A Framework for Knowledge Derivation Incorporating Trust and Quality of Data

Today, across all major industries gaining insight from data is seen as an essential part of business. However, while data gathering is becoming inexpensive and relatively easy, analysis and ultimately deriving knowledge from it is increasingly difficult. In many cases, there is the problem of too much data such that important insights are hard to find. The problem is often not lack of data but whether knowledge derived from it is trustworthy. This means distinguishing "good" from "bad" insights based on factors such as context and reputation. Still, modeling trust and quality of data is complex because of the various conditions and relationships in heterogeneous environments. The new TrustKnowOne framework and architecture developed in this dissertation addresses these issues by describing an approach to fully incorporate trust and quality of data with all its aspects into the knowledge derivation process. This is based on Berlin, an abstract graph model we developed that can be used to model various approaches to trustworthiness and relationship assessment as well as decision making processes. In particular, processing, assessment, and evaluation approaches are implemented as graph expressions that are evaluated on graph components modeling the data. We have implemented and applied our framework to three complex scenarios using real data from public data repositories. As part of their evaluation we highlighted how our approach exhibits both the formalization and flexibility necessary to model each of the realistic scenarios. The implementation and evaluation of these scenarios confirms the advantages of the TrustKnowOne framework over current approaches

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Contributions to Ensemble Classifiers with Image Analysis Applications

