Towards simulating the emergence of environmentally responsible behavior among natural resource users : an integration of complex systems theory, machine learning and geographic information science

La gouvernance pour le développement durable comporte de nombreux défis. L'un de ces défis consiste à mieux comprendre les systèmes socio-écologiques gouvernés. Dans de tels systèmes, l'apprentissage par essais et erreurs implique le risque de conséquences inattendues, irréversibles et néfastes. De plus, en raison de la complexité des systèmes socio-écologiques, les leçons tirées d'expériences à petite échelle ne peuvent pas toujours être applicables à des problèmes à grande échelle. Un autre aspect difficile des problèmes de développement durable est que ces problèmes sont souvent multidisciplinaires et composés de composants qui sont chacun étudiés individuellement dans une discipline différente, mais il existe peu d'informations sur leur comportement ensemble. Un troisième défi de la gouvernance pour le développement durable est qu'il est souvent nécessaire d'impliquer les parties prenantes dans des actions de gestion et des mesures d'intervention coûteuses pour les individus qui y participent. De plus, dans de nombreuses situations de ce type, les incitations financières et l'application des réglementations se soldent par un échec et ne constituent donc pas des options de gouvernance. Dans cette thèse, les défis ci-dessus sont abordés dans un exemple de contrôle des perturbations forestières avec une approche intégrée. Pour éviter le problème des effets indésirables irréversibles et pour permettre des expériences répétées, une approche de simulation est utilisée. Pour relever le défi de la multidisciplinarité des problèmes des systèmes socio-écologiques, deux modèles sont développés indépendamment - portant sur les aspects sociaux et écologiques du système de l'étude - et ils sont ensuite couplés de telle sorte que la sortie de chaque modèle est utilisée comme entrée pour l'autre modèle. Pour résoudre le problème de l'engagement des parties prenantes, un plan est proposé pour la promotion d'un comportement respectueux de l'environnement. Ce plan est basé sur l'offre de reconnaissance à ceux qui adoptent volontairement le comportement responsable. Le modèle écologique de cette étude, qui simule la propagation d'une perturbation forestière, est construit à l'aide de l’apprentissage automatique supervisé. Le modèle social de cette étude, qui simule l'émergence d'une nouvelle norme de comportement, est construit à l'aide de l'apprentissage par renforcement. Les deux modèles sont testés et validés avant couplage. Le modèle couplé est ensuite utilisé comme un laboratoire virtuel, où plusieurs expériences sont réalisées dans un cadre hypothétique et selon différents scénarios. Chacune de ces expériences est une simulation. A travers ces simulations, cette étude montre qu'avec un algorithme de prise de décision approprié et avec suffisamment de temps pour l'interaction entre une entité gouvernante et la société, il est possible de créer une motivation pour un comportement responsable dans la société. En d'autres termes, il est possible d'encourager la participation volontaire des acteurs à l'action pour le développement durable, sans que l'entité gouvernante ait besoin d'utiliser des incitations financières ou d'imposer son autorité. Ces résultats peuvent être applicables à d'autres contextes où un comportement responsable des individus ou des entreprises est recherché afin d'atténuer l'impact d'une perturbation, de protéger une ressource écologique, ou de faciliter une transition sectorielle vers la durabilité.Governance for sustainable development involves many challenges. One of those challenges is to gain insight about the social-ecological systems being governned. In such systems, learning by trial and error involve the risk of unexpected, irreversible and adverse consequences. Moreover, due to complexity of social-ecological systems, lessons learned from small scale experiments may not be applicable in large-scale problems. Another challenging aspect of problems of sustainable development is that these problems are often multidisciplinary and comprised of components that are each studied individually in a different discipline, but little information exists about their behavior together as a whole. A third challenge in governance for sustainable development is that often it is necessary to involve stakeholders in management actions and intervention measures that are costly for individuals who participate in them. Moreover, in many of these situations financial incentives or enforcement of regulations result in failure, and are thus not options for governance. In this thesis, the above challenges are addressed in an example case of forest disturbance control with an integrated approach. To avoid the problem of irreversible adverse effects and to allow repeated experiments, a simulation approach is used. To tackle the challenge of multidisciplinarity of problems of social-ecological systems, two models are independently developed – pertaining to social and ecological aspects of the system of the study – and they are subsequently coupled in such a way that the output of each model served as an input for the other. To address the problem of engagement of stakeholders, a scheme is proposed for promotion of environmentally responsible behavior. This scheme is based on offering recognition to those who voluntarily perform the responsible behavior. The ecological model of this study, which simulates the spread of a forest disturbance, is built using Supervised Machine Learning. The social model of this study, which simulates the emergence of a new norm of behavior, is built using Reinforcement Learning. Both models are tested and validated before coupling. The coupled model is then used as a virtual laboratory, where several experiments are performed in a hypothetical setting and under various scenarios. Each such experiment is a simulation. Through these simulations, this study shows that with an appropriate decision-making algorithm and with sufficient time for interaction between a governing entity and the society, it is possible to create motivation for responsible behavior in the society. In other words, it is possible to encourage voluntary participation of stakeholders in action for sustainable development, without the need for the governing entity to use financial incentives or impose its authority. These results may be applicable to other contexts where responsible behavior by individuals or enterprises is sought in order to mitigate the impact of a disturbance, protect an ecological resource, or facilitate a sectoral transition towards sustainability

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Novel approaches to anonymity and privacy in decentralized, open settings

