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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 availab.e.

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

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Reducing cache contention in a multi-core processor via a scheduler

Reducing cache contention in a multi-core processor via a sc...

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International Conference on Advanced Computer Theory and Engineering

作者： Shekofteh, S. Kazem Deldari, Hossein Khalkhali, Maryam Baradaran Parallel and Distributed Processing Lab. Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Computer Engineering Islamic Azad University Mashhad Branch Mashhad Iran

ISBN: (纸本)9781424465408

Multi-core architectures, which have multiple processing units on a single chip, are widely viewed as a way to achieve higher processor performance. Well scheduling of running threads on these processors will result in achieving higher performance. Modern multi-core systems are designed to allow clusters of cores to share various hardware structures, such as last-level caches, memory controllers, and interconnections, as well as prefetching hardware. Without considering these shared resources, scheduling the threads will cause serious degradation in overall performance of the system. In this paper we propose a novel algorithm to schedule the threads that considers these potential contentions to keep away from. The simulation results showed that the proposed scheduler would avoid from lots of contentions between threads on various resources especially on shared caches. © 2010 IEEE.

关键词： Scheduling

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Coverage problem in wireless sensor network: A survey

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Journal of Networks 2010年第9期5卷 1033-1040页

作者： Fan, Gao Jun Jin, Shi Yao National Lab of Parallel and Distributed Processing National University of Defense Technology Chang Sha China

Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The coverage of WSN has answered the questions about quality of service (surveillance) which can be provided by WSN. Therefore, maximizing coverage using the resource constrained nodes is a non-trivial problem. The coverage problem for wireless sensor network (WSN) has been studied extensively in recent years, especially when combined with connectivity and energy efficiency. In this paper we present a survey of coverage problem. And besides some basic design considerations in coverage of WSN we describe two challenges, namely, maximizing network lifetime and network connectivity. We also provide a brief summary and comparison of existing coverage schemes. © 2010 ACADEMY PUBLISHER.

关键词： Wireless sensor networks

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Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling 48

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

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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 lab.led 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 unlab.led 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 lab.led 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 hybrid multicasting for large-scale data dissemination applications

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

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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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Linking stack overflow to issue tracker for issue resolution 2014

Linking stack overflow to issue tracker for issue resolution

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6th Asia-Pacific Symposium on Internetware, Internetware 2014

作者： Wang, Tao Yin, Gang Wang, Huaimin Yang, Cheng Zou, Peng Key Lab. of Parallel and Distributed Computing Colledge of Computer National University of Defence Technology ChangSha Hunan China Academy of Equipment Command and Technology Beijing100000 China

ISBN: (纸本)9781450334013

Issue resolution is a central task for software development. The efficiency of issue fixing largely relies on the issue report quality. Stack Overflow, which hosts rich and real-Time posts about programming-specific problems, is a valuable external source of knowledge for issue resolution. In this paper, we present CrossLink, an analysis framework that automatically introduce related posts in Stack Overflow to issues in Android Issue Tracker. This helps developers to leverage the abundant and professional knowledge from massive programmers in Stack Overflow for issue resolution. CrossLink explores the semantic similarities as well as the temporal associations between the two types of repositories to recommend Stack Overflow posts to Android issues. The internal links in Stack Overflow are also employed to improve the linking accuracy. The experiments prove the effectiveness of CrossLink with precision of 62.51% for top-10 recommendations, which is significantly higher than the state-of-Art method. © 2014 ACM.

关键词： Android (operating system)

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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...

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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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The parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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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： 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...

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

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

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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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