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Users of ambient intelligence environments have been overwhelmed by the huge numbers of social media available, thus identifying the social media tailored to the user's need is becoming an important question to be discussed. This paper adapts the Katz proximity measure, for the use in social tagging system, to help users in ambient environment find relevant media suited to their interests. The method models the ternary relations among user, resource and tag as a weighted, undirected tripartite graph, then apply the Katz proximity measure to tripartite graph. Experiments on two real datasets are implemented and compared with many state-of-the-art algorithms. The experimental results prove that the adaptation of the Katz algorithm with the tripartite structure yields a significant improvement, and successfully ranks relevant search results according to the user's interests. [ABSTRACT FROM AUTHOR]

This article provides a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization. A publicly available dataset is used where the position data of multiple RF tags worn on different parts of the human body are acquired asynchronously and nonuniformly. In this study, curves fitted to the data are resampled uniformly and then segmented. We investigate the effect on system accuracy of varying the relevant system parameters. We compare various curve-fitting, segmentation, and classification techniques and present the combination resulting in the best performance. The classifiers are validated using 5-fold and subject-based leave-one-out cross validation, and for the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30%, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%, respectively. The results indicate that the system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. [ABSTRACT FROM AUTHOR]

Highlights: [•] Tag frequency, recency, and duration were combined to model the personalized preference. [•] The social network was utilized to find similar users in the collaborative filtering. [•] The incorporated system was applied to the social resource sharing systems. [Copyright &y& Elsevier]

The single-event effect sensitivity of inverse-mode biased SiGe HBTs in both bulk and SOI technology platforms are investigated, for the first time, using digital circuits and stand-alone device test structures. Comparisons of heavy-ion broad beam data of shift register circuits constructed with forward-mode and inverse-mode biased SiGe HBTs from a first-generation, complementary SOI SiGe BiCMOS process, reveal an improvement in SEU mitigation for the inverse-mode shift register architecture. Full 3D TCAD simulations highlight the differences in transient current origination between forward and inverse-mode biased devices, illustrating the impact of doping profiles on ion-induced shunt duration. To extend the analysis to a bulk platform, new fourth-generation npn, SiGe HBTs were biased in both the forward and inverse-mode and irradiated at NRL using the two photon absorption measurement system. These measurements support the analysis of transient origination using 3D TCAD simulations. Furthermore, the isolation of the output terminal from the sensitive subcollector-substrate junction is experimentally demonstrated for the inverse-mode bias. Fully coupled mixed-mode simulations predict a significant reduction in sensitive area for inverse-mode shift registers built in a bulk SiGe platform. [ABSTRACT FROM AUTHOR]

While personalization is key to increase the usability of online services, disclosing one's preferences is undesirable from a privacy perspective, because it enables profiling through the linkage of what may otherwise be unlinkable service invocations. This paper considers an easily implementable class of obfuscation strategies as a means to mitigate these risks, and examines its privacy/utility tradeoff. Our results are based on simulations that take place within a modular evaluation framework that can seamlessly accommodate real-world data. We conducted experiments with different simulated behaviors and using two preference populations, namely a population of maximally diverse preferences and one consisting of the movie preferences of some Netflix users. We measure utility in a way that is specific to the application of preference obfuscation. Privacy is measured in terms of unlinkability, with respect to two different adversaries. Our results show that reasonable privacy/utility tradeoffs require the disclosure of only small amounts of preference information. [ABSTRACT FROM PUBLISHER]

Online social media services such as Flickr and Zooomr allow users to share their images with the others for social interaction. An important feature of these services is that the users manually annotate their images with the freely-chosen tags, which can be used as indexing keywords for image search and other applications. However, since the tags are generally provided by grassroots Internet users, there is still a gap between these tags and the actual content of the images. This deficiency has significantly limited tag-based applications while, on the other hand, poses a new challenge to the multimedia research community. It calls for a series of research efforts for processing these unqualified tags, especially in making use of content analysis techniques to improve the descriptive power of the tags with respect to the image contents. This paper provides a comprehensive survey of the technical achievements in the research area of content-based tag processing for social images, covering the research aspects on tag ranking, tag refinement and tag-to-region assignment. We review the research advances for each topic and present a brief suggestion for future promising directions. [ABSTRACT FROM AUTHOR]

One of the recent web developments has focused on the opportunities it presents for social tagging through user participation and collaboration. As a result, social tagging has changed the traditional online communication process. The interpretation of tagging between humans and machines may create new problems if essential questions about how social tagging corresponds to online communications, what objects the tags refer to, who the interpreters are, and why they are engaged are not explored systematically. Since such reasoning is an interpretation of social tagging among humans, tags and machines, it is a complex issue that calls for deep reflection. In this paper, we investigate the relevance of the potential problems raised by social tagging through the framework of C.S. Peirce's semiotics. We find that general phenomena of social tagging can be well classified by Peirce's 10 classes of signs for reasoning. This suggests that regarding social tagging as a sign and systematically analyzing the interpretation are positively associated with the 10 classes of signs. Peircean semiotics can be used to examine the dynamics and determinants of tagging; hence, the various uses of this categorization schema may have implications for the design and development of information systems and web applications. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0’s participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging. [ABSTRACT FROM AUTHOR]

The article discusses how Weblogging works and its application to social welfare policy research. It is defined as a Webpage that contains information called posts that are arranged in reverse chronological order and are identified by an anchor tag and marked with a permanent link. Examples of political blogs and directories for locating them are presented. It cites the reason that Social Welfare Policy scholars should not ignore blogs and bloggers.

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. [ABSTRACT FROM AUTHOR]

The article looks at some examples of microformats, an approach to encoding semistructured information in ordinary Extensible Hyper Text Markup Language. The most successful microformat is tagging. By displaying or surfacing items to which other users have applied the same tag or by suggesting additional tags for a given item, tags power an exciting bottom-up process for collaboratively organizing information. The microformats community is an open wiki, mailing list and Internet relay chat channel that has proven to be scalable and accommodating.

The article looks into the trends and developments in the application of tagging and subject cataloging in the development of digital libraries. The release of the December 2005 Final Report of the Bibliographic Services Task Force of the University of California Libraries have suggested that direct access to the full content usually is just click away, metadata standards that emphasize retrieval are more useful than descriptive cataloging practices. The launch of TagWorld tries to improve upon groundbreaking applications to release the masses' pent-up impulses to tag and share digital content. However, there are several problems that may hamper the application of tagging in managing digital content.

Presents the cover/table of contents for this issue of the periodical. [ABSTRACT FROM PUBLISHER]