The Covid-19 pandemic has affected the world globally and the way humans conduct their work today. Face-to-face meetings have since before the pandemic been the most standard and effective way to do business, but due to the recommendations from the Swedish Public Health Agency workshops conducted by ERP-consultants does now have to be executed digitally. The transition from physical to digital meetings has imposed major challenges for consultants such as replicating the communication, lack of engagement and a higher risk for becoming fatigued. However, there is little to no research from an ERP-consultant's perspective on how to facilitate and conduct workshops as well as how it has now been affected by the ongoing global pandemic. Therefore, the purpose of the study is to gain an understanding of workshops conducted by ERP-consultants, comparing the process of a traditional workshop to digital workshops. In addition, understanding which strengths and weaknesses can be found with traditional and digital workshops. The study also aims to create guidelines that will help ERP-consultants to achieve higher quality in digital workshops. The data was gathered via semi-structured interviews with five ERP-consultants and was split into two stages; first stage aimed at identifying strength and weaknesses, further the second stage of interviews aimed at evaluating the created guidelines. From the interviews several strengths and weaknesses were identified, and the weaknesses acted as a foundation for the creation of the five guidelines. In conclusion, 1) there are strengths in traditional workshops that are lost in digital workshops, and 2) there are added strengths in digital workshops that could not be fulfilled in traditional workshops.  Lastly, the study found that the traditional workshop and the face-to-face communication can not be fully replaced by the digital as it is human nature to connect and socialize with other people.

The aim of this work is to realize a system in the Matlab-Simulink environment, which will be able to detect and recognize the human face from the input image. The created model will actually simulate the biometric security systems working on the principle of face recognition. The work is divided into two parts. In the first part, several methods for face detection from image are described. We focused on the symptomatic oriented and color segmentation methods. The pattern matching method is also described and implemented; the advantage ofthe pattern matching that it can be used either for face detection or face recognition. The second part of this work contains a description of the face recognition. Where PCA (Principal Component Analysis) are used for this task, this part of the work also includes experimental results of tests performed on our methods.

Diploma thesis is focused on the application of PVD coatings for face milling. The theoretical part, systematically, deals with the division of coating methods. PVD method is stressed here. Treatment of material before coating is described as well. The machine, the machine tool and cutting material is described in the experimental section. The aim of the work was comparison of coating exchangeable cutting tips (VBD) to uncoated ones. Based on experiment, it is clear that the use of PVD coatings resulted in reduction of cutting powers and lifetime was increased.

Från och med 1 april 2022 klassas inte längre Covid-19 som en allmänfarlig- eller samhällsfarlig sjukdom och restriktionerna som tidigare tvingade fram digital vägledning i skolor gäller inte längre.Studiens syfte och frågeställning är att undersöka hur studie- och yrkesvägledare förhåller sig till digital vägledning efter covid-19 pandemin och vilka fördelar- samt nackdelar de upplevt med digital vägledning.I denna kvalitativa studie intervjuades sex studie- och yrkesvägledare där alla informanterna var verksamma på olika grundskolor. Intervjuerna har gjorts med utgångspunkt i en semistrukturerad intervjuguide.Genom att analysera intervjuerna med hjälp av Malténs kommunikationsteori och Egans The Skilled Helper Model har vi besvarat vårt syfte och frågeställning. Egan och Maltén beskriver bland annat att icke-verbal kommunikation är en viktig komponent i kommunikation då denna utgör en stor del av samtal.Resultaten i denna studie visar att digital vägledning missar en del av den icke-verbala kommunikationen då parterna till viss del inte kan se varandras kroppsspråk.Det framkommer även att samtliga informanter har uppfattningen att den digitala vägledningen aldrig kan ersätta det fysiska mötet face-to-face men att det finns fler fördelar än nackdelar med digital vägledning.I studien lyfts också att vissa särskilda grupper, till exempel elever med NPF-diagnos och hemmasittare gynnas av digital vägledning.

