Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems

Using self-definition to predict the influence of procedural justice on organizational, interpersonal, and job/task-oriented citizenship behaviors

An integrative self-definition model is proposed to improve our understanding of how procedural justice affects different outcome modalities in organizational behavior. Specifically, it is examined whether the strength of different levels of self-definition (collective, relational, and individual) each uniquely interact with procedural justice to predict organizational, interpersonal, and job/task-oriented citizenship behaviors, respectively. Results from experimental and (both single and multisource) field data consistently revealed stronger procedural justice effects (1) on organizational-oriented citizenship behavior among those who define themselves strongly in terms of organizational characteristics, (2) on interpersonal-oriented citizenship behavior among those who define themselves strongly in terms of their interpersonal relationships, and (3) on job/task-oriented citizenship behavior among those who define themselves weakly in terms of their distinctiveness or uniqueness. We discuss the relevance of these results with respect to how employees can be motivated most effectively in organizational settings

Willing and able: action-state orientation and the relation between procedural justice and employee cooperation

Existing justice theory explains why fair procedures motivate employees to adopt cooperative goals, but it fails to explain how employees strive towards these goals. We study self-regulatory abilities that underlie goal striving; abilities that should thus affect employees’ display of cooperative behavior in response to procedural justice. Building on action control theory, we argue that employees who display effective self-regulatory strategies (action oriented employees) display relatively strong cooperative behavioral responses to fair procedures. A multisource field study and a laboratory experiment support this prediction. A subsequent experiment addresses the process underlying this effect by explicitly showing that action orientation facilitates attainment of the cooperative goals that people adopt in response to fair procedures, thus facilitating the display of actual cooperative behavior. This goal striving approach better integrates research on the relationship between procedural justice and employee cooperation in the self-regulation and the work motivation literature. It also offers organizations a new perspective on making procedural justice effective in stimulating employee cooperation by suggesting factors that help employees reach their adopted goals

Outlier detection and classification in sensor data streams for proactive decision support systems

A paper has a deal with the problem of quality assessment in sensor data streams accumulated by proactive decision support systems. The new problem is stated where outliers need to be detected and to be classified according to their nature of origin. There are two types of outliers defined; the first type is about misoperations of a system and the second type is caused by changes in the observed system behavior due to inner and external influences. The proposed method is based on the data-driven forecast approach to predict the values in the incoming data stream at the expected time. This method includes the forecasting model and the clustering model. The forecasting model predicts a value in the incoming data stream at the expected time to find the deviation between a real observed value and a predicted one. The clustering method is used for taxonomic classification of outliers. Constructive neural networks models (CoNNS) and evolving connectionists systems (ECS) are used for prediction of sensors data. There are two real world tasks are used as case studies. The maximal values of accuracy are 0.992 and 0.974, and F1 scores are 0.967 and 0.938, respectively, for the first and the second tasks. The conclusion contains findings how to apply the proposed method in proactive decision support systems

On ethically solvent leaders : the roles of pride and moral identity in predicting leader ethical behavior.

The popular media has repeatedly pointed to pride as one of the key factors motivating leaders to behave unethically. However, given the devastating consequences that leader unethical behavior may have, a more scientific account of the role of pride is warranted. The present study differentiates between authentic and hubristic pride and assesses its impact on leader ethical behavior, while taking into consideration the extent to which leaders find it important to their self-concept to be a moral person. In two experiments we found that with higher levels of moral identity, authentically proud leaders are more likely to engage in ethical behavior than hubristically proud leaders, and that this effect is mediated by leaders’ motivation to act selflessly. A field survey among organizational leaders corroborated that moral identity may bring the positive effect of authentic pride and the negative effect of hubristic pride on leader ethical behavior to the forefront

Embedding of chips in flex: a global optimisation from thermal, mechanical and electrical RF perspective

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Motivation to cooperate in organisations: the case of prototypical leadership and procedural fairness

The present paper explores how leader prototypicality and procedural fairness connect in stimulating follower cooperation. We, first of all, argue that leader prototypicality (the extent to which a leader represents the group identity) enhances positive perceptions about the future. It does so by positively influencing perceptions of the leader's procedural fairness (at least among strongly identifying group members). Such perceptions of procedural fairness, in turn, stimulate follower cooperation. Secondly, we argue that leader prototypicality also facilitates the enactment of fair procedures by increasing the effectiveness that a procedurally fair treatment has on follower cooperation. We present an overview of very recent studies that support both arguments and conclude that group based dynamics, which determine the group prototype, have important influences on the effectiveness with which leaders can stimulate cooperation by means of procedural fairness.</span