Experiences using video technologies in teaching programming

This paper deals with experiences of the author and other staff at UiS in using recorded lectures and other video technologies in teaching.. F ewer students show up for the lectures than they used to before recording and streaming was introduced,, which means that the teacher has much less contact with the students.. The videos are used b y students who cannot or do not want to attend the regular lectures.. The videos are also used for repetition and checking things that students struggle with.. Only a few students watch whole recorded lectures,, the majority just watch smaller parts of them.. Recorded lectures are popular with the students.. There appears to be little difference in student performance as measured in exam results before and after recorded lectures was introduced

UDIT 2022 Preface

The Norwegian conference for education and didactics in IT (UDIT) is a track under the NIKT conference. The UDIT track deals with teaching computer science. Most of the presenters at NIKT are also teachers, and UDIT is meant to facilitate discussion between the staff of all IT departments in Norway on the pedagogical aspect of being a university professor, including teaching, supervision, assessment, etc. This is the third year that UDIT has its own volume in the conference proceedings, which should be seen as evidence that UDIT is no longer a newcomer, but a well-established part of NIKT. In addition, this is the eighth year that UDIT has been arranged as a separate track at NIKT. UDIT 2022 received 11 full paper submissions, of which the program committee accepted five for publication and two more for presentation. New this year is that 5-minute lightning talks have replaced the posters. Three lightning talks have been accepted for presentation this year. We want to thank all those who submitted a paper to UDIT2022. We would also like to give our sincere thanks to the program committee members and the external reviewers who did the essential job of reviewing the submissions. Without authors and reviewers, no conference! We expect you will enjoy this year’s version of NIKT, hosted by the University of Agder

Experiences using video technologies in teaching programming

This paper deals with experiences of the author and other staff at UiS in using recorded lectures and other video technologies in teaching. Fewer students show up for the lectures than they used to before recording and streaming was introduced, which means that the teacher has much less contact with the students. The videos are used by students who cannot or do not want to attend the regular lectures. The videos are also used for repetition and checking things that students struggle with. Only a few students watch whole recorded lectures, the majority just watch smaller parts of them. Recorded lectures are popular with the students. There appears to be little difference in student performance as measured in exam results before and after recorded lectures was introduced.publishedVersio

Representing uncertainty in spatial and spatiotemporal databases

The theme of this thesis is uncertainty in spatial and spatiotemporal databases. Due to lack of accurate measurements, or rapid changes in time, spatial and spatiotemporal data are often uncertain. This thesis presents new abstract and discrete models for uncertain spatial and spatiotemporal information. The models are based on the principle that one knows that the uncertain object, regardless of type, must be within a certain area. The first part of this thesis concerns an abstract model. To this author’sknowledge, this is the first attempt to create a general type system for uncertainty with spatial data. Individual uncertain types have been modelled before, but no work has studied points, lines and regions and used the same principles to model all three. It also seems to be the first model to handle temporal as well as spatial uncertainty. This thesis contains mathematical definitions of uncertain points, lines, regions and temporal versions of these. The thesis also contains definitions of relevant operations on these types. These operations are also evaluated for their usefulness with regard to uncertain data. The second part of this thesis concerns three discrete models which are all based on the abstract model mentioned earlier. One of these is an advanced model that manages to model almost all of the aspects of the abstract model, but at the cost of increased need for storage space. It is also difficult to compute probabilities in a consistent manner for this model.The second model is of medium complexity, and balances storage use and modelling power. It also has the advantage that computing probabilities in a consistent manner is much easier than for the advanced model. The third model is an attempt to bring the storage space needed as low as possible. It therefore has somewhat limited modelling power. Unlike the two other discrete models, it cannot be extended to handle spatiotemporal data. The handling of uncertain spatiotemporal data is based on how crisp spatiotemporal data are handled in [GBE+00] and [FGNS00]. This thesis makes two important additions to these models so that they can handle uncertain data. First, it presents ways of generating a sliced representation when the times the snapshots were taken are uncertain. Second, it details how operations change as a result of uncertainty. The Initial and Final operations exemplify this as in the crisp case they return the initial and final shapes of an object, but they cannot be defined in the uncertain case. This thesis discusses how these operations can be replaced in the uncertain case

Detecting flu outbreaks based on spatiotemporal information from urban systems - designing a novel study

This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal flu epidemics from a spatial perspective. We believe that combining travel data with epidemiological data will be the first step to develop a tool to predict future epidemics and to better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants. In this paper, we describe and discuss the design of the geospatial project, in which we will draw upon data sources available from the Norwegian cities of Oslo and Bergen. Historical datasets from public transport and road traffic will serve as an initial indication of whether changes in daily transport patterns corresponds to seasonal flu data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly flu activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of flu outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.publishedVersio

Automatic diagnostic tool for predicting cancer grade in bladder cancer patients using deep learning

The most common type of bladder cancer is urothelial carcinoma, which is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists. We propose a pipeline, called TRI grade , that will identify diagnostic relevant regions in the whole-slide image (WSI) and collectively predict the grade of the current WSI. The system consists of two main models, trained on weak slide-level grade labels. First, a WSI is segmented into the different tissue classes (urothelium, stroma, muscle, blood, damaged tissue, and background). Next, tiles are extracted from the diagnostic relevant urothelium tissue from three magnification levels (25x, 100x, and 400x) and processed sequentially by a convolutional neural network (CNN) based model. Ten models were trained for the slide-level grading experiment, where the best model achieved an F1-score of 0.90 on a test set consisting of 50 WSIs. The best model was further evaluated on a smaller segmentation test set, consisting of 14 WSIs where low- and high-grade regions were annotated by a pathologist. The TRI grade pipeline achieved an average F1-score of 0.91 for both the low-grade and high-grade classes.publishedVersio