Enhancing trustability in MMOGs environments

Massively Multiplayer Online Games (MMOGs; e.g., World of Warcraft), virtual worlds (VW; e.g., Second Life), social networks (e.g., Facebook) strongly demand for more autonomic, security, and trust mechanisms in a way similar to humans do in the real life world. As known, this is a difficult matter because trusting in humans and organizations depends on the perception and experience of each individual, which is difficult to quantify or measure. In fact, these societal environments lack trust mechanisms similar to those involved in humans-to-human interactions. Besides, interactions mediated by compute devices are constantly evolving, requiring trust mechanisms that keep the pace with the developments and assess risk situations. In VW/MMOGs, it is widely recognized that users develop trust relationships from their in-world interactions with others. However, these trust relationships end up not being represented in the data structures (or databases) of such virtual worlds, though they sometimes appear associated to reputation and recommendation systems. In addition, as far as we know, the user is not provided with a personal trust tool to sustain his/her decision making while he/she interacts with other users in the virtual or game world. In order to solve this problem, as well as those mentioned above, we propose herein a formal representation of these personal trust relationships, which are based on avataravatar interactions. The leading idea is to provide each avatar-impersonated player with a personal trust tool that follows a distributed trust model, i.e., the trust data is distributed over the societal network of a given VW/MMOG. Representing, manipulating, and inferring trust from the user/player point of view certainly is a grand challenge. When someone meets an unknown individual, the question is “Can I trust him/her or not?”. It is clear that this requires the user to have access to a representation of trust about others, but, unless we are using an open source VW/MMOG, it is difficult —not to say unfeasible— to get access to such data. Even, in an open source system, a number of users may refuse to pass information about its friends, acquaintances, or others. Putting together its own data and gathered data obtained from others, the avatar-impersonated player should be able to come across a trust result about its current trustee. For the trust assessment method used in this thesis, we use subjective logic operators and graph search algorithms to undertake such trust inference about the trustee. The proposed trust inference system has been validated using a number of OpenSimulator (opensimulator.org) scenarios, which showed an accuracy increase in evaluating trustability of avatars. Summing up, our proposal aims thus to introduce a trust theory for virtual worlds, its trust assessment metrics (e.g., subjective logic) and trust discovery methods (e.g., graph search methods), on an individual basis, rather than based on usual centralized reputation systems. In particular, and unlike other trust discovery methods, our methods run at interactive rates.MMOGs (Massively Multiplayer Online Games, como por exemplo, World of Warcraft), mundos virtuais (VW, como por exemplo, o Second Life) e redes sociais (como por exemplo, Facebook) necessitam de mecanismos de confiança mais autónomos, capazes de assegurar a segurança e a confiança de uma forma semelhante à que os seres humanos utilizam na vida real. Como se sabe, esta não é uma questão fácil. Porque confiar em seres humanos e ou organizações depende da percepção e da experiência de cada indivíduo, o que é difícil de quantificar ou medir à partida. Na verdade, esses ambientes sociais carecem dos mecanismos de confiança presentes em interacções humanas presenciais. Além disso, as interacções mediadas por dispositivos computacionais estão em constante evolução, necessitando de mecanismos de confiança adequados ao ritmo da evolução para avaliar situações de risco. Em VW/MMOGs, é amplamente reconhecido que os utilizadores desenvolvem relações de confiança a partir das suas interacções no mundo com outros. No entanto, essas relações de confiança acabam por não ser representadas nas estruturas de dados (ou bases de dados) do VW/MMOG específico, embora às vezes apareçam associados à reputação e a sistemas de reputação. Além disso, tanto quanto sabemos, ao utilizador não lhe é facultado nenhum mecanismo que suporte uma ferramenta de confiança individual para sustentar o seu processo de tomada de decisão, enquanto ele interage com outros utilizadores no mundo virtual ou jogo. A fim de resolver este problema, bem como os mencionados acima, propomos nesta tese uma representação formal para essas relações de confiança pessoal, baseada em interacções avatar-avatar. A ideia principal é fornecer a cada jogador representado por um avatar uma ferramenta de confiança pessoal que segue um modelo de confiança distribuída, ou seja, os dados de confiança são distribuídos através da rede social de um determinado VW/MMOG. Representar, manipular e inferir a confiança do ponto de utilizador/jogador, é certamente um grande desafio. Quando alguém encontra um indivíduo desconhecido, a pergunta é “Posso confiar ou não nele?”. É claro que isto requer que o utilizador tenha acesso a uma representação de confiança sobre os outros, mas, a menos que possamos usar uma plataforma VW/MMOG de código aberto, é difícil — para não dizer impossível — obter acesso aos dados gerados pelos utilizadores. Mesmo em sistemas de código aberto, um número de utilizadores pode recusar partilhar informações sobre seus amigos, conhecidos, ou sobre outros. Ao juntar seus próprios dados com os dados obtidos de outros, o utilizador/jogador representado por um avatar deve ser capaz de produzir uma avaliação de confiança sobre o utilizador/jogador com o qual se encontra a interagir. Relativamente ao método de avaliação de confiança empregue nesta tese, utilizamos lógica subjectiva para a representação da confiança, e também operadores lógicos da lógica subjectiva juntamente com algoritmos de procura em grafos para empreender o processo de inferência da confiança relativamente a outro utilizador. O sistema de inferência de confiança proposto foi validado através de um número de cenários Open-Simulator (opensimulator.org), que mostrou um aumento na precisão na avaliação da confiança de avatares. Resumindo, a nossa proposta visa, assim, introduzir uma teoria de confiança para mundos virtuais, conjuntamente com métricas de avaliação de confiança (por exemplo, a lógica subjectiva) e em métodos de procura de caminhos de confiança (com por exemplo, através de métodos de pesquisa em grafos), partindo de uma base individual, em vez de se basear em sistemas habituais de reputação centralizados. Em particular, e ao contrário de outros métodos de determinação do grau de confiança, os nossos métodos são executados em tempo real

