The third-party tracking ecosystem continuously evolves in scope, therefore, understanding of it is at best elusive. In this thesis, we investigate this complex ecosystem from three dimen-sions. Firstly, we examine third-party trackers from a geographical perspective. We observe a non-uniform presence of local third-party trackers between regions and countries within re-gions, with some trackers focusing on specific regions and countries. Secondly, we focus on how trackers share user-specific identifiers (IDs). We identify user-specific IDs that we suspect are used to track users. We find a significant amount of ID-sharing practices across different organ-isations providing various service categories. Our observations reveal that ID-sharing happens at a large scale regardless of the user profile size and profile condition such as logged-in and logged-out. Finally, we quantify the effect of tracker-blockers, a popular option for the users to protect their privacy, on the page-load performance. The effect of such tools on the over-all user browsing experience is questionable as the blockage of trackers can disrupt the general website loading process. The tracker-blockers we studied have a considerable negative effect on page-load performance. Unexpectedly, we find that even highly popular websites are negatively affected. This thesis points to significant gaps in our knowledge about the inner workings of this complex ecosystem. Moreover, it highlights some of the challenges that we face when attempting to preserve user's privacy by using tracker-blockers.

Background: Users are an important but underestimated driver of innovation and entrepreneurship. Therefore, they have a positive impact on the competitive position of companies, the development of industries and the wealth of societies as a whole. Our study focuses on the occurrence and development of user entrepreneurship in the esports industry, which is a modern and fast-growing industry that is also characterized by its over-energetic, over-enthusiastic and over-dynamic users. One compelling case of user entrepreneurship can be observed in the game series “Super Smash Bros.” where users have developed an esports scene out of the game without the active involvement of its publisher Nintendo. Research Purpose: The development of an in-depth understanding of how user entrepreneurship evolves and works in the esports industry. Research Problem: Both, user entrepreneurship and the esports industry, are relatively new research areas that have not yet been sufficiently investigated. As user entrepreneurship is assumed to be more likely in industries that are characterized by uncertainty, ambiguity and evolving demands, and in which the product or service provides enjoyment, we deem the esports industry to provide facilitating conditions for its emergence. Therefore, a deeper understanding of genesis and mechanics has the potential to apply those learnings within the industry and to other industries which may benefit from user entrepreneurship as well. Research Question: How do users and the environment in the esports industry enable the occurrence and flourishing of user entrepreneurship? Method: Ontology – Relativism; Epistemology – Social Constructionism; Methodology – Exploratory Single Embedded Case Study; Data Collection – 12 Semi-structured Interviews supported by Online Forum Narratives; Sampling – Purposeful selection of the first Interviewees followed by Snowball Sampling; Data Analysis – Content Analysis (creation of a tree-diagram based on quotes, sub-categories, generic categories and main categories) Conclusion: We developed a model that represents the most important factors for user entrepreneurship apparent in the esports industry and describes how they enable its occurrence and flourishing. Thereby we contribute to an understanding of the interdependence between user- and environmental-specific enabling factors for user entrepreneurship. Our results suggest that the presence of a supportive environment fosters the user entrepreneur’s motivation, knowledge and skills. Practical Implications: Emerging from our findings, implications for producer firms, individual user entrepreneurs and user entrepreneurship communities were developed on how to purposely foster user entrepreneurship and benefit from its occurrence.

Post-implantation facial nerve stimulation is a common side-effect of cochlear electrical stimulation. Facial nerve stimulation can often be resolved through adjustments in speech processor fitting but, in some instances, exhibit limited benefit or may have a detrimental effect on speech perception. In this study, the apical reference stimulation mode was investigated as a potential intervention to facial nerve stimulation. Firstly, a model refinement software tool was developed to improve the accuracy of models created by an automated workflow. Secondly, the refined model of the human cochlea, facial nerve and electrode array, coupled with a neural model, was used to predict excitations of auditory and facial nerve fibres. Finally, psychoacoustic tests were used to determine auditory comfort and threshold levels for the apical reference stimulation mode while simultaneously capturing electromyography data. The refinement tool illustrated an improved accuracy compared to measured data. Models predicted a desirable outcome for apical reference stimulation, as facial nerve fibre thresholds were higher and auditory thresholds were lower, in direct comparison to conventional monopolar stimulation. Psychoacoustic tests illustrated decreased auditory thresholds and increased dynamic range during apical reference stimulation. Furthermore, apical reference stimulation resulted in lower electromyography energy levels, compared to conventional monopolar stimulation, which suggests a reduction in facial nerve stimulation. Subjective feedback corroborated that apical reference stimulation alleviated facial nerve stimulation. This suggests that apical reference stimulation may be a viable strategy to alleviate facial nerve stimulation considering the improvements in dynamic range and auditory thresholds, complemented with a reduction in facial nerve stimulation / Dissertation (MEng (Bioengineering))--University of Pretoria, 2019. / NRF / Electrical, Electronic and Computer Engineering / MEng (Bioengineering) / Unrestricted

