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Alabdallah, A., Ohlsson, M., Pashami, S. & Rögnvaldsson, T. (2024). The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models. Artificial Intelligence in Medicine, 148, Article ID 102781.
Open this publication in new window or tab >>The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
2024 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 148, article id 102781Article in journal (Refereed) Published
Abstract [en]

The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events. 

Place, publisher, year, edition, pages
Elsevier B.V., 2024
Keywords
Concordance Index, Evaluation metric, Survival analysis, Variational encoder–decoder, Machine Learning, Neural Networks, Computer, Bioinformatics, Forecasting, Learning systems, Signal encoding, Encoder-decoder, Evaluation metrics, Fine-grained analysis, Performance, Prediction modelling, Survival prediction, Weighted harmonic means, artificial neural network, Deep learning
National Category
Mathematics
Identifiers
urn:nbn:se:ri:diva-71927 (URN)10.1016/j.artmed.2024.102781 (DOI)2-s2.0-85184733529 (Scopus ID)
Note

This research was performed under the CAISR+ project funded by the Swedish Knowledge Foundation 

Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-02-27Bibliographically approved
Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance. In: Proceedings of the 15th European Wave and Tidal Energy Conference: . Paper presented at The 15th European Wave and Tidal Energy Conference.
Open this publication in new window or tab >>A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance
2023 (English)In: Proceedings of the 15th European Wave and Tidal Energy Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.

Keywords
Linear potential flow, machine learning, recurrent neural network, floating bodies, wave energy
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72107 (URN)10.36688/ewtec-2023-321 (DOI)
Conference
The 15th European Wave and Tidal Energy Conference
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Fu, J., Tan, J., Yin, W., Pashami, S. & Björkman, M. (2023). Component attention network for multimodal dance improvisation recognition. In: : . Paper presented at 25th International Conference on Multimodal Interaction, ICMI 2023. Paris, France. 9 October 2023 through 13 October 2023 (pp. 114-118). Association for Computing Machinery
Open this publication in new window or tab >>Component attention network for multimodal dance improvisation recognition
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2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Dance improvisation is an active research topic in the arts. Motion analysis of improvised dance can be challenging due to its unique dynamics. Data-driven dance motion analysis, including recognition and generation, is often limited to skeletal data. However, data of other modalities, such as audio, can be recorded and benefit downstream tasks. This paper explores the application and performance of multimodal fusion methods for human motion recognition in the context of dance improvisation. We propose an attention-based model, component attention network (CANet), for multimodal fusion on three levels: 1) feature fusion with CANet, 2) model fusion with CANet and graph convolutional network (GCN), and 3) late fusion with a voting strategy. We conduct thorough experiments to analyze the impact of each modality in different fusion methods and distinguish critical temporal or component features. We show that our proposed model outperforms the two baseline methods, demonstrating its potential for analyzing improvisation in dance

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
Arts computing; Attention network; Dance recognition; Data driven; Down-stream; Fusion methods; Improvization; Multi-modal; Multi-modal fusion; Performance; Research topics; Motion estimation
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-67967 (URN)10.1145/3577190.3614114 (DOI)2-s2.0-85175844284 (Scopus ID)
Conference
25th International Conference on Multimodal Interaction, ICMI 2023. Paris, France. 9 October 2023 through 13 October 2023
Available from: 2023-11-24 Created: 2023-11-24 Last updated: 2024-02-06Bibliographically approved
Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). Estimation of nonlinear forces acting on floating bodies using machine learning. In: J. W. Ringsberg, C. Guedes Soares (Ed.), Advances in the Analysis and Design of Marine Structures: (pp. 63-72). CRC Press
Open this publication in new window or tab >>Estimation of nonlinear forces acting on floating bodies using machine learning
2023 (English)In: Advances in the Analysis and Design of Marine Structures / [ed] J. W. Ringsberg, C. Guedes Soares, CRC Press, 2023, p. 63-72Chapter in book (Other academic)
Abstract [en]

Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.

Place, publisher, year, edition, pages
CRC Press, 2023
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72114 (URN)10.1201/9781003399759 (DOI)9781003399759 (ISBN)
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Pashami, S., Nowaczyk, S., Fan, Y., Jakubowski, J., Paiva, N., Davari, N., . . . Gama, J. (2023). Explainable Predictive Maintenance. arXiv
Open this publication in new window or tab >>Explainable Predictive Maintenance
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2023 (English)In: arXivArticle in journal (Refereed) Epub ahead of print
Abstract [en]

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of “black box” Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 & 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability’s crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community’s attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-69223 (URN)10.48550/arxiv.2306.05120 (DOI)
Note

The paper is funded from the XPM project funded by the National Science Centre, Poland under CHIST-ERA programme (NCN UMO-2020/02/Y/ ST6/00070), French National Research Agency(ANR) under CHIST-ERA programme (ANR-21-CHR4-0003), Swedish Research Council under grantCHIST-ERA-19-XAI-012 and Portuguese Funding Agency, FCT - Fundação para a Ciência e a Tecnologia under CHIST-ERA programme (CHIST-ERA/0004/2019). The research has been supportedby a grant from the Priority Research Area (DigiWorld) under the Strategic Programme ExcellenceInitiative at Jagiellonian University

Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-06-11Bibliographically approved
Eskilsson, C., Pashami, S., Holst, A. & Palm, J. (2023). Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems. In: The Proceedings of the 33rd International Ocean and Polar Engineering Conference: . Paper presented at The 33rd International Ocean and Polar Engineering Conference. , 33, Article ID 307.
Open this publication in new window or tab >>Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems
2023 (English)In: The Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023, Vol. 33, article id 307Conference paper, Published paper (Refereed)
Abstract [en]

We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.

