云环境下用户隐私属性及其分类研究

为了保护用户隐私,在云计算环境对用户隐私的影响基础上,通过对用户隐私的分类研究,设计出一种云环境下用户隐私分类方法。对其中隐私的属性进行研究,利用XML进行属性描述。最后通过设计的电信行业CRM用户隐私保护系统来对云环境下用户隐私分类器的应用作出验证。     

一种基于上下文的用户兴趣模型

当前的上下文觉察计算研究大多没有为用户提供个性化的服务,而且对在手持设备上的个性化上下文觉察服务的用户模型研究也不多见。依托用在PC上的个性化用户模型试探性的研究手持设备上加入上下文信息的用户兴趣模型,并对设计过程给出详细论述,通过模拟实验证明模型的有效性,并对未来手持设备上用户模型进行展望。     

电子政务用户群体细分及关系模型研究*

从电子政务用户的角度出发,首先对电子政务用户的相关文献进行了论述,接着对电子政务的用户群体进行研究,并从宏观与微观、电子政务系统以及政府等角度对电子政务的用户进行了分类.在此基础上,构建了电子政务的用户群体关系模型.最后,讨论了该模型在电子政务研究与实践中的应用,并对以后的研究提出了展望.     

浅谈移动APP产品的用户研究方法

对于一款移动APP来说,无论是在开发的初期,还是在上线以后,都有必要对用户进行分析和研究。初期的研究可以帮助产品明确定位和功能需求,而后期的研究则可以帮助产品持续改进、更新。所以,产品经理和设计开发者都应将用户研究放在移动APP开发的重要位置。本文将对用户研究的意义和实质进行分析,并提出用户研究的几种方法。     

百度贴吧用户兴趣分类

百度贴吧作为百度集团旗下独立品牌,是目前为止全球最大的中文社区。但目前对百度贴吧用户兴趣识别研究甚少。本文根据用户在百度贴吧的相关行为数据,利用改进的K-Means算法对用户兴趣进行聚类的分类识别,进而识别贴吧用户兴趣,为研究用户兴趣贴吧推荐奠定基础。     

用户新闻信息浏览行为和感知价值研究

随着互联网技术的发展,用户信息行为的研究越来越受到重视,浏览行为是用户信息行为的重要组成部分,浏览行为的研究对提升用户体验具有重要意义.以今日头条用户为研究对象,基于顾客感知价值理论,使用访谈法与问卷调查法相结合的方法,探索影响用户浏览新闻的行为的因素.研究发现,用户在移动端浏览新闻场景下,只有实用价值对新闻浏览行为正...     

用户网络行为模拟技术研究

用户网络行为模拟对于网络服务提供商提升服务质量、提高用户体验方面具有非常重要的意义。首先对用户网络行为的研究背景及意义进行了阐述,然后对用户网络行为相关技术的研究进展进行了分类介绍,包括用户个人行为中的细粒度行为和粗粒度行为以及群体行为中的统计模型和生成模型,在此基础上,对这一领域的未来研究方向进行了展望。     

在线用户打分行为长记忆效应与信任关系研究

对在线打分行为的动态研究能够帮助深入理解社交网络用户集群行为和信任关系的演化机制,当前许多在线系统用户能够通过对物品进行打分传达自己的观点。通过去趋势波动分析法研究了用户打分行为在信任关系建立前后的长记忆效应,并通过随机化打分时间和信任时间建立零模型,最后进行用户打分行为异质性分析。采用Epinions数据集进行实证研究,结果表明用户打分的长记忆效应在信任关系建立前出现下降趋势（8.06%）,并于之后逐步回升（8.43%）,而在两个零模型中赫斯特指数分别稳定在0.5和0.6左右,且用户长记忆效应变动与用户度呈正相关,Pearson相关系数分别为0.9358和0.9278。该工作有助于深入理解用户集群行为和信任关系的动态演化机制。     

