https://ecp.ep.liu.se/index.php/sais <p>The SAIS workshop has since its first edition been a forum for building the Swedish AI research community and nurturing networks across academia and industry. Researchers and practitioners in AI and related disciplines, in Sweden and the rest of the world, have been invited to join us in exchanging knowledge, news, and results on AI-related theory and applications.</p> Linköping University Electronic Press en-US Swedish Artificial Intelligence Society 1650-3686 https://ecp.ep.liu.se/index.php/sais/article/view/713 During the last decade, we have witnessed a rapid development of extended reality (XR) technologies such as augmented reality (AR) and virtual reality (VR). Further, there have been tremendous advancements in artificial intelligence (AI) and machine learning (ML). These two trends will have a significant impact on future digital societies. The vision of an immersive, ubiquitous, and intelligent virtual space opens up new opportunities for creating an enhanced digital world in which the users are at the center of the development process, so-called intelligent realities (IRs). The “Human-Centered Intelligent Realities” (HINTS) profile project will develop concepts, principles, methods, algorithms, and tools for human-centered IRs, thus leading the way for future immersive, user-aware, and intelligent interactive digital environments. The HINTS project is centered around an ecosystem combining XR and communication paradigms to form novel intelligent digital systems. HINTS will provide users with new ways to understand, collaborate with, and control digital systems. These novel ways will be based on visual and data-driven platforms which enable tangible, immersive cognitive interactions within real and virtual realities. Thus, exploiting digital systems in a more efficient, effective, engaging, and resource-aware condition. Moreover, the systems will be equipped with cognitive features based on AI and ML, which allow users to engage with digital realities and data in novel forms. This paper describes the HINTS profile project and its initial results. Veronica Sundstedt Veselka Boeva Hans-Jürgen Zepernick Prashant Goswami Abbas Cheddad Kurt Tutschku Håkan Grahn Emiliano Casalicchio Markus Fiedler Emilia Mendes Shahrooz Abghari Yan Hu Valeria Garro Thi My Chinh Chu Lars Lundberg Patrik Arlos Copyright (c) 2023 Veronica Sundstedt, Veselka Boeva, Hans-Jürgen Zepernick, Prashant Goswami, Abbas Cheddad, Kurt Tutschku, Håkan Grahn, Emiliano Casalicchio, Markus Fiedler, Emilia Mendes, Shahrooz Abghari, Yan Hu, Valeria Garro, Thi My Chinh Chu, Lars Lundberg, Patrik Arlos https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 9 17 10.3384/ecp199001 https://ecp.ep.liu.se/index.php/sais/article/view/714 Intensifying climate change will lead to more extreme weather events, including heavy rainfall and drought. Accurate streamflow prediction models which are adaptable and robust to new circumstances in a changing climate will be an important source of information for decisions on climate adaptation efforts, especially regarding mitigation of the risks of and damages associated with flooding. In this work we propose a machine learning-based approach for predicting water flow intensities in inland watercourses based on the physical characteristics of the catchment areas, obtained from geospatial data (including elevation and soil maps, as well as satellite imagery), in addition to temporal information about past rainfall quantities and temperature variations. We target the one-day-ahead regime, where a fully convolutional neural network model receives spatio-temporal inputs and predicts the water flow intensity in every coordinate of the spatial input for the subsequent day. To the best of our knowledge, we are the first to tackle the task of dense water flow intensity prediction; earlier works have considered the prediction of flow intensities at a sparse set of locations at a time. An extensive set of model evaluations and ablations are performed, which empirically justify our various design choices. Code and preprocessed data have been made publicly available at https://github.com/aleksispi/fcn-water-flow. Aleksis Pirinen Olof Mogren Mårten Västerdal Copyright (c) 2023 Aleksis Pirinen, Olof Mogren, Mårten Västerdal https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 18 27 10.3384/ecp199002 https://ecp.ep.liu.se/index.php/sais/article/view/715 Climate-induced disasters are and will continue to be on the rise, and thus search-and-rescue (SAR) operations, where the task is to localize and assist one or several people who are missing, become increasingly relevant. In many cases the rough location may be known and a UAV can be deployed to explore a given, confined area to precisely localize the missing people. Due to time and battery constraints it is often critical that localization is performed as efficiently as possible. In this work we approach this type of problem by abstracting it as an aerial view goal localization task in a framework that emulates a SAR-like setup without requiring access to actual UAVs. In this framework, an agent operates on top of an aerial