Risk taking by motorcyclists : rider training and stages of change

Motorcyclists in Australia have been found to be 30 times more likely to be killed per kilometre travelled than car occupants and 40 times more likely to be seriously injured. One approach to preventing motorcycle-related injury is through training and education. While there is traditionally a major focus on developing riding skills during training for motorcyclists, there is also a need for training to promote safe riding to reduce subsequent risk taking. The Transtheoretical Model, commonly known as the ‘Stages of Change’ model, provides a rationale to support incremental behaviour change for risky riding that may be facilitated through motorcycle rider training and education. A sample of 438 learner motorcyclists attended a rider training program in Queensland, Australia, with the stages of change to adopt a safe riding mindset and safe riding practices being measured upon commencement of the course (Time 1) and then again upon completion (Time 2). A small subset of the original sample (n=45) responded at follow up 24 months post training (Time 3). Consistent with the aims of training, results showed a significant shift from the contemplation stage to the subsequent stages of change for participants between Time 1 and Time 2. Progression to the later stages in the model was found for the subset of participants that responded at the Time 3 follow up. Issues of questionnaire design and the utility of the Transtheoretical Model for motorcycle rider training are discussed.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data.

Preliminary results from a real time control system using optical information

Successful results from training an adaptive controller to use optical information to balance an inverted pendulum are presented in comparison to the training requirements using traditional controller inputs. Results from research into the psychology of the sense of balance in humans are presented as the motivation for the investigation of this new type of controller. The simulated model of the inverted pendulum and the virtual reality environments used to provide the optical input are described The successful introduction of optical information is found to require the preservation of at least two of the traditional input types and entail increased training time for the adaptive controller and reduced performance (measured as the time the pendulum remains upright).

Simple adaptive momentum: new algorithm for training multilayer perceptrons

The speed of convergence while training is an important consideration in the use of neural nets. The authors outline a new training algorithm which reduces both the number of iterations and training time required for convergence of multilayer perceptrons, compared to standard back-propagation and conjugate gradient descent algorithms.

Impact of patient training patterns on peritonitis rates in a large national cohort study

Background. Ideal training methods that could ensure best peritoneal dialysis (PD) outcome have not been defined in previous reports. The aim of the present study was to evaluate the impact of training characteristics on peritonitis rates in a large Brazilian cohort.Methods. Incident patients with valid data on training recruited in the Brazilian Peritoneal Dialysis Multicenter Study (BRAZPD II) from January 2008 to January 2011 were included. Peritonitis was diagnosed according to International Society for Peritoneal Dialysis guidelines; incidence rate of peritonitis (episodes/patient-months) and time to the first peritonitis were used as end points.Results. Two thousand two hundred and forty-three adult patients were included in the analysis: 59 +/- 16 years old, 51.8% female, 64.7% with <= 4 years of education. The median training time was 15 h (IQI 10-20 h). Patients were followed for a median of 11.2 months (range 3-36.5). The overall peritonitis rate was 0.29 per year at risk (1 episode/41 patient-months). The mean number of hours of training per day was 1.8 +/- 2.4. Less than 1 h of training/day was associated with higher incidence rate when compared with the intervals of 1-2 h/day (P = 0.03) and > 2 h/day (P = 0.02). Patients who received a cumulative training of > 15 h had significantly lower incidence of peritonitis compared with < 15 h (0.26 per year at risk versus 0.32 per year at risk, P = 0.01). The presence of a caregiver and the number of people trained were not significantly associated with peritonitis incidence rate. Training in the immediate 10 days after implantation of the catheter was associated with the highest peritonitis rate (0.32 per year), compared with training prior to catheter implantation (0.28 per year) or > 10 days after implantation (0.23 per year). More experienced centers had a lower risk for the first peritonitis (P = 0.003).Conclusions. This is the first study to analyze the association between training characteristics and outcomes in a large cohort of PD patients. Low training time (particularly < 15 h), smaller center size and the timing of training in relation to catheter implantation were associated with a higher incidence of peritonitis. These results support the recommendation of a minimum amount of training hours to reduce peritonitis incidence regardless of the number of hours trained per day.

Simulator training on pulsatile vascular models significantly improves surgical skills and the quality of carotid patch plasty

Vascular surgeons perform numerous highly sophisticated and delicate procedures. Due to restrictions in training time and the advent of endovascular techniques, new concepts including alternative environments for training and assessment of surgical skills are required. Over the past decade, training on simulators and synthetic models has become more sophisticated and lifelike. This study was designed to evaluate the impact of a 3-day intense training course in open vascular surgery on both specific and global vascular surgical skills.

