On the Importance of Considering Country-specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream

In the field of music recommender systems, country-specific aspects have received little attention, although it is known that music perception and preferences are shaped by culture; and culture varies across countries. Based on the LFM-1b dataset (including 53,258 users from 47 countries), we show that there are significant country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Results indicate that, for instance, Finnish users’ listening behavior is farther away from the global mainstream, while United States’ listeners are close to the global mainstream. Relying on rating prediction experiments, we tailor recommendations to a user’s level of preference for mainstream (defined on a global level and on a country level) and the user’s country. Results suggest that, in terms of rating prediction accuracy, a combination of these two filtering strategies works particularly well for users of countries far away from the global mainstream

Syntactic and Semantic Analysis and Visualization of Unstructured English Texts

People have complex thoughts, and they often express their thoughts with complex sentences using natural languages. This complexity may facilitate efficient communications among the audience with the same knowledge base. But on the other hand, for a different or new audience this composition becomes cumbersome to understand and analyze. Analysis of such compositions using syntactic or semantic measures is a challenging job and defines the base step for natural language processing. In this dissertation I explore and propose a number of new techniques to analyze and visualize the syntactic and semantic patterns of unstructured English texts. The syntactic analysis is done through a proposed visualization technique which categorizes and compares different English compositions based on their different reading complexity metrics. For the semantic analysis I use Latent Semantic Analysis (LSA) to analyze the hidden patterns in complex compositions. I have used this technique to analyze comments from a social visualization web site for detecting the irrelevant ones (e.g., spam). The patterns of collaborations are also studied through statistical analysis. Word sense disambiguation is used to figure out the correct sense of a word in a sentence or composition. Using textual similarity measure, based on the different word similarity measures and word sense disambiguation on collaborative text snippets from social collaborative environment, reveals a direction to untie the knots of complex hidden patterns of collaboration

Von Requirements zu Privacy Explanations: Ein nutzerzentrierter Ansatz für Usable Privacy

