Open Gateway冲破平台界限

在Oracle OpenWorld′97大会上,Oracle宣布了基于NT平台的Oracle Open Gateway套件,再次承诺:NT平台的用户借助该产品可访问任意平台的任意数据。基于NT的Open Gateway套件包括用于MS SQL Server以及基于Oracle 8技术的Sysbase的第一个网关,该产品是Oracle Open     

集成Docker容器的OpenStack云平台性能研究

针对Open Stack云平台虚拟机未能较好利用宿主物理机资源的问题,结合基于组件工作的Open Stack在云平台管理中的高效性和Docker容器虚拟化的快速部署优势,搭建基于Docker容器技术的Open Stack云平台。对云平台管理的Docker容器中实际运行的客户机操作系统进行计算、调度、内存访问、文件读写等基础性能测试,做出相关性能的评估与分析,并在多实例运行状态下将其与Open Stack传统虚拟机进行性能比较。分析结果表明,该云平台能够利用Docker容器的轻量级虚拟化优势,优化云计算管理系统的计算性能和文件读写性能。     

基于知识图谱的中成药智能问答平台构建

近年来,中成药的使用和生产规模不断增大,产生了越来越多的中成药数据,中成药的普及与发展是一关键性问题。本项目结合知识图谱和多标签文本分类与命名实体识别的自然语言处理、语音识别等智能问答技术搭建中成药智能问答平台。在用户文本提问或语音提问后,平台会根据提问在海量中成药信息中迅速准确地查询相关中成药信息,并呈现相关中成药的知识图谱辅助用户理解。知识图谱可以将中成药很好的存储起来,智能问答可以帮助用户了解中成药,使用知识图谱结合智能问答技术对中成药普及与发展具有一定的意义。     

基于Tableau实现MOOC学习行为数据可视化分析

MOOC平台记录了大量学习行为数据,使用Tableau对edX上哈佛大学和麻省理工学院2013年开设的两门计算机导论类课程的公开数据进行可视化分析并得出分析结论.     

基于OpenFlow的发布/订阅系统的管理与安全

软件定义网络(Software Defined Network,SDN),是一种新型网络创新架构,其实现技术Open Flow通过将网络设备控制面与数据面分离开来,从而实现了网络流量的灵活控制,为核心网络及应用的创新提供了良好的平台。本文针对发布/订阅系统的数据时效性及安全上的问题,利用Open Flow这一具有集中管控能力的新型网络平台,实现了包括绘制全局拓扑图、管理设备信息、管理流量信息、管理用户订阅及策略信息在内的发布/订阅管理系统。在安全方面,通过对用户特征进行分析,使用数据挖掘算法查找可疑用户,找到后对其订阅限制的方式实现了对订阅攻击的有效防御。从而使得发布/订阅系统更加易于管理,更加安全和可靠。     

数据平台的设计和实现以及大赛中的应用

得益于大数据相关的开源软件蓬勃发展,中小企业和机构也可以进行大数据平台的搭建和应用。但是数据平台在实际应用中仍然有很多挑战,例如如何进行开放数据的共享和计算,并保证代码安全等。基于开源软件设计了一个数据平台架构来让用户一站式地共享数据和计算,同时跟踪和审核代码。根据此设计搭建了一个生产环境,并给出了基本的测试来验证平台的可用性。把平台开放给外界使用,成功地支持了上海开放数据创新应用大赛(Shanghai Open Data Apps,SODA)中的大量用户的数据共享和计算需求。     

OPhone平台持久化存储方案分析

OPhone平台基于Linux和Android，经中国移动的创新研发，设计出新颖独特的用户操作界面，增强了浏览器能力和WAP兼容性，优化了Open CORE、WebKit等业内知名引擎，增加了游戏、Widget、Java ME等在内的先进平台中间件。数据存储是0Phone平台上非常重要的功能，本文主要介绍OPhone平台向开发者提供的四种存储方式。     

基于OpenStack的私有云平台设计

云计算是当前计算机领域非常热门的研究方向,在此背景下,阐述了云计算、私有云的相关概念及其应用。详细介绍了开源云计算框架Open Stack及其核心组件。提出了一个基于Open Stack的私有云平台架构方案,在实际工作中部署了私有云平台并对相应的计算、存储、网络、镜像等资源进行管理。     

基于链接预测思想的问句检索方法研究

问答服务系统的一个重要功能是问题检索,即根据用户的提问,在已有的问答对数据中查找与用户提问相似的其他问题,将这些问题的答案直接返回给用户。问题检索任务所面临的主要困难是如何计算两个问句之间的语义相似度,提出利用链接预测模型计算问句之间的关联程度,将链接预测模型与语言模型相结合,设计出一种新的问句检索方法。通过在真实问答对数据上进行实验,表明该方法可以有效计算问句之间的语义相似度,其性能优于传统的计算方法。     

基于微信订阅号的辅助教学平台

介绍利用微信订阅号作为课堂教学的辅助平台,提示学生课前预习、课后巩固,设计实现趣味问答等环节,阐述如何将辅助教学平台部署到学生每天都离不开的手机中,提高学习效率。     

