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Combined Surface-Wave and Resistivity Imaging for Shallow Subsurface CharacterizationTufekci, Sinan 21 September 2009 (has links)
No description available.
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Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer:Schob, Stefan, Meyer, Hans Jonas, Dieckow, Julia, Pervinder, Bhogal, Pazaitis, Nikolaos, Höhn, Anne Kathrin, Garnov, Nikita, Horvath-Rizea, Diana, Hoffmann, Karl-Titus, Surov, Alexey 11 January 2024 (has links)
Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of
thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that
DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has
decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole
tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid
carcinomas which had already gained the capacity to metastasize lymphatically from those not yet
being able to spread via the lymphatic system. The secondary aim was to reflect prognostically
important tumor-biological features like cellularity and proliferative activity with ADC histogram
analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status,
extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these
patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm2
. Whole tumor
ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed
significant correlations with immunohistopathological parameters. Most importantly, ADC histogram
skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal
positive thyroid carcinoma. Conclusions: histogram analysis of whole ADC tumor volumes has
the potential to provide valuable information on tumor biology in thyroid carcinoma. However,
further studies are warranted.
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[en] A FRAMEWORK APPROACH FOR QUALITY FEATURE ANALYSIS OF GENOME ASSEMBLIES / [pt] UMA ABORDAGEM DE FRAMEWORK PARA ANÁLISE DE MEDIDAS DE QUALIDADE DA MONTAGEM DE GENOMASGUILHERME BORBA NEUMANN 06 December 2019 (has links)
[pt] A área de pesquisa em Montagem de Genomas tem evoluído rapidamente, adaptando-se às novas tecnologias de sequenciamento e modernos ambientes computacionais. Existem diversos softwares montadores que usam múltiplas abordagens, porém persiste o questionamento sobre a qualidade da montagem ao final do processo. Assim que uma montagem é finalizada, muitas medidas de qualidade podem ser geradas, a fim de que a montagem seja qualificada. Todavia, essas medidas apenas fornecem aos biólogos valores quantitativos acerca da montagem. Nós propomos nesta pesquisa um framework de domínio para o processo de análise de medidas pós montagem de genomas. Nosso objetivo é de prover a interpretação dos dados e avaliação da qualidade das montagens a partir do Framework. O Genome Assembly Analysis Framework (GAAF) foi projetado para trabalhar com espécies, montadores e medidas distintas. Para validar nossa proposta, foram realizados testes com o GAAF que permitem entender como o mesmo pode ser utilizado e de que maneira ele pode ser instanciado e/ou estendido. / [en] The Genome Assembly research area has quickly evolved, adapting to new sequencing technologies and modern computational environments. There exist many assembler software that consider multiple approaches. However, at the end of the process, one can always question the quality of assemblies. When an assembly is accomplished, some quality features may be generated, in order to qualify it. Nonetheless, the features do not directly tell one about assembly quality, but only bring to the biologists quantitative assembly descriptions. We propose a Domain Framework for the feature analysis process post-genome Assembly. Our goal is to enable data interpretation and assembly quality evaluation. The Genome Assembly Analysis Framework (GAAF) was designed to work with distinct species, assemblers and features. In order to validate our proposal, we have run a few practical experiments with GAAF, which make us understand the way
it can be used, instantiated and extended.
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Music discovery methods using perceptual features / Användning av metoder baserade på perceptuella särdrag för att upptäcka musikNysäter, Richard January 2017 (has links)
Perceptual features are qualitative features used to describe music properties in relation to human perception instead of typical musical theory concepts such as pitches and chords. This report describes a music discovery platform which uses three different methods of music playlist generation to investigate if and how perceptual features work when used for music discovery. One method abstracts away the complexity of perceptual features and the other two lets users use them directly. Two user testing sessions were performed to evaluate the browser and compare the different methods. Test participants found the playlist generation to work well in general, and especially found the method which uses emotions as an interface to be intuitive, enjoyable and something they would use to find new music. The other two methods which let users directly interact with perceptual features were less popular, especially among users without musical education. Overall, using perceptual features for music discovery was successful, although methods should be chosen with the intended audience in mind. / Perceptuella särdrag är kvalitativt framtagna särdrag som beskriver musik med fokus på mänsklig perception snarare än musikteoribegrepp som tonhöjd och ackord. Den här rapporten beskriver en musikhemsida som använder tre olika metoder för att generera spellistor med avsikt att undersöka om och hur perceptuella särdrag fungerar för att hitta ny musik. En metod abstraherar bort perceptuella särdragens komplexitet och de andra två metoderna låter testare använda dem utan abstraktion. Två användbarhetstest utfördes för att utvärdera musikhemsidan och jämföra de olika metoderna. Testanvändare tyckte överlag att genereringen av spellistor fungerade bra och att speciellt metoden som använde känslor som gränssnitt var intuitiv, rolig att använda och en metod de skulle använda för att hitta ny musik. De andra två metoderna som tillät användare att direkt använda perceptuella särdrag var mindre populära, speciellt bland användare utan musikutbildning. Överlag var användandet av perceptuella särdrag för att hitta musik en framgång, dock bör metoderna väljas utifrån användarnas kunskap.
