MC2: MPEG-7 content modelling communities

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe use of multimedia content on the web has grown significantly in recent years. Websites such as Facebook, YouTube and Flickr cater for enormous amounts of multimedia content uploaded by users. This vast amount of multimedia content requires comprehensive content modelling otherwise retrieving relevant content will be challenging. Modelling multimedia content can be an extremely time consuming task that may seem impossible particularly when undertaken by individual users. However, the advent of Web 2.0 and associated communities, such as YouTube and Flickr, has shown that users appear to be more willing to collaborate in order to take on enormous tasks such as multimedia content modelling. Harnessing the power of communities to achieve comprehensive content modelling is the primary focus of this research. The aim of this thesis is to explore collaborative multimedia content modelling and in particular the effectiveness of existing multimedia content modelling tools, taking into account the key development challenges of existing collaborative content modelling research and the associated modelling tools. Four research objectives are pursued in order to achieve this; first, design a user experiment to study users’ tagging behaviour with existing multimedia tagging tools and identify any relationships between such user behaviour; second, design and develop a framework for MPEG-7 content modelling communities based on the results of the experiment; third, implement an online service as a proof of concept of the framework; fourth, validate the framework through the online service during a repeat of the initial user experiment. This research contributes first, a conceptual model of user behaviour visualised as a fuzzy cognitive map and, second, an MPEG-7 framework for multimedia content modelling communities (MC2) and its proof of concept as an online service. The fuzzy cognitive model embodies relationships between user tagging behaviour and context and provides an understanding of user priorities in the description of content features and the relationships that exist between them. The MC2 framework, developed based on the fuzzy cognitive model, is deep-rooted in user content modelling behaviour and content preferences. A proof of concept of the MC2 framework is implemented as an online service in which all metadata is modelled using MPEG-7. The online service is validated, first, empirically with the same group of users and through the same experiment that led to the development of the fuzzy cognitive model and, second, functionally against the folksonomy and MPEG-7 content modelling tools used in the initial experiment. The validation demonstrates that MC2 has the advantages without the shortcomings of existing multimedia tagging tools by harnessing the ease of use of folksonomy tools while producing comprehensive structured metadata.Supported by UK Engineering and Physical Sciences Research Council (EPSRC

“I thought you were okay”: Participatory Design with Young Adults to Fight Multiparty Privacy Conflicts in Online Social Networks

International audienceWhile sharing multimedia content on Online Social Networks (OSNs) has many benefits, exposing other people without obtaining permission could cause Multiparty Privacy Conflicts (MPCs). Earlier studies developed technical solutions and dissuasive approaches to address MPCs. However, none of these studies involved OSN users who have experienced MPCs, in the design process, possibly overlooking the valuable experiences these individuals might have accrued. To fill this gap, we recruited participants specifically from this population of users, and involved them in participatory design sessions aiming at ideating solutions to reduce the incidence of MPCs. To frame the activities of our participants, we borrowed terminology and concepts from a well known framework used in the justice systems. Over the course of several design sessions, our participants designed 10 solutions to mitigate MPCs. The designed solutions leverage different mechanisms, including preventing MPCs from happening, dissuading users from sharing, mending the harm, and educating users about the community standards. We discuss the open design and research opportunities suggested by the designed solutions and we contribute an ideal workflow that synthesizes the best of each solution. This study contributes to the innovation of privacy-enhancing technologies to limit the incidences of MPCs in OSNs

Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications throughout the world, the amount of data stored on the Web has been growing exponentially. In this new digital era, a large number of companies have emerged with the purpose of ltering the information available on the web and provide users with interesting items. The algorithms and models used to recommend these items are called Recommender Systems. These systems are applied to a large number of domains, from music, books, or movies to dating or Point-of-Interest (POI), which is an increasingly popular domain where users receive recommendations of di erent places when they arrive to a city. In this thesis, we focus on exploiting the use of contextual information, especially temporal and sequential data, and apply it in novel ways in both traditional and Point-of-Interest recommendation. We believe that this type of information can be used not only for creating new recommendation models but also for developing new metrics for analyzing the quality of these recommendations. In one of our rst contributions we propose di erent metrics, some of them derived from previously existing frameworks, using this contextual information. Besides, we also propose an intuitive algorithm that is able to provide recommendations to a target user by exploiting the last common interactions with other similar users of the system. At the same time, we conduct a comprehensive review of the algorithms that have been proposed in the area of POI recommendation between 2011 and 2019, identifying the common characteristics and methodologies used. Once this classi cation of the algorithms proposed to date is completed, we design a mechanism to recommend complete routes (not only independent POIs) to users, making use of reranking techniques. In addition, due to the great di culty of making recommendations in the POI domain, we propose the use of data aggregation techniques to use information from di erent cities to generate POI recommendations in a given target city. In the experimental work we present our approaches on di erent datasets belonging to both classical and POI recommendation. The results obtained in these experiments con rm the usefulness of our recommendation proposals, in terms of ranking accuracy and other dimensions like novelty, diversity, and coverage, and the appropriateness of our metrics for analyzing temporal information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones en todo el mundo, la cantidad de datos almacenados en la red ha crecido exponencialmente. En esta nueva era digital, han surgido un gran n umero de empresas con el objetivo de ltrar la informaci on disponible en la red y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos utilizados para recomendar estos art culos reciben el nombre de Sistemas de Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios, desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs, en ingl es), un dominio cada vez m as popular en el que los usuarios reciben recomendaciones de diferentes lugares cuando llegan a una ciudad. En esta tesis, nos centramos en explotar el uso de la informaci on contextual, especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa tanto en la recomendaci on cl asica como en la recomendaci on de POIs. Creemos que este tipo de informaci on puede utilizarse no s olo para crear nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas m etricas para analizar la calidad de estas recomendaciones. En una de nuestras primeras contribuciones proponemos diferentes m etricas, algunas derivadas de formulaciones previamente existentes, utilizando esta informaci on contextual. Adem as, proponemos un algoritmo intuitivo que es capaz de proporcionar recomendaciones a un usuario objetivo explotando las ultimas interacciones comunes con otros usuarios similares del sistema. Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y 2019, identi cando las caracter sticas comunes y las metodolog as utilizadas. Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking. Adem as, debido a la gran di cultad de realizar recomendaciones en el ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos para utilizar la informaci on de diferentes ciudades y generar recomendaciones de POIs en una determinada ciudad objetivo. En el trabajo experimental presentamos nuestros m etodos en diferentes conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los resultados obtenidos en estos experimentos con rman la utilidad de nuestras propuestas de recomendaci on en t erminos de precisi on de ranking y de otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de apropiadas son nuestras m etricas para analizar la informaci on temporal y los sesgos en las recomendaciones producida

