Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

Enhancing Privacy and Fairness in Search Systems

Following a period of expedited progress in the capabilities of digital systems, the society begins to realize that systems designed to assist people in various tasks can also harm individuals and society. Mediating access to information and explicitly or implicitly ranking people in increasingly many applications, search systems have a substantial potential to contribute to such unwanted outcomes. Since they collect vast amounts of data about both searchers and search subjects, they have the potential to violate the privacy of both of these groups of users. Moreover, in applications where rankings influence people's economic livelihood outside of the platform, such as sharing economy or hiring support websites, search engines have an immense economic power over their users in that they control user exposure in ranked results. This thesis develops new models and methods broadly covering different aspects of privacy and fairness in search systems for both searchers and search subjects. Specifically, it makes the following contributions: (1) We propose a model for computing individually fair rankings where search subjects get exposure proportional to their relevance. The exposure is amortized over time using constrained optimization to overcome searcher attention biases while preserving ranking utility. (2) We propose a model for computing sensitive search exposure where each subject gets to know the sensitive queries that lead to her profile in the top-k search results. The problem of finding exposing queries is technically modeled as reverse nearest neighbor search, followed by a weekly-supervised learning to rank model ordering the queries by privacy-sensitivity. (3) We propose a model for quantifying privacy risks from textual data in online communities. The method builds on a topic model where each topic is annotated by a crowdsourced sensitivity score, and privacy risks are associated with a user's relevance to sensitive topics. We propose relevance measures capturing different dimensions of user interest in a topic and show how they correlate with human risk perceptions. (4) We propose a model for privacy-preserving personalized search where search queries of different users are split and merged into synthetic profiles. The model mediates the privacy-utility trade-off by keeping semantically coherent fragments of search histories within individual profiles, while trying to minimize the similarity of any of the synthetic profiles to the original user profiles. The models are evaluated using information retrieval techniques and user studies over a variety of datasets, ranging from query logs, through social media and community question answering postings, to item listings from sharing economy platforms.Nach einer Zeit schneller Fortschritte in den Fähigkeiten digitaler Systeme beginnt die Gesellschaft zu erkennen, dass Systeme, die Menschen bei verschiedenen Aufgaben unterstützen sollen, den Einzelnen und die Gesellschaft auch schädigen können. Suchsysteme haben ein erhebliches Potenzial, um zu solchen unerwünschten Ergebnissen beizutragen, weil sie den Zugang zu Informationen vermitteln und explizit oder implizit Menschen in immer mehr Anwendungen in Ranglisten anordnen. Da sie riesige Datenmengen sowohl über Suchende als auch über Gesuchte sammeln, können sie die Privatsphäre dieser beiden Benutzergruppen verletzen. In Anwendungen, in denen Ranglisten einen Einfluss auf den finanziellen Lebensunterhalt der Menschen außerhalb der Plattform haben, z. B. auf Sharing-Economy-Plattformen oder Jobbörsen, haben Suchmaschinen eine immense wirtschaftliche Macht über ihre Nutzer, indem sie die Sichtbarkeit von Personen in Suchergebnissen kontrollieren. In dieser Dissertation werden neue Modelle und Methoden entwickelt, die verschiedene Aspekte der Privatsphäre und der Fairness in Suchsystemen, sowohl für Suchende als auch für Gesuchte, abdecken. Insbesondere leistet die Arbeit folgende Beiträge: (1) Wir schlagen ein Modell für die Berechnung von fairen Rankings vor, bei denen Suchsubjekte entsprechend ihrer Relevanz angezeigt werden. Die Sichtbarkeit wird im Laufe der Zeit durch ein Optimierungsmodell adjustiert, um die Verzerrungen der Sichtbarkeit für Sucher zu kompensieren, während die Nützlichkeit des Rankings beibehalten bleibt. (2) Wir schlagen ein Modell für die Bestimmung kritischer Suchanfragen vor, in dem für jeden Nutzer Aanfragen, die zu seinem Nutzerprofil in den Top-k-Suchergebnissen führen, herausgefunden werden. Das Problem der Berechnung von exponierenden Suchanfragen wird als Reverse-Nearest-Neighbor-Suche modelliert. Solche kritischen Suchanfragen werden dann von einem Learning-to-Rank-Modell geordnet, um die sensitiven Suchanfragen herauszufinden. (3) Wir schlagen ein Modell zur Quantifizierung von Risiken für die Privatsphäre aus Textdaten in Online Communities vor. Die Methode baut auf einem Themenmodell auf, bei dem jedes Thema durch einen Crowdsourcing-Sensitivitätswert annotiert wird. Die Risiko-Scores sind mit der Relevanz eines Benutzers mit kritischen Themen verbunden. Wir schlagen Relevanzmaße vor, die unterschiedliche Dimensionen des Benutzerinteresses an einem Thema erfassen, und wir zeigen, wie diese Maße mit der Risikowahrnehmung von Menschen korrelieren. (4) Wir schlagen ein Modell für personalisierte Suche vor, in dem die Privatsphäre geschützt wird. In dem Modell werden Suchanfragen von Nutzer partitioniert und in synthetische Profile eingefügt. Das Modell erreicht einen guten Kompromiss zwischen der Suchsystemnützlichkeit und der Privatsphäre, indem semantisch kohärente Fragmente der Suchhistorie innerhalb einzelner Profile beibehalten werden, wobei gleichzeitig angestrebt wird, die Ähnlichkeit der synthetischen Profile mit den ursprünglichen Nutzerprofilen zu minimieren. Die Modelle werden mithilfe von Informationssuchtechniken und Nutzerstudien ausgewertet. Wir benutzen eine Vielzahl von Datensätzen, die von Abfrageprotokollen über soziale Medien Postings und die Fragen vom Q&A Forums bis hin zu Artikellistungen von Sharing-Economy-Plattformen reichen

Artificial Intelligence for Online Review Platforms - Data Understanding, Enhanced Approaches and Explanations in Recommender Systems and Aspect-based Sentiment Analysis

The epoch-making and ever faster technological progress provokes disruptive changes and poses pivotal challenges for individuals and organizations. In particular, artificial intelligence (AI) is a disruptive technology that offers tremendous potential for many fields such as information systems and electronic commerce. Therefore, this dissertation contributes to AI for online review platforms aiming at enabling the future for consumers, businesses and platforms by unveiling the potential of AI. To achieve this goal, the dissertation investigates six major research questions embedded in the triad of data understanding of online consumer reviews, enhanced approaches and explanations in recommender systems and aspect-based sentiment analysis

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov