La inclusión del terrorismo entre los crímenes internacionales previstos en el Estatuto de la Corte Penal Internacional

La Resolución E, aprobada por la Conferencia Diplomática de Plenipotenciarios de las Naciones Unidas sobre el establecimiento de una Corte Penal Internacional, pospuso para la Conferencia de Revisión la inclusión del terrorismo entre los crímenes internacionales sobre los que tiene competencia la Corte Penal Internacional. En vísperas de la celebración de esta Conferencia de Revisión, la presente monografía contiene un estudio muy completo de los diversos y abundantes instrumentos jurídicos internacionales de lucha contra el terrorismo. A partir de los mismos, se realiza una doble propuesta alternativa de inclusión del terrorismo como crimen internacional en el Estatuto de la Corte Penal Internacional, ya sea como crimen internacional autónomo, ya sea como un supuesto concreto de crimen contra la humanidad

Applying sentiment analysis on Spanish tweets using BETO

Emotion analysis of messages using machine learning techniques is a difficult and cumbersome task requiring a major effort to obtain reliable results. This challenge is even more pronounced when the target language is not English, but Spanish. To overcome this challenge, this paper describes how UPC Team applied sentiment analysis on social media messages (in particular, on Twitter) written in Spanish and, related to events that took place in April 2019 from different domains. To this aim, we present a machine learning model based on BERT and describe the results obtained to reach an accuracy of 65% approx. and the 12th position in the ranking, for this second edition of the contest for emotion detection of Spanish tweets [email protected] ReviewedPostprint (published version

Applying transfer learning to sentiment analysis in social media

Context: Sentiment analysis is an NLP technique that can be used to automatically obtain the sentiment of a crowd of end-users regarding a software application. However, applying sentiment analysis is a difficult task, especially considering the need of obtaining enough good quality data for training a Machine Learning (ML) model. To address this challenge, transfer learning can help us save time and get better performance results with a limited amount of data. Objective: In this paper, we aim at identifying to which degree transfer learning improves the results of sentiment analysis of messages shared by end-users in social media. Method: We propose a tool-supported framework able to monitor and analyze the sentiment of tweets with different ML models and settings. Using the proposed framework, we apply transfer learning and conduct a set of experiments with multiple datasets. Results: The performance of different ML models with transfer learning from different datasets are obtained and discussed, showing how different factors affect the results, and discussing how they have to be considered when applying transfer learning.This work has been partially supported by the Spanish project DOGO4ML (contract PID2020-117191RB-I00).Peer ReviewedPostprint (author's final draft

Merging datasets for emotion analysis

Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB.Peer ReviewedPostprint (author's final draft

Software-based dialogue systems: Survey, taxonomy and challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the inancial support of his predoctoral grant FPI-UPC. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Requirements for a Nutrition Education Demonstrator

[Context and Motivation] Development of innovative ICT-based applications is a complex process involving collaboration of all relevant disciplines. This complexity arises due to differences in terminology, knowledge and often also the ways of working between developers in the disciplines involved. [Question/problem] Advances in each discipline bring a rich design environment of theories, models, methods and techniques. Making a selection from these makes the development of distributed applications very challenging, often requiring a holistic approach to address the needs of the disciplines involved. This paper describes early stage requirements acquisition of a mobile nutrition education demonstrator which supports overweight persons in adopting healthier dietary behaviour. [Principal idea/results] We present a novel way to combine and use known requirements acquisition methods involving a two stage user needs analysis based on scenarios which apply a theory-based model of behavioural change and are onstructed in two phases. The first phase scenarios specify an indicative description reflecting the use of the transtheoretical model of behavioural change. In the second phase, a handshake protocol adds elements of optative system-oriented descriptions to the scenarios such that the intended system can support the indicative description. [Contribution] The holistic and phased approach separates design concerns to which each of the disciplines contributes with their own expertise and domain principles. It preserves the applied domain principles in the design and it bridges gaps in terminology, knowledge and ways of working

Early Triassic-Early Jurassic Bivalve Diversity Dynamics

Bivalves are a highly diversified molluscan class, with a long history dating from early Cambrian times (Cope, 2000). Although the group already showed a steady diversification trend during the Paleozoic, it only became highly successful and expanded rapidly from the Mesozoic onward. The Triassic was, for bivalves, first a recovery period and later a biotic diversification event. It was also the time bivalves first fully exploited their evolutionary novelties.Facultad de Ciencias Naturales y Muse