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Intention oriented itinerary recommendation by bridging physical trajectories and online social networks

Ruan Jian Xue Bao/Journal of Software 2012年第SUPPL.期23卷 159-168页

作者： Meng, Xiang-Xu Wang, Xiao-Dong Zhou, Xing-Ming National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha 410073 China

Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendation framework to help the user transfer from intentions to itinerary planning, which join physical trajectories and information of location based social networks. The main contributions are: (1) Build the category based activity scheduling model;(2) Design and implement the category tree based POI (point or interest) query strategy and algorithm;(3) Propose the Voronoi graph based GPS trajectory analysis method to build traffic information networks;(4) Combine social networks with traffic information networks to implement category based recommendation by ant colony algorithm. The study conducts experiments on datasets from FourSquare and GeoLife project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.

关键词： Trajectories

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Big Data-aided Study of the Physical Process of Volume Ignition 6

Big Data-aided Study of the Physical Process of Volume Ignit...

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2022 6th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2022

作者： Liu, Pan Zhou, Yinzhao Xiong, Wenbin Liu, Chencheng Liu, Jie Liu, Gongjie Tang, Zhangchun Gao, Qiang Tianfu Innovation Energy Establishment Chengdu610000 China Nuclear and Radiation Safety Center MEE Beijing100000 China Central South University of Forestry and Technology Changsha410000 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Beijng PARATERA Technology Corp. Ltd Beijing100000 China University of Electronic Science and Technology of China Chengdu610000 China

Volume ignition is a method of igniting a fuel as a whole by simultaneously achieving ignition conditions throughout the fuel zone. The basic criterion for ignition is that the thermonuclear energy is greater than the energy leakage at the fuel boundary, resulting in self-sustaining heating and deep combustion. Deuterium-tritium fuels are wrapped in medium to high Z media to reduce radiative leakage and achieve lower-temperature holistic ignition and non-equilibrium combustion, ultimately allowing the fuel to achieve high combustion efficiency. Volume ignition is the use of energy balance relations under the local thermodynamic equilibrium approximation to establish the energy balance equation for thermonuclear systems, and the system ignition threshold is obtained by solving this equation. By understanding the physical process, we believe that the non-equilibrium process is universal to the volume ignition process. Changes in external factors (density, boundaries, albedo, etc.) at the moment of ignition can have a significant impact on the development direction of the system, and an ignition system with a large surface density can nevertheless withstand a large amount of reverse work and continue to burn. The design of the ignition target tries to avoid these factors through margin design, but conversely, the rational use of these laws can further improve the design margin of the capsule. With the aid of big data, the volume ignition method is easier to calculate and has a shorter iteration time. The traditional way is to propose a model, set the material, and then perform the calculation while using big data can set any model and material for calculation. In this paper, a simple comparison will be made to find out that the efficiency of the physical design of a volume ignition target will be effectively improved with the aid of big data. Volume ignition targets can be used not only in Z-pinch-driven systems but also for laser-driven volume ignition. © Published un

关键词： Ignition

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Preface

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2003年 2834卷 VI页

作者： Zhou, Xingming National Laboratory for Parallel and Distributed Processing ChangshaHunan China

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F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

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An effective control scheme to ensure real-time causal order in large-scale distributed interactive simulations

An effective control scheme to ensure real-time causal order...

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International Conference on Communication Software and Networks, ICCSN

作者： Hangjun Zhou Sha Fu Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China Department of Information Management Hunan University of Finance and Economics Changsha Hunan China

When a large-scale distributed interactive simulation system is running on WAN, the sites usually disperse over a wide area in geography, which results in the simulation clock of each site is hardly to be accurately synchronized with that of other sites. The asynchronous clocks and large transmission latency on WAN bring on a problem for the large-scale simulations to preserve the real-time causal order delivery of received events at each site. In this article, we analyze the indirect way to compare the values of asynchronous simulation clocks at first, and then propose a novel scheme which can select the reconstructible causal control information for each message so as to ensure the causal ordering of events in real time. Experiments demonstrate that the scheme can weaken the effect of network latency, reduce the overhead of the transmission amount of control information and improve the causal order consistency in asynchronous distributed simulations.

关键词： Clocks Educational institutions

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GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

arXiv

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

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A Runtime Verification Based Trace-Oriented Monitoring Framework for Cloud Systems

A Runtime Verification Based Trace-Oriented Monitoring Frame...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Jingwen Zhou Zhenbang Chen Ji Wang Zibin Zheng Wei Dong College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Cloud computing provides a new paradigm for resource utilization and sharing. However, the reliability problems, like system failures, often happen in cloud systems and bring enormous loss. Trace-oriented monitoring is an important runtime method to improve the reliability of cloud systems. In this paper, we propose to bring runtime verification into trace-oriented monitoring, to facilitate the specification of monitoring requirements and to improve the efficiency of monitoring cloud systems. Based on a data set collected from a cloud storage system in a real environment, we validate our approach by monitoring the critical properties of the storage system. The preliminary experimental results indicate the promise of our approach.

关键词： Monitoring Runtime Cloud computing Educational institutions Reliability Google Computers

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On implementation of a scalable wallet-size cluster computing system for multimedia applications

On implementation of a scalable wallet-size cluster computin...

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5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Lee, Liang-Teh Li, Kuan-Ching Yang, Chao-Tung Tseng, Chia-Ying Liu, Kang-Yuan Hung, Chih-Hung Dept. of Computer Science and Engineering Tatung University Taipei104 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung433 Taiwan High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Networks and Multimedia Institute Institute for Information Industry Taipei106 Taiwan

ISBN: (纸本)9783540239857

In order to obtain a low-cost, low-power consumption and small sized digital home processing center, especially for multimedia applications, we propose a scalable wallet-size cluster computing system in this paper. Such system uses low-cost, low-power consumption and small sized microprocessors as computing nodes to form a cluster system. The main reason why we are interested to investigate and build such system is to increase the computing power and to accelerate the H.264 video processing. We analyzed the H.264 decoder scheme and distributed the most heavy-load computations in the decoder scheme to each computing node (or microprocessor) of the cluster system for the processing of signals, in parallel. To run multimedia applications in this cluster system, an efficient real-time video processing is implemented. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Computing power

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A consistency algorithm for causal and totally ordered event delivery in DVE

A consistency algorithm for causal and totally ordered event...

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International Conference on Computer Science and Information Technology (CSIT)

作者： Hangjun Zhou Wei Zhang Yuxing Peng Sikun Li Department of Information Management Hunan College of Finance and Economics Changsha Hunan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

How to preserve causal and totally ordered event delivery is an important issue in real-time serverless DVE(distributed Virtual Environment). However, most of the related works are designed to maintain causal order merely or time stamped order with intensive computation and bandwidth overhead. In this paper, we proposed a novel distributed algorithm to maintain the before-and-after relationship between events, both causal and concurrent, of DVE at each individual node. Several simulation experiments are carried out to evaluate the performance of our algorithm and the results demonstrate that the algorithm is effective in preserving causal and totally ordered event delivery and more efficient than the previous algorithms.

关键词： Artificial neural networks Silicon Educational institutions

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DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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