This study explores the role of social identity in human–robot interactions, focusing on a scenario where a humanoid robot functions as a bartender with either a positive or negative personality. Conducted with 28 participants, the experiment utilized the Big-5 questionnaire to assess personality traits and the Godspeed questionnaire to gauge perceptions of the robot. The research sought to determine if users could perceive the robot’s distinct identities and if these perceptions were influenced by the participants’ personality traits. The findings indicated that participants could effectively discern the robot’s different personalities, validating the potential for programming robots to convey specific social identities. Despite the limited sample size, the results suggest that participants’ initial emotional states and personality traits significantly influenced their perceptions, suggesting that customizing a robot’s identity to match the interlocutor’s personality can enhance the interaction experience. As a preliminary investigation, this study contributes valuable insights into human–robot interaction dynamics and lays the groundwork for future research in the development of socially integrated robotic systems.

Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long time, relatively few studies have addressed the related problem of feature drift. In this paper, a variation of the QuickReduct algorithm suitable to process data streams is proposed and tested: it builds an evolving reduct that dynamically selects the relevant features in the stream, removing the redundant ones and adding the newly relevant ones as soon as they become such. Tests on five publicly available datasets with an artificially injected drift have confirmed the effectiveness of the proposed method.

Record linkage aims to identify records from multiple data sources that refer to the same entity of the real world. It is a well known data quality process studied since the second half of the last century, with an established pipeline and a rich literature of case studies mainly covering census, administrative or health domains. In this paper, a method to recognize matching records from real municipalities and banks through multiple similarity criteria and a Neural Network classifier is proposed: starting from a labeled subset of the available data, first several similarity measures are combined and weighted to build a feature vector, then a Multi-Layer Perceptron (MLP) network is trained and tested to find matching pairs. For validation, seven real datasets have been used (three from banks and four from municipalities), purposely chosen in the same geographical area to increase the probability of matches. The training only involved two municipalities, while testing involved all sources (municipalities vs. municipalities, banks vs banks and and municipalities vs. banks). The proposed method scored remarkable results in terms of both precision and recall, clearly outperforming threshold-based competitors.