Abusive supervision (". .. subordinates' perceptions of the extent to which their supervisors engage in the sustained display of hostile verbal and nonverbal behaviors, excluding physical contact"; Tepper, 2000, p. 178) is a type of workplace mistreatment that has gained momentum in research and practice. Indeed, "the boss" may be one of the major factors that can lead employees to experience stress in their jobs (Michie, 2002) and literature shows far-reaching consequences of abusive supervision for employee attitudes and behavior (Martinko, Harvey, Brees, & Mackey, 2013). In addition to affecting employee well-being, abusive supervision affects employees' discretionary behavior. Abusive supervision predicts reductions in positive discretionary behaviors (e.g., Xu, Huang, Lam, & Miao, 2012) such as organizational citizenship behavior (OCB). These desirable OCBs can entail behaviors such as helping colleagues (individual-directed OCB; OCBI) or attending nonmandatory organizational meetings (organization-directed OCB; OCBO). Abusive supervision also relates positively to negative discretionary behaviors (Mitchell & Ambrose, 2007), such as counterproductive work behaviors (CWB). CWBs can entail behaviors such as ridiculing or embarrassing coworkers (individual-directed CWB) or leaving work early and taking longer breaks (organization-directed CWB). Recent research suggests that, when employees have better resources to cope with abuse, they may show less of the detrimental behavioral effects (e.g.

In the recent decade it has been witnessed that raster images are the primary source of information for numerous applications such as bio-medical, law enforcement, geographical information system (GIS), photography, astronomy, etc. Primarily, the quality of raster images compromises due to the surrounding factors of these applications. Because, it is very difficult to control surrounding parameter (light, motion, distance) while acquiring images. Therefore, the image acquisition in these applications is very much prone to the noise. In the literature, researchers have targeted this issue and have already devised classical image filters for image de-noising. Afterwards, in the recent years the performance of classical filtering was further improved by employing two dimensional adaptive filters (2-DAF) for image de-noising and enhancement. In the literature, researchers have reported the performance comparisons of various 2-DAF specifically for image restoration, enhancement, estimation, and de-noising. In this paper an extended version of one dimensional fractional least mean square (1-DFLMS) to two dimensional fractional least mean square (2-DFLMS) is presented. Moreover the performance of the proposed algorithm has been rigorously compared with the existing and most employed 2-DAF algorithm namely, two dimensional least mean square (2-DLMS), two dimensional variable step size least mean square (2-DVSSLMS). The simulation results illustrate the notable performance edge of the proposed algorithm with the existing approaches.Keywords: Image de-noising, two-dimensional adaptive filtering, least mean square (LMS), variable step size least mean square (VSSLMS), fractional least mean square (FLMS)

In recent times, the prediction of personality traits with automated and programmed systems has caught human attention. Specifically, the use of multimodal data to predict personality types is the most considerable talk in artificial intelligence. There are a variety of techniques and methods available for personality type identification. The most popular and highly used personality type identifier is the Myers Briggs Type Indicator (MBTI) type indicator among all methods. In this paper, an exhaustive comparative analysis of all machine learning classical algorithms implementing the MBTI framework will be presented by giving a numerical and graphical representation of performance measures. To experience this study, a supervised machine learning approach is used to perform and analyze different classifiers using the phenomena of MBTI. The models are learned from a dataset to make predictions. The results show that the Ensemble Bagged Trees algorithm gives an overall good training accuracy of 98.4% and test accuracy of 70.75% at a moderate prediction speed of 11 K - Obs/sec by taking a training time of 14 sec. Other than that Coarse Tree algorithm in training time is 0.94009/sec and prediction speed 390 (K - Obs/sec), Fine KNN and Weighted KNN algorithm in training accuracy of 99.20% and Ensemble Boosted Trees algorithm in testing accuracy of 75.51% shows the efficient outcome respectively.

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