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

Abstraction, Visualization, and Evolution of Process Models

The increasing adoption of process orientation in companies and organizations has resulted in large process model collections. Each process model of such a collection may comprise dozens or hundreds of elements and captures various perspectives of a business process, i.e., organizational, functional, control, resource, or data perspective. Domain experts having only limited process modeling knowledge, however, hardly comprehend such large and complex process models. Therefore, they demand for a customized (i.e., personalized) view on business processes enabling them to optimize and evolve process models effectively. This thesis contributes the proView framework to systematically create and update process views (i.e., abstractions) on process models and business processes respectively. More precisely, process views abstract large process models by hiding or combining process information. As a result, they provide an abstracted, but personalized representation of process information to domain experts. In particular, updates of a process view are supported, which are then propagated to the related process model as well as associated process views. Thereby, up-to-dateness and consistency of all process views defined on any process model can be always ensured. Finally, proView preserves the behaviour and correctness of a process model. Process abstractions realized by views are still not sufficient to assist domain experts in comprehending and evolving process models. Thus, additional process visualizations are introduced that provide text-based, form-based, and hierarchical representations of process models. Particularly, these process visualizations allow for view-based process abstractions and updates as well. Finally, process interaction concepts are introduced enabling domain experts to create and evolve process models on touch-enabled devices. This facilitates the documentation of process models in workshops or while interviewing process participants at their workplace. Altogether, proView enables domain experts to interact with large and complex process models as well as to evolve them over time, based on process model abstractions, additional process visualizations, and process interaction concepts. The framework is implemented in a proof-ofconcept prototype and validated through experiments and case studies

Recommending access control decisions to social media users

Social media has become an integral part of the Internet and has revolutionized interpersonal communication. The lines of separation between content creators and content consumers have blurred as normal users have platforms such as social media sites, blogs and microblogs at their disposal on which they can create and consume content as well as have the opportunity to interact with other users. This change has also led to several well documented privacy problems for the users. The privacy problems faced by social media users can be categorized into institutional privacy (related to the social network provider) and social privacy (related to the interpersonal communication between social media users) problems. The work presented in this thesis focuses on the social privacy issues that affect users on social media due to their interactions with members in their network who may represent various facets of their lives (such as work, family, school, etc.). In such a scenario, it is imperative for them to be able to appropriately control access to their information such that it reaches the appropriate audience. For example, a person may not want to share the same piece of information with their boss at work and their family members. These boundaries are defined by the nature of relationships people share with each other and are enforced by controlling access during communication. In real life, people are accustomed to do this but it becomes a greater challenge while interacting online. The primary contribution of the work presented in this thesis is to design an access control recommendation mechanism for social media users which would ease the burden on the user while sharing information with their contacts on the social network. The recommendation mechanism presented in this thesis, REACT (REcommending Access Control decisions To social media users), leverages information defining interpersonal relationships between social media users in conjunction with information about the content in order to appropriately represent the context of information disclosure. Prior research has pointed towards ways in which to employ information residing in the social network to represent social relationships between individuals. REACT relies on extensive empirical evaluation of such information in order to identify the most suitable types of information which can be used to predict access control decisions made by social media users. In particular, the work in this thesis advances the state of art in the following ways: (i) An empirical study to identify the most appropriate network based community detection algorithm to represent the type of interpersonal relationships in the resulting access control recommendation mechanism. This empirical study examines a goodness of fit of the communities produced by 8 popular network based community detection algorithms with the access control decisions made by social media users. (ii) Systematic feature engineering to derive the most appropriate profile attribute to represent the strength or closeness between social media users. The relationship strength is an essential indicator of access control preferences and the endeavor is to identify the minimal subset of attributes which can accurately represent this in the resulting access control recommendation mechanism. (iii) The suitable representation of interpersonal relationships in conjunction with information about the content that result in the design of an access control recommendation mechanism, REACT, which considers the overall context of information disclosure and is shown to produce highly accurate recommendations