一种基于QML 和GPU 编程的实时显示软件

为实现国产自主可控,基于国产CPU 和麒麟操作系统,采用Qt 元语言(qt meta language,QML)和图形 处理器(graphics processing unit,GPU)编程的模式设计一种跨平台的实时显示软件。详细阐述基于QML 的软件架构 设计,并对软件设计中的QML、C++及GPU 的通信模式、QML 下CPU+GPU 编程实时显示性能优化方法以及QML 显示控件设计等关键技术进行说明,验证其应用。结果证明：该模型具有可靠、稳定、高效和跨平台等特点,满足 实时显示应用要求。     

基于循环神经网络的汉语语言模型建模方法

语言模型是语音识别系统的重要组成部分,目前的主流是n-gram模型。然而n-gram模型存在一些不足,对语句中长距信息描述差、数据稀疏是影响模型性能的两个重要因素。针对不足,研究者提出循环神经网络(Recurrent Neural Network,RNN)建模技术,在英语语言模型建模上取得了较好的效果。根据汉语特点将RNN建模方法应用于汉语语言建模,并结合两种模型的优点,提出了模型融合构建方法。实验结果表明:相比传统的n-gram语言模型,采用RNN训练的汉语语言模型困惑度(Per PLexity,PPL)有了下降,在对汉语电话信道的语音识别上,系统错误率也有下降,将两种语言模型融合后,系统识别错误率更低。     

基于自然语言理解的需求聚类和需求优选方法研究

为了实现大数量软件需求的优选,提出了一种基于自然语言理解的需求聚类和需求优选方法。该方法首先基于自然语言处理技术对需求进行深层次语言理解,找到相同语义的需求并只保留一条。随后,采用基于超图分割的需求聚类方法对大量需求进行聚类。最后,提出了需求优选目标函数,该目标函数根据需求聚类结果为需求优先级打分,并在综合考虑需求与类别的相似性、类别的权重以及聚类标准的权重的基础上,最终给出一个合理的需求优选结果。在大规模真实的需求集合上进行了实验,实验结果表明:基于自然语言处理技术的需求聚类性能优异;需求聚类对后续的需求优选起有着非常重要的作用;基于超图分割的需求聚类,以及在此基础上提出的需求优选函数相对于基线方法有着明显的提高。     

工学结合的专业外语课程改革与实践

通过分析工科专业外语教学现状,提出了课程改革实施方案:课程标准的制定、以专业为驱动的教学内容改革、以需求为导向的新教材开发、"教-学-做"一体化的实践教学改革。     

Computational personality recognition from Facebook text: psycholinguistic features,words and facets

ABSTRACT

Advances in the Natural Language Processing (NLP) and machine learning fields have led to the development of automated methods for the recognition of personality traits from text available from social media and similar sources. Systems of this kind exploit the close relation between lexical knowledge and personality models – such as the well-known Big Five model – to provide information about the author of an input text in a non-intrusive fashion, and at a low cost. Although now a well-established research topic in the field, the computational recognition of personality traits from text still leaves a number of research questions worth further exploration. In particular, this paper attempts to shed light on three main issues: (i) whether we may develop psycholinguistics-motivated models of personality recognition when such knowledge sources are not available for the target language under consideration; (ii) whether the use of psycholinguistic knowledge may be still superior to contemporary word vector representations; and (iii) whether we may infer certain personality facets from a corpus that does not explicitly convey this information. In this paper these issues are dealt with in a series of individual experiments of personality recognition from Facebook text, whose initial results should aid the future development of more robust systems of this kind.     

命名实体识别技术综述

命名实体识别是自然语言处理中的热点研究方向之一,目的是识别文本中的命名实体并将其归纳到相应的实体类型中。首先阐述了命名实体识别任务的定义、目标和意义,分析提出了命名实体识别的主要难点在于领域命名实体识别局限性、命名实体表述多样性和歧义性、命名实体的复杂性和开放性;然后介绍了命名实体识别研究的发展进程,从最初的规则和字典方法到传统的统计学习方法再到现在的深度学习方法,不断地将新技术应用到命名实体识别研究中以提高性能;接着系统梳理了当下命名实体识别任务中的若干热门研究点,分别是匮乏资源下的命名实体识别、细粒度命名实体识别、嵌套命名实体识别以及命名实体链接;最后针对评判命名实体识别模型的好坏,总结了常用的若干数据集和实验测评指标,并给出了未来的研究建议。     

Comparing the expressiveness of downward fragments of the relation algebra with transitive closure on trees

Motivated by the continuing interest in the tree data model, we study the expressive power of downward navigational query languages on trees and chains. Basic navigational queries are built from the identity relation and edge relations using composition and union. We study the effects on relative expressiveness when we add transitive closure, projections, coprojections, intersection, and difference; this for Boolean queries and path queries on labeled and unlabeled structures. In all cases, we present the complete Hasse diagram. In particular, we establish, for each query language fragment that we study on trees, whether it is closed under difference and intersection.     

基于BERT-Att-biLSTM模型的医学信息分类研究

目前,对互联网上虚假健康信息的研究多集中于谣言识别,而对医学信息自动分类的研究较少。采用基于双向编码的语言表征模型和注意力增强的双向长短时记忆模型(BERT-Att-BiLSTM模型),对健康信息文本进行分类,实现自动识别虚假健康信息。实验结果表明,BERT-Att-BiLSTM模型可以高效地对医学信息进行分类,其中BERT模型相较于BiLSTM模型,性能提升明显;与融合Word2Vec的BiLSTM模型相比,BERT-Att-BiLSTM模型效果更佳。     

Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.     

GoBIS: An integrated framework to analyse the goal and business process perspectives in information systems

ContextOrganisational reengineering, continuous process improvement, alignment among complementary analysis perspectives, and information traceability are some current motivations to promote investment and scientific effort for integrating goal and business process perspectives. Providing support to integrate information systems analysis becomes a challenge in this complex setting.ObjectiveThe GoBIS framework integrates two goal and business process modelling approaches: i^⁎ (a goal-oriented modelling method) and Communication Analysis (a communication-oriented business process modelling method).MethodIn this paper, we describe the methodological integration of both methods with the aim of fulfilling several criteria: i) to rely on appropriate theories; ii) to provide abstract and concrete syntaxes; iii) to provide scenarios of application; iv) to develop tool support; v) to provide demonstrable benefits to potential adopters.ResultsWe provide guidelines for using the two modelling methods in a top-down analysis scenario. The guidelines are validated by means of a comparative experiment and a focus-group session with students.ConclusionsFrom a practitioner viewpoint (modeller and/or analyst), the guidelines facilitate the traceability between goal and business process models, the experimental results highlight the benefits of GoBIS in performance and usability perceptions, and demonstrate an improvement on the completeness of the latter having an impact on efficiency. From a researcher perspective, the validation has produced useful feedback for future research.