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Topic change in robot-moderated group discussions : Investigating machine learning approaches for topic change in robot-moderated discussions using non-verbal features / Ämnesbyte i robotmodererade gruppdiskussioner : Undersöka maskininlärningsmetoder för ämnesändring i robotmodererad diskussion med hjälp av icke-verbala egenskaperHadjiantonis, Georgios January 2024 (has links)
Moderating group discussions among humans can often be challenging and require certain skills, particularly in deciding when to ask other participants to elaborate or change the current topic of the discussion. Recent research on Human-Robot Interaction in groups has demonstrated the positive impact of robot behavior on the quality and effectiveness of the interaction and their ability to shape the dynamics of the group and promote social behavior. In light of this, there is the potential of using social robots as discussion moderators to facilitate engaging and productive discussions among humans. Previous work on topic management in conversational agents was predominantly based on human engagement and topic personalization, with the agent having an active/central role in the conversation. This thesis focuses exclusively on the moderation of group discussions; instead of moderating the topic based on evaluated human engagement, the thesis builds upon previous research on non-verbal cues related to discussion topic structure and turntaking to determine whether participants intend to continue discussing the current topic in a content-free manner. This thesis investigates the suitability of machine-learning models and the contribution of different audiovisual non-verbal features in predicting appropriate topic changes. For this purpose, we utilized pre-recorded interactions between a robot moderator and human participants, which we annotated and from which we extracted acoustic and body language-related features. We provide an analysis of the performance of sequential and nonsequential machine learning approaches using different sets of features, as well as a comparison with rule-based heuristics. The results indicate promising performance in classifying between cases when a topic change was inappropriate versus when a topic change could or should change, outperforming rule-based approaches and demonstrating the feasibility of using machine learning models for topic moderation. Regarding the type of models, the results suggest no distinct advantage of sequential over non-sequential modeling approaches, indicating the effectiveness of simpler non-sequential data models. Acoustic features exhibited comparable and, in some cases, improved overall performance and robustness compared to using only body language-related features or a combination of both types. In summary, this thesis provides a foundation for future research in robot-mediated topic moderation in groups using non-verbal cues, presenting opportunities to further improve social robots with topic moderation capabilities. / Att moderera gruppdiskussioner mellan människor kan ofta vara utmanande och kräver vissa färdigheter, särskilt när det gäller att bestämma när man ska be andra deltagare att utveckla eller ändra det aktuella ämnet för diskussionen. Ny forskning om människa-robotinteraktion i grupper har visat den positiva effekten av robotbeteende på interaktionens kvalitet och effektivitet och deras förmåga att forma gruppens dynamik och främja socialt beteende. I ljuset av detta finns det potential att använda sociala robotar som diskussionsmoderatorer för att underlätta engagerande och produktiva diskussioner bland människor. Tidigare arbete med ämneshantering hos konversationsagenter baserades till övervägande del på mänskligt engagemang och ämnesanpassning, där agenten hade en aktiv/central roll i samtalet. Denna avhandling fokuserar uteslutande på moderering av gruppdiskussioner; istället för att moderera ämnet baserat på utvärderat mänskligt engagemang, bygger avhandlingen på tidigare forskning om icke-verbala ledtrådar relaterade till diskussionsämnesstruktur och turtagning för att avgöra om deltagarna avser att fortsätta diskutera det aktuella ämnet på ett innehållsfritt sätt. Denna avhandling undersöker lämpligheten av maskininlärningsmodeller och bidraget från olika audiovisuella icke-verbala funktioner för att förutsäga lämpliga ämnesändringar. För detta ändamål använde vi förinspelade interaktioner mellan en robotmoderator och mänskliga deltagare, som vi kommenterade och från vilka vi extraherade akustiska och kroppsspråksrelaterade funktioner. Vi tillhandahåller en analys av prestandan för sekventiell och ickesekventiell maskininlärningsmetoder med olika