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Improving Parallel Corpus Quality for Chinese-Vietnamese Statistical Machine Translation

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Journal of Beijing Institute of Technology 2018年第1期27卷 127-136页

作者： Huu-anh Tran Yuhang Guo Ping Jian Shumin Shi Heyan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing Engineering research center of High Volume Language Information Processing and cloud computing Application Beijing Institute of Technology

The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a very difficult task especially for low resource languages such as Chinese-Vietnamese. Fortunately,multilingual user generated contents（ UGC）,such as bilingual movie subtitles,provide us access to automatic construction of the parallel corpus. Although the amount of UGC parallel corpora can be considerable,the original corpus is not suitable for statistical machine translation（ SMT） systems. The corpus may contain translation errors,sentence mismatching,free translations,etc. To improve the quality of the bilingual corpus for SMT systems,three filtering methods are proposed： sentence length difference,the semantic of sentence pairs,and machine learning. Experiments are conducted on the Chinese to Vietnamese translation *** results demonstrate that all the three methods effectively improve the corpus quality,and the machine translation performance（ BLEU score） can be improved by 1. 32.

关键词： parallel corpus filtering low resource languages bilingual movie subtitles machine translation Chinese-Vietnamese translation

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PSVM: a preference-enhanced SVM model using preference data for classification

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Science China(Information Sciences) 2017年第12期60卷 165-178页

作者： Lerong MA Dandan SONG Lejian LIAO Jingang WANG Engineering research center of High Volume Language Information Processing and cloud computing Applications School of Computer Science and Technology Beijing Institute of Technology College of Mathematics and Computer Science Yan'an University Search Business Department Alibaba Group

Classification is an essential task in data mining, machine learning and pattern recognition *** classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine(PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization(SMO)algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation(KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI,Stat Lib and ***. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics.

关键词： preference SVM classification sampling sequential minimal optimization(SMO)

Multi-DAGs Scheduling Integrating with Security and Availability in cloud Environment

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Chinese Journal of Electronics 2015年第4期24卷 709-716页

作者： LIU Yaqiu SHAO Hongrun JING Weipeng QIU Zhaowen The College of Information and Computer Engineering Northeast Forestry University Heilongjiang Province Engineering Technology research Centre for Forestry Ecological Big Data Storage and High Performance (cloud) computing

In view of the issue concerns multiple Directed acyclic graphs(DAGs) scheduling in multi-tenant cloud computing environment, a scheduling strategy that integrate security and availability is proposed to satisfy the tenants’ requirements for resource security and availability, as thus it can not only protect the users’ privacy and data security but also advance the success rate. The proposal assesses resource reputation to ensure jobs can be scheduled onto relatively security nodes; during task scheduling, it classifies the DAGs to achieve fairness; in the process of resources allocation, the objective function would maximize the user’s security satisfaction and minimize the deviation of availability; meanwhile, it takes advantage of "time chips" flexibly to promote resource utilization rate; afterwards, we present a Greedy algorithm integrating with security and availability(GISA) to implement the strategy. The experimental results show the correctness and superior of the novel strategy.

关键词： Multi-DAGs scheduling Security Availability Resource utilization

Advancing brain-inspired computing with hybrid neural networks

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National Science Review 2024年第5期11卷 56-71页

作者： Faqiang Liu Hao Zheng Songchen Ma Weihao Zhang Xue Liu Yansong Chua Luping Shi Rong Zhao center for Brain-Inspired computing research Optical Memory National Engineering Research CenterTsinghua University-China Electronics Technology HIK Group Co.Joint Research Center for Brain-inspired ComputingIDGMc Govern Institute for Brain Research Department of Precision Instrument Tsinghua University Neuromorphic computing Laboratory China Nanhu Academy of Electronics and Information Technology

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain–computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network(HNN), which integrates computer-science-oriented artificial neural networks(ANNs) with neuroscience-oriented spiking neural networks(SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

关键词： brain-inspired computing hybrid neural network dual-brain driven multi-network integration neuromorphic system

Quantum multicast schemes of different quantum states via non-maximally entangled channels with multiparty involvement

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Chinese Physics B 2021年第9期30卷 101-106页

作者： Yan Yu Nan Zhao Chang-Xing Pei Wei Li State Key Laboratory of Integrated Services Networks Xidian UniversityXi'an 710071China cloud computing center of Xi'an Branch of Shaanxi Telecom China TelecomXi'an 710002China

Due to the unavoidable interaction between the quantum channel and its ambient environment,it is difficult to generate and maintain the maximally ***,the research on multiparty information transmission via non-maximally entangled channels is of academic value and general ***,we utilize the non-maximally entangled channels to implement two multiparty remote state preparation schemes for transmitting different quantum information from one sender to two receivers *** first scheme is adopted to transmit two different four-qubit cluster-type entangled states to two receivers with a certain *** order to improve success probabilities of such multicast remote state preparation using non-maximally entangled channels,we put forward the second scheme,which deals with the situation that is a synchronous transfer of an arbitrary single-qubit state and an arbitrary two-qubit state from one sender to two *** particular,its success probability can reach 100%in principle,and independent of the entanglement degree of the shared non-maximally entangled ***,in the second scheme,the auxiliary particle is not required.

