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1.	Wilhelmsson, Kenneth, 1976 (författare) Huvudansatser för parsningsmetoder. Om programutvecklingens förutsättningar i en svensk kontext 2016 Rapport (övrigt vetenskapligt/konstnärligt)abstract Syftet med denna text var att ge en inblick i området (syntaktisk) parsning. Tanken var att ge en bild av utvecklingen som var 1) fri från alltför tekniska detaljer, då området är programmeringstekniskt, och 2) beskriven ur ett svenskt perspektiv. Bakgrunden till valet av ämne till texten, som var tänkt att finnas med i antologin Text och kontext, var att parsning är relativt okänt för många personer verksamma inom närliggande områden, samtidigt som det är ett absolut nyckelbegrepp för den som ägnar sig åt datorlingvistik eller språkteknologi. Målet var alltså att ge en ganska allmän utifrånblick på några centrala sidor av utvecklingen, samtidigt som det tydligt är så att den som själv arbetat med utveckling kan ha starka åsikter och preferenser rörande metodval, något som i ärlighetens namn kanske inte heller denna text är lösgjord från. Hur ska det göras? Konsten att utveckla automatisk syntaxanalys av naturlig text kan läras ut från ett flertal perspektiv. Det kan t.ex. ske med fokus på användandet av en viss grammatikformalism, med fokus på beräkningssnabbhet, med fokus på entydiggörande av möjliga ambiguiteter. Tolkningsval kan göras med hjälp av antingen handskrivna regler eller inhämtad statistik. En sorts huvudtema i denna text är hur metoder för parsning på senare år uppvisar förändringar som kanske kan förklaras med att programmen har fått andra användningsområden och att metoderna har anpassats därefter (en annan tolkning är att flera senare system inte längre gör parsning i strikt mening). När detta tänkta ”kapitel” var färdigt fick det kommentaren att det inte var anpassat för antologins målgrupp. Det fick skrivas en annan kapiteltext, men det kom samtidigt ett förslag att publicera texten om parsning här som denna rapport.
2.	Wilhelmsson, Kenneth, 1976 (författare) Autentiska och artificiella frågor till svensk text Automatisk frågegenerering jämfört med användares frågor för informationsåtkomst : Authentic and artificial questions to Swedish text Automatically generated questions versus user-generated questions for information access 2015 Annan publikation (övrigt vetenskapligt/konstnärligt)abstract Informationssökning mot ostrukturerade datakällor som fri text är ett av de områden där användargränssnitt med fri formulering i naturligt språk har tagits fram. I ett sådant, eventuellt AI-betonat, system kan några grundläggande svårigheter från användarperspektivet märkas. En sådan svårighet är att en användare inte känner till huruvida en fråga som hon avser att ställa egentligen kan besvaras av den aktuella texten. Denna svårighet, tillsammans med andra, som de kraftiga variationsmöjligheterna för formen för ett giltigt svar på en ställd fråga, riskerar att leda till att användarintrycken av systemtypen blir negativa. De moment som behöver ingå i ett sådant frågebaserat informationssystems funktionssätt måste på något sätt inbegripa en mappning av frågeled i frågan (t.ex. när) till den form och grammatisk funktion som svaret i texten måste ha (för frågan när normalt ett tidsadverbial). Bland annat denna iakttagelse inbjuder till användning av automatisk frågegenerering (question generation, QG). Frågegenerering innebär att frågor som en naturlig text besvarar initialt utvinns av ett program som samlar in dem i explicit form. Tanken för användning i informationssökning är att en användare i gränssnittet enbart ska kunna ställa just dessa frågor, vilka faktiskt besvaras av texten. Denna studie gäller just de frågor som ett automatiskt frågegenereringssystem för svenska kan, och genom vidare utveckling, skulle kunna generera för godtycklig digital svensk text. Även om mängden automatiskt genererade frågor och frågeformuleringar kan bli mycket stor, utrymmesmässigt många gånger större än ursprungstexten, så är det tydligt att den beskrivna metoden för frågegenerering för svenska inte kan och troligen inte heller kommer att kunna förmås att skapa alla de frågor och frågeformuleringar som en vanlig användare skulle anse att en viss text besvarar. Men hur väl fungerar då automatiskt genererade frågor i detta sammanhang? Denna uppsats kretsar kring en användarundersökning där undersökningsdeltagare har ombetts att formulera frågor som texter besvarar, och som anses vara relevanta frågor. Den resulterande samlingen frågor undersöktes och kategoriserades. Resultatet av undersökningens huvudfråga visar att bara 20-25 % av användarnas frågeformuleringar skulle kunna genereras direkt automatiskt med aktuell ansats – utan vissa informationstekniska förbättringar. Uppsatsen föreslår viss ny terminologi för detta outforskade område, bl.a. för att skilja mellan de olika grader av processkrav som generering av olika frågeslag från text kräver.
