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检索条件"机构=National Lab of Parallel and Distributed Processing"

共 68 条记录，以下是21-30 订阅

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Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling 48

Raw Ultrasound-Based Phonetic Segments Classification Via Ma...

引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： You, Kang Liu, Bo Xu, Kele Xiong, Yunsheng Xu, Qisheng Feng, Ming Csapó, Tamás Gábor Zhu, Boqing Tongji University Shanghai China National University of Defense Technology Changsha China National Key Lab of Parallel and Distributed Processing Changsha China Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781728163277

Ultrasound tongue imaging is widely used in clinical linguistics and phonetics. Recently, deep neural networks, especially convolutional neural networks, have been widely used in the interpretation and analysis of ultrasound tongue images (UTI). Despite achieving satisfactory performance, deep models rely on a large amount of manually labeled data, which is often difficult to obtain in practical settings. To address this issue, this paper focuses on how to utilize a large amount of unlabeled UTI data to improve the performance of UTI classification task. Specifically, we explore self-supervised learning with masking modeling strategy. By predicting the masked part, our pre-trained model enables the neural network to infer contextual information. Then, we fine-tune the pre-trained model with a small amount of labeled data. Compared with the previous competing algorithms, our method can improve the classification accuracy by an average of 13.33% in four different scenarios. © 2023 IEEE.

关键词： Mask modeling Masked auto-encoder Self-supervised learning Ultrasound tongue imaging.

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A Comparative Study of parallel Prefix Adders in FPGA Implementation of EAC

A Comparative Study of Parallel Prefix Adders in FPGA Implem...

引用

12th Euromicro Conference on Digital System Design, Architectures, Methods and Tools

作者： Liu, Feng Fariborz, F. F. Mohamed, Otmane Ait Chen, Gang Song, Xiaoyu Tan, Qingping National Lab. of Parallel Distributed Processing Hunan China ECE Department Concordia University Montreal QC Canada Lingcore Lab. Portland OR United States ECE Department Portland State University Portland OR United States

ISBN: (纸本)9780769537825

Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and interconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.

关键词： Adders

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A hybrid multicasting for large-scale data dissemination applications

A hybrid multicasting for large-scale data dissemination app...

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Hairong Zhong Xiaojian Liu Shiyao Jin National Lab for Parallel & Distributed Processing National University of Defense Technology Hunan Province China National Lab for Parallel & Distributed Processing National University of Defense Technology Changsha Hunan Province China

ISBN: (纸本)0780382730

Large-scale data dissemination applications, which need large numbers of multicast groups to filter information, were often restricted by the poor support of the relevant network routers. So we proposed hybrid multicasting (HM), which could take the advantage of IP multicasting, and was not limited by the network routers at the same time. Compared with other one-to-many data transfer methods, such as duplicate unicasting, broadcasting and IP multicasting, HM could exploit the capabilities and behavior of the underlying communication network and provided better performance. Basing on the application characteristics, we gave some theorems to guide how to select transfer methods, and presented a new data dissemination allocation algorithm, information Flow Merge with the Hybrid Multicasting First (FM/HMF). Tests showed that FM/HMF could produce better outcome and executes faster than nowadays-prevalent IP multicast group allocation strategies.

关键词： Large-scale systems Application software Broadcasting Filters Multicast algorithms Testing Bandwidth distributed processing Computer science Electronic mail

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AOP-based high-level power estimation in SystemC 10

AOP-based high-level power estimation in SystemC

引用

20th Great Lakes Symposium on VLSI, GLSVLSI 2010

作者： Liu, Feng Tan, Qingping Song, Xiaoyu Abbasi, Naeem National Lab. of Parallel Distributed Processing China ECE Dept. Portland State University Portland OR United States ECE Dept. Concordia University Montreal QC Canada

The paper presents a novel high-level power modeling and estimation framework. The approach is based on a synergic integration of aspect-oriented programming(AOP) and SystemC. Macro module modeling and power estimatio... 详细信息

ISBN: (纸本)9781450300124

关键词： Aspect oriented programming

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The parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

引用

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： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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Hermes: An Optimization of HyperLogLog Counting in Real-time Data processing

Hermes: An Optimization of HyperLogLog Counting in Real-time...

