Questo lavoro studia la distribuzione della dimensione d'impresa sulla base di due datasets. Il primo è l'indagine micro1 di istat, che include tutte le imprese manifatturiere con più di 20 addetti sopravvissute dal 1989 al 1997. Il secondo è il file Cerved riguardante l'universo delle imprese del settore meccanico (atecodk29), dal 1997 al 2002. Lo scopo generale della tesi è quello di espolare la possibilità di trovare nuove regolarità empiriche riguardanti la distribuzione della dimensione d'impresa, sulla base della passata evidenza empirica che attesta la (in)capacità di Lognormale e Pareto di modellare in modo soddisfacente la dimensione d'impresa nell'intero arco dimensionale. Vengono per questo proposti due modelli mai utilizzati prima. Gli stessi vengono poi convalidati su differenti variabili dimensionali e a diversi livelli di aggregazione. La tesi cerca anche di esplicitare al meglio le implicazioni economiche dei modelli parametrici di distribuzione adottati secondo diversi punti di vista. / The present work studies the firm size distribution of Italian manufacturing industries on the basis of two datasets. The first is the Micro1 survey carried out by ISTAT, which recorded all manufacturing firms with 20 employees and more surviving from 1989 to 1997. The second is the Cerved file regarding all firms of the mechanical sector (DK29) from 1997 to 2002. The general aim of this research is to explore the possibility to find new empirical regularities in the size distribution of firms, building on the relevant past evidence about the (in) capacity of the Lognormal and Pareto distribution of satisfactorily modelling the whole size range. Two unused statistical models are proposed and validated on different size proxies and at different levels of data aggregation. The thesis also addresses the economic implications of parametric models of firm size distribution in different aspects.

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This thesis deals with the role of experiences at camps for children and youth and the possibilities of subsequent work with these experiences. The theoretical part describes and differentiates between the Czech terms prožitek, zážitek and zkušenost (experience). Special attention is given to the process of experience-based education and six basic experiential learning models are specified. Feedback and targeted feedback are also mentioned with special focus being given to facilitation, its rules and techniques. The thesis also informs about the connection between work with feedback and dramaturgy. Qualitative research finds out whether camp leaders work with the participants' experiences, clarifies the phenomena (not) leading to this fact, describes their experience with the form of processing the experiences and determines if they have some theoretical knowledge in this field.

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

Yes / This survey provides an insightful overview of recent advancements in facial recognition technology, mainly focusing on multi-modal face recognition and its applications in security biometrics and identity verification. Central to this study is the Sejong Face Database, among other prominent datasets, which facilitates the exploration of intricate aspects of facial recognition, including hidden and heterogeneous face recognition, cross-modality analysis, and thermal-visible face recognition. This paper delves into the challenges of accurately identifying faces under various conditions and disguises, emphasising its significance in security systems and sensitive sectors like banking. The survey highlights novel contributions such as using Generative Adversarial Networks (GANs) to generate synthetic disguised faces, Convolutional Neural Networks (CNNs) for feature extractions, and Fuzzy Extractors to integrate biometric verification with cryptographic security. The paper also discusses the impact of quantum computing on encryption techniques and the potential of post-quantum cryptographic methods to secure biometric systems. This survey is a critical resource for understanding current research and prospects in biometric authentication, balancing technological advancements with ethical and privacy concerns in an increasingly digital society. / European Union’s Horizon-Marie Skłodowska-Curie Actions (MSCA)-RISE-2019-2023, Marie Skłodowska-Curie, Research, and Innovation Staff Exchange (RISE), titled: Secure and Wireless Multimodal Biometric Scanning Device for Passenger Verification Targeting Land and Sea Border Control

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.

The paper shows how crowdfunding campaigns aimed at African startups depend on the factors of their success and the actions of sponsors. Crowdfunding has emerged as an important financial solution to solve the problems that arise when using conventional financing methods in Africa, such as, for example, high-interest loans. Based on the study of regional, temporal and technological factors, this study suggests practical ways to improve crowdfunding mechanisms using machine learning models such as logistic regression, random forest, support vector machines, XGBoost. / В работе показано, как краудфандинговые кампании, ориентированные на африканские стартапы, зависят от факторов их успеха и действий спонсоров. Краудфандинг появился как важное финансовое решение, позволяющее решать проблемы, возникающие при использовании обычных способов финансирования в Африке, таких как, например, займы под высокие проценты. На основе изучения региональных, временных и технологических факторов это исследование предлагает практические способы улучшения краудфандинговых механизмов с применением моделей машинного обучения, таких как логистическая регрессия, случайный лес, методы опорных векторов, XGBoost.

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)