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IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Zhenpeng Xu Xinhai Chen Lihua Chi Jie Liu Chunye Gong Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170060

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination.

关键词： Convolution Computational fluid dynamics Convolutional neural networks Jacobian matrices Artificial intelligence distributed processing Software engineering

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Adaptive admission control of multimedia traffic in high-speed networks

Adaptive admission control of multimedia traffic in high-spe...

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IEEE International Symposium on Intelligent Control (ISIC)

作者： S. Jagannathan A. Tohmaz A. Chronopoulos H.G. Cheung Embedded Systems and Networking Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Parallel and Distributed Systems Lab Department of Computer Science University of Texas-San Antonio TX USA

This paper proposes a novel real-time adaptive admission control (AAC) scheme with a desired quality of service (QoS) guarantee and high network utilization in high speed networks. The QoS is given in terms of service delay, which is defined as the time it takes for a source to get admitted into the network after it initiates its intended request, packet/cell losses, and transmission delay (time taken to complete transmission from its initiation). AAC uses the following information - the available capacity from a novel adaptive bandwidth estimation scheme, a congestion indicator derived from a congestion controller, peak cell rate estimate from new sources, along with the desired QoS metrics, and outputs an 'admit' or 'reject' decision signal to the new sources while guaranteeing QoS and network utilization. Simulation results are presented by streaming ON/OFF and video data into the network. Results show that the proposed AAC admits significantly more traffic compared to other available admission control schemes thereby guaranteeing high network utilization while maintaining the desired QoS.

关键词： Programmable control Adaptive control Admission control Communication system traffic control High-speed networks Quality of service Delay effects Propagation losses Bandwidth Streaming media

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Flexible DOR routing for virtualization of multicore chips

Flexible DOR routing for virtualization of multicore chips

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IEEE International Symposium on System-on-Chip

作者： Tor Skeie Frank Olaf Sem-Jacobsen Samuel Rodrigo José Flich Davide Bertozzi Simone Medardoni Simula Research Laboratory Networking and Distributed Systems Lysaker Norway Parallel Architectures Group Technical University of Valencia Valencia Spain ENDIF Department of Engineering University of Ferrara Ferrara Italy

The expected increase in number of cores on a single chip leads to the necessity of high-performance on chip interconnects (NoC). Furthermore, in order to fully utilize the abundance of cores, the chip is expected to support a number of applications running on the chip simultaneously. It is therefore necessary to partition the chip to support numerous applications without any risk of interference between them. The success of this depends on the flexibility of the underlying routing algorithm. This paper presents a flexible routing algorithm based on dimension ordered routing, which supports a large variety of irregular (2-D and 3-D) mesh topologies. The algorithm provides high efficiency at very low additional complexity, as is confirmed by experimental results.

关键词： Routing Multicore processing Network-on-a-chip Network topology Partitioning algorithms Energy consumption Interference Delay Virtual machining parallel architectures

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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International Conference on Computer and Communications (ICCC)

作者： Zhe Zhang Jie Liu Lihua Chi Xinhai Chen Science and Technology on Parallel and Distributed Processing Laboratory & Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%.

关键词： Recurrent neural networks Computational modeling Bit error rate Semantics Speech recognition Feature extraction Task analysis

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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SAWSDL-iMatcher: A Customizable and Effective Semantic Web Service Matchmaker

SSRN

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SSRN 2018年

作者： Wei, Dengping Wang, Ting Wang, Ji Bernstein, Abraham National University of Defense Technology Changsha410073 China University of Zürich Binzmühlestrasse 14 ZürichCH-8050 Switzerland National Laboratory for Parallel and Distributed Processing Changsha410073 China Dynamic and Distributed Information Systems Group

As the number of publicly available services grows, discovering proper services becomes an important issue and has attracted amount of attempts. This paper presents a new customizable and effective matchmaker, called SAWSDL-iMatcher. It supports a matchmaking mechanism, named iXQuery, which extends XQuery with various similarity joins for SAWSDL service discovery. Using SAWSDL-iMatcher, users can flexibly customize their preferred matching strategies according to different application requirements. SAWSDL-iMatcher currently supports several matching strategies, including syntactic and semantic matching strategies as well as several statistical-model-based matching strategies which can effectively aggregate similarity values from matching on various types of service description information such as service name, description text, and semantic annotation. Besides, we propose a semantic matching strategy to measure the similarity among SAWSDL semantic annotations. These matching strategies have been evaluated in SAWSDL-iMatcher on SAWSDL-TC2 and Jena Geography Dataset (JGD). The evaluation shows that different matching strategies are suitable for different tasks and contexts, which implies the necessity of a customizable matchmaker. In addition, it also provides evidence for the claim that the effectiveness of SAWSDL service matching can be significantly improved by statistical-model-based matching strategies. Our matchmaker is competitive with other matchmakers on benchmark tests at S3 contest 2009. © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Soft-Constrained Nonnegative Matrix Factorization via Normalization

Soft-Constrained Nonnegative Matrix Factorization via Normal...

