A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.     

动态文本会话抽取技术研究

对海量的短文数据进行自动分析和挖掘,从中获取有价值的隐含知识已经成为一项迫切的需求。动态文本会话抽取是针对MSN,QQ等动态数据,将属于同一会话但相互交错的不同消息文本组织在一起,属于在线话题发现追踪的新兴领域,在信息检索,文本挖掘和话题检测追踪等方面有着重要应用。首先介绍了文本会话抽取的必要性和重要性,介绍其主要研究内容和结果评测方法;然后对其中多个研究内容提出一个统一研究框架,并对该框架中的关键技术进行了详细分析;最后指出该领域中的关键问题及难点,并对未来研究做出展望。     

Exploring Social Networks and Improving Hypertext Results for Cloud Solutions

Internet technologies are constantly evolving as well as the way people use them. Search engines help users to find higher and better relevant results to their searches. Cloud Computing is an evolution of the Internet services and provides a step further ecosystem that can be used to improve the search of more relevant results. Each search engine is based on different modules in order to retrieve the results expected by users using specific keywords. Social networks appear as a reliable Web technology that can directly support a content search. Several studies have been performed showing the growth of social networks in people lives. Using the cloud computing paradigm it is possible to propose a more scalable and efficient way to explore public information available on online social networks. This paper includes the analyses of several social networks services, available contents, cloud-crawlers, and information extraction. In order to collect relevant data from social networks, a social crawler on cloud is proposed. The new approach provides a cloud-based crawler for low-cost, effective, and personalized search models. Moreover, a new algorithm to rank Web documents is proposed and demonstrated. The proposed system is evaluated in comparison with the top Internet search engine, Google, its behavior is very promising, and it is ready for use.     

All-Learning: The state of the art of the models and the methodologies educational with ICT

This paper presents a systematic review of models and methodologies that integrate information and communication technologies (ICT) and education. The systematic review was based on the methodology of Kitchenham. The steps used and developed correspond to the steps proposed in the methodology. The starting point of the review are the research questions, then keywords, selection of the databases, definition of the inclusion and exclusion criteria, the definition of the search chains, search process and selection of papers, the analyzes of the paper and the results of the systematic review to answer the questions posed. In the systematic review, 919 papers were found in 6 academic databases and 129 relevant papers were selected. The work developed intends to know the different models and methodologies that integrate the ICT and the education. Develop an analysis and characterize to find common elements among models and methodologies. The idea is to find limitations, disadvantages and spaces that allow to propose a new model. This systematic review is the first step in the development of a doctoral research in which the development of a U-Learning model based on Connective Learning and Experience Learning is proposed.     

An AI-based communication system for motor and speech disabledpersons: design methodology and prototype testing

An intelligent communication device is developed to assist the nonverbal, motor disabled in the generation of written and spoken messages. The device is centered on a knowledge base of the grammatical rules and message elements. A "belief" reasoning scheme based on both the information from external sources and the embedded knowledge is used to optimize the process of message search. The search for the message elements is conceptualized as a path search in the language graph, and a special frame architecture is used to construct and to partition the graph. Bayesian "belief" reasoning from the Dempster-Shafer theory of evidence is augmented to cope with time-varying evidence. An "information fusion" strategy is also introduced to integrate various forms of external information. Experimental testing of the prototype system is discussed.     

本体论在Web信息检索中的应用

文章提出了一种基于本体论的Web信息检索模型，它利用领域本体知识库对用户的查询要求进行语义扩展，并且将检索到的文档在语义层次上进一步过滤，从而克服了目前的检索工具仅提供基于关键字的检索．而忽视关键字本身语义信息所造成的错检和漏检。     

一种基于汉字笔顺特征的关键词变体匹配方法

近年来,垃圾短消息呈现出包含大量拆分字、形近字现象,这种短消息可以绕过监控系统的关键词审查。由于拆分字、形近字数量众多、变化灵活,将其加入关键词库将使得关键词库冗余。对此,本文提出了一种基于汉字笔顺特征的关键词变体匹配方法,基于汉字笔顺特征,首先合并垃圾短消息中的拆分字,然后通过建立索引查找短消息中包含的疑似关键词,最后提出了“金字塔匹配法”匹配关键词。本文提出的方法有效降低了关键词库的冗余度,提高了关键词匹配效率。     

