Integrating time forgetting mechanisms into topic-based user interest profiling

The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.

Cipher cities : effective user interface design for building a digital social network around location-based gaming

In this paper we explore what is required of a User Interface (UI) design in order to encourage participation around playing and creating Location-Based Games (LBGs). To base our research in practice, we present Cipher Cities, a web based system. Through the design of this system, we investigate how UI design can provide tools for complex content creation to compliment and encourage the use of mobile phones for designing, distributing, and playing LBGs. Furthermore we discuss how UI design can promote and support socialisation around LBGs through the design of functional interface components and services such as groups, user profiles, and player status listings.

User friendly guide for rehabilitation or strengthening of bridge structures using fiber reinforced polymer composites

A worldwide interest is being generated in the use of fibre reinforced polymer composites (FRP) in rehabilitation of reinforced concrete structures. As a replacement for the traditional steel plates or external post-tensioning in strengthening applications, various types of FRP plates, with their high strength to weight ratio and good resistance to corrosion, represent a class of ideal material in external retrofitting. Within the last ten years, many design guidelines have been published to provide guidance for the selection, design and installation of FRP systems for external strengthening of concrete structures. Use of these guidelines requires understanding of a number of issues pertaining to different properties and structural failure modes specific to these materials. A research initiative funded by the CRC for Construction Innovation was undertaken (primarily at RMIT) to develop a decision support tool and a user friendly guide for use of fibre reinforced polymer composites in rehabilitation of concrete structures. The user guidelines presented in this report were developed after industry consultation and a comprehensive review of the state of the art technology. The scope of the guide was mainly developed based on outcomes of two workshops with Queensland Department of Main Roads (QDMR). The document covers material properties, recommended construction requirements, design philosophy, flexural, shear and torsional strengthening of beams and strengthening of columns. In developing this document, the guidelines published on FIB Bulletin 14 (2002), Task group 9.3, International Federation of Structural Concrete (FIB) and American Concrete Institute Committee 440 report (2002) were consulted in conjunction with provisions of the Austroads Bridge design code (1992) and Australian Concrete Structures code AS3600 (2002). In conclusion, the user guide presents design examples covering typical strengthening scenarios.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Cipher cities

Cipher Cities was a practice-led research project developed in 3 stages between 2005 and 2007 resulting in the creation of a unique online community, ‘Cipher Cities’, that provides simple authoring tools and processes for individuals and groups to create their own mobile events and event journals, build community profile and participate in other online community activities. Cipher Cities was created to revitalise peoples relationship to everyday places by giving them the opportunity and motivation to create and share complex digital stories in simple and engaging ways. To do so we developed new design processes and methods for both the research team and the end user to appropriate web and mobile technologies. To do so we collaborated with ethnographers, designers and ICT researchers and developers. In teams we ran a series of workshops in a wide variety of cities in Australia to refine an engagement process and to test a series of iteratively developed prototypes to refine the systems that supported community motivation and collaboration. The result of the research is 2 fold: 1. a sophisticated prototype for researchers and designers to further experiment with community engagement methodologies using existing and emerging communications technologies. 2. A ‘human dimensions matrix’. This matrix assists in the identification and modification of place based interventions in the social, technical, spatial, cultural, pedagogical conditions of any given community. This matrix has now become an essential part of a number of subsequent projects and assists design collaborators to successfully conceptualise, generate and evaluate interactive experiences. the research team employed practice-led action research methodologies that involved a collaborative effort across the fields of interaction design and social science, in particular ethnography, in order to: 1. seek, contest, refine a design methodology that would maximise the successful application of a dynamic system to create new kinds of interactions between people, places and artefacts’. 2. To design and deploy an application that intervenes in place-based and mobile technologies and offers people simple interfaces to create and share digital stories. Cipher Cities was awarded 3 separate CRC competitive grants (over $270,000 in total) to assist 3 stages of research covering the development of the Ethnographic Design Methodologies, the development of the tools, and the testing and refinement of both the engagement models and technologies. The resulting methodologies and tools are in the process of being commercialised by the Australasian CRC for Interaction Design.

User-centered video quality assessment for scalable video coding of H.264/AVC standard

Scalable video coding of H.264/AVC standard enables adaptive and flexible delivery for multiple devices and various network conditions. Only a few works have addressed the influence of different scalability parameters (frame rate, spatial resolution, and SNR) on the user perceived quality within a limited scope. In this paper, we have conducted an experiment of subjective quality assessment for video sequences encoded with H.264/SVC to gain a better understanding of the correlation between video content and UPQ at all scalable layers and the impact of rate-distortion method and different scalabilities on bitrate and UPQ. Findings from this experiment will contribute to a user-centered design of adaptive delivery of scalable video stream.

