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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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An Outpost Vector placement evaluation of an incremental learning algorithm for Support Vector Machine

An Outpost Vector placement evaluation of an incremental lea...

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International Joint Conference on Neural Networks (IJCNN)

作者： Piyabute Fuangkhon Thitipong Tanprasert Distributed and Parallel Computing Research Laboratory Faculty of Science and Technology Assumption University Bangkok Thailand

Outpost Vector model synthesizes new vectors at the boundary of two classes of data in order to increase the level of accuracy of classification. This paper presents a performance evaluation of four different placements of outpost vectors in an incremental learning algorithm for Support Vector Machine (SVM) on a non-complex problem. The algorithm generates outpost vectors from selected new samples, selected prior samples, both samples, or generates no outpost vector at all. After that, they are included in the final training set, as well as new samples and prior samples, based on the specified parameters. The experiments are conducted with a 2-dimension partition problem. The distribution of training and test samples is created in a limited location of a 2-dimension donut ring. There are two classes of data which are represented as 0 and 1. The context of the problem is assumed to shift 45 degrees in counterclockwise direction. Every consecutive partition is set to have different class of data. The experimental results show that the placement of outpost vectors generated from only selected new samples yields the highest level of accuracy of classification for both new data and old data. As a result, using samples from different part of the algorithm to generate outpost vectors affects the level of accuracy of classification.

关键词： Training Support vector machines Accuracy Shape Data models Context Vectors

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An Economical Power Management for High Performance Computers

An Economical Power Management for High Performance Computer...

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International Conference on Computational and Information sciences (ICCIS)

作者： Huiquan Wang Xinhai Xu Jing Zhou Hao Zhou Guibin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan P. R. China

The large amount of energy consumption of high performance computers represent significant financial cost. If we consider the specific situation of electricity price, a measure which makes the energy consumption the lowest is usually not the best on economy. This paper proposes an economical approach for designing a power management plan that reduces the assessment of energy consumption of high performance computers. By taking account of the electricity price in different periods during the day, we assign the workload to each period properly to make the electricity fee as low as possible. A simulation of the cases of a single task and multi-task demonstrates how our approach can be used. The results of the simulation show that our approach can save the electricity fee produced by high performance computers effectively. In the single task case, the proposed approach saves the electricity fee of 10.08% on average, and in the multi-task case the proportion of saved fee is 12.67%.

关键词： Electricity Supercomputers Energy consumption Mathematical model Power demand Equations

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Memory Access Analysis of Many-core System with Abundant Bandwidth

Memory Access Analysis of Many-core System with Abundant Ban...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Chuan Tang Dan Liu Zuocheng Xing Peng Yang Zhe Wang Jiang Xu Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781479986712

Many-core system is main architecture trend currently. One of the dominating challenges for on-chip manycore system is the memory wall. However traditional research primarily focus on the limited bandwidth. To solve this problem, many-core system is aided with large cache, and a lot of complex approaches about memory and cache are adopted aiming at relaxing the pressure of bandwidth and improving the efficiency of cache. All these methods generate much cost of area and power. In this paper, we are motivated by the feature of abundant bandwidth and low latency of optical interconnect. We analyze the characteristics of memory access on 64 cores system under the case of high bandwidth which can be assumed to benefit from optical interconnect, considering the sensibility with bandwidth and cache for different benchmarks. Finally, we discuss about promising basic frameworks suitable for manycore system with optical interconnect.

关键词： Bandwidth Benchmark testing Optical interconnections Random access memory Delays Hidden Markov models Computers

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Virtual Frame Aggregation: Clustered Channel Access in Wireless Networks

Virtual Frame Aggregation: Clustered Channel Access in Wirel...

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IEEE International Conference on Communications

作者： Xuan Dong Shaohe Lv Chunsheng Zhu Rukhsana Ruby Xiaodong Wang Xingming Zhou Victor C. M. Leung National Laboratory of Parallel and Distributed Processing National University of Defense Technology Department of Electrical and Computer Engineering The University of British Columbia

ISBN: (纸本)9781467364300

Coordination among users is inevitable in wireless communication for efficient medium access. Even though the data rate of individual user increases significantly, the performance of wireless network does not grow up accordingly due to the high MAC coordination overhead. In this paper, we present VFA, namely virtual frame aggregation, to achieve high coordination efficiency by amortizing the overhead over multiple transmissions. VFA provides a novel way to construct a winner cluster and allow the winners to transmit without interruption. Specifically, in a multicarrier network, every contending node chooses a subcarrier and the nodes are ordered by the index of the chosen subcarrier. When there are some subcarriers chosen by two or more nodes, an additional slot is exploited to reorder the collided nodes. Finally, all ordered nodes form a cluster and the transmissions are issued sequentially and uninterruptedly. Simulation results show that usually two slots are enough to construct a sufficiently large winner cluster. Moreover, VFA achieves a notable throughput gain over IEEE 802.11 as high as 120% with better fairness under various scenarios.

关键词： Wireless communication Antennas Wires

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Modeling Full Information with Graph Network for Joint Entity-Relation Extraction

Modeling Full Information with Graph Network for Joint Entit...