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Measuring Green Space Efficacy in Hangzhou, China

Urban green spaces are often claimed to mitigate environmental health issues associated with urban development, although empirical evidence is lacking. This Interactive Qualifying Project analyzed the efficacy of parks in reducing pollution concentrations and increasing the well- being of Hangzhou’s residents. By measuring particulate matter and canopy coverage, surveying park-goers, and conducting ArcGIS spatial analysis, we conclude that parks are effective at improving quality of life but are not effective at reducing air pollution, demonstrating that other variables mediate pollution concentrations

Synthesizing stream control

For the management of reactive systems, controllers must coordinate time, data streams, and data transformations, all joint by the high level perspective of their control flow. This control flow is required to drive the system correctly and continuously, which turns the development into a challenge. The process is error-prone, time consuming, unintuitive, and costly. An attractive alternative is to synthesize the system instead, where the developer only needs to specify the desired behavior. The synthesis engine then automatically takes care of all the technical details. However, while current algorithms for the synthesis of reactive systems are well-suited to handle control, they fail on complex data transformations due to the complexity of the comparably large data space. Thus, to overcome the challenge of explicitly handling the data we must separate data and control. We introduce Temporal Stream Logic (TSL), a logic which exclusively argues about the control of the controller, while treating data and functional transformations as interchangeable black-boxes. In TSL it is possible to specify control flow properties independently of the complexity of the handled data. Furthermore, with TSL at hand a synthesis engine can check for realizability, even without a concrete implementation of the data transformations. We present a modular development framework that first uses synthesis to identify the high level control flow of a program. If successful, the created control flow then is extended with concrete data transformations in order to be compiled into a final executable. Our results also show that the current synthesis approaches cannot replace existing manual development work flows immediately. During the development of a reactive system, the developer still may use incomplete or faulty specifications at first, that need the be refined after a subsequent inspection. In the worst case, constraints are contradictory or miss important assumptions, which leads to unrealizable specifications. In both scenarios, the developer needs additional feedback from the synthesis engine to debug errors for finally improving the system specification. To this end, we explore two further possible improvements. On the one hand, we consider output sensitive synthesis metrics, which allow to synthesize simple and well structured solutions that help the developer to understand and verify the underlying behavior quickly. On the other hand, we consider the extension of delay, whose requirement is a frequent reason for unrealizability. With both methods at hand, we resolve the aforementioned problems and therefore help the developer in the development phase with the effective creation of a safe and correct reactive system.Um reaktive Systeme zu regeln müssen Steuergeräte Zeit, Datenströme und Datentransformationen koordinieren, die durch den übergeordneten Kontrollfluss zusammengefasst werden. Die Aufgabe des Kontrollflusses ist es das System korrekt und dauerhaft zu betreiben. Die Entwicklung solcher Systeme wird dadurch zu einer Herausforderung, denn der Prozess ist fehleranfällig, zeitraubend, unintuitiv und kostspielig. Eine attraktive Alternative ist es stattdessen das System zu synthetisieren, wobei der Entwickler nur das gewünschte Verhalten des Systems festlegt. Der Syntheseapparat kümmert sich dann automatisch um alle technischen Details. Während aktuelle Algorithmen für die Synthese von reaktiven Systemen erfolgreich mit dem Kontrollanteil umgehen können, versagen sie jedoch, sobald komplexe Datentransformationen hinzukommen, aufgrund der Komplexität des vergleichsweise großen Datenraums. Daten und Kontrolle müssen demnach getrennt behandelt werden, um auch große Datenräumen effizient handhaben zu können. Wir präsentieren Temporal Stream Logic (TSL), eine Logik die ausschließlich die Kontrolle einer Steuerung betrachtet, wohingegen Daten und funktionale Datentransformationen als austauschbare Blackboxen gehandhabt werden. In TSL ist es möglich Kontrollflusseigenschaften unabhängig von der Komplexität der zugrunde liegenden Daten zu beschreiben. Des Weiteren kann ein auf TSL beruhender Syntheseapparat die Realisierbarkeit einer Spezifikation prüfen, selbst ohne die konkreten Implementierungen der Datentransformationen zu kennen. Wir präsentieren ein modulares Grundgerüst für die Entwicklung. Es verwendet zunächst den Syntheseapparat um den übergeordneten Kontrollfluss zu erzeugen. Ist dies erfolgreich, so wird der resultierende Kontrollfluss um die konkreten Implementierungen der Datentransformationen erweitert und anschließend zu einer ausführbare Anwendung kompiliert. Wir zeigen auch auf, dass bisherige Syntheseverfahren bereits existierende manuelle Entwicklungsprozesse noch nicht instantan ersetzen können. Im Verlauf der Entwicklung ist es auch weiterhin möglich, dass der Entwickler zunächst unvollständige oder fehlerhafte Spezifikationen erstellt, welche dann erst nach genauerer Betrachtung des synthetisierten Systems weiter verbessert werden können. Im schlimmsten Fall sind Anforderungen inkonsistent oder wichtige Annahmen über das Verhalten fehlen, was zu unrealisierbaren Spezifikationen führt. In beiden Fällen benötigt der Entwickler zusätzliche Rückmeldungen vom Syntheseapparat, um Fehler zu identifizieren und die Spezifikation schlussendlich zu verbessern. In diesem Zusammenhang untersuchen wir zwei mögliche Erweiterungen. Zum einen betrachten wir ausgabeabhängige Metriken, die es dem Entwickler erlauben einfache und wohlstrukturierte Lösungen zu synthetisieren die verständlich sind und deren Verhalten einfach zu verifizieren ist. Zum anderen betrachten wir die Erweiterung um Verzögerungen, welche eine der Hauptursachen für Unrealisierbarkeit darstellen. Mit beiden Methoden beheben wir die jeweils zuvor genannten Probleme und helfen damit dem Entwickler während der Entwicklungsphase auch wirklich das reaktive System zu kreieren, dass er sich auch tatsächlich vorstellt

