Self-determination theory as applied to the design of a software learning system using whole-body controls

Whole-body computer control interfaces present new opportunities to engage children with games for learning. Stomp is a suite of educational games that use such a technology, allowing young children to use their whole body to interact with a digital environment projected on the floor. To maximise the effectiveness of this technology, tenets of self-determination theory (SDT) are applied to the design of Stomp experiences. By meeting user needs for competence, autonomy, and relatedness our aim is to increase children's engagement with the Stomp learning platform. Analysis of Stomp's design suggests that these tenets are met. Observations from a case study of Stomp being used by young children show that they were highly engaged and motivated by Stomp. This analysis demonstrates that continued application of SDT to Stomp will further enhance user engagement. It also is suggested that SDT, when applied more widely to other whole-body multi-user interfaces, could instil similar positive effects.

Preliminary reliability and validity testing of a self-efficacy questionnaire for Chinese family caregivers

Objectives: To develop and test preliminary reliability and validity of a Self-Efficacy Questionnaire for Chinese Family Caregivers (SEQCFC). Methods: A cross-sectional survey of 196 family caregivers (CGs) of people with dementia (CGs) was conducted to determine the factor structure of a SEQCFC of people with dementia. Following factor analyses, preliminary testing was performed, including internal consistency, 4-week test retest reliability, and construct and convergent validity. Results: Factor analyses with direct oblimin rotation were performed. Eight items were removed and five subscales(selfefficacy for gathering information about treatment, symptoms and health care; obtaining support; responding to behaviour disturbances; managing household, personal and medical care; and managing distress associated with caregiving) were identified. The Cronbach’s alpha coefficients for the whole scale and for each subscale were all over 0.80. The 4-week testretest reliabilities for the whole scale and for each subscale ranged from 0.64 to 0.85. The convergent validity was acceptable. Conclusions: Evidence for the preliminary testing of the SEQCFC was encouraging. A future follow-up study using confirmatory factor analysis with a new sample from different recruitment centres in Shanghai will be conducted. Future psychometric property testings of the questionnaire will be required for CGs from other regions of mainland China.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

Comparison of self-reported driving behaviours and experiences of immediate-uptake and delayed-uptake licence holders

The objective of this paper was to explore experiences of ‘immediate-uptake’ (intermediate licensure at age 17-18 years, n = 928) and ‘delayed-uptake’ (intermediate licensure at age 19-20 years, n = 158) driver’s licence holders in the Australian state of Queensland. In Queensland, the graduated driver licence program applies to all novices irrespective of age. Drivers who obtained a Provisional 1 (intermediate) (P1) licence completed a survey exploring pre-Licence and Learner experiences, including the Behaviour of Young Novice Drivers Scale (BYNDS). Six months later, 351 drivers from this sample (n = 300 immediate-uptake) completed a survey exploring P1 driving. Delayed-uptake Learners reported significantly more difficulty gaining driving practice, which appeared to be associated with significantly greater engagement in unsupervised driving during the Learner period. Whilst a larger proportion of delayed-uptake novices, particularly males, reported the use of more active punishment avoidance strategies (avoiding Police, talking themselves out of a ticket) in the P1 phase, there was no significant difference in the BYNDS scores in the Learner and P1 phases according to licence-uptake category. Delayed-uptake novices report more difficulty meeting GDL requirements and place themselves at increased risk by driving unsupervised during the Learner licence phase. Additional efforts such as mentoring programs which can support the delayed-uptake Learner in meeting their GDL obligations merit further consideration to allow this novice group to gain the full benefits of the GDL program and to reduce their risk of harm in the short-term.

