INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

Humanization of robots: Is it really such a good idea?

Abstract: Funding information National Funds provided by the Portuguese Foundation for Science and Technology (FCT), Grant/Award Numbers: UID/PSI/03125/2013, PTDC/EEI-SII/7174/2014, SFRH/BD/110223/2015, CIPPSI/04345/2013 Abstract The aim of this review was to examine the pros and cons of humanizing social robots following a psychological perspective. As such, we had six goals. First, we defined what social robots are. Second, we clarified the meaning of humanizing social robots. Third, we presented the theoretical backgrounds for promoting humanization. Fourth, we conducted a review of empirical results of the positive effects and the negative effects of humanization on human–robot interaction (HRI). Fifth, we presented some of the political and ethical problems raised by the humanization of social robots. Lastly, we discussed the overall effects of the humanization of robots in HRI and suggested new avenues of research and development.

A metadata geoparsing system for place name recognition and resolution in metadata records

Abstract: This paper describes an approach for performing recognition and resolution of place names mentioned over the descriptive metadata records of typical digital libraries. Our approach exploits evidence provided by the existing structured attributes within the metadata records to support the place name recognition and resolution, in order to achieve better results than by just using lexical evidence from the textual values of these attributes. In metadata records, lexical evidence is very often insufficient for this task, since short sentences and simple expressions are predominant. Our implementation uses a dictionary based technique for recognition of place names (with names provided by Geonames), and machine learning for reasoning on the evidences and choosing a possible resolution candidate. The evaluation of our approach was performed in data sets with a metadata schema rich in Dublin Core elements. Two evaluation methods were used. First, we used cross-validation, which showed that our solution is able to achieve a very high precision of 0,99 at 0,55 recall, or a recall of 0,79 at 0,86 precision. Second, we used a comparative evaluation with an existing commercial service, where our solution performed better on any confidence level (p

Relating gaming habits with student performance in a gamified learning experience

Abstract: Gamified education is a novel concept, and early trials show its potential to engage students and improve their performance. However, little is known about how different students learn with gamification, and how their gaming habits influence their experience. In this paper we present a study where data regarding student performance and gaming preferences, from a gamified engineering course, was collected and analyzed. We performed cluster analysis to understand what different kinds of students could be observed in our gamified experience, and how their behavior could be correlated to their gaming characteristics. We identified four main student types: the Achievers, the Regular students, the Halfhearted students, and the Underachievers, all representing different strategies towards the course and with different gaming preferences. Here we will thoroughly describe each student type and address how different gaming preferences might have impacted the students' learning experience.