Reevaluating the magic spell\u27 : examining empowerment, stress, and workplace outcomes

Empowerment has long been believed to positively influence workplace outcomes such as performance and satisfaction, but empirical and anecdotal evidence suggest this influence is frequently weak. The present study explores the theoretical links among aspects of structural and psychological empowerment, challenge and hindrance stress appraisals, and employee performance and well-being within workplace settings. Hypotheses were tested with data obtained from individual employees and their supervisors from a diverse range of industries and organizations. Results demonstrate that accountability positively affects appraisals of challenge and hindrance stress; felt hindrance stress adversely affects employee well-being; proactive personality moderates the relationship between authority-sharing and challenge stress; and locus of control moderates the relationship between empowerment practices and challenge stress appraisal. These findings broaden the focus of prior research by addressing why the so-called “magic spell” of empowerment may sometimes fail to improve performance and well-being

CARBEN: Composite Adversarial Robustness Benchmark

Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. This paper demonstrates how CAA's attack order affects the resulting image, and provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level and their rapid evaluation of model prediction. A leaderboard to benchmark adversarial robustness against CAA is also introduced.Comment: IJCAI 2022 Demo Track; The demonstration is at https://hsiung.cc/CARBEN

Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations

Model robustness against adversarial examples of single perturbation type such as the $\ell_{p}$-norm has been widely studied, yet its generalization to more realistic scenarios involving multiple semantic perturbations and their composition remains largely unexplored. In this paper, we first propose a novel method for generating composite adversarial examples. Our method can find the optimal attack composition by utilizing component-wise projected gradient descent and automatic attack-order scheduling. We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation. Results obtained using ImageNet and CIFAR-10 datasets indicate that GAT can be robust not only to all the tested types of a single attack, but also to any combination of such attacks. GAT also outperforms baseline $\ell_{\infty}$-norm bounded adversarial training approaches by a significant margin

Chilling susceptibility in mungbean varieties is associated with their differentially expressed genes

Additional file 4: Table S3. Validation of microarray data by qRT-PCR in mungbean seedlings