Career Adapt-Abilities Scale—Iran Form: Psychometric properties and relationships with career satisfaction and entrepreneurial intentions

This study examined the psychometric properties of a Persian translation of the Career Adapt-Abilities Scale (CAAS—Iran Form) and its relationships with career satisfaction, business opportunity identification, and entrepreneurial intentions. It was hypothesized that career adaptability relates positively to these three outcomes, even when controlling for demographic and employment characteristics. Data were provided by 204 workers from Iran. Results showed that the overall CAAS score and sub-dimension scores (concern, control, curiosity, and confidence) were highly reliable. Moreover, confirmatory factor analyses indicated that the CAAS—Iran Form measures four distinct dimensions that can be combined into a higher-order career adaptability factor. Findings also demonstrated criterion-related validity of the scale with regard to career satisfaction and entrepreneurial intentions. In contrast, overall career adaptability was not significantly related to opportunity identification, while concern related positively, and control related negatively to opportunity identification. Overall, the CAAS—Iran Form has very good psychometric properties and predicts important career outcomes, suggesting that it can be used for career counseling and future research with Persian-speaking workers.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.