| 1. |
- Strannegård, Claes, 1962, et al.
(author)
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Ecosystem Models Based on Artificial Intelligence
- 2022
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In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022. - : IEEE.
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Conference paper (peer-reviewed)abstract
- Ecosystem models can be used for understanding general phenomena of evolution, ecology, and ethology. They can also be used for analyzing and predicting the ecological consequences of human activities on specific ecosystems, e.g., the effects of agriculture, forestry, construction, hunting, and fishing. We argue that powerful ecosystem models need to include reasonable models of the physical environment and of animal behavior. We also argue that several well-known ecosystem models are unsatisfactory in this regard. Then we present the open-source ecosystem simulator Ecotwin, which is built on top of the game engine Unity. To model a specific ecosystem in Ecotwin, we first generate a 3D Unity model of the physical environment, based on topographic or bathymetric data. Then we insert digital 3D models of the organisms of interest into the environment model. Each organism is equipped with a genome and capable of sexual or asexual reproduction. An organism dies if it runs out of some vital resource or reaches its maximum age. The animal models are equipped with behavioral models that include sensors, actions, reward signals, and mechanisms of learning and decision-making. Finally, we illustrate how Ecotwin works by building and running one terrestrial and one marine ecosystem model.
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| 2. |
- 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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| 3. |
- Kucher, Kostiantyn, et al.
(author)
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Text Visualization Techniques : Taxonomy, Visual Survey, and Community Insights
- 2015
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In: Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15). - : IEEE. - 9781467368797 ; , s. 117-121
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Conference paper (peer-reviewed)abstract
- Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data.
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| 4. |
- Le, Minh Ha, et al.
(author)
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AnonFACES: Anonymizing Faces Adjusted to Constraints on Efficacy and Security
- 2020
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In: WPES 2020 - Proceedings of the 19th Workshop on Privacy in the Electronic Society. - New York, NY, USA : ACM. ; , s. 87-100, s. 87-100
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Conference paper (peer-reviewed)abstract
- Image data analysis techniques such as facial recognition can threaten individuals’ privacy. Whereas privacy risks often can be reduced by adding noise to the data, this approach reduces the utility of the images. For this reason, image de-identification techniques typically replace directly identifying features (e.g., faces, car number plates) present in the data with synthesized features, while still preserving other non-identifying features. As of today, existing techniques mostly focus on improving the naturalness of the generated synthesized images, without quantifying their impact on privacy. In this paper, we propose the first methodology and system design to quantify, improve, and tune the privacy-utility trade-off, while simultaneously also improving the naturalness of the generated images. The system design is broken down into three components that address separate but complementing challenges. This includes a two-step cluster analysis component to extract low-dimensional feature vectors representing the images (embedding) and to cluster the images into fixed-sized clusters. While the importance of good clustering mostly has been neglected in previous work, we find that our novel approach of using low-dimensional feature vectors can improve the privacy-utility trade-off by better clustering similar images. The use of these embeddings has been found particularly useful when wanting to ensure high naturalness and utility of the synthetically generated images. By combining improved clustering and incorporating StyleGAN, a state-of-the-art Generative Neural Network, into our solution, we produce more realistic synthesized faces than prior works, while also better preserving properties such as age, gender, skin tone, or even emotional expressions. Finally, our iterative tuning method exploits non-linear relations between privacy and utility to identify good privacy-utility trade-offs. We note that an example benefit of these improvements is that our solution allows car manufacturers to train their autonomous vehicles while complying with privacy laws.
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| 5. |
- 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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| 6. |
- Heintz, Fredrik, 1975-, et al.
(author)
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Introducing Programming and Digital Competence in Swedish K-9 Education
- 2017
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In: Informatics in Schools. - Cham : Springer. - 1611-3349 .- 0302-9743. - 9783319714820 - 9783319714837 ; , s. 117-128
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Conference paper (peer-reviewed)abstract
- The role of computer science and IT in Swedish schools has varied throughout the years. In fall 2014, the Swedish government gave the National Agency for Education (Skolverket) the task of preparing a proposal for K–9 education on how to better address the competences required in a digitalized society. In June 2016, Skolverket handed over a proposal introducing digital competence and programming as interdisciplinary traits, also providing explicit formulations in subjects such as mathematics (programming, algorithms and problem-solving), technology (controlling physical artifacts) and social sciences (fostering aware and critical citizens in a digital society). In March 2017, the government approved the new curriculum, which needs to be implemented by fall 2018 at the latest. We present the new K–9 curriculum and put it in a historical context. We also describe and analyze the process of developing the revised curriculum, and discuss some initiatives for how to implement the changes.
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| 7. |
- Huang, Zeyang, 1998-, et al.
(author)
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Towards an Exploratory Visual Analytics System for Multivariate Subnetworks in Social Media Analysis
- 2022
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In: Poster Abstracts, IEEE Visualization and Visual Analytics (VIS '22). - : IEEE.
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Conference paper (peer-reviewed)abstract
- Identifying sociolinguistic attributes of inter-community interactions is essential for understanding the polarization of social network communities. A wide range of computational text and network analysis methods may be applicable for this task, however, interpretation of the respective results and investigation of particularly interesting cases and subnetworks are difficult due to the scale and complexity of the data, e.g., for the Reddit platform. In this poster paper, we present an interactive visual analysis interface that facilitates network exploration and comparison at different topological and multivariate attribute scales. Users are able to investigate text- and network-based properties of social network community interactions, identify anomalies of conflict starters, or gain insight into multivariate anomalies behind groups of negative social media posts.
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| 8. |
- Kucher, Kostiantyn, Dr. 1989-, et al.
(author)
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An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics : Position Paper
- 2022
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In: Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond — Methodological Approaches to Visualization (BELIV '22). - : IEEE. - 9798350396294 - 9798350396300 ; , s. 28-37
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Conference paper (peer-reviewed)abstract
- Appropriate evaluation and experimental design are fundamental for empirical sciences, particularly in data-driven fields. Due to the successes in computational modeling of languages, for instance, research outcomes are having an increasingly immediate impact on end users. As the gap in adoption by end users decreases, the need increases to ensure that tools and models developed by the research communities and practitioners are reliable, trustworthy, and supportive of the users in their goals. In this position paper, we focus on the issues of evaluating visual text analytics approaches. We take an interdisciplinary perspective from the visualization and natural language processing communities, as we argue that the design and validation of visual text analytics include concerns beyond computational or visual/interactive methods on their own. We identify four key groups of challenges for evaluating visual text analytics approaches (data ambiguity, experimental design, user trust, and "big picture" concerns) and provide suggestions for research opportunities from an interdisciplinary perspective.
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| 9. |
- Kucher, Kostiantyn, et al.
(author)
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Analysis of VINCI 2009–2017 Proceedings
- 2018
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In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 97-101
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Conference paper (peer-reviewed)abstract
- Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.
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| 10. |
- Kucher, Kostiantyn, et al.
(author)
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Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
- 2018
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In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18). - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450365017 ; , s. 102-103
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Conference paper (peer-reviewed)abstract
- The analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, stances, arguments, topics, terms, and text documents are usually connected with many-to-many relationships for such tasks. Exploratory visual analysis with interactive tools can help the analysts to get an overview of the topics and opinions, identify particularly interesting documents, and describe main themes of various arguments. In our previous work, we introduced an interactive tool called Topics2Themes that was used for topic and theme analysis of vaccination-related discussion texts with a limited set of stance categories. In this poster paper, we describe an application of Topics2Themes to a different genre of data, namely, political comments from Reddit, and multiple sentiment and stance categories detected with automatic classifiers.
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