Im Zeitalter der fortschreitenden Digitalisierung, in dem die Technologie zunehmend in unsere Gesellschaft eindringt, rücken sogenannte human values wie Ethik, Fairness, Privatsphäre und Vertrauen weiter in den Mittelpunkt. Digitale Informationssysteme dringen immer stärker in private und berufliche Bereiche vor und bieten den Nutzern Unterstützung, schnell und einfach mit anderen Menschen in Kontakt zu treten, bei der Informationsbeschaffung und helfen bei der Erledigung täglicher Aufgaben. Im Gegenzug geben die Nutzer bereitwillig große Mengen an persönlichen Daten an diese Systeme weiter. Diese Datenerfassung bedeutet jedoch, dass die Privatsphäre der Nutzer zunehmend gefährdet ist. Daher ist die Aufklärung der Nutzer über die gesammelten Informationen und ihre anschließende Verarbeitung der Schlüssel, die Privatsphäre der Nutzer zu schützen. Der Gesetzgeber hat Datenschutzerklärungen als Mittel zur Kommunikation von Datenpraktiken eingeführt. Leider erweisen sich diese Dokumente für die Endnutzer als praktisch nutzlos, da sie umfangreich, vage formuliert und mit Fachausdrücken gespickt sind, die oft ein tieferes Fachwissen erfordern. Das Ergebnis ist ein Mangel an nutzerorientierten Lösungen zur transparenten und verständlichen Vermittlung von Datenpraktiken. Um diese Lücke zu schließen, wird in dieser Arbeit das Konzept der Erklärbarkeit als entscheidender Qualitätsaspekt zur Verbesserung der Kommunikation zwischen Systemen und Nutzern in Bezug auf Datenpraktiken in einer klaren, verständlichen und nachvollziehbaren Weise untersucht. Zu diesem Zweck wird ein Ansatz vorgeschlagen, der aus drei Theorien besteht, die durch sieben Artefakte gestützt werden, die die Rolle der Erklärbarkeit im Kontext der Privatsphäre skizzieren und Leitlinien für die Kommunikation von Datenschutzinformationen aufstellen. Diese Theorien und Artefakte sollen Software-Experten unterstützen, (a) privatsphärerelevante Aspekte zu identifizieren, (b) diese kontextrelevant und verständlich an den Nutzer zu kommunizieren, um (c) datenschutzfreundliche Systeme zu designen. Um die Wirksamkeit des vorgeschlagenen Ansatzes zu validieren, wurden Evaluierungen durchgeführt, darunter Literaturrecherchen, Workshops und Nutzerstudien. Die Ergebnisse bestätigen die Eignung der entwickelten Theorien und Artefakte und bieten eine vielversprechende Grundlage für die Entwicklung datenschutzfreundlicher, fairer und transparenter Systeme.In the era of ongoing digitalization, where technology increasingly infiltrates our society, fun-damental human values such as ethics, fairness, privacy, and trust have taken center stage. Digital systems have seamlessly penetrated both personal and professional spheres, offering users swift connectivity, information access, and assistance in their daily routines. In exchange, users willingly share copious amounts of personal data with these systems. However, this data collection means that that users’ privacy sphere is increasingly at stake. Therefore, educating users about the information being collected and its subsequent processing is key to protect users’ privacy sphere. Legislation has established privacy policies as a means of communicating data practices. Unfortunately, these documents often prove fruitless for end users due to their extensive, va-gue, and jargon-laden nature, replete with legal terminology that often requires a deeper level of specialized knowledge. The result is a lack of user-centric solutions to communicate privacy information transparently and understandably. To bridge this gap, this thesis explores the concept of explainability as a crucial quality aspect for improving communication between systems and users concerning data practices, in a clear, understandable, and comprehensible manner. To this end, this thesis proposes an approach consisting of three theories supported by seven artifacts that outline the role of explainability in the context of privacy and provide guidelines for communicating privacy information. These theories and artifacts are intended to help software professionals (a) to identify privacy-relevant aspects, (b) to communicate them to users in a contextually relevant and understandable way, and (c) to design privacy-aware systems. To validate the efÏcacy of the proposed approach, evaluations were conducted, including literature reviews, workshops, and user studies. The results endorse the suitability of the de-veloped theories and artifacts, offering a promising foundation for developing privacy-aware, fair, and transparent systems

Machine learning techniques for music information retrieval

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015The advent of digital music has changed the rules of music consumption, distribution and sales. With it has emerged the need to effectively search and manage vast music collections. Music information retrieval is an interdisciplinary field of research that focuses on the development of new techniques with that aim in mind. This dissertation addresses a specific aspect of this field: methods that automatically extract musical information exclusively based on the audio signal. We propose a method for automatic music-based classification, label inference, and music similarity estimation. Our method consist in representing the audio with a finite set of symbols and then modeling the symbols time evolution. The symbols are obtained via vector quantization in which a single codebook is used to quantize the audio descriptors. The symbols time evolution is modeled via a first order Markov process. Based on systematic evaluations we carried out on publicly available sets, we show that our method achieves performances on par with most techniques found in literature. We also present and discuss the problems that appear when computers try to classify or annotate songs using the audio as the only source of information. In our method, the separation of quantization process from the creation and training of classification models helped us in that analysis. It enabled us to examine how instantaneous sound attributes (henceforth features) are distributed in term of musical genre, and how designing codebooks specially tailored for these distributions affects the performance of ours and other classification systems commonly used for this task. On this issue, we show that there is no apparent benefit in seeking a thorough representation of the feature space. This is a bit unexpected since it goes against the assumption that features carry equally relevant information loads and somehow capture the specificities of musical facets, implicit in many genre recognition methods. Label inference is the task of automatically annotating songs with semantic words - this tasks is also known as autotagging. In this context, we illustrate the importance of a number of issues, that in our perspective, are often overlooked. We show that current techniques are fragile in the sense that small alterations in the set of labels may lead to dramatically different results. Furthermore, through a series of experiments, we show that autotagging systems fail to learn tag models capable to generalize to datasets of different origins. We also show that the performance achieved with these techniques is not sufficient to be able to take advantage of the correlations between tags.Fundação para a Ciência e a Tecnologia (FCT