The growing presence of drones in human spaces has sparked curiosity regarding their role as social creatures. One approach to conveying the social aspects of robotic devices is to incorporate emotions. However, the social effects of emotions depend on their perceived appropriateness by humans. In this work, we investigate the psychological effects of appropriate vs. inappropriate emotions displayed on a drone in a delivery scenario. Through an online study (N =97), we observe significant differences in how people ascribe social attributes to a drone, understand both its functional acceptance (e.g., ease of use, usefulness), social acceptance (e.g., drone as an interaction partner), and assess its social competencies and human-like attributes. Overall, for a given situation of interaction, the drone is perceived more (resp. less) positively when displaying appropriate (resp. inappropriate) emotions. We conclude with a discussion on the use of emotions in drones and their psychological effects on users. This work contributes to a deeper understanding of emotion appropriateness and social interactions in robotics.

Assigning lifelike qualities to robotic agents (Anthropomorphism) is associated with complex affective interpretations of their behavior. These anthropomorphized perceptions are traditionally elicited through robots' designs. Yet, aerial robots (or drones) present a special case due to their – traditionally – non-anthropomorphic design, and prior research shows conflicting evidence on their perception as either person-like, animal-like, or machine-like. In this work, we explore how people perceive drones in a cross-dimensional space between these three dimensions by varying the affective state presented on the drone. To capture these perceptions, we developed a novel measurement instrumentAnZoMa. We describe the design, use, and deployment of the instrument in an online study (N=98). The study results suggest that different drone emotions triggered people to attribute various characteristics to the drone (e.g., interaction metaphors, traits, and features) and variations in acceptability of drone affective states. These results demonstrate the interdependencies between affective perceptions and anthropomorphism of drones. We conclude by discussing the necessity to integrate cross-dimensional perception of anthropomorphism in human-drone interaction and affective computing. This work contributes a novel tool to measure the dimensions and gravity of anthropomorphism and insights into interdependencies between different affective states displayed on drones and their anthropomorphized perception.

Socially assistive robots (SARs) aim to provide assistance through social interaction. Previous studies contributed to understanding users’ perceptions and preferences regarding existing commercially available SARs. Yet very few studies regarding SARs’ appearance used designated SAR designs, and even fewer evaluated isolated visual qualities (VQs). In this work, we aim to assess the effect of isolated VQs systematically. To achieve this, we first conducted market survey and deconstructed the VQs attributed to SARs. Then, a reconstruction of body structure, outline, and color scheme was done, resulting in the creation of 30 new SAR models that differ in their VQs, allowing us to isolate one character at a time. We used these new designs to evaluate users’ preferences and perceptions in two empirical studies. Our empirical findings link VQs with perceptions of SAR characteristics. These can lead to forming guidelines for the industrial design processes of new SARs to match user expectations.

Digitization in the battlefield enables bi-directional graphic communication, i.e., sharing pictures or video feeds derived from unmanned systems, among distributed elements. Introducing robotic technologies at the battalion level is aimed to support shorter OODA (observe-orient-decide-act) cycles and more agility. Our focus is on developing bi-directional graphic communication tools for observer-executer teams as alternative ways to communicate. Following the Design Science Research methodology, four closely related consecutive studies with subject matter experts (SMEs) demonstrate how we first learn about phenomena and then use the knowledge to design interaction tools for communication. We first study the human-human observer-executer teams. Then, as the design of the interaction tools evolves, we transfer design principles into communication tools between an executer and the unmanned system (Wizard of Oz study). Our work highlights the potential of implementing and using bi-directional graphic communication to enhance battlefield understanding in addition to verbal communication (speech or chat). We demonstrate the necessity of user-centered development and evaluation for design and application. Further, we emphasize the role of the unmanned system operator (observer) and raise questions regarding how to further progress in teaming with intelligent unmanned systems.

Real-time continuous workload assessment is important for researchers and developers of tools that aim to reduce human operators' cognitive workload, especially in dynamic environments, as the military environment, where task demands and workload change rapidly. Most workload measurement techniques provide a single retrospective value or require expensive high-end sensing equipment. This study aimed to introduce an affordable continuous machine learning (ML) based workload assessment tool, that can provide real-time workload scores. Using experienced military unmanned aerial vehicle (UAV) operators in a simulated operational setting, muscle behavior represented by their interaction with a joystick was modeled to predict Subjective Workload Assessment Technique (SWAT) scores. Data were obtained from six professional participants. Four machine learning (ML) modeling methodologies were tested on each participant's data. It has been shown that after running an ML setup phase for each participant, an already in use available tool as the UAV joystick controller can be used to predict SWAT scores at any given time. By implementing the approach presented in this study, researchers can more accurately evaluate various aspects of the human operator's cognitive workload, and developers can evaluate the progression of their solutions on operators' cognitive workload over time.

Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3-D vision-based human-skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing toward a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users' performance, while the NASA-TLX questionnaire was used to evaluate the users' workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F (2, 61) = 84.874, p <; 0.001), accuracy (F (2, 29) = 4.937, p = 0.016), and workload (F (2, 68) = 11.948, p <; 0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: The participants completed the tasks faster with less workload.

The editor's decision where and how to place items on a screen is crucial for the design of information displays, such as websites. We developed a statistical model that can facilitate automating this process by predicting the perceived importance of screen items from their location and size. The model was developed based on a 2-step experiment in which we asked participants to rate the importance of text articles that differed in size, screen location, and title size. Articles were either presented for 0.5 seconds or for unlimited time. In a stepwise regression analysis, the model's variables accounted for 65% of the variance in the importance ratings. In a validation study, the model predicted 85% of the variance of the mean apparent importance of screen items. The model also predicted individual raters' importance perception ratings. We discuss the implications of such a model in the context of automating layout generation. An automated system for layout generation can optimize data presentation to suit users' individual information and display preferences.

Human-robot collaboration is essential when the robot operates in unstructured environments which change dynamically and require high perception capabilities. The design of such collaborative systems requires the system to be predictable, i.e., the system's response should be repeatable and as close as possible to the response expected by its users. This should enable users to comprehend and learn how the system operates and foresee the system's responses to their commands and actions. We present a study of remote controlling a robot. This study investigated the expectations, perception, behavior and preferences of users while issuing a “forward” movement command. The study aimed to determine if users expect and prefer a system response that is identical in the distance of movement (repeated quantity), or an adaptive movement whose size depends on the environment (i.e., repeated rule, such as movement until another command, until junctions and/or obstacles). Speech was the only modality of interaction in the two experiments performed. The results show that the movement manner adaptable to the environment is preferred. Although the manner of movement (set step size vs. continuous movement, stopping at junctions vs. not stopping) may affect the overall performance, especially in the learning stage, these differences are not always perceived by the users. Results indicate that a robot's response could be qualitatively similar rather than identical in quantity or quality (the direction is constant, but the movement and feedback manners may vary). Furthermore, the overall gained user experience compensates for minor variations in the system's response.

Technological developments and the addition of new features to existing applications or services require the inclusion of security mechanisms to protect the user. When using these mechanisms the user faces a tradeoff between more risky and more efficient or safer and less efficient use of the system. We discuss this tradeoff and present a novel complementary experimental system which provides researchers and corporations the ability to explore and model the usability and security tradeoff in the context of user interaction with security systems and psychological acceptability, even before the actual development and implementation processes have ended.

Detecting unknown malicious code (malcode) is a challenging task. Current common solutions, such as anti-virus tools, rely heavily on prior explicit knowledge of specific instances of malcode binary code signatures. During the time between its appearance and an update being sent to anti-virus tools, a new worm can infect many computers and cause significant damage. We present a new host-based intrusion detection approach, based on analyzing the behavior of the computer to detect the presence of unknown malicious code. The new approach consists on classification algorithms that learn from previous known malcode samples which enable the detection of an unknown malcode. We performed several experiments to evaluate our approach, focusing on computer worms being activated on several computer configurations while running several programs in order to simulate background activity. We collected 323 features in order to measure the computer behavior. Four classification algorithms were applied on several feature subsets. The average detection accuracy that we achieved was above 90% and for specific unknown worms even above 99%.

Systems increasingly provide operators with the ability to move between different levels of automation from entirely manual modes, over partly automated modes (in which some task components are performed by the operator) and up to fully automated modes. It is still unclear what determines the relative efficiency of different levels of automation. An experimental system was developed to study these issues. In the system operators had to detect faulty items, based on partial information. Two levels of automation were tested: aided detection (operators received cues from a failure detection system), and approval (operators could change decisions of the failure detection system). An experiment assessed operators' performance with the different levels of automation and with low and high validity failure detection systems. Results showed that operators adjusted their response to the diagnostic value of the automation. Also, operators responded more strongly to low-validity cues in the approval condition than in the aided detection condition. These results point to some of the complex issues that need to be considered when choosing the appropriate level of automation for a system.