134 p.Ésta tesis tiene dos aspectos fundamentales, por un lado, la propuesta denuevas arquitecturas de clasificadores y, por otro, su aplicación a el análisis deimagen.Desde el punto de vista de proponer nuevas arquitecturas de clasificaciónla tesis tiene dos contribucciones principales. En primer lugar la propuestade un innovador ensemble de clasificadores basado en arquitecturas aleatorias,como pueden ser las Extreme Learning Machines (ELM), Random Forest (RF) yRotation Forest, llamado Hybrid Extreme Rotation Forest (HERF) y su mejoraAnticipative HERF (AHERF) que conlleva una selección del modelo basada enel rendimiento de predicción para cada conjunto de datos específico. Ademásde lo anterior, proveemos una prueba formal tanto del AHERF, como de laconvergencia de los ensembles de regresores ELMs que mejoran la usabilidad yreproducibilidad de los resultados.En la vertiente de aplicación hemos estado trabajando con dos tipos de imágenes:imágenes hiperespectrales de remote sensing, e imágenes médicas tanto depatologías específicas de venas de sangre como de imágenes para el diagnósticode Alzheimer. En todos los casos los ensembles de clasificadores han sido la herramientacomún además de estrategias especificas de aprendizaje activo basadasen dichos ensembles de clasificadores. En el caso concreto de la segmentaciónde vasos sanguíneos nos hemos enfrentado con problemas, uno relacionado conlos trombos del Aneurismas de Aorta Abdominal en imágenes 3D de tomografíacomputerizada y el otro la segmentación de venas sangineas en la retina. Losresultados en ambos casos en términos de rendimiento en clasificación y ahorrode tiempo en la segmentación humana nos permiten recomendar esos enfoquespara la práctica clínica.Chapter 1Background y contribuccionesDado el espacio limitado para realizar el resumen de la tesis hemos decididoincluir un resumen general con los puntos más importantes, una pequeña introducciónque pudiera servir como background para entender los conceptos básicosde cada uno de los temas que hemos tocado y un listado con las contribuccionesmás importantes.1.1 Ensembles de clasificadoresLa idea de los ensembles de clasificadores fue propuesta por Hansen y Salamon[4] en el contexto del aprendizaje de las redes neuronales artificiales. Sutrabajo mostró que un ensemble de redes neuronales con un esquema de consensogrupal podía mejorar el resultado obtenido con una única red neuronal.Los ensembles de clasificadores buscan obtener unos resultados de clasificaciónmejores combinando clasificadores débiles y diversos [8, 9]. La propuesta inicialde ensemble contenía una colección homogena de clasificadores individuales. ElRandom Forest es un claro ejemplo de ello, puesto que combina la salida de unacolección de árboles de decisión realizando una votación por mayoría [2, 3], yse construye utilizando una técnica de remuestreo sobre el conjunto de datos ycon selección aleatoria de variables.2CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 31.2 Aprendizaje activoLa construcción de un clasificador supervisado consiste en el aprendizaje de unaasignación de funciones de datos en un conjunto de clases dado un conjunto deentrenamiento etiquetado. En muchas situaciones de la vida real la obtenciónde las etiquetas del conjunto de entrenamiento es costosa, lenta y propensa aerrores. Esto hace que la construcción del conjunto de entrenamiento sea unatarea engorrosa y requiera un análisis manual exaustivo de la imagen. Esto se realizanormalmente mediante una inspección visual de las imágenes y realizandoun etiquetado píxel a píxel. En consecuencia el conjunto de entrenamiento esaltamente redundante y hace que la fase de entrenamiento del modelo sea muylenta. Además los píxeles ruidosos pueden interferir en las estadísticas de cadaclase lo que puede dar lugar a errores de clasificación y/o overfitting. Por tantoes deseable que un conjunto de entrenamiento sea construido de una manera inteligente,lo que significa que debe representar correctamente los límites de clasemediante el muestreo de píxeles discriminantes. La generalización es la habilidadde etiquetar correctamente datos que no se han visto previamente y quepor tanto son nuevos para el modelo. El aprendizaje activo intenta aprovecharla interacción con un usuario para proporcionar las etiquetas de las muestrasdel conjunto de entrenamiento con el objetivo de obtener la clasificación másprecisa utilizando el conjunto de entrenamiento más pequeño posible.1.3 AlzheimerLa enfermedad de Alzheimer es una de las causas más importantes de discapacidaden personas mayores. Dado el envejecimiento poblacional que es una realidaden muchos países, con el aumento de la esperanza de vida y con el aumentodel número de personas mayores, el número de pacientes con demencia aumentarátambién. Debido a la importancia socioeconómica de la enfermedad enlos países occidentales existe un fuerte esfuerzo internacional focalizado en laenfermedad del Alzheimer. En las etapas tempranas de la enfermedad la atrofiacerebral suele ser sutil y está espacialmente distribuida por diferentes regionescerebrales que incluyen la corteza entorrinal, el hipocampo, las estructuras temporaleslateral e inferior, así como el cíngulo anterior y posterior. Son muchoslos esfuerzos de diseño de algoritmos computacionales tratando de encontrarbiomarcadores de imagen que puedan ser utilizados para el diagnóstico no invasivodel Alzheimer y otras enfermedades neurodegenerativas.CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 41.4 Segmentación de vasos sanguíneosLa segmentación de los vasos sanguíneos [1, 7, 6] es una de las herramientas computacionalesesenciales para la evaluación clínica de las enfermedades vasculares.Consiste en particionar un angiograma en dos regiones que no se superponen:la región vasculares y el fondo. Basándonos en los resultados de dicha particiónse pueden extraer, modelar, manipular, medir y visualizar las superficies vasculares.Éstas estructuras son muy útiles y juegan un rol muy imporntate en lostratamientos endovasculares de las enfermedades vasculares. Las enfermedadesvasculares son una de las principales fuentes de morbilidad y mortalidad en todoel mundo.Aneurisma de Aorta Abdominal El Aneurisma de Aorta Abdominal (AAA)es una dilatación local de la Aorta que ocurre entre las arterias renal e ilíaca. Eldebilitamiento de la pared de la aorta conduce a su deformación y la generaciónde un trombo. Generalmente, un AAA se diagnostica cuando el diámetro anterioposteriormínimo de la aorta alcanza los 3 centímetros [5]. La mayoría delos aneurismas aórticos son asintomáticos y sin complicaciones. Los aneurismasque causan los síntomas tienen un mayor riesgo de ruptura. El dolor abdominalo el dolor de espalda son las dos principales características clínicas que sugiereno bien la reciente expansión o fugas. Las complicaciones son a menudo cuestiónde vida o muerte y pueden ocurrir en un corto espacio de tiempo. Por lo tanto,el reto consiste en diagnosticar lo antes posible la aparición de los síntomas.Imágenes de Retina La evaluación de imágenes del fondo del ojo es una herramientade diagnóstico de la patología vascular y no vascular. Dicha inspecciónpuede revelar hipertensión, diabetes, arteriosclerosis, enfermedades cardiovascularese ictus. Los principales retos para la segmentación de vasos retinianos son:(1) la presencia de lesiones que se pueden interpretar de forma errónea comovasos sanguíneos; (2) bajo contraste alrededor de los vasos más delgados, (3)múltiples escalas de tamaño de los vasos.1.5 ContribucionesÉsta tesis tiene dos tipos de contribuciones. Contribuciones computacionales ycontribuciones orientadas a una aplicación o prácticas.CHAPTER 1. BACKGROUND Y CONTRIBUCCIONES 5Desde un punto de vista computacional las contribuciones han sido las siguientes:¿ Un nuevo esquema de aprendizaje activo usando Random Forest y el cálculode la incertidumbre que permite una segmentación de imágenes rápida,precisa e interactiva.¿ Hybrid Extreme Rotation Forest.¿ Adaptative Hybrid Extreme Rotation Forest.¿ Métodos de aprendizaje semisupervisados espectrales-espaciales.¿ Unmixing no lineal y reconstrucción utilizando ensembles de regresoresELM.Desde un punto de vista práctico:¿ Imágenes médicas¿ Aprendizaje activo combinado con HERF para la segmentación deimágenes de tomografía computerizada.¿ Mejorar el aprendizaje activo para segmentación de imágenes de tomografíacomputerizada con información de dominio.¿ Aprendizaje activo con el clasificador bootstrapped dendritic aplicadoa segmentación de imágenes médicas.¿ Meta-ensembles de clasificadores para detección de Alzheimer conimágenes de resonancia magnética.¿ Random Forest combinado con aprendizaje activo para segmentaciónde imágenes de retina.¿ Segmentación automática de grasa subcutanea y visceral utilizandoresonancia magnética.¿ Imágenes hiperespectrales¿ Unmixing no lineal y reconstrucción utilizando ensembles de regresoresELM.¿ Métodos de aprendizaje semisupervisados espectrales-espaciales concorrección espacial usando AHERF.¿ Método semisupervisado de clasificación utilizando ensembles de ELMsy con regularización espacial