uppsättningar funktioner, samt en jämförelse med regelbaserad heuristik. Resultaten indikerar lovande prestation när det gäller att klassificera mellan fall när ett ämnesbyte var olämpligt kontra när ett ämnesbyte kunde eller borde ändras, överträffande regelbaserade tillvägagångssätt och demonstrerar genomförbarheten av att använda maskininlärningsmodeller för ämnesmoderering. När det gäller typen av modeller tyder resultaten inte på någon tydlig fördel med sekventiella metoder framför icke-sekventiella modelleringsmetoder, vilket indikerar effektiviteten hos enklare icke-sekventiella datamodeller. Akustiska funktioner uppvisade jämförbara och, i vissa fall, förbättrade övergripande prestanda och robusthet jämfört med att endast använda kroppsspråksrelaterade funktioner eller en kombination av båda typerna.svis ger denna avhandling en grund för framtida forskning inom robotmedierad ämnesmoderering i grupper som använder icke-verbala ledtrådar, och presenterar möjligheter att förbättra sociala robotar ytterligare med ämnesmodererande förmåga.
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Voice for Decision Support in Healthcare Applied to Chronic Obstructive Pulmonary Disease Classification : A Machine Learning ApproachIdrisoglu, Alper January 2024 (has links)
Background: Advancements in machine learning (ML) techniques and voice technology offer the potential to harness voice as a new tool for developing decision-support tools in healthcare for the benefit of both healthcare providers and patients. Motivated by technological breakthroughs and the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, numerous studies aim to investigate the diagnostic potential of ML algorithms in the context of voice-affecting disorders. This thesis focuses on respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and explores the potential of a decision support tool that utilizes voice and ML. This exploration exemplifies the intricate relationship between voice and overall health through the lens of applied health technology (AHT. This interdisciplinary nature of research recognizes the need for accurate and efficient diagnostic tools. Objective: The objectives of this licentiate thesis are twofold. Firstly, a Systematic Literature Review (SLR) thoroughly investigates the current state of ML algorithms in detecting voice-affecting disorders, pinpointing existing gaps and suggesting directions for future research. Secondly, the study focuses on respiratory health, specifically COPD, employing ML techniques with a distinct emphasis on the vowel "A". The aim is to explore hidden information that could potentially be utilized for the binary classification of COPD vs no COPD. The creation of a new Swedish COPD voice classification dataset is anticipated to enhance the experimental and exploratory dimensions of the research. Methods: In order to have a holistic view of a research field, one of the commonly utilized methods is to scan and analyze the literature. Therefore, Paper I followed the methodology of an SLR where existing journal publications were scanned and synthesized to create a holistic view in the realm of ML techniques employed to experiment on voice-affecting disorders. Based on the results from the SLR, Paper II focused on the data collection and experimentation for the binary classification of COPD, which was one of the gaps identified in the first study. Three distinct ML algorithms were investigated on the collected datasets through voice features, which consisted of recordings collected through a mobile application from participants 18 years old and above, and the most utilized performance measures were computed for the best outcome. Results: The summary of findings from Paper I reveals the dominance of Support Vector Machine (SVM) classifiers in voice disorder research, with Parkinson's Disease and Alzheimer's Disease as the most studied disorders. Gaps in research include underrepresented disorders, limited datasets in terms of number of participants, and a lack of interest in longitudinal studies. Paper II demonstrates promising results in COPD classification using ML and a newly developed dataset, offering insights into potential decision support tools for COPD diagnosis. Conclusion: The studies covered in this dissertation provide a comprehensive literature summary of ML techniques used to support decision-making on voice-affecting disorders for clinical outcomes. The findings contribute to understanding the diagnostic potential of using ML on vocal features and highlight avenues for future research and technology development. Nonetheless, the experiment reveals the potential of employing voice as a digital biomarker for COPD diagnosis using ML.
