Wireless networks are more vulnerable to attacks such as eavesdropping and intercepting than traditional wireline networks because of the inherent broadcast feature of the wireless medium. Nowadays, security is becoming more and more important with the rapid pace of innovation of wireless communication technologies such as 5G. Exploiting the unique characteristics of the physical layer of wireless networks to perform device authentication and enhance wireless security appears promising recently. In this paper, we investigate the carrier frequency offset (CFO) caused by the imperfection of the transceiver parts and further implement device authentication by extracting and analyzing the visibility graph (VG) features of CFO series. Firstly, with the fine estimation of CFO for each frame, a time series of CFO can be formed. VG is then extracted and the degree distributions are used for features extracting and matching. Finally, a security authentication scheme of combined CFO values and the features derived from the VG of CFO series is proposed. Experimental results and analyses show that our proposed scheme can successfully achieve device authentication with tolerable computational overhead.

Educational materials that utilize generative AI (e.g., ChatGPT) have been developed, thus, allowing students to learn through conversations with robots or agents. However, if these artificial entities provide incorrect information (hallucinating), it could lead to confusion among students. To investigate whether students can detect lies from these artificial entities, we conducted an experiment using the social robot Furhat and we make it engage in various types of deceptive interactions. Twenty-two Japanese middle school students participated in ten teaching sessions with Furhat using a human and an anime facial appearances while employing different types of deception: Lying, Paltering, Pandering, and Bullshit. The results revealed that the majority of students were deceived by those lies. Additionally, the robot's facial appearance (i.e., social agency) affected both the learning effectiveness and the likelihood of being deceived. We conclude that an anime robot face is recommended to be used as it excelled in learning effectiveness as it attracts students attention. An anime face also provided protection against deceptive techniques due to its low social agency which leads to ineffectiveness in persuasion and deception. This study underscores the importance of preparing AI-based educational tools and scripts carefully to prevent the dissemination of false information produced through generative AI hallucinations to students.

Establishing a secret and reliable wireless communication is a challenging task that is of paramount importance. In this paper, we investigate the physical layer security of a legitimate transmission link between a user that assists an Intrusion Detection System (IDS) in detecting eavesdropping and jamming attacks in the presence of an adversary that is capable of conducting an eavesdropping or a jamming attack. The user is being faced by a challenge of whether to transmit, thus becoming vulnerable to an eavesdropping or a jamming attack, or to keep silent and consequently his/her transmission will be delayed. The adversary is also facing a challenge of whether to conduct an eavesdropping or a jamming attack that will not get him/her to be detected. We model the interactions between the user and the adversary as a two-state stochastic game. Explicit solutions characterize some properties while highlighting some interesting strategies that are being embraced by the user and the adversary. Results show that our proposed system outperform current systems in terms of communication secrecy.

Social robots are taking major roles in our lives through many sectors, ranging from eHealth to industry. However, social robots that can interact with people are expensive. In this study, we evaluate what makes an inexpensive retro-projected Face-Display/robot different from the advanced social robot “Furhat”. How will a retro-projected robot be perceived compared to an advanced social robot? We built a retro-projected robot and compared it with Furhat. Seventy-two participants interacted with both robots in a between-subjects study design. We compare the two robots using different HRI questionnaires. Furthermore, we investigate which robot face is preferred to interact with, a human face (due to social agency) or an anime face (due to Japanese anime culture). In a retro-projected robot, a human face is preferable as it is rated higher in terms of anthropomorphization, trust, competence, and empathy. An anime face was rated higher in warmth, attractiveness, pleasantness, and comfort to see. In Furhat, few differences were noticed between the human and anime faces. A human face was perceived to be more emphatically responsive, and an anime face was rated highly in trustworthiness, warmth, competence, and empathic understanding. Furhat excelled significantly in the aspects of anthropomorphization, likeability, trustworthiness, warmth, competence, empathic responsiveness, attractiveness, and pleasantness to see. On the contrary, the retro-projected robot excelled in inducing social discomfort. Interestingly, both robots were perceived similarly in aspects of perceived intelli-gence and safety, empathic understanding, and comfort to see. An anime face on either robot is significantly more pleasant and is always perceived to be young in age. We deduce that retro-projected robots are inferior to Furhat in most HRI aspects and similar in a few of them. Thus, retro-projected robots can serve effectively in fields where the three aforementioned indifferent aspects are the main requirements in an application. Moreover, whenever aesthetics and visual pleasantness are needed, an anime face is preferred over a human one.