3.	Wilhelmsson, Kenneth (författare) Automatic Question Generation from Swedish Documents as a Tool for Information Extraction 2011 Ingår i: Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. ; , s. 323-326 Konferensbidrag (refereegranskat)abstract An implementation of automatic question generation (QG) from raw Swedish text is presented. QG is here chosen as an alternative to natural query systems where any query can be posed and no indication is given of whether the current text database includes the information sought for. The program builds on parsing with grammatical functions from which corresponding questions are generated and it incorporates the article database of Swedish Wikipedia. The pilot system is meant to work with a text shown in the GUI and auto-completes user input to help find available questions. The act of question generation is here described together with early test results regarding the current produced questions.
4.	Amundin, Mats, et al. (författare) A proposal to use distributional models to analyse dolphin vocalisation 2017 Ingår i: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 31-32 Konferensbidrag (refereegranskat)abstract This paper gives a brief introduction to the starting points of an experimental project to study dolphin communicative behaviour using distributional semantics, with methods implemented for the large scale study of human language.
5.	Yun, Yixiao, 1987, et al. (författare) Maximum-Likelihood Object Tracking from Multi-View Video by Combining Homography and Epipolar Constraints 2012 Ingår i: 6th ACM/IEEE Int'l Conf on Distributed Smart Cameras (ICDSC 12), Oct 30 - Nov.2, 2012, Hong Kong. - 9781450317726 ; , s. 6 pages- Konferensbidrag (refereegranskat)abstract This paper addresses problem of object tracking in occlusion scenarios, where multiple uncalibrated cameras with overlapping fields of view are used. We propose a novel method where tracking is first done independently for each view and then tracking results are mapped between each pair of views to improve the tracking in individual views, under the assumptions that objects are not occluded in all views and move uprightly on a planar ground which may induce a homography relation between each pair of views. The tracking results are mapped by jointly exploiting the geometric constraints of homography, epipolar and vertical vanishing point. Main contributions of this paper include: (a) formulate a reference model of multi-view object appearance using region covariance for each view; (b) define a likelihood measure based on geodesics on a Riemannian manifold that is consistent with the destination view by mapping both the estimated positions and appearances of tracked object from other views; (c) locate object in each individual view based on maximum likelihood criterion from multi-view estimations of object position. Experiments have been conducted on videos from multiple uncalibrated cameras, where targets experience long-term partial or full occlusions. Comparison with two existing methods and performance evaluations are also made. Test results have shown effectiveness of the proposed method in terms of robustness against tracking drifts caused by occlusions.
6.	Kavathatzopoulos, Iordanis (författare) New technologies in the education of native language 2004 Ingår i: Greek language education in Scandinavia. ; , s. 73-76 Konferensbidrag (refereegranskat)abstract Education in mother tongue as well as any education, demands the use of adequate methods and tools to be effective. New technology offers many possibilities for this purpose. In the present paper different IT solutions are discussed and their contribution to the goal of learning the mother tongue are examined. Necessary conditions for successful learning are supposed to be the careful choice and the continuous adaptation of used IT tools to the psychological learning process of the child.
7.	Ellison, Magnus, et al. (författare) En implementation för domänoberoende textkategorisering 2001 Annan publikation (övrigt vetenskapligt/konstnärligt)
8.	Hammarstedt, Martin, et al. (författare) Sparv 5 Developer’s Guide 2022 Rapport (övrigt vetenskapligt/konstnärligt)abstract The Sparv Pipeline developed by Språkbanken Text is a text analysis tool run from the command line. This Developer’s Guide describes its general structure and key concepts and serves as an API documentation. Most importantly, it describes how to write plugins for Sparv 5 so that you can add your own functions to the toolkit.
9.	Norlund, Tobias, 1991, et al. (författare) Transferring Knowledge from Vision to Language: How to Achieve it and how to Measure it? 2021 Ingår i: Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 149-162, Punta Cana, Dominican Republic. - : Association for Computational Linguistics. Konferensbidrag (refereegranskat)abstract Large language models are known to suffer from the hallucination problem in that they are prone to output statements that are false or inconsistent, indicating a lack of knowledge. A proposed solution to this is to provide the model with additional data modalities that complements the knowledge obtained through text. We investigate the use of visual data to complement the knowledge of large language models by proposing a method for evaluating visual knowledge transfer to text for uni- or multimodal language models. The method is based on two steps, 1) a novel task querying for knowledge of memory colors, i.e. typical colors of well-known objects, and 2) filtering of model training data to clearly separate knowledge contributions. Additionally, we introduce a model architecture that involves a visual imagination step and evaluate it with our proposed method. We find that our method can successfully be used to measure visual knowledge transfer capabilities in models and that our novel model architecture shows promising results for leveraging multimodal knowledge in a unimodal setting.
10.	Kucher, Kostiantyn, et al. (författare) Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena 2016 Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116 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.
