Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.     

Academic Misconduct in Nigerian Medical Schools-A Report from Focus Group Discussions among House Officers

Concern is growing as research continues to find evidence of academic misconduct among medical students. There is, however, paucity of information on this issue among medical students and medical graduates in Africa. We determined the perceptions and attitude of house officers on academic misconduct within Nigerian medical schools. We conducted 7 focus group discussions (FGDs) among pre-registration house-officers (PRHOs) working in a Nigerian Teaching hospital between October and November 2013. A FGD guide containing 7 broad questions related to their perception and attitude on academic misconduct was employed. Ethnographic content analysis and manual coding were adopted in identifying themes, issues and representative quotations. A total of 58 participants out of an eligible pool of 76 PRHOs were recruited. All the groups associated academic misconduct chiefly with cheating in examinations, and perceived it as a commonplace problem. “It is a part of school life”, admitted one PRHO. Copying during exam appeared to be the commonest form of cheating. Another respondent opined that “The lecturers should be more concerned with helping students understand better. They should not make passing an exam a do-or-die affair.” The perception and attitudes towards academic misconduct may be a reflection of the prevailing moral decline in the contemporary Nigerian society. Underlying the reasons adduced by the participants for getting involved in academic misconduct is the fear of failure in examinations. Apart from consolidating formal and informal medical ethics education, the medical schools should ensure that the opportunities and pressures to indulge in academic misconduct are minimized.     

Ethical Leadership: An Integrative Review and Future Research Agenda

Over the past decade, ethical leadership has increasingly become one of the most popular topics in the areas of leadership and business ethics. As a result, there now exists a substantial body of empirical research addressing ethical leadership issues, but the findings reported by this body of research are highly fragmented. The topic has advanced to the stage where a review and synthesis of existing literature can provide great value and help move the scholarly conversation forward. The primary purposes of this article are to (a) review empirical findings from the ethical leadership literature utilizing a framework consisting of the antecedents, mediators, moderators and outcomes of ethical leadership, and (b) suggest a set of interesting research opportunities, thereby facilitating future investigation. We base our synthesis on a review of 62 empirical studies on ethical leadership that were published between 2005 and mid-2015.     

Parental Involvement in Primary School Education: its Relationship with Children’s Academic Performance and Psychosocial Competence through Engaging Children with School

The benefits of parental involvement in children’s education have been well established but increasing evidence suggests that overparenting may have adverse effects on children. The question of whether excessive parental involvement hinders children’s academic and psychosocial development warrants further investigations. This study examined the associations of parental educational involvement at home and in school with academic performance and psychological health of 507 Chinese Grade 3 schoolchildren in Hong Kong. Parents reported on their level of involvement in children’s schooling and their children’s psychosocial issues. Children were surveyed to determine their school engagement, and their Chinese language and mathematics attainment was assessed. We also explored the underlying mechanism by testing children’s engagement with school as a mediator of the relationships. Our results showed that home-based parental educational involvement was positively associated with children’s language competence and psychosocial wellbeing, and the associations were linked through engaging children with school. However, the benefits reached a plateau at higher level of parental involvement in children’s learning at home. School-based parental involvement had an indirect effect on children’s prosocial behavior through school engagement. These findings highlight the significance of optimal level of parental involvement in children’s education at home for children’s development.     

Different contexts change the impression of animacy

The aim of this research was to explore the effect of different spatiotemporal contexts on the perceptual saliency of animacy, and the extent of the relationship between animacy and other related properties such as emotions and intentionality. Paired-comparisons and ratings were used to compare the impressions of animacy elicited by a small square moving on the screen, either alone or in the context of a second square. The context element was either static or moving showing an animate-like or a physical-like trajectory, and the target object moved either toward it or away from it. The movement of the target could also include animacy cues (caterpillar-like expanding/contracting phases). To determine the effect of different contexts on the emergence of emotions and intentions, we also recorded and analysed the phenomenological reports of participants. The results show that the context significantly influences the perception of animacy, which is stronger in dynamic contexts than in static ones, and also when the target is moving away from the context element than when it is approaching it. The free reports reveal different proportions in emotional or intentional attributions in the different conditions: in particular, the "moving away" condition is related to negative emotions, while the "approaching" condition evokes positive emotions. Overall, the results suggest that animacy is a graded concept that can be articulated in more general characteristics, like simple aliveness, and more specific ones, like intentions or emotions, and that the spatiotemporal contingencies of the context play a crucial role in making them evident.     

A comparison of positive vicarious learning and verbal information for reducing vicariously learned fear

Research with children has demonstrated that both positive vicarious learning (modelling) and positive verbal information can reduce children's acquired fear responses for a particular stimulus. However, this fear reduction appears to be more effective when the intervention pathway matches the initial fear learning pathway. That is, positive verbal information is a more effective intervention than positive modelling when fear is originally acquired via negative verbal information. Research has yet to explore whether fear reduction pathways are also important for fears acquired via vicarious learning. To test this, an experiment compared the effectiveness of positive verbal information and positive vicarious learning interventions for reducing vicariously acquired fears in children (7–9 years). Both vicarious and informational fear reduction interventions were found to be equally effective at reducing vicariously acquired fears, suggesting that acquisition and intervention pathways do not need to match for successful fear reduction. This has significant implications for parents and those working with children because it suggests that providing children with positive information or positive vicarious learning immediately after a negative modelling event may prevent more serious fears developing.     

Automated Item Generation with Recurrent Neural Networks

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.     

Counterpossibles in Science: The Case of Relative Computability

I develop a theory of counterfactuals about relative computability, i.e. counterfactuals such as

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