| 1. |
- Rexhepi, Hanife, 1984-, et al.
(författare)
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Cancer patients’ information seeking behavior related to online electronic healthcare records
- 2021
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Ingår i: Health Informatics Journal. - : Sage Publications. - 1460-4582 .- 1741-2811. ; 27:3, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Patients’ online access to their EHR together with the rapid proliferation of medical information on the Internet has changed how patients use information to learn about their health. Patients’ tendency to turn to the Internet to find information about their health and care is well-documented. However, little is known about patients’ information seeking behavior when using online EHRs. By using information horizons as an analytical tool this paper aims to investigate the information behavior of cancer patients who have chosen to view their EHRs (readers) and to those who have not made that option (non-readers). Thirty interviews were conducted with patients. Based on information horizons, it seems that non-reading is associated with living in a narrower information world in comparison to readers. The findings do not suggest that the smallness would be a result of active avoidance of information, or that it would be counterproductive for the patients. The findings suggest, however, that EHRs would benefit from comprehensive linking to authoritative health information sources to help users to understand their contents. In parallel, healthcare professionals should be more aware of their personal role as a key source of health information to those who choose not to read their EHRs.
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| 2. |
- Frezza, S., et al.
(författare)
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Modelling competencies for computing education beyond 2020 : A research based approach to defining competencies in the computing disciplines
- 2018
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Ingår i: Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE. - New York, NY, USA : Association for Computing Machinery. - 9781450362238 ; , s. 148-174, s. 148-174
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Konferensbidrag (refereegranskat)abstract
- How might the content and outcomes of tertiary education programmes be described and analysed in order to understand how they are structured and function? To address this question we develop a framework for modelling graduate competencies linked to tertiary degree programmes in the computing disciplines. While the focus of our work is computing the framework is applicable to education more broadly. The work presented here draws upon the pioneering curricular document for information technology (IT2017), curricular competency frameworks, other related documents such as the software engineering competency model (SWECOM), the Skills Framework for the Information Age (SFIA), current research in competency models, and elicitation workshop results from recent computing conferences. The aim is to inform the ongoing Computing Curricula (CC2020) project, an endeavour supported by the Association for Computing Machinery (ACM) and the IEEE Computer Society. We develop the Competency Learning Framework (CoLeaF), providing an internationally relevant tool for describing competencies. We argue that this competency based approach is well suited for constructing learning environments and assists degree programme architects in dealing with the challenge of developing, describing and including competencies relevant to computer and IT professionals. In this paper we demonstrate how the CoLeaF competency framework can be applied in practice, and though a series of case studies demonstrate its effectiveness and analytical power as a tool for describing and comparing degree programmes in the international higher education landscape.
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| 3. |
- Winblad, Kjell, 1985-
(författare)
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Dynamic Adaptations of Synchronization Granularity in Concurrent Data Structures
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The multicore revolution means that programmers have many cores at their disposal in everything from phones to large server systems. Concurrent data structures are needed to make good use of all the cores. Designing a concurrent data structure that performs well across many different scenarios is a difficult task. The reason for this is that the best synchronization granularity and data organization vary between scenarios. Furthermore, the number of parallel threads and the types of operations that are accessing a data structure may even change over time.This dissertation tackles the problem mentioned above by proposing concurrent data structures that dynamically adapt their synchronization granularity and organization based on usage statistics collected at run-time. Two of these data structures (one lock-free and one lock-based) implement concurrent sets with support for range queries and other multi-item operations. These data structures adapt their synchronization granularity based on detected contention and the number of items that are involved in multi-item operations such as range queries. This dissertation also proposes a concurrent priority queue that dynamically changes its precision based on detected contention.Experimental evaluations of the proposed data structures indicate that they outperform non-adaptive data structures over a wide range of scenarios because they adapt their synchronization based on usage statistics. Possible practical consequences of the work described in this dissertation are faster parallel programs and a reduced need to manually tune the synchronization granularities of concurrent data structures.
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| 4. |
- Koriakina, Nadezhda, 1991-, et al.
(författare)
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Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
- 2024
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
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Tidskriftsartikel (refereegranskat)abstract
- The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
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| 5. |
- Franco, Alejandro A., et al.
(författare)
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Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality?
- 2019
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Ingår i: Chemical Reviews. - : American Chemical Society (ACS). - 0009-2665 .- 1520-6890. ; 119:7, s. 4569-4627
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Tidskriftsartikel (refereegranskat)abstract
- This review addresses concepts, approaches, tools, and outcomes of multiscale modeling used to design and optimize the current and next generation rechargeable battery cells. Different kinds of multiscale models are discussed and demystified with a particular emphasis on methodological aspects. The outcome is compared both to results of other modeling strategies as well as to the vast pool of experimental data available. Finally, the main challenges remaining and future developments are discussed.
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| 6. |
- Liu, Fei, et al.
(författare)
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Infrared-visible image registration for augmented reality-based thermographic building diagnostics
- 2015
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Ingår i: Visualization in Engineering. - : Springer. - 2213-7459. ; 3, s. 16:1-15
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIn virtue of their capability to measure temperature, thermal infrared cameras have been widely used in building diagnostics for detecting heat loss, air leakage, water damage etc. However, the lack of visual details in thermal infrared images makes the complement of visible images a necessity. Therefore, it is often useful to register images of these two modalities for further inspection of architectures. Augmented reality (AR) technology, which supplements the real world with virtual objects, offers an ideal tool for presenting the combined results of thermal infrared and visible images. This paper addresses the problem of registering thermal infrared and visible façade images, which is essential towards developing an AR-based building diagnostics application.MethodsA novel quadrilateral feature is devised for this task, which models the shapes of commonly present façade elements, such as windows. The features result from grouping edge line segments with the help of image perspective information, namely, vanishing points. Our method adopts a forward selection algorithm to determine feature correspondences needed for estimating the transformation model. During the formation of the feature correspondence set, the correctness of selected feature correspondences at each step is verified by the quality of the resulting registration, which is based on the ratio of areas between the transformed features and the reference features.Results and conclusionsQuantitative evaluation of our method shows that registration errors are lower than errors reported in similar studies and registration performance is usable for most tasks in thermographic inspection of building façades.