An SVD-Entropy and bilinearity based product ranking algorithm using heterogeneous data

E-commerce websites, besides selling products and services, pay ample emphasis on providing a platform for consumers to share their opinions about past and potential purchases. They share such opinions as product reviews (star ratings, plain text, etc.) and answering product related questions (Q&A data). There are several machine learning and classification approaches available to scrutinize this review data, e.g., algorithms based on Entropy measures, Bilinear Similarity, stochastic methods, etc. In this paper, we review some of the prevalent review classification techniques and present a hybrid approach, involving Singular Value Decomposition (SVD), Entropy and Bilinear Similarity measures, that uses heterogeneous product data and simultaneously analyze and rank products for customers. With experimental results, we show that our approach effectively ranks products using (1) text reviews (2) Q&A data (3) five-star rating of products and has 10% improved prediction accuracy as compared to the individual approaches. Also, using SVD, we achieve a 35% runtime efficiency for our algorithm while only sacrificing 1% of the prediction accuracy.     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

多任务高并发数据处理平台的技术研究

本文提出了基于HL7协议及数据流处理技术的多任务高并发数据处理平台。这个平台提供开放的接口,可以进行数据传输及交换,并能够与其他兼容HL7的医疗系统交换病人病历信息。在数据处理方面,该平台使用超大型存储设备和严格的安全管理方案,以确保数据处理的速度和数据的真实性。     

Piazza: mediation and integration infrastructure for Semantic Web data

The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to provide interoperability—both between sites with different terminologies and with existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world’s data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies.This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, and it maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it.     

基于案例推理技术的自学习答疑引擎

将基于案例的推理技术引入传统的自动答疑系统，并结合用户交互机制，提出了一种基于案例推理的自学习答疑引擎。该引擎通过对问题的关键字描述来触发案例，并根据它们与问题的关联程度进行排序；同时，利用用户端的反馈及时有效地对关键字的权重进行维护，提高了答疑系统的效率准确性。     

StackExchange问答社区知识传播

StackExchange是目前最流行的问答社区集结地之一.本文利用StackExchange中具有美国地理信息的用户构建StackExchange问答社区在美国境内的知识传播图谱,对传播网络的统计特征进行了分析,提取出问答社区类网站的传播模式,获取得到网络用户的知识分享方式.我们发现StackExchange中的问答社区在分享知识过程中,传播源往往不止一个.同时,我们为问答社区构建了知识传播图谱,发现这些传播图谱具有相似的统计特征,这意味着不同的问答社区可能具有类似的知识传播模式.     

基于知识图谱、TF-IDF和BERT模型的冬奥知识问答系统

传统信息检索技术已经不能满足人们对信息获取效率的要求,智能问答系统应运而生,并成为自然语言处理领域一个非常重要的研究热点。本文针对中文的冬奥问答领域,提出了基于知识图谱、词频-逆文本频率指数 (term frequency-inverse document frequency,TF-IDF)和自注意力机制的双向编码表示(bidirectional encoder representation from transformers,BERT)的3种冬奥问答系统模型。本文首次构建了冬奥问答数据集,并将上述3种方法集成在一起,应用于冬奥问答领域,用户可以使用本系统来快速准确地获取冬奥内容相关的问答知识。进一步,对3种模型的效果进行了测评,测量了3种模型各自的回答可接受率。实验结果显示BERT模型的整体效果略优于知识图谱和TDIDF模型,BERT模型对3类问题的回答可接受率都超过了96%,知识图谱和TDIDF模型对于复合统计问答对的回答效果不如BERT模型。     

问答系统研究综述

问答系统是人工智能和自然语言处理领域中具有广泛发展前景的研究方向之一.早期的问答系统限定以自然语言形式进行提问和回答,近年来,随着多模态知识图谱、多模态预训练模型的发展,支持文字、图片、音频、视频等多种模态间信息查询的广义问答系统逐渐成为新的研究热点,其以多媒体方式展示结果,更加直观、全面.本文根据问答系统任务对象的变化,将问答系统划分为3种类型:专用问答系统、通用问答系统和多模态问答系统.分析了这3种类型的问答系统发展过程中所面临的问题,着重总结每个阶段所采用的关键技术与方法,同时对问答系统在工业上的应用进行了举例说明,并对未来研究方向进行了展望.     

What are mobile developers asking about? A large scale study using stack overflow

The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums.     

深度学习视阈下MOOC学习者流失预测及干预研究

MOOC（Massive Open Online Courses）在为学习者提供优质课程的同时，低完成率成为影响其有效推广的重要因素。通过对edX开放数据集分析发现，学习者的逐渐流失是导致MOOC课程低完成率的因素之一，且学习行为与成绩之间存在复杂的相关性；基于线性回归和深度神经网络（Deep Neural Network，DNN）预测学习者的成绩，实验证明，DNN能够更好地拟合学习行为与成绩之间复杂的相关性，实现对成绩更加精准的预测，预警学习者流失；对预测的潜在流失学习者迭代进行个性化的教学干预，提高MOOC课程的完成率。