Mixed-Initiative Human-Automated Agents Teaming: Towards a Flexible Cooperation Framework

The recent progress in robotics and artificial intelligence raises the question of the efficient artificial agents interaction with humans. For instance, artificial intelligence has achieved technical advances in perception and decision making in several domains ranging from games to a variety of operational situations, (e.g. face recognition [51] and firefighting missions [23]). Such advanced automated systems still depend on human operators as far as complex tactical, legal or ethical decisions are concerned. Usually the human is considered as an ideal agent, that is able to take control in case of automated (artificial) agent's limit range of action or even failure (e.g embedded sensor failures or low confidence in identification tasks). However, this approach needs to be revised as revealed by several critical industrial events (e.g. aviation and nuclear power-plant) that were due to conflicts between humans and complex automated system [13]. In this context, this paper reviews some of our previous works related to human-automated agents interaction driving systems. More specifically, a mixed-initiative cooperation framework that considers agents' non-deterministic actions effects and inaccuracies about the human operator state estimation. This framework has demonstrated convincing results being a promising venue for enhancing human-automated agent(s) teaming

Do agile managed information systems projects fail due to a lack of emotional intelligence?

YesAgile development methodologies (ADM) have become a widely implemented project management approach in Information Systems (IS). Yet, along with its growing popularity, the amount of concerns raised in regard to human related challenges caused by applyingADMare rapidly increasing. Nevertheless, the extant scholarly literature has neglected to identify the primary origins and reasons of these challenges. The purpose of this study is therefore to examine if these human related challenges are related to a lack of Emotional Intelligence (EI) by means of a quantitative approach. Froma sample of 194 agile practitioners, EI was found to be significantly correlated to human related challenges in agile teams in terms of anxiety, motivation, mutual trust and communication competence. Hence, these findings offer important new knowledge for IS-scholars, project managers and human resource practitioners, about the vital role of EI for staffing and training of agile managed IS-projects

Who Said or What Said? Estimating Ideological Bias in Views Among Economists

There exists a long-standing debate about the influence of ideology in economics. Surprisingly, however, there is no concrete empirical evidence to examine this critical issue. Using an online randomized controlled experiment involving economists in 19 countries, we examine the effect of ideological bias on views among economists. Participants were asked to evaluate statements from prominent economists on different topics, while source attribution for each statement was randomized without participants’ knowledge. For each statement, participants either received a mainstream source, an ideologically different less-/non-mainstream source, or no source. We find that changing source attributions from mainstream to less-/non-mainstream, or removing them, significantly reduces economists’ reported agreement with statements. Using a model of Bayesian updating we examine two competing hypotheses as potential explanations for these results: unbiased Bayesian updating versus ideologically-biased Bayesian updating. While we find no evidence in support of unbiased updating, our results are consistent with biased Bayesian updating. More specifically, we find that changing/removing sources (1) has no impact on economists’ reported confidence with their evaluations; (2) similarly affects experts/non-experts in relevant areas; and (3) affects those at the far right of the political spectrum much more significantly than those at the far left. Finally, we find significant heterogeneity in our results by gender, country, PhD completion country, research area, and undergraduate major, with patterns consistent with the existence of ideological bias

Experiences of Medical Residents and Faculty Members on Adhering to Professional Commitments and Training during Covid Pandemic: A Qualitative Study

 &#x0D; Introduction: During the COVID-19 pandemic era, adhering to professional commitments can be a major challenge. This study aims to document the experiences of medical residents and faculty members on the challenges of adhering to professional commitments during the COVID-19 pandemia and its impacts on clinical education.&#x0D; Methods: This qualitative study (using a descriptive phenomenology and purposive sampling method) was conducted in 2020, comprising 14 participants (faculty members and infectious diseases residents of Qazvin University of Medical Sciences) to share their experiences on the challenges of strict adherence to professional behavior. Data were collected through in-depth semi-structured interviews. The transcribed interviews were analyzed based on the Colaizzi approach.&#x0D;  Result: Ninety extracted primary codes are classified to two main areas: problems in adherence to professional commitments and the impact of COVID-19 pandemia on educational and research activities mandatory for professionalism. In the first area, the three main themes includes reducing effective patient-physician communication, non-maleficence and violating justice, and the second, two main themes includes a rise in non-professional behavior when dealing with patients (cutting down the quantity and quality of training, reducing supervision and decreased clinical engagement with patients) and a rise in non-professional behaviors in the research area.&#x0D; Conclusion: The most important problem negatively affecting professional commitments during corona epidemy is role overload and the deterioration of physician-patient relationship. Due to the influence of role modeling on student professional behavior, it is helpful to include the training of related skills that can reduce the incidence of these problems in the clinical settings.</jats:p

Modeling and Perception of Deformable One-Dimensional Objects

Abstract — Recent advances in the modeling of deformable one-dimensional objects (DOOs) such as surgical suture, rope, and hair show significant promise for improving the simulation, perception, and manipulation of such objects. An important application of these tasks lies in the area of medical robotics, where robotic surgical assistants have the potential to greatly reduce surgeon fatigue and human error by improving the accuracy, speed, and robustness of surgical tasks such as suturing. However, different types of DOOs exhibit a variety of bending and twisting behaviors that are highly dependent on material properties. This paper proposes an approach for fitting simulation models of DOOs to observed data. Our approach learns an energy function such that observed DOO configurations lie in local energy minima. Our experiments on a variety of DOOs show that models fitted to different types of DOOs using our approach enable accurate prediction of future configurations. Additionally, we explore the application of our learned model to the perception of DOOs. I