Delivery Drones - Just a Hype? Towards Autonomous Air Mobility Services at Scale

While hype often arises around emerging technologies, delivery drones have received a significant share of attention in recent years. A variety of applications for drone networks formed, from delivering medical goods to drone-delivered pizza. Nevertheless, high expectations did not yet result in a widespread deployment of drones to improve logistic networks. We conducted semi-structured interviews with drone and aviation experts to derive a taxonomy of challenges for autonomous drone operations and gain practical insight into promising solution approaches that could transform the current hype into sound business models. Our findings comprise a multitude of operational, technical, social and legal issues that have not been identified in literature. Societal adaption and the development and interaction with AI-based systems pose a major challenge to provide autonomous air mobility services in the near future

Design for Acceptance and Intuitive Interaction: Teaming Autonomous Aerial Systems with Non-experts

In recent years, rapid developments in artificial intelligence (AI) and robotics have enabled transportation systems such as delivery drones to strive for ever-higher levels of autonomy and improve infrastructure in many industries. Consequently, the significance of interaction between autonomous systems and humans with little or no experience is steadily rising. While acceptance of delivery drones remains low among the general public, a solution for intuitive interaction with autonomous drones to retrieve packages is urgently needed so that non-experts can also benefit from the technology. We apply a design science research approach and develop a mobile application as a solution instantiation for both challenges. We conduct one expert and one non-expert design cycle to integrate necessary domain knowledge and ensure acceptance of the artifact by potential non-expert users. The results show that teaming of non-experts with complex autonomous systems requires rethinking common design requirements, such as ensuring transparency of AI-based decisions

A Framework for Developing Cross-Sectional Surveys

Although the use of cross-sectional surveys is widespread in Information Systems (IS) research and related disciplines, few papers address the survey development process. In order to ensure a standardized approach, comparable and valid results, as well as to guide researchers in quantitative research methods, this paper presents a framework for the survey development process in IS. Based on a Design Science Research (DSR) methodology, the framework was derived from a structured literature review of leading IS journals and refined by three focus group discussions among IS experts. The framework includes several steps and considerations on the sample size, variable selection, their order in the survey, protection against bias, ensuring validity and reliability, and testing before administering the survey with a focus on documentation and reporting. Our framework supports quantitative research by providing a structured approach to create reliable and credible surveys

Promoting Learning Through Explainable Artificial Intelligence: An Experimental Study in Radiology

The deployment of machine learning (ML)-based decision support systems (DSSs) in high-risk environments such as radiology is increasing. Despite having achieved high decision accuracy, they are prone to errors. Thus, they are primarily used to assist radiologists in their decision making. However, collaborative decision making poses risks to the decision maker, e.g. automation bias and long-term performance degradation. To address these issues, we propose combining findings of the research streams of explainable artificial intelligence and education to promote human learning through interaction with ML-based DSSs. We provided radiologists with explainable vs non-explainable decision support that was high- vs low-performing in a between-subject experimental study to support manual segmentation of 690 brain tumor scans. Our results show that explainable ML-based DSSs improved human learning outcomes and prevented false learning triggered by incorrect decision support. In fact, radiologists were able to learn from errors made by the low-performing explainable ML-based DSS

Designing the Organizational Metaverse for Effective Socialization

The metaverse is a virtual world that merges physical, virtual, and augmented reality, enabling collaboration between online users and offering limitless opportunities for connectivity and integration. While the metaverse has gained significant attention in organizations, it presents social challenges as organizations have unprecedented insight and influence over individuals\u27 thoughts and beliefs. Our review is based on a theoretical framework and examines the impact of the environment, collaboration, avatars, and individual behavior on organizational socialization. We develop a conceptual model for the socialization process in the metaverse, contributing to a deep understanding of this emerging field and providing a research agenda for future work

Approximate average head models for EEG source imaging

We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual’s MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.Fil: Valdés Hernández, Pedro A.. Centro Cubano de Neurociencias; CubaFil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; ArgentinaFil: Ojeda Gonzalez, Alejandro. Centro Cubano de Neurociencias; CubaFil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Alemán Gómez, Yasser. Centro Cubano de Neurociencias; CubaFil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Valdés Sosa, Pedro A.. Centro Cubano de Neurociencias; Cub