| 1. |
- Kucher, Kostiantyn, et al.
(författare)
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Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena
- 2016
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Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116
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Tidskriftsartikel (refereegranskat)abstract
- Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.
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| 2. |
- Wiqvist, Samuel, et al.
(författare)
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Partially Exchangeable Networks and architectures for learning summary statistics in Approximate Bayesian Computation
- 2019
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Ingår i: Proceedings of the 36th International Conference on Machine Learning. - : PMLR. ; 2019-June, s. 11795-11804
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Konferensbidrag (refereegranskat)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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| 3. |
- Snickars, Pelle
(författare)
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100 miljoner ord : Reflektioner kring forskningsarbete med storskaliga dataset som historisk empiri
- 2022
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Ingår i: Historisk Tidskrift. - 0345-469X. ; 142:3, s. 320-352
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Tidskriftsartikel (refereegranskat)abstract
- A hundred million words: Reflections on historical research with large-scale textual datasets as empirical evidenceThe research project Welfare State Analytics: Text Mining and Modelling Swedish Politics, Media & Culture, 1945–1989 uses probabilistic methods and text-mining models to study three massive textual datasets from Swedish politics, news media, and literary culture. By topic modelling and distant reading a dataset from some 3,100 Swedish Government Official Reports, findings have been made which previous historical scholarship has neglected – or rather, cannot detect because of the limitations of traditional, smallscale examinations of only a few such reports. This article presents some of the project’s findings, but concentrates on the practical issues of curating large-scale textual datasets, and thus the possibilities – and shortcomings – of digital history research practices.Large-scale textual datasets, often containing hundreds of millions of words, are a new type of empirical material that presents the historian with fresh challenges. The preparation of datasets is usually a resource-intensive task, where algorithmic machine learning is combined with the manual curation of data, a process that compiles the empirical material into datasets (in different versions).Plainly, historical empirical material must be compiled into datasets to enable large-scale analyses, and such work can be laborious, as it depends on extensive programming efforts; what may come as a surprise is how complicated the relationship between data and empirical material can be in a digital-historical context, and the fact that preparing datasets is usually an iterative procedure that fundamentally changes the historical sources. In this type of research, compiled empirical material will usually result in several datasets, depending not only on how effective the available software is to curate and correct errors but also the specific research questions – given that data can be modelled in many ways. The relationship between empirical material and curated datasets is therefore complex, and highly dependent on both software and research practices.
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| 4. |
- Berntsson Svensson, Richard, et al.
(författare)
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Prioritization of quality requirements : State of practice in eleven companies
- 2011
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Ingår i: 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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Konferensbidrag (refereegranskat)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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| 5. |
- Medved, Dennis
(författare)
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Applications of Machine Learning on Natural Language Processing and Biomedical Data
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Machine learning is ubiquitous in today’s society, with promising applicationsin the field of natural language processing (NLP), so that computers can handlehuman language better, and within the medical community, with the promiseof better treatments. Machine learning can be seen as a subfield of artificialintelligence (AI), where AI is used to describe a machine that mimics cognitivefunctions that humans associate with other human minds, such as learning orproblem solving.In this thesis we explore how machine learning can be used to improve classification of picture, by using associated text. We then shift our focus to biomedical data, specifically heart transplantation patients. We show how the data can be represented as a graph database using the resource description framework (RDF).After that we use the data with logistic regression and the Spark framework, toperform feature search to predict the survival probability of the patients. In thetwo last articles we use artificial neural networks (ANN) first to predict patientsurvival, and compare it with a logistic regression approach, and last to predict the outcome of patients awaiting heart transplantation.We plan to do simulation of different allocation policies, for donor hearts, usingthese kind of ANNs, to be able to asses their impact on predicted earned survivaltime.
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| 6. |
- Tahmasebi, Nina, 1982, et al.
(författare)
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Visions and open challenges for a knowledge-based culturomics
- 2015
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Ingår i: International Journal on Digital Libraries. - : Springer Science and Business Media LLC. - 1432-5012 .- 1432-1300. ; 15:2-4, s. 169-187
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Tidskriftsartikel (refereegranskat)abstract
- The concept of culturomics was born out of the availability of massive amounts of textual data and the interest to make sense of cultural and language phenomena over time. Thus far however, culturomics has only made use of, and shown the great potential of, statistical methods. In this paper, we present a vision for a knowledge-based culturomics that complements traditional culturomics. We discuss the possibilities and challenges of combining knowledge-based methods with statistical methods and address major challenges that arise due to the nature of the data; diversity of sources, changes in language over time as well as temporal dynamics of information in general. We address all layers needed for knowledge-based culturomics, from natural language processing and relations to summaries and opinions.
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| 7. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 8. |
- Bååth, Rasmus, et al.
(författare)
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A prototype based resonance model of rhythm categorization
- 2014
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Ingår i: i-Perception. - : SAGE Publications. - 2041-6695. ; 5:6, s. 548-558
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Tidskriftsartikel (refereegranskat)abstract
- Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems’ model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.
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| 9. |
- Larsson, Måns, 1989, et al.
(författare)
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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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Ingår i: 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 LNCS, s. 564-579
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Konferensbidrag (refereegranskat)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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| 10. |
- Park, Soon Young, et al.
(författare)
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How to improve data quality in dog eye tracking
- 2023
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Ingår i: Behavior Research Methods. - : Springer Science and Business Media LLC. - 1554-3528. ; 55:4, s. 1513-1536
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Tidskriftsartikel (refereegranskat)abstract
- Pupil-corneal reflection (P-CR) eye tracking has gained a prominent role in studying dog visual cognition, despite methodological challenges that often lead to lower-quality data than when recording from humans. In the current study, we investigated if and how the morphology of dogs might interfere with tracking of P-CR systems, and to what extent such interference, possibly in combination with dog-unique eye-movement characteristics, may undermine data quality and affect eye-movement classification when processed through algorithms. For this aim, we have conducted an eye-tracking experiment with dogs and humans, and investigated incidences of tracking interference, compared how they blinked, and examined how differential quality of dog and human data affected the detection and classification of eye-movement events. Our results show that the morphology of dogs' face and eye can interfere with tracking methods of the systems, and dogs blink less often but their blinks are longer. Importantly, the lower quality of dog data lead to larger differences in how two different event detection algorithms classified fixations, indicating that the results of key dependent variables are more susceptible to choice of algorithm in dog than human data. Further, two measures of the Nyström & Holmqvist (Behavior Research Methods, 42(4), 188-204, 2010) algorithm showed that dog fixations are less stable and dog data have more trials with extreme levels of noise. Our findings call for analyses better adjusted to the characteristics of dog eye-tracking data, and our recommendations help future dog eye-tracking studies acquire quality data to enable robust comparisons of visual cognition between dogs and humans.
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