Exploring an opinion network for taste prediction: an empirical study

We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means.Comment: 9 pages, 4 figure

Social network analysis in DBpedia

Unser Leben verlagert sich immer mehr in Richtung netzbasierter Umgebungen. Wir schreiben E-Mails, telefonieren mit Mobiltelefonen und kommunizieren mit Freunden in Social Media Plattformen, von Facebook bis Wikipedia. Das schafft eine große Anzahl an verwertbaren Daten für die Soziale Netzwerkanalyse. Diese Methode erlaubt es, basierend auf dem Medium spezielle Kommunikations-Schemata zu analysieren, Verhaltensmuster bei starken und schwachen Beziehungen, Beziehungen bei denen sich Akteure mögen oder ablehnen zu untersuchen. Mit ihr kann man auch Aussagen treffen, wie wichtig einzelne Akteure in Relation zu anderen im Netzwerk sind. Netzwerk Technologien entwickeln sich kontinuierlich weiter. Ein gutes Beispiel dafür ist die Erweiterung des World Wide Web zum sogenannten Web of Data. Hier werden Standards geschaffen, um die den Webseiten zugrunde liegenden Daten einheitlich, offen und maschinenlesbar zu gestalten. Das Web of Data, auch Linked Data genannt, hat eine große Gemeinde und eine schnell wachsende Anzahl an frei verfügbaren, maschinenlesbaren Daten. Das leuchtende Zentrum dieser verlinkten Daten ist die DBpedia, welche Daten aus der Wikipedia extrahiert und anhand der Linked Data Prinzipien aufbereitet. Diese Arbeit versucht die frei verfügbaren Daten des Web of Data mit der Methode der Sozialen Netzwerkanalyse zu verbinden. Um das umzusetzen, wollen wir Daten von der DBpedia extrahieren und die extrahierten Akteure analysieren, um daraus konkrete Aussagen herleiten zu können. Konkret möchten wir jeweils ein Netzwerk von Schriftstellern, Wissenschaftlern, Fußballspielern und Architekten extrahieren um, unter anderem, Fragen zu beantworten wie „Wer ist der wichtigste Schriftsteller/Wissenschaftler der Geschichte?“, „Welchen Transfermustern folgen Fußballspieler?“ und „Arbeiten Architekten in Teams?“. Die Beantwortung solcher Fragen gibt Aufschluss darüber, ob die Soziale Netzwerkanalyse in Verbindung mit der DBpedia grundsätzlich möglich ist. Auch Ziel dieser Studie ist es, herauszufinden ob dieser Ansatz brauchbar ist für die Sozialwissenschaft.Daily Life is more and more affected by modern forms of communication and media. In the world of today, we live our lives within network based environments. We check e-mails, make mobile phone calls and interact on social media platforms – starting from Facebook or Twitter up to Wikipedia. The high volume of raw computable data leads to research topics about social network analysis. Using this method, it is possible to reveal distinct patterns of interaction. Depending on the communication media, it allows the investigation of behavioral patterns of strong and weak relationships, relationships of liking and disliking someone, or even dividing important actors from less-important actors within a network system. Besides, network technology does not stand still. It is constantly expanding, enhancing and restructuring itself. A great new vision of the World Wide Web is the enhancement to uniform standards on the underlying data to a Web of Data. The Web of Data, or Linked Data, already has a huge community and a fast growing amount of freely accessible, machine-readable data. The nucleus and crystallization point of the Web of Data is DBpedia, which provides a machine-readable representation of the entire Wikipedia contents as Linked Data on the Web. This thesis seeks to connect the data of Linked Data with the method of the social network analysis. In order to achieve this, we would like to extract networks from DBpedia and analyze the extracted actors to draw a valid conclusion about using DBpedia as a source of data for social network analysis. To assure that social network analysis on DBpedia is possible and reasonable, we will exemplarily analyze networks of writers, scientists, soccer players and architects to answer questions like “Who is the most important writer/scientist in history?”, “Which transfer patterns do soccer players follow?” or “Do architects work in teams?”. Another topic of this thesis is the usability and usefulness of this whole approach in social science

Egocentric Vision-based Action Recognition: A survey

[EN] The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.We gratefully acknowledge the support of the Basque Govern-ment's Department of Education for the predoctoral funding of the first author. This work has been supported by the Spanish Government under the FuturAAL-Context project (RTI2018-101045-B-C21) and by the Basque Government under the Deustek project (IT-1078-16-D)

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

The insider on the outside: a novel system for the detection of information leakers in social networks

Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles

Linked Data : A Paradigm Shift for Publishing and Using Biography Collections on the Semantic Web

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