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Publications (10 of 29) Show all publications
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
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
Paylar, B., Längkvist, M., Jass, J. & Olsson, P.-E. (2023). Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in Daphnia magna Toxicogenomics. Biology, 12(5), Article ID 692.
Open this publication in new window or tab >>Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in Daphnia magna Toxicogenomics
2023 (English)In: Biology, E-ISSN 2079-7737, Vol. 12, no 5, article id 692Article in journal (Refereed) Published
Abstract [en]

Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
Zn, bioavailability, biomarker, machine learning, water hardness
National Category
Water Engineering
Identifiers
urn:nbn:se:oru:diva-106096 (URN)10.3390/biology12050692 (DOI)000995573200001 ()37237504 (PubMedID)2-s2.0-85160308477 (Scopus ID)
Funder
Knowledge Foundation, 20180027Örebro University, 1214-NT3060
Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2024-01-02Bibliographically approved
Rahaman, G. M., Längkvist, M. & Loutfi, A. (2022). Deep Learning based Aerial Image Segmentation for Computing Green Area Factor. In: 2022 10th European Workshop on Visual Information Processing (EUVIP): . Paper presented at 10th European Workshop on Visual Information Processing (EUVIP), Lisbon, Portugal, September 11-14, 2022. IEEE
Open this publication in new window or tab >>Deep Learning based Aerial Image Segmentation for Computing Green Area Factor
2022 (English)In: 2022 10th European Workshop on Visual Information Processing (EUVIP), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The Green Area Factor(GYF) is an aggregate norm used as an index to quantify how much eco-efficient surface exists in a given area. Although the GYF is a single number, it expresses several different contributions of natural objects to the ecosystem. It is used as a planning tool to create and manage attractive urban environments ensuring the existence of required green/blue elements. Currently, the GYF model is gaining rapid attraction by different communities. However, calculating the GYF value is challenging as significant amount of manual effort is needed. In this study, we present a novel approach for automatic extraction of the GYF value from aerial imagery using semantic segmentation results. For model training and validation a set of RGB images captured by Drone imaging system is used. Each image is annotated into trees, grass, soil/open surface, building, and road. A modified U-net deep learning architecture is used for the segmentation of various objects by classifying each pixel into one of the semantic classes. From the segmented image we calculate the class-wise fractional area coverages that are used as input into the simplified GYF model called Sundbyberg for calculating the GYF value. Experimental results yield that the deep learning method provides about 92% mean IoU for test image segmentation and corresponding GYF value is 0.34.

Place, publisher, year, edition, pages
IEEE, 2022
Series
European Workshop on Visual Information Processing, ISSN 2471-8963, E-ISSN 2164-974X
Keywords
green Area index, deep learning, CNN, image, segmentation, urban planning, semantic classification
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:oru:diva-102544 (URN)10.1109/EUVIP53989.2022.9922743 (DOI)000886233300019 ()2-s2.0-85141101986 (Scopus ID)9781665466233 (ISBN)9781665466240 (ISBN)
Conference
10th European Workshop on Visual Information Processing (EUVIP), Lisbon, Portugal, September 11-14, 2022
Available from: 2022-12-05 Created: 2022-12-05 Last updated: 2022-12-05Bibliographically approved
Blad, S., Längkvist, M., Klügl, F. & Loutfi, A. (2022). Empirical analysis of the convergence of Double DQN in relation to reward sparsity. In: Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY (Ed.), 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022: Proceedings. Paper presented at 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022 (pp. 591-596). IEEE
Open this publication in new window or tab >>Empirical analysis of the convergence of Double DQN in relation to reward sparsity
2022 (English)In: 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022: Proceedings / [ed] Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, IEEE, 2022, p. 591-596Conference paper, Published paper (Refereed)
Abstract [en]

Q-Networks are used in Reinforcement Learning to model the expected return from every action at a given state. When training Q-Networks, external reward signals are propagated to the previously performed actions leading up to each reward. If many actions are required before experiencing a reward, the reward signal is distributed across all those actions, where some actions may have greater impact on the reward than others. As the number of significant actions between rewards increases, the relative importance of each action decreases. If actions have too small importance, their impact might be over-shadowed by noise in a deep neural network model, potentially causing convergence issues. In this work, we empirically test the limits of increasing the number of actions leading up to a reward in a simple grid-world environment. We show in our experiments that even though the training error surpasses the reward signal attributed to each action, the model is still able to learn a smooth enough value representation.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
reinforcement learning, deep q-learning, reward sparsity
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:oru:diva-102850 (URN)10.1109/ICMLA55696.2022.00102 (DOI)000980994900087 ()2-s2.0-85152213586 (Scopus ID)9781665462839 (ISBN)9781665462846 (ISBN)
Conference
21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knowledge Foundation, 20190128Knut and Alice Wallenberg Foundation
Available from: 2022-12-22 Created: 2022-12-22 Last updated: 2023-08-21Bibliographically approved
Sjöqvist, H., Längkvist, M. & Javed, F. (2020). An Analysis of Fast Learning Methods for Classifying Forest Cover Types. Applied Artificial Intelligence, 34(10), 691-709
Open this publication in new window or tab >>An Analysis of Fast Learning Methods for Classifying Forest Cover Types
2020 (English)In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 34, no 10, p. 691-709Article in journal (Refereed) Published
Abstract [en]