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PERSON RE-IDENTIFICATION USING RGB-DEPTH CAMERASOliver Moll, Javier 29 December 2015 (has links)
[EN] The presence of surveillance systems in our lives has drastically increased during the last years. Camera networks can be seen in almost every crowded public and private place, which generate huge amount of data with valuable information. The automatic analysis of data plays an important role to extract relevant information from the scene. In particular, the problem of person re-identification is a prominent topic that has become of great interest, specially for the fields of security or marketing. However, there are some factors, such as changes in the illumination conditions, variations in the person pose, occlusions or the presence of outliers that make this topic really challenging. Fortunately, the recent introduction of new technologies such as depth cameras opens new paradigms in the image processing field and brings new possibilities. This Thesis proposes a new complete framework to tackle the problem of person re-identification using commercial rgb-depth cameras. This work includes the analysis and evaluation of new approaches for the modules of segmentation, tracking, description and matching. To evaluate our contributions, a public dataset for person re-identification using rgb-depth cameras has been created.
Rgb-depth cameras provide accurate 3D point clouds with color information. Based on the analysis of the depth information, an novel algorithm for person segmentation is proposed and evaluated. This method accurately segments any person in the scene, and naturally copes with occlusions and connected people. The segmentation mask of a person generates a 3D person cloud, which can be easily tracked over time based on proximity.
The accumulation of all the person point clouds over time generates a set of high dimensional color features, named raw features, that provides useful information about the person appearance. In this Thesis, we propose a family of methods to extract relevant information from the raw features in different ways. The first approach compacts the raw features into a single color vector, named Bodyprint, that provides a good generalisation of the person appearance over time. Second, we introduce the concept of 3D Bodyprint, which is an extension of the Bodyprint descriptor that includes the angular distribution of the color features. Third, we characterise the person appearance as a bag of color features that are independently generated over time. This descriptor receives the name of Bag of Appearances because its similarity with the concept of Bag of Words. Finally, we use different probabilistic latent variable models to reduce the feature vectors from a statistical perspective. The evaluation of the methods demonstrates that our proposals outperform the state of the art. / [ES] La presencia de sistemas de vigilancia se ha incrementado notablemente en los últimos anños. Las redes de videovigilancia pueden verse en casi cualquier espacio público y privado concurrido, lo cual genera una gran cantidad de datos de gran valor. El análisis automático de la información juega un papel importante a la hora de extraer información relevante de la escena. En concreto, la re-identificación de personas es un campo que ha alcanzado gran interés durante los últimos años, especialmente en seguridad y marketing. Sin embargo, existen ciertos factores, como variaciones en las condiciones de iluminación, variaciones en la pose de la persona, oclusiones o la presencia de artefactos que hacen de este campo un reto. Afortunadamente, la introducción de nuevas tecnologías como las cámaras de profundidad plantea nuevos paradigmas en la visión artificial y abre nuevas posibilidades. En esta Tesis se propone un marco completo para abordar el problema de re-identificación utilizando cámaras rgb-profundidad. Este trabajo incluye el análisis y evaluación de nuevos métodos de segmentación, seguimiento, descripción y emparejado de personas. Con el fin de evaluar las contribuciones, se ha creado una base de datos pública para re-identificación de personas usando estas cámaras.
Las cámaras rgb-profundidad proporcionan nubes de puntos 3D con información de color. A partir de la información de profundidad, se propone y evalúa un nuevo algoritmo de segmentación de personas. Este método segmenta de forma precisa cualquier persona en la escena y resuelve de forma natural problemas de oclusiones y personas conectadas. La máscara de segmentación de una persona genera una nube de puntos 3D que puede ser fácilmente seguida a lo largo del tiempo.
