Le sentiment d’agentivité est défini comme le sentiment de contrôler nos actions, et à travers elles, les évènements du monde extérieur. Cet ensemble phénoménologique dépend de notre capacité d’apprendre les contingences entre nos actions et leurs résultats, et un algorithme classique pour modéliser cela vient du domaine de l’apprentissage par renforcement. Dans cette thèse, nous avons utilisé l’approche de modélisation cognitive pour étudier l’interaction entre agentivité et apprentissage par renforcement. Tout d’abord, les participants réalisant une tâche d’apprentissage par renforcement tendent à avoir plus d’agentivité. Cet effet est logique, étant donné que l’apprentissage par renforcement consiste à associer une action volontaire et sa conséquence. Mais nous avons aussi découvert que l’agentivité influence l’apprentissage de deux manières. Le mode par défaut pour apprendre des contingences action-conséquence est que nos actions ont toujours un pouvoir causal. De plus, simplement choisir une action change l’apprentissage de sa conséquence. En conclusion, l’agentivité et l’apprentissage par renforcement, deux piliers de la psychologie humaine, sont fortement liés. Contrairement à des ordinateurs, les humains veulent être en contrôle, et faire les bons choix, ce qui biaise notre aquisition d’information. / Sense of agency or subjective control can be defined by the feeling that we control our actions, and through them effects in the outside world. This cluster of experiences depend on the ability to learn action-outcome contingencies and a more classical algorithm to model this originates in the field of human reinforcementlearning. In this PhD thesis, we used the cognitive modeling approach to investigate further the interaction between perceived control and reinforcement learning. First, we saw that participants undergoing a reinforcement-learning task experienced higher agency; this influence of reinforcement learning on agency comes as no surprise, because reinforcement learning relies on linking a voluntary action and its outcome. But our results also suggest that agency influences reinforcement learning in two ways. We found that people learn actionoutcome contingencies based on a default assumption: their actions make a difference to the world. Finally, we also found that the mere fact of choosing freely shapes the learning processes following that decision. Our general conclusion is that agency and reinforcement learning, two fundamental fields of human psychology, are deeply intertwined. Contrary to machines, humans do care about being in control, or about making the right choice, and this results in integrating information in a one-sided way.