关键词： quantum multicast communications non-maximally entangled states remote state preparation

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A New ETL Approach Based on Data Virtualization

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Journal of Computer Science & Technology 2015年第2期30卷 311-323页

作者：郭树盛袁子牧孙傲冰岳强 State Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100190 China cloud computing center Chinese Academy of Sciences Dongguan 523808 China

ETL （Extract-Transform-Load） usually includes three phases： extraction, transformation, and loading. In building data warehouse, it plays the role of data injection and is the most time-consuming activity. Thus it is necessary to improve the performance of ETL. In this paper, a new ETL approach, TEL （Transform-Extract-Load） is proposed. The TEL approach applies virtual tables to realize the transformation stage before extraction stage and loading stage, without data staging area or staging database which stores raw data extracted from each of the disparate source data systems. The TEL approach reduces the data transmission load, and improves the performance of query from access layers. Experimental results based on our proposed benchmarks show that the TEL approach is feasible and practical.

关键词： cloud computing big data ETL heterogeneous database data virtualization

Truth Discovery with Memory Network

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Tsinghua Science and Technology 2017年第6期22卷 609-618页

作者： Luyang Li Bing Qin Wenjing Ren Ting Liu research center for Social computing and Information Retrieval Harbin Institute of Technology Harbin 150001 China

Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility of statements. These methods use real numbers, which have a lower representation capability than vectors to represent the reliability. In addition, neural networks have not been used for truth discovery. In this work, we propose memory-network-based models to address truth discovery. Our proposed models use feedforward and feedback memory networks to learn the representation of the credibility of statements. Specifically, our models adopt a memory mechanism to learn the reliability of sources for truth prediction. The proposed models use categorical and continuous data during model learning by automatically assigning different weights to the loss function on the basis of their own effects. Experimental results show that our proposed models outperform state-of-the-art methods for truth discovery.

关键词： truth discovery memory networks source reliability

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A syntactic path-based hybrid neural network for negation scope detection

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Frontiers of Computer Science 2020年第1期14卷 84-94页

作者： Lydia LAZIB Bing QIN Yanyan ZHAO Weinan ZHANG Ting LIU research center for Social computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China

The automatic detection of negation is a crucial task in a wide-range of natural language processing(NLP)applications,including medical data mining,relation extraction,question answering,and sentiment *** this paper,we present a syntactic path-based hybrid neural network architecture,a novel approach to identify the scope of negation in a *** hybrid architecture has the particularity to capture salient information to determine whether a token is in the scope or not,without relying on any human *** approach combines a bidirectional long shortterm memory(Bi-LSTM)network and a convolutional neural network(CNN).The CNN model captures relevant syntactic features between the token and the cue within the shortest syntactic path in both constituency and dependency parse *** Bi-LSTM learns the context representation along the sentence in both forward and backward *** evaluate our model on the Bioscope corpus,and get 90.82%F-score(78.31%PCS)on the abstract sub-corpus,outperforming features-dependent approaches.

关键词： natural language processing negation scope detection convolutional neural network recurrent neural network syntactic path

An online service-oriented performance profiling tool for cloud computing systems

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Frontiers of Computer Science 2013年第3期7卷 431-445页

作者： Haibo MI Huaimin WANG Yangfan ZHOU Michael Rung-Tsong LY~ Hua CAI Gang YIN National Lab for Parallel & Distributed Processing National University of Defense Technology Changsha 410073 China Shenzhen research Institute The Chinese University of Hong Kong Shenzhen 518000 China computing Platform Alibaba Cloud Computing Company Hangzhou 310000 China

The growing scale and complexity of component interactions in cloud computing systems post great challenges for operators to understand the characteristics of system performance. Profiling has long been proved to be an effective approach to performance analysis; however, existing approaches confront new challenges that emerge in cloud computing systems. First, the efficiency of the profiling becomes of critical concern; second, service-oriented profiling should be considered to support separation-of-concerns performance analysis. To address the above issues, in this paper, we present P-Tracer, an online performance profiling tool specifically tailored for cloud computing systems. P-Tracer constructs a specific search engine that proactively processes performance logs and generates a particular index for fast queries; second, for each service, P-Tracer retrieves a statistical insight of performance characteristics from multi-dimensions and provides operators with a suite of web-based interfaces to query the critical information. We evaluate P- Tracer in the aspects of tracing overheads, data preprocessing scalability and querying efficiency. Three real-world case studies that happened in Alibaba cloud computing platform demonstrate that P-Tracer can help operators understand soft-ware behaviors and localize the primary causes of performance anomalies effectively and efficiently.

关键词： cloud computing performance profiling performance anomaly visual analytics