Proper mapping and classification of Forest cover types are integral in understanding the processes governing the interaction mechanism of the surface with the atmosphere. In the presence of massive satellite and aerial measurements, a proper manual categorization has become a tedious job. In this study, we implement three different modest machine learning classifiers along with three statistical feature selectors to classify different cover types from cartographic variables. Our results showed that, among the chosen classifiers, the standard Random Forest Classifier together with Principal Components performs exceptionally well, not only in overall assessment but across all seven categories. Our results are found to be significantly better than existing studies involving more complex Deep Learning models.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2020
National Category
Other Natural Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-83354 (URN)10.1080/08839514.2020.1771523 (DOI)000550104300001 ()2-s2.0-85086860115 (Scopus ID)
Funder
The Jan Wallander and Tom Hedelius Foundation, P18-0201
Available from: 2020-06-18 Created: 2020-06-18 Last updated: 2020-08-19Bibliographically approved
Landin, C., Tahvili, S., Haggren, H., Längkvist, M., Muhammad, A. & Loutfi, A. (2020). Cluster-Based Parallel Testing Using Semantic Analysis. In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest): . Paper presented at 2nd IEEE International Conference on Artificial Intelligence Testing (AITest 2020), Oxford, United Kingdom, August 3-6, 2020 (pp. 99-106). IEEE
Open this publication in new window or tab >>Cluster-Based Parallel Testing Using Semantic Analysis
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2020 (English)In: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), IEEE, 2020, p. 99-106Conference paper, Published paper (Refereed)
Abstract [en]

Finding a balance between testing goals and testing resources can be considered as a most challenging issue, therefore test optimization plays a vital role in the area of software testing. Several parameters such as the objectives of the tests, test cases similarities and dependencies between test cases need to be considered, before attempting any optimization approach. However, analyzing corresponding testing artifacts (e.g. requirement specification, test cases) for capturing the mentioned parameters is a complicated task especially in a manual testing procedure, where the test cases are documented as a natural text written by a human. Thus, utilizing artificial intelligence techniques in the process of analyzing complex and sometimes ambiguous test data, is considered to be working in different industries. Test scheduling is one of the most popular and practical ways to optimize the testing process. Having a group of test cases which are required the same system setup, installation or testing the same functionality can lead to a more efficient testing process. In this paper, we propose, apply and evaluate a natural language processing-based approach that derives test cases' similarities directly from their test specification. The proposed approach utilizes the Levenshtein distance and converts each test case into a string. Test cases are then grouped into several clusters based on their similarities. Finally, a set of cluster-based parallel test scheduling strategies are proposed for execution. The feasibility of the proposed approach is studied by an empirical evaluation that has been performed on a Telecom use-case at Ericsson in Sweden and indicates promising results.

Place, publisher, year, edition, pages
IEEE, 2020
Series
IEEE International Conference on Artificial Intelligence Testing (AITest)
Keywords
Software Testing, Natural Language Processing, Test Optimization, Semantic Similarity, Clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-88654 (URN)10.1109/AITEST49225.2020.00022 (DOI)000583824000015 ()2-s2.0-85092313008 (Scopus ID)978-1-7281-6984-2 (ISBN)
Conference
2nd IEEE International Conference on Artificial Intelligence Testing (AITest 2020), Oxford, United Kingdom, August 3-6, 2020
Funder
Knowledge FoundationVinnova
Available from: 2021-01-19 Created: 2021-01-19 Last updated: 2023-10-05Bibliographically approved
Alirezaie, M., Längkvist, M. & Loutfi, A. (2020). Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning. In: Ilaria Tiddi, Freddy Lécué, Pascal Hitzler (Ed.), Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges. IOS Press
Open this publication in new window or tab >>Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning
2020 (English)In: Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges / [ed] Ilaria Tiddi, Freddy Lécué, Pascal Hitzler, IOS Press, 2020Chapter in book (Refereed)
Abstract [en]

