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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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Machine learning-based orchestration of containers: A taxonomy and future directions

arXiv

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arXiv 2021年

作者： Zhong, Zhiheng Xu, Minxian Rodriguez, Maria Alejandra Xu, Chengzhong Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Grattan Street Parkville MelbourneVICVIC 3010 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University Town 1068 Xueyuan Avenue Guangdong Shenzhen518055 China State Key Lab of IOTSC University of Macau Avenida da Universidade Taipa 999078 China

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted. © 2021, CC0.

关键词： Containers

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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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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on Intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information Systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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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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Elastic and Efficient Virtual Network Provisioning for cloud-Based Multi-tier Applications

Elastic and Efficient Virtual Network Provisioning for Cloud...

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International Conference on Parallel Processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity cloud computing Algorithm design and analysis Load modeling Resource management

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Structure of campus network based on reconfigurable architecture

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Tongxin Xuebao/Journal on Communications 2013年第SUPPL.2期34卷 107-110页

作者： Yuan, Hu-Sheng Feng, Hua Li, Hui Zhong, Zhi-Bin Pan, Kai Network and Information Center of Shenzhen University Town Shenzhen 518055 China Peking University Shenzhen Graduate School Shenzhen 518055 China Shenzhen Key Lab. of Cloud Computing Technology and Application Shenzhen 518055 China Shenzhen Engineering Lab. of Converged Network Technology Shenzhen 518055 China Huaqiang Vocational Technical School Shenzhen 518000 China

Because of the petrifaction of the existing network, it is difficult to achieve the controllability, adaptability and expansibility in current structure. Simultaneously, in order to have better campus network services in teaching, researching and studying, a new structure based on reconfigurable architecture for campus network was designed based on service set handling capacity of various kinds of business in reconfigurable network as the central idea. After the evolution from tradition to flat structure, a more condensed structure was proposed by adopting virtualization technology on switches and routers. It has the advantages of high performance, high reliability, manageability and augmentability.

关键词： Virtualization

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Motion vehicles tracking in intelligent transportation visual surveillance systems

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2012年第SUPPL.2期36卷 216-222页

作者： Wu, Lin-Feng Xiong, Gang Qian, Shou-Yi Xie, Li Zhejiang Provincial Key Lab. of Information Network Technology Department of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Cloud Computing Industrial Technology Innovation and Incubation Center Dongguan Research Institute of CASIA Chinese Academy of Sciences Dongguan 523808 China Wenzhou Research Institute of Electronics and Information Wenzhou 325003 China

In terms of the difficulty of vehicle tracking in complex environment of the visual surveillance system, an object tracking algorithm is proposed for the applications in practical visual surveillance systems for intelligent traffic. A block-based Gaussian mixture background modeling method for object detection is presented to reduce the computational complexity of moving vehicle object abstraction. An adaptive tracking algorithm fused with color features and texture features is described to better adapt the traffic scene variation. The experimental results show that the proposed algorithm can effectively deal with the complex urban traffic conditions and the tracking performance is better than the conventional particle filter method and single feature based non-adaptive object tracking method.

关键词： Tracking (position)

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Wenwang: Toward Effectively Generating Code Beyond Standalone Functions via Generative Pre-trained Models

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Hao Yu Bo Shen Jiaxin Zhang Shaoxin Lin Lin Li Guangtai Liang Ying Li Qianxiang Wang Tao Xie The Hong Kong University of Science and Technology China Huawei Cloud Computing Technologies Co. Ltd. China Huawei Noah’s Ark Lab China National Research Center of Software Engineering Peking University China Key Lab of HCST (PKU) MOE SCS Peking University China

Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. After being pre-trained on a large-scale corpus of code, a model is further fine-tuned with datasets specifically for the target downstream task, e.g., generating code from natural language description. The target code being generated can be classified into two types: a standalone function, i.e., a function that invokes or accesses only built-in functions and standard libraries, and a non-standalone function, i.e., a function that invokes or accesses user-defined functions or third-party *** effectively generate code especially non-standalone functions (largely ignored by existing work), in this article, we present Wenwang, an approach to improving the capability of a pre-trained model on generating code beyond standalone functions. Wenwang consists of two components: a fine-tuning dataset named WenwangData and a fine-tuned model named WenwangCoder. Compared with existing fine-tuning datasets, WenwangData additionally covers non-standalone functions. Besides the docstring and code snippet for a function, WenwangData also includes its contextual information collected via program analysis. Based on PanGu-Coder, we produce WenwangCoder by fine-tuning PanGu-Coder on WenwangData with our context-aware fine-tuning technique so that the contextual information can be fully leveraged during code generation. On CoderEval and HumanEval, WenwangCoder outperforms three state-of-the-art models with similar parameter sizes (at the scale of around 300M), namely CodeGen, PanGu-Coder, and PanGu-FT. Although WenwangCoder does not outperform ChatGPT on HumanEval, WenwangCoder with smaller model parameter sizes can achieve similar effects to ChatGPT on CoderEval. Our experimental results also shed light on a number of promising optimization directions based on

关键词： Code Generation Pre-trained Language Models Fine-tuning Dataset

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面向微博的情感影响最大化模型

面向微博的情感影响最大化模型

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第29届中国数据库学术会议

作者： OU Gaoyan 欧高炎 CHEN Wei 陈薇 WANG Tengjiao 王腾蛟 LEI Kai 雷凯 YANG Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 Chin 高可信软件技术教育部重点实验室北京100871 北京大学信息科学技术学院北京100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School Peki 北京大学深圳研究生院深圳市云计算关键技术与应用重点实验室广东深圳518055

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型—... 详细信息

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型——情感影响分配模型(SID),证明了SID模型下的情感影响最大化问题是一个NP难问题,给出了一个具有精度保证的贪心算法。在真实的微博数据上的实验结果表明,SID模型能够有效地找出情感影响最大化的节点集,同时具有很高的扩展性。

关键词：社交网络情感分析信息传播模型分配算法

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