Human key point tracking such as face detection and recognition has become an increasingly popular research topic. It is a platform independent functionality and already being implemented on a wide range of platforms. Android is one such platform that runs on mobile phones and top of many edge devices such as car devices and smart home appliances. In the current times, AI and ML related applications are slightly moving into those edge devices due to various reasons such as security and low latency. The hardware enhancements are also backing this trend that happened over the last few years. Many solutions and algorithms have been proposed in this context, and various frameworks and models have also been developed. Even though there are different models available, they tend to deliver varying results in terms of performance. Evaluating these different alternatives to find an optimized solution is a problem worth addressing. In this thesis project, several selected face detection and recognition models have been implemented in an Android device, and their performance been evaluated. Google ML Kit showed the best results among the face detection methods since it took only around 68 milliseconds on average to detect a face. Out of the three face recognition algorithms evaluated, FaceNet was the most accurate as it showed an accuracy above 95% for most cases. Meanwhile, MobileFaceNet was the fastest algorithm, and it took only around 90 milliseconds on average to produce and output. Eventually, a face recognition application was also developed using the best performing models selected from the experiment.

Recent developments suggest that the need for contact between parents and adult children is expected to grow, while paid labour is re-organized to include more flexible work schedules and locations. In parallel we view a pressure to increase sustainable mobility through reducing car driving. Against this background, this paper addresses the question: to what extent the frequency of contact between parents and their adult children living out of home is associated with time allocated to work, including commuting time, and with automobility? Face-to-face and telecommunication based contact is considered. Regression analysis of survey data collected in the Netherlands was performed and results suggest that face-to-face contact was significantly associated with work and commute duration, car ownership, car commuting and distance. Telecommunication based contact was mainly associated with work duration, degree of urbanization and distance. Automobility seemed to be more important for women than for men. The policy implication is a potential trade-off between policies that aim at strengthening sustainable mobility behaviour and policies that lead to an increase in the reliance on informal care.

Face recognition is often described as the process of identifying and verifying people in a photograph by their face. Researchers have recently given this field increased attention, continuously improving the underlying models. The objective of this study is to implement a real-time face recognition system using one-shot learning. “One shot” means learning from one or few training samples. This paper evaluates different methods to solve this problem. Convolutional neural networks are known to require large datasets to reach an acceptable accuracy. This project proposes a method to solve this problem by reducing the number of training instances to one and still achieving an accuracy close to 100%, utilizing the concept of transfer learning.

