Understanding the asymmetric perceptions of smartphone security from security feature perspective: A comparative study

Smartphones are being used and relied on by people more than ever before. The open connectivity brings with it great convenience and leads to a variety of risks that cannot be overlooked. Smartphone vendors, security policy designers, and security application providers have put a variety of practical efforts to secure smartphones, and researchers have conducted extensive research on threat sources, security techniques, and user security behaviors. Regrettably, smartphone users do not pay enough attention to mobile security, making many efforts futile. This study identifies this gap between technology affordance and user requirements, and attempts to investigate the asymmetric perceptions toward security features between developers and users, between users and users, as well as between different security features. These asymmetric perceptions include perceptions of quality, perceptions of importance, and perceptions of satisfaction. After scoping the range of smartphone security features, this study conducts an improved Kano-based method and exhaustively analyzes the 245 collected samples using correspondence analysis and importance satisfaction analysis. The 14 security features of the smartphone are divided into four Kano quality types and the perceived quality differences between developers and users are compared. Correspondence analysis is utilized to capture the relationship between the perceived importance of security features across different groups of respondents, and results of importance-satisfaction analysis provide the basis for the developmental path and resource reallocation strategy of security features. This article offers new insights for researchers as well as practitioners of smartphone security.     

Tactile colour pictogram to improve artwork appreciation of people with visual impairments

A recent development in tactile technology enables an improvement in the appreciation of the visual arts for people with visual impairment (PVI). The tactile sense, in conjunction with, or a possibly as an alternative to, the auditory sense, would allow PVIs to approach artwork in a more self‐driven and engaging way that would be difficult to achieve with just an auditory stimulus. Tactile colour pictograms (TCPs), which are raised geometric patterns, are ideographic characters that are designed to enable PVIs to identify colours and interpret information by touch. In this article, three TCPs are introduced to code colours in the Munsell colour system. Each colour pattern consists of a basic cell size of 10 mm × 10 mm to represent the patterns consistently in terms of regular shape. Each TCP consists of basic geometric patterns that are combined to create primary, secondary, and tertiary colour pictograms of shapes indicating colour hue, intensity and lightness. Each TCP represents 29 colours including six hues; they were then further expanded to represent 53 colours. Two of them did not increase the cell size, the other increased the cell size 1.5 times for some colours, such as yellow‐orange, yellow, blue, and blue‐purple. Our proposed TCPs use a slightly larger cell size compared to most tactile patterns currently used to indicate colour, but code for more colours. With user experience and identification tests, conducted with 23 visually impaired adults, the effectiveness of the TCPs suggests that they were helpful for the participants.     

基于NR-Transformer的集群作业运行时间预测

高性能集群的作业调度通常使用作业调度系统来实现，准确填写作业运行时间能在很大程度上提升作业调度效率。现有的研究通常使用机器学习的预测方式，在预测精度和实用性上还存在一定的提升空间。为了进一步提高集群作业运行时间预测的准确率，考虑先对集群作业日志进行聚类，将作业类别信息添加到作业特征中，再使用基于注意力机制的NR-Transformer网络对作业日志数据建模和预测。在数据处理上，根据与预测目标的相关性、特征的完整性和数据的有效性，从历史日志数据集中筛选出7维特征，并按作业运行时间的长度将其划分为多个作业集，再对各作业集分别进行训练和预测。实验结果表明，相比于传统机器学习和BP神经网络，时序神经网络结构有更好的预测性能，其中NR-Transformer在各作业集上都有较好的性能。     

小微数据包高效无线传输技术的发展和趋势

5G-NR和B5G系统都需能高效地支持小微数据包(块)的传输,且在不同的无线传输技术(如宽带或超低时延)之间快速灵活地切换。系统梳理和比较和小微数据包(块)相关的多种无线传输技术,如两步RACH、预配置授权和公共资源竞争等,分析它们对3GPP各个系统和各个RAN协议的影响和复杂度优劣等,并进一步展望其未来发展和应用趋势。     

基于用户体验的压路机操纵手柄交互品质研究

目的以压路机操纵手柄为对象,探讨交互品质研究方法,改进用户体验。方法对交互品质进行解析,由用户体验与交互品质的关联性,提出基于用户体验的交互品质研究方法;面向压路机操纵手柄典型任务进行用户体验研究,梳理压路机手柄的操作流程,并对交互行为进行聚类分析,结合语义分析构建用户体验词库,运用半结构式访谈与问卷调查,以定量与定性方法相结合对驾驶员的操纵体验进行调研;以专家研讨的形式进行驾驶员的操纵体验向交互特征的映射,构建交互特征集,进而对操纵手柄的交互品质进行分析与阐释。结果得出了压路机操纵手柄的交互特征与交互品质。结论通过用户体验可以映射交互特征,得到了交互品质,对于从交互品质的角度指导交互设计提供了借鉴。     