La acumulación de todas las nubes de puntos de una persona a lo largo del tiempo genera un conjunto de características de color de grandes dimensiones, denominadas características base, que proporcionan información útil de la apariencia de la persona. En esta Tesis se propone una familia de métodos para extraer información relevante de las características base. La primera propuesta compacta las características base en un vector único de color, denominado Bodyprint, que proporciona una buena generalización de la apariencia de la persona a lo largo del tiempo. En segundo lugar, se introducen los Bodyprints 3D, definidos como una extensión de los Bodyprints que incluyen información angular de las características de color. En tercer lugar, la apariencia de la persona se caracteriza mediante grupos de características de color que se generan independientemente a lo largo del tiempo. Este descriptor recibe el nombre de Grupos de Apariencias debido a su similitud con el concepto de Grupos de Palabras. Finalmente, se proponen diferentes modelos probabilísticos de variables latentes para reducir los vectores de características desde un punto de vista estadístico. La evaluación de los métodos demuestra que nuestras propuestas superan los métodos del estado del arte. / [CA] La presència de sistemes de vigilància s'ha incrementat notòriament en els últims anys. Les xarxes de videovigilància poden veure's en quasi qualsevol espai públic i privat concorregut, la qual cosa genera una gran quantitat de dades de gran valor. L'anàlisi automàtic de la informació pren un paper important a l'hora d'extraure informació rellevant de l'escena. En particular, la re-identificaciò de persones és un camp que ha aconseguit gran interès durant els últims anys, especialment en seguretat i màrqueting. No obstant, hi ha certs factors, com variacions en les condicions d'il.luminació, variacions en la postura de la persona, oclusions o la presència d'artefactes que fan d'aquest camp un repte. Afortunadament, la introducció de noves tecnologies com les càmeres de profunditat, planteja nous paradigmes en la visió artificial i obri noves possibilitats. En aquesta Tesi es proposa un marc complet per abordar el problema de la re-identificació mitjançant càmeres rgb-profunditat. Aquest treball inclou l'anàlisi i avaluació de nous mètodes de segmentació, seguiment, descripció i emparellat de persones. Per tal d'avaluar les contribucions, s'ha creat una base de dades pública per re-identificació de persones emprant aquestes càmeres.
Les càmeres rgb-profunditat proporcionen núvols de punts 3D amb informació de color. A partir de la informació de profunditat, es defineix i s'avalua un nou algorisme de segmentació de persones. Aquest mètode segmenta de forma precisa qualsevol persona en l'escena i resol de forma natural problemes d'oclusions i persones connectades. La màscara de segmentació d'una persona genera un núvol de punts 3D que pot ser fàcilment seguida al llarg del temps.
L'acumulació de tots els núvols de punts d'una persona al llarg del temps genera un conjunt de característiques de color de grans dimensions, anomenades característiques base, que hi proporcionen informació útil de l'aparença de la persona. En aquesta Tesi es proposen una família de mètodes per extraure informació rellevant de les característiques base. La primera proposta compacta les característiques base en un vector únic de color, anomenat Bodyprint, que proporciona una bona generalització de l'aparença de la persona al llarg del temps. En segon lloc, s'introdueixen els Bodyprints 3D, definits com una extensió dels Bodyprints que inclouen informació angular de les característiques de color. En tercer lloc, l'aparença de la persona es caracteritza amb grups de característiques de color que es generen independentment a llarg del temps. Aquest descriptor reb el nom de Grups d'Aparences a causa de la seua similitud amb el concepte de Grups de Paraules. Finalment, es proposen diferents models probabilístics de variables latents per reduir els vectors de característiques des d'un punt de vista estadístic. L'avaluació dels mètodes demostra que les propostes presentades superen als mètodes de l'estat de l'art. / Oliver Moll, J. (2015). PERSON RE-IDENTIFICATION USING RGB-DEPTH CAMERAS [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59227
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A system for modeling social traits in realistic faces with artificial intelligenceFuentes Hurtado, Félix José 14 May 2018 (has links)
Los seres humanos han desarrollado especialmente su capacidad perceptiva para procesar caras y extraer información de las características faciales. Usando nuestra capacidad conductual para percibir rostros, hacemos atribuciones tales como personalidad, inteligencia o confiabilidad basadas en la apariencia facial que a menudo tienen un fuerte impacto en el comportamiento social en diferentes dominios. Por lo tanto, las caras desempeñan un papel fundamental en nuestras relaciones con otras personas y en nuestras decisiones cotidianas.
Con la popularización de Internet, las personas participan en muchos tipos de interacciones virtuales, desde experiencias sociales, como juegos, citas o comunidades, hasta actividades profesionales, como e-commerce, e-learning, e-therapy o e-health. Estas interacciones virtuales manifiestan la necesidad de caras que representen a las personas reales que interactúan en el mundo digital: así surgió el concepto de avatar. Los avatares se utilizan para representar a los usuarios en diferentes escenarios y ámbitos, desde la vida personal hasta situaciones profesionales. En todos estos casos, la aparición del avatar puede tener un efecto no solo en la opinión y percepción de otra persona, sino en la autopercepción, que influye en la actitud y el comportamiento del sujeto. De hecho, los avatares a menudo se emplean para obtener impresiones o emociones a través de expresiones no verbales, y pueden mejorar las interacciones en línea o incluso son útiles para fines educativos o terapéuticos. Por lo tanto, la posibilidad de generar avatares de aspecto realista que provoquen un determinado conjunto de impresiones sociales supone una herramienta muy interesante y novedosa, útil en un amplio abanico de campos.
