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8th International Conference on Advanced Data Mining and Applications, ADMA 2012

作者： Li, Xiang Wang, Huaimin Yin, Gang Wang, Tao Yang, Cheng Yu, Yue Tang, Dengqing National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China College of Mechatronics Engineering and Automation National University of Defense Technology Changsha China

ISBN: (纸本)9783642355264

By amassing 'wisdom of the crowd', social tagging systems draw more and more academic attention in interpreting Internet folk knowledge. In order to uncover their hidden semantics, several researches have attempted to induce an ontology-like taxonomy from tags. As far as we know, these methods all need to compute an overall or relative generality for each tag, which is difficult and error-prone. In this paper, we propose an agglomerative hierarchical clustering framework which relies only on how similar every two tags are. We enhance our framework by integrating it with a topic model to capture thematic correlations among tags. By experimenting on a designated online tagging system, we show that our method can disclose new semantic structures that supplement the output of previous approaches. Finally, we demonstrate the effectiveness of our method with quantitative evaluations. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Taxonomies

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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MCHITS: Monte carlo based method for hyperlink induced topic search on networks

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Journal of Networks 2013年第10期8卷 2376-2382页

作者： Jin, Zhaoyan Shi, Dianxi Wu, Quanyuan Fan, Hua National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

Hyperlink Induced Topic Search (HITS) is the most authoritative and most widely used personalized ranking algorithm on networks. The HITS algorithm ranks nodes on networks according to power iteration, and has high complexity of computation. This paper models the HITS algorithm with the Monte Carlo method, and proposes Monte Carlo based algorithms for the HITS computation. Theoretical analysis and experiments show that the Monte Carlo based approximate computing of the HITS ranking reduces computing resources a lot while keeping higher accuracy, and is significantly better than related works. © 2013 ACADEMY PUBLISHER.

关键词： Monte Carlo methods

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CNA: A Dataset for Parsing Discourse Structure on Chinese News Articles

CNA: A Dataset for Parsing Discourse Structure on Chinese Ne...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhenliang Guo Zhen Huang Yong Dou Xiubin Yu Sijie Wang Zhongwu Chen Xinxin Su Xiaohang Liu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

Discourse structure analysis has shown to be useful for many artificial intelligence (AI) tasks such as text sum-marization and text categorization. However, for the Chinese news domain, the discourse structure analysis system is still immature due to the limitation of the lack of expert-annotated datasets. In this paper, we present CNA, a Chinese news corpus containing 1155 news articles annotated by human experts, which covers four domains and four news media sources. Next, we implement several text classification methods as baselines. Experimental results demonstrate that document-level method can achieve a better performance, and we further propose a document-level neural network model with multiple sentence features which achieves the state-of-the-art performance. In the end, we analyze the content type distribution of each sentence in CNA and the prediction errors of our model that occurred on the test set. The codes and dataset will be open-sourced at https://***/gzl98/Chinese_Discourse_Profiling.

关键词： Analytical models Codes Text categorization Neural networks Predictive models Media Artificial intelligence

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A simple model for the energy-efficient optimal real-time multiprocessor scheduling

A simple model for the energy-efficient optimal real-time mu...

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2012 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2012

作者： Wu, Fei Jin, Shiyao Wang, Yuming College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai 201620 China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781467300865

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, it becomes an urgent problem of real-time energy-efficient scheduling in multiprocessor systems to reduce system energy consumption while meeting real-time constraints. In this paper, we introduce a unifying energy-efficient real-time scheduling theory with the Deadline Partitioning Fair Dynamic Voltage Frequency Scaling (DPF-DVFS) policy for multiprocessor systems. The proposed model can offer a unifying, extendable, on-line and simple method to realize energy-efficient real-time scheduling with low scheduling overhead. We then present an optimal energy-efficient scheduling algorithm DPF-DVFS-WRAP for periodic tasks. © 2012 IEEE.

关键词： Real time systems

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Offline Imitation Learning Using Reward-free Exploratory Data 5

Offline Imitation Learning Using Reward-free Exploratory Dat...

