Developing a model to predict aircraft maintenance performance

[Abstract]: A three-pronged approach was adopted to the investigation of causes of maintenance errors in army aviation. In the first phase of the research, analysis of maintenance incident reports suggested that individuals were mostly at fault, making errors because they failed to follow procedures and were inadequately supervised. Interviews with maintenance technicians, on the other hand, put the spotlight on organisational variables, such as pressures created by poor planning. In the third phase, a survey instrument administered to 448 maintenance workers was used to develop a structural model that predicted 34% of the variance in psychological health, 16% of the variance in turnover intentions, and 16% of the variance in self-reported maintenance errors. Implications of these findings are discussed

Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

Market information acquisition: a prerequisite for successful strategic entrepreneurship

AbstractThis paper investigates on the types of information used by managers and entrepreneurs, so as to conduct market research and to evaluate market potential.The authors examine five major sets of variables to understand their impact on firms’ information market search effort. Empirical results based on a survey of Greek enterprises provide support for these factors in predicting firms’ market information acquisition. Findings on structural and administrative characteristics of the firms support the notion that companies engaged in greater market information search and evaluation of market potential tend to develop and implement complex penetration and development market strategies, in order to maximize their business performance in the examined market

Future work selves : how salient hoped-for identities motivate proactive career behaviors

The term future work self refers to an individual's representation of himself or herself in the future that reflects his or her hopes and aspirations in relation to work. The clearer and more accessible this representation, the more salient the future work self. An initial study with 2 samples (N = 397; N = 103) showed that future work self salience was distinct from established career concepts and positively related to individuals' proactive career behavior. A follow-up longitudinal analysis, Study 2 (N = 53), demonstrated that future work self salience had a lagged effect on proactive career behavior. In Study 3 (N = 233), we considered the role of elaboration, a further attribute of a future work self, and showed that elaboration motivated proactive career behavior only when future work self salience was also high. Together the studies suggest the power of future work selves as a motivational resource for proactive career behavior. (PsycINFO Database Record (c) 2012 APA, all rights reserved

The Relationship Between Employee Perceptions of the Employment Game and Their Perceptions of Cooperative Knowledge Behavior in High Tech Firms

The relationship between knowledge sharing and organizational performance for high technology start-up companies is not well understood. Using game theory and the concept of competitive advantage through human resource management, I examine employee perceptions of the employment game relating to cooperative knowledge behavior and firm performance as an entry point into researching organizational knowledge utilization. I draw upon classical game theory to develop four measures of perceptions critical to game playing and apply these to organizational situations via a survey instrument. I propose that perceptions of the employment game held by organization members are determinants of cooperative knowledge sharing and subsequently firm performance. I analyze survey data gathered from high-tech workers using both regression and path analysis techniques. The results from this study offer new insights into methods for measuring both the connections between knowledge work and firm performance and the perceptions critical for fostering collaborative knowledge work in high tech firms. Results of the study show a significant relationship between the game theory construct of reciprocity, knowledge building behavior and firm performance. The mediation model was weakly supported but shows potential usefulness for further research in the field of strategic human resource management

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page