Esta tesis propone un método novedoso para generar caras de aspecto realistas con un perfil social asociado que comprende 15 impresiones diferentes. Para este propósito, se completaron varios objetivos parciales.
En primer lugar, las características faciales se extrajeron de una base de datos de caras reales y se agruparon por aspecto de una manera automática y objetiva empleando técnicas de reducción de dimensionalidad y agrupamiento. Esto produjo una taxonomía que permite codificar de manera sistemática y objetiva las caras de acuerdo con los grupos obtenidos previamente. Además, el uso del método propuesto no se limita a las características faciales, y se podría extender su uso para agrupar automáticamente cualquier otro tipo de imágenes por apariencia.
En segundo lugar, se encontraron las relaciones existentes entre las diferentes características faciales y las impresiones sociales. Esto ayuda a saber en qué medida una determinada característica facial influye en la percepción de una determinada impresión social, lo que permite centrarse en la característica o características más importantes al diseñar rostros con una percepción social deseada.
En tercer lugar, se implementó un método de edición de imágenes para generar una cara totalmente nueva y realista a partir de una definición de rostro utilizando la taxonomía de rasgos faciales antes mencionada.
Finalmente, se desarrolló un sistema para generar caras realistas con un perfil de rasgo social asociado, lo cual cumple el objetivo principal de la presente tesis.
La principal novedad de este trabajo reside en la capacidad de trabajar con varias dimensiones de rasgos a la vez en caras realistas. Por lo tanto, en contraste con los trabajos anteriores que usan imágenes con ruido, o caras de dibujos animados o sintéticas, el sistema desarrollado en esta tesis permite generar caras de aspecto realista eligiendo los niveles deseados de quince impresiones: Miedo, Enfado, Atractivo, Cara de niño, Disgustado, Dominante, Femenino, Feliz, Masculino, Prototípico, Triste, Sorprendido, Amenazante, Confiable e Inusual.
Los prometedores resultados obtenidos permitirán investigar más a fondo cómo modelar l / Humans have specially developed their perceptual capacity to process faces and to extract information from facial features. Using our behavioral capacity to perceive faces, we make attributions such as personality, intelligence or trustworthiness based on facial appearance that often have a strong impact on social behavior in different domains. Therefore, faces play a central role in our relationships with other people and in our everyday decisions.
With the popularization of the Internet, people participate in many kinds of virtual interactions, from social experiences, such as games, dating or communities, to professional activities, such as e-commerce, e-learning, e-therapy or e-health. These virtual interactions manifest the need for faces that represent the actual people interacting in the digital world: thus the concept of avatar emerged. Avatars are used to represent users in different scenarios and scopes, from personal life to professional situations. In all these cases, the appearance of the avatar may have an effect not only on other person's opinion and perception but on self-perception, influencing the subject's own attitude and behavior. In fact, avatars are often employed to elicit impressions or emotions through non-verbal expressions, and are able to improve online interactions or even useful for education purposes or therapy. Then, being able to generate realistic looking avatars which elicit a certain set of desired social impressions poses a very interesting and novel tool, useful in a wide range of fields.
This thesis proposes a novel method for generating realistic looking faces with an associated social profile comprising 15 different impressions. For this purpose, several partial objectives were accomplished.
First, facial features were extracted from a database of real faces and grouped by appearance in an automatic and objective manner employing dimensionality reduction and clustering techniques. This yielded a taxonomy which allows to systematically and objectively codify faces according to the previously obtained clusters. Furthermore, the use of the proposed method is not restricted to facial features, and it should be possible to extend its use to automatically group any other kind of images by appearance.
Second, the existing relationships among the different facial features and the social impressions were found. This helps to know how much a certain facial feature influences the perception of a given social impression, allowing to focus on the most important feature or features when designing faces with a sought social perception.
Third, an image editing method was implemented to generate a completely new, realistic face from just a face definition using the aforementioned facial feature taxonomy.
Finally, a system to generate realistic faces with an associated social trait profile was developed, which fulfills the main objective of the present thesis.
