Translating competency statements into desired outcomes : a representational model for conceptualising the competency acquisition process

The need to develop effective and efficient training programs has been recognised by all sectors engaged in training. In responding to the above need, focus has been directed to developing good competency statements and performance indicators to measure the outcomes. Very little has been done to understand how the competency statements get translated into good performance. To conceptualise this translation process, a representational model based on an information processing paradigm is proposed and discussed. It is argued that learners’ prior knowledge and the effectiveness of the instructional material are two variables that have significant bearing on how effectively the competency knowledge is translated into outcomes. To contextualise the model examples from apprentice training are used.

Review of the under the limit drink driving rehabilitation program

The combination of alcohol and driving is a major health and economic burden to most communities in industrialised countries. The total cost of crashes for Australia in 1996 was estimated at approximately 15 billion dollars and the costs for fatal crashes were about 3 billion dollars (BTE, 2000). According to the Bureau of Infrastructure, Transport and Regional Development and Local Government (2009; BITRDLG) the overall cost of road fatality crashes for 2006 $3.87 billion, with a single fatal crash costing an estimated $2.67 million. A major contributing factor to crashes involving serious injury is alcohol intoxication while driving. It is a well documented fact that consumption of liquor impairs judgment of speed, distance and increases involvement in higher risk behaviours (Waller, Hansen, Stutts, & Popkin, 1986a; Waller et al., 1986b). Waller et al. (1986a; b) asserts that liquor impairs psychomotor function and therefore renders the driver impaired in a crisis situation. This impairment includes; vision (degraded), information processing (slowed), steering, and performing two tasks at once in congested traffic (Moskowitz & Burns, 1990). As BAC levels increase the risk of crashing and fatality increase exponentially (Department of Transport and Main Roads, 2009; DTMR). According to Compton et al. (2002) as cited in the Department of Transport and Main Roads (2009), crash risk based on probability, is five times higher when the BAC is 0.10 compared to a BAC of 0.00. The type of injury patterns sustained also tends to be more severe when liquor is involved, especially with injuries to the brain (Waller et al., 1986b). Single and Rohl (1997) reported that 30% of all fatal crashes in Australia where alcohol involvement was known were associated with Breadth Analysis Content (BAC) above the legal limit of 0.05gms/100ml. Alcohol related crashes therefore contributes to a third of the total cost of fatal crashes (i.e. $1 billion annually) and crashes where alcohol is involved are more likely to result in death or serious injury (ARRB Transport Research, 1999). It is a major concern that a drug capable of impairment such as is the most available and popular drug in Australia (Australian Institute of Health and Welfare, 2007; AIHW). According to the AIHW (2007) 89.9% of the approximately 25,000 Australians over the age of 14 surveyed had consumed at some point in time, and 82.9% had consumed liquor in the previous year. This study found that 12.1% of individuals admitted to driving a motor vehicle whilst intoxicated. In general males consumed more liquor in all age groups. In Queensland there were 21503 road crashes in 2001, involving 324 fatalities and the largest contributing factor was alcohol and or drugs (Road Traffic Report, 2001). 23438 road crashes in 2004, involving 289 fatalities and the largest contributing factor was alcohol and or drugs (DTMR, 2009). Although a number of measures such as random breath testing have been effective in reducing the road toll (Watson, Fraine & Mitchell, 1995) the recidivist drink driver remains a serious problem. These findings were later supported with research by Leal, King, and Lewis (2006). This Queensland study found that of the 24661 drink drivers intercepted in 2004, 3679 (14.9%) were recidivists with multiple drink driving convictions in the previous three years covered (Leal et al., 2006). The legal definition of the term “recidivist” is consistent with the Transport Operations (Road Use Management) Act (1995) and is assigned to individuals who have been charged with multiple drink driving offences in the previous five years. In Australia relatively little attention has been given to prevention programs that target high-risk repeat drink drivers. However, over the last ten years a rehabilitation program specifically designed to reduce recidivism among repeat drink drivers has been operating in Queensland. The program, formally known as the “Under the Limit” drink driving rehabilitation program (UTL) was designed and implemented by the research team at the Centre for Accident Research and Road Safety in Queensland with funding from the Federal Office of Road Safety and the Institute of Criminology (see Sheehan, Schonfeld & Davey, 1995). By 2009 over 8500 drink-drivering offenders had been referred to the program (Australian Institute of Crime, 2009).

Motivational Interviewing changes the treatment trajectory of group Cognitive-Behavioural Therapy for anxiety

Anxiety disorders have been viewed as manifestations of broad underlying predisposing personality constructs such as neuroticism combined with more specific individual differences of unhelpful information processing styles. Given the high prevalence of anxiety and the significant impairment that it causes, there is an important need to continue to explore successful treatments for this disorder. Research indicates that there is still room for significantly improving attrition rates and treatment adherence. Traditionally Motivational Interviewing (MI) has been used to facilitate health behaviour change. Recently MI has been applied to psychotherapy and has been shown to improve the outcome of CBT. However, these studies have been limited to only considering pre- and post-treatment measures and neglected to consider when changes occur along the course of therapy. This leaves the unanswered question of what is the impact of pre-treatment MI on the treatment trajectory of therapy. This study provides preliminary research into answering this question by tracking changes on a weekly basis along the course of group CBT. Prior to group CBT, 40 individuals with a principal anxiety disorder diagnosis were randomly assigned to receive either 3 individual sessions of MI or placed on a waitlist control group. All participants then received the same dosage of 10 weekly 2 hour sessions of group CBT. Tracking treatment outcome trajectory over the course of CBT, the pre-treatment MI group, compared to the control group, experienced a greater improvement early on in the course of therapy in their symptom distress, interpersonal relationships and quality of life. This early advantage over the control group was then maintained throughout therapy. These results not only demonstrate the value of adding MI to CBT, but also highlight the immediacy of MI effects. Further research is needed to determine the robustness of these effects to inform clinical implications of how to best apply MI to improve treatment adherence to CBT for anxiety disorders.

