| 1. |
- Wiqvist, Samuel, et al.
(author)
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Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation
- 2019
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In: Proceedings of the 36th International Conference on Machine Learning. - : PMLR. ; 2019-June, s. 11795-11804
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Conference paper (peer-reviewed)abstract
- We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show that any block-switch invariant function has a PEN-like representation. The DeepSets architecture is a special case of PEN and we can therefore also target fully exchangeable data. We employ PENs to learn summary statistics in approximate Bayesian computation (ABC). When comparing PENs to previous deep learning methods for learning summary statistics, our results are highly competitive, both considering time series and static models. Indeed, PENs provide more reliable posterior samples even when using less training data.
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| 2. |
- Berntsson Svensson, Richard, et al.
(author)
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Prioritization of quality requirements : State of practice in eleven companies
- 2011
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In: 2011 IEEE 19th International Requirements Engineering Conference, RE 2011; Trento; 29 August 2011 through 2 September 2011. - Trento : IEEE. - 9781457709234 ; , s. 69-78, s. 69-78
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Conference paper (peer-reviewed)abstract
- Requirements prioritization is recognized as an important but challenging activity in software product development. For a product to be successful, it is crucial to find the right balance among competing quality requirements. Although literature offers many methods for requirements prioritization, the research on prioritization of quality requirements is limited. This study identifies how quality requirements are prioritized in practice at 11 successful companies developing software intensive systems. We found that ad-hoc prioritization and priority grouping of requirements are the dominant methods for prioritizing quality requirements. The results also show that it is common to use customer input as criteria for prioritization but absence of any criteria was also common. The results suggests that quality requirements by default have a lower priority than functional requirements, and that they only get attention in the prioritizing process if decision-makers are dedicated to invest specific time and resources on QR prioritization. The results of this study may help future research on quality requirements to focus investigations on industry-relevant issues.
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| 3. |
- Larsson, Måns, 1989, et al.
(author)
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A projected gradient descent method for crf inference allowing end-to-end training of arbitrary pairwise potentials
- 2018
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319781983 ; 10746, s. 564-579
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Conference paper (peer-reviewed)abstract
- Are we using the right potential functions in the Conditional Random Field models that are popular in the Vision community? Semantic segmentation and other pixel-level labelling tasks have made significant progress recently due to the deep learning paradigm. However, most state-of-the-art structured prediction methods also include a random field model with a hand-crafted Gaussian potential to model spatial priors, label consistencies and feature-based image conditioning. In this paper, we challenge this view by developing a new inference and learning framework which can learn pairwise CRF potentials restricted only by their dependence on the image pixel values and the size of the support. Both standard spatial and high-dimensional bilateral kernels are considered. Our framework is based on the observation that CRF inference can be achieved via projected gradient descent and consequently, can easily be integrated in deep neural networks to allow for end-to-end training. It is empirically demonstrated that such learned potentials can improve segmentation accuracy and that certain label class interactions are indeed better modelled by a non-Gaussian potential. In addition, we compare our inference method to the commonly used mean-field algorithm. Our framework is evaluated on several public benchmarks for semantic segmentation with improved performance compared to previous state-of-the-art CNN+CRF models.
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| 4. |
- Hagberg, Rasmus, et al.
(author)
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Using Program Analysis to Identify the Use of Vulnerable Functions
- 2021
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In: 18th International Conference on Security and Cryptography, SECRYPT 2021. - : SCITEPRESS - Science and Technology Publications. - 9789897585241 ; , s. 520-530
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Conference paper (peer-reviewed)abstract
- Open-Source Software (OSS) is increasingly used by software applications. It allows for code reuse, but also comes with the problem of potentially being affected by the vulnerabilities that are found in the OSS libraries. With large numbers of OSS components and a large number of published vulnerabilities, it becomes challenging to identify and analyze which OSS components need to be patched and updated. In addition to matching vulnerable libraries to those used in software products, it is also necessary to analyze if the vulnerable functionality is actually used by the software. This process is both time-consuming and error-prone. Automating this process presents several challenges, but has the potential to significantly decrease vulnerability exposure time. In this paper, we propose a modular framework for analyzing if software code is using the vulnerable part of a library, by analyzing and matching the call graphs of the software with changes resulting from security patches. Further, we provide an implementation of the framework targeting Java and the Maven dependency management system. This allows us to identify 20% of the dependencies in our sample projects as false positives. We also identify and discuss challenges and limitations in our approach
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| 5. |
- Heintz, Fredrik, et al.