11.	Wilhelmsson, Kenneth, 1976 (författare) Om förutsättningarna för språkligt datorstöd på ordnivån och uppåt 2017 Ingår i: Text och kontext - perspektiv på textanalys / Karin Helgesson, Hans Lundqvist, Anna Lyngfelt, Andreas Nord & Åsa Wengelin (red.). - Malmö : Gleerups. - 9789140693648 ; , s. 207-228 Bokkapitel (övrigt vetenskapligt/konstnärligt)
12.	Täckström, Oscar, et al. (författare) Uncertainty Detection as Approximate Max-Margin Sequence Labelling 2010 Ingår i: CoNLL 2010. - : Association for Computational Linguistics. ; , s. 84-91 Konferensbidrag (refereegranskat)abstract This paper reports experiments for the CoNLL 2010 shared task on learning to detect hedges and their scope in natural language text. We have addressed the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection in the biological domain we use an L1-regularised binary support vector machine, while for sentence level weasel detection in the Wikipedia domain, we use an L2-regularised approach. We model the in-sentence uncertainty cue and scope detection task as an L2-regularised approximate maximum margin sequence labelling problem, using the BIO-encoding. In addition to surface level features, we use a variety of linguistic features based on a functional dependency analysis. A greedy forward selection strategy is used in exploring the large set of potential features. Our official results for Task 1 for the biological domain are 85.2 F1-score, for the Wikipedia set 55.4 F1-score. For Task 2, our official results are 2.1 for the entire task with a score of 62.5 for cue detection. After resolving errors and final bugs, our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes: 39.6 and cues: 78.5.
13.	Wilhelmsson, Kenneth, 1976 (författare) Automatisk generering av frågor som svensk text besvarar: ett informationssystem 2010 Ingår i: Röster från Humanisten, 2010. ; 2010 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract Vilken information kan en text sägas innehålla? Ett enkelt svar är ”de frågor som den besvarar.” I vilken grad går det i så fall att automatiskt generera dessa frågor och därmed programmera ett frågebesvarande informationssystem för svensk text? Ett prototypsystem för denna uppgift har skapats som en del av ett avhandlingsprojekt inom språkteknologi. Det vore till exempel möjligt att vidareutveckla det system som här visas till en allmän teknisk tjänst, t.ex. webbaserad, som ger användare möjlighet att söka efter information med naturligt språk i en valfri digital text. Denna text tar upp de allmänna förutsättningarna för automatisk generering av de frågor som en svensk text besvarar. Själva den teoretiska uppgiften har egenskaper som kan sägas vara lingvistiska eller informationsteoretiska. För att skapa det program som här beskrivs har dessutom naturligtvis en programmeringsinsats krävts, men denna kommer inte att tas upp här, den rent praktiska sidan av uppgiften är möjlig att lösa på många sätt. http://www.hum.gu.se/samverkan/popularvetenskap/roster-fran-humanisten-2010/ http://hdl.handle.net/2320/7176
14.	Skeppstedt, Maria, Dr. 1977-, et al. (författare) From word clouds to Word Rain: Revisiting the classic word cloud to visualize climate change texts 2024 Ingår i: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 23:3, s. 217-238 Tidskriftsartikel (refereegranskat)abstract Word Rain is a development of the classic word cloud. It addresses some of the limitations of word clouds, in particular the lack of a semantically motivated positioning of the words, and the use of font size as a sole indicator of word prominence. Word Rain uses the semantic information encoded in a distributional semantics-based language model – reduced into one dimension – to position the words along the x-axis. Thereby, the horizontal positioning of the words reflects semantic similarity. Font size is still used to signal word prominence, but this signal is supplemented with a bar chart, as well as with the position of the words on the y-axis. We exemplify the use of Word Rain by three concrete visualization tasks, applied on different real-world texts and document collections on climate change. In these case studies, word2vec models, reduced to one dimension with t-SNE, are used to encode semantic similarity, and TF-IDF is used for measuring word prominence. We evaluate the technique further by carrying out domain expert reviews.
15.	Dobnik, Simon, 1977 (författare) Coordinating spatial perspective in discourse 2012 Ingår i: Proceedings of the Workshop on Vision and Language 2012 (VL'12): The 2nd Annual Meeting of the EPSRC Network on Vision and Language. Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract We present results of an on-line data collection experiment where we investigate the assignment and coordination of spatial perspective between a pair of dialogue participants situated in a constrained virtual environment.
16.	Ferro, Nicola, et al. (författare) PROMISE Retreat Report Prospects and Opportunities for Information Access Evaluation 2013 Ingår i: ACM SIGIR Forum. - : Association for Computing Machinery (ACM). - 0163-5840 .- 1558-0229. ; 46:2, s. 60-84 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract The PROMISE network of excellence organized a two-days brainstorming workshop on 30th and 31st May 2012 in Padua, Italy, to discuss and envisage future directions and perspectives for the evaluation of information access and retrieval systems in multiple languages and multiple media. This document reports on the outcomes of this event and provides details about the six envisaged research lines: search applications; contextual evaluation; challenges in test collection design and exploitation; component-based evaluation; ongoing evaluation; and signal-aware evaluation. The ultimate goal of the PROMISE retreat is to stimulate and involve the research community along these research lines and to provide funding agencies with effective and scientifically sound ideas for coordinating and supporting information access research.
17.	Kucher, Kostiantyn, et al. (författare) Visual Analysis of Sentiment and Stance in Social Media Texts 2018 Ingår i: EuroVis 2018 - Posters. - : Eurographics - European Association for Computer Graphics. - 9783038680659 ; , s. 49-51 Konferensbidrag (refereegranskat)abstract Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.
