Evaluating parent-child group music therapy programmes: challenges and successes for Sing & Grow

Sing & Grow is an early intervention music therapy programme for families with children from birth to 3 years of age, who are socially, economically, or physically disadvantaged. It aims to improve parenting skills and confidence, promote positive parent–child interactions, stimulate child development, and provide social networking opportunities. Music and song activities are used in a therapeutic context to enhance parenting skills, improve parent–child interactions, provide essential developmental stimulation for children, promote social support for parenting, and strengthen links between parents and community services.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Quality delivery of mobile video : in-depth understanding of user requirements

The increase of powerful mobile devices has accelerated the demand for mobile videos. Previous studies in mobile video have focused on understanding of mobile video usage, improvement of video quality, and user interface design in video browsing. However, research focusing on a deep understanding of users’ needs for a pleasing quality delivery of mobile video is lacking. In particular, what quality-delivery mode users prefer and what information relevant to video quality they need requires attention. This paper presents a qualitative interview study with 38 participants to gain an insight into three aspects: influencing factors of user-desired video quality, user-preferred quality-delivery modes, and user-required interaction information of mobile video. The results show that user requirements for video quality are related to personal preference, technology background and video viewing experience, and the preferred quality-delivery mode and interactive mode are diverse. These complex user requirements call for flexible and personalised quality delivery and interaction of mobile video.

Query expansion using term relationships in language models for information retrieval

Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

Implementable efficient time and energy consumption trajectories design for an autonomous underwater vehicle

In this paper we consider the implementation of time and energy efficient trajectories onto a test-bed autonomous underwater vehicle. The trajectories are losely connected to the results of the application of the maximum principle to the controlled mechanical system. We use a numerical algorithm to compute efficient trajectories designed using geometric control theory to optimize a given cost function. Experimental results are shown for the time minimization problem.

Saving bitrate vs. pleasing users : where is the break-even point in mobile video quality?

This paper presents a comprehensive study to find the most efficient bitrate requirement to deliver mobile video that optimizes bandwidth, while at the same time maintains good user viewing experience. In the study, forty participants were asked to choose the lowest quality video that would still provide for a comfortable and long-term viewing experience, knowing that higher video quality is more expensive and bandwidth intensive. This paper proposes the lowest pleasing bitrates and corresponding encoding parameters for five different content types: cartoon, movie, music, news and sports. It also explores how the lowest pleasing quality is influenced by content type, image resolution, bitrate, and user gender, prior viewing experience, and preference. In addition, it analyzes the trajectory of users’ progression while selecting the lowest pleasing quality. The findings reveal that the lowest bitrate requirement for a pleasing viewing experience is much higher than that of the lowest acceptable quality. Users’ criteria for the lowest pleasing video quality are related to the video’s content features, as well as its usage purpose and the user’s personal preferences. These findings can provide video providers guidance on what quality they should offer to please mobile users.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Process mining manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

A semantics-based approach to querying process model repositories

As business process management technology matures, organisations acquire more and more business process models. The resulting collections can consist of hundreds, even thousands of models and their management poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks. This query language is independent of the particular process modelling notation used, but we will demonstrate how it can be used in the context of Petri nets by showing how the semantic relationships can be determined for these nets in such a way that state space explosion is avoided as much as possible. An experiment with three large process model repositories shows that queries expressed in our language can be evaluated efficiently.

Was By The Northern Coast

‘Was by the Northern Coast’ was an installation at MetroArts in Brisbane. A pile of warped timber, evocative of a dismantled boat, sits in the middle of the gallery space on a bed of carefully-laid bands of polyester insulation and pine battening. From within the wood stack, the sound of dripping water indicates the flow of water created by a silent internal pump. The sound of water intermingles with a soft soundtrack of Kulning, an archaic form of Scandinavian song. In ‘Was by the Northern Coast’, the detritus of timber mimics the Romantic sublime of the mountain peak and nautical wreckage while the snowy drifts of the Northern European landscape become mistranslated as a field of artificial ceiling insulation. In employing such slippages, the work attempted to create the imaginative landscape of an aesthetic displaced by distance and time.

Exploring regional differences in Chinese consumer acceptance of new mobile technology : a qualitative study

This study examines consumer adoption of 3G mobile technology in China. The qualitative study involved 45 in-depth interviews undertaken in three major Chinese cities to explore the beliefs and attitudes which determine Chinese consumers’ acceptance of the mobile technological innovation. The findings are compared and contrasted against those reported in Western studies. The variations underpinning adoption of 3G between consumers in the three regional cities were identified. Specifically, it was found that the regions differed in terms of the relative importance of the identified adoption determinants, such as perceived social outcomes for using the innovation and the effects of social influence on the adoption. These findings provide subtle insight into the nature of Chinese consumers’ responses to new mobile technologies and a better understanding of variations among regional Chinese consumers.

From bust to boom : towards a strategic cognition perspective on Australian mining firms’ adaption

While the role of executives’ cognition in organisations’ responses to change is a central topic in strategic cognition research, changes in firms’ environment are typically not measured directly but described either as an event (for example, new industry legislation) or represented by a time period (e.g. when a new technology impacted an industry). The Australian mining sector has witnessed a historically significant change in demand for its products and we begin by developing measures of changes in supply and demand for key commodities during the period 1992-2008. We identify sub-groups of firms based on their activities and commodity sector and examine the relation of these variables to executives’ cognition and to firms’ CapEx. We find industry, firm and cognitive variables are related to both strategic cognition and firms’ CapEx.