基于日志分析的中文输入法用户行为研究

与拼音文字不同,用户在进行中文输入时需要借助输入法软件完成从拼音串到汉字串的转换过程,输入法因此成为中文用户进行人机交互的基础性工具,而输入法的相关技术研发也一直是学术界与产业界的关注热点。在中文输入法技术的研究中,用户的行为特点对输入法软件的词库建立、算法设计、交互方式设计与性能评价等多方面都有着至关重要的作用,但由于数据获取与分析的困难,这方面的相关研究尚不多见。该文利用某中文输入法在用户许可下收集的超过4.1亿条用户输入行为记录,进行了中文输入法用户行为的分析研究,针对不同类别应用程序的输入词频差异,不同用户在同类应用程序中的不同候选词条的选择等行为特点进行了挖掘分析,研究结果会对深入了解中文输入法用户行为,进而改进输入法软件性能具有一定的指导意义。     

一种微博预警算法

以新浪微博为研究对象,基于用户特征将用户对微博转发量的影响力进行量化,提出了一种微博预警算法。首先,分别研究了大转发量与小转发量的微博作者的用户基本特征,获得其中对关键用户与非关键用户具有良好区分度的特征,并基于信息增益的特征选择法获得用户特征对用户关键性的区分度。随后,基于特征加权模型,提出了一种用户对微博转发量的影响力的量化算法。最后,提出了一种微博预警算法,该算法对给定的新发布的微博,以其作者及已有转发用户的特征就用户对该微博转发量的影响力进行量化,当影响力超过一定阈值时,输出预警信息。该算法可以有效控制敏感微博在网络上的传播及扩散。     

新的基于鼠标行为的持续身份认证方法

With the rapid development of Internet technologies, security issues have always been the hot topics. Continuous identity authentication based on mouse behavior plays a crucial role in protecting computer systems, but there are still some problems to be solved. Aiming at the problems of low authentication accuracy and long authentication latency in mouse behavior authentication method, a new continuous identity authentication method based on mouse behavior was proposed. The method divided the user’s mouse event sequence into corresponding mouse behaviors according to different types, and mined mouse behavior characteristics from various aspects based on mouse behaviors. Thereby, the differences in mouse behavior of different users can be better represented, and the authentication accuracy can be improved. Besides, the importance of mouse behavior features was obtained by the ReliefF algorithm, and on this basis, the irrelevant or redundant features of mouse behavior were removed by combining the neighborhood rough set to reduce model complexity and modeling time. Moreover binary classification was adopted. The algorithm performed the training of the authentication model. During identity authentication, the authentication model was used to obtain a classification score based on the mouse behavior collected each time, and then the user’s trust value was updated in combination with the trust model. When the user’s trust value fell below the threshold of the trust model, it might be judged as illegal user. The authentication effect of the proposed method was simulated on the Balabit and DFL datasets. The results show that, compared with the methods in other literatures, this method not only improves the authentication accuracy and reduces the authentication latency, but also has a certain robustness to the illegal intrusion of external users. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.     

An approach to user knowledge acquisition in product design

As the world increasingly moves towards a knowledge-based economy, user requirements become an important factor for enterprises to drive product collaborative design evolution. To map user requirements to the product model, user requirements are generally extracted into knowledge that can be used for design decisions. However, because users are interest-driven participants and not professional design engineers, the effect of user knowledge acquisition is not ideal. There are significant challenges for rapid knowledge acquisition with dynamic user requirements. This paper presents an approach to user knowledge acquisition in the product design process, which obtains the tangible requirements of users under the premise that users are adequate for participation. In this approach, the typical information flow is divided into four stages: submission, interaction, knowledge discovery, and model evolution. In the submission stage, natural language processing technology is used to transform text form solutions into data, so that computer technology can be applied to manage large-scale user requirements. In the interaction stage, users are helped to improve their solutions by the iterative recommendation process. In the knowledge discovery stage, after less concerned partial solutions are removed and vacant items are predicted to be supplemented, the final collection of user design information is obtained. Finally, based on rough set theory, design knowledge can be extracted to support the decision of the product model. The washing machine design project is used as a case study to explain the implementation of the proposed approach.     