Cognitive Models and Computational Approaches for improving Situation Awareness Systems

2016 - 2017The world of Internet of Things is pervaded by complex environments with smart services available every time and everywhere. In such a context, a serious open issue is the capability of information systems to support adaptive and collaborative decision processes in perceiving and elaborating huge amounts of data. This requires the design and realization of novel socio-technical systems based on the “human-in-the-loop” paradigm. The presence of both humans and software in such systems demands for adequate levels of Situation Awareness (SA). To achieve and maintain proper levels of SA is a daunting task due to the intrinsic technical characteristics of systems and the limitations of human cognitive mechanisms. In the scientific literature, such issues hindering the SA formation process are defined as SA demons. The objective of this research is to contribute to the resolution of the SA demons by means of the identification of information processing paradigms for an original support to the SA and the definition of new theoretical and practical approaches based on cognitive models and computational techniques. The research work starts with an in-depth analysis and some preliminary verifications of methods, techniques, and systems of SA. A major outcome of this analysis is that there is only a limited use of the Granular Computing paradigm (GrC) in the SA field, despite the fact that SA and GrC share many concepts and principles. The research work continues with the definition of contributions and original results for the resolution of significant SA demons, exploiting some of the approaches identified in the analysis phase (i.e., ontologies, data mining, and GrC). The first contribution addresses the issues related to the bad perception of data by users. We propose a semantic approach for the quality-aware sensor data management which uses a data imputation technique based on association rule mining. The second contribution proposes an original ontological approach to situation management, namely the Adaptive Goal-driven Situation Management. The approach uses the ontological modeling of goals and situations and a mechanism that suggests the most relevant goals to the users at a given moment. Lastly, the adoption of the GrC paradigm allows the definition of a novel model for representing and reasoning on situations based on a set theoretical framework. This model has been instantiated using the rough sets theory. The proposed approaches and models have been implemented in prototypical systems. Their capabilities in improving SA in real applications have been evaluated with typical methodologies used for SA systems. [edited by Author]XXX cicl

