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.     

基于MSP430的RFID读写器的设计

设计一种基于MSP430单片机的多协议RFID读写器。分析RFID概念、应用现状及系统构成,介绍两款主要芯片的功能特点,给出系统硬件组成结构．提出系统的软件组成模块及软件流程图。系统顺利通过测试,其设计达到相应的技术指标要求,具有良好的市场应用前景。     

在.NET中利用自定义模板快速开发网站

利用自定义模板,将界面设计和代码编写工作分离,使两项工作可以并行进行,并且充分利用已有代码,避免重复编写相同代码。文中介绍了模板的实现原理和具体实现算法。     

应用列对象的Tag属性实现多表数据动态查询

探讨在PB开发的应用程序中利用数据窗口列对象的Tag属性,结合自行设计的一个更改SELECT语句中的Where子句的全局函数,实现应用程序多表数据的动态查询。该种方法是PB开发的应用程序中实现数据查询的一种高效、简洁且行之有效的方法。     

用子空间粒子群聚类算法识别Folksonomy标签冗余的研究

Wcb2.。标签系统中经常包含很多冗余的标签,标签冗余会增加用户选择喜好项目时的负担,从而影响用户建模和对推荐系统的评估。标签数据集通常存在着大量不相关或是冗余的特征,而不同簇之间的相关特征子集又是不一样的,所以应该从不同的特征子集中来发现簇。提出使用子空间粒子群聚类识别标签冗余,算法采用指数型变权类似K-means的目标函数,该函数对变量权值的改变更加敏感。在此基础上利用粒子群优化目标函数搜寻得到全局最优的标签聚类,提高抽取冗余标签的准确度实验结果表明,此算法具有较强的全局搜索能力,应用于标签冗余识别获得了更好的精度。     

基于好友关系和标签的混合协同过滤算法

针对传统推荐算法存在数据稀疏影响推荐效果的问题,考虑到社交网络中的链路预测能够综合考虑用户节点之间的拓扑结构,以及好友关系能反映用户的兴趣爱好,提出了一种融合好友关系和标签信息的推荐算法。首先,借助网络资源分配算法对社交网络的结构信息进行特征提取;然后,利用TF-IDF构建合理的社会化标签模型;最后,利用线性模型融合两方面的信息,从而实现推荐。在Last.fm和Delicious数据集上的实验表明,与传统算法相比,所提算法在推荐的召回率和准确率指标上有显著提高。     

基于张量分解的个性化标签推荐算法

基于互联网的社会标签推荐系统为广大用户提供了一个信息共享平台,让用户以"标签"的形式为其浏览的物品标注信息。标签既描述了物品语义,又反映了用户偏好。标签系统的最大优势在于可以发挥群体的智能,获得用户对物品比较准确的关键词描述,而准确的标签信息是提升个性化推荐系统性能的重要资源。然而,现存的标签推荐系统面临的问题是:由于兴趣的不同,不同的用户对于同一物品可能会打不同的标签,或者是同一标签对于不同用户可能会蕴含不同的语义。因此如何有效获取用户、物品、标签3者之间潜在的语义关联成为标签推荐系统需要解决的主要问题。为此引入三维张量模型,利用三维张量的3个维度来分别描述社会标签推荐系统中3种类型的实体:用户、物品、标签。在基于历史标签数据(标签元数据)构建初始张量的基础上,应用高阶奇异值分解(HOSVD)方法降低张量维度,同时实现3种类型实体之间潜在的语义关联分析,从而进一步提高标签推荐系统的准确性。实验结果表明,该方法较当前两种典型的标签推荐算法(FolkRank和PR)在准确率和召回率性能指标上有明显提升。     

Electromagnetic spectrum chipless radio frequency identification: A review

Over the past few years, Radio Frequency IDentification (RFID) has stridden significantly due to its adoption in many business and daily life activities. RFID empowers end-to-end and item-by-item visibility, letting the customers or decision-makers effectively trail the personnel and resources for improved management. Significant research and development in this area have transformed the inexpensive, miniaturized technology of RFIDs without chip inside. Chipless Radio Frequency IDentification (CRFID) is a unique wireless technology based on radar principle to sense, identify, and track the objects. CRFIDs are simplified and miniaturized tags of traditional RFID, which can widely be used in small and cheap objects/applications in our daily life. This paper reviews the definition, principle, and most recent development in CRFIDs. Based on the reported researches, the ElectroMagnetic Spectrum (EMS) method is typical, which has been concerned by many RFID researchers. Therefore, this paper reviews different types of EMS CRFID tag and classifies these tags based on structure design emphasizing their significant geometries in comparison with performance parameters. Antenna and coding methods of the EMS CRFID are also elaborated, contrasting the tag capacity and coding performance. Finally, we briefly summarize the challenges and limitations in this emerging field and look forward to the prospects of future CRFID.     

基于特征的轻量级RFID安全搜索协议

在RFID技术的应用中,需要对特定的RFID标签进行有效安全的搜索。由于低成本的RFID标签的计算能力和硬件资源的限制,使RFID标签搜索协议的有效性和安全性更难保证。从减少RFID标签的计算量为出发点,提出一个轻量级的基于物品特征的RFID安全搜索协议,以解决隐私泄露、前向安全、跟踪、重放攻击和假冒等安全问题,并对该协议给出基于BAN逻辑的形式化证明。在该协议中,RFID标签只存储自己的标识符和一个秘密数,在通信过程中只进行一次随机数运算,因此,该协议更适合于低成本RFID系统中对标签的搜索。     

Detecting topic-based communities in social networks: A study in a real software development network

In social network analysis, a key issue is the detection of meaningful communities. This problem consists of finding groups of people who are both connected and semantically aligned. In the software development context, identifying communities considering both collaborations between developers and their skills can help to address critical elements or issues in a project. However, a large amount of data and the lack of data structure make it difficult to analyze these networks’ content. In this paper, we propose a framework for detecting overlapping semantic communities and their influential members. We also propose an ontology to extract topics of interest through tag enrichment in a Q&A forum. An evaluation was conducted in a large network of software developers built with Stack Overflow’s data, showing that the proposed framework and ontology can find real communities of developers. The evaluation indicates that their members are semantic aligned and still active in the detected topics of interest, and the quantitative analysis showed that the detected communities have high internal connectivity.