Understanding the behaviour and influence of automated social agents

Soft-bound submitted: Fri 23 Feb 2018 Corrections submitted: Mon 30 Jul 2018 Corrections approved: Tue 7 Aug 2018 Apollo submitted: Wed 22 Aug 2018 Hard-bound submitted: Fri 24 Aug 2018Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.Sep'14-Aug'17, Marie Curie ITN METRICS, Early-Stage Researcher Sep'17, UMobile, Research Associate Oct'17-Mar'18, EPSRC Global Challenges Research Fund, Research Associat

AUTOMATED ANALYSIS OF NATURAL-LANGUAGE REQUIREMENTS USING NATURAL LANGUAGE PROCESSING

Natural Language (NL) is arguably the most common vehicle for specifying requirements. This dissertation devises automated assistance for some important tasks that requirements engineers need to perform in order to structure, manage, and elaborate NL requirements in a sound and effective manner. The key enabling technology underlying the work in this dissertation is Natural Language Processing (NLP). All the solutions presented herein have been developed and empirically evaluated in close collaboration with industrial partners. The dissertation addresses four different facets of requirements analysis: • Checking conformance to templates. Requirements templates are an effective tool for improving the structure and quality of NL requirements statements. When templates are used for specifying the requirements, an important quality assurance task is to ensure that the requirements conform to the intended templates. We develop an automated solution for checking the conformance of requirements to templates. • Extraction of glossary terms. Requirements glossaries (dictionaries) improve the understandability of requirements, and mitigate vagueness and ambiguity. We develop an auto- mated solution for supporting requirements analysts in the selection of glossary terms and their related terms. • Extraction of domain models. By providing a precise representation of the main concepts in a software project and the relationships between these concepts, a domain model serves as an important artifact for systematic requirements elaboration. We propose an automated approach for domain model extraction from requirements. The extraction rules in our approach encompass both the rules already described in the literature as well as a number of important extensions developed in this dissertation. • Identifying the impact of requirements changes. Uncontrolled change in requirements presents a major risk to the success of software projects. We address two different dimen- sions of requirements change analysis in this dissertation: First, we develop an automated approach for predicting how a change to one requirement impacts other requirements. Next, we consider the propagation of change from requirements to design. To this end, we develop an automated approach for predicting how the design of a system is impacted by changes made to the requirements

