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Neelakantan, S., Norell, J., Hansson, A., Längkvist, M. & Loutfi, A. (2024). Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation. Applied Computing and Geosciences, 21, Article ID 100153.
Open this publication in new window or tab >>Neural network approach for shape-based euhedral pyrite identification in X-ray CT data with adversarial unsupervised domain adaptation
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2024 (English)In: Applied Computing and Geosciences, E-ISSN 2590-1974, Vol. 21, article id 100153Article in journal (Refereed) Published
Abstract [en]

We explore an attenuation and shape-based identification of euhedral pyrites in high-resolution X-ray Computed Tomography (XCT) data using deep neural networks. To deal with the scarcity of annotated data we generate a complementary training set of synthetic images. To investigate and address the domain gap between the synthetic and XCT data, several deep learning models, with and without domain adaption, are trained and compared. We find that a model trained on a small set of human annotations, while displaying over-fitting, can rival the human annotators. The unsupervised domain adaptation approaches are successful in bridging the domain gap, which significantly improves their performance. A domain-adapted model, trained on a dataset that fuses synthetic and real data, is the overall best-performing model. This highlights the possibility of using synthetic datasets for the application of deep learning in mineralogy.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Mineral identification, Unsupervised domain adaptation, Deep convolutional neural network, Semantic segmentation, Euhedral pyrites
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-111189 (URN)10.1016/j.acags.2023.100153 (DOI)001155327400001 ()2-s2.0-85182272087 (Scopus ID)
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI and Robotics funded by the Swedish Knowledge Foundation Dnr:20190128 and in collaboration with the industrial partner Orexplore Technologies.

Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2024-02-14Bibliographically approved
Akalin, N., Kiselev, A., Kristoffersson, A. & Loutfi, A. (2023). A Taxonomy of Factors Influencing Perceived Safety in Human-Robot Interaction. International Journal of Social Robotics, 15, 1993-2004
Open this publication in new window or tab >>A Taxonomy of Factors Influencing Perceived Safety in Human-Robot Interaction
2023 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 15, p. 1993-2004Article in journal (Refereed) Published
Abstract [en]

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human-robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human-robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human-robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Perceived safety, Human-robot interaction, Comfort, Sense of control, Trust
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-107200 (URN)10.1007/s12369-023-01027-8 (DOI)001024550100001 ()2-s2.0-85164166548 (Scopus ID)
Funder
Örebro University
Available from: 2023-08-01 Created: 2023-08-01 Last updated: 2024-02-05Bibliographically approved
Landin, C., Zhao, X., Längkvist, M. & Loutfi, A. (2023). An Intelligent Monitoring Algorithm to Detect Dependencies between Test Cases in the Manual Integration Process. In: 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW): . Paper presented at 16th IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW 2023), Dublin, Ireland, April 16-20, 2023 (pp. 353-360). IEEE
Open this publication in new window or tab >>An Intelligent Monitoring Algorithm to Detect Dependencies between Test Cases in the Manual Integration Process
2023 (English)In: 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), IEEE, 2023, p. 353-360Conference paper, Published paper (Refereed)
Abstract [en]