Automatic understanding of multimodal content for Web-based learning

Web-based learning has become an integral part of everyday life for all ages and backgrounds. On the one hand, the advantages of this learning type, such as availability, accessibility, flexibility, and cost, are apparent. On the other hand, the oversupply of content can lead to learners struggling to find optimal resources efficiently. The interdisciplinary research field Search as Learning is concerned with the analysis and improvement of Web-based learning processes, both on the learner and the computer science side. So far, automatic approaches that assess and recommend learning resources in Search as Learning (SAL) focus on textual, resource, and behavioral features. However, these approaches commonly ignore multimodal aspects. This work addresses this research gap by proposing several approaches that address the question of how multimodal retrieval methods can help support learning on the Web. First, we evaluate whether textual metadata of the TIB AV-Portal can be exploited and enriched by semantic word embeddings to generate video recommendations and, in addition, a video summarization technique to improve exploratory search. Then we turn to the challenging task of knowledge gain prediction that estimates the potential learning success given a specific learning resource. We used data from two user studies for our approaches. The first one observes the knowledge gain when learning with videos in a Massive Open Online Course (MOOC) setting, while the second one provides an informal Web-based learning setting where the subjects have unrestricted access to the Internet. We then extend the purely textual features to include visual, audio, and cross-modal features for a holistic representation of learning resources. By correlating these features with the achieved knowledge gain, we can estimate the impact of a particular learning resource on learning success. We further investigate the influence of multimodal data on the learning process by examining how the combination of visual and textual content generally conveys information. For this purpose, we draw on work from linguistics and visual communications, which investigated the relationship between image and text by means of different metrics and categorizations for several decades. We concretize these metrics to enable their compatibility for machine learning purposes. This process includes the derivation of semantic image-text classes from these metrics. We evaluate all proposals with comprehensive experiments and discuss their impacts and limitations at the end of the thesis.Web-basiertes Lernen ist ein fester Bestandteil des Alltags aller Alters- und Bevölkerungsschichten geworden. Einerseits liegen die Vorteile dieser Art des Lernens wie Verfügbarkeit, Zugänglichkeit, Flexibilität oder Kosten auf der Hand. Andererseits kann das Überangebot an Inhalten auch dazu führen, dass Lernende nicht in der Lage sind optimale Ressourcen effizient zu finden. Das interdisziplinäre Forschungsfeld Search as Learning beschäftigt sich mit der Analyse und Verbesserung von Web-basierten Lernprozessen. Bisher sind automatische Ansätze bei der Bewertung und Empfehlung von Lernressourcen fokussiert auf monomodale Merkmale, wie Text oder Dokumentstruktur. Die multimodale Betrachtung ist hingegen noch nicht ausreichend erforscht. Daher befasst sich diese Arbeit mit der Frage wie Methoden des Multimedia Retrievals dazu beitragen können das Lernen im Web zu unterstützen. Zunächst wird evaluiert, ob textuelle Metadaten des TIB AV-Portals genutzt werden können um in Verbindung mit semantischen Worteinbettungen einerseits Videoempfehlungen zu generieren und andererseits Visualisierungen zur Inhaltszusammenfassung von Videos abzuleiten. Anschließend wenden wir uns der anspruchsvollen Aufgabe der Vorhersage des Wissenszuwachses zu, die den potenziellen Lernerfolg einer Lernressource schätzt. Wir haben für unsere Ansätze Daten aus zwei Nutzerstudien verwendet. In der ersten wird der Wissenszuwachs beim Lernen mit Videos in einem MOOC-Setting beobachtet, während die zweite eine informelle web-basierte Lernumgebung bietet, in der die Probanden uneingeschränkten Internetzugang haben. Anschließend erweitern wir die rein textuellen Merkmale um visuelle, akustische und cross-modale Merkmale für eine ganzheitliche Darstellung der Lernressourcen. Durch die Korrelation dieser Merkmale mit dem erzielten Wissenszuwachs können wir den Einfluss einer Lernressource auf den Lernerfolg vorhersagen. Weiterhin untersuchen wir wie verschiedene Kombinationen von visuellen und textuellen Inhalten Informationen generell vermitteln. Dazu greifen wir auf Arbeiten aus der Linguistik und der visuellen Kommunikation zurück, die seit mehreren Jahrzehnten die Beziehung zwischen Bild und Text untersucht haben. Wir konkretisieren vorhandene Metriken, um ihre Verwendung für maschinelles Lernen zu ermöglichen. Dieser Prozess beinhaltet die Ableitung semantischer Bild-Text-Klassen. Wir evaluieren alle Ansätze mit umfangreichen Experimenten und diskutieren ihre Auswirkungen und Limitierungen am Ende der Arbeit