Extração de conhecimento a partir de fontes semi-estruturadas

The increasing number of small, cheap devices, full of sensing capabilities lead to an untapped source of data that can be explored to improve and optimize multiple systems, from small-scale home automation to large-scale applications such as agriculture monitoring, traffic flow and industrial maintenance prediction. Yet, hand in hand with this growth, goes the increasing difficulty to collect, store and organize all these new data. The lack of standard context representation schemes is one of the main struggles in this area. Furthermore, conventional methods for extracting knowledge from data rely on standard representations or a priori relations. These a priori relations add latent information to the underlying model, in the form of context representation schemes, table relations, or even ontologies. Nonetheless, these relations are created and maintained by human users. While feasible for small-scale scenarios or specific areas, this becomes increasingly difficult to maintain when considering the potential dimension of IoT and M2M scenarios. This thesis addresses the problem of storing and organizing context information from IoT/M2M scenarios in a meaningful way, without imposing a representation scheme or requiring a priori relations. This work proposes a d-dimension organization model, which was optimized for IoT/M2M data. The model relies on machine learning features to identify similar context sources. These features are then used to learn relations between data sources automatically, providing the foundations for automatic knowledge extraction, where machine learning, or even conventional methods, can rely upon to extract knowledge on a potentially relevant dataset. During this work, two different machine learning techniques were tackled: semantic and stream similarity. Semantic similarity estimates the similarity between concepts (in textual form). This thesis proposes an unsupervised learning method for semantic features based on distributional profiles, without requiring any specific corpus. This allows the organizational model to organize data based on concept similarity instead of string matching. Another advantage is that the learning method does not require input from users, making it ideal for massive IoT/M2M scenarios. Stream similarity metrics estimate the similarity between two streams of data. Although these methods have been extensively researched for DNA sequencing, they commonly rely on variants of the longest common sub-sequence. This PhD proposes a generative model for stream characterization, specially optimized for IoT/M2M data. The model can be used to generate statistically significant data’s streams and estimate the similarity between streams. This is then used by the context organization model to identify context sources with similar stream patterns. The work proposed in this thesis was extensively discussed, developed and published in several international publications. The multiple contributions in projects and collaborations with fellow colleagues, where parts of the work developed were used successfully, support the claim that although the context organization model (and subsequent similarity features) were optimized for IoT/M2M data, they can potentially be extended to deal with any kind of context information in a wide array of applications.O número crescente de dispositivos pequenos e baratos, repletos de capacidades sensoriais, criou uma nova fonte de dados que pode ser explorada para melhorar e otimizar vários sistemas, desde domótica em ambientes residenciais até aplicações de larga escala como monitorização agrícola, gestão de tráfego e manutenção preditiva a nível industrial. No entanto, este crescimento encontra-se emparelhado com a crescente dificuldade em recolher, armazenar e organizar todos estes dados. A inexistência de um esquema de representação padrão é uma das principais dificuldades nesta área. Além disso, métodos de extração de conhecimento convencionais dependem de representações padrão ou relações definidas a priori. No entanto estas relações são definidas e mantidas por utilizadores humanos. Embora seja viável para cenários de pequena escala ou áreas especificas, este tipo de relações torna-se cada vez mais difícil de manter quando se consideram cenários com a dimensão associado a IoT e M2M. Esta tese de doutoramento endereça o problema de armazenar e organizar informação de contexto de cenários de IoT/M2M, sem impor um esquema de representação ou relações a priori. Este trabalho propõe um modelo de organização com d dimensões, especialmente otimizado para dados de IoT/M2M. O modelo depende de características de machine learning para identificar fontes de contexto similares. Estas caracteristicas são utilizadas para aprender relações entre as fontes de dados automaticamente, criando as fundações para a extração de conhecimento automática. Quer machine learning quer métodos convencionais podem depois utilizar estas relações automáticas para extrair conhecimento em datasets potencialmente relevantes. Durante este trabalho, duas técnicas foram desenvolvidas: similaridade semântica e similaridade entre séries temporais. Similaridade semântica estima a similaridade entre conceitos (em forma textual). Este trabalho propõe um método de aprendizagem não supervisionado para features semânticas baseadas em perfis distributivos, sem exigir nenhum corpus específico. Isto permite ao modelo de organização organizar dados baseado em conceitos e não em similaridade de caracteres. Numa outra vantagem importante para os cenários de IoT/M2M, o método de aprendizagem não necessita de dados de entrada adicionados por utilizadores. A similaridade entre séries temporais são métricas que permitem estimar a similaridade entre várias series temporais. Embora estes métodos tenham sido extensivamente desenvolvidos para sequenciação de ADN, normalmente dependem de variantes de métodos baseados na maior sub-sequencia comum. Esta tese de doutoramento propõe um modelo generativo para caracterizar séries temporais, especialmente desenhado para dados IoT/M2M. Este modelo pode ser usado para gerar séries temporais estatisticamente corretas e estimar a similaridade entre múltiplas séries temporais. Posteriormente o modelo de organização identifica fontes de contexto com padrões temporais semelhantes. O trabalho proposto foi extensivamente discutido, desenvolvido e publicado em diversas publicações internacionais. As múltiplas contribuições em projetos e colaborações com colegas, onde partes trabalho desenvolvido foram utilizadas com sucesso, permitem reivindicar que embora o modelo (e subsequentes técnicas) tenha sido otimizado para dados IoT/M2M, podendo ser estendido para lidar com outros tipos de informação de contexto noutras áreas.The present study was developed in the scope of the Smart Green Homes Project [POCI-01-0247-FEDER-007678], a co-promotion between Bosch Termotecnologia S.A. and the University of Aveiro. It is financed by Portugal 2020 under the Competitiveness and Internationalization Operational Program, and by the European Regional Development Fund.Programa Doutoral em Informátic