In a world where artificial intelligence and machine learning aregrowing and spreading in society, its impact and consequence forpeople is increasing. The technology is used in services that peopleuse every day. Both privately but also in a commercial context, forexample social media and to identify fraud in the banking sector.Previous studies show that machine learning models can givediscriminatory outcomes when it comes to, among other things,gender and ethnicity. This study aims to investigate how, in systemdevelopment projects where machine learning is used, one works tocounteract discriminatory outcomes. The study examines both thefactors that contribute to the emergence of discriminatoryoutcomes, as well as the solutions that exist to counteract theproblem. The study is conducted at a global IT consultingcompany.To investigate the area, a study, with qualitative researchmethodology, has been conducted. The empirical material has beencollected through six semi-structured interviews. All respondentswho participated in the study work within the same organization, indifferent projects and with varying experiences in the area. Therespondents have been selected through a subjective selectionbased on their experience in the field in relation to the purpose ofthe study.The results of the study show that the decisive factor for theemergence of discrimination is the training data which the modelsare trained with. The majority of solutions to counteractdiscriminatory outcomes have also been identified. The results ofthe study differ to some extent from the previous research done inthe field. Regarding factors, previous research and the results of thestudy agree that data is the decisive factor that contributes todiscriminatory outcomes arising from machine learning models.The main difference among the solutions is that previous researchshows more specific techniques, which are used to identify ormitigate discriminatory outcomes, while the results of the studyshow softer values and almost no specific techniques at all. In theresults of the study, for example, the individual is seen as a centralpart of the process instead of automatic techniques and tools.The study concludes that data is the most decisive factor indiscriminatory outcomes in machine learning models. The modelsare not discriminatory in themselves, they only reflect the trainingdata. If the data contains discrimination, the model will learn thisand ultimately give discriminatory outcomes. The very basicproblem for this is the human being, who creates the prejudices thatexist in society and from which the data is collected. At the sametime, man is a central part of the process of reducing discriminatoryoutcomes and is needed to counteract this problem. / I en värld där artificiell intelligens och maskininlärning växer ochsprids i samhället ökar samtidigt dess påverkan och konsekvens förmänniskor. Tekniken används i tjänster som människor användervarje dag. Både privat men även i ett kommersiellt sammanhang,exempelvis sociala medier och för att identifiera bedrägerier inombanksektorn. Tidigare studier visar att maskininlärningsmodellerkan ge diskriminerande utfall när det kommer till bland annat könoch etnicitet. Denna studie syftar till att undersöka hur man, isystemutvecklingsprojekt där maskininlärning används, arbetar föratt motverka diskriminerande utfall. Studien undersöker både vilkafaktorer som bidrar till att diskriminerande utfall uppstår, samtvilka lösningar som finns för att motverka problemet. Studiengenomförs på ett globalt IT-konsultbolag.För att undersöka området har en studie, med kvalitativforskningsmetodik genomförts. Det empiriska materialet harsamlats in via sex stycken semistrukturerade intervjuer. Samtligarespondenter som deltagit i studien arbetar inom sammaorganisation i olika systemutvecklingsprojekt samt med varierandeerfarenheter inom området. Respondenterna har valts ut genom ettsubjektivt urval baserad på deras erfarenhet inom området samt irelation med studiens syfte.Studiens resultat visar att den mest avgörande faktorn för uppkomstav diskriminering är träningsdatat som modellerna tränas med.Flertalet lösningar för att motverka diskriminerande utfall har ävenidentifierats i studien. Studiens resultat skiljer sig till viss del motden tidigare forskning som gjorts inom området. Gällande faktorerär tidigare forskning och studiens resultat eniga om att datat är denavgörande faktorn som bidrar att diskriminerande utfall uppstårfrån maskininlärningsmodeller. Den största skillnaden blandlösningarna är att tidigare forskning visar på mer specifika teknikeroch verktyg som används för att identifiera eller mildradiskriminerande utfall, medan resultatet i studien visar mer mjukavärden och nästan inga specifika tekniker alls. I studiens resultatses exempelvis den enskilda individen som en central del iprocessen istället för automatiska tekniker och verktyg. Vidareframkommer det i resultatet blandade åsikter gällande ansvaret förmaskininlärningsmodeller samt behov av regleringar på området.Studiens slutsats är att datat är den mest avgörande faktorn till attdiskriminerande utfall uppstår i maskininlärningsmodeller.Modellerna är inte diskriminerande i sig, utan de speglar bara8. Handledare9. Examinator10. Termin11. Övrigt/AnmärkningKomplettera i alla blanka fält. Gråmarkerade fält skall kompletteras när det finns anledning. I annatfall ska de avlägsnas. För mer information se ”HANDLÄGGNING AV RAPPORT, DEL AV SJÄLVSTÄNDIGT ARBETE(EXAMENSARBETE), INOM NMT”, MIUN 2015/XXX. Det är examinator som är ansvarig för innehållet idetta dokument.träningsdatat. Om datat innehåller diskriminering kommermodellen att lära sig detta och slutligen ge diskriminerande utfall.Själva grundproblemet till detta är människan som skapat defördomar som finns i samhället vilket är där träningsdatat samlas infrån. Samtidigt visar studiens resultat att människan idag är encentral del i processen med att både motverka och identifieradiskriminerande utfall från maskininlärningsmodeller

The task of the judge of difficulty in trampoline gymnastics is to check the elements and difficulty values entered on the competition cards and the difficulty of each element according to a numeric system. To do this, the judge must count all somersaults and twists for each jump during a routine and thus record the difficulty of the routine. This assessment can be automated with the help of inertial measurement units (IMUs) and facilitate the judges’ task during the competition. Currently, there is no known reliable method for the automated detection and recognition of the various elements to determine the difficulty of an exercise in trampoline gymnastics. Accordingly, a total of 2076 jumps and 50 different jump types were recorded over the course of several training sessions. In the first instance, 10 different jump types were used to train different machine learning (ML) models. Eight ML models were used for the automatic jump classification. Supervised learning approaches include a naive classifier, deep feedforward neural network, convolutional neural network, k‑nearest neighbors, Gaussian naive Bayes, support-vector classification, gradient boosting classifier, and stochastic gradient descent. When all classifiers were compared for accuracy, i.e., how many jumps were correctly detected by the ML model, the deep feedforward neural network and the convolutional neural network provided the best matches with 96.4 and 96.1%, respectively. The findings of this study will help to develop the automated classification of sensor-based data to support the judge and, simultaneously, for automated training logging.

