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检索条件"机构=Key Lab. of Cloud Computing Technology"

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A general co/decoder of network coding in HDL

A general co/decoder of network coding in HDL

引用

IEEE International Symposium on Network Coding

作者： Zhang, Minglong Li, Hui Chen, Fuxing Hou, Hanxu An, Huiyao Wang, Wei Huang, Jiaqing Shenzhen Key Lab. of Cloud Computing Tech. and Application Shenzhen Graduate School Peking Univ. 518055 China Dept. of Electronic and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781612841403

This paper presents a practical and general coder and decoder of network coding (NC) with HDL (Hardware Description Language) logic for wire-speed nodes in multisource multicast networks. The NC coders apply random linear network coding (RLNC) and the decoders recover the original packets by Cramer's rule. All these mathematical operations are carried out in the Galois Field (2 8). The structures and algorithms of NC coder and decoder were designed in detail and implemented in HDL with NetFPGA boards provided by Stanford University. Comparing with traditional stored-and-forward mechanism, network emulations showed that networks with wire-speed NC coder and decoder nodes could achieve the capacity bound of max-flow min-cut theorem in case of bottlenecks, and the end-to-end delay was guaranteed on a small constant. © 2011 IEEE.

关键词： Network coding

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Energy-efficient network routing with discrete cost functions

Energy-efficient network routing with discrete cost function...

引用

9th Annual Conference on Theory and Applications of Models of Computation, TAMC 2012

作者： Wang, Lin Fernández Anta, Antonio Zhang, Fa Hou, Chenying Liu, Zhiyong Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Institute IMDEA Networks Spain China State Key Lab. for Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Graduate University of Chinese Academy of Sciences China

ISBN: (纸本)9783642299513

Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization network routing problem with discrete cost functions. We formulate this problem as an integer program, which is proved to be NP-hard. Then a constant approximation algorithm is developed. In our proposed method, we first transform the program into a continuous-cost network routing problem, and then we approximate the optimal solution by a two-step rounding process. We show by analysis that, for uniform demands, our method provides a constant approximation for the uniform network routing problem with discrete costs. A bicriteria network routing problem is also developed so that a trade-off can be made between energy consumption and network delay. Analytical results for this latter model are also presented. © 2012 Springer-Verlag.

关键词： Network routing

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A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science 20

A Multi-Dimensional Assessment Model and Its Application in ...

引用

21st Annual Conference of the Special Interest Group in Information technology Education, SIGITE 2020

作者： Luo, Jiwen Lu, Feng Wang, Tao Key Lab. of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450370455

Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in large-scale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms. © 2020 ACM.

关键词： Students

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A real-time attitude recognition by eye-tracking 11

A real-time attitude recognition by eye-tracking

引用

3rd International Conference on Internet Multimedia computing and Service, ICIMCS 2011

作者： Lv, Yanpeng Wang, Shangfei Shen, Peijia Key Lab. of Computing and Communicating Software of Anhui Province School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781450309189

Attitude, which is the outward manifestation of human psychology information, is inevitable in everyday situation. Attitude recognition by artificial intelligence has become a hotspot and produced promising results in recent years. In this paper, we propose an attitude recognition method based on eye-tracking. We first improve the eye location algorithm in OpenCV and assume that the trajectory of eyes-center is the same with that of head. Then using eye location method, the center of eyes is calculated. By analysis of displacement of eyes' centers in each two successive frames, sub-result is obtained. The final result is determined by voting algorithm. Experimental results indicate that this method could recognize positive and negative attitudes in real time. We applied our method to "KeJia", an intelligent service robot and it worked well. © 2011 ACM.

