Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

Understanding trust in ms-commerce: The roles of reported experience,linguistic style,profile photo,emotional, and cognitive trust

This paper examines what influences trust in mobile social commerce environment. Drawing on trust-based acceptance model (i.e. cognitive and emotional trust) and online review features (i.e. profile photo, linguistic style, and reported experience), we examine how these factors affect trust in mobile social commerce. Hypotheses were tested using survey data. The results of our model showed that there are significant influences of profile photo, reported experience, cognitive, and emotional trust towards trust in ms-commerce. This work contributes to existing literature by examining the roles of previous trust in mobile payments and online reviews on trust in mobile social commerce.     

Owner’s Risk Allocation and Contractor’s Role Behavior in a Project: A Parallel-mediation Model

Risk allocation decisions are of critical importance in project management. The present study proposes an explanation for how risk allocation in a contract motivates a contractor to cooperate with a project owner. Theories of risk allocation and trust were used to motivate the research. Using a survey methodology, we collected data concerning 124 construction projects in China. We found that risk allocation influenced the contractor’s role behavior through the contractor’s feeling of being trusted but not the contractor’s trust in the owner. Feeling of being trusted partially mediated the effect of risk allocation on the contractor’s in-role (i.e., contractual) behavior and fully mediated the effect on extra-role behavior. The study introduces a social and psychological view of the impacts of risk allocation to the project and engineering management literature. We contribute to theory by arguing and demonstrating the mediating effect of trust on the relationship between risk allocation and contractor behavior. From a practical standpoint, we conclude that contractual risk allocation has a significant impact on building a trusting relationship between owners and contractors and that contractors who feel trusted perform both contractually mandated actions and actions external to the contract more diligently, resulting in the likelihood of improved outcomes for both parties.     

Web服务在信用信息工程中的应用

目前的社会信用信息工程中,许多的政府部门都建立了自己的信用信息数据库,但在政府各级部门数据库之间进行数据交流和共享存在着许多的问题,在许多地方,数据共享还是一片空白。Web服务技术是应用程序通过内联网或者因特网发布和利用软件服务的一种标准机制。利用WEB服务可以方便地实现不同系统之间的数据交换和集成。该文提出利用web服务技术来解决信用信息工程中的信息数据共享问题。并在此基础上提出利用WEB服务工作流技术将信用评估整合到电子商务过程,以实现可信用电子商务     

Construction and Preliminary Performance of Reedbed Treatment Systems at Castle Espie Wildfowl and Wetlands Trust Centre, Northern Ireland

In 1993, the Wildfowl and Wetlands Trust constructed a demonstration reedbed treatment system at its Castle Espie Visitor Centre in Northern Ireland. The objectives of the project included (a) the treatment of septic-tank effluent, (b) the protection of water quality in Strangford Lough, and (c) the establishment of a research base for constructed wetland systems. Parallel beds of equally sized sub-surface and surface flow were constructed and planted, and a research project was initiated to provide data on performance.     

基于聚类分析的新书推荐

针对大多数图书馆管理系统中的个性化服务中的新书推荐只是定时将新到馆的图书罗列出来让读者浏览进行推荐,推荐效果差的问题。本文在比较三大常用的推荐技术的优缺点后,采用基于聚类分析的推荐算法推荐新书,并具体给出实现的方法。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

卷积融合文本和异质信息网络的学术论文推荐算法

针对交互数据稀疏和推荐多样性问题,基于卷积协同过滤推荐框架提出卷积融合文本和异质信息网络的学术论文推荐算法（WN-APR）。首先学习不同语义下用户和论文的多样化特征,缓解数据稀疏问题;然后基于外积设计不同语义特征相互增强的方式融合它们,并使用三维卷积神经网络代替二维卷积神经网络充分挖掘不同特征对性能的影响;最后改进贝叶斯个性化排序损失函数增强推荐多样性。在CiteuLike-a、CiteuLike-t数据集上的实验结果表明,相比于基线模型,WN-APR在准确率和多样性的四个指标上都有所提升。