18.	Ryazanov, Igor, et al. (författare) Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences 2021 Ingår i: Journal of Marine Science and Engineering. - : MDPI AG. - 2077-1312. ; 9:2 Tidskriftsartikel (refereegranskat)abstract Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences.
19.	Al Sabbagh, Khaled, 1987, et al. (författare) Improving Data Quality for Regression Test Selection by Reducing Annotation Noise 2020 Ingår i: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194 Konferensbidrag (refereegranskat)abstract Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
20.	Fredriksson, Teodor, 1992, et al. (författare) Machine learning models for automatic labeling: A systematic literature review 2020 Ingår i: ICSOFT 2020 - Proceedings of the 15th International Conference on Software Technologies. - : SCITEPRESS - Science and Technology Publications. ; , s. 552-566 Konferensbidrag (refereegranskat)abstract Automatic labeling is a type of classification problem. Classification has been studied with the help of statistical methods for a long time. With the explosion of new better computer processing units (CPUs) and graphical processing units (GPUs) the interest in machine learning has grown exponentially and we can use both statistical learning algorithms as well as deep neural networks (DNNs) to solve the classification tasks. Classification is a supervised machine learning problem and there exists a large amount of methodology for performing such task. However, it is very rare in industrial applications that data is fully labeled which is why we need good methodology to obtain error-free labels. The purpose of this paper is to examine the current literature on how to perform labeling using ML, we will compare these models in terms of popularity and on what datatypes they are used on. We performed a systematic literature review of empirical studies for machine learning for labeling. We identified 43 primary studies relevant to our search. From this we were able to determine the most common machine learning models for labeling. Lack of unlabeled instances is a major problem for industry as supervised learning is the most widely used. Obtaining labels is costly in terms of labor and financial costs. Based on our findings in this review we present alternate ways for labeling data for use in supervised learning tasks.
21.	Hamon, Thierry, et al. (författare) Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words 2012 Ingår i: BioNLP. - 9781937284206 - 1937284204 ; , s. 109-117 Konferensbidrag (refereegranskat)abstract The acquisition of semantic resources and relations is an important task for several applications, such as query expansion, information retrieval and extraction, machine translation. However, their validity should also be computed and indicated, especially for automatic systems and applications. We exploit the compositionality based methods for the acquisition of synonymy relations and of indicators of these synonyms. We then apply pagerank-derived algorithm to the obtained semantic graph in order to filter out the acquired synonyms. Evaluation performed with two independent experts indicates that the quality of synonyms is systematically improved by 10 to 15% after their filtering.
22.	Hammarstedt, Martin, et al. (författare) Korp 6 - Technical Report 2017 Rapport (övrigt vetenskapligt/konstnärligt)
23.	Kucher, Kostiantyn, et al. (författare) Visual Analysis of Stance Markers in Online Social Media 2014 Ingår i: Poster Abstracts of IEEE VIS 2014. - : IEEE. ; , s. 259-260 Konferensbidrag (refereegranskat)abstract Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and 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 results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.
24.	Samoaa, Hazem Peter, et al. (författare) A systematic mapping study of source code representation for deep learning in software engineering 2022 Ingår i: Iet Software. - : Institution of Engineering and Technology (IET). - 1751-8806 .- 1751-8814. ; 16:4, s. 351-385 Tidskriftsartikel (refereegranskat)abstract The usage of deep learning (DL) approaches for software engineering has attracted much attention, particularly in source code modelling and analysis. However, in order to use DL, source code needs to be formatted to fit the expected input form of DL models. This problem is known as source code representation. Source code can be represented via different approaches, most importantly, the tree-based, token-based, and graph-based approaches. We use a systematic mapping study to investigate i detail the representation approaches adopted in 103 studies that use DL in the context of software engineering. Thus, studies are collected from 2014 to 2021 from 14 different journals and 27 conferences. We show that each way of representing source code can provide a different, yet orthogonal view of the same source code. Thus, different software engineering tasks might require different (combinations of) code representation approaches, depending on the nature and complexity of the task. Particularly, we show that it is crucial to define whether the DL approach requires lexical, syntactical, or semantic code information. Our analysis shows that a wide range of different representations and combinations of representations (hybrid representations) are used to solve a wide range of common software engineering problems. However, we also observe that current research does not generally attempt to transfer existing representations or models to other studies even though there are other contexts in which these representations and models may also be useful. We believe that there is potential for more reuse and the application of transfer learning when applying DL to software engineering tasks.
25.	Kucher, Kostiantyn, Dr. 1989-, et al. (författare) An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper 2022 Ingår i: Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond — Methodological Approaches to Visualization (BELIV '22). - : IEEE. - 9798350396294 - 9798350396300 ; , s. 28-37 Konferensbidrag (refereegranskat)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.