引用

International Joint Conference on Neural Networks

作者： Yunxiang Zhao Songtao Guo Yuanyuan Yang National Lab for Parallel and Distributed Processing Southwest University Stony Brook University

ISBN: (纸本)9781509006212

HyperLogLog Counting is widely used in cardinality estimation. It is the foundation of many algorithms in data analysis, commodity recommendation and database optimization. Facing the large scale internet business like electronic commerce, internet companies have an urgent requirement of distributed real-time cardinality estimation with high accuracy and low time cost. In this paper, we propose a distributed real-time cardinality estimation algorithm named Hermes. Hermes adjusts the estimated cardinality dynamically according to the result of HyperLogLog Counting and also optimizes the data distribution strategy of existing distributed cardinality estimation algorithms. Experiments have been carried out and the results show that Hermes has lower estimation error and time cost compared with existing algorithms.

关键词： Estimation Real-time systems Algorithm design and analysis distributed databases Optimization Sparks Companies

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hi-RF:Incremental Learning Random Forest for Large-Scale Multi-class Data Classification

hi-RF:Incremental Learning Random Forest for Large-Scale Mul...

引用

2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Tingting Xie Changjian Wang Yuxing Peng National Lab for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

ISBN: (纸本)9781510835368

In recent years,dynamically growing data and largescale data classification *** traditional methods struggle to balance the precision and computational burden when data and its number of classes ***,some methods are with weak precision,and the others are *** this paper,we propose an incremental learning method,namely,heterogeneous incremental Nearest Class Mean Random Forest(hi-RF),to handle this *** is a heterogeneous method that either replaces trees or updates trees leaves in the random forest adaptively,to reduce the computational time in comparable performance,when data of new classes ***,to keep the accuracy,one proportion of trees are replaced by new NCM decision trees;to reduce the computational load,the rest trees are updated their leaves probabilities *** of all,outof-bag estimation and out-of-bag boosting are proposed to balance the accuracy and the computational *** experiments were conducted and demonstrated its comparable precision with much less computational time.

关键词： large scale multi-class classification Incremental Learning random forest heterogeneous incremental Nearest Class Mean Random Forest

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BSE-MAML: Model Agnostic Meta-Reinforcement Learning via Bayesian Structured Exploration

BSE-MAML: Model Agnostic Meta-Reinforcement Learning via Bay...

引用

IEEE International Conference on Services Computing (SCC)

作者： Haonan Wang Yiyun Zhang Dawei Feng Dongsheng Li Feng Huang National Lab for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

Deep reinforcement learning (RL) is playing an increasingly important role in web services such as news recommendation, vulnerability detection, and personalized services. Exploration is a key component of RL, which determines whether these RL-based applications could find effective solutions eventually. In this paper, we propose a novel gradient-based fast adaptation approach for model agnostic meta-reinforcement learning via Bayesian structure exploration (BSE-MAML). BSE-MAML could effectively learn exploration strategies from prior experience by updating policy with embedding latent space via a Bayesian mechanism. Coherent stochasticity injected by latent space are more efficient than random noise, and can produce exploration strategies to perform well in novel environment. We have conducted extensive experiments to evaluate BSE-MAML. Experimental results show that BSE-MAML achieves better performance in exploration in realistic environments with sparse rewards, compared to state-of-the-art meta-RL algorithms, RL methods without learning exploration strategies, and task-agnostic exploration approaches.

关键词： Adaptation models Web services Stochastic processes Service computing Probabilistic logic Bayes methods Task analysis

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Property-oriented test generation from UML Statecharts 04

Property-oriented test generation from UML Statecharts

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Shuhao Li Ji Wang Zhi-ChangQi National Laboratory for Parallel and Distributed Processing Changsha P.R. China National Lab. for Parallel & Distributed Process. Changsha China

ISBN: (纸本)0769521312

UML Statecharts is an important modeling formalism with hierarchy, concurrency, broadcast-communication mechanisms and data variables. These facilities help the developers to describe the complex behaviors of an object along its lifecycle naturally. However, the ease of modeling is at the expense of testing: the system developed from Statecharts usually has numerous behaviors and is therefore extremely difficult to receive comprehensive and in-depth testing. This work presents an approach to deriving targeted test sequences from UML Statecharts according to tester-specified temporal logic properties. Using this approach, testing efforts can be focused on specific properties of the system and usually only a small portion of the total behaviors will be tested. This method suits well the occasions when the testers are interested in just some specific properties of the system or when they have to focus on its critical properties in case that limited budget is available.

关键词： Unified modeling language Automatic testing Software testing Software engineering

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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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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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