The main novelty of this work resides in the ability to work with several trait dimensions at a time on realistic faces. Thus, in contrast with the previous works that use noisy images, or cartoon-like or synthetic faces, the system developed in this thesis allows to generate realistic looking faces choosing the desired levels of fifteen impressions, namely Afraid, Angry, Attractive, Babyface, Disgusted, Dominant, Feminine, Happy, Masculine, Prototypical, Sad, Surprised, Threatening, Trustworthy and Unusual.
The promising results obtained in this thesis will allow to further investigate how to model social perception in faces using a completely new approach. / Els sers humans han desenvolupat especialment la seua capacitat perceptiva per a processar cares i extraure informació de les característiques facials. Usant la nostra capacitat conductual per a percebre rostres, fem atribucions com ara personalitat, intel·ligència o confiabilitat basades en l'aparença facial que sovint tenen un fort impacte en el comportament social en diferents dominis. Per tant, les cares exercixen un paper fonamental en les nostres relacions amb altres persones i en les nostres decisions quotidianes.
Amb la popularització d'Internet, les persones participen en molts tipus d'inter- accions virtuals, des d'experiències socials, com a jocs, cites o comunitats, fins a activitats professionals, com e-commerce, e-learning, e-therapy o e-health. Estes interaccions virtuals manifesten la necessitat de cares que representen a les persones reals que interactuen en el món digital: així va sorgir el concepte d'avatar. Els avatars s'utilitzen per a representar als usuaris en diferents escenaris i àmbits, des de la vida personal fins a situacions professionals. En tots estos casos, l'aparició de l'avatar pot tindre un efecte no sols en l'opinió i percepció d'una altra persona, sinó en l'autopercepció, que influïx en l'actitud i el comportament del subjecte. De fet, els avatars sovint s'empren per a obtindre impressions o emocions a través d'expressions no verbals, i poden millorar les interaccions en línia o inclús són útils per a fins educatius o terapèutics. Per tant, la possibilitat de generar avatars d'aspecte realista que provoquen un determinat conjunt d'impressions socials planteja una ferramenta molt interessant i nova, útil en un ampla varietat de camps.
Esta tesi proposa un mètode nou per a generar cares d'aspecte realistes amb un perfil social associat que comprén 15 impressions diferents. Per a este propòsit, es van completar diversos objectius parcials.
En primer lloc, les característiques facials es van extraure d'una base de dades de cares reals i es van agrupar per aspecte d'una manera automàtica i objectiva emprant tècniques de reducció de dimensionalidad i agrupament. Açò va produir una taxonomia que permet codificar de manera sistemàtica i objectiva les cares d'acord amb els grups obtinguts prèviament. A més, l'ús del mètode proposat no es limita a les característiques facials, i es podria estendre el seu ús per a agrupar automàticament qualsevol altre tipus d'imatges per aparença.
En segon lloc, es van trobar les relacions existents entre les diferents característiques facials i les impressions socials. Açò ajuda a saber en quina mesura una determinada característica facial influïx en la percepció d'una determinada impressió social, la qual cosa permet centrar-se en la característica o característiques més importants al dissenyar rostres amb una percepció social desitjada.
En tercer lloc, es va implementar un mètode d'edició d'imatges per a generar una cara totalment nova i realista a partir d'una definició de rostre utilitzant la taxonomia de trets facials abans mencionada.
Finalment, es va desenrotllar un sistema per a generar cares realistes amb un perfil de tret social associat, la qual cosa complix l'objectiu principal de la present tesi.
La principal novetat d'este treball residix en la capacitat de treballar amb diverses dimensions de trets al mateix temps en cares realistes. Per tant, en contrast amb els treballs anteriors que usen imatges amb soroll, o cares de dibuixos animats o sintètiques, el sistema desenrotllat en esta tesi permet generar cares d'aspecte realista triant els nivells desitjats de quinze impressions: Por, Enuig, Atractiu, Cara de xiquet, Disgustat, Dominant, Femení, Feliç, Masculí, Prototípic, Trist, Sorprés, Amenaçador, Confiable i Inusual.