image (proxy for a search area) and is tasked with localizing a goal that is described in terms of visual cues. To further mimic the situation on an actual UAV, the agent is not able to observe the search area in its entirety, not even at low resolution, and thus it has to operate solely based on partial glimpses when navigating towards the goal. To tackle this task, we propose AiRLoc, a reinforcement learning (RL)-based model that decouples exploration (searching for distant goals) and exploitation (localizing nearby goals). Extensive evaluations show that AiRLoc outperforms heuristic search methods as well as alternative learnable approaches, and that it generalizes across datasets, e.g. to disaster-hit areas without seeing a single disaster scenario during training. We also conduct a proof-of-concept study which indicates that the learnable methods outperform humans on average. Code and models have been made publicly available at https://github.com/aleksispi/airloc. Aleksis Pirinen Anton Samuelsson John Backsund Kalle Åström Copyright (c) 2023 Aleksis Pirinen, Anton Samuelsson, John Backsund, Kalle Åström https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 28 37 10.3384/ecp199003 https://ecp.ep.liu.se/index.php/sais/article/view/716 The Urdarbrunnen project is a Saab-led exploratory initiative that aims to develop an operator-assisted AI-enabled mission system for basic autonomous functions. In its first iteration, presented in this project paper, the system is designed to be capable of performing the search task of a combat search and rescue mission in a complex and dynamic environment, while providing basic human machine interaction support for remote operators. The system enables a team of agents to cooperatively plan and execute a search mission while also interfacing with the WARA-PS core system that allows human operators and other agents to monitor activities and interact with each other. The aim of the project is to develop the system iteratively, with each iteration incorporating feedback from simulations and real-world experiments. In future work, the capability of the system will be extended to incorporate additional tasks for other scenarios, making it a promising starting point for the integration of autonomous capabilities in a future air force. Ella Olsson Mikael Nilsson Kristoffer Bergman Daniel de Leng Stefan Carlén Emil Karlsson Bo Granbom Copyright (c) 2023 Ella Olsson, Mikael Nilsson, Kristoffer Bergman, Daniel de Leng, Stefan Carlén, Emil Karlsson, Bo Granbom https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 38 45 10.3384/ecp199004 https://ecp.ep.liu.se/index.php/sais/article/view/717 The one-pixel attack is an image attack method for creating adversarial instances with minimal perturbations, i.e., pixel modification. The attack method makes the adversarial instances difficult to detect as it only manipulates a single pixel in the image. In this paper, we study four different defense approaches against adversarial attacks, and more specifically the one-pixel attack, over three different models. The defense methods used are: data augmentation, spatial smoothing, and Gaussian data augmentation used during both training and testing. The empirical experiments involve the following three models: all convolutional network (CNN), network in network (NiN), and the convolutional neural network VGG16. Experiments were executed and the results show that Gaussian data augmentation performs quite poorly when applied during the prediction phase. When used during the training phase, we see a reduction in the number of instances that could be perturbed by the NiN model. However, the CNN model shows an overall significantly worse performance compared to no defense technique. Spatial smoothing shows an ability to reduce the effectiveness of the one-pixel attack, and it is on average able to defend against half of the adversarial examples. Data augmentation also shows promising results, reducing the number of successfully perturbed images for both the CNN and NiN models. However, data augmentation leads to slightly worse overall model performance for the NiN and VGG16 models. Interestingly, it significantly improves the performance for the CNN model. We conclude that the most suitable defense is dependent on the model used. For the CNN model, our results indicate that a combination of data augmentation and spatial smoothing is a suitable defense setup. For the NiN and VGG16 models, a combination of Gaussian data augmentation together with spatial smoothing is more promising. Finally, the experiments indicate that applying Gaussian noise during the prediction phase is not a workable defense against the one-pixel attack. Victor Arvidsson Ahmad Al-Mashahedi Martin Boldt Copyright (c) 2023 Victor Arvidsson, Ahmad Al-Mashahedi, Martin Boldt https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 49 57 10.3384/ecp199005 https://ecp.ep.liu.se/index.php/sais/article/view/718 A recent report by the Swedish Authority for Privacy Protection (IMY) evaluates the potential of jointly training and exchangingmachine learningmodels between two healthcare providers. In relation to the privacy problems