Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Understanding personal and contextual factors to increase motivation in gamified systems

Gamification, the use of game elements in non-game contexts, has been shown to help people reaching their goals, affect people's behavior and enhance the users' experience within interactive systems. However, past research has shown that gamification is not always successful. In fact, literature reviews revealed that almost half of the interventions were only partially successful or even unsuccessful. Therefore, understanding the factors that have an influence on psychological measures and behavioral outcomes of gamified systems is much in need. In this thesis, we contribute to this by considering the context in which gamified systems are applied and by understanding personal factors of users interacting with the system. Guided by Self-Determination Theory, a major theory on human motivation, we investigate gamification and its effects on motivation and behavior in behavior change contexts, provide insights on contextual factors, contribute knowledge on the effect of personal factors on both the perception and effectiveness of gamification elements and lay out ways of utilizing this knowledge to implement personalized gamified systems. Our contribution is manifold: We show that gamification affects motivation through need satisfaction and by evoking positive affective experiences, ultimately leading to changes in people's behavior. Moreover, we show that age, the intention to change behavior, and Hexad user types play an important role in explaining interpersonal differences in the perception of gamification elements and that tailoring gamified systems based on these personal factors has beneficial effects on both psychological and behavioral outcomes. Lastly, we show that Hexad user types can be partially predicted by smartphone data and interaction behavior in gamified systems and that they can be assessed in a gameful way, allowing to utilize our findings in gamification practice. Finally, we propose a conceptual framework to increase motivation in gamified systems, which builds upon our findings and outlines the importance of considering both contextual and personal factors. Based on these contributions, this thesis advances the field of gamification by contributing knowledge to the open questions of how and why gamification works and which factors play a role in this regard.Gamification, die Nutzung von Spielelementen in spielfremden Kontexten, kann nachweislich Menschen helfen, ihre Ziele zu erreichen, das Verhalten von Menschen zu beeinflussen und die Erfahrung der User in interaktiven Systemen zu verbessern. Allerdings hat die bisherige Forschung gezeigt, dass Gamification nicht immer erfolgreich ist. So haben Literaturübersichten ergeben, dass fast die Hälfte der Interventionen nur teilweise erfolgreich oder sogar erfolglos waren. Daher besteht ein großer Bedarf, die Faktoren zu verstehen, die einen Einfluss auf psychologische Maße sowie auf das Verhalten von Usern in gamifizierten Systemen haben. In dieser Arbeit tragen wir dazu bei, indem wir den Kontext, in dem gamifizierte Systeme eingesetzt werden, betrachten und persönliche Faktoren von Usern, die mit dem System interagieren, verstehen. Geleitet von der Selbstbestimmungstheorie, einer der wichtigsten Theorien zur menschlichen Motivation, untersuchen wir Gamification und dessen Auswirkungen auf Motivation und Verhalten in Kontexten zur Verhaltensänderung. Wir liefern Erkenntnisse über kontextuelle Faktoren, tragen Wissen über den Einfluss persönlicher Faktoren auf die Wahrnehmung und Effektivität von Gamification-Elementen bei und bieten Möglichkeiten, dieses Wissen für die Implementierung personalisierter gamifizierter Systeme zu nutzen. Unser Beitrag ist mannigfaltig: Wir zeigen, dass Gamification die Motivation durch Bedürfnisbefriedigung und durch das Hervorrufen positiver affektiver Erfahrungen beeinflusst, was letztlich zu Verhaltensänderungen führen kann. Darüber hinaus zeigen wir, dass das Alter, die Absicht, das Verhalten zu ändern, und Hexad-Usertypen eine wichtige Rolle bei der Erklärung von interpersonellen Unterschieden in der Wahrnehmung von Gamification-Elementen spielen. Ebenso zeigen unsere Resultate dass die Anpassung von gamifizierten Systemen auf Basis dieser persönlichen Faktoren positive Auswirkungen auf psychologische und verhaltensbezogene Ergebnisse hat. Letztlich zeigen wir, dass Hexad-Usertypen teilweise durch Smartphone-Daten und Interaktionsverhalten in gamifizierten Systemen vorhergesagt werden können und dass sie auf spielerische Art und Weise erhoben werden können. Dies ermöglicht, unsere Erkenntnisse in der Gamification-Praxis zu nutzen. Auf Basis dieser Ergebnisse schlagen wir ein konzeptuelles Framework zur Steigerung der Motivation in gamifizierten Systemen vor, das die Wichtigkeit der Berücksichtigung sowohl kontextueller als auch persönlicher Faktoren hervorhebt. Diese Erkenntnisse bereichern das Forschungsfeld Gamification, indem sie Wissen zu den offenen Fragen, wie und warum Gamification funktioniert und welche Faktoren in diesem Zusammenhang eine Rolle spielen, beitragen

Virtual reality, gamification, and mobile multimedia for cystic fibrosis education and management

Cystic Fibrosis (CF) is a rare condition and is the most common life-limiting genetic disease affecting Caucasians; and Ireland has the highest occurrence of this condition in the world. As CF is a rare disease, common conditions and diseases such as asthma, diabetes, and heart disease are given precedence by researchers and practitioners who design and implement multimedia solutions. As life expectancy for CF patients is expected to rise, it is postulated that the development of multimedia interventions may aid CF adults in the management of their disease. This research, therefore, aims to investigate if and how the use of multimedia can be of benefit to Cystic Fibrosis knowledge and education. To achieve this, a systematic scoping literature review was conducted which yielded 12 manuscripts. From these papers it was observed that there is paucity in available multimedia for medical professionals, games for CF adults, and management applications for CF adults. These three observations serve as the objectives of each Chapter within the thesis. Each Chapter begins by investigating the observation further before designing and implementing a multimedia solution. The results of this research produced a 3D interactive virtual reality tool for medical professionals, a general mHealth design and development pipeline/framework, a serious game with data analysis system for CF adults, three e-learning tools for CF adults, and a CF patient passport app. All multimedia solutions were evaluated with their target audience, and each result is presented. This research concludes that multimedia can be of benefit to the education and management of Cystic Fibrosis. Feedback from testing with both medical professionals and CF adults demonstrates that these cohorts indeed want these multimedia solutions and find them beneficial. However, further investigation and research is required to evaluate the benefits these solutions have. Therefore this thesis also identifies further areas of interest and makes recommendations for future research