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2021年第四届算法、计算和人工智能国际会议

作者： Qian Wan Luona Wei Xinhai Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology College of Systems Engineering National University of Defense Technology Laboratory of Software Engineering for Complex Systems National University of Defense Technology

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the Co NLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model.

关键词： Named Entity Recognition Relation Extraction Natural Language processing Graph Network Deep Learning

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HybridSwap: A scalable and synthetic framework for guest swapping on virtualization platform

HybridSwap: A scalable and synthetic framework for guest swa...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Pengfei Zhang Xi Li Rui Chu Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Information Science and Engineering Central South University Changsha China

In IaaS cloud environments, peak memory demand caused by hotspot applications in Virtual Machine (VM) often results in performance degradation within and outside of this VM. Some solutions such as host swapping and ballooning for memory consolidation and overcommitment have been proposed. These solutions, however, have no help for addressing guest swapping issues inside VM. Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between VMM and it. Our goal is to alleviate the performance degradation by decreasing disk I/O operations generated by guest swapping. Based on the insight analysis of behavioral features of guest swapping, we design HybridSwap, a distributed scalable framework which organize surplus memory in all hosts within data center into virtual pools for swapping. This framework builds up a synthetic swapping mechanism in a peer-to-peer way, which VM can adaptively choose suitable pools for swapping. We implement the prototype of HybridSwap and evaluate it with different benchmarks. The results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed. Even in some cases, it shows 2-5 times of performance promotion compared with the baseline setup.

关键词： Benchmark testing Virtualization Semantics Servers Operating systems Degradation Instruction sets

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ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations

arXiv

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arXiv 2023年

作者： Yan, Junjun Chen, Xinhai Wang, Zhichao Zhoui, Enqiang Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN. Specifically, ST-PINN introduces a pseudo label based self-learning algorithm during training. It employs governing equation as the pseudo-labeled evaluation index and selects the highest confidence examples from the sample points to attach the pseudo labels. To our best knowledge, we are the first to incorporate a self-training mechanism into physics-informed learning. We conduct experiments on five PDE problems in different fields and scenarios. The results demonstrate that the proposed method allows the network to learn more physical information and benefit convergence. The ST-PINN outperforms existing physics-informed neural network methods and improves the accuracy by a factor of 1.33x-2.54x. The code of ST-PINN is available at GitHub: https://***/junjun-yan/ST-PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Routing with uncertainty in wireless mesh networks

Routing with uncertainty in wireless mesh networks

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International Workshop on Quality of Service

作者： Fajun Chen Jiangchuan Liu Zongpeng Li Yijie Wang Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense Technology China School of Computing Science Simon Fraser University Department of Computer Science University of Caglary

Existing routing protocols for Wireless Mesh Networks (WMNs) are generally optimized with statistical link measures, while not addressing on the intrinsic uncertainty of wireless links. We show evidence that, with the transient link uncertainties at PHY and MAC layers, a pseudo-deterministic routing protocol that relies on average or historic statistics can hardly explore the full potentials of a multi-hop wireless mesh. We study optimal WMN routing using probing-based online anypath forwarding, with explicit consideration of transient link uncertainties. We show the underlying connection between WMN routing and the classic Canadian Traveller Problem (CTP). Inspired by a stochastic recoverable version of CTP (SRCTP), we develop a practical SRCTP-based online routing algorithm under link uncertainties. We study how dynamic next hop selection can be done with low cost, and derive a systematic selection order for minimizing transmission delay. We conduct simulation studies to verify the effectiveness of the SRCTP algorithms under diverse network configurations. In particular, compared to deterministic routing, reduction of end-to-end delay (51.15~73.02%) and improvement on packet delivery ratio (99.76%) are observed.

关键词： Uncertainty Wireless mesh networks Delay Routing protocols Physical layer Statistics Stochastic processes Relays Computer networks Concurrent computing

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Aircraft Detection in Remote Sensing Images via CNN Multi-scale Feature Representation

Aircraft Detection in Remote Sensing Images via CNN Multi-sc...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Jia-qi WANG Xin NIU Peng ZHANG Yong DOU Fei XIA National Laboratory for Parallel and Distributed Processing National University of Defense Technology Institute of Electronic Information Warfare Naval University of Engineering

Aircraft detection in remote sensing images is an intractable *** current aircraft detection methods have limited representative capabilities and heavy computational *** paper studies how to apply multi-scale feature representation of Convolutional Neural Networks(CNN) to aircraft detection by qualitatively and quantitatively analyzing the performance of Single Shot Detection(SSD) *** first,we find that low-level detectors are not robust enough to detect as the semantic gap *** we propose a data driven hyper-parameter selection method to alleviate this problem by determining appropriate hyper-parameters of sliding window and default box ***,we employ a multi-scale training strategy to enhance low-level predictive ***,we propose an accurate and efficient aircraft detection *** results illustrate that our method could achieve 96.84% AP at 20 FPS on NVIDIA TITAN *** with original SSD method,our proposed approach achived 2.13% AP improvement.

关键词： Aircraft detection Remote sensing Single Shot Detection(SSD) CNN

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