Dissecting regional heterogeneity and modeling transcriptional cascades in brain organoids

Over the past decade, there has been a rapid expansion in the development and utilization of brain organoid models, enabling three-dimensional in vivo-like views of fundamental neurodevelopmental features of corticogenesis in health and disease. Nonetheless, the methods used for generating cortical organoid fates exhibit widespread heterogeneity across different cell lines. Here, we show that a combination of dual SMAD and WNT inhibition (Triple-i protocol) establishes a robust cortical identity in brain organoids, while other widely used derivation protocols are inconsistent with respect to regional specification. In order to measure this heterogeneity, we employ single-cell RNA-sequencing (scRNA-Seq), enabling the sampling of the gene expression profiles of thousands of cells in an individual sample. However, in order to draw meaningful conclusions from scRNA-Seq data, technical artifacts must be identified and removed. In this thesis, we present a method to detect one such artifact, empty droplets that do not contain a cell and consist mainly of free-floating mRNA in the sample. Furthermore, from their expression profiles, cells can be ordered along a developmental trajectory which recapitulates the progression of cells as they differentiate. Based on this ordering, we model gene expression using a Bayesian inference approach in order to measure transcriptional dynamics within differentiating cells. This enables the ordering of genes along transcriptional cascades, statistical testing for differences in gene expression changes, and measuring potential regulatory gene interactions. We apply this approach to differentiating cortical neural stem cells into cortical neurons via an intermediate progenitor cell type in brain organoids to provide a detailed characterization of the endogenous molecular processes underlying neurogenesis.Im letzten Jahrzent hat die Entwicklung und Nutzung von Organoidmodellen des Gehirns stark zugenommen. Diese Modelle erlauben dreidimensionale, in-vivo ähnliche Einblicke in fundamentale Aspekte der neurologischen Entwicklung des Hirnkortex in Gesundheit und Krankheit. Jedoch weisen die Methoden, um die Entwicklung kortikaler Organoide zu verfolgen, starke Heterogenität zwischen verschiedenen Zelllinien auf. Hier weisen wir nach, dass eine Kombination dualer SMAD und WNT Hemmung (Triple-i Protokoll) eine konstante kortikale Zuordnung in Hirnorganoiden erzeugt, während andere, weit verbreitete und genutzte Protokolle in Bezug auf kortikale Spezifizierung keine konstanten Ergebnisse liefern. Um die Heterogenität zu messen, haben wir Einzelzell-RNA Sequenzierung (scRNA-Seq) benutzt, wodurch die Erfassung der Genexpression von Tausenden von Zellen in einer Probe möglich ist. Um jedoch sinnvolle Schlüsse aus diesen scRNA-Seq Daten zu ziehen, müssen technische Artifakte identifiziert und aus den Daten entfernt werden. In dieser Dissertation stellen wir eine Methode vor, um eines solcher Artifakte zu erkennen: leere Tröpfchen (ohne Zellen), die hauptsächlich aus freischwebender mRNA in der Probe bestehen. Weiterhin können Zellen anhand ihrer Genexpressionsprofile entlang einer Entwicklungsschiene angeordnet werden, die die Entwicklung der Zellen während ihrer Differenzierung rekapituliert. Auf der Grundlage dieser Entwicklungsreihenfolge modellieren wir die Genexpression mit einem Bayes’schen Inferenzansatz, um die Dynamik der Transkription in sich differenzierenden Zellen zu messen. Dies ermöglicht das Anordnen von Genen entlang einer Transkriptionskaskade, sowie statistische Untersuchungen in Hinblick auf Unterschiede in der Veränderung von Genexpression, und das Messen des Einflusses möglicher Regulationsgene. Wir wenden diese Methode an, um kortikale neuronale Stammzellen zu untersuchen, die sich über einen intermediären Vorläuferzelltyp in kortikale Neuronen in Hirnorganoiden differenzieren, und um eine detaillierte Charakterisierung der molekularen Prozesse zu liefern, die der Neurogenese zugrunde liegen

