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.

Skins for smokes and the use of cultural content and imagery

[Extract] For just $5.29 Australians can now purchase "Skins" from local, independent grocers to cover their cigarette packet with the Aboriginal or Torres Strait Islander flag. We argue that this use of cultural content and copyright' imagery on cigarette packets negates health promotion efforts, such as Australia's recent introduction of plain packaging laws and the subsequent dismissal of a legal challenge from the tobacco industry.

A combined method for mitigating sparsity problem in tag recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

The microbial content of vacuum cleaner bag dust and emitted bioaerosols : implications for human exposures indoors

Vacuum cleaners can release large concentrations of particles, both in their exhaust air and from resuspension of settled dust. However, the size, variability and microbial diversity of these emissions are unknown, despite evidence to suggest they may contribute to allergic responses and infection transmission indoors. This study aimed to evaluate bioaerosol emission from various vacuum cleaners. We sampled the air in an experimental flow tunnel where vacuum cleaners were run and their airborne emissions sampled with closed-face cassettes. Dust samples were also 35 collected from the dust bag. Total bacteria, total archaea, Penicillium/Aspergillus and total Clostridium cluster 1 were quantified with specific qPCR protocols and emission rates were calculated. Clostridium botulinum, as well as antibiotic resistance genes were detected in each sample using endpoint PCR. Bacterial diversity was also analyzed using denaturing gel electrophoresis (DGGE), image analysis and band sequencing. We demonstrated that emission of bacteria and moulds (Pen/Asp) can reach values as high as 1E05/min and that those emissions are not related to each other. The bag dust bacterial and mould content was also consistently across the vacuums we assessed, reaching up to 1E07 bacteria or moulds equivalent/g. Antibiotic resistance genes were detected in several samples. No archaea or C. botulinum were detected in any air samples. Diversity analyses showed that most bacteria are from human sources, in keeping with other recent results. These results highlight the potential capability of vacuum cleaners to disseminate appreciable quantities of moulds and human-associated bacteria indoors and their role as a source of exposure to bioaerosols.

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.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Booster vaccination of toddlers with reduced antigen content diphtheria-tetanus-acellular pertussis vaccine

Immunogenicity and reactogenicity of DTPa and reduced antigen dTpa booster vaccines were compared to a hepatitis A control vaccine in DTPa-primed toddlers aged 18-20 months. Post-booster, all DTPa and dTpa recipients were seroprotected against diphtheria and tetanus, and >= 93.3% had a booster response to pertussis. There were similar reactogenicity rates in the DTPa and dTpa vaccine recipients. Few Grade 3 symptoms were reported. Just over one in four children in the control group had diphtheria antibody at or potentially below the correlate of protection benchmark (0.016 IU/ml). Larger studies should evaluate potential benefits of reduced antigen vaccines and seroprotection in children who do not receive a booster dose of DTPa at this age, including protection against diphtheria until subsequent booster doses are given. (C) 2009 Elsevier Ltd. All rights reserved.

Educating for digital futures : what the learning strategies of digital content professionals can teach higher education

Beginning in the second half of the 20th century, ICTs transformed many societies from industrial societies in which manufacturing was the central focus, into knowledge societies in which dealing effectively with data and information has become a central element of work (Anderson, 2008). To meet the needs of the knowledge society, universities must reinvent their structures and processes, their curricula and pedagogic practices. In addition to this, of course higher education is itself subject to the sweeping influence of ICTs. But what might effective higher education look like in the 21st century? In designing higher education systems and learning experiences which are responsive to the learning needs of the future and exploit the possibilities offered by ICTs, we can learn much from the existing professional development strategies of people who are already successful in 21st century fields, such as digital media. In this study, I ask: (1) what are the learning challenges faced by digital media professionals in the 21st century? (2) what are the various roles of formal and informal education in their professional learning strategies at present? (3) how do they prefer to acquire needed capabilities? In-depth interviews were undertaken with successful Australian digital media professionals working in micro businesses and SMEs to answer these questions. The strongest thematic grouping that emerged from the interviews related to the need for continual learning and relearning because of the sheer rate of change in the digital media industries. Four dialectical relationships became apparent from the interviewees’ commentaries around the learning imperatives arising out of the immense and continual changes occurring in the digital content industries: (1) currency vs best practice (2) diversification vs specialisation of products and services (3) creative outputs vs commercial outcomes (4) more learning opportunities vs less opportunity to learn. These findings point to the importance of ‘learning how to learn’ as a 21st century capability. The interviewees were ambivalent about university courses as preparation for professional life in their fields. Higher education was described by several interviewees as having relatively little value-add beyond what one described as “really expensive credentialling services.” For all interviewees in this study, informal learning strategies were the preferred methods of acquiring the majority of knowledge and skills, both for ongoing and initial professional development. Informal learning has no ‘curriculum’ per se, and tends to be opportunistic, unstructured, pedagogically agile and far more self-directed than formal learning (Eraut, 2004). In an industry impacted by constant change, informal learning is clearly both essential and ubiquitous. Inspired by the professional development strategies of the digital media professionals in this study, I propose a 21st century model of the university as a broad, open learning ecology, which also includes industry, professionals, users, and university researchers. If created and managed appropriately, the university learning network becomes the conduit and knowledge integrator for the latest research and industry trends, which students and professionals alike can access as needed.

A comparison of the histochemical and image-derived proteoglycan content of articular cartilage

There are several methods for determining the proteoglycan content of cartilage in biomechanics experiments. Many of these include assay-based methods and the histochemistry or spectrophotometry protocol where quantification is biochemically determined. More recently a method based on extracting data to quantify proteoglycan content has emerged using the image processing algorithms, e.g., in ImageJ, to process histological micrographs, with advantages including time saving and low cost. However, it is unknown whether or not this image analysis method produces results that are comparable to those obtained from the biochemical methodology. This paper compares the results of a well-established chemical method to those obtained using image analysis to determine the proteoglycan content of visually normal (n=33) and their progressively degraded counterparts with the protocols. The results reveal a strong linear relationship with a regression coefficient (R2) = 0.9928, leading to the conclusion that the image analysis methodology is a viable alternative to the spectrophotometry.

Cultural content matters: A critical sociology of language and literacy curriculum

A theoretical rationale, policy analysis and research agenda for a critical sociology of language and literacy curriculum, outlining the agenda for a political economy of textbooks.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

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.

Review of community facilities in Australian retirement villages : a content analysis

Aim Facilities in retirement villages form a supportive environment for older residents. The purpose of this paper is to investigate the provision of these facilities in retirement villages, which are regarded as a viable accommodation option for the ever-increasing ageing population in Australia. Method A content analysis of 124 retirement villages operated by 22 developers in Queensland and South Australia was conducted for the research purpose. Results The most widely provided facilities are community centres, libraries, barbeque facilities, hairdressers/salons and billiards/snooker/pool tables. Commercial operators provide more facilities than not-for-profit organisations and larger retirement villages normally have more facilities due to the economics of scale involved. Conclusions The results of the study provide a useful reference for providing facilities within retirement villages that may support the quality lifestyles for the older residents.