Curiosity to cupboard : self reported disengagement with energy use feedback over time

This paper discusses findings made during a study of energy use feedback in the home (eco-feedback), well after the novelty has worn off. Contributing towards four important knowledge gaps in the research, we explore eco-feedback over longer time scales, focusing on instances where the feedback was not of lasting benefit to users rather than when it was. Drawing from 23 semi-structured interviews with Australian householders, we found that an initially high level of engagement gave way over time to disinterest, neglect and in certain cases, technical malfunction. Additionally, preconceptions concerned with the “purpose” of the feedback were found to affect use. We propose expanding the scope of enquiry for eco-feedback in several ways, and describe how eco-feedback that better supports decision-making in the “maintenance phase”, i.e. once the initial novelty has worn off, may be key to longer term engagement.

A critical analysis of business process management education and alignment with industry demand : an Australian perspective

Business Process Management (BPM) is accepted globally as an organizational approach to enhance productivity and drive cost efficiencies. Studies confirm a shortage of BPM skilled professionals with limited opportunities to develop the required BPM expertise. This study investigates this gap starting from a critical analysis of BPM courses offered by Australian universities and training institutions. These courses were analyzed and mapped against a leading BPM capability framework to determine how well current BPM education and training offerings in Australia address the core capabilities required by BPM professionals globally. To determine the BPM skill-sets sought by industry, online recruitment advertisements were collated, analyzed, and mapped against this BPM capability framework. The outcomes provide a detailed overview on the alignment of available BPM education/training and industry demand. These insights are useful for BPM professionals and their employers to build awareness of the BPM capabilities required for a BPM mature organization. Universities and other training institutions will benefit from these results by understanding where demand is, where the gaps are, and what other BPM education providers are supplying. This structured comparison method could continue to provide a common ground for future discussion across university-industry boundaries and continuous alignment of their respective practices.

Implementing open licensing in government open data initiatives : a review of Australian government practice

As support grows for greater access to information and data held by governments, so does awareness of the need for appropriate policy, technical and legal frameworks to achieve the desired economic and societal outcomes. Since the late 2000s numerous international organizations, inter-governmental bodies and governments have issued open government data policies, which set out key principles underpinning access to, and the release and reuse of data. These policies reiterate the value of government data and establish the default position that it should be openly accessible to the public under transparent and non-discriminatory conditions, which are conducive to innovative reuse of the data. A key principle stated in open government data policies is that legal rights in government information must be exercised in a manner that is consistent with and supports the open accessibility and reusability of the data. In particular, where government information and data is protected by copyright, access should be provided under licensing terms which clearly permit its reuse and dissemination. This principle has been further developed in the policies issued by Australian Governments into a specific requirement that Government agencies are to apply the Creative Commons Attribution licence (CC BY) as the default licensing position when releasing government information and data. A wide-ranging survey of the practices of Australian Government agencies in managing their information and data, commissioned by the Office of the Australian Information Commissioner in 2012, provides valuable insights into progress towards the achievement of open government policy objectives and the adoption of open licensing practices. The survey results indicate that Australian Government agencies are embracing open access and a proactive disclosure culture and that open licensing under Creative Commons licences is increasingly prevalent. However, the finding that ‘[t]he default position of open access licensing is not clearly or robustly stated, nor properly reflected in the practice of Government agencies’ points to the need to further develop the policy framework and the principles governing information access and reuse, and to provide practical guidance tools on open licensing if the broadest range of government information and data is to be made available for innovative reuse.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

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.

Acquiring user information needs for recommender systems

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.

Ontology-based product's reputation model

Different reputation models are used in the web in order to generate reputation values for products using uses' review data. Most of the current reputation models use review ratings and neglect users' textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And that's why the use of users' textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for users' extracted opinions about products' features from users' text. Our model uses features ontology in order to define general features and sub-features of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Downunder grads

Downunder Grads is a 4-part series (4 x 26') screened on the Special Broadcasting Station in 2008. It's the start of the semester at one of Australia's top universities and 37,000 students, are about to embark on their studies. University is make or break time; a time that can change people forever. From free education in the 1980s to HECS fee debt and a large increase in full fee paying students, university education is now big business. Through a variety of character stories this four-part series explores the contemporary Australian university experience of both international and Australian students from a range of different backgrounds.

Strategies and solutions for housing sustainability : building information files and performance certificates

The overall aim of this research project was to provide a broader range of value propositions (beyond upfront traditional construction costs) that could transform both the demand side and supply side of the housing industry. The project involved gathering information about how building information is created, used and communicated and classifying building information, leading to the formation of an Information Flow Chart and Stakeholder Relationship Map. These were then tested via broad housing industry focus groups and surveys. The project revealed four key relationships that appear to operate in isolation to the whole housing sector and may have significant impact on the sustainability outcomes and life cycle costs of dwellings over their life cycle. It also found that although a lot of information about individual dwellings does already exist, this information is not coordinated or inventoried in any systematic manner and that national building information files of building passports would present value to a wide range of stakeholders.

Physical activity promotion as a school priority : what do parents think?

The demands and responsibilities placed on schools in contemporary education systems are vast. However, with growing obesity levels and physical inactivity, the prevention of chronic disease has focused on youth populations, with schools playing the focal educative asset in this strategy. Parents play a decisive role in their child’s educational setting, and as fee and tax payers, are ultimately a consumer. Parents (82 males and 208 females) of secondary school children were recruited from three private (n=151) and two government schools (n=150) in Brisbane, Australia. The mean (standard deviation) age was 44.57 (6.21) years. Participants responded to a series of questions about physical activity at their child’s school, in addition to completing the International Physical Activity Questionnaire. Data were analysed using descriptive statistics, frequency distributions and logistic regressions. Parents were deemed sufficiently physically active if they participated in at least 150 minutes of moderate-to-vigorous physical activity per week. Overall, 83 (59.7%) parents from private and 60 (50.8%) parents from government schools were deemed sufficiently physically active. Concerning whether physical activity promotion should be a priority at their child’s school, 111 (73.5%) parents from private schools either agreed or strongly agreed, as opposed to 97 (64.7%) parents from government schools. Logistic regressions indicated that the concept of physical activity promotion being prioritised at schools was dependent on whether the child attended a private school (OR =1.34, z = 2.30, p = 0.02), and whether the participant was sufficiently active (OR =.71, z = -2.48, p = 0.01). Physical activity promotion within schools may provide substantial future benefits on a population scale. The demands on schools may need to be addressed to meet the needs of students and the desires of their parents.