Resting metabolic rate is associated with hunger, self-determined meal size, and daily energy intake and may represent a marker for appetite

Background: There are strong logical reasons why energy expended in metabolism should influence the energy acquired in food-intake behavior. However, the relation has never been established, and it is not known why certain people experience hunger in the presence of large amounts of body energy. Objective: We investigated the effect of the resting metabolic rate (RMR) on objective measures of whole-day food intake and hunger. Design: We carried out a 12-wk intervention that involved 41 overweight and obese men and women [mean ± SD age: 43.1 ± 7.5 y; BMI (in kg/m2): 30.7 ± 3.9] who were tested under conditions of physical activity (sedentary or active) and dietary energy density (17 or 10 kJ/g). RMR, daily energy intake, meal size, and hunger were assessed within the same day and across each condition. Results: We obtained evidence that RMR is correlated with meal size and daily energy intake in overweight and obese individuals. Participants with high RMRs showed increased levels of hunger across the day (P < 0.0001) and greater food intake (P < 0.00001) than did individuals with lower RMRs. These effects were independent of sex and food energy density. The change in RMR was also related to energy intake (P < 0.0001). Conclusions: We propose that RMR (largely determined by fat-free mass) may be a marker of energy intake and could represent a physiologic signal for hunger. These results may have implications for additional research possibilities in appetite, energy homeostasis, and obesity. This trial was registered under international standard identification for controlled trials as ISRCTN47291569.

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.

Neighbourhood disadvantage, individual-level socioeconomic position, and self-reported chronic arthritis : a cross-sectional multilevel study

Objective: To examine the association between individual- and neighborhood-level disadvantage and self-reported arthritis. Methods: We used data from a population-based cross-sectional study conducted in 2007 among 10,757 men and women ages 40–65 years, selected from 200 neighborhoods in Brisbane, Queensland, Australia using a stratified 2-stage cluster design. Data were collected using a mail survey (68.5% response). Neighborhood disadvantage was measured using a census-based composite index, and individual disadvantage was measured using self-reported education, household income, and occupation. Arthritis was indicated by self-report. Data were analyzed using multilevel modeling. Results: The overall rate of self-reported arthritis was 23% (95% confidence interval [95% CI] 22–24). After adjustment for sociodemographic factors, arthritis prevalence was greatest for women (odds ratio [OR] 1.5, 95% CI 1.4–1.7) and in those ages 60–65 years (OR 4.4, 95% CI 3.7–5.2), those with a diploma/associate diploma (OR 1.3, 95% CI 1.1–1.6), those who were permanently unable to work (OR 4.0, 95% CI 3.1–5.3), and those with a household income <$25,999 (OR 2.1, 95% CI 1.7–2.6). Independent of individual-level factors, residents of the most disadvantaged neighborhoods were 42% (OR 1.4, 95% CI 1.2–1.7) more likely than those in the least disadvantaged neighborhoods to self-report arthritis. Cross-level interactions between neighborhood disadvantage and education, occupation, and household income were not significant. Conclusion: Arthritis prevalence is greater in more socially disadvantaged neighborhoods. These are the first multilevel data to examine the relationship between individual- and neighborhood-level disadvantage upon arthritis and have important implications for policy, health promotion, and other intervention strategies designed to reduce the rates of arthritis, indicating that intervention efforts may need to focus on both people and places.

Context-aware device self-configuration using self-organizing maps

Modern mobile computing devices are versatile, but bring the burden of constant settings adjustment according to the current conditions of the environment. While until today, this task has to be accomplished by the human user, the variety of sensors usually deployed in such a handset provides enough data for autonomous self-configuration by a learning, adaptive system. However, this data is not fully available at certain points in time, or can contain false values. Handling potentially incomplete sensor data to detect context changes without a semantic layer represents a scientific challenge which we address with our approach. A novel machine learning technique is presented - the Missing-Values-SOM - which solves this problem by predicting setting adjustments based on context information. Our method is centered around a self-organizing map, extending it to provide a means of handling missing values. We demonstrate the performance of our approach on mobile context snapshots, as well as on classical machine learning datasets.

Variability in estimation of self-reported dietary intake data from elite athletes resulting from coding by different sports dietitians

