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.

Improving a hybrid literary book recommendation system through author ranking

Abstract: Literary reading is an important activity for individuals and can be a long term commitment, making book choice an important task for book lovers and public library users. In this paper, we present a hybrid recommendation system to help readers decide which book to read next. We study book and author recommendations in a hybrid recommendation setting and test our algorithm on the LitRec data set. Our hybrid method combines two item-based collaborative filtering algorithms to predict books and authors that the user will like. Author predictions are expanded into a booklist that is subsequently aggregated with the former book predictions. Finally, the resulting booklist is used to yield the top-n book recommendations. By means of various experiments, we demonstrate that author recommendation can improve overall book recommendation.

Apt-pbo: solving the software dependency problem using pseudo-boolean optimization

Abstract: The installation of software packages depends on the correct resolution of dependencies and conflicts between packages. This problem is NP-complete and, as expected, is a hard task. Moreover, today's technology still does not address this problem in an acceptable way. This paper introduces a new approach to solving the software dependency problem in a Linux environment, devising a way for solving dependencies according to available packages and user preferences. This work introduces the "apt-pbo" tool, the first publicly available tool that solves dependencies in a complete and optimal way.

Dynamic Load Balancing for Real-Time Video Encoding on Heterogeneous CPU+GPU Systems

Abstract: The high computational demands and overall encoding complexity make the processing of high definition video sequences hard to be achieved in real-time. In this manuscript, we target an efficient parallelization and RD performance analysis of H.264/AVC inter-loop modules and their collaborative execution in hybrid multi-core CPU and multi-GPU systems. The proposed dynamic load balancing algorithm allows efficient and concurrent video encoding across several heterogeneous devices by relying on realistic run-time performance modeling and module-device execution affinities when distributing the computations. Due to an online adjustment of load balancing decisions, this approach is also self-adaptable to different execution scenarios. Experimental results show the proposed algorithm's ability to achieve real-time encoding for different resolutions of high-definition sequences in various heterogeneous platforms. Speed-up values of up to 2.6 were obtained when compared to the video inter-loop encoding on a single GPU device, and up to 8.5 when compared to a highly optimized multi-core CPU execution. Moreover, the proposed algorithm also provides an automatic tuning of the encoding parameters, in order to meet strict encoding constraints.

Artificial intelligence moving serious gaming: Presenting reusable game AI components

Abstract: This article provides a comprehensive overview of artificial intelligence (AI) for serious games. Reporting about the work of a European flagship project on serious game technologies, it presents a set of advanced game AI components that enable pedagogical affordances and that can be easily reused across a wide diversity of game engines and game platforms. Serious game AI functionalities include player modelling (real-time facial emotion recognition, automated difficulty adaptation, stealth assessment), natural language processing (sentiment analysis and essay scoring on free texts), and believable non-playing characters (emotional and socio-cultural, non-verbal bodily motion, and lip-synchronised speech), respectively. The reuse of these components enables game developers to develop high quality serious games at reduced costs and in shorter periods of time. All these components are open source software and can be freely downloaded from the newly launched portal at gamecomponents.eu. The components come with detailed installation manuals and tutorial videos. All components have been applied and validated in serious games that were tested with real end-users.

BSig: evaluating the statistical significance of biclustering solutions

Abstract: Statistical evaluation of biclustering solutions is essential to guarantee the absence of spurious relations and to validate the high number of scientific statements inferred from unsupervised data analysis without a proper statistical ground. Most biclustering methods rely on merit functions to discover biclusters with specific homogeneity criteria. However, strong homogeneity does not guarantee the statistical significance of biclustering solutions. Furthermore, although some biclustering methods test the statistical significance of specific types of biclusters, there are no methods to assess the significance of flexible biclustering models. This work proposes a method to evaluate the statistical significance of biclustering solutions. It integrates state-of-the-art statistical views on the significance of local patterns and extends them with new principles to assess the significance of biclusters with additive, multiplicative, symmetric, order-preserving and plaid coherencies. The proposed statistical tests provide the unprecedented possibility to minimize the number of false positive biclusters without incurring on false negatives, and to compare state-of-the-art biclustering algorithms according to the statistical significance of their outputs. Results on synthetic and real data support the soundness and relevance of the proposed contributions, and stress the need to combine significance and homogeneity criteria to guide the search for biclusters.