26.	Beckerman, Carina, 1956- (författare) Implications of Transforming the Patient Record into a Knowledge Management System : Initiating a Movement of Coordination and Enhancement 2008 Ingår i: The ICFAI University Journal of Knowledge Management. - New Dehli : The ICFAI University Press. - 0972-9216. ; Nov:6 Tidskriftsartikel (refereegranskat)abstract Today there is often a need to re-innovate who you are and what you do and re-think the tools that are used and the business models that guide action. The purpose of this paper is to show how transforming a document, such as a patient record, might start a horizontal and vertical movement, a movement of coordination and enhancement in an organizational setting, such as a hospital clinic. The observations presented here and the conclusions drawn were obtained during a three year case study following implications of constructing and computerizing a patient record at three different hospitals. The results were then analyzed, interpreted and discussed within a framework combining theories about knowledge management and with cognitive theories about use of interpretative schemes and representations. This paper tries to look beyond the implications of reconstructing a patient record on a micro-level or explore if it is good or bad to computerize it. Instead this paper theorizes about how re-thinking the interpretative scheme for what a patient record is and how it may be used might restructure a health care setting. It proposes that what the employees want to achieve with the knowledge management system depends on what strategy they have for it.
27.	Balouji, Ebrahim, 1985, et al. (författare) A LSTM-based Deep Learning Method with Application to Voltage Dip Classification 2018 Ingår i: 2018 18TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER (ICHQP). - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2164-0610. - 9781538605172 - 9781538605172 ; 2018-May Konferensbidrag (refereegranskat)abstract In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposedmethod is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.
28.	Proceedings of the workshop on Nordic language research infrastructure at NODALIDA 2013, May 22-24, 2013, Oslo, Norway : NEALT Proceedings Series 20 2013 Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)
29.	Lindgren, Helena, Professor, et al. (författare) The wasp-ed AI curriculum : A holistic curriculum for artificial intelligence 2023 Ingår i: INTED2023 Proceedings. - : IATED. - 9788409490264 ; , s. 6496-6502 Konferensbidrag (refereegranskat)abstract Efforts in lifelong learning and competence development in Artificial Intelligence (AI) have been on the rise for several years. These initiatives have mostly been applied to Science, Technology, Engineering and Mathematics (STEM) disciplines. Even though there has been significant development in Digital Humanities to incorporate AI methods and tools in higher education, the potential for such competences in Arts, Humanities and Social Sciences is far from being realised. Furthermore, there is an increasing awareness that the STEM disciplines need to include competences relating to AI in humanity and society. This is especially important considering the widening and deepening of the impact of AI on society at large and individuals. The aim of the presented work is to provide a broad and inclusive AI Curriculum that covers the breadth of the topic as it is seen today, which is significantly different from only a decade ago. It is important to note that with the curriculum we mean an overview of the subject itself, rather than a particular education program. The curriculum is intended to be used as a foundation for educational activities in AI to for example harmonize terminology, compare different programs, and identify educational gaps to be filled. An important aspect of the curriculum is the ethical, legal, and societal aspects of AI and to not limit the curriculum to the STEM subjects, instead extending to a holistic, human-centred AI perspective. The curriculum is developed as part of the national research program WASP-ED, the Wallenberg AI and transformative technologies education development program.
30.	Alfalahi, Alyaa, et al. (författare) Expanding a dictionary of marker words for uncertainty and negation using distributional semantics 2015 Ingår i: 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 Konferensbidrag (refereegranskat)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.
31.	Ju, Qi, et al. (författare) Learning to Rank from Structures in Hierarchical Text Classification 2013 Ingår i: Advances in Information Retrieval; 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013; P. Serdyukov et al. (ed). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 0302-9743. - 9783642369728 ; Lecture Notes in Computer Science 7814, s. 183-194 Konferensbidrag (refereegranskat)abstract In this paper, we model learning to rank algorithms based on structural dependencies in hierarchical multi-label text categorization (TC). Our method uses the classification probability of the binary classifiers of a standard top-down approach to generate k-best hypotheses. The latter are generated according to their global probability while at the same time satisfy the structural constraints between father and children nodes. The rank is then refined using Support Vector Machines and tree kernels applied to a structural representation of hypotheses, i.e., a hierarchy tree in which the outcome of binary one-vs-all classifiers is directly marked in its nodes. Our extensive experiments on the whole Reuters Corpus Volume 1 show that our models significantly improve over the state of the art in TC, thanks to the use of structural dependecies.
32.	Simistira Liwicki, Foteini, et al. (författare) Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition 2023 Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 10 Tidskriftsartikel (refereegranskat)abstract The recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.
33.	Kalimikeraki, Katerina, et al. (författare) Computer assisted second language learning 2005 Ingår i: Open and Distance Education and Education Technology. - 1790-3254. ; 1:2, s. 74-97 Tidskriftsartikel (refereegranskat)abstract To teach Greek and to teach in Greek are two different things. Children who learn Greek as a second/foreign language are expected to manage both conditions simultaneously. That is, to learn the language as well as all the curriculum taught in this language: history, geography, religion, science etc. Compared to their classmates, who are native speakers, these children have to work double as hard to keep up with the rest of the class. Different approaches have been applied in recent years to facilitate second language acquisition in elementary schools all over the world. Pioneers in the field of education have argued that educators must find stimulating methods to mobilize their students’ intelligences in order to achieve their pedagogical goals. Based on the indisputable assumption that children today are enveloped by the world of high-tech media and that computer games can be highly addictive, educators can borrow from the entertainment industry and create engaging and successful gateways to learning. In the appealing environment of the “virtual classroom” and with the appropriate “mind tool” at hand, both learning of a second language and learning in that second language can be effectively enhanced. Based on psychological research on language and learning we will discuss different ways to use certain ICT (Information and Communication Technology) systems in Greek language education and to create a home language environment at distance. Our main hypothesis is that learning and using Greek presupposes real activities in a Greek language environment and that information technology systems have the ability to create such an environment. The paper describes a number of computer assisted project-based lessons on second language learning and presents their theoretical background.