This study explores the potential of artificial intelligence (AI) in enhancing the diagnosis of brain tumours, specifically through a comparative analysis of two advanced deep learning (DL) models, ResNet50 and YOLOv8, applied to detect and classify brain tumours in MRI images. The study addresses the critical need for rapid and accurate diagnostic tools in the medical field, given the complexity and diversity of brain tumours. The research was motivated by the potential benefits AI could offer to medical diagnostics, particularly in terms of speed and accuracy, which are crucial for effective patient treatment and outcomes. The performance of the ResNet50 and YOLOv8 models was evaluated on a dataset of 7023 MRI images across four tumour types. Key metrics used were accuracy, precision, recall, specificity, F1-score, and processing time, to identify which model performs better in detecting and classifying brain tumours. The findings demonstrates that although both models exhibit high performance, YOLOv8 surpasses ResNet50 in most metrics, particularly showing advantages in speed. The findings highlight the effectiveness advanced DL models in medical image analysis, providing a significant advancement in brain tumour diagnosis. By offering a thorough comparative analysis of two commonly used DL models, aligning with ongoing approaches to integrate AI into practical medical application, and highlighting their potential uses, this study advances the area of medical AI providing insight into the knowledge required for the deployment of future AI diagnostic tools.

During the past thirty years several models for lifelong education after retirement have been developed worldwide, one of them being the University of the Third Age (U3A). This study explored the contributions of the U3A to the educational needs of older adults and evaluated the benefits they perceived from their participation in U3A by means of a literature study and an empirical investigation. The latter used a survey to explore the experiences of U3A members of two U 3As and presidents of 68 U3As in Victoria, Australia by means of two different questionnaires. As only 1.47 percent of the over-55 population of Victoria are U3A members, the survey also investigated barriers to U3A participation in general and with special reference to the male population. The findings indicated that member respondents were very satisfied with their U3A experiences which had made substantial differences in their lives. Both male and female respondents saw personal, mental, social, and physical improvement as a result of U3A participation. The majority indicated that participation had improved their intellectual development. Significant differences in the perceptions of male and female participants emerged: female members outnumbered males by three to one. Both the presidents and the members expressed some programmatic concerns, primarily obtaining tutors and classroom availability. The subject areas covered by courses presented were extensive. There was a difference in the subjects desired by males and female respondents; very few courses are offered in science and economics. Some barriers to participation identified are a lack of awareness of U3A, the stereotypical attitudinal barrier of `I am too old' and negative past educational experiences. Moreover, U3As should increase marketing endeavours. Although most U3As advertise, almost a third of the respondents indicated that they would have joined earlier if aware of U3As. A contributing factor appears to be a virtual lack of research and information provided in educational academic journals and other media about lifelong education after retirement. Based on the findings, recommendations were made for future research and for improved practice in the U3A environment as a means to enhance the quality of life for older adults. / Educational Studies / D.Ed. (Comparative Education)

During the past thirty years several models for lifelong education after retirement have been developed worldwide, one of them being the University of the Third Age (U3A). This study explored the contributions of the U3A to the educational needs of older adults and evaluated the benefits they perceived from their participation in U3A by means of a literature study and an empirical investigation. The latter used a survey to explore the experiences of U3A members of two U 3As and presidents of 68 U3As in Victoria, Australia by means of two different questionnaires. As only 1.47 percent of the over-55 population of Victoria are U3A members, the survey also investigated barriers to U3A participation in general and with special reference to the male population. The findings indicated that member respondents were very satisfied with their U3A experiences which had made substantial differences in their lives. Both male and female respondents saw personal, mental, social, and physical improvement as a result of U3A participation. The majority indicated that participation had improved their intellectual development. Significant differences in the perceptions of male and female participants emerged: female members outnumbered males by three to one. Both the presidents and the members expressed some programmatic concerns, primarily obtaining tutors and classroom availability. The subject areas covered by courses presented were extensive. There was a difference in the subjects desired by males and female respondents; very few courses are offered in science and economics. Some barriers to participation identified are a lack of awareness of U3A, the stereotypical attitudinal barrier of `I am too old' and negative past educational experiences. Moreover, U3As should increase marketing endeavours. Although most U3As advertise, almost a third of the respondents indicated that they would have joined earlier if aware of U3As. A contributing factor appears to be a virtual lack of research and information provided in educational academic journals and other media about lifelong education after retirement. Based on the findings, recommendations were made for future research and for improved practice in the U3A environment as a means to enhance the quality of life for older adults. / Educational Studies / D.Ed. (Comparative Education)

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.