智能人机界面Agent的用户模型及其构建方法

智能Agent是计算机领域中自适应人机界面需要研究的关键问题之一。文章论述了智能Agent的定义、功能和组成。给出了用户模型的结构框图,探讨了用户模型及其构建方法,并通过实例介绍了几个典型的智能Agent系统。     

带有用户意识的计算机多病毒传播模型

为改进计算机系统及网络的安全性和可信度,提出一种带有用户意识的计算机多病毒传播模型。综合考虑人为因素和客观因素对病毒传播的影响,采用稳定性分析和实验仿真的方法,在理论上证明无病毒平衡态是全局稳定的,地方病平衡态是局部渐进稳定的。实验结果表明,在该模型中,通过提高用户意识可以有效地控制计算机病毒的传播。     

一种新的用户界面设计模型

用户界面是人和计算机之间的通信媒介,友好的用户界面能提高用户使用的效率.为了增强用户界面的有效性、正确性、友好性,提高人机交互的效率,在研究已有用户界面设计模型、用户参与设计方式和领域建模的基础上,提出一种以最终用户、领域专家、开发人员为设计中心的新的用户界面设计模型.     

IT产品用户体验质量的模糊综合评价研究

提出了一种基于可用性测试的旨在对产品使用过程中的用户体验质量进行综合评价的模型。首先结合ISO9241-11有关可用性的定义,建立了包括人-机交互角度、用户角度和观察者角度的用户体验质量的360°指标体系模型;然后针对可用性概念的模糊性及其综合评估中的问题,应用层次分析法(AHP)确定了指标体系中相关因素的权重,并根据模糊隶属度函数对用户体验质量评价标准进行了模糊化处理;最后采用模糊评价方法对用户体验质量进行评价。实例表明用户体验质量的综合性评估模型可以有效地对用户体验质量进行综合性评价。     

基于EFI和双核处理器的协处理器模型

为了提高双核处理器系统的性能,提出一种基于可扩展固件接口(EFI)、利用双核技术和IPI协议实现的协处理器模型。在EFI的DXE阶段,利用IPI协议引导启动一个与EFI系统并行的用户操作系统,将EFI系统作为一个后台服务系统(协处理器)处理用户系统的RPC请求。实验证明该分工协助的方式可以提高双核处理器系统的整体性能。     

面向个性化服装推荐的判断优化模型

针对已有服装推荐算法中服装色调与用户颜色特征不协调的问题,将服装搭配的四季色彩理论与计算机视觉领域的推荐方法相结合,提出一种面向个性化服装推荐的判断优化模型。提出四季色彩判断模型,对根据输入的人脸图像提取得到的用户颜色特征集进行分类;建立优化处理模型,根据四季色彩判断模型结果和用户所需风格进行优化处理并获得服装预推荐结果;通过用户评分及反馈机制,提高优化结果,得到最终推荐结果。实验表明,该方法能实现与用户颜色特征相协调的个性化的服装推荐,并且在实际应用中具有较高的准确率。     

Modeling context-aware and intention-aware in-car infotainment systems

It is fundamental to understand users’ intentions to support them when operating a computer system with a dynamically varying set of functions, e.g., within an in-car infotainment system. The system needs to have sufficient information about its own and the user’s context to predict those intentions. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling contextual information and user intentions is currently not supported. However, manually creating software that understands the current context and predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user’s intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible user intentions according to the current context. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user’s behavior. We demonstrate feasibility of our approach using an industrial case study of an intention-aware in-car infotainment system. Finally, we show how modeling of contextual information and modeling user intentions can be combined by using model transformation.     

Computer-related success and failure: a longitudinal field study of the factors influencing computer-related performance

This study used a path analysis of longitudinal data collected from 75 manufacturing employees participating in a computer training course, to test a model of the intrapersonal processes impacting computer-related performance. Gender, computer experience, and attributional style were found to be predictive of computer attitudes, which were in turn related to computer efficacy, task-specific performance expectations, and post-performance anxiety. Computer training was effective in raising user efficacy levels and improving computer performance. In addition, post-training efficacy was predictive of subsequent computer performance. Finally, performance outcomes and future performance expectations were predictive of users’ affective reactions.