L’analyse de visages 3D y compris la reconnaissance des visages et des expressions faciales 3D est devenue un domaine actif de recherche ces dernières années. Plusieurs méthodes ont été développées en utilisant des images 2D pour traiter ces problèmes. Cependant, ces méthodes présentent un certain nombre de limitations dépendantes à l’orientation du visage, à l’éclairage, à l’expression faciale, et aux occultations. Récemment, le développement des capteurs d’acquisition 3D a fait que les données 3D deviennent de plus en plus disponibles. Ces données 3D sont relativement invariables à l’illumination et à la pose, mais elles restent sensibles à la variation de l’expression. L’objectif principal de cette thèse est de proposer de nouvelles techniques de reconnaissance/vérification de visages et de reconnaissance d’expressions faciales 3D. Tout d’abord, une méthode de reconnaissance de visages en utilisant des matrices de covariance comme des descripteurs de régions de visages est proposée. Notre méthode comprend les étapes suivantes : le prétraitement et l’alignement de visages, un échantillonnage uniforme est ensuite appliqué sur la surface faciale pour localiser un ensemble de points de caractéristiques. Autours de chaque point, nous extrayons une matrice de covariance comme un descripteur de région du visage. Deux méthodes d’appariement sont ainsi proposées, et différentes distances (géodésiques / non-géodésique) sont appliquées pour comparer les visages. La méthode proposée est évaluée sur troisbases de visages GAVAB, FRGCv2 et BU-3DFE. Une description hiérarchique en utilisant trois niveaux de covariances est ensuite proposée et validée. La deuxième partie de cette thèse porte sur la reconnaissance des expressions faciales 3D. Pour ce faire, nous avons proposé d’utiliser les matrices de covariances avec les méthodes noyau. Dans cette contribution, nous avons appliqué le noyau de Gauss pour transformer les matrices de covariances en espace d’Hilbert. Cela permet d’utiliser les algorithmes qui sont déjà implémentés pour l’espace Euclidean (i.e. SVM) dans cet espace non-linéaire. Des expérimentations sont alors entreprises sur deux bases d’expressions faciales 3D (BU-3DFE et Bosphorus) pour reconnaître les six expressions faciales prototypiques. / 3D face analysis including 3D face recognition and 3D Facial expression recognition has become a very active area of research in recent years. Various methods using 2D image analysis have been presented to tackle these problems. 2D image-based methods are inherently limited by variability in imaging factors such as illumination and pose. The recent development of 3D acquisition sensors has made 3D data more and more available. Such data is relatively invariant to illumination and pose, but it is still sensitive to expression variation. The principal objective of this thesis is to propose efficient methods for 3D face recognition/verification and 3D facial expression recognition. First, a new covariance based method for 3D face recognition is presented. Our method includes the following steps : first 3D facial surface is preprocessed and aligned. A uniform sampling is then applied to localize a set of feature points, around each point, we extract a matrix as local region descriptor. Two matching strategies are then proposed, and various distances (geodesic and non-geodesic) are applied to compare faces. The proposed method is assessed on three datasetsincluding GAVAB, FRGCv2 and BU-3DFE. A hierarchical description using three levels of covariances is then proposed and validated. In the second part of this thesis, we present an efficient approach for 3D facial expression recognition using kernel methods with covariance matrices. In this contribution, we propose to use Gaussian kernel which maps covariance matrices into a high dimensional Hilbert space. This enables to use conventional algorithms developed for Euclidean valued data such as SVM on such non-linear valued data. The proposed method have been assessed on two known datasets including BU-3DFE and Bosphorus datasets to recognize the six prototypical expressions.

In order to minimize the effects of COVID-19 on teaching and learning, educational institutions around the world moved quickly to transfer education from traditional classrooms to an online learning environment. The main objective of this study was to determine the effects of COVID-19 on students and instructors of several world languages at Brigham Young University (Provo, Utah), highlighting the viability or possible limitations of online learning in university teaching and learning during COVID-19. Thus, this study investigated the effects this transition had on students' learning as well as instructors' teaching practices. Using the thematic analysis described by Braun and Clarke (2013) to analyze which factors influenced the instructors' and students' adaptations to a new online teaching and learning environment during this period of transition from the traditional classroom to online learning, ensured a comprehensive study of the qualitative data. Using survey data, I was able to make conclusions derived directly from the data collected. Results of this study indicated that most learners and instructors felt that the following factors are necessary for online learning to be successful and beneficial: A well designed, rigorous curriculum; technological and emotional support before and during the transition; constant feedback from instructors; opportunities for student-to-student collaboration; and opportunities for instructors and students to build relationships. These results support previous research, which has also shown that the online environment requires more than simply transferring content from a traditional to an online setting. Instructors must be organized and know how to implement the course curriculum and use the right methods for the new environment to avoid factors that trigger stress and anxiety, thus blocking their learning. Therefore, appropriate training from the institutions as well should be ensured before transitioning to an online environment.

The diploma thesis investigates the measurable progress in the ability of upper-secondary school students to form and use the passive voice after having been exposed to two online lessons on this grammar item. The theoretical part describes distance education, its varieties, and the tools that are assumed to make it effective. The empirical part of the thesis is based on the analysis of the two online lessons,the analysis of the students' development based on short pre- and post-tests developed to evaluate the students' knowledge, and an interview with two of the students.The thesis will attempt to reach a conclusion on whether there is a measurable development in students' understanding of the subject matter when taught in an online environment.The thesis proved that despite the distance form of education of the two lessons, the acquisitionof the grammar item was successful.