Acquisition 3D et visualisation d'objets culturels pour les applications de la réalité augmentée

Au cours des dernières décennies, le domaine de la reconstruction 3D a connu une croissance rapide et de nombreuses techniques ont été présentées par les scientifiques. L'enveloppe visuelle et la stéréovision sont deux de ces techniques, et sont classées parmi les techniques IBMR. Les enveloppes visuelles utilisent la forme d'un objet dérivée des images prises sous différents angles pour créer un modèle 3D approximatif de l'objet. La stéréovision calcule la dimension 3D (profondeur) en comparant deux images de la même scène qui ont été prises sous deux angles différents. Les pixels correspondants dans les deux images sont ensuite calculés pour déduire une carte de disparité qui est utilisée pour déterminer les profondeurs. Les deux techniques présentent des inconvénients lorsqu'elles sont utilisées seules, et l'un des objectifs de cette recherche est de surmonter ces problèmes et de développer une technique de reconstruction 3D efficace et robuste. Notre étude a abouti à la proposition d’une méthode innovante pour estimer l'enveloppe visuelle. Deux approches ont été combinées, à savoir, « la correspondance de caractéristiques » et « l’approche par bloc ». Ce qui a permis de reconstruire des objets avec des gains considérables de temps et sans perte de qualité. Nous avons utilisé les informations géométriques présentes dans les images pour réduire l'espace de recherche des algorithmes stéréoscopiques, réduisant ainsi le temps d'exécution de plus de la moitié du temps initial. Dans le cadre de cette recherche, nous avons eu l’occasion de reconstruire des objets culturels réels et d’intégrer leur modèles 3D à des applications de réalité augmentée.

Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study

Symbiotic deep learning for medical image analysis with applications in real-time diagnosis for fetal ultrasound screening

The last hundred years have seen a monumental rise in the power and capability of machines to perform intelligent tasks in the stead of previously human operators. This rise is not expected to slow down any time soon and what this means for society and humanity as a whole remains to be seen. The overwhelming notion is that with the right goals in mind, the growing influence of machines on our every day tasks will enable humanity to give more attention to the truly groundbreaking challenges that we all face together. This will usher in a new age of human machine collaboration in which humans and machines may work side by side to achieve greater heights for all of humanity. Intelligent systems are useful in isolation, but the true benefits of intelligent systems come to the fore in complex systems where the interaction between humans and machines can be made seamless, and it is this goal of symbiosis between human and machine that may democratise complex knowledge, which motivates this thesis. In the recent past, datadriven methods have come to the fore and now represent the state-of-the-art in many different fields. Alongside the shift from rule-based towards data-driven methods we have also seen a shift in how humans interact with these technologies. Human computer interaction is changing in response to data-driven methods and new techniques must be developed to enable the same symbiosis between man and machine for data-driven methods as for previous formula-driven technology. We address five key challenges which need to be overcome for data-driven human-in-the-loop computing to reach maturity. These are (1) the ’Categorisation Challenge’ where we examine existing work and form a taxonomy of the different methods being utilised for data-driven human-in-the-loop computing; (2) the ’Confidence Challenge’, where data-driven methods must communicate interpretable beliefs in how confident their predictions are; (3) the ’Complexity Challenge’ where the aim of reasoned communication becomes increasingly important as the complexity of tasks and methods to solve also increases; (4) the ’Classification Challenge’ in which we look at how complex methods can be separated in order to provide greater reasoning in complex classification tasks; and finally (5) the ’Curation Challenge’ where we challenge the assumptions around bottleneck creation for the development of supervised learning methods.Open Acces

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Maximising the diagnostic value of structural MRI in the diagnosis of dementia: a comprehensive study of post-mortem proven cases

This thesis investigates the use of atrophy patterns from structural brain imaging to distinguish different dementia pathologies, including Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia pathologies. Using gold standard histopathology to stratify groups, analysis is based on 3D-T1-weighted imaging acquired during life in patients who attended clinic in one of three European centres. As well as comparison of disease groups with healthy controls, more clinically relevant comparisons between disease groups are performed to identify features that may be useful for differential diagnosis. The image analysis techniques used in this thesis range from simple visual assessment to more advanced machine learning. Visual rating scales were found to be reliable, quick to perform, and when used in combination, could achieve diagnostic accuracy equal to unstructured visual assessment by dementia experts. Voxel based morphometry, used to provide a comprehensive estimate of global patterns of atrophy in pathologically distinct dementias, confirmed findings in the literature based on clinical data, and identified novel regions of interest for further study. A fully automated diagnostic approach using multi-atlas segmentation propagation and support vector classifiers, revealed brain volume differences between pathologically distinct groups, yet with several technical limitations to address. Since histopathological diagnosis is rare in such a large, pathologically diverse cohort, this thesis also considers opportunities to develop the dataset into a shared resource for the dementia research community. To this end, a web application was developed to allow the data to be shared between collaborating centres, with plans to adapt this into a teaching resource. In summary, this thesis uses a variety of analysis techniques to identify imaging features that may be useful for the differential diagnosis of dementia pathologies. Various opportunities are explored to maximise the value that can be derived from this unique and valuable dataset