Modular training in vascular and endovascular surgery exemplified by the Vascular International Foundation and School

The partial shift from patient to model is a reasonable and necessary paradigm shift in surgery in order to increase patient safety and to adapt to the reduced training time periods in hospitals and increased quality demands. Since 1991 the Vascular International Foundation and School has carried out many training courses with more than 2,500 participants. The modular build training system allows to teach many open vascular and endovascular surgical techniques on lifelike models with a pulsatile circulation. The simulation courses cannot replace training in operating rooms but are suitable for supporting the cognitive and associative stages for achieving motor skills. Scientific evaluation of the courses has continually shown that the training principle established since 1991 can lead to significant learning success. They are extremely useful not only for beginners but also for experienced vascular surgeons. They can help to shorten the learning curve, to learn new techniques or to refine previously used techniques in all stages of professional development. Keywords Advanced training · Advanced training regulations · Training model · Vascular International · Certification

Mixture density network training by computation in parameter space

Training Mixture Density Network (MDN) configurations within the NETLAB framework takes time due to the nature of the computation of the error function and the gradient of the error function. By optimising the computation of these functions, so that gradient information is computed in parameter space, training time is decreased by at least a factor of sixty for the example given. Decreased training time increases the spectrum of problems to which MDNs can be practically applied making the MDN framework an attractive method to the applied problem solver.

Social influences on risky driving behaviours among young drivers in Oman

Young drivers represent approximately 20% of the Omani population, yet account for over one third of crash injuries and fatalities on Oman's roads. Internationally, research has demonstrated that social influences play an important role within young driver safety, however, there is little research examining this within Arab gulf countries. This study sought to explore young driver behaviour using Akers' social learning theory. A self-report survey was conducted by 1319 (72.9% male and 27.1% female) young drivers aged 17-25 years. A hierarchical regression model was used to investigate the contribution of social learning variables (norms and behaviour of significant others, personal attitudes towards risky behaviour, imitation of significant others, beliefs about the rewards and punishments offered by risky behaviour), socio-demographic characteristics (age and gender), driving experience (initial training, time driving and previous driving without supervision) and sensitivity to rewards and punishments upon the self-reported risky driving behaviours of young drivers. It was found that 39.6% of the young drivers reported that they have been involved in at least one crash since the issuance of their driving licence and they were considered ‘at fault’ in 60.7% of these crashes. The hierarchical multiple regression models revealed that socio-demographic characteristics and driving experience alone explained 14.2% of the variance in risky driving behaviour. By introducing social learning factors into the model a further 37.0% of variance was explained. Finally, 7.9% of the variance in risky behaviour could be explained by including individual sensitivity to rewards and punishments. These findings and the implications are discussed.

"Mut ei tää oo hei midsommarista!" - ruotsin kielen viestinnällinen suullinen harjoittelu yhteistoiminnallisten skeema- ja elaborointitehtävien avulla

Communicative oral practice in Swedish through collaborative schema-based and elaboration tasks The general aim of this study was to learn how to better understand foreign language communicative oral practice and to develop it as part of communicative language teaching. The language-specific aim was to study how Swedish was being practised communicatively and orally in a classroom context as part of the didactic teaching-studying-learning process, and how the students' communicative oral practice in Swedish was carried out through collaborative schema-based and elaboration tasks. The scientific problem of this study focused on the essence of foreign language communicative oral proficiency. The research questions were concerned with 1) the students' involvement in carrying out the given oral tasks; 2) the features of communication and interaction strategies; 3) thematic vocabulary, and 4) the students' experiences and conceptions of the communicative oral tasks used. The study consisted of two groups of students from a Helsinki-area school (a group of upper secondary school students, Swedish Level A, Courses 2 and 3, n=9; and a group of basic education students, Swedish Level B, Course 2, n=13). The study was carried out as a pedagogically oriented case study which included certain features of ethnographic research and where the students' teacher acted as a researcher of her own work. The communicative oral practice contained five different tasks. The research data were gathered through systematic observation, audio recordings and by a questionnaire. The data were analysed through ethnographic content analysis methods. The main research finding was that a good deal of social interaction, collaboration and communication took place between the students when involved in communicative oral practice in Swedish. The students took almost optimal advantage of the allocated training time. They mostly used Swedish when participating in interactional communication. Finnish was mostly used by the students when they were deciding how to carry out a given task, aiming at intersubjectivity or negotiating meaning. The students were relaxed when practising Swedish. They also asked for and gave linguistic help in the spirit of collaborative learning principles. This resulted in interaction between students that highlighted certain features of negotiation of meaning, scaffolding and collaborative dialogue. Asking for and giving help in language issues concentrated mainly on vocabulary, and only in a few cases on grammar or pronunciation. The students also needed the teacher as a mentor. As well, the students had an enjoyable time when practising, which was most often related to carrying out the oral tasks. The thematic vocabulary used by the students corresponded well to the thematic lexis that served as a basis for the practice. At its most efficient, this lexis was most evident when the basic education students were carrying out schema-based tasks. The students' questionnaire answers agreed with the research findings gained through systematic observation and the analysis of audio recordings. The communicative tasks planned by the teacher and implemented by the students were very much in line. The language-didactic theory as presented in this study and the research findings can be widely utilised in pre-service and in-service teacher education, as well as, more generally, when developing communicative language teaching. Key words: communicative oral practice; the Swedish language; foreign language; didactic teaching-studying-learning process; communicative language teaching; collaborative task; schema-based task; elaboration task.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.