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Robots (and computers) are increasingly being used in scenarios where they interact socially with people. How people react to these agents is telling about the perceived empathy of such agents. Mistreatment of robots (or computers) by co-workers might provoke such telling reactions. This study examines perceived mistreatment directed towards a robot in comparison to a computer. This will provide some understanding of how people feel about robots in collaborative social settings. We conducted a two by two between-subjects study with 80 participants. Participants worked cooperatively with either a robot or a computer agent. An experiment confederate would either act aggressively or neutrally towards the agent. We hypothesized that people would not perceive aggressive speech as mistreatment when an agent was capable of emotional feelings and similar to themselves; that participants would perceive the robot as more similar in appearance and emotionally capable to themselves than a computer; and so would observe more mistreatment with a robot. The final results supported our hypotheses; the participants observed greater mistreatment for the robot, but not the computer. Also participants felt significantly more sympathetic towards the robot and believed that it was much more emotionally capable.

Homophily, a person’s bias for having ties with people who are similar to themselves in social ways, has a vital role in creating a social connection between people. Studying homophily in human-robot interactions can provide valuable insights for improving those interactions. In this paper, we investigate whether similar interests have a positive effect on a human-robot interaction similar to the positive impact it can have on human-human interaction. We explore whether sharing similar interests can affect trust. This experiment consisted of two NAO robots; each gave differing speeches. For each participant, their national origin was asked in the pre-questionnaire, and during the sessions, one of the robot’s topics was either personalized or not to their national origin. Since one robot shared a familiar topic, we expected to observe bonding between humans and the robot. We gathered data from a post-questionnaire and analyzed them. The results summarize the hypotheses here. We conclude that homophily plays a significant role in human-robot interaction, affecting trust in a robot partner.

Cloud computing has gain wide-spread popularity in recent years. Despite its numerous advantages, security is still one of the most challenging issues associated with it. An intrusion detection system (IDS), as a common security tool, can be used to increase the level of security in cloud computing. An IDS need to be accurate, efficient, adaptive and extensible. Cloud features, such as being highly distributed, generating large volume of data, variety and dynamism of the provided services on one hand and different security needs of users on the other hand has made traditional IDSs inefficient for this environment. In this paper and by using data mining methods, an efficient policy driven detection strategy for intrusion detection has been proposed for the cloud environment. The proposed approach classifies different security needs, based on CIA triad model, into groups of users with the same security requirements and then selects the appropriate policy. By grouping similar users/security requirements and tuning each IDS accordingly, the proposed approach has been able to improve IDS efficiency. Results of our simulations show that the proposed approach decreases the total detection time by 21% in average while preserving adequate detection coverage. Improving IDS efficiency implies that it also processes a bigger volume of data due to reduction in time, better use of resources and also loads balancing between groups.