Finding a balance between meeting test coverage and minimizing the testing resources is always a challenging task both in software (SW) and hardware (HW) testing. Therefore, employing machine learning (ML) techniques for test optimization purposes has received a great deal of attention. However, utilizing machine learning techniques frequently requires large volumes of valuable data to be trained. Although, the data gathering is hard and also expensive, manual data analysis takes most of the time in order to locate the source of failure once they have been produced in the so-called fault localization. Moreover, by applying ML techniques to historical production test data, relevant and irrelevant features can be found using strength association, such as correlation- and mutual information-based methods. In this paper, we use production data records of 100 units of a 5G radio product containing more than 7000 test results. The obtained results show that insightful information can be found after clustering the test results by their strength association, most linear and monotonic, which would otherwise be challenging to identify by traditional manual data analysis methods.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW, ISSN 2159-4848
Keywords
Terms Test Optimization, Machine Learning, Fault Localization, Dependence Analysis, Mutual Information
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-107727 (URN)10.1109/ICSTW58534.2023.00066 (DOI)001009223100052 ()2-s2.0-85163076493 (Scopus ID)9798350333350 (ISBN)9798350333367 (ISBN)
Conference
16th IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW 2023), Dublin, Ireland, April 16-20, 2023
Funder
Knowledge FoundationVinnova
Available from: 2023-08-28 Created: 2023-08-28 Last updated: 2023-10-05Bibliographically approved
Liao, Q., Sun, D., Zhang, S., Loutfi, A. & Andreasson, H. (2023). Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment. IEEE Transactions on Image Processing, 32, 550-564
Open this publication in new window or tab >>Fuzzy Cluster-based Group-wise Point Set Registration with Quality Assessment
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2023 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 32, p. 550-564Article in journal (Refereed) Published
Abstract [en]

This article studies group-wise point set registration and makes the following contributions: "FuzzyGReg", which is a new fuzzy cluster-based method to register multiple point sets jointly, and "FuzzyQA", which is the associated quality assessment to check registration accuracy automatically. Given a group of point sets, FuzzyGReg creates a model of fuzzy clusters and equally treats all the point sets as the elements of the fuzzy clusters. Then, the group-wise registration is turned into a fuzzy clustering problem. To resolve this problem, FuzzyGReg applies a fuzzy clustering algorithm to identify the parameters of the fuzzy clusters while jointly transforming all the point sets to achieve an alignment. Next, based on the identified fuzzy clusters, FuzzyQA calculates the spatial properties of the transformed point sets and then checks the alignment accuracy by comparing the similarity degrees of the spatial properties of the point sets. When a local misalignment is detected, a local re-alignment is performed to improve accuracy. The proposed method is cost-efficient and convenient to be implemented. In addition, it provides reliable quality assessments in the absence of ground truth and user intervention. In the experiments, different point sets are used to test the proposed method and make comparisons with state-of-the-art registration techniques. The experimental results demonstrate the effectiveness of our method.The code is available at https://gitsvn-nt.oru.se/qianfang.liao/FuzzyGRegWithQA

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Quality assessment, Measurement, Three-dimensional displays, Registers, Probability distribution, Point cloud compression, Optimization, Group-wise registration, registration quality assessment, joint alignment, fuzzy clusters, 3D point sets
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-102755 (URN)10.1109/TIP.2022.3231132 (DOI)000908058200002 ()
Funder
Vinnova, 2019- 05878Swedish Research Council Formas, 2019-02264
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-04-03Bibliographically approved
Kalidindi, S. S., Banaee, H., Karlsson, H. & Loutfi, A. (2023). Indoor temperature prediction with context-aware models in residential buildings. Building and Environment, 244, Article ID 110772.
Open this publication in new window or tab >>Indoor temperature prediction with context-aware models in residential buildings
2023 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 244, article id 110772Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel approach for predicting average indoor temperature in residential buildings, utilizing contextual factors of the rise of the building and geographical location. The proposed approach employs advanced deep learning architectures, such as Long Short-Term Memory (LSTM) and Transformers, to create generalized predictive models applicable to a range of residential buildings. The models are trained using historical data from 18 residential buildings over a period of 6 to 10 years, where the buildings are located in different climate zones. Testing is done on nine different data sets representing three different locations and three different types of buildings. The study demonstrates that incorporating the context of building rise significantly improves the models' predictive performance. Specifically, the transformer-based models show improvements in R2 of 4%-27% in a 6 h prediction horizon. The proposed approach explicitly using context information significantly improves the accuracy of predicting, making learnt models a good starting point for optimizing district heating distribution.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Residential buildings, Indoor temperature prediction, Context-aware models, Long Short-Term Memory (LSTM), Transformer
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-109061 (URN)10.1016/j.buildenv.2023.110772 (DOI)001075152300001 ()2-s2.0-85171620775 (Scopus ID)
Funder
Knowledge Foundation, 20190128
Note

This work has been supported by the Industrial Graduate School Collaborative AI & Robotics funded by the Swedish Knowledge Foundation Dnr:20190128 and in collaboration with industrial partner Eco-Guard AB, Sweden.

Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Somasundaram, K., Kiselev, A. & Loutfi, A. (2023). Intelligent Disobedience: A Novel Approach for Preventing Human Induced Interaction Failures in Robot Teleoperation. In: HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. Paper presented at 18th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023), Stockholm, Sweden, March 13-16, 2023 (pp. 142-145). New York: Association for Computing Machinery
Open this publication in new window or tab >>Intelligent Disobedience: A Novel Approach for Preventing Human Induced Interaction Failures in Robot Teleoperation
2023 (English)In: HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, New York: Association for Computing Machinery , 2023, p. 142-145Conference paper, Published paper (Refereed)
Abstract [en]

Failures are natural and unavoidable events in any form of interaction, especially in human-robot interactions (HRI). Throughout the literature, the definition and classification of failures are diverse, depending on the source and application domain. However, the tolerance to the aftereffect of these failures is low in teleoperation due to its unstructured application domains. One such type of failure is called human induced interaction failure. This is an interesting and often overlooked failure type, due to the perspective that robots are designed always to obey the instructions given by the human operators. Regardless of the degree of automation that the robot is equipped with. But what if the instructions provided are faulty, dangerous, or misleading. This paper addresses the above mentioned research gap. It introduces a framework based on the concept of Intelligent Disobedience (ID), derived from guide dog training methods, to manage human induced interaction failures in teleoperation scenarios.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery, 2023
Keywords
intelligent disobedience, failures in HRI, interaction failures, human errors
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-108836 (URN)10.1145/3568294.3580060 (DOI)001054975700023 ()2-s2.0-85150451985 (Scopus ID)9781450399708 (ISBN)
Conference
18th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023), Stockholm, Sweden, March 13-16, 2023
Funder
Knowledge Foundation, 20190128
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2023-10-10Bibliographically approved
Giaretta, A. & Loutfi, A. (2023). On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3839-3851
Open this publication in new window or tab >>On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds
2023 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 14, no 4, p. 3839-3851Article in journal (Refereed) Published
Abstract [en]

Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Counting, Smart Home, Multi-occupancy, Graph Theory, Independent Set
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-94963 (URN)10.1007/s12652-021-03514-0 (DOI)000701599000001 ()2-s2.0-85115885370 (Scopus ID)
Note

Funding agency:

Örebro University

Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2023-06-12Bibliographically approved
Gutiérrez Maestro, E., Banaee, H. & Loutfi, A. (2023). Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems. In: : . Paper presented at IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology, Portomaso, St. Julians, Malta, December 7-9, 2023.
Open this publication in new window or tab >>Stress Lingers: Recognizing the Impact of Task Order on Design of Stress and Emotion Detection Systems
2023 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

This paper examines the significance of the priming effect in designing and developing models for recognizing of affective states. Using a public dataset, often considered a benchmark in automatic stress recognition, the significance of the priming effect is explicated. Two experimental setups confirm the importance of task ordering in this problem. The results demonstrate the statistical significance of the model's confusion when the subject has previously experienced stress and illustrate the importance for the Affective Computing community to develop methods to mitigate the priming effect where the order of tasks impacts how data should be modelled. 

Keywords
Artificial Intelligence, Deep Learning, Digital Health
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-111192 (URN)
Conference
IEEE EMBS International Conference on Data Science and Engineering in Healthcare, Medicine & Biology, Portomaso, St. Julians, Malta, December 7-9, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2024-01-29Bibliographically approved
Harrisont, K., Perugia, G., Correia, F., Somasundaram, K., van Waveren, S., Paiva, A. & Loutfi, A. (2023). The Imperfectly Relatable Robot: An Interdisciplinary Workshop on the Role of Failure in HRI. In: HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction. Paper presented at 18th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023), Stockholm, Sweden, March 13-16, 2023 (pp. 917-919). New York: Association for Computing Machinery
Open this publication in new window or tab >>The Imperfectly Relatable Robot: An Interdisciplinary Workshop on the Role of Failure in HRI
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2023 (English)In: HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, New York: Association for Computing Machinery , 2023, p. 917-919Conference paper, Published paper (Refereed)
Abstract [en]