34.	Kalimikeraki, Katerina, et al. (författare) Computers as thinking tools for the acquisition of second language 2006 Ingår i: Educator as researcher. - : 2nd Bureau of Elementary Education of East Attica, Koropi GR. - 9606312151 ; , s. 139-149 Bokkapitel (populärvet., debatt m.m.)abstract Many different approaches have taken place recently to facilitate second language learning in elementary schools throughout the world. Research in this area shows that teachers have to use the suitable motivators to stimulate pupils' thinking in order to achieve their educational goals. Children live in a world dominated by information technology therefore computer games may be used for educational purposes and to create a successful and pleasant learning environment. Our main hypothesis is that learning and use of Greek language demand real activities in a Greek language environment, and that systems of information technology have the ability to create such an environment that supports learning.
35.	Yu, Yinan, 1985, et al. (författare) climateBUG: A data-driven framework for analyzing bank reporting through a climate lens 2024 Ingår i: Expert Systems with Applications. - 0957-4174 .- 1873-6793. ; 239 Tidskriftsartikel (refereegranskat)abstract This paper applies computational linguistics learning methods to the banking industry and climate change fields. We introduce our data-driven framework, climateBUG, with the aim of detecting latent information about how banks discuss their activities related to climate change using natural language processing (NLP). This framework consists of an ingestion pipeline, a configurable database, and a set of API’s. In addition, climateBUG offers two standalone components, namely a unique annotated corpus of approximately 1.1M statements from EU banks’ annual and sustainability reporting and a deep learning model adapted to the semantics of the corpus. When benchmarking on classification performance, our model outperforms other models with similar scopes due to its stronger domain relevance. We also provide examples of how the framework can be applied from a user perspective.
36.	Huhnstock, Nikolas Alexander, 1988-, et al. (författare) An Infinite Replicated Softmax Model for Topic Modeling 2019 Ingår i: Modeling Decisions for Artificial Intelligence. - Cham : Springer. - 9783030267728 - 9783030267735 ; , s. 307-318 Konferensbidrag (refereegranskat)abstract In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.
37.	Hammarstedt, Martin, et al. (författare) Sparv 5 User Manual 2022 Rapport (övrigt vetenskapligt/konstnärligt)abstract The Sparv Pipeline developed by Språkbanken Text is a text analysis tool run from the command line. This user manual describes how to get Sparv 5 up and running on your own machine, how to configure it and how to use it for annotating your own corpora.
38.	Barreiro, Anabela, et al. (författare) Multi3Generation : Multitask, Multilingual, Multimodal Language Generation 2022 Ingår i: Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. - : European Association for Machine Translation. ; , s. 345-346 Konferensbidrag (refereegranskat)abstract This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generatio(CA18231), an interdisciplinary networof research groups working on different aspects of language generation. This "meta-paper" will serve as reference for citationof the Action in future publications. It presents the objectives, challenges and a the links for the achieved outcomes.
39.	Martins, Rafael Messias, et al. (författare) StanceXplore : Visualization for the Interactive Exploration of Stance in Social Media 2017 Konferensbidrag (refereegranskat)abstract The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier.
40.	Kucher, Kostiantyn, et al. (författare) Active learning and visual analytics for stance classification with ALVA 2017 Ingår i: ACM Transactions on Interactive Intelligent Systems. - New York, NY, USA : Association for Computing Machinery. - 2160-6455 .- 2160-6463. ; 7:3 Tidskriftsartikel (refereegranskat)abstract The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine-learning methods creates an opportunity to gain insight into the speakers' attitudes toward their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. To facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy to select suitable candidate utterances for manual annotaion. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics to improve the understanding of stance phenomena and to build a st ance classifier for applications such as social media monitoring.
