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.

Integration of an Assisted P2P Live Streaming Service in Community Network Clouds

Abstract: Wireless community networks (CNs) are large-scale, self-organized and decentralized communication infrastructures built and operated by citizens for citizens. CN cloud infrastructures have been recently introduced to run services inside the network, without the need to consume them from the Internet. We have developed a Linux-based distribution code-named Cloudy, which fosters the service deployment and automation in CN clouds. In this paper, we present two ways provisioned by Cloudy to integrate the services and improve the user QoS in these clouds. First, we present a distributed service discovery mechanism that helps users with service quality metrics to choose the best service from a pool of instances. Second, we experiment with a live video streaming service deployed in CN environments, using more than 50 real CN nodes distributed across Europe for the evaluation. Our analysis shows that, tuning the vital parameters of this service as neighborhood peer selection strategy and source node dispersion strategy, improves the video streaming QoS in the CNs. Our results indicate that both ways help the user to experience improved service performance. Automated service selection, needed once the number of micro service providers becomes larger, is the next step that can be built upon our results.

cmMUS: a tool for circumscription-based MUS membership testing

Abstract: This article presents cmMUS--a tool for deciding whether a clause belongs to some minimal unsatisfiable subset (MUS) of a given formula. While MUS-membership has a number of practical applications, related with understanding the causes of unsatisfiability, it is computationally challenging--it is Σ2P-complete. The presented tool cmMUS solves the problem by translating it to propositional circumscription, a well-known problem from the area of nonmonotonic reasoning. The tool constantly outperforms other approaches to the problem, which is demonstrated on a variety of benchmarks.

iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

Abstract: iCO2 is a networked game that aims to collect human driving behavior data at scale and help researchers better understand how people apply eco-driving practices. iCO2 is an integrated multiplatform game that can be accessed via Web client on Facebook and iPad clients, and supports a shared traffic experience among its users. Here, the authors describe iCO2 and discuss two key efforts and their results. First, they investigated the scalability of Distributed Virtual Environments (DiVE), the networking framework they developed for multiuser 3D virtual world applications. The "area of interest" method that DiVE uses was tested in a laboratory setting and showed promising results on two important metrics: bandwidth and frames per second. Second, they ran a small marketing campaign to collect data from users interacting with the iPad version of iCO2 "in the wild." They then analyzed the users' acceleration behavior, an important aspect of eco-driving.

e-Motional Learning in Primary Schools: FearNot! An Anti-Bullying Intervention Based on Virtual Role-Play with Intelligent Synthetic Characters.

Abstract: Addressing the problems of bullying in schools, this paper presents a novel and highly innovative pedagogical approach, building on the immersive power of virtual role-play. Educational role-play is widely accepted as a powerful instrument to change attitudes and behaviour, but faces some difficulties and disadvantages when applied to sensitive social issues in the classroom. This paper shows how the FearNot! software application, developed within the scope of the EU-funded projects VICTEC (Virtual ICT with Empathic Characters) and eCIRCUS (Education through Characters with emotional-Intelligence and Role-playing Capabilities that Understand Social interaction) uses virtual role-play and autonomous agents to provide children aged eight to eleven years of age with the opportunity to visit a virtual school environment populated by 3D animated synthetic characters that engage in bullying episodes. The characters’ actions and the storyline are created as improvised dramas by use of emergent narrative, resulting in unscripted and highly believable interaction experiences for the learner. While the students are spectators to the bullying episodes that unfold among the FearNot! characters, the victimised character starts a conversation with the student in between the episodes, describing their experiences with bullying and how they feel as a result to it, and asking the student for advice. The aim of this approach and particularly of this interaction sequence in between the virtual bullying episodes is to sensitise primary school students to the potential problems that victims of persistent aggressive behaviour are facing: By triggering an empathic relationship between learners and characters, learners understand and vicariously feel into the plight of the victimised character. Empirical evidence from bullying research implies that bullies are regularly reinforced by bystanders that witness the bullying and turn their attention to it, but do not actively intervene to end it (Craig & Pepler 1996; Lean 1998; Salmivalli 1999; Hawkins et al. 2001). Hence, this intervention strategy targets these bystanders to stand up to the bully and help the victim, due to their heightened awareness and sensitivity to the grave consequences victims face. Preliminary evaluation results indicate that the children were willing to immerse themselves in the virtual drama and that they empathically engage with the characters, attributing a range of emotions to the characters depending on the events that happen within the respective scenario. An ongoing long-term intervention in school in the UK and Germany covers several interactions with the software over a ten week period of time.

Text based classification of companies in CrunchBase

Abstract: This paper introduces two fuzzy fingerprint based text classification techniques that were successfully applied to automatically label companies from CrunchBase, based purely on their unstructured textual description. This is a real and very challenging problem due to the large set of possible labels (more than 40) and also to the fact that the textual descriptions do not have to abide by any criteria and are, therefore, extremely heterogeneous. Fuzzy fingerprints are a recently introduced technique that can be used for performing fast classification. They perform well in the presence of unbalanced datasets and can cope with a very large number of classes. In the paper, a comparison is performed against some of the best text classification techniques commonly used to address similar problems. When applied to the CrunchBase dataset, the fuzzy fingerprint based approach outperformed the other techniques.