基于MVC的校园闲置物品置换系统设计

随着经济水平的提高、移动支付的出现,国内大学快递行业飞速发展,学生网购量日益增多,二手闲置物品也在增加。目前咸鱼市场有较多不稳定因素,存在众多欺诈行为,校园内跳蚤市场缺乏有效管理。因此,笔者构建了校园闲置物品置换系统,其能够在校园范围内实现校园闲置物品的重新利用,发挥闲置物品的价值。     

物联网环境下基于情境的语义Web服务选择

当前Web服务海量增加，物联网应用技术快速发展、不断普及，而现有的Web服务选择算法低效、用户匹配度低。针对该问题提出一种物联网环境下基于情境的语义Web服务选择方法。该方法应用QoS参数的无量纲化与语义Web服务动态选择方式，将物联网环境下服务与语义Web服务相结合，并根据用户需求针对QoS选择最优的服务集。实验表明，该方法能有效地提高用户服务动态选择的成功率。     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

基于NB分类器重访概率预测的Web缓存替换策略

Web缓存是用来解决网络访问延迟和网络拥塞问题，缓存替换策略直接影响缓存的命中率。为此，提出一种朴素贝叶斯（NB）分类器重访概率预测的Web缓存替换策略；根据用户之前访问日志，通过分区操作提取多项特征来表示每次访问的对象，并构建特征数据集；训练NB分类器，用来确定缓存中对象被再次访问的概率，为对象分配权重；结合LRU策略来合理删除一些对象。仿真结果表明，提出的策略在保证较高命中率的同时有效降低了执行时间。     

基于儿童使用主体的建成环境评价尺度研究

评价尺度是建筑使用后评价的基 本问题之一，它是使用者评判建成环境好 坏、优劣的重要测度标准。为弥补目前环境 评价尺度研究方面的缺失，建立与儿童使用 主体相一致的建成环境评价尺度，对儿童常 用语义量词进行汇总和筛选，采用五级线 分刻度方法进行问卷调查研究。基于儿童的 理解，通过问卷试验分析，结果表明：双侧 5级区间评价尺度为：很（负面）；有点（负 面）；中等；有点（正面）；很（正面）；单侧 5级区间评价尺度为：不；好像有点；比较； 很；特别。     

互联网Web前端应用中的问题分析

随着Web的发展,在该领域存在的问题也逐渐显露出现,为了从根本上解决Web前端应用过程中存在的问题,需要对Web前端设计进行系统化分析。近年来,Web前端在实际应用过程中因结构设计不完善出现了很多问题,严重影响Web工作的正常运转,针对Web前端问题而提出的研究与防范措施迫在眉睫。目前,基于Web前端结构设计工作的特殊性,可以看出Web前端设计项目至关重要。文章主要介绍了Web前端的具体结构设计以及Web前端在实际应用过程中存在的问题,并提出了相应的解决对策。     

Modeling Smart Grid neighborhoods with the ENERsip ontology

The electricity consumption in the buildings sector has been steadily increasing during the last decade, up to the point that energy efficiency in this sector has become a major problem for governments, utilities, customers, and the environment. The foreseen high penetration of distributed micro-generation facilities based on renewables can help to reduce the environmental footprint of buildings and households, although the complexity of managing effectively the electric grid increases dramatically under these conditions. The IEEE 2030 standard for interoperability in the Smart Grid remarks upon the importance of well-defined data models in such complex scenarios and puts emphasis on the benefits of ontologies and OWL (Web Ontology Language) for this purpose. This paper presents an OWL-based ontology that formally defines the vocabulary and taxonomy and captures the engineering and business semantics of this domain of knowledge (i.e., energy efficiency in the so-called nZEN – nearly Zero-Energy Neighborhoods). This ontology has been defined under the scope of the EU (European Union) research project ENERsip. The paper also highlights the main benefits the ontology brought to all the phases of the project life cycle, as well as how future work can make the most out of it.     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

Data collection mode effect on feeling thermometer questions: A comparison of face-to-face and Web surveys

Feeling thermometer questions are widely used in political science research to estimate people’s attitudes and feelings toward a political object, like a political figure or an organization. Given the popularity of the feeling thermometer question in population surveys, more work is needed to explore the measurement of this question type. This study examines the data collection mode effect on feeling thermometers. Using the 2012 American National Election Studies, we find that the measurement of feeling thermometers is not exactly comparable between face-to-face and Web surveys. Face-to-face respondents tend to provide warmer feelings, while Web respondents give relatively more reliable responses in comparison. In both survey modes, respondents are most likely to select the response options that are verbally labeled although the effect is more striking in face-to-face than Web survey. The item nonresponse between these two modes does not differ in a meaningful way. This study ends by discussing future research directions on feeling thermometer questions.