A routine activity for a sports dietitian is to estimate energy and nutrient intake from an athlete's self-reported food intake. Decisions made by the dietitian when coding a food record are a source of variability in the data. The aim of the present study was to determine the variability in estimation of the daily energy and key nutrient intakes of elite athletes, when experienced coders analyzed the same food record using the same database and software package. Seven-day food records from a dietary survey of athletes in the 1996 Australian Olympic team were randomly selected to provide 13 sets of records, each set representing the self-reported food intake of an endurance, team, weight restricted, and sprint/power athlete. Each set was coded by 3-5 members of Sports Dietitians Australia, making a total of 52 athletes, 53 dietitians, and 1456 athlete-days of data. We estimated within- and between- athlete and dietitian variances for each dietary nutrient using mixed modeling, and we combined the variances to express variability as a coefficient of variation (typical variation as a percent of the mean). Variability in the mean of 7-day estimates of a nutrient was 2- to 3-fold less than that of a single day. The variability contributed by the coder was less than the true athlete variability for a 1-day record but was of similar magnitude for a 7-day record. The most variable nutrients (e.g., vitamin C, vitamin A, cholesterol) had approximately 3-fold more variability than least variable nutrients (e.g., energy, carbohydrate, magnesium). These athlete and coder variabilities need to be taken into account in dietary assessment of athletes for counseling and research.

Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.

How to stop binge drinking and speeding motorists : effects of relational-interdependent self-construal and self-referencing on attitudes toward social marketing

How can marketers stop speeding motorists and binge drinking? Two experiments show that the beliefs consumers have about the degree to which they define themselves in terms of their close relationships (i.e., relational-interdependent self-construal (RISC)) offer useful insights into the effectiveness of communications for two key social marketing issues—road safety (Study 1, New Zealand sample) and alcohol consumption (Study 2, English sample). Further, self-referencing is a mechanism for these effects. Specifically, people who define themselves in terms of their close relationships (high-RISCs) respond most favorably to advertisements featuring a dyadic relationship (two people), and this favorable response is mediated by self-referencing. In contrast, people who do not include close relationships in their sense of self (low-RISCs) respond most favorably to self-reference advertisements featuring solitary models.

A self-tuning feedforward active noise control system

This paper proposes a self-tuning feedforward active noise control (ANC) system with online secondary path modeling. The step-size parameters of the controller and modeling filters have crucial rule on the system performance. In literature, these parameters are adjusted by trial-and-error. In other words, they are manually initialized before system starting, which require performing extensive experiments to ensure the convergence of the system. Hence there is no guarantee that the system could perform well under different situations. In the proposed method, the appropriate values for the step-sizes are obtained automatically. Computer simulation results indicate the effectiveness of the proposed method.

The relationship between self-esteem and psychological adjustment in young adolescents

The study adopts a multi-dimensional construct of self-esteem to examine the relationship between self-perception and psychological adjustment in order to identify specific dimensions that discriminate between disturbed and non-disturbed groups. The disturbed group (n = 33) is derived from a clinical sample and are matched with a non-disturbed group (n = 33) of adolescents. Results indicate that dimensional self-concept scores are significantly lower for clinical subjects while there are no significant differences between groups on the mathematics, honesty, and physical ability dimensions. These findings provide a more fine grained understanding of the relationship between self-esteem and psychological adjustment and emphasize the need to examine self-esteem in terms of its particular dimensions.

Gifted early adolescents’ negotiating identity : A case study of self-presentation theory

Academically gifted students are recognised as possessing considerable achievement potential. Yet many fail to perform at a level commensurate with their ability. Often gifted students in early adolescence are faced with a forced choice between fulfilment of potential and achieving stable positive relationships with peers. This choice can affect their achievement and may have far-reaching personal and social costs. This case study explored the viability of self-presentation theory to explain students' ways of negotiating their sense of self whilst developing public identity and the concomitant affects on achievement and the fulfilment of potential. It examined how gifted students moderate their images in their learning and extra-curricular environments. Further, the study identifies those self-presentation strategies adopted that either facilitate or hinder achievement. This study may assist parents, educators and school counsellors to provide greater support for gifted adolescents.

Application of theory in developing peer-support based intervention to improve self-management for cardiac patients with diabetes

The biosafety of carbon nanomaterial needs to be critically evaluated with both experimental and theoretical validations before extensive biomedical applications. In this letter, we present an analysis of the binding ability of two dimensional monolayer carbon nanomaterial on actin by molecular simulation to understand their adhesive characteristics on F-actin cytoskeleton. The modelling results indicate that the positively charged carbon nanomaterial has higher binding stability on actin. Compared to crystalline graphene, graphene oxide shows higher binding influence on actin when carrying 11 positive surface charge. This theoretical investigation provides insights into the sensitivity of actin-related cellular activities on carbon nanomaterial.