Focusing on failure to improve human-robot interactions represents a novel approach that calls into question human expectations of robots, as well as posing ethical and methodological challenges to researchers. Fictional representations of robots (still for many non-expert users the primary source of expectations and assumptions about robots) often emphasize the ways in which robots surpass/perfect humans, rather than portraying them as fallible. Thus, to encounter robots that come too close, drop items or stop suddenly starts to close the gap between fiction and reality. These kinds of failures - if mitigated by explanation or recovery procedures - have the potential to make the robot a little more relatable and human-like. However, studying failures in human-robot interaction requires producing potentially difficult or uncomfortable interactions in which robots failing to behave as expected may seem counterintuitive and unethical. In this space, interdisciplinary conversations are the key to untangling the multiple challenges and bringing themes of power and context into view. In this workshop, we invite researchers from across the disciplines to an interactive, interdisciplinary discussion around failure in social robotics. Topics for discussion include (but are not limited to) methodological and ethical challenges around studying failure in HRI, epistemological gaps in defining and understanding failure in HRI, sociocultural expectations around failure and users' responses.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery, 2023
Keywords
Failure, Interdisciplinary, Ethics, Methodology, Social Robotics, Human-Robot Interaction, Faulty robots
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-108854 (URN)10.1145/3568294.3579952 (DOI)001054975700203 ()2-s2.0-85150440939 (Scopus ID)9781450399708 (ISBN)
Conference
18th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023), Stockholm, Sweden, March 13-16, 2023
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2023-10-10Bibliographically approved
Morillo-Mendez, L., Schrooten, M. G. S., Loutfi, A. & Martinez Mozos, O. (2022). Age-Related Differences in the Perception of Robotic Referential Gaze in Human-Robot Interaction. International Journal of Social Robotics, 1-13
Open this publication in new window or tab >>Age-Related Differences in the Perception of Robotic Referential Gaze in Human-Robot Interaction
2022 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, p. 1-13Article in journal (Refereed) Epub ahead of print
Abstract [en]

There is an increased interest in using social robots to assist older adults during their daily life activities. As social robots are designed to interact with older users, it becomes relevant to study these interactions under the lens of social cognition. Gaze following, the social ability to infer where other people are looking at, deteriorates with older age. Therefore, the referential gaze from robots might not be an effective social cue to indicate spatial locations to older users. In this study, we explored the performance of older adults, middle-aged adults, and younger controls in a task assisted by the referential gaze of a Pepper robot. We examined age-related differences in task performance, and in self-reported social perception of the robot. Our main findings show that referential gaze from a robot benefited task performance, although the magnitude of this facilitation was lower for older participants. Moreover, perceived anthropomorphism of the robot varied less as a result of its referential gaze in older adults. This research supports that social robots, even if limited in their gazing capabilities, can be effectively perceived as social entities. Additionally, this research suggests that robotic social cues, usually validated with young participants, might be less optimal signs for older adults.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12369-022-00926-6.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Aging, Gaze following, Human-robot interaction, Non-verbal cues, Referential gaze, Social cues
National Category
Gerontology, specialising in Medical and Health Sciences Robotics
Identifiers
urn:nbn:se:oru:diva-101615 (URN)10.1007/s12369-022-00926-6 (DOI)000857896500001 ()36185773 (PubMedID)2-s2.0-85138680591 (Scopus ID)
Funder
European Commission, 754285Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Funding agency:

RobWell project - Spanish Ministerio de Ciencia, Innovacion y Universidades RTI2018-095599-A-C22

Available from: 2022-10-04 Created: 2022-10-04 Last updated: 2023-12-08Bibliographically approved
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