Exploring the effect of achievements on students attending university orientation

University orientation is a key event for new students that aids in the transition from a school to a university environment. A smartphone orientation application was built to aid students attending the event. Achievements were added to the application in an attempt to engage students further with the orientation activities and application. An exploratory field study was undertaken to evaluate the effect of the achievement system on participants attending orientation. Forty-six new students were recruited to test the orientation application. Twenty-six participants used a gamified version of the orientation application and twenty participants used a non-gamified version. While the gamification was generally well received, no impact on user experience was evident. Some effect on engagement with orientation activities was shown. Participants who used the gamified system reported the game elements as fun, but some negative issues arose, such as cheating.

Acceptability-based QoE management for user-centric mobile video delivery : a field study evaluation

Effective Quality of Experience (QoE) management for mobile video delivery – to optimize overall user experience while adapting to heterogeneous use contexts – is still a big challenge to date. This paper proposes a mobile video delivery system to emphasize the use of acceptability as the main indicator of QoE to manage the end-to-end factors in delivering mobile video services. The first contribution is a novel framework for user-centric mobile video system that is based on acceptability-based QoE (A-QoE) prediction models, which were derived from comprehensive subjective studies. The second contribution is results from a field study that evaluates the user experience of the proposed system during realistic usage circumstances, addressing the impacts of perceived video quality, loading speed, interest in content, viewing locations, network bandwidth, display devices, and different video coding approaches, including region-of-interest (ROI) enhancement and center zooming

Technicolouring the fridge : reducing food waste through uses of colour-coding and cameras

Domestic food wastage is a growing problem for the environment and food security. Some causes of domestic food wastes are attributed to a consumer’s behaviours during food purchasing, storage and consumption, such as: excessive food purchases and stockpiling in storage. Recent efforts in human-computer interaction research have examined ways of influencing consumer behaviour. The outcomes have led to a number of interventions that assist users with performing everyday tasks. The Internet Fridge is an example of such an intervention. However, new pioneering technologies frequently confront barriers that restrict their future impact in the market place, which has prompted investigations into the effectiveness of behaviour changing interventions used to encourage more sustainable practices. In this paper, we investigate and compare the effectiveness of two interventions that encourage behaviour change: FridgeCam and the Colour Code Project. We use FridgeCam to examine how improving a consumer’s food supply knowledge can reduce food stockpiling. We use the Colour Code Project to examine how improving consumer awareness of food location can encourage consumption of forgotten foods. We explore opportunities to integrate these interventions into commercially available technologies, such as the Internet Fridge, to: (i) increase the technology’s benefit and value to users, and (ii) promote reduced domestic food wastage. We conclude that interventions improving consumer food supply and location knowledge can promote behaviours that reduce domestic food waste over a longer term. The implications of this research present new opportunities for existing and future technologies to play a key role in reducing domestic food waste.

Designing for grassroots food production : an event-based urban agriculture community

As urbanisation of the global population has increased above 50%, growing food in urban spaces increases in importance, as it can contribute to food security, reduce food miles, and improve people’s physical and mental health. Approaching the task of growing food in urban environments is a mixture of residential growers and groups. Permablitz Brisbane is an event-centric grassroots community that organises daylong ‘working bee’ events, drawing on permaculture design principles in the planning and design process. Permablitz Brisbane provides a useful contrast from other location-centric forms of urban agriculture communities (such as city farms or community gardens), as their aim is to help encourage urban residents to grow their own food. We present findings and design implications from a qualitative study with members of this group, using ethnographic methods to engage with and understand how this group operates. Our findings describe four themes that include opportunities, difficulties, and considerations for the creation of interventions by Human-Computer Interaction (HCI) designers.