关键词： Eye tracking

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Context-adaptive matrix factorization for multi-context recommendation 15

Context-adaptive matrix factorization for multi-context reco...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Man, Tong Shen, Huawei Huang, Junming Cheng, Xueqi CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China CompleX Lab. Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781450337946

Data sparsity is a long-standing challenge for recommender systems based on collab.rative filtering. A promising solution for this problem is multi-context recommendation, i.e., leveraging users' explicit or implicit feedback from multiple contexts. In multi-context recommendation, various types of interactions between entities (users and items) are combined to alleviate data sparsity of a single context in a collective manner. Two issues are crucial for multi-context recommendation: (1) How to differentiate context-specific factors from entity-intrinsic factors shared across contexts? (2) How to capture the salient phenomenon that some entities are insensitive to contexts while others are remarkably context-dependent? Previous methods either do not consider context-specific factors, or assume that a context imposes equal influence on different entities, limiting their capability of combating data sparsity problem by taking full advantage of multiple contexts. In this paper, we propose a context-adaptive matrix factorization method for multi-context recommendation by simultaneously modeling context-specific factors and entity-intrinsic factors in a unified model. We learn an entity-intrinsic latent factor for every entity, and a context-specific latent factor for every entity in each context. Meanwhile, using a context-entity mixture parameter matrix we explicitly model the extent to which each context imposes influence on each entity. Experiments on two real scenarios demonstrate that our method consistently outperforms previous multi-context recommendation methods on all different sparsity levels. Such a consistent performance promotion forms the unique superiority of our method, enabling it to be a reliable model for multi-context recommendation. © 2015 ACM.

关键词： Collab.rative filtering

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SleepMG: Multimodal Generalizable Sleep Staging with Inter-modal Balance of Classification and Domain Discrimination 24

SleepMG: Multimodal Generalizable Sleep Staging with Inter-m...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ma, Shuo Zhang, Yingwei Zhang, Qiqi Chen, Yiqiang Wang, Haoran Jia, Ziyu Beijing Key Lab. of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shandong Normal University Jinan China Beijing Key Lab. of Mobile Computing and Pervasive Device Institute of Computing Technology Peng Cheng Lab. Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

Sleep staging is crucial for sleep tracking and health assessment. Polysomnography (PSG), containing multiple modalities such as electroencephalography, electrooculography, electromyography, and electrocardiography, is the fundamental means of sleep staging. However, due to performance differences in both classification and domain discrimination across modalities in PSG, existing domain generalization methods face a dilemma of modal imbalance. To balance inter-modal differences and achieve highly accurate cross-domain sleep staging, we propose SleepMG, a Multimodal Generalizable Sleep staging method. SleepMG assesses the classification and domain discrimination performances of each modality and further defines the modal performance metrics by calculating the variance between the performance score and the average performance of each modality. Guided by these metrics, the gradients of the classifier and domain discriminator are adaptively adjusted, placing greater emphasis on poorly-balanced modalities while reducing emphasis on well-balanced modalities. Experimental results on public sleep staging datasets demonstrate that SleepMG outperforms state-of-the-art sleep staging methods, effectively balancing multiple modalities as evidenced by the visual experiment of modal imbalance degree. © 2024 Owner/Author.

关键词： Electroencephalography

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Improving rating estimation in recommender using demographic data and expert opinions

Improving rating estimation in recommender using demographic...

引用

2011 IEEE 2nd International Conference on Software Engineering and Service Science, ICSESS 2011

作者： Yun, Long Yang, Yan Wang, Jing Zhu, Ge Dept. of Computer Science and Technology Heilongjiang University Key Lab. of Database and Parallel Computing Heilongjiang Province Harbin China

ISBN: (纸本)9781424496969

Memory-based collab.rative filtering algorithms have been widely adopted in many popular recommender systems, however the rating data are very sparse, which affects prediction accuracy greatly. To solve this problem, we use expert opinions to improve prediction accuracy. Firstly, we propose a novel similarity measure in order to highlight users' background. Then, combining users' ratings with expert opinions, the prediction get a right balance in both expert professional opinions and similar users. Finally, since SVD-based collab.rative filtering algorithms shows good performance on the prevention of noise, we use it to smooth the prediction. Experiments of MovieLens have shown that our proposed method improves recommendation quality obviously. © 2011 IEEE.

