现代壁挂在室内环境中的美学意义

现代壁挂的起源与发展以及独特的编织艺术形式，给现代人带来了精神和物质享受，同时也表现了现代壁挂在特定环境中的美学意义。     

在视觉艺术教学中如何培养大学生的视觉文化鉴赏力

当下视觉文化对于大学生的影响日益深入,如何培养大学生对视觉文化理性、客观、科学的鉴赏能力,这是我们应该予以最大关注的内容.教师正确的引导、加强自身学习和艺术实践是提高视觉文化鉴赏能力的必要手段,除此,在视觉艺术教学中培养大学生的视觉文化鉴赏能力还可以通过以下四个方面入手:一、学习视觉艺术的语言;二、在视觉艺术中发展各种观念;三、在视觉艺术中传递和阐述意图;四.在情景中了解视觉艺术.     

利用语言实验室优化英语教学效果

充分利用各种现代教学设备在语言实验室进行英语教学,可以大大提高学生的英语听说能力、语言综合运用能力和语言交际能力,同时也能培养学生的观察、记忆、想象能力和创新精神,从而优化英语教学效果。     

国际视野下的中小学教师领导力开发

教师领导力作为教师发展的有效途径,为学校的发展注入了新动力。中小学教师领导力在开发过程中,受到多种因素的影响和制约,主要表现在观念、组织、资源、知识技能四个层面的限制。分析这些因素,对于指导人们在实践中更有效地开发教师领导力具有积极的意义。     

视觉文化视野下的英语听力教学模式探析

摄影摄像技术的诞生和发展,带来了新的文化范式——视觉文化的崛起,而这一现象引发了人们学习习惯和思维方式的转变。英语专业学生的听力课程教学顺应新的时代潮流,在听力课程改革中将视觉、听觉元素有机结合在一起,发挥多媒体课件、影像资料和网络环境的作用,引领听力教学进入视听时代,建立起听力课程新的教学模式。     

Quality and relevance of domain-specific search: A case study in mental health

When searching for health information, results quality can be judged against available scientific evidence: Do search engines return advice consistent with evidence based medicine? We compared the performance of domain-specific health and depression search engines against a general-purpose engine (Google) on both relevance of results and quality of advice. Over 101 queries, to which the term ‘depression’ was added if not already present, Google returned more relevant results than those of the domain-specific engines. However, over the 50 treatment-related queries, Google returned 70 pages recommending for or against a well studied treatment, of which 19 strongly disagreed with the scientific evidence. A domain-specific index of 4 sites selected by domain experts was only wrong in 5 of 50 recommendations. Analysis suggests a tension between relevance and quality. Indexing more pages can give a greater number of relevant results, but selective inclusion can give better quality.     

Internet上的军事体育信息资源与检索

如何从Internet上获取军事体育信息资源,目前尚未见诸报导。探讨了Internet上军事体育信息资源的分布与检索问题,从军事体育官方网站、军事体育相关网站、网络数据库和搜索引擎等4个方面,阐述了Internet上军事体育信息资源的获取方法。     

Document replication strategies for geographically distributed web search engines

Large-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index. Compared to a centralized, single-center search engine, this architecture offers lower query response times as the network latencies between the users and data centers are reduced. However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers. As a remedy to this scalability problem, we propose a document replication framework in which documents are selectively replicated on data centers based on regional user interests. Within this framework, we propose three different document replication strategies, each optimizing a different objective: reducing the potential search quality loss, the average query response time, or the total query workload of the search system. For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers. Moreover, we investigate the performance impact of query forwarding and result caching. We evaluate our strategies via detailed simulations, using a large query log and a document collection obtained from the Yahoo! web search engine.     

试论搜索引擎的现状与发展

随着互联网的不断发展,搜索引擎现在已成为网络用户获取信息的一个不可或缺的检索工具.搜索引擎目前存在诸多问题,例如查全率和查准率、功能问题、作弊问题、安全性问题、信息更新问题、规范化问题等.本文就这些问题进行一些探讨.     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.