Instructing anger management skills for mothers of mentally retarded children: effects on mother-child relationship

AbstractThe purpose of the present research is to examine the effects of anger management training on mother's relationship with their educable mentally retarded and slow learner children. The design of this study is quasi-experimental with pretest-posttest and control group. This study was conducted on 46 mothers who were assigned equally into experimental and control groups. The anger management training was implemented on experimental group in seven sessions, 2hours each. Data were collected utilizing Anger Evaluation Scale and Multidimensional Anger Inventory. An analysis of covariance was used for this research. Findings showed that intensity and frequency of anger decreased and the use of anger control strategies increased, indicating the effectiveness of anger management techniques

Automatic alignment of surgical videos using kinematic data

Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.Comment: Accepted at AIME 201

Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis

Recurrent auto-encoder model summarises sequential data through an encoder structure into a fixed-length vector and then reconstructs the original sequence through the decoder structure. The summarised vector can be used to represent time series features. In this paper, we propose relaxing the dimensionality of the decoder output so that it performs partial reconstruction. The fixed-length vector therefore represents features in the selected dimensions only. In addition, we propose using rolling fixed window approach to generate training samples from unbounded time series data. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed using additional visualisation and unsupervised clustering techniques. The proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose, where clusters of the vector representations can reflect the operating states of the industrial system.Comment: Accepted paper at the 19th International Conference on Engineering Applications of Neural Networks (EANN 2018

Mental health literacy: a cross-cultural approach to knowledge and beliefs about depression, schizophrenia and generalized anxiety disorder

Many families worldwide have at least one member with a behavioral or mental disorder, and yet the majority of the public fails to correctly recognize symptoms of mental illness. Previous research has found that Mental Health Literacy (MHL)—the knowledge and positive beliefs about mental disorders—tends to be higher in European and North American cultures, compared to Asian and African cultures. Nonetheless quantitative research examining the variables that explain this cultural difference remains limited. The purpose of our study was fourfold: (a) to validate measures of MHL cross-culturally, (b) to examine the MHL model quantitatively, (c) to investigate cultural differences in the MHL model, and (d) to examine collectivism as a predictor of MHL. We validated measures of MHL in European American and Indian samples. The results lend strong quantitative support to the MHL model. Recognition of symptoms of mental illness was a central variable: greater recognition predicted greater endorsement of social causes of mental illness and endorsement of professional help-seeking as well as lesser endorsement of lay help-seeking. The MHL model also showed an overwhelming cultural difference; namely, lay help-seeking beliefs played a central role in the Indian sample, and a negligible role in the European American sample. Further, collectivism was positively associated with causal beliefs of mental illness in the European American sample, and with lay help-seeking beliefs in the Indian sample. These findings demonstrate the importance of understanding cultural differences in beliefs about mental illness, particularly in relation to help-seeking beliefs

Meaningful Rule Discovery and Adaptive Classification of Multi-Dimensional Time Series Data

The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the current value of a stream. However, for many problems the actual values are irrelevant, whereas the shape of the current time series pattern may foretell the future. The handful of research efforts that consider this variant of the problem have met with limited success. In particular, it is now understood that most of these efforts allow the discovery of spurious rules. We believe the reason why rule discovery in real-valued time series has failed thus far is because most efforts have more or less indiscriminately applied the ideas of symbolic stream rule discovery to real-valued rule discovery. We feel that the lack of progress in this pursuit can be attributed to two related factors: the lack of effective algorithms for rule discovery in one dimensional time series, resulting in poor-quality and random rules; less accurate classifiers built for multi-dimensional time series in order to make accurate predictions. In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW's efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant.In this dissertation, we strive to solve these problems. The contribution of this dissertation is as follows:First, we show why the idea of applying symbolic stream rule discovery to real-valued rule discovery is not directly suitable for rule discovery in time series. Beyond our novel definitions/representations, which allow for meaningful and extendable specifications of rules, we further show novel algorithms that allow us to quickly discover high quality rules in very large datasets that accurately predict the occurrence of future eventsFinally, we show that the two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted

The impact of anger management training based on cognitive - behavioral approach on behavioral problems of adolescent girls

Researchers in the field of clinical psychology have emphasized the effective role of interventions based on cognitive-behavioral approach on managing adolescent behavioral problems. Studying anger management training as well as the efficient ways to moderate behavior problems in adolescents is considered to be an important issue. The purpose of the current study was to investigate and determine the effects of anger management training based on cognitive-behavioral approach on behavioral problems of adolescent girls. This Study used a quasi-experimental design with pretest-posttest and control group. 142 adolescent girls (12-15 age) were selected by convenience sampling method and randomly assigned into two experimental and control groups. Anger management training program based on cognitive-behavioral approach was designed for seven two-hour sessions. Using Achenbach Problem Behavior Scale-Youth Self Report Form (YSR) (1991), the behavioral problems of the participants were measured before and after the workshop. The data were analyzed by multivariate analysis of covariance. Results showed that the post-test scores of the experimental group were decreased significantly in all dimensions (P<0.01). This study indicated that anger management program based on cognitive-behavioral approach can reduce behavioral problems in adolescent girls

Friendships with Peers with Severe Disabilities: American and Iranian Secondary Students&apos; Ideas about Being a Friend

Abstract: We used the Student Friendship Perception Survey (SFPS

Anger in mothers of children with disabilities: effects of occupation an level of education

AbstractIn order to investigate anger in mothers of educable mentally retarded and slow learner children, based on their occupation and level of education, a total of 100 mothers completed the Multidimensional Anger Inventory (Siegel, 1986) and a demographic information questionnaire. Data were analyzed using two-way analysis of variance. Results showed a significant relationship between level of education with anger arousal and anger-in. More precisely, mothers with higher educational level showed lower anger arousal and anger-in. No significant differences were found between anger of mothers with different occupational status (housewife versus employed). Confirming the relationship between anger and level of education, the present research can be helpful in devising educational policies for mothers.© 2011 Published by Elsevier Ltd

Anger management instruction for mothers: a cognitive behavioural approach

AbstractThe aim of the current study was to evaluate the effectiveness of a cognitive-behavioral anger management program for mothers. The design of this study was quasi-experimental with pretest-posttest without control group. 22 mothers who were volunteers participated in this research and anger management program was implemented for seven sessions, each session two hours per week. Data were collected utilizing State-Trait Anger Expression Inventory and Anger Evaluation Scale to assess parents’ anger toward their children. Findings of this research indicate the effectiveness of anger management training based on cognitive-behavioural approach on reducing anger in mothers. Finally, implications of the findings, research limitations and suggestions for future research are discussed