Conveying identity with mobile content

A series of mobile phone prototypes called The Swarm have been developed in response to the user needs identified in a three-year empirical study of young people’s use of mobile phones. The prototypes take cues from user led innovation and provide multiple avatars that allow individuals to define and manage their own virtual identity. This paper briefly maps the evolution of the prototypes and then describes how the pre-defined, color coded avatars in the latest version are being given greater context and personalization through the use of digital images. This not only gives ‘serendipity a nudge’ by allowing groups to come together more easily, it provides contextual information that can reduce gratuitous contact.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Impact of zooming and enhancing Region of Interests for optimizing user experience on mobile sports video

In mobile videos, small viewing size and bitrate limitation often cause unpleasant viewing experiences, which is particularly important for fast-moving sports videos. For optimizing the overall user experience of viewing sports videos on mobile phones, this paper explores the benefits of emphasizing Region of Interest (ROI) by 1) zooming in and 2) enhancing the quality. The main goal is to measure the effectiveness of these two approaches and determine which one is more effective. To obtain a more comprehensive understanding of the overall user experience, the study considers user’s interest in video content and user’s acceptance of the perceived video quality, and compares the user experience in sports videos with other content types such as talk shows. The results from a user study with 40 subjects demonstrate that zooming and ROI-enhancement are both effective in improving the overall user experience with talk show and mid-shot soccer videos. However, for the full-shot scenes in soccer videos, only zooming is effective while ROI-enhancement has a negative effect. Moreover, user’s interest in video content directly affects not only the user experience and the acceptance of video quality, but also the effect of content type on the user experience. Finally, the overall user experience is closely related to the degree of the acceptance of video quality and the degree of the interest in video content. This study is valuable in exploiting effective approaches to improve user experience, especially in mobile sports video streaming contexts, whereby the available bandwidth is usually low or limited. It also provides further understanding of the influencing factors of user experience.

Content and functionality of alcohol and other drug websites : results of an online survey

Background: There is a growing trend for individuals to seek health information from online sources. Alcohol and other drug (AOD) use is a significant health problem worldwide, but access and use of AOD websites is poorly understood. ----- ----- Objective: To investigate content and functionality preferences for AOD and other health websites. Methods: An anonymous online survey examined general Internet and AOD-specific usage and search behaviors, valued features of AOD and health-related websites (general and interactive website features), indicators of website trustworthiness, valued AOD website tools or functions, and treatment modality preferences. ----- ----- Results: Surveys were obtained from 1214 drug (n = 766) and alcohol website users (n = 448) (mean age 26.2 years, range 16-70). There were no significant differences between alcohol and drug groups on demographic variables, Internet usage, indicators of website trustworthiness, or on preferences for AOD website functionality. A robust website design/navigation, open access, and validated content provision were highly valued by both groups. While attractiveness and pictures or graphics were also valued, high-cost features (videos, animations, games) were minority preferences. Almost half of respondents in both groups were unable to readily access the information they sought. Alcohol website users placed greater importance on several AOD website tools and functions than did those accessing other drug websites: online screening tools (χ²2 = 15.8, P < .001, n = 985); prevention programs (χ²2 = 27.5, P < .001, n = 981); tracking functions (χ²2 = 11.5, P = .003, n = 983); self help treatment programs (χ²2 = 8.3, P = .02, n = 984); downloadable fact sheets for friends (χ²2 = 11.6, P = .003, n = 981); or family (χ²2 = 12.7, P = .002, n = 983). The most preferred online treatment option for both the user groups was an Internet site with email therapist support. Explorations of demographic differences were also performed. While gender did not affect survey responses, younger respondents were more likely to value interactive and social networking features, whereas downloading of credible information was most highly valued by older respondents. ----- ----- Conclusions: Significant deficiencies in the provision of accessible information on AOD websites were identified, an important problem since information seeking was the most common reason for accessing these websites, and, therefore, may be a key avenue for engaging website users in behaviour change. The few differences between AOD website users suggested that both types of websites may have similar features, although alcohol website users may more readily be engaged in screening, prevention and self-help programs, tracking change, and may value fact sheets more highly. While the sociodemographic differences require replication and clarification, these differences support the notion that the design and features of AOD websites should target specific audiences to have maximal impact.

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data).

Trans digital storytelling : everyday activism, mutable identity and the problem of visibility

Mainstream representations of trans people typically run the gamut from victim to mentally ill and are almost always articulated by non-trans voices. The era of user-generated digital content and participatory culture has heralded unprecedented opportunities for trans people who wish to speak their own stories in public spaces. Digital Storytelling, as an easy accessible autobiographic audio-visual form, offers scope to play with multi-dimensional and ambiguous representations of identity that contest mainstream assumptions of what it is to be ‘male’ or ‘female’. Also, unlike mainstream media forms, online and viral distribution of Digital Stories offer potential to reach a wide range of audiences, which is appealing to activist oriented storytellers who wish to confront social prejudices. However, with these newfound possibilities come concerns regarding visibility and privacy, especially for storytellers who are all too aware of the risks of being ‘out’ as trans. This paper explores these issues from the perspective of three trans storytellers, with reference to the Digital Stories they have created and shared online and on DVD. These examplars are contextualised with some popular and scholarly perspectives on trans representation, in particular embodied and performed identity. It is contended that trans Digital Stories, while appearing in some ways to be quite conventional, actually challenge common notions of gender identity in ways that are both radical and transformative.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.