introduces its design and implementation process. To achieve rapid development, by analysis and abstraction, we extract main features of common e-government systems and make them into configurable functions. The powerful form builder, the explicit authority management method and the BPMN 2.0 based workflow engine can significantly simplify the development and deployment process, promote the inter-system information exchange, and improve development efficiency.

关键词： Engines Electronic government Process control Tools Monitoring

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Exploring the Benefits of Introducing Network Coding into Named Data Networking

Exploring the Benefits of Introducing Network Coding into Na...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Kai Lei Tao Chen Chen Peng Zhi Tan Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) School of Electronics and Computer Engineering (SECE) Peking University Shenzhen P.R. China GuangDong Poya Information & Technology Co. LTD Guangzhou P.R. China

ISBN: (纸本)9781479979820

In recent years, the focus to optimize network transmission efficiency has evolved to adopt methods that let those intermediate data transferring nodes get involved with routing, forwarding and caching. In other words, the new network architecture designs become in favor of hop-to-hop model, instead of traditional TCP-like end-to-end model. Named data networking is a promising future internet data oriented architecture which uses names instead of addresses and exchanges or forwards interest/data pair packets at each node along the path to route data for delivery. And meanwhile Network coding (NC) is a content oriented and effective method to reduce redundancy, increase network throughput and improve robustness. Nonetheless, due to NDN's current preliminary research, less research has combined these two technologies together. This paper presents some new thoughts to study on the benefits brought by integrating network coding to NDN, which can effectively improve network utilization, strengthen caching privacy, and also promote development of the NDN architecture itself.

关键词： Network coding Security Peer-to-peer computing Communication networks Encoding Bandwidth Internet

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Combining parametric and nonparametric topic model to discover microblog event

Combining parametric and nonparametric topic model to discov...

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International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)

作者： Shengbing Liu Li Liu Ruzhong Cheng Peking University Beijing Beijing CN Dept. of Electronic and Computer Engineering South University of Science and Technology of China Shenzhen P.R. China Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University Shenzhen P.R. China

Compared with traditional news media, microblog holds overwhelming superiority in fast-diffusion and comprehensive coverage of topics. Microblog becomes an effective, particular and important carrier of affair information and many other text analysis tasks, e.g., event discovering based on microblog have special significance. Common tools of content analysis, such as topic model, however, experience severe data sparsity problems due to short length of microblog. Following previous researchers' idea, such as separating personal interest post from global event post, we further differentiate general topics from event topics and adopt nonparametric method to model the birth and death of event. We conduct experiments on Twitter data set, and the experimental results demonstrate that our method can not only discover event effectively, but also mine higher quality general topics.

关键词： Computational modeling Educational institutions Coherence Media Data models Twitter Computational linguistics

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Extracting Unknown Words from Sina Weibo via Data Clustering

Extracting Unknown Words from Sina Weibo via Data Clustering

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IEEE International Conference on Communications

作者： Kai Lei WeiYang Zhang Kai Zhang Kuai Xu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronics and Computer Engineering (SECE) Peking University School of Mathematical and Natural Sciences Arizona State University

ISBN: (纸本)9781467364300

Sina Weibo, a Twitter-like microblogging site attracting over 240 million monthly active users to tweet, retweet, and comment, has rapidly become one of the most popular social media sites in China. As many users create new and innovative words on their tweets and comments, it is necessary to extract these emerging words, which do not exist in today's Chinese vocabulary or dictionary. Towards this end, this paper proposes a novel method based on data clustering of Weibo users and tweets for extracting unknown words from Weibo tweets and comments. Specifically, relying on the similarity of the users who post the tweets, we apply a hierarchical clustering to divide Weibo data into distinct groups, e.g., sports, news stories, movies, before extraction. Comparing with the method of unclustered Weibo data, our experimental results have successfully demonstrated the benefits of the proposed data clustering scheme for improving the recall and accuracy of extracting unknown Chinese words from tweets and comments.

关键词： Entropy Context Data mining Clustering algorithms Social network services Clustering methods Couplings

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Profiling the Followers of the Most Influential and Verified Users on Sina Weibo

Profiling the Followers of the Most Influential and Verified...

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IEEE International Conference on Communications

作者： Huiyu Wang Kai Lei Kuai Xu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronics and Computer Engineering (SECE) Peking University School of Mathematical and Natural Sciences Arizona State University

ISBN: (纸本)9781467364300

The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and news-papers. The most influential and verified users, also called big-V accounts on Sina Weibo often attract million of followers and fans, creating massive "celebrity-centric" social networks on the social media, which play a key role in disseminating breaking news, latest events, and controversial opinions on social issues. Given the importance of these accounts, it is very crucial to understand social networks and user influence of these accounts and profile their followers' behaviors. Towards this end, this paper monitors a selected group of influential users on Sina Weibo and collects their tweet streams as well as retweeting and commenting activities on these tweets from their followers. Our analysis on tweet data streams from Sina Weibo reveals when and what the followers comment on the tweets of these influential users, and discovers different temporal patterns and word diversity in the comments. Based on the insight gained from follower characteristics, we further develop simple and intuitive algorithms for classifying the followers into spammers and normal fans. Our experimental results demonstrate that the proposed algorithms are able to achieve an average accuracy of 95.20% in detecting spammers from the followers who have commented on the tweets of these influential accounts.

关键词： Classification algorithms Fans Social network services Media Feature extraction Standards Entropy

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The Characterization of Multi-source Multicast with Network Coding

The Characterization of Multi-source Multicast with Network ...

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2012 IEEE 14th International Conference on Communication technology

作者： Hanxu Hou Hui Li Shenzhen Key Lab of Cloud Computing Technology & Application Shenzhen Graduate SchoolPeking University Shenzhen Eng.Lab of Converged Network Tech.Shenzhen Graduate SchoolPeking University

ISBN: (纸本)9781467321006

The capacity region of the single-source multicast network coding has an explicit Max-flow Min-cut *** for multi-source multicast networks the problem is still *** this paper,we mainly discuss the case of independent encoding multi-source multicast network using inter-session network *** propose a multi-source independent encoding theorem for this problem which characterizes the admissible coding rate region of independent encoding for relevant multiple *** theorem is proposed by the paper according to the strongly typical sequences and random *** also point out the connections between our theorem and the general multi- source network coding problem,of which the results are computable and can be used to design the multi-source network coding algorithm.

关键词： network coding multi-source multicast correlated data computable

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Relation-Guided Representation Learning

arXiv

引用

arXiv 2020年

作者： Kang, Zhao Lu, Xiao Liang, Jian Bai, Kun Xu, Zenglin School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Cloud and Smart Industries Group Tencent Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Center for Artificial Intelligence Peng Cheng Lab Shenzhen China

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most dominant structures which are able to reconstruct the data from a latent space and neglect rich latent structural information. In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning. Different from previous work, our framework well preserves the relations between samples. Since the prediction of pairwise relations themselves is a fundamental problem, our model adaptively learns them from data. This provides much flexibility to encode real data manifold. The important role of relation and representation learning is evaluated on the clustering task. Extensive experiments on benchmark data sets demonstrate the superiority of our approach. By seeking to embed samples into subspace, we further show that our method can address the large-scale and out-of-sample problem. Our source code is publicly available at: https://***/nbShawnLu/RGRL. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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