Predicting continuity of online conversations on Reddit

Online conversation has been facilitating human society, which has encouraged people not only to share news, products, or daily events, but also to develop ideas. A popular type of online conversation is threaded conversation, where a person initiates a conversation with a new topic by uploading a post, then others reply to the post or the comments made by other participants in a recursive manner. Even though the growing importance of understanding and predicting the development process of threaded conversations has attracted research community to investigate continuity of online threaded conversations from a perspective of individual posts or comments, little work has focused on predicting the continuity or persistence of online conversations. In this paper, we propose a deep learning model to predict continuity of threaded conversations – whether there will be a newly-arrived comment or not. Cooperating with popular pre-trained text embedding models and graph neural network models, the proposed model captures text, structural, and temporal characteristics of the threaded conversation by an observable time for the final prediction. We evaluate the proposed model with two different types of the threaded conversations, self-introductory Q&A and discussion, which demonstrates that the proposed model can accurately predict the continuity of the conversations regardless of types of threaded conversations. We believe that the proposed methodology and the results can provide the potential insights to advertisers, opinion leaders, or platform designers who want to understand and predict the evolutionary process of threaded conversations.     

信息检索中的聚类分析技术

信息检索/搜索引擎技术的快速发展使得信息的查全率有较大提高,而查准率以及人们获取信息的效率改善却不明显。文本聚类和多文档关键词的自动生成技术将有助于解决这一问题。其基本思想是对检索到的部分文档进行聚类处理,并对每类文档自动生成关键词,从而帮助用户判断各个类别的文档和检索需求是否相关。该文提出文档相关度和类别相关度的概念,并利用词频信息以及知网(HOWNET)中词的概念计算模型计算类别相关度,将其作为聚类合并的依据。信息获取的仿真实验表明文档检索效率有较大提高。     

Search recommendation model based on user search behavior and gradual forgetting collaborative filtering strategy

The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose.By analyzing user's dynamic search behavior,the paper introduces a new method of using a keyword query graph to express user's dynamic search behavior,and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user.To reflect the dynamic changes of the user's preference,the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model.By calculating the similarity between each two users,the model can do the recommendation based on neighbors and be used to construct the personalized search engine.     

抗关键词猜测的授权可搜索加密方案

大多数可搜索加密方案仅支持对单关键词集的搜索,且数据使用者不能迅速对云服务器返回的密文进行有效性判断,同时考虑到云服务器具有较强的计算能力,可能会对关键词进行猜测,且没有对数据使用者的身份进行验证。针对上述问题,该文提出一个对数据使用者身份验证的抗关键词猜测的授权多关键词可搜索加密方案。方案中数据使用者与数据属主给授权服务器进行授权,从而验证数据使用者是否为合法用户;若验证通过,则授权服务器利用授权信息协助数据使用者对云服务器返回的密文进行有效性检测;同时数据使用者利用服务器的公钥和伪关键词对关键词生成陷门搜索凭证,从而保证关键词的不可区分性。同时数据属主在加密时,利用云服务器的公钥、授权服务器的公钥以及数据使用者的公钥,可以防止合谋攻击。最后在随机预言机模型下证明了所提方案的安全性,并通过仿真实验验证,所提方案在多关键词环境下具有较好的效率。     

How Does Insightful and Emotional Disclosure Bring Potential Health Benefits? Study Based on Online Support Groups for Women With Breast Cancer

Building upon 2 theoretical models (the cognitive adaptation model and the emotional exposure–habituation model), this research focused on 2 aspects of disclosure content—insights and emotions—and examined how women with breast cancer benefit from written disclosure in online support groups. Using survey data collected at baseline and after 4 months and messages posted in bulletin‐board‐type online groups in between, we analyzed how the content of disclosive messages predicted health outcomes. Disclosure of insights led to greater improvements in health self‐efficacy, emotional well‐being, and functional well‐being, which was mediated by lowered breast cancer concerns. Disclosure of negative emotions had no main effects on health outcomes, but weakened the unfavorable association between concerns at baseline and functional well‐being at follow‐up.     

CBM数据库收录1971-2006年激光与护理文献计量分析

目的:计量分析我国1971-2006年发表的激光治疗与护理文献,以期展现这一研究成果的全貌,对研究者有所帮助.方法:从CBMDISC局域网版,检索1971-2006年国内有关激光与护理的文献,用FOXPRO对检索字段编程,并用EXCEL排序,分项计量统计分析.结果:共检获文献1297篇,自1994年后文献数量呈逐年上升趋势,合作率为69.31%,合作度为2.66人,主要作者为刘鲁霞(8篇),主要发文期刊为激光杂志(83篇),主要发文机构为第三军医大学附属新桥医院(48篇),主要发文地区为山东省(151篇),主要研究方向为眼病(341篇)等.结论:从文献外部特征描绘了我国近35年来激光与护理研究的概况,有助于研究者在了解全貌的基础上取得更好成绩.     