identified therein, this article explores the trade-off between utility and privacy when using privacyenhancing technologies (PETs) in combination with federated learning. Results are reported from numerical experiments with standard text-book machine learning models under both differential privacy (DP) and FullyHomomorphic Encryption (FHE). The results indicate that FHE is a promising approach for privacy-preserving federated learning, with the CKKS scheme being more favorable in terms of computational performance due to its support of SIMD operations and compact representation of encrypted vectors. The results for DP are more inconclusive. The article briefly discusses the current regulatory context and aspects that lawmakers may consider to enable an AI leap in Swedish healthcare while maintaining data protection. Rickard Brännvall Helena Linge Johan Östman Copyright (c) 2023 Rickard Brännvall, Helena Linge, Johan Östman https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 58 67 10.3384/ecp199006 https://ecp.ep.liu.se/index.php/sais/article/view/719 During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However, implementing and generating value from AI in the customer life cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both the model performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer life cycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regarding outcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating both policy outputs and outcomes to ensure reliable and trustworthy AI. Jim Ahlstrand Martin Boldt Anton Borg Håkan Grahn Copyright (c) 2023 Jim Ahlstrand, Martin Boldt, Anton Borg, Håkan Grahn https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 68 76 10.3384/ecp199007 https://ecp.ep.liu.se/index.php/sais/article/view/720 This paper is motivated by Floridi’s recent claim that Large Language Models like ChatGPT can be seen as ‘intelligence-free’ agents. Where I do not agree with Floridi that such systems are intelligence-free, my paper does question whether they can be called agents, and if so, what kind. I argue for the adoption of a more restricted understanding of agent in AI-research, one that comes closer in its meaning to how the term is used in the philosophies of mind, action, and agency. I propose such a more narrowing understanding of agent, suggesting that an agent can be seen as entity or system that things can be ‘up to’, that can act autonomously in a way that is best understood on the basis of Husserl’s notion of indeterminate determinability. Maud van Lier Copyright (c) 2023 Maud van Lier https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 79 84 10.3384/ecp199008 https://ecp.ep.liu.se/index.php/sais/article/view/721 Manufacturers of high-end professional products are committed to delivering outstanding customer-quality experiences. They maintain databases of customer complaints and repair service jobs data to monitor product quality. Analyzing the text data from service jobs can help identify common problems, recurring issues, and patterns that impact customer satisfaction, and aid manufacturers in taking corrective actions to improve product design, manufacturing processes, and customer support services. However, distinguishing legitimate quality issues from a brief, domain-specific text in service jobs remains a challenge. This study aims to automate the classification of technical service repair job data into legitimate quality issues or non-issues to assist individuals in the quality field department in a large company. To achieve this goal, we developed a comprehensive pipeline based on natural language processing and machine learning techniques including raw text preprocessing, dealing with imbalance class distribution, feature extraction, and classification. In this study, We evaluate several feature extraction and machine learning classification methods and perform the Friedman test followed by Nemenyi post-hoc analysis to find the best-performing model. Our results show that the passive-aggressive classifier achieved the highest average accuracy of 94% and 89% average macro F1-score when trained on TF-IDF vectors. Rakhshanda Jabeen Morgan Ericsson Jonas Nordqvist Copyright (c) 2023 Rakhshanda Jabeen, Morgan Ericsson, Jonas Nordqvist https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 85 93 10.3384/ecp199009 https://ecp.ep.liu.se/index.php/sais/article/view/722 By identifying and characterising the narratives told in news media we can better understand political and societal processes. The problem is challenging from the perspective of natural language processing because it requires a combination of quantitative and qualitative methods. This paper reports on work in progress, which aims to build a human-in-the-loop pipeline for analysing how the variation of narrative themes across different domains, based on topic modelling and word embeddings. As an illustration, we study the language associated with the threat narrative in British news media. Igor Ryazanov Johanna Björklund Copyright (c) 2023 Igor Ryazanov, Johanna Björklund https://creativecommons.org/licenses/by/4.0/ 2023-06-09 2023-06-09 94 99 10.3384/ecp199010