Ceremony analysis : strengths and weaknesses

We investigate known security flaws in the context of security ceremonies to gain an understanding of the ceremony analysis process. The term security ceremonies is used to describe a system of protocols and humans which interact for a specific purpose. Security ceremonies and ceremony analysis is an area of research in its infancy, and we explore the basic principles involved to better understand the issues involved.We analyse three ceremonies, HTTPS, EMV and Opera Mini, and use the information gained from the experience to establish a list of typical flaws in ceremonies. Finally, we use that list to analyse a protocol proven secure for human use. This leads to a realisation of the strengths and weaknesses of ceremony analysis.

Multi-modal summarization of key events and top players in sports tournament videos

To detect and annotate the key events of live sports videos, we need to tackle the semantic gaps of audio-visual information. Previous work has successfully extracted semantic from the time-stamped web match reports, which are synchronized with the video contents. However, web and social media articles with no time-stamps have not been fully leveraged, despite they are increasingly used to complement the coverage of major sporting tournaments. This paper aims to address this limitation using a novel multimodal summarization framework that is based on sentiment analysis and players' popularity. It uses audiovisual contents, web articles, blogs, and commentators' speech to automatically annotate and visualize the key events and key players in a sports tournament coverage. The experimental results demonstrate that the automatically generated video summaries are aligned with the events identified from the official website match reports.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

Recognition of expertise : an important concept in the acquisition of nephrology nursing expertise

The present article, which is abstracted from a larger study into the acquisition and exercise of nephrology nursing expertise, aims to explore the concept of recognition of expertise. The study used grounded theory methodology and involved 17 registered nurses who were practising in a metropolitan renal unit in New South Wales, Australia. Concurrent data collection and analysis was undertaken, incorporating participant observations and interviews. According to nurses in this study, patients, doctors and other nurses recognized that some nurses were experts while others were not. In addition, being trusted, being a role model and teaching others were important components of being recognized as an expert nephrology nurse. Of importance for nursing, the results of the present study indicate that knowledge and experience are not sufficient to ensure expert practice; recognition of expertise by others is an important function of expertise acquisition.

Exponentiated gradient algorithms for large-margin structured classification

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

Geometry vs appearance for discriminating between posed and spontaneous emotions

Spontaneous facial expressions differ from posed ones in appearance, timing and accompanying head movements. Still images cannot provide timing or head movement information directly. However, indirectly the distances between key points on a face extracted from a still image using active shape models can capture some movement and pose changes. This information is superposed on information about non-rigid facial movement that is also part of the expression. Does geometric information improve the discrimination between spontaneous and posed facial expressions arising from discrete emotions? We investigate the performance of a machine vision system for discrimination between posed and spontaneous versions of six basic emotions that uses SIFT appearance based features and FAP geometric features. Experimental results on the NVIE database demonstrate that fusion of geometric information leads only to marginal improvement over appearance features. Using fusion features, surprise is the easiest emotion (83.4% accuracy) to be distinguished, while disgust is the most difficult (76.1%). Our results find different important facial regions between discriminating posed versus spontaneous version of one emotion and classifying the same emotion versus other emotions. The distribution of the selected SIFT features shows that mouth is more important for sadness, while nose is more important for surprise, however, both the nose and mouth are important for disgust, fear, and happiness. Eyebrows, eyes, nose and mouth are important for anger.

Optimistic linear programming gives logarithmic regret for irreducible MDPs

We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P) log T of the reward obtained by the optimal policy, where C(P) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.

Current AI in games : a review

As the graphics race subsides and gamers grow weary of predictable and deterministic game characters, game developers must put aside their “old faithful” finite state machines and look to more advanced techniques that give the users the gaming experience they crave. The next industry breakthrough will be with characters that behave realistically and that can learn and adapt, rather than more polygons, higher resolution textures and more frames-per-second. This paper explores the various artificial intelligence techniques that are currently being used by game developers, as well as techniques that are new to the industry. The techniques covered in this paper are finite state machines, scripting, agents, flocking, fuzzy logic and fuzzy state machines decision trees, neural networks, genetic algorithms and extensible AI. This paper introduces each of these technique, explains how they can be applied to games and how commercial games are currently making use of them. Finally, the effectiveness of these techniques and their future role in the industry are evaluated.

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Acoustic component detection for automatic species recognition in environmental monitoring

Automatic species recognition plays an important role in assisting ecologists to monitor the environment. One critical issue in this research area is that software developers need prior knowledge of specific targets people are interested in to build templates for these targets. This paper proposes a novel approach for automatic species recognition based on generic knowledge about acoustic events to detect species. Acoustic component detection is the most critical and fundamental part of this proposed approach. This paper gives clear definitions of acoustic components and presents three clustering algorithms for detecting four acoustic components in sound recordings; whistles, clicks, slurs, and blocks. The experiment result demonstrates that these acoustic component recognisers have achieved high precision and recall rate.