Els prometedors resultats obtinguts en esta tesi permetran investigar més a fons com modelar la percepció social en les cares utilitzant un enfocament complet / Fuentes Hurtado, FJ. (2018). A system for modeling social traits in realistic faces with artificial intelligence [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/101943
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Soil Genesis and Vegetation Response to Amendments and Microtopography in Two Virginia Coastal Plain Created WetlandsOtt, Emily Thomas 12 June 2018 (has links)
Wetlands serve important ecosystem functions such as carbon sequestration but are often affected by disturbances like urban development, agriculture, and road building. For wetlands created to mitigate losses, it is important that the ecosystem functions successfully replicate those of natural wetlands. Created wetlands have frequently not provided these functions due to issues including low organic carbon (OC), high soil bulk density (BD), lost topsoil, incorrect hydrology, and failure of targeted vegetation establishment. Organic matter (OM) amendments help created wetlands attain these functions quicker, but, their long-term effects are seldom reported. This research's purpose was to measure the long-term effects of treatments at a sandy tidal freshwater wetland created in 2003 (WWE) and a fine-textured, non-tidal wetland created in 2002 (CCW). We tested OM treatments, topsoil amendment, and microtopography effects on soil and vegetation properties at WWE and OM treatments at CCW. Pedogenic changes in soil morphology, physical and chemical properties were detected by comparing data to previous studies at these sites. At both sites, litter and biomass parameters were measured to estimate total mass C. Herbaceous biomass was measured at WWE. At WWE, no long-term OM treatment effects from 78 or 156 Mg ha-1 were observed. Soils in pits had higher OC, lower BD, and lower chroma than soils on mounds. Sandy and loamy HSFI's developed at WWE within four years, but there were fewer sandy indicators after 12 years. Loamy HSFI's were lost at CCW from 2003 to 2016. Plots at WWE that were amended with topsoil had higher soil mass C than the sandy soil due to a finer texture, but total mass C did not vary. At CCW, long-term OM treatment effects were observed, including lower BD, higher soil mass C, and higher tree mass C with increasing compost rates up to 224 Mg ha-1. Overall, the ideal compost loading rate for constructed wetlands varied with wetland type and mitigation goals. Compost rates of 112 Mg ha-1 are sufficient for short term establishment of wetland vegetation and hydric soil properties, but higher rates near 224 Mg ha-1 may be required for effects that last over 10 years. / Ph. D. / Wetlands are unique habitats that provide environmental benefits such as carbon storage but are often negatively affected by human disturbances such as urban development and road construction. When wetlands are constructed to mitigate natural wetland losses, it is important that they successfully provide the benefits of the wetlands they replace. Created wetlands have frequently not functioned like natural wetlands due to soil issues including low organic carbon (OC) and high soil density (BD). Organic matter (OM) amendments such as composted yard waste help created wetlands attain these functions quickly after construction compared to unamnded wetlands. The purpose of this study was to measure long-term (greater than 10 years) effects of OM treatments on soil and vegetation properties at two different created wetlands. The two wetlands were a sandy tidal freshwater wetland created in 2003 (WWE) and a fine-textured, compacted, non-tidal wetland created in 2002 (CCW). Previous soil data were compared to recent soil samples to detect changes in physical and chemical soil properties over time. At WWE, soils in pits accumulated more OM, were higher in carbon, lower in BD, and had greyer color than soils in mounds. Hydric soil field indicators developed from upland soil within four years after construction at WWE. There were no long term compost effects on soil properties compared to a fertilized control, but the compost rates used were low compared to other recommendations, and the wetland was constructed carefully to avoid compaction. There were much higher rates of compost applied at CCW, which produced lower BD, higher soil mass C, and higher tree biomass. We recommend applying OM and avoiding compaction during wetland construction. Ideal OM loading rate depends on wetland type (soil texture, hydrology) and mitigation goals. In the fine-textured, compacted wetland studied here, compost rates of 112 Mg ha⁻¹ are ideal for short term establishment of wetland vegetation and soil properties, but higher rates near 224 Mg ha⁻¹ may be required for long term effects.
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Управление позиционированием малого предприятия в онлайн-среде : магистерская диссертация / Managing the online-positioning of a small companyАржеловская, А. А., Arzhelovskaya, A. A. January 2024 (has links)
The master's thesis provides a rationale for theoretical positions and the development of practical recommendations, which contribute to the planning and organization of effective positioning, and include the algorithm for applying positioning tools for small companies online. / В данной работе проводится обоснование теоретических положений и разработка практических рекомендаций, способствующих планированию и организации результативного позиционирования, включающих алгоритм применения инструментов позиционирования для малых предприятий в онлайн-среде.
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