DIY media architecture: Open and participatory approaches to community engagement

Media architecture’s combination of the digital and the physical can trigger, enhance, and amplify urban experiences. In this paper, we examine how to bring about and foster more open and participatory approaches to engage communities through media architecture by identifying novel ways to put some of the creative process into the hands of laypeople. We review technical, spatial, and social aspects of DIY phenomena with a view to better understand maker cultures, communities, and practices. We synthesise our findings and ask if and how media architects as a community of practice can encourage the ‘open-sourcing’ of information and tools allowing laypeople to not only participate but become active instigators of change in their own right. We argue that enabling true DIY practices in media architecture may increase citizen control. Seeking design strategies that foster DIY approaches, we propose five areas for further work and investigation. The paper begs many questions indicating ample room for further research into DIY Media Architecture.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Day in the Lab : Urban Informatics Research Lab, Queensland University of Technology

The Urban Informatics Research Lab brings together a group of people who focus their research on interdisciplinary topics at the intersection of social, spatial, and technical research domains—that is, people, place, and technology. Those topics are spread across the breadth of urban life—its contemporary issues and its needs, as well as the design opportunities that we have as individuals, groups, communities, and as a whole society. The lab’s current research areas include urban planning and design, civic innovation, mobility and transportation, education and connected learning, environmental sustainability, and food and urban agriculture. The common denominator of the lab’s approach is user-centered design research directed toward understanding, conceptualizing, developing, and evaluating sociotechnical practices as well as the opportunities afforded by innovative digital technology in urban environments.

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

Multi-ciphersuite security of the Secure Shell (SSH) protocol

The Secure Shell (SSH) protocol is widely used to provide secure remote access to servers, making it among the most important security protocols on the Internet. We show that the signed-Diffie--Hellman SSH ciphersuites of the SSH protocol are secure: each is a secure authenticated and confidential channel establishment (ACCE) protocol, the same security definition now used to describe the security of Transport Layer Security (TLS) ciphersuites. While the ACCE definition suffices to describe the security of individual ciphersuites, it does not cover the case where parties use the same long-term key with many different ciphersuites: it is common in practice for the server to use the same signing key with both finite field and elliptic curve Diffie--Hellman, for example. While TLS is vulnerable to attack in this case, we show that SSH is secure even when the same signing key is used across multiple ciphersuites. We introduce a new generic multi-ciphersuite composition framework to achieve this result in a black-box way.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Report on INEX 2013

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data (Social Book Search track); searching structured or semantic data (Linked Data track); and focused retrieval (Snippet Retrieval and Tweet Contextualization tracks). INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with (other activities in) CLEF and for the second time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results.

Report on INEX 2012

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX'12 evaluation campaign, which consisted of a five tracks: Linked Data, Relevance Feedback, Snippet Retrieval, Social Book Search, and Tweet Contextualization. INEX'12 was an exciting year for INEX in which we joined forces with CLEF and for the first time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums.

Item reputation-aware recommender systems

Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on users’ interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.

Coding livecoding

Livecoding is an artistic programming practice in which an artist's low-level interaction can be observed with sufficiently high fidelity to allow for transcription and analysis. This paper presents the first reported" coding" of livecoding videos. From an identified corpus of videos available on the web, we coded performances of two different livecoding artists, recording both the (textual) programming edit events and the musical effect of these edits.

Privacy in the new era of visible and sharable energy-use information

A new era of visible and sharable electricity information is emerging. Where eco-feedback is installed, households can now visualise many aspects of their energy consumption and share this information with others through Internet platforms such as social media. Despite providing users with many affordances, eco-feedback information can make public previously private actions from within the intimate setting of the family home. This paper represents a study focussing specifically on the privacy aspects of nascent ways for viewing and sharing this new stream of personal information. It explores the nuances of privacy related to eco-feedback both within and beyond the family home. While electricity consumption information may not be considered private itself, the household practices which eco-feedback systems makes visible may be private. We show that breaches of privacy can occur in unexpected ways and have the potential to cause distress. The paper concludes with some suggestions for how to realise the benefits of sharing energy consumption information whist effectively maintaining individuals’ conceptions of adequate privacy.