Keywords
Wave-body interaction; hierarchical modelling; linear potential flow; hybrid modeling; machine learning; recurrent neural net- work.
National Category
Marine Engineering
Identifiers
urn:nbn:se:ri:diva-72110 (URN)
Conference
The 33rd International Ocean and Polar Engineering Conference
Funder
Swedish Energy Agency, 50196-1
Available from: 2024-03-02 Created: 2024-03-02 Last updated: 2024-03-08Bibliographically approved
Gutkin, R., Wirje, A., Nilsson-Lindén, H., Brunklaus, B., Pashami, S., Lundahl, J., . . . Andersson, O. (2023). Safe to circulate: public report.
Open this publication in new window or tab >>Safe to circulate: public report
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2023 (English)Report (Other academic)
Publisher
p. 15
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ri:diva-67531 (URN)
Note

Project within FFI: Accelerate the transition to sustainable road transport 

Available from: 2023-10-15 Created: 2023-10-15 Last updated: 2024-02-26Bibliographically approved
Davari, N., Pashami, S., Veloso, B., Nowaczyk, S., Fan, Y., Pereira, P. M., . . . Gama, J. (2022). A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume 13205 LNCS, Pages 39 - 522022: . Paper presented at 20th International Symposium on Intelligent Data Analysis, IDA 2022Rennes20 April 2022 through 22 April 2022 (pp. 39-52). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set
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2022 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume 13205 LNCS, Pages 39 - 522022, Springer Science and Business Media Deutschland GmbH , 2022, p. 39-52Conference paper, Published paper (Refereed)
Abstract [en]

This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022, The Author(s)

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2022
Keywords
Autoencoder, Fault detection, LSTM, Outliers, Time series, Anomaly detection, Buses, Engines, Network layers, Anomaly detection frameworks, Auto encoders, Case-studies, Data set, Data-driven anomalies, Detection framework, Faults detection, Multi-layers, Times series, Volvo bus, Long short-term memory
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-59249 (URN)10.1007/978-3-031-01333-1_4 (DOI)2-s2.0-85128784943 (Scopus ID)9783031013324 (ISBN)
Conference
20th International Symposium on Intelligent Data Analysis, IDA 2022Rennes20 April 2022 through 22 April 2022
Note

 Funding details: 2020-00767; Funding details: Fundação para a Ciência e a Tecnologia, FCT; Funding details: Vetenskapsrådet, VR; Funding text 1: This work was supported by the CHIST-ERA grant CHIST-ERA-19-XAI-012, project CHIST-ERA/0004/2019 funded by FCT - Funda¸cão para a Ciência e Tecnologia and project 2020-00767 funded by Swedish Research Council.

Available from: 2022-06-13 Created: 2022-06-13 Last updated: 2023-11-06Bibliographically approved
Pena, F. J., Gonzalez, A. L., Pashami, S., Al-Shishtawy, A. & Payberah, A. H. (2022). Siambert: Siamese Bert-based Code Search. In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022: . Paper presented at 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, 13 June 2022 through 14 June 2022. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Siambert: Siamese Bert-based Code Search
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2022 (English)In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Code Search is a practical tool that helps developers navigate growing source code repositories by connecting natural language queries with code snippets. Platforms such as StackOverflow resolve coding questions and answers; however, they cannot perform a semantic search through the code. Moreover, poorly documented code adds more complexity to search for code snippets in repositories. To tackle this challenge, this paper presents Siambert, a BERT-based model that gets the question in natural language and returns relevant code snippets. The Siambert architecture consists of two stages, where the first stage, inspired by Siamese Neural Network, returns the top K relevant code snippets to the input questions, and the second stage ranks the given snippets by the first stage. The experiments show that Siambert outperforms non-BERT-based models having improvements that range from 12% to 39% on the Recall@1 metric and improves the inference time performance, making it 15x faster than standard BERT models

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2022
Keywords
Codes (symbols), Natural language processing systems, Code search, Natural language queries, Natural languages, Neural-networks, Performance, Semantic search, Source code repositories, Semantics
National Category
Economics and Business
Identifiers
urn:nbn:se:ri:diva-60199 (URN)10.1109/SAIS55783.2022.9833051 (DOI)2-s2.0-85136132400 (Scopus ID)9781665471268 (ISBN)
Conference
34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, 13 June 2022 through 14 June 2022
Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2023-11-06Bibliographically approved
Englund, C., Aksoy, E. E., Alonso-Fernandez, F., Cooney, M. D., Pashami, S. & Åstrand, B. (2021). AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities, 4(2), 783-802
Open this publication in new window or tab >>AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control
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2021 (English)In: Smart Cities, ISSN 2624-6511, Vol. 4, no 2, p. 783-802Article in journal (Refereed) Published
Abstract [en]

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
smart cities, artificial intelligence, perception, smart traffic control, driver modeling
National Category
Computer Sciences
Identifiers
urn:nbn:se:ri:diva-55191 (URN)10.3390/smartcities4020040 (DOI)
Available from: 2021-07-05 Created: 2021-07-05 Last updated: 2023-11-06Bibliographically approved
Projects
Data-Driven Predictive Maintenance for Trucks [2016-03451_Vinnova]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3272-4145

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