Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

UX Curve: A method for evaluating long-term user experience

The goal of user experience design in industry is to improve customer satisfaction and loyalty through the utility, ease of use, and pleasure provided in the interaction with a product. So far, user experience studies have mostly focused on short-term evaluations and consequently on aspects relating to the initial adoption of new product designs. Nevertheless, the relationship between the user and the product evolves over long periods of time and the relevance of prolonged use for market success has been recently highlighted. In this paper, we argue for the cost-effective elicitation of longitudinal user experience data. We propose a method called the “UX Curve” which aims at assisting users in retrospectively reporting how and why their experience with a product has changed over time. The usefulness of the UX Curve method was assessed in a qualitative study with 20 mobile phone users. In particular, we investigated how users’ specific memories of their experiences with their mobile phones guide their behavior and their willingness to recommend the product to others. The results suggest that the UX Curve method enables users and researchers to determine the quality of long-term user experience and the influences that improve user experience over time or cause it to deteriorate. The method provided rich qualitative data and we found that an improving trend of perceived attractiveness of mobile phones was related to user satisfaction and willingness to recommend their phone to friends. This highlights that sustaining perceived attractiveness can be a differentiating factor in the user acceptance of personal interactive products such as mobile phones. The study suggests that the proposed method can be used as a straightforward tool for understanding the reasons why user experience improves or worsens in long-term product use and how these reasons relate to customer loyalty.     

一种可信软件设计方法及可信性评价

针对可信计算组织TCG(Trusted Computing Group)的信任链无法保障软件运行时动态可信的问题,对该信任链进行扩充,引入对软件运行时动态可信性的检测,提出了可信引擎驱动的可信软件信任链模型,并在此基础上提出了一种可信软件设计方法及可信性评价策略.通过引入描述软件可信行为轨迹的可信视图,在可信软件检查点处植入检查点传感器,将软件可信性融入软件设计中.通过对软件的完整性度量以及运行过程中软件行为轨迹的监测,实现软件的可信性保障.实验分析表明:采用该方法设计的软件能够有效地检测软件异常,并且成功检测软件异常的能力明显优于基于TCG信任链的软件.     

支持多种安全级别的文件加密工具设计

针对目前加密工具存在加密算法单一、密钥管理不方便、密文文件共享不安全、文件重要性未区分等问题,设计了一个支持多种安全级别的文件加密工具。该工具的密钥随机生成,按用户分组组织,保存于密钥文件中,并按照文件的重要性,把它分成一般、中等和高3个安全级别。一般和中等安全级别文件加解密时,自动从密钥文件中查找密钥,而后者解密时需要进行用户认证,高安全级别文件的密钥不在密钥文件中,加密时随机生成,用投票者的公钥加密后保存到服务器,解密时需要所有投票者同意才能解密。该工具加密时随机选择加密算法,密钥无需用户记忆,通过密钥管理保证不同级别密文文件共享的安全,既具有操作的方便性,又具有较高的安全性。     

网格访问控制与授权模型的设计与实现

访问控制和资源授权是网格系统中资源与用户的关系策略的集合,分析了访问控制与资源授权的设计原则,提出了一种基于禁止表和允许表的网格用户访问控制层次式AB4L访问控制模型.给出了该模型的形式化定义,叙述了基于Postgres数据库的资源访问控制模型和授权的实现方法,并从完备性、可扩展性、自主控制和安全性方面对该模型进行了性...     

基于加权马尔可夫链的主动用户行为预测模型

建立有效的用户行为预测模型,准确地预测用户的上网行为,是当前网络主动管理地关键,传统的Markov模型是一种简单而有效的预测模型,但它存在测准确率低、预测覆盖率低以及存储复杂度高等缺点.提出了基于加权马尔可夫链模型,通过分析用户行为特征和最优状态分类的方法,预测网络用户行为.最后通过实验结果表明了该模型的可行性和实用性...     

基于极速神经网络的协作过滤方法研究

协作过滤是一种有效的个性化推荐技术,针对该技术随着用户和资源的增多,数据的高维稀疏特性严重导致推荐质量的下降和计算速度减慢的问题,研究并实现了一种基于极速神经网络的协作过滤方法。采用主成分分析解决数据高维稀疏性问题,采用极速神经网络技术解决计算速度慢的问题。实验结果表明,该方法具有良好的泛化性能和学习速度,能很好的满足个性化资源推荐的需求。     

非线性电路的簇发现象及分岔机制

通过在立方非线性Hartley模型中引入交变的电流源,并选定适当的参数和激励频率,建立了具有快慢行为的两时间尺度周期激励电路系统．由Hopf分岔的产生条件,推导了对应自治系统Hopf分岔的第一Lyapunov系数解析表达式,并在数值计算中得到了验证．结合该系数,重点分析了系统中的快慢行为,给出了典型的周期簇发现象及其相应的分岔模式,并利用自治系统和转换相图从分岔角度指出了该种簇发现象的产生机理．     

随机非线性动态系统数值模拟与性态分析

针对具有随机参数非线性系统和随机非线性系统的数值模拟与性态分析问题,提出将蒙特卡罗模拟办法与数值积分法相结合,建立了迭代积分算法求取随机非线性系统在不副随机序列作用下的数值解集,分析了计算误差．其次,利用数值解集的样本值进行解过程性态分析,建立了系统状态的概率分布、均方和矩函数的数值汁箅公式,实现了该类系统数值模拟分析的完整算法和程序流程设计．最后,通过两类典型示例系统的数值仿真结粜验证本文算法与流程的正确性和有效性．     

Understanding risk-taking behavior of groups: A “decision analysis” perspective

Past research has extensively investigated the effect of media, especially focusing on how anonymity increases risk-related behaviors of groups when using computer-mediated communication (CMC). This study extends prior research by examining the differences in group risk-taking behaviors between face-to-face groups and completely non-anonymous CMC groups (i.e., groups working in a fully identified, synchronous CMC environment similar to popular instant messaging systems utilized widely within organizations). Drawing on the “decision analysis” perspective, a key framework for understanding organizational decision-making, the study also examines the effects of the firm's risk preferences as well as the type of information distribution among group members (i.e., full information known to all group members versus partial information know by only some of the members) on the groups' risk-taking behaviors. Results from a laboratory experiment using student subjects found no differences in risk-taking behaviors between CMC and face-to-face groups; additionally, no differences were found related to how information was distributed among group members. A significant effect was found, however, for the risk preference of the firm, showing that risk-neutral firms influenced groups to make riskier decisions than groups from risk-averse firms. Finally, groups within risk-neutral firms receiving partial information made riskier decisions than groups receiving full information. The implications of these results for future research and practice are examined.