Open Intellectual Property Strategies for Emerging Technologies - An Exploratory, Mixed-Method Investigation of Patent Pledges

Patent owners increasingly employ strategies called patent pledges. This strategy involves the owner offering a broad availability of their active patents for free or at a reasonable fee. Patent pledges span across multiple industries from automotive over information and communication technology to biotechnology. This research investigates patent pledges through an exploratory stance and by utilising a mixed-methods approach. The existing literature is inconsistent and lacks academic rigour. Most patent pledge studies are conceptual and lack the sufficient evidence to offer authoritative results. The first problem is although there exist several preliminary definitions of patent pledges, none are sufficiently derived from empirical data. A second problem is that that existing literature is inconclusive on the reasons why patent owners employ patent pledges. Previous scholars have predominantly relied on findings from studies of other sharing mechanisms, as well as the broad literature on open innovation and patent law. A third point is that research on the effect of patent pledges on technology diffusion remains unclear, because existing studies have utilised research methods with significant limitations and deliver contradictory results. This research addressed each problem in three distinct studies. Study 1 focused on the definition and taxonomy of patent pledges and derived its results from 60 patent pledge statements of patent owners. This secondary data was analysed by an abductive approach through multiple coding cycles. The result was a three-dimensional taxonomy of patent pledges. The patent pledge taxonomy was then extended to a patent licensing taxonomy which compromised every common type of patent licensing approach. Study 2 explored the motivation behind patent pledges and utilised case study research with two data sets: primary data from 22 semi-structured interviews and secondary data from 50 patent pledge statements. The study was able to access renowned experts directly involved in designing and executing patent pledges. Experts included presidents of patent departments from global firms and a former president of a national patent office. Study 3 developed an abstract agent-based model to examine the effects of patent pledges on technology diffusion. The simulation model enabled the investigation of inter-firm technology diffusion of two competing technologies, where only one was subject to a patent pledge. Two cases were considered: in case I both technologies were similar, but in case II the pledged technology was inferior. Study 1 revealed that • eight patent pledge types exist; • the most frequent patent pledge type is subject to certain conditions but can be accessed by the unrestricted public free of charge and that • the majority of patent pledges occur in the are of information and communication technologies. Study 2 revealed that • thirteen patent pledge motives in three categories exist; • the main goal of patent pledges is to drive technology diffusion and that • most other motives for patent pledges also relate to the goal of fostering technology diffusion. Study 3 revealed that • the relationship between the strength of patent pledges and their effect on technology adoption rates is not linear; • patent pledges can lead to a market share ’win’ of an inferior technology that competes with a superior alternative and that • the time period in which the adopter category ’Early Majority’ adopts is, in most cases, the crucial phase that determines the success of patent pledges. The three studies provide a substantial intervention in several ways. The empirical development of a patent pledge definition and two taxonomies facilitate the distinction to other licensing approaches and set a common ground for future research. The taxonomies constitute managerial tools that can be used to visualise patent licensing landscapes for specific units of interest. Insights about motives enable a better understanding of patent pledges and help practitioners as well as policymakers making informed decisions. Finally, the investigation of patent pledge effects on technology diffusion adds to the diffusion literature and supports (i) firms that need to evaluate whether or not to conduct a patent pledge; (ii) firms that need to react to patent pledges of competitors; and (iii) firms that face the decision whether to adopt a pledged technology.- Engineering and Physical Sciences Research Council (EPSRC) DTP Studentship - Research and Development Management Association (RADMA) Doctoral Studies Award 201