为了满足使用者需求而设计——上海地铁研究·使用者需求

在上海地铁研究中,我们发现了许多使用者的不满。这些不满来自于设计师对使用者的缺乏了解和缺乏关注。通过对上海地铁真实的使用者、真实的地点和真实的时间的调研,我们分析出25项上海地铁使用者的需求。并提出希望设计师能够从这些使用者的需求出发,去作为使用者带来良好体验的设计。     

Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems

Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance.     

Adaptive training interfaces for less-experienced,elderly users of electronic devices

A great number of complex electronic devices are now part of our everyday lives. While many of us learn to handle these products by trial and error; others, especially older users with little experience in using electronic devices, need support. In order to allow the user maximum flexibility in terms of learning time and location, a training programme is presented which is implemented as part of the software embedded in the product itself. Particular focus is placed on the effect of adaptive training on learning. In this study, the training versions differed in their ability to adjust their complexity to the user's experience (adaptive user interface complexity) and their capability to support the learner by prompting them during the learning process (adaptive training advice). The results show that the adjustment of complexity had a positive effect on users’ experience: elderly users who trained with an adaptive interface were more successful in learning to use a mobile phone. Adaptive training advice, however, was found to have no significant effects on learners’ success and reduced their self-efficacy. This work offers guidelines on how to design integrated training applications for electronic devices that successfully help elderly users with little prior experience.     

Probability-based location anonymity algorithm

As one of the most effective location privacy preservation technologies, the k-anonymity model provides safeguards for location privacy of the mobile client against vulnerabilities for abuse by constructing an anonymous area of k users including the protected one. However, most existing k-anonymity models only utilize the users who are sending requests at recent time. If there are not enough requesting users, the generated anonymous area of the k-anonymity model will be larger than expected. In this paper, a Probability-based Location Anonymity (PLA) algorithm is proposed for protecting location privacy of the mobile users in a road network. The PLA model takes advantage of the historical path track of the users who are not sending the request currently, and then computes the probability into the anonymous section so that it can greatly reduce the size of the anonymous area. Experimental results show that the PLA algorithm is superior to the k-anonymity and it increases its anonymous efficiency enormously.     

非时隙结构认知无线网络中非授权用户传输时延分析

在非时隙结构认知无线网络中,以随机侦听多址方案为基础构建了系统的数学模型,利用柯尔莫哥洛夫费勒前向方程得出系统的稳态概率,并采用拉普拉斯斯帝尔阶斯变换推导出非授权用户数据包的平均传输时延.数值分析表明,信道忙的概率和平均退避时间与传输时延呈正比关系,不同的载波侦听多址方案对传输时延影响很大,其中全侦听多址方案的传输时延最小.     

移动搜索终端用户行为调查研究

移动搜索正在深入到移动互联网用户的生活,为更好地了解移动搜索终端用户行为,以提供更好的服务,对移动搜索终端用户的行为进行了调查。共发出调查问卷522份,收到有效问卷508份,通过对有效问卷进行深入研究,分析移动用户使用移动搜索的行为习惯,得出移动搜索呈现本地化、使用需求碎片化、使用习惯已经形成、用户年龄较为集中等结果,为探索移动搜索新业务提供有效的数据支持,并从搜索精度、搜索方式、提高搜索质量等方面对移动搜索的发展提出合理化建议。     

中国的铸造生产能力与铸件出口贸易

2001年度中国灰铸铁，铸钢和可锻铸铁三类铸件的产量均大于或相当于日、美、德三国同类铸件产量的总和。尚欠发达的中国相关工业如何能为如此巨大的铸造工业提供足够的订单?美国按相关工业分类预测铸件年需求量的经验值得我们借鉴。中国铸件出口以廉价低档铸件为主，量值长期在低位徘徊的局面应予改变。2003年度美国各类进口铸件预计可达220万t，销售总金额约为40亿美元。     

协同过滤技术的改进研究

协同过滤算法应用于个性化推荐系统中取得了巨大成功,它是通过用户项目评分数据,以用户之间或者项目之间相互协作的方式来产生推荐。然而,邻居用户的相似度计算不精确一直是阻碍推荐系统推荐精度进一步提高的主要因素。从提高用户间相似度计算精度出发,提出了一种改进算法,该算法通过考虑不同特征、加强平均值影响、惩罚热门项目的比重,对用户的相似度计算方法进行改进,以期生成更加合理的邻居用户集,最后,根据评分预测公式进行预测,最终产生推荐。在MovieLens数据集上的实验表明,改进算法计算用户间的相似度更加精确,推荐算法的预测精确度有了显著提高。     

面向最终用户编程的服务虚拟化模型

为了解决面向最终用户编程问题,提出了一种基于动态语义的服务虚拟化模型。该模型以SOA思想理论为基础,采用面向服务计算和Web服务技术,结合本体服务描述语言和描述逻辑,建立服务虚拟化模型,从而实现面向最终用户编程。该模型有效地提高了模型的查全率和查准率,能够在一定程度上解决软件危机。     

WLAN拨号客户端方案

随着中国电信WLAN业务开放和深入推进,客户对WLAN业务体验飞速增长,本文就WLAN业务拨号方式进行多方位的分析,并提出WLAN专用拨号客户端的建议和发展思路。