me as al, you as bobby, me as bobby, you as al

In ‘me as al, you as bobby, me as bobby, you as al’, appropriated footage is looped and supplemented with superimposed text, creating a scenario where Robert De Niro and Al Pacino endlessly stalk each other, with their readied-guns chased by hovering words. These titans of Hollywood screen acting represent opposing approaches to the construction of filmic identity, and as the text labels loosely adhere to one weapon and the next, the action on screen becomes an investigation of the subjective and objective potential within screen surrogate constructions of personalized identity. The work was included in the group show 'Vernacular Terrain' (curated by Lubi Thomas and Steven Danzig) for the Songzhuang Art Museum, Beijing.

Bending and blurring blended learning boundaries [Extended Abstract]

This presentation presents a blended learning model that provides greater opportunity for learning to be self-managed and personalized.

Designing a self-service technology to increase self-efficacy and maintain breastfeeding

Despite the significant health benefits attributed to breastfeeding, rates in countries, such as Australia, continue to remain static or to decline. Typically, the tangible support offered for women to support breastfeeding behaviours takes the form of face-to-face advice from health professionals, peer counselling via not-for-profit organizations such as the ABA, and provision of information through websites, pamphlets, and books. Prior research indicates that face-to-face support is more effective than telephone contact (Britton, McCormic, Renfrew, Wade, & King, 2009). Given the increasing costs associated with the provision of personalized face-to-face professional support and the need for some women to maximize privacy, discretion, and judgment-free consultations, there is a gap that could be filled by the use of m-technologies such as text messaging and other social media. The research team developed MumBubConnect; a two-way SMS system which combined the personalized aspects of face-to-face contact but maintained levels of privacy. The use of SMS was immediate, portable, and overcame many of the barriers associated with embarrassment. An Page 205 of 312 online survey of 130 breastfeeding mothers indicated that MumBubConnect facilitated the seeking of social support using m-technology, increased self-efficacy and maintained the desire behaviour.

Meaning or measurement? Researching the social contexts of health and settlement among newly-arrived refugee youth in Melbourne, Australia

What are the most appropriate methodological approaches for researching the psychosocial determinants of health and wellbeing among young people from refugee backgrounds over the resettlement period? What kinds of research models can involve young people in meaningful reflections on their lives and futures while simultaneously yielding valid data to inform services and policy? This paper reports on the methods developed for a longitudinal study of health and wellbeing among young people from refugee backgrounds in Melbourne, Australia. The study involves 100 newly-arrived young people 12 to 18 years of age, and employs a combination of qualitative and quantitative methods implemented as a series of activities carried out by participants in personalized settlement journals. This paper highlights the need to think outside the box of traditional qualitative and/or quantitative approaches for social research into refugee youth health and illustrates how integrated approaches can produce information that is meaningful to policy makers, service providers and to the young people themselves.

Personalization in tag ontology learning for recommendation making

Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users’ information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users’ tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.

Time-aware topic recommendation based on micro-blogs

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com.

Behavioral biometrics for persistent single sign-on

Driven by the rapid development of ubiquitous and pervasive computing, personalized services and applications are deployed to support our lives. Accordingly, the number of interfaces and devices (smartphone, tablet computer, etc.) provided to access and consume these services is growing continuously. To simplify the complexity of managing many accounts with different credentials, Single Sign-On (SSO) solutions have been introduced. However, a single password for many accounts represents a single-point-of-failure. Furthermore, once initiated SSO session is a high potential risk when the working station is left unlocked and unattended. In this paper, we present a conception of a Persistent Single Sign-On (PSSO) for ubiquitous home environments by involving the capabilities of Behavioral Biometrics to check the identity of the user continuously in an unobtrusive manner.

Hot topic detection in news blogs from the perspective of W2T

News blog hot topics are important for the information recommendation service and marketing. However, information overload and personalized management make the information arrangement more difficult. Moreover, what influences the formation and development of blog hot topics is seldom paid attention to. In order to correctly detect news blog hot topics, the paper first analyzes the development of topics in a new perspective based on W2T (Wisdom Web of Things) methodology. Namely, the characteristics of blog users, context of topic propagation and information granularity are unified to analyze the related problems. Some factors such as the user behavior pattern, network opinion and opinion leader are subsequently identified to be important for the development of topics. Then the topic model based on the view of event reports is constructed. At last, hot topics are identified by the duration, topic novelty, degree of topic growth and degree of user attention. The experimental results show that the proposed method is feasible and effective.