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International Joint Conference on Neural Networks

作者： Long Lan Naiyang Guan Xiang Zhang Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Semi-supervised clustering aims at boosting the clustering performance on unlabeled samples by using labels from a few labeled samples. Constrained NMF (CNMF) is one of the most significant semi-supervised clustering methods, and it factorizes the whole dataset by NMF and constrains those labeled samples from the same class to have identical encodings. In this paper, we propose a novel soft-constrained NMF (SCNMF) method by softening the hard constraint in CNMF. Particularly, SCNMF factorizes the whole dataset into two lower-dimensional factor matrices by using multiplicative update rule (MUR). To utilize the labels of labeled samples, SCNMF iteratively normalizes both factor matrices after updating them with MURs to make encodings of labeled samples close to their label vectors. It is therefore reasonable to believe that encodings of unlabeled samples are also close to their corresponding label vectors. Such strategy significantly boosts the clustering performance even when the labeled samples are rather limited, e.g., each class owns only a single labeled sample. Since the normalization procedure never increases the computational complexity of MUR, SCNMF is quite efficient and effective in practices. Experimental results on face image datasets illustrate both efficiency and effectiveness of SCNMF compared with both NMF and CNMF.

关键词： Matrix decomposition Encoding Vectors Clustering methods Clustering algorithms Time complexity Face

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Oscillatory behaviour of the RBF-FD approximation accuracy under increasing stencil size

arXiv

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arXiv 2023年

作者： Kolar-Požun, Andrej Jančič, Mitja Rot, Miha Kosec, Gregor Jožef Stefan Institute Parallel and Distributed Systems Laboratory Ljubljana Slovenia Faculty of Mathematics and Physics University of Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia

When solving partial differential equations on scattered nodes using the Radial Basis Function generated Finite Difference (RBF-FD) method, one of the parameters that must be chosen is the stencil size. Focusing on Polyharmonic Spline RBFs with monomial augmentation, we observe that it affects the approximation accuracy in a particularly interesting way - the solution error oscillates under increasing stencil size. We find that we can connect this behaviour with the spatial dependence of the signed approximation error. Based on this observation we are then able to introduce a numerical quantity that indicates whether a given stencil size is locally optimal. © 2023, CC BY.

关键词： Finite difference method

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Box-constrained Projective Nonnegative Matrix Factorization via Augmented Lagrangian Method

Box-constrained Projective Nonnegative Matrix Factorization ...

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International Joint Conference on Neural Networks

作者： Xiang Zhang Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Centre for Quantum Computation & Intelligent Systems and the Faculty of Engineering and Information Technology University of Technology

ISBN: (纸本)9781479914821

Projective non-negative matrix factorization (PNMF) projects a set of examples onto a subspace spanned by a non-negative basis whose transpose is regarded as the projection matrix. Since PNMF learns a natural parts-based representation, it has been successfully used in text mining and pattern recognition. However, it is non-trivial to analyze the convergence of the optimization algorithms for PNMF because its objective function is non-convex. In this paper, we propose a Box-constrained PNMF (BPNMF) method to overcome this deficiency of PNMF. In particular, BPNMF introduces an auxiliary variable, i.e., the coefficients of examples, and incorporates the following two types of constraints: 1) each entry of the basis is non-negative and upper-bounded, i.e., box-constrained, and 2) the coefficients equal to the projected points of the examples. The first box constraint makes the basis to be bound and the second equality constraint keeps its equivalence to PNMF. Similar to PNMF, BPNMF is difficult because the objective function is non-convex. To solve BPNMF, we developed an efficient algorithm in the frame of augmented Lagrangian multiplier (ALM) method and proved that the ALM-based algorithm converges to local minima. Experimental results on two face image datasets demonstrate the effectiveness of BPNMF compared with the representative methods.

关键词： Accuracy Linear programming Training Convergence Optimization Face Educational institutions

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PyMW - A Python module for desktop grid and volunteer computing

PyMW - A Python module for desktop grid and volunteer comput...

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International Symposium on parallel and distributed Processing (IPDPS)

作者： Eric M. Heien Yusuke Takata Kenichi Hagihara Adam Kornafeld Graduate School of Information Science and Technology Osaka University Toyonaka Osaka Japan Laboratory of Parallel and Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

We describe a general purpose master-worker parallel computation Python module called PyMW. PyMW is intended to support rapid development, testing and deployment of large scale master-worker style computations on a desktop grid or volunteer computing environment. This module targets non-expert computer users by hiding complicated task submission and result retrieval procedures behind a simple interface. PyMW also provides a unified interface to multiple computing environments with easy extension to support additional environments. In this paper, we describe the internal structure and external interface to the PyMW module and its support for the Condor computing environment and the Berkeley Open Infrastructure for Network Computing (BOINC) platform. We demonstrate the effectiveness and scalability of PyMW by performing master-worker style computations on a desktop grid using Condor and a BOINC volunteer computing project.

关键词： Grid computing Concurrent computing Computer interfaces Large-scale systems Virtual colonoscopy distributed computing Computer networks Software packages Packaging Information science

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