The Internet has undergone dramatic changes in the last two decades, evolving from a mere communication network to a global multimedia platform in which billions of users actively exchange information. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy that existing technology is failing to keep pace with. In this dissertation, we present the results of two lines of research that developed two novel approaches to anonymity and privacy in decentralized, open settings. First, we examine the issue of attribute and identity disclosure in open settings and develop the novel notion of (k,d)-anonymity for open settings that we extensively study and validate experimentally. Furthermore, we investigate the relationship between anonymity and linkability using the notion of (k,d)-anonymity and show that, in contrast to the traditional closed setting, anonymity within one online community does necessarily imply unlinkability across different online communities in the decentralized, open setting. Secondly, we consider the transitive diffusion of information that is shared in social networks and spread through pairwise interactions of user connected in this social network. We develop the novel approach of exposure minimization to control the diffusion of information within an open network, allowing the owner to minimize its exposure by suitably choosing who they share their information with. We implement our algorithms and investigate the practical limitations of user side exposure minimization in large social networks. At their core, both of these approaches present a departure from the provable privacy guarantees that we can achieve in closed settings and a step towards sound assessments of privacy risks in decentralized, open settings.Das Internet hat in den letzten zwei Jahrzehnten eine drastische Transformation erlebt und entwickelte sich dabei von einem einfachen Kommunikationsnetzwerk zu einer globalen Multimedia Plattform auf der Milliarden von Nutzern aktiv Informationen austauschen. Diese Transformation hat zwar einen gewaltigen Nutzen und vielfältige Vorteile für die Gesellschaft mit sich gebracht, hat aber gleichzeitig auch neue Herausforderungen und Gefahren für online Privacy mit sich gebracht mit der die aktuelle Technologie nicht mithalten kann. In dieser Dissertation präsentieren wir zwei neue Ansätze für Anonymität und Privacy in dezentralisierten und offenen Systemen. Mit unserem ersten Ansatz untersuchen wir das Problem der Attribut- und Identitätspreisgabe in offenen Netzwerken und entwickeln hierzu den Begriff der (k, d)-Anonymität für offene Systeme welchen wir extensiv analysieren und anschließend experimentell validieren. Zusätzlich untersuchen wir die Beziehung zwischen Anonymität und Unlinkability in offenen Systemen mithilfe des Begriff der (k, d)-Anonymität und zeigen, dass, im Gegensatz zu traditionell betrachteten, abgeschlossenen Systeme, Anonymität innerhalb einer Online Community nicht zwingend die Unlinkability zwischen verschiedenen Online Communitys impliziert. Mit unserem zweiten Ansatz untersuchen wir die transitive Diffusion von Information die in Sozialen Netzwerken geteilt wird und sich dann durch die paarweisen Interaktionen von Nutzern durch eben dieses Netzwerk ausbreitet. Wir entwickeln eine neue Methode zur Kontrolle der Ausbreitung dieser Information durch die Minimierung ihrer Exposure, was dem Besitzer dieser Information erlaubt zu kontrollieren wie weit sich deren Information ausbreitet indem diese initial mit einer sorgfältig gewählten Menge von Nutzern geteilt wird. Wir implementieren die hierzu entwickelten Algorithmen und untersuchen die praktischen Grenzen der Exposure Minimierung, wenn sie von Nutzerseite für große Netzwerke ausgeführt werden soll. Beide hier vorgestellten Ansätze verbindet eine Neuausrichtung der Aussagen die diese bezüglich Privacy treffen: wir bewegen uns weg von beweisbaren Privacy Garantien für abgeschlossene Systeme, und machen einen Schritt zu robusten Privacy Risikoeinschätzungen für dezentralisierte, offene Systeme in denen solche beweisbaren Garantien nicht möglich sind

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Recommending Tags for Images: Deep Learning Approaches for Personalized Tag Recommendation

Social media has become an integral part of numerous individuals as well as organizations, with many services being used frequently by a majority of people. Along with its widespread use, the amount of information explodes when people use these services. This demands for efficient tools as well as methods to assist data management and retrieval. Annotating resources by keywords, known as the tagging task, is a solution to improve categorizability and findability of resources. However, tagging is a human, time-consuming task, which requires the user's focus to figure out many keywords in a short moment and manually enter them into the system. To encourage users to tag their resources more correctly and frequently, tag recommendation is adopted into the social tagging systems to suggest relevant keywords for resources. In this thesis, we will address the problem of personalized tag recommendation for images and present ways to solve this problem by combining the advantages of the user relation with the images' content. In order to suggest tags for unobserved images, their visual contents are used to replace the index-based information of the image entity in the tagging relations. Because the limitation of low-level features does not show the "content" of images, we propose to utilize a deep learning based approach to learn high-level visual features concurrently with the scoring-tag estimator. For the tag predictor, a latent factor model or a multi-layer perceptron is selected to compute scores of tags by which the top selected tags are sorted in descending order. As a further development upon our findings, we examine the inside and outside context of images to enhance the accuracy of estimators. Regarding the image-inside context, we are motivated by the fact that objects, such as cars or cats are influential on the user's selection criteria. Regarding the image-outside context, the image's surrounding text contributes to the clarity of the image's content for different users. We consider these contextual features as a supporting part which is combined with the mainly visual representation to enhance the tag recommendation performance. Finally, as an additional technique, transfer learning is also adapted to support the proposed models to overcome the limitations of too small training data and boost up their performance. This thesis demonstrates the usefulness and versatility of deep learning approaches for tag recommendation and highlights the importance of the learned image's content in predicting personalized tags. Directions for future work include semantic enhancements to context-based representation and extensions of the content-aware approaches to different recommendation scenarios