关键词： Collab.rative filtering

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A new evidence fusion approach for decision of proactive service

A new evidence fusion approach for decision of proactive ser...

引用

2010 2nd International Conference on Computational Intelligence and Natural computing, CINC 2010

作者： Zhang, De-Gan Li, Chao Li, Wei-Wei Chen, Xu-Yan School of Computer Science and Technology Tianjin University of Technology 300191 China Tianjin Key Lab. of Intelligent Computing and Novel Software Technology Tianjin 300191 China

ISBN: (纸本)9781424477036

Multi-source heterogeneous information fusion is an extremely challenging problem because of the complex and highly irregular nature of environment. We consider decision fusion problem in the presence of m-dependent evidences in this paper. A new evidence fusion approach for decision of proactive service has introduced, which is extending of Class D-S Evidence method. We applied this approach in our active space project for seamless nomadic service of pervasive computing paradigm, good results have been attained. The validity of approach of this paper has been tested. ©2010 IEEE.

关键词： Ubiquitous computing

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PIModel: A pragmatic ITS model based on instructional automata theory

PIModel: A pragmatic ITS model based on instructional automa...

引用

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004

作者： Si, Jinxin Yue, Xiaoli Cao, Cungen Sui, Yuefei Key Lab. of Intelligent Info. Proc. Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

It is a vital and challenging issue in AI community to get the "Right Information" to the "Right People" in the "Right Language" in the "Right Timeframe" in the "Right Leve... 详细信息

ISBN: (纸本)1577352017

It is a vital and challenging issue in AI community to get the "Right Information" to the "Right People" in the "Right Language" in the "Right Timeframe" in the "Right Level of Granularity". More and more researchers recognized that a well-designed instructional plan in intelligent tutoring systems will open up new possibilities of genuinely intelligent knowledge delivery for future education society. This paper proposes a novel three-layer student-state model and discusses the core elements in the architecture of a practical ITS model based on the theory of instructional automata The main advantages of instruction automata theory lie in that it can not only generate, regulate, update and implement instructional plans for individualized learner in the efficient and effective way, but also provide a uniform and extensible domain-independent environment for ITS designers and engineers.

关键词： Automata theory

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Web Service Recommendation Based on Knowledge Graph Enhanced

Web Service Recommendation Based on Knowledge Graph Enhanced

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2023 International Conference on cloud computing, Performance computing, and Deep Learning, CCPCDL 2023

作者： Huang, Xinyi Zhao, Gansen School of Computer Science South China Normal University Guang Zhou China Guangzhou Key Lab on Cloud Computing Security and Assessment Technology Guang Zhou China

ISBN: (纸本)9781510666474

Mashup is an information system development project based on Web services. It can build new business processes by mixing data, resources and APIs from different sites or applications, and meet new user needs. However, in recent years, the number of Web API is increasing quickly, and it is difficult for developers and users to find suitable Mashups, which influences the development and functional efficiency of information systems. Therefore, designing an accurate and efficient Web service recommendation method to improve the functional characteristics of information systems has become a research hotspot. At present, many algorithms, like matrix factorization and collab.rative filtering, often have serious cold start and sparse problems. In order to alleviate the above problems and capture as much information as possible, we proposed a multi-modal knowledge graph enhancement recommendation method (MKE), which combines structured information and unstructured information of Mashup and Web API. Firstly, the feature information is mined from the text description of API and Mashup to assist the construction of the service recommendation system. Then, the knowledge graph is constructed by using the related attributes and relationships of Mashup and API, as well as the entity and relationship of Mashup and API, and the text features. The low-dimensional embedded Trans model provides Top-K recommendation results for the specified Mashup description. Finally, experiments are carried out on the latest ProgrammableWeb dataset, and the experimental results show that the recommendation performance of this method is better than that of RippleNet, NCF, CKE, KGCN. © 2023 SPIE.

关键词： Knowledge graph

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