In this chapter we focus the use of knowledge representation and reasoning (KRR) methods as a guide to machine learning algorithms whereby relevant contextual knowledge can be leveraged upon. In this way, the learning methods improve performance by taking into account causal relationships behind errors. Performance improvement can be obtained by focusing the learning task on aspects that are particularly challenging (or prone to error), and then using added knowledge inferred by the reasoner as a means to provide further input to learning algorithms. Said differently, the KRR algorithms guide the learning algorithms, feeding it labels and data in order to iteratively reduce the errors calculated by a given cost function. This closed loop system comes with the added benefit that errors are also made more understandable to the human, as it is the task of the KRR system to contextualize the errors from the ML algorithm in accordance with its knowledge model. This represents a type of explainable AI that is focused on interpretability. This chapter will discuss the benefits of using KRR methods with ML methods in this way, and demonstrate an approach applied to satellite data for the purpose of improving classification and segmentation task.

Place, publisher, year, edition, pages
IOS Press, 2020
Series
Studies on the Semantic Web ; 47
National Category
Computer Sciences
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-87438 (URN)10.3233/SSW200017 (DOI)978-1-64368-080-4 (ISBN)978-1-64368-081-1 (ISBN)
Available from: 2020-11-17 Created: 2020-11-17 Last updated: 2020-12-10Bibliographically approved
Längkvist, M., Persson, A. & Loutfi, A. (2020). Learning Generative Image Manipulations from Language Instructions. In: : . Paper presented at Concepts in Action: Representation, Learning, and Application (CARLA 2020), Virtual workshop, September 22-23, 2020.
Open this publication in new window or tab >>Learning Generative Image Manipulations from Language Instructions
2020 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

This paper studies whether a perceptual visual system can simulate human-like cognitive capabilities by training a computational model to predict the output of an action using language instruction. The aim is to ground action words such that an AI is able to generate an output image that outputs the effect of a certain action on an given object. The output of the model is a synthetic generated image that demonstrates the effect that the action has on the scene. This work combines an image encoder, language encoder, relational network, and image generator to ground action words, and then visualize the effect an action would have on a simulated scene. The focus in this work is to learn meaningful shared image and text representations for relational learning and object manipulation.

Keywords
image manipulation, predictive learning, relational network, cognitive learning, image generation
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-88913 (URN)
Conference
Concepts in Action: Representation, Learning, and Application (CARLA 2020), Virtual workshop, September 22-23, 2020
Available from: 2021-01-25 Created: 2021-01-25 Last updated: 2021-01-26Bibliographically approved
Landin, C., Hatvani, L., Tahvili, S., Haggren, H., Längkvist, M., Loutfi, A. & Håkansson, A. (2020). Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases. In: The Fifteenth International Conference on Software Engineering Advances: . Paper presented at The Fifteenth International Conference on Software Engineering Advances (ICSEA 2020), Porto, Portugal, October 18-22, 2020 (pp. 90-97). International Academy, Research and Industry Association (IARIA)
Open this publication in new window or tab >>Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases
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2020 (English)In: The Fifteenth International Conference on Software Engineering Advances, International Academy, Research and Industry Association (IARIA) , 2020, p. 90-97Conference paper, Published paper (Refereed)
Abstract [en]

Software testing is still heavily dependent on human judgment since a large portion of testing artifacts, such as requirements and test cases are written in a natural text by experts. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases should be distinguished based on their properties, such as their dependencies and similarities. Knowing the mentioned properties at an early stage of the testing process can be utilized for several test optimization purposes, such as test case selection, prioritization, scheduling,and also parallel test execution. In this paper, we apply, evaluate, and compare the performance of two deep learning algorithmsto detect the similarities between manual integration test cases. The feasibility of the mentioned algorithms is later examined in a Telecom domain by analyzing the test specifications of five different products in the product development unit at Ericsson AB in Sweden. The empirical evaluation indicates that utilizing deep learning algorithms for finding the similarities between manual integration test cases can lead to outstanding results.

Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2020
Series
International Conference on Software Engineering Advances, E-ISSN 2308-4235
Keywords
Natural Language Processing, Deep Learning, Software Testing, Semantic Analysis, Test Optimization
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-88921 (URN)978-1-61208-827-3 (ISBN)
Conference
The Fifteenth International Conference on Software Engineering Advances (ICSEA 2020), Porto, Portugal, October 18-22, 2020
Projects
TESTOMAT Project - The Next Level of Test Automation
Available from: 2021-01-25 Created: 2021-01-25 Last updated: 2023-10-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0579-7181

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