Image Super-Resolution (ISR) is a technology that aims to increase image resolution while preserving as much content and detail as possible. In this study, we evaluate four different Deep Learning models (EDSR, LapSRN, ESPCN, and FSRCNN) to determine their effectiveness in increasing the resolution of lowresolution images. The study builds on previous research in the field as well as the results of the comparison between the different deep learning models. The problem statement for this study is: “Which of the four Deep Learning-based models, EDSR, LapSRN, ESPCN, and FSRCNN, generates an upscaled image with the best quality from a low-resolution image on a dataset of Abyssinian cats, with a factor of four, based on quantitative results?” The study utilizes a dataset consisting of pictures of Abyssinian cats to evaluate the performance and results of these different models. Based on the quantitative results obtained from RMSE, PSNR, and Structural Similarity (SSIM) measurements, our study concludes that EDSR is the most effective Deep Learning-based model. / Bildsuperupplösning (ISR) är en teknik som syftar till att öka bildupplösningen samtidigt som så mycket innehåll och detaljer som möjligt bevaras. I denna studie utvärderar vi fyra olika Deep Learning modeller (EDSR, LapSRN, ESPCN och FSRCNN) för att bestämma deras effektivitet när det gäller att öka upplösningen på lågupplösta bilder. Studien bygger på tidigare forskning inom området samt resultatjämförelser mellan olika djupinlärningsmodeller. Problemet som studien tar upp är: “Vilken av de fyra Deep Learning-baserade modellerna, EDSR, LapSRN, ESPCN och FSRCNN generarar en uppskalad bild med bäst kvalité, från en lågupplöst bild på ett dataset med abessinierkatter, med skalningsfaktor fyra, baserat på kvantitativa resultat?” Studien använder en dataset av bilder på abyssinierkatter för att utvärdera prestandan och resultaten för dessa olika modeller. Baserat på de kvantitativa resultaten som erhölls från RMSE, PSNR och Structural Similarity (SSIM) mätningar, drar vår studie slutsatsen att EDSR är den mest effektiva djupinlärningsmodellen.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. This thesis presents a critical examination of the efficacy of deep learning models in comparison to classical approaches for time series anomaly detection. Contrary to the widespread belief in the superiority of deep learning models, our research findings suggest that their performance may be misleading and the progress illusory. Through rigorous experimentation and evaluation, we reveal that classical models outperform deep learning counterparts in various scenarios, challenging the prevailing assumptions. In addition to model performance, our study delves into the intricacies of evaluation metrics commonly employed in time series anomaly detection. We uncover how it inadvertently inflates the performance scores of models, potentially leading to misleading conclusions. By identifying and addressing these issues, our research contributes to providing valuable insights for researchers, practitioners, and decision-makers in the field of time series anomaly detection, encouraging a critical reevaluation of the role of deep learning models and the metrics used to assess their performance. / Tidsperiods avvikelsedetektering har varit ett långvarigt forskningsområde med tillämpningar inom olika områden. Under de senaste åren har det uppstått ett ökat intresse för att tillämpa djupinlärningsmodeller på detta problemområde. Denna avhandling presenterar en kritisk granskning av djupinlärningsmodellers effektivitet jämfört med klassiska metoder för tidsperiods avvikelsedetektering. I motsats till den allmänna övertygelsen om överlägsenheten hos djupinlärningsmodeller tyder våra forskningsresultat på att deras prestanda kan vara vilseledande och framsteg illusoriskt. Genom rigorös experimentell utvärdering avslöjar vi att klassiska modeller överträffar djupinlärningsalternativ i olika scenarier och därmed utmanar de rådande antagandena. Utöver modellprestanda går vår studie in på detaljerna kring utvärderings-metoder som oftast används inom tidsperiods avvikelsedetektering. Vi avslöjar hur dessa oavsiktligt överdriver modellernas prestandapoäng och kan därmed leda till vilseledande slutsatser. Genom att identifiera och åtgärda dessa problem bidrar vår forskning till att erbjuda värdefulla insikter för forskare, praktiker och beslutsfattare inom området tidsperiods avvikelsedetektering, och uppmanar till en kritisk omvärdering av djupinlärningsmodellers roll och de metoder som används för att bedöma deras prestanda.