Correlates of resilience among university students in Oman: a cross-sectional study.

BACKGROUND: Resilience has a paramount role in dealing with different life stressors and has a great impact on mental health. AIM: To assess the level of resilience among university students and explore the relation between resilience and a number of variables including psychological well-being, self-esteem and physical health. METHODS: A cross-sectional design was utilized. Data was collected from 676 university students. Students were required to complete a demographic sheet, Brief Resilience Scale, World Health Organization Well-Being Index, Rosenberg Self-Esteem Scale and a physical health survey. RESULTS: Overall, less than half of the participants have reported low levels of resilience (45.3%; n = 306). Regular sleep, perceived stress, WHO well-being index, self-esteem, and having a cumulative GPA of more than two, were factors that significantly predicted positive resilience in multivariate analysis. CONCLUSION: Resilience is a necessary skill among university students that requires more academic attention. Factors predicting positive resilience should be considered when implementing mental health promotion programs.

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation.

We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model-support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker's gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker's gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.