基于微博用户兴趣话题的相似用户挖掘

相似用户挖掘是提高社交网络服务质量的重要途径，在面向大数据的社交网络时代，准确的相似用户挖掘对于用户和互联网企业等都有重要的意义，而根据用户自己的兴趣话题挖掘的相似用户更符合相似用户的要求。提出了一种基于用户兴趣话题进行相似用户挖掘的方法。该方法首先使用TextRank话题提取方法对用户进行兴趣话题提取，再对用户发表内容进行训练，计算出所有词之间的相似度。提出CP（Corresponding Position similarity）、CPW（Corresponding Position Weighted similarity）、AP（All Position similarity）、APW（All Position Weighted similarity）四种用户兴趣话题词相似度计算方法，通过用户和相似用户间关注、粉丝重合率验证相似用户挖掘效果，APW similarity的相似用户的关注/粉丝重合百分比为1.687%，优于提出的其他三种算法，分别提高了26.3%、2.8%、12.4%，并且比传统的文本相似度方法Jaccard相似度、编辑距离算法、余弦相似度分别提高了20.4%、21.2%、45.0%。因此APW方法可以更加有效地挖掘出用户的相似用户。     

Information Security Policy Compliance: Leadership and Trust

ABSTRACT

The purpose of this study was to examine the influence of trust variables (trust: competence, trust: benevolence, trust: integrity) on leadership regarding the organization’s information security policy (ISP) compliance. An instrument with four constructs was used to collect data from 474 non-management subjects from various organizations in the USA. Collected data were analyzed through multiple regression procedure. Results revealed that all trust variables (trust: competence, trust: benevolence, trust: integrity) were influential in predicting the leadership regarding the organization’s ISP compliance. The findings are discussed and implications for practice are outlined. Conclusion, limitations, and recommendations for future research are drawn.     

Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component‐based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run‐time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component‐based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component‐based architectures by using model transformation. Our aim is to generate at run‐time a dynamic model transformation, because the rules describing their behavior are not pre‐set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run‐time a rule subset from a repository. Selected rules are used to generate, through a higher‐order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre‐set transformation, which enables the dynamic adaptation of component‐based architectures. Copyright © 2014 Copyright © 2014 John Wiley & Sons, Ltd.     

融合信任和基于概率矩阵分解的推荐算法

针对推荐精度不准确、数据稀疏、恶意推荐的问题，提出融合信任基于概率矩阵分解（PMF）的新推荐模型。首先，通过建立基于信任的协同过滤模型（CFMTS）将改进的信任机制融入到协同过滤推荐算法中。信任值通过全局信任及局部信任计算获得，其中局部信任利用了信任传播机制计算用户的直接信任值和间接信任值得到，全局信任采用信任有向图的方式计算得到。然后，将信任值与评分相似度融合以解决数据稀疏、恶意推荐的问题。同时，将CFMTS融入到PMF模型中以建立新的推荐模型——融合信任基于概率矩阵分解模型（MPMFFT），通过梯度下降算法对用户特征向量和项目特征向量进行计算以产生预测评分值，进一步提高推荐系统的精准度。通过实验将提出的MPMFFT与经典的PMF、社交信息的矩阵分解（SocialMF）、社交信息的推荐（SoRec）、加权社交信息的推荐（RSTE）等模型进行了结果的对比和分析，在公开的真实数据集Epinions上MPMFFT的平均绝对误差（MAE）和均方根误差（RMSE）比最优的RSTE模型分别降低2.9%和1.5%，同时在公开的真实数据集Ciao上MPMFFT的MAE和RMSE比最优的SocialMF模型分别降低1.1%和1.8%，结果证实了模型能在一定程度上解决数据稀疏、恶意推荐问题，有效提高推荐质量。     

融合偏好度与网络结构的推荐算法

针对传统推荐系统追求推荐列表的准确率而忽略推荐的多样性以及数据集信息缺失等问题，提出了融合偏好度与网络结构的推荐算法。通过用户历史反馈数据分析用户偏好度，将偏好度与二部图随机游走推荐算法融合，初步得出项目推荐列表；利用用户-标签二部图，挖掘用户不跟随大众的喜好标签，得到推荐项目列表；根据模型融合得到最终的推荐结果。实验表明，新算法在保持较好精确率和召回率的情况下，有效提高了推荐的多样性。