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5th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2022

作者： Wang, Hao Feng, Dawei Ding, Bo Li, Wei College of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Independent Researcher GuangZhou China

ISBN: (纸本)9781450398343

Offline imitative learning(OIL) is often used to solve complex continuous decision-making tasks. For these tasks such as robot control, automatic driving and etc., it is either difficult to design an effective reward for learning or very expensive and time-consuming for agents to collect data interactively with the environment. However, the data used in previous OIL methods are all gathered by reinforcement learning algorithms guided by task-specific rewards, which is not a true reward-free premise and still suffers from the problem of designing an effective reward function in real tasks. To this end, we propose the reward-free exploratory data driven offline imitation learning (ExDOIL) framework. ExDOIL first trains an unsupervised reinforcement learning agent by interacting with the environment, and collects enough unsupervised exploration data during training;Then, a task independent yet simple and efficient reward function is used to relabel the collected data;Finally, an agent is trained to imitate the expert to complete the task through a conventional RL algorithm such as TD3. Extensive experiments on continuous control tasks demonstrate that the proposed framework can achieve better imitation performance(28% higher episode returns on average) comparing with previous SOTA method(ORIL) without any task-specific rewards. © 2022 ACM.

关键词： Reinforcement learning

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Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPUs 22nd

Optimizing Yinyang K-Means Algorithm on ARMv8 Many-Core CPU...

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22nd International Conference on Algorithms and Architectures for parallel processing, ICA3PP 2022

作者： Zhou, Tianyang Wang, Qinglin Yin, Shangfei Hao, Ruochen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China

ISBN: (纸本)9783031226762

K-Means algorithm is one of the most common clustering algorithms widely applied in various data analysis applications. Yinyang K-Means algorithm is a popular enhanced K-Means algorithm that avoids most unnecessary calculations using triangle inequality. However, Yinyang K-Means algorithm is time-consuming when the problem size is large. Due to the influence of performance and energy-efficiency, ARM CPUs have appeared in high performance computing. Therefore, it is very interesting to accelerate Yinyang K-Means algorithm on ARM CPUs. In this paper, we propose an efficient parallel implementation of Yinyang K-Means algorithm on ARMv8 many-core CPUs by means of vectorization, NUMA affinity memory optimization and data layout optimization. The experiment on two ARMv8 many-core CPUs has shown that our implementation can achieve up to 5.6 times faster than the open-source multi-threaded one of Yinyang K-Means algorithm. To the best of our knowledge, this is the first work that studies the optimization of Yinyang K-Means algorithms on ARMv8 CPUs. © 2023, Springer Nature Switzerland AG.

关键词： K-means clustering

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Cloudroid: A cloud framework for transparent and QoS-aware robotic computation outsourcing

arXiv

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arXiv 2017年

作者： Hu, Ben Wang, Huaimin Zhang, Pengfei Ding, Bo Che, Huimin National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China

Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing the computation to the cloud. However, exploiting the potential of cloud resources in robotic software is difficult, because it involves complex code modification and extensive (re)configuration procedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. In this paper, we propose a transparent and QoS-aware software framework called Cloudroid for cloud robotic applications. This framework supports direct deployment of existing robotic software packages to the cloud, transparently transforming them into Internet-accessible cloud services. And with the automatically generated service stubs, robotic applications can outsource their computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintain the specific QoS property such as request response time, even in a highly dynamic and resource-competitive environment. We evaluated Cloudroid based on a group of typical robotic scenarios and a set of software packages widely adopted in real-world robot practices. Results show that robots capability can be enhanced significantly without code modification and specific QoS objectives can be guaranteed. In certain tasks, the"cloud + robot" setup shows improved performance in orders of magnitude compared with the robot native setup. Copyright © 2017, The Authors. All rights reserved.

关键词： Quality of service

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Jammer Localization for Wireless Sensor Networks

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Chinese Journal of Electronics 2023年第4期20卷 735-738页

作者： SUN Yanqiang WANG Xiaodong ZHOU Xingming National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circle-covering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.

关键词： Location awareness Wireless communication Geometry Wireless sensor networks Sensitivity Simulation Jamming

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Investor: A Light-Weight Speculation Scheme for the Open Internet

Investor: A Light-Weight Speculation Scheme for the Open Int...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Huiba Li Shengyun Liu Yuxing Peng Xicheng Lu National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Investor is a novel speculation scheme that targets at the open Internet. It makes use of standard HTTP and speculation by convention to fit into this environment. Investor is a runtime technique that doesn't modify the languages, compilers or binary formats of the applications, so it is compatible well with existing programs or libraries. Investor can overcome some form of control dependency and data dependency. Preliminary experiments show that Investor can significantly improve the overall performance of the applications.

关键词： Speculation, distributed Systems, Internet, HTTP

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