41.	Megyesi, Beáta, 1971-, et al. (författare) Decryption of historical manuscripts: the DECRYPT project 2020 Ingår i: Cryptologia. - : Informa UK Limited. - 0161-1194 .- 1558-1586. ; 44:6, s. 545-559 Tidskriftsartikel (refereegranskat)abstract Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
42.	Axelsson, Agnes, 1992- (författare) Adaptive Robot Presenters : Modelling Grounding in Multimodal Interaction 2023 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract This thesis addresses the topic of grounding in human-robot interaction, that is, the process by which the human and robot can ensure mutual understanding. To explore this topic, the scenario of a robot holding a presentation to a human audience is used, where the robot has to process multimodal feedback from the human in order to adapt the presentation to the human's level of understanding.First, the use of behaviour trees to model real-time interactive processes of the presentation is addressed. A system based on the behaviour tree architecture is used in a semi-automated Wizard-of-oz experiment, showing that audience members prefer an adaptive system to a non-adaptive alternative.Next, the thesis addresses the use of knowledge graphs to represent the content of the presentation given by the robot. By building a small, local knowledge graph containing properties (edges) that represent facts about the presentation, the system can iterate over that graph and consistently find ways to refer to entities by referring to previously grounded content. A system based on this architecture is implemented, and an evaluation using simulated users is presented. The results show that crowdworkers comparing different adaptation strategies are sensitive to the types of adaptation enabled by the knowledge graph approach.In a face-to-face presentation setting, feedback from the audience can potentially be expressed through various modalities, including speech, head movements, gaze, facial gestures and body pose. The thesis explores how such feedback can be automatically classified. A corpus of human-robot interactions is annotated, and models are trained to classify human feedback as positive, negative or neutral. A relatively high accuracy is achieved by training simple classifiers with signals found mainly in the speech and head movements.When knowledge graphs are used as the underlying representation of the system's presentation, some consistent way of generating text, that can be turned into speech, is required. This graph-to-text problem is explored by proposing several methods, both template-based and methods based on zero-shot generation using large language models (LLMs). A novel evaluation method using a combination of factual, counter-factual and fictional graphs is proposed. Finally, the thesis presents and evaluates a fully automated system using all of the components above. The results show that audience members prefer the adaptive system to a non-adaptive system, matching the results from the beginning of the thesis. However, we note that clear learning results are not found, which means that the entertainment aspects of the presentation are perhaps more prominent than the learning aspects.
43.	Lambers, L., et al. (författare) Granularity of conflicts and dependencies in graph transformation systems: A two-dimensional approach 2019 Ingår i: Journal of Logical and Algebraic Methods in Programming. - : Elsevier BV. - 2352-2208 .- 2352-2216. ; 103, s. 105-129 Tidskriftsartikel (refereegranskat)abstract Conflict and dependency analysis (CDA) is a static analysis for the detection of conflicting and dependent rule applications in a graph transformation system. The state-of-the-art CDA technique, critical pair analysis, provides all potential conflicts and dependencies in minimal context as critical pairs, for each pair of rules. Yet, critical pairs can be hard to understand; users are mainly interested in core information about conflicts and dependencies occurring in various combinations. In this paper, we present an approach to conflicts and dependencies in graph transformation systems based on two dimensions of granularity. The first dimension refers to the overlap considered between the rules of a given rule pair; the second one refers to the represented amount of context information about transformations in which the conflicts occur. We introduce a variety of new conflict notions, in particular, conflict atoms, conflict reasons, and minimal conflict reasons, relate them to the existing conflict notions of critical pairs and initial conflicts, and position all of these notions within our granularity approach. Finally, we introduce dual concepts for dependency analysis. As we discuss in a running example, our approach paves the way for an improved CDA technique. (C) 2018 Elsevier Inc. All rights reserved.
44.	Nass, Michel, 1968-, et al. (författare) Improving Web Element Localization by Using a Large Language Model 2024 Ingår i: Software testing, verification & reliability. - : John Wiley & Sons. - 0960-0833 .- 1099-1689. Tidskriftsartikel (refereegranskat)abstract Web-based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but don't grasp the context and meaning of elements and words. The emergence of Large Language Models (LLMs) like GPT-4, which can show human-like reasoning abilities on some tasks, offers new opportunities for software engineering and web element localization. This paper introduces and evaluates VON Similo LLM, an enhanced web element localization approach. Using an LLM, it selects the most likely web element from the top-ranked ones identified by the existing VON Similo method, ideally aiming to get closer to human-like selection accuracy. An experimental study was conducted using 804 web element pairs from 48 real-world web applications. We measured the number of correctly identified elements as well as the execution times, comparing the effectiveness and efficiency of VON Similo LLM against the baseline algorithm. In addition, motivations from the LLM were recorded and analyzed for all instances where the original approach failed to find the right web element. VON Similo LLM demonstrated improved performance, reducing failed localizations from 70 to 39 (out of 804), a 44 percent reduction. Despite its slower execution time and additional costs of using the GPT-4 model, the LLMs human-like reasoning showed promise in enhancing web element localization. LLM technology can enhance web element identification in GUI test automation, reducing false positives and potentially lowering maintenance costs. However, further research is necessary to fully understand LLMs capabilities, limitations, and practical use in GUI testing.
45.	Chen, Yiting, et al. (författare) GraspAda: Deep Grasp Adaptation through Domain Transfer 2023 Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - 1050-4729. - 9798350323658 ; 2023-May Konferensbidrag (refereegranskat)abstract Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.
