| 1. |
- Amundin, Mats, et al.
(author)
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A proposal to use distributional models to analyse dolphin vocalisation
- 2017
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In: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 31-32
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Conference paper (peer-reviewed)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.
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| 2. |
- Schötz, Susanne, et al.
(author)
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Phonetic Characteristics of Domestic Cat Vocalisations
- 2017
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In: Proceedings of the 1st International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots, VIHAR 2017. - 9782956202905 ; , s. 5-6
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Conference paper (peer-reviewed)abstract
- The cat (Felis catus, Linneaus 1758) has lived around or with humans for at least 10,000 years, and is now one of the most popular pets of the world with more than 600 millionindividuals. Domestic cats have developed a more extensive, variable and complex vocal repertoire than most other members of the Carnivora, which may be explained by their social organisation, their nocturnal activity and the long period of association between mother and young. Still, we know surprisingly little about the phonetic characteristics of these sounds, and about the interaction between cats and humans.Members of the research project Melody in human–cat communication (Meowsic) investigate the prosodic characteristics of cat vocalisations as well as the communication between human and cat. The first step includes a categorisation of cat vocalisations. In the next step it will be investigated how humans perceive the vocal signals of domestic cats. This paper presents an outline of the project which has only recently started.
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| 3. |
- Kucher, Kostiantyn, et al.
(author)
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Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena
- 2016
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In: Information Visualization. - : Sage Publications. - 1473-8716 .- 1473-8724. ; 15:2, s. 93-116
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Journal article (peer-reviewed)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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| 4. |
- Zhang, Chi, 1992, et al.
(author)
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Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings
- 2023
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In: IEEE Intelligent Vehicles Symposium, Proceedings. - Anchorage, Alaska, Canada, : IEEE. - 9798350346916 - 9798350346923
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Conference paper (peer-reviewed)abstract
- Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.
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| 5. |
- Kucher, Kostiantyn, et al.
(author)
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Visual Analysis of Sentiment and Stance in Social Media Texts
- 2018
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In: EuroVis 2018 - Posters. - : Eurographics - European Association for Computer Graphics. - 9783038680659 ; , s. 49-51
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Conference paper (peer-reviewed)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.
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| 6. |
- Daoud, Adel, 1981, et al.
(author)
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Using Satellite Images and Deep Learning to Measure Health and Living Standards in India
- 2023
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In: Social Indicators Research. - : SPRINGER. - 0303-8300 .- 1573-0921. ; 167:1-3, s. 475-505
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Journal article (peer-reviewed)abstract
- Using deep learning with satellite images enhances our understanding of human development at a granular spatial and temporal level. Most studies have focused on Africa and on a narrow set of asset-based indicators. This article leverages georeferenced village-level census data from across 40% of the population of India to train deep models that predicts 16 indicators of human well-being from Landsat 7 imagery. Based on the principles of transfer learning, the census-based model is used as a feature extractor to train another model that predicts an even larger set of developmental variables—over 90 variables—included in two rounds of the National Family Health Survey (NFHS). The census-based-feature-extractor model outperforms the current standard in the literature for most of these NFHS variables. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep models that track human development at an unprecedented geographical and temporal resolution.
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| 7. |
- Liu, Yang, et al.
(author)
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Movement Status Based Vision Filter for RoboCup Small-Size League
- 2012
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In: Advances in Automation and Robotics, Vol. 2. - Berlin, Heidelberg : Springer. - 9783642256455 - 9783642256462 ; , s. 79-86
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Book chapter (other academic/artistic)abstract
- Small-size soccer league is a division of the RoboCup (Robot world cup) competitions. Each team uses its own designed hardware and software to compete with othersunder defined rules. There are two kinds of data which the strategy system will receive from the dedicated server, one of them is the referee commands, and the other one is vision data. However, due to the network delay and the vision noise, we have to process the data before we can actually use it. Therefore, a certain mechanism is needed in this case.Instead of using some prevalent and complex algorithms, this paper proposes to solve this problem from simple kinematics and mathematics point of view, which can be implemented effectively by hobbyists and undergraduate students. We divide this problem by the speed status and deal it in three different situations. Testing results show good performance with this algorithm and great potential in filtering vision data thus forecasting actual coordinates of tracking objects.
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| 8. |
- Kucher, Kostiantyn, et al.
(author)
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Visual Analysis of Stance Markers in Online Social Media
- 2014
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In: Poster Abstracts of IEEE VIS 2014. - : IEEE. ; , s. 259-260
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Conference paper (peer-reviewed)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.
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| 9. |
- 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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| 10. |
- Bleser, Gabriele, et al.
(author)
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Cognitive Learning, Monitoring and Assistance of Industrial Workflows Using Egocentric Sensor Networks
- 2015
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:6
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Journal article (peer-reviewed)abstract
- Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or repetitive tasks. This burden on the workers can be eased by introducing smart assistance systems. This article presents a scalable concept and an integrated system demonstrator designed for this purpose. The basic idea is to learn workflows from observing multiple expert operators and then transfer the learnt workflow models to novice users. Being entirely learning-based, the proposed system can be applied to various tasks and domains. The above idea has been realized in a prototype, which combines components pushing the state of the art of hardware and software designed with interoperability in mind. The emphasis of this article is on the algorithms developed for the prototype: 1) fusion of inertial and visual sensor information from an on-body sensor network (BSN) to robustly track the user’s pose in magnetically polluted environments; 2) learning-based computer vision algorithms to map the workspace, localize the sensor with respect to the workspace and capture objects, even as they are carried; 3) domain-independent and robust workflow recovery and monitoring algorithms based on spatiotemporal pairwise relations deduced from object and user movement with respect to the scene; and 4) context-sensitive augmented reality (AR) user feedback using a head-mounted display (HMD). A distinguishing key feature of the developed algorithms is that they all operate solely on data from the on-body sensor network and that no external instrumentation is needed. The feasibility of the chosen approach for the complete action-perception-feedback loop is demonstrated on three increasingly complex datasets representing manual industrial tasks. These limited size datasets indicate and highlight the potential of the chosen technology as a combined entity as well as point out limitations of the system.
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