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

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

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

Abstract: We used the Student Friendship Perception Survey (SFPS

The Effectiveness of Resiliency based on Islamic Spirituality Training on Mental Health and Spiritual Resiliency among Mothers of Slow Pace (Mentally Retarded) Children

Background & aim: Birth and presence of slow pace children in each family can be considered as challenging and adverse event that probably leads to stress and frustration and mental health related complications. According to several studies that show positive and significant relationship between resiliency and values and religious beliefs and their impact on mental health,the present study was conducted to evaluate the effectiveness of resiliency skills training based on Islamic spirituality in promoting mental health and spiritual resilience among mothers of Slow Pace children. Methods: The present study used a semi-experimental design with pre test-post test which was conducted among mothers of Slow Pace Children in Dehdasht, Iran, and the countryside using random sampling, in which 30 of these mothers were randomly divided into two experimental and control groups, participated in this study. Twelve sessions of resiliency training based on Islamic spirituality were held for experimental group of 15 people.The tools used in this study included a mental health questionnaire-28 (Ghq) and resiliency based on Islamic spirituality researcher made scale that were completed by individuals in pre and post tests. Finally, collected data were analyzed by multivariate analysis of covariance (MANCOVA). Results: Analysis of data using multivariate analysis of covariance showed that utilization of Intervention program among mothers of Slow Pace children in experimental group was significantly (P>0/05) effective on mental health and components of resiliency based on Islamic spirituality. In other words, spiritual resiliency skills training was led to improve depressive symptoms, social functioning and components of spiritual resiliency such as patience, contentment, Submission and thanksgiving. Conclusion: The results of the present study indicated that through changes in attitude of Slow Pace children's mothers, resiliency skills training based on Islamic spirituality can improve mental health and components of resilience-based on Islamic spirituality and helps them keep their mental health despite the exposure to chronic stress and tension