(author)
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Computing at School in Sweden - Experiences from Introducing Computer Science within Existing Subjects
- 2015
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In: Informatics in Schools. Curricula, Competences, and Competitions /Lecture Notes in Computer Science and General Issues. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319253954 - 9783319253961 ; 9378, s. 118-130
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Conference paper (peer-reviewed)abstract
- Computing is no longer considered a subject area only relevant for a narrow group of professionals, but rather as a vital part of general education that should be available to all children and youth. Since making changes to national curricula takes time, people are trying to find other ways of introducing children and youth to computing. In Sweden, several current initiatives by researchers and teachers aim at finding ways of working with computing within the current curriculum. In this paper we present case studies based on a selection of these initiatives from four major regions in Sweden and based on these case studies we present our ideas for how to move forward on introducing computational thinking on a larger scale in Swedish education.
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| 6. |
- Alfalahi, Alyaa, et al.
(author)
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Expanding a dictionary of marker words for uncertainty and negation using distributional semantics
- 2015
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In: EMNLP 2015 - 6th International Workshop on Health Text Mining and Information Analysis, LOUHI 2015 : Proceedings of the Workshop - Proceedings of the Workshop. - : Association for Computational Linguistics. - 9781941643327 ; , s. 90-96
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Conference paper (peer-reviewed)abstract
- Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word.
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| 7. |
- Heintz, Fredrik, 1975-, et al.
(author)
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Introducing Programming and Digital Competence in Swedish K-9 Education
- 2017
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In: Informatics in Schools. - Cham : Springer. - 1611-3349 .- 0302-9743. - 9783319714820 - 9783319714837 ; , s. 117-128
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Conference paper (peer-reviewed)abstract
- The role of computer science and IT in Swedish schools has varied throughout the years. In fall 2014, the Swedish government gave the National Agency for Education (Skolverket) the task of preparing a proposal for K–9 education on how to better address the competences required in a digitalized society. In June 2016, Skolverket handed over a proposal introducing digital competence and programming as interdisciplinary traits, also providing explicit formulations in subjects such as mathematics (programming, algorithms and problem-solving), technology (controlling physical artifacts) and social sciences (fostering aware and critical citizens in a digital society). In March 2017, the government approved the new curriculum, which needs to be implemented by fall 2018 at the latest. We present the new K–9 curriculum and put it in a historical context. We also describe and analyze the process of developing the revised curriculum, and discuss some initiatives for how to implement the changes.
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| 8. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(author)
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An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper
- 2022
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In: Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond — Methodological Approaches to Visualization (BELIV '22). - : IEEE. - 9798350396294 - 9798350396300 ; , s. 28-37
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Conference paper (peer-reviewed)abstract
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.
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| 9. |
- Kucher, Kostiantyn, et al.
(author)
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DoSVis : Document Stance Visualization
- 2018
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In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18). - : SciTePress. - 9789897582899 ; , s. 168-175
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Conference paper (peer-reviewed)abstract
- Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer’s attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature.
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| 10. |
- Kucher, Kostiantyn, et al.
(author)
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Methodology and Applications of Visual Stance Analysis : An Interactive Demo
- 2016
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In: International Symposium on Digital Humanities, Växjö 7-8 November 2016. - : Linnaeus University. ; , s. 56-57
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Conference paper (peer-reviewed)abstract
- Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages.
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