本論文では，漸進型遺伝的プログラミングを用いて特定ユーザを対象にした感情認識モデルを進化的に実現する方法論について提案した．特徴量の木構造で解を表現する遺伝的プログラミングを用い，時間情報も含め顔表情データを取得できる汎用センサの情報を基にユーザ適応型の感情認識モデルを進化させた．同時に遺伝的プログラミングの非決定性，汎化性の欠如，過適応に対処するため，進化を漸進的に展開する機構を組み込んだ漸進型遺伝的プログラミング法を開発した． / This research proposes a model to tackle challenges common in Emotion Recognition based on facial expression. First, we use pervasive sensor and environment, enabling natural expressions of user, as opposed to unnatural expressions on a large dataset. Second, the model analyzes relevant temporal information, unlike many other researches. Third, we employ user-specific approach and adaptation to user. We also show that our evolved model by genetic programming can be analyzed on how it really works and not a black-box model. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Applications for sensor-based human activity recognition use the latest algorithms for the detection and classification of human everyday activities, both for online and offline use cases. The insights generated by those algorithms can in a next step be used within a wide broad of applications such as safety, fitness tracking, localization, personalized health advice and improved child and elderly care.In order for an algorithm to be performant, a significant amount of annotated data from a specific target audience is required. However, a satisfying data collection process is cost and labor intensive. This also may be unfeasible for specific target groups as aging effects motion patterns and behaviors. One main challenge in this application area lies in the ability to identify relevant changes over time while being able to reuse previously annotated user data. The accurate detection of those user-specific patterns and movement behaviors therefore requires individual and adaptive classification models for human activities.The goal of this degree work is to compare several supervised classifier performances when trained and tested on a newly iterative user-specific human activity classification approach as described in this report. A qualitative and quantitative data collection process was applied. The tree-based classification algorithms Decision Tree, Random Forest as well as XGBoost were tested on custom based datasets divided into three groups. The datasets contained labeled motion data of 21 volunteers from wrist worn sensors.Computed across all datasets, the average performance measured in recall increased by 5.2% (using a simulated leave-one-subject-out cross evaluation) for algorithms trained via the described approach compared to a random non-iterative approach. / Sensorbaserad aktivitetsigenkänning använder sig av det senaste algoritmerna för detektion och klassificering av mänskliga vardagliga aktiviteter, både i uppoch frånkopplat läge. De insikter som genereras av algoritmerna kan i ett nästa steg användas inom en mängd nya applikationer inom områden så som säkerhet, träningmonitorering, platsangivelser, personifierade hälsoråd samt inom barnoch äldreomsorgen.För att en algoritm skall uppnå hög prestanda krävs en inte obetydlig mängd annoterad data, som med fördel härrör från den avsedda målgruppen. Dock är datainsamlingsprocessen kostnadsoch arbetsintensiv. Den kan dessutom även vara orimlig att genomföra för vissa specifika målgrupper, då åldrandet påverkar rörelsemönster och beteenden. En av de största utmaningarna inom detta område är att hitta de relevanta förändringar som sker över tid, samtidigt som man vill återanvända tidigare annoterad data. För att kunna skapa en korrekt bild av det individuella rörelsemönstret behövs därför individuella och adaptiva klassificeringsmodeller.Målet med detta examensarbete är att jämföra flera olika övervakade klassificerares (eng. supervised classifiers) prestanda när dem tränats med hjälp av ett iterativt användarspecifikt aktivitetsklassificeringsmetod, som beskrivs i denna rapport. En kvalitativ och kvantitativ datainsamlingsprocess tillämpades. Trädbaserade klassificeringsalgoritmerna Decision Tree, Random Forest samt XGBoost testades utifrån specifikt skapade dataset baserade på 21 volontärer, som delades in i tre grupper. Data är baserad på rörelsedata från armbandssensorer.Beräknat över samtlig data, ökade den genomsnittliga sensitiviteten med 5.2% (simulerad korsvalidering genom utelämna-en-individ) för algoritmer tränade via beskrivna metoden jämfört med slumpvis icke-iterativ träning.