A theoretical basis for using virtual worlds as a personalised process visualisation approach

Communication processes are vital in the lifecycle of BPM projects. With this in mind, much research has been performed into facilitating this key component between stakeholders. Amongst the methods used to support this process are personalized process visualisations. In this paper, we review the development of this visualization trend, then, we propose a theoretical analysis framework based upon communication theory. We use this framework to provide theoretical support to the conjecture that 3D virtual worlds are powerful tools for communicating personalised visualisations of processes within a workplace. Meta requirements are then derived and applied, via 3D virtual world functionalities, to generate example visualisations containing personalized aspects, which we believe enhance the process of communcation between analysts and stakeholders in BPM process (re)design activities.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

Post-genomic science & society : Getting our fangs into omics science and its implications

Genomics and genetic findings have been hailed with promises of unlocked codes and new frontiers of personalized medicine. Despite cautions about gene hype, the strong cultural pull of genes and genomics has allowed consideration of genomic personhood. Populated by the complicated records of mass spectrometer, proteomics, which studies the human protein, has not achieved either the funding or the popular cultural appeal proteomics scientists had hoped it would. While proteomics, being focused on the proteins that actually indicate and create disease states, has a more direct potential for clinical applications than genomic risk predictions, culturally, it has not provided the material for identity creation. In our ethnographic research, we explore how proteomic scientists attempting to shape an appeal to personhood through which legitimacy may be defined.

An ontology-based method for sparsity problem in tag recommendation

Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

Current status of pharmacogenomics testing for anti-tumor drug therapies : approaches to non-melanoma skin cancer

Skin cancer is one of the most commonly occurring cancer types, with substantial social, physical, and financial burdens on both individuals and societies. Although the role of UV light in initiating skin cancer development has been well characterized, genetic studies continue to show that predisposing factors can influence an individual's susceptibility to skin cancer and response to treatment. In the future, it is hoped that genetic profiles, comprising a number of genetic markers collectively involved in skin cancer susceptibility and response to treatment or prognosis, will aid in more accurately informing practitioners' choices of treatment. Individualized treatment based on these profiles has the potential to increase the efficacy of treatments, saving both time and money for the patient by avoiding the need for extensive or repeated treatment. Increased treatment responses may in turn prevent recurrence of skin cancers, reducing the burden of this disease on society. Currently existing pharmacogenomic tests, such as those that assess variation in the metabolism of the anticancer drug fluorouracil, have the potential to reduce the toxic effects of anti-tumor drugs used in the treatment of non-melanoma skin cancer (NMSC) by determining individualized appropriate dosage. If the savings generated by reducing adverse events negate the costs of developing these tests, pharmacogenomic testing may increasingly inform personalized NMSC treatment.

A high-throughput panel for identifying clinically relevant mutation profiles in melanoma

Success with molecular-based targeted drugs in the treatment of cancer has ignited extensive research efforts within the field of personalized therapeutics. However, successful application of such therapies is dependent on the presence or absence of mutations within the patient's tumor that can confer clinical efficacy or drug resistance. Building on these findings, we developed a high-throughput mutation panel for the identification of frequently occurring and clinically relevant mutations in melanoma. An extensive literature search and interrogation of the Catalogue of Somatic Mutations in Cancer database identified more than 1,000 melanoma mutations. Applying a filtering strategy to focus on mutations amenable to the development of targeted drugs, we initially screened 120 known mutations in 271 samples using the Sequenom MassARRAY system. A total of 252 mutations were detected in 17 genes, the highest frequency occurred in BRAF (n = 154, 57%), NRAS (n = 55, 20%), CDK4 (n = 8, 3%), PTK2B (n = 7, 2.5%), and ERBB4 (n = 5, 2%). Based on this initial discovery screen, a total of 46 assays interrogating 39 mutations in 20 genes were designed to develop a melanoma-specific panel. These assays were distributed in multiplexes over 8 wells using strict assay design parameters optimized for sensitive mutation detection. The final melanoma-specific mutation panel is a cost effective, sensitive, high-throughput approach for identifying mutations of clinical relevance to molecular-based therapeutics for the treatment of melanoma. When used in a clinical research setting, the panel may rapidly and accurately identify potentially effective treatment strategies using novel or existing molecularly targeted drugs