False Identity Detection Using Complex Sentences

The use of faked identities is a current issue for both physical and online security. In this paper, we test the differences between subjects who report their true identity and the ones who give fake identity responding to control, simple, and complex questions. Asking complex questions is a new procedure for increasing liars' cognitive load, which is presented in this paper for the first time. The experiment consisted in an identity verification task, during which response time and errors were collected. Twenty participants were instructed to lie about their identity, whereas the other 20 were asked to respond truthfully. Different machine learning (ML) models were trained, reaching an accuracy level around 90-95% in distinguishing liars from truth tellers based on error rate and response time. Then, to evaluate the generalization and replicability of these models, a new sample of 10 participants were tested and classified, obtaining an accuracy between 80 and 90%. In short, results indicate that liars may be efficiently distinguished from truth tellers on the basis of their response times and errors to complex questions, with an adequate generalization accuracy of the classification models

Improving social game engagement on Facebook through enhanced socio-contextual information

In this paper we describe the results of a controlled study of a social game, Magpies, which was built on the Facebook Online Social Network (OSN) and enhanced with contextual social information in the form of a variety of social network indices. Through comparison with a concurrent control trial using an identical game without the enhanced social information, it was shown that the additional contextual data increased the frequency of social activity between players engaged in the game. Despite this increase in activity, there was little increase in growth of the player-base when compared to the control condition. These findings corroborate previous work that showed how socio-contextual enhancement can increase performance on task-driven games, whilst also suggesting that it can increase activity and engagement when provided as context for non task-driven game environments

You Can't Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality

Virtual and Augmented Reality (VR, AR) are increasingly gaining traction thanks to their technical advancement and the need for remote connections, recently accentuated by the pandemic. Remote surgery, telerobotics, and virtual offices are only some examples of their successes. As users interact with VR/AR, they generate extensive behavioral data usually leveraged for measuring human behavior. However, little is known about how this data can be used for other purposes. In this work, we demonstrate the feasibility of user profiling in two different use-cases of virtual technologies: AR everyday application ($N=34$) and VR robot teleoperation ($N=35$). Specifically, we leverage machine learning to identify users and infer their individual attributes (i.e., age, gender). By monitoring users' head, controller, and eye movements, we investigate the ease of profiling on several tasks (e.g., walking, looking, typing) under different mental loads. Our contribution gives significant insights into user profiling in virtual environments

An exploratory fNIRS study with immersive virtual reality: a new method for technical implementation

For over two decades Virtual Reality (VR) has been used as a useful tool in several fields, from medical and psychological treatments, to industrial and military applications. Only in recent years researchers have begun to study the neural correlates that subtend VR experiences. Even if the functional Magnetic Resonance Imaging (fMRI) is the most common and used technique, it suffers several limitations and problems. Here we present a methodology that involves the use of a new and growing brain imaging technique, functional Near-infrared Spectroscopy (fNIRS), while participants experience immersive VR. In order to allow a proper fNIRS probe application, a custom-made VR helmet was created. To test the adapted helmet, a virtual version of the line bisection task was used. Participants could bisect the lines in a virtual peripersonal or extrapersonal space, through the manipulation of a Nintendo Wiimote ® controller in order for the participants to move a virtual laser pointer. Although no neural correlates of the dissociation between peripersonal and extrapersonal space were found, a significant hemodynamic activity with respect to the baseline was present in the right parietal and occipital areas. Both advantages and disadvantages of the presented methodology are discussed

Facing with Collaborative Robots : The Subjective Experience in Senior and Younger Workers

In the past few years, collaborative robots (i.e., cobots) have been largely adopted within industrial manufacturing. Although robots can support companies and workers in carrying out complex activities and improving productivity, human factors related to cobot operators have not yet been thoroughly investigated. The present study aims to understand the subjective experience of younger and senior workers interacting with an industrial collaborative robot. Results show that workers' acceptance of cobots is high, regardless of age and control modality used. Interesting differences between seniors and younger adults emerged in the evaluations of user experience, usability, and perceived workload of participants and are detailed and commented in the last part of the work.Peer reviewe

Investigating Proactive Search Support in Conversations

Conversations among people involve solving disputes, building common ground, and reinforce mutual beliefs and assumptions. Conversations often require external information that can support these human activities. In this paper, we study how a spoken conversation can be supported by a proactive search agent that listens to the conversation, detects entities mentioned in the conversation, and proactively retrieves and presents information related to the conversation. A total of 24 participants (12 pairs) were involved in informal conversations, using either the proactive search agent or a control condition that did not support conversational analysis or proactive information retrieval. Data comprising transcripts, interaction logs, questionnaires, and interviews indicated that the proactive search agent effectively augmented the conversations, affected the conversations' topical structure, and reduced the need for explicit search activity. The findings also revealed key challenges in the design of proactive search systems that assist people in natural conversations.Peer reviewe

IoT Systems for Healthy and Safe Life Environments

The past two years have been sadly marked by the worldwide spread of the SARS-Cov-19 pandemic. The first line of defense against this and other pandemic threats is to respect interpersonal distances, use masks, and sanitize hands, air, and objects. Some of these countermeasures are becoming part of our daily lives, as they are now considered good practices to reduce the risk of infection and contagion. In this context, we present \emph{Safe Place}, a modular system enabled by \gls{iot} that is designed to improve the safety and healthiness of living environments. %\textcolor{blue}{ This system combines several sensors and actuators produced by different vendors with self-regulating procedures and \gls{ai} algorithms to limit the spread of viruses and other pathogens, and increase the quality and comfort offered to people while minimizing the energy consumption.%} We discuss the main objectives of the system and its implementation, showing preliminary results that assess its potentials in enhancing the conditions of living and working spaces