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| 7. |
- Ström, Emanuel, et al.
(författare)
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Photon-Counting CT Reconstruction With a Learned Forward Operator
- 2022
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Ingår i: IEEE Transactions on Computational Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 2573-0436 .- 2333-9403. ; 8, s. 536-550
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Tidskriftsartikel (refereegranskat)abstract
- Photon-Counting CT is an emerging imaging technology that promises higher spatial resolution and the possibility for material decomposition in the reconstruction. A major difficulty in Photon-Counting CT is to efficiently model cross-talk between detectors. In this work, we accelerate image reconstruction tasks for Photon-Counting CT by modelling the cross-talk with an appropriately trained deep convolutional neural network. The main result relates to proving convergence when using such a learned cross-talk model in the context of second-order optimisation methods for spectral CT. Another is to evaluate the method through numerical experiments on small-scale CT acquisitions generated using a realistic physics model. Using the reconstruction with a full cross-talk model as ground truth, the learned cross-talk model results in a 20 dB increase in peak-signal-to noise ratio compared to ignoring crass-talk altogether. At the same time, it effectively cuts the computation time of the full cross-talk model in half. Furthermore, the learned cross-talk model generalises well to both unseen data and unseen detector settings. Our results indicate that such a partially learned forward operator is a suitable way of modelling data generation in Photon-Counting CT with a computational benefit that becomes more noticeable for realistic problem sizes.
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| 8. |
- Wingkvist, Anna, 1976-, et al.
(författare)
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A Meta-model Describing the Development Process of Mobile Learning
- 2009
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Ingår i: Advances in Web Based Learning. - Berlin / Heidelberg : Springer. - 9783642034251 ; , s. 454-463
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Bokkapitel (refereegranskat)abstract
- This paper presents a meta-model to describe the development process of mobile learning initiatives. These initiatives are often small scale trials that are not integrated in the intended setting, but carried out outside of the setting. This results in sustainability issues, i.e., problems to integrate the results of the initiative as learning aids. In order to address the sustainability issues, and in turn help to understand the scaling process, a meta-model is introduced. This meta-model divides the development into four areas of concern, and the life cycle of any mobile learning initiative into four stages. The meta-model was developed by analyzing and describing how a podcasting initiative was developed, and is currently being evaluated as a tool to both describe and evaluate mobile learning initiatives. The meta-model was developed based on a mobile learning initiative, but the meta-model itself is extendible to other forms of technology-enhanced learning.
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| 9. |
- Alghamdi, Fayiq, 1985-
(författare)
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Dimensions of Professionalism : A Study of Computer Science Teaching in Saudi Arabia
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In Saudi Arabia, new computing education programs have been introduced in alignment with the Saudi Vision 2030, which is a plan launched in 2017 to reduce Saudi Arabia's reliance on oil, diversify its economy, and develop its health, education, recreation, infrastructure and tourism. Computer science is a rapidly changing area, which places high demands on teachers in the subject to develop both their subject and pedagogical competence. This thesis explores computer science teachers’ perspectives on professional development from three viewpoints—the Saudi Teaching Competencies Standard, engagement in teachers’ awards and self-directed learning. The thesis examines the efforts of computer science teachers as they develop new pedagogies during their teaching careers as a result of the new regulations. The main question is ‘How do Saudi Arabian computer science teachers develop their teaching professionalism?’ Conclusions draw on the outcomes of four sub-studies. A mixed-methods approach consisting of interviews and questionnaires was used to collect data. The participants comprised 389 computer science teachers from different Saudi Arabian cities with different demographics and different teaching experience. The analysis drew on a theoretical framework that integrates elements of the Theory of Reasoned Action, the Theory of Planned Behaviour and the Adult Learning Theory. A model for pedagogical change was developed and used to understand how and why computer science teachers change their educational pedagogy. The model explains the teachers’ shift in pedagogy and answers the question of how and why computer science teachers adopt a new pedagogical strategy. The studies show that both internal and external factors motivate the study participants to engage in competency development. In the Saudi model, the Saudi Teaching Competencies Standard and awards are external factors as they include a preparatory period of intensive skills development. Teachers' experience from this informs the picture of Saudi teachers' training that is presented in the dissertation. Indeed, the trial participants stated that they mainly used self-directed learning for their competence development, drawing on internal motivation. One reason for this was that they felt that many of the skills development programs offered lacked timeliness and relevance. The studies on which the dissertation is based have been conducted in Saudi Arabia, but the results also provide insights into general challenges associated with regulating teachers' competence and the design of in-service training for teachers. The results clearly point out the importance of teachers' participation in the development of the profession in order for changes to be accepted and incorporated into their profession. Behavior change theories can be used to understand and predict how new regulations and pedagogical strategies will be received, and if they are likely to be accepted or rejected by teachers. These theories, therefore, constitute a useful tool in regulating teaching and the teaching profession.
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| 10. |
- Beck, Daniel, et al.
(författare)
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Learning Structural Kernels for Natural Language Processing
- 2015
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Ingår i: Transactions of the Association for Computational Linguistics. - Stroudsburg, PA : Association for Computational Linguistics. - 2307-387X. ; 3, s. 461-473
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Tidskriftsartikel (refereegranskat)abstract
- Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
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