46.	Höglund, Lars, 1946, et al. (författare) Maskininlärningsbaserad indexering av digitaliserade museiartefakter - projektrapport 2012 Rapport (övrigt vetenskapligt/konstnärligt)abstract Projektet har genomfört försök med maskinbaserad analys och maskininlärning för automatisk indexering och analys av bilder som stöd för registrering av föremål i museibestånd. Resultaten visar att detta är möjligt för avgränsade delmängder i kombination med maskininlärning som stöd för, men inte som ersättning för, manuell analys. Projektet har också funnit behov av utveckling av ett användargränssnitt för både text och bildsökning och utvecklat en prototyplösning för detta, vilket finns dokumenterat i denna rapport och i ett separat appendix till rapporten. Materialet utgör grundunderlag för implementeringar som innebär utökade sökmöjligheter, effektivare registrering samt ett användarvänligt gränssnitt. Arbetet ligger i framkant av forskningsområdets resultat och etablerade metoder och kombinerar statististiska, lingvistiska och datavetenskapliga metoder. Se länk till rapport och även länk till appendix längre ned.
47.	Singh, Avinash, 1986-, et al. (författare) Verbal explanations by collaborating robot teams 2021 Ingår i: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 12:1, s. 47-57 Tidskriftsartikel (refereegranskat)abstract In this article, we present work on collaborating robot teams that use verbal explanations of their actions and intentions in order to be more understandable to the human. For this, we introduce a mechanism that determines what information the robots should verbalize in accordance with Grice’s maxim of quantity, i.e., convey as much information as is required and no more or less. Our setup is a robot team collaborating to achieve a common goal while explaining in natural language what they are currently doing and what they intend to do. The proposed approach is implemented on three Pepper robots moving objects on a table. It is evaluated by human subjects answering a range of questions about the robots’ explanations, which are generated using either our proposed approach or two further approaches implemented for evaluation purposes. Overall, we find that our proposed approach leads to the most understanding of what the robots are doing. In addition, we further propose a method for incorporating policies driving the distribution of tasks among the robots, which may further support understandability.
48.	Fredriksson, Teodor, 1992, et al. (författare) Machine Learning Algorithms for Labeling: Where and How They are Used? 2022 Ingår i: SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings. Konferensbidrag (refereegranskat)abstract With the increased availability of new and better computer processing units (CPUs) as well as graphical processing units (GPUs), the interest in statistical learning and deep learning algorithms for classification tasks has grown exponentially. These classification algorithms often require the presence of fully labeled instances during the training period for maximum classification accuracy. However, in industrial applications, data is commonly not fully labeled, which both reduces the prediction accuracy of the learning algorithms as well as increases the project cost to label the missing instances. The purpose of this paper is to survey the current state-of-the-art literature on machine learning algorithms that are used for assisted or automatic labeling and to understand where these are used. We performed a systematic mapping study and identified 52 primary studies relevant to our research. This paper provides three main contributions. First, we identify the existing machine learning algorithms for labeling and we present a taxonomy of these algorithms. Second, we identify the datasets that are used to evaluate the algorithms and we provide a mapping of the datasets based on the type of data and the application area. Third, we provide a process to support people in industry to optimally label their dataset. The results presented in this paper can be used by both researchers and practitioners aiming to improve the missing labels with the aid of machine algorithms or to select appropriate datasets to compare new state-of-the art algorithms in their respective application area.
49.	Snickars, Pelle (författare) 100 miljoner ord : Reflektioner kring forskningsarbete med storskaliga dataset som historisk empiri 2022 Ingår i: Historisk Tidskrift. - 0345-469X. ; 142:3, s. 320-352 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.
50.	Kusetogullari, Huseyin, et al. (författare) DIGITNET : A Deep Handwritten Digit Detection and Recognition Method Using a New Historical Handwritten Digit Dataset 2021 Ingår i: Big Data Research. - : Elsevier. - 2214-5796 .- 2214-580X. ; 23 Tidskriftsartikel (refereegranskat)abstract This paper introduces a novel deep learning architecture, named DIGITNET, and a large-scale digit dataset, named DIDA, to detect and recognize handwritten digits in historical document images written in the nineteen century. To generate the DIDA dataset, digit images are collected from 100,000 Swedish handwritten historical document images, which were written by different priests with different handwriting styles. This dataset contains three sub-datasets including single digit, large-scale bounding box annotated multi-digit, and digit string with 250,000, 25,000, and 200,000 samples in Red-Green-Blue (RGB) color spaces, respectively. Moreover, DIDA is used to train the DIGITNET network, which consists of two deep learning architectures, called DIGITNET-dect and DIGITNET-rec, respectively, to isolate digits and recognize digit strings in historical handwritten documents. In DIGITNET-dect architecture, to extract features from digits, three residual units where each residual unit has three convolution neural network structures are used and then a detection strategy based on You Look Only Once (YOLO) algorithm is employed to detect handwritten digits at two different scales. In DIGITNET-rec, the detected isolated digits are passed through 3 different designed Convolutional Neural Network (CNN) architectures and then the classification results of three different CNNs are combined using a voting scheme to recognize digit strings. The proposed model is also trained with various existing handwritten digit datasets and then validated over historical handwritten digit strings. The experimental results show that the proposed architecture trained with DIDA (publicly available from: https://didadataset.github.io/DIDA/) outperforms the state-of-the-art methods.

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