一种基于概念抽取的相关词推荐模型

搜索引擎已经成为Internet上非常有用的信息检索工具，而查询关键词的质量则是影响搜索效果的最重要的因素之一。相关词推荐技术可以通过向用户提供高质量的查询词，帮助用户缩小查询范围，提高搜索结果的查准率。文章提出一种新的基于概念抽取的相关词推荐模型，该模型克服了传统相关词推荐模型的一些不足，提供精确度更高的相关词作为二次搜索关键词，使信息搜索达到更好的效果。     

Turn a Blind Eye If You Care: Impacts of Attitude Consistency,Importance, and Credibility on Seeking of Political Information and Implications for Attitudes

Abundant media outlets allow for much diversity of political messages and selective use among citizens. A 2‐session online field study examined impacts of attitude consistency, attitude importance, and source credibility on selective exposure to political messages and subsequent attitude accessibility. The first session assessed attitudes and their accessibility. In the second session, participants browsed online search results that featured attitude‐consistent and attitude‐discrepant messages associated with sources of either high or low credibility; selective reading was tracked. Then attitude accessibility was measured again. Participants spent less time with attitude‐discrepant messages compared to attitude‐consistent messages; this pattern was particularly pronounced among participants with higher attitude importance. Low importance fostered exposure to high‐credibility messages. Exposure to attitude‐discrepant, high‐credibility messages reduced attitude accessibility.     

一种基于本体的信息检索模型

为解决传统的基于关键词的信息检索只能从语法上分析关键词、进行关键词的机械匹配,导致检索缺乏语义的问题,提出了一个基于本体的语义网检索模型,并以此为基础构建一个系统原型.实践结果表明,该检索模型能够在一定程度上改善检索效果.     

一种基于本体语义的网格服务发现模型

在网格环境中,网格服务的发现机制对于系统的性能和效率有着重要的意义和作用。目前网格服务发现模型多数是集中式的、基于关键字的服务匹配模型,缺乏灵活性和推理能力,发现结果难以令人满意。提出了一种基于本体论语义和Agent代理的网格服务发现模型。采用Agent代理技术进行服务搜索,不同的代理能够提供不同领域的本体知识库,从而达到多领域的服务发现;利用本体论语义进行服务的匹配,从而达到智能化的服务发现。该模型提高了服务发现的准确率,同时也增强了系统的可维护性、灵活性和扩展性以及服务管理的自治性和容错性。     

Knowledge Based Image Annotation Refinement

Recently, images on the Web and personal computers are prevalent around the human’s life. To retrieve effectively these images, there are many (Automatic Image Annotation) AIA algorithms. However, it still suffers from low-level accuracy since it couldn’t overcome the semantic-gap between low-level features (‘color’, ‘texture’ and ‘shape’) and high-level semantic meanings (e.g., ‘sky’, ‘beach’). Namely, AIA techniques annotates images with many noisy keywords. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. Furthermore, We can re-formulate the removal of erroneous keywords from image annotation problem into graph-partitioning problem, which is weighted MAX-CUT problem. It is possible that we have too many candidate keywords for web-images. Hence, we need to have deterministic polynomial time algorithm for MAX-CUT problem. We show that finding optimal solution for removing noisy keywords in the graph is NP-Complete problem and propose a new methodology for Knowledge Based Image Annotation Refinement (KBIAR) using a deterministic polynomial time algorithm, namely, randomized approximation graph algorithm. Finally, we demonstrate the superiority of this algorithm over traditional one including the most recent work for a benchmark dataset.     

A Novel Dual Pointer Approach for Entity Mention Extraction

The named entity extraction task aims to extract entity mentions from the unstructured text, including names of people, places, institutions and so on. It plays an important role in many Natural language processing (NLP) tasks, such as knowledge bases construction, automatic question answering system and information extraction. Most of the existing entity extraction studies are based on the long text data, which are easier to annotate due to the sufficient contextual information. Extracting entities from short texts such as search queries, conversations is still a challenging task. This paper proposes a dual pointer approach for entity mention extraction, it extracts one entities by two position pointers of the input sentence. The end-to-end deep neural networks model based on the proposed approach can extract the entities by serially generating the dual pointers. The evaluation results on the Chinese public dataset show that the model achieves the state-of-the-art results over the baseline models.