Communicating Dominance in a Nonanthropomorphic Robot Using Locomotion

Dominance is a key aspect of interpersonal relationships. To what extent do nonverbal indicators related to dominance status translate to a nonanthropomorphic robot? An experiment (N = 25) addressed whether a mobile robot's motion style can influence people's perceptions of its status. Using concepts from improv theater literature, we developed two motion styles across three scenarios (robot makes lateral motions, approaches, and departs) to communicate a robot's dominance status through nonverbal expression. In agreement with the literature, participants described a motion style that was fast, in the foreground, and more animated as higher status than a motion style that was slow, in the periphery, and less animated. Participants used fewer negative emotion words to describe the robot with the purportedly high-status movements versus the purportedly low-status movements, but used more negative emotion words to describe the robot when it made departing motions that occurred in the same style. This result provides evidence that guidelines from improvisational theater for using nonverbal expression to perform interpersonal status can be applied to influence perception of a nonanthropomorphic robot's status, thus suggesting that useful models for more complicated behaviors might similarly be derived from performance literature and theory

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

New Updates in E-Learning

This book presents state-of-the-art educational technologies and teaching methodologies and discusses future educational philosophies in support of the global academic society. New Updates in E-Learning is a collection of chapters addressing important issues related to effective utilization of the Internet and Cloud Computing, virtual robotics, and real-life application of hybrid educational environments to enhance student learning regardless of geographical location or other constraints. Over ten chapters, the book discusses the current and future evolution of educational technologies and methodologies and the best academic practices in support of providing high-quality education at all academic levels

Information and Communication Technologies in Tourism 2022

This open access book presents the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 29th Annual International eTourism Conference, which assembles the latest research presented at the ENTER2022 conference, which will be held on January 11–14, 2022. The book provides an extensive overview of how information and communication technologies can be used to develop tourism and hospitality. It covers the latest research on various topics within the field, including augmented and virtual reality, website development, social media use, e-learning, big data, analytics, and recommendation systems. The readers will gain insights and ideas on how information and communication technologies can be used in tourism and hospitality. Academics working in the eTourism field, as well as students and practitioners, will find up-to-date information on the status of research

Aeronautical Engineering: A continuing bibliography with indexes (supplement 165)

This bibliography lists 466 reports, articles and other documents introduced into the NASA scientific and technical information system in August 1983

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Harnessing Big Data and Machine Learning for Event Detection and Localization

Anomalous events are rare and significantly deviate from expected pattern and other data instances, making them hard to predict. Correctly and timely detecting anomalous severe events can help reduce risks and losses. Many anomalous event detection techniques are studied in the literature. Recently, big data and machine learning based techniques have shown a remarkable success in a wide range of fields. It is important to tailor big data and machine learning based techniques for each application; otherwise it may result in expensive computation, slow prediction, false alarms, and improper prediction granularity.First, we aim to address the above challenges by harnessing big data and machine learning techniques for fast and reliable prediction and localization of severe events. Firstly, to improve storage failure prediction, we develop a new lightweight and high performing tensor decomposition-based method, named SEFEE, for storage error forecasting in large-scale enterprise storage systems. SEFEE employs tensor decomposition technique to capture latent spatio-temporal information embedded in storage event logs. By utilizing the latent spatio-temporal information, we can make accurate storage error forecasting without training requirements of typical machine learning techniques. The training-free method allows for live prediction of storage errors and their locations in the storage system based on previous observations that had been used in tensor decomposition pipeline to extract meaningful latent correlations. Moreover, we propose an extension to include severity of the errors as contextual information to improve the accuracy of tensor decomposition which in turn improves the prediction accuracy. We further provide detailed characterization of NetApp dataset to provide additional insight into the dynamics of typical large-scale enterprise storage systems for the community.Next, we focus on another application -- AI-driven Wildfire prediction. Wildfires cause billions of dollars in property damages and loss of lives, with harmful health threats. We aim to correctly detect and localize wildfire events in the early stage and also classify wildfire smoke based on perceived pixel density of camera images. Due to the lack of publicly available dataset for early wildfire smoke detection, we first collect and process images from the AlertWildfire camera network. The images are annotated with bounding boxes and densities for deep learning methods to use. We then adapt a transformer-based end-to-end object detection model for wildfire detection using our dataset. The dataset and detection model together form as a benchmark named the Nevada smoke detection benchmark, or Nemo for short. Nemo is the first open-source benchmark for wildfire smoke detection with the focus of the early incipient stage. We further provide a weakly supervised Nemo version to enable wider support as a benchmark