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  • 1.
    Alabdallah, A.
    et al.
    Halmstad University, Sweden.
    Ohlsson, Mathias
    Halmstad University, Sweden; Lund University, Sweden.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Halmstad University, Sweden.
    Rögnvaldsson, T.
    Halmstad University, Sweden.
    The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models2024In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 148, article id 102781Article in journal (Refereed)
    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. 

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  • 2.
    Bouguelia, Mohamed-Rafik
    et al.
    Halmstad University, Sweden.
    Karlsson, Alexander
    University of Skövde, Sweden.
    Pashami, Sepideh
    Halmstad University, Sweden.
    Nowaczyk, Sławomir
    Halmstad University, Sweden.
    Holst, Anders
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Mode tracking using multiple data streams2018In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, p. 33-46Article in journal (Refereed)
    Abstract [en]

    Most existing work in information fusion focuses on combining information with well-defined meaning towards a concrete, pre-specified goal. In contradistinction, we instead aim for autonomous discovery of high-level knowledge from ubiquitous data streams. This paper introduces a method for recognition and tracking of hidden conceptual modes, which are essential to fully understand the operation of complex environments, and an important step towards building truly intelligent aware systems. We consider a scenario of analyzing usage of a fleet of city buses, where the objective is to automatically discover and track modes such as highway route, heavy traffic, or aggressive driver, based on available on-board signals. The method we propose is based on aggregating the data over time, since the high-level modes are only apparent in the longer perspective. We search through different features and subsets of the data, and identify those that lead to good clusterings, interpreting those clusters as initial, rough models of the prospective modes. We utilize Bayesian tracking in order to continuously improve the parameters of those models, based on the new data, while at the same time following how the modes evolve over time. Experiments with artificial data of varying degrees of complexity, as well as on real-world datasets, prove the effectiveness of the proposed method in accurately discovering the modes and in identifying which one best explains the current observations from multiple data streams.

  • 3.
    Davari, Narjes
    et al.
    INESC TEC, Portugal.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Halmstad University, Sweden.
    Veloso, Bruno
    INESC TEC, Portugal; University Portucalense, Portugal.
    Nowaczyk, Stawomir
    Halmstad University, Sweden.
    Fan, Yuantao
    Halmstad University, Sweden.
    Pereira, Pedro Mota
    Metro of Porto, Portugal.
    Ribeiro, Rita
    INESC TEC, Portugal; University of Porto, Portugal.
    Gama, Joao
    INESC TEC, Portugal; University of Porto, Portugal.
    A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set2022In: 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 (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)

  • 4.
    Englund, Cristofer
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems. Halmstad University, Sweden.
    Aksoy, Eren E.
    Halmstad University, Sweden.
    Alonso-Fernandez, Fernando
    Halmstad University, Sweden.
    Cooney, Martin D.
    Halmstad University, Sweden.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Halmstad University, Sweden.
    Åstrand, Björn
    Halmstad University, Sweden.
    AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control2021In: Smart Cities, ISSN 2624-6511, Vol. 4, no 2, p. 783-802Article in journal (Refereed)
    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.

  • 5.
    Eskilsson, Claes
    et al.
    Dep. of the Built Environment, Aalborg University.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Holst, Anders
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Palm, Johannes
    Sigma Energy & Marine, Sweden.
    A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance2023In: Proceedings of the 15th European Wave and Tidal Energy Conference, 2023Conference 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.

  • 6.
    Eskilsson, Claes
    et al.
    RISE Research Institutes of Sweden, Safety and Transport, Maritime department. Aalborg University, Denmark.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Holst, Anders
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Palm, Johannes
    Sigma Energy & Marine, Sweden.
    Estimation of nonlinear forces acting on floating bodies using machine learning2023In: 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.

  • 7.
    Eskilsson, Claes
    et al.
    RISE Research Institutes of Sweden, Safety and Transport, Maritime department.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Holst, Anders
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Palm, Johannes
    Sigma Energy & Marine, Sweden.
    Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems2023In: The Proceedings of the 33rd International Ocean and Polar Engineering Conference, 2023, Vol. 33, article id 307Conference 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.

  • 8.
    Fu, Jia
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.
    Tan, Jiarui
    RISE Research Institutes of Sweden.
    Yin, Wenjie
    RISE Research Institutes of Sweden.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Björkman, Mårten
    RISE Research Institutes of Sweden.
    Component attention network for multimodal dance improvisation recognition2023Conference 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

  • 9.
    Gutkin, Renaud
    et al.
    Volvo Cars, Sweden.
    Wirje, Anders
    Nilsson-Lindén, Hanna
    RISE Research Institutes of Sweden, Materials and Production, Product Realisation Methodology.
    Brunklaus, Birgit
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Lundahl, Jenny
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Essvik, Krister
    RISE Research Institutes of Sweden, Materials and Production, Manufacturing Processes.
    Enebog, Emma
    RISE Research Institutes of Sweden, Materials and Production, Product Realisation Methodology.
    Jonasson, Christian
    RISE Research Institutes of Sweden, Digital Systems, Smart Hardware.
    Andersson, Oscar
    RISE Research Institutes of Sweden, Materials and Production, Manufacturing Processes.
    Safe to circulate: public report2023Report (Other academic)
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  • 10.
    Holst, Anders
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Pashami, Sepideh
    Halmstad University, Sweden .
    Bae, Juhee
    University of Skövde, Sweden .
    Incremental causal discovery and visualization2019In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper (Refereed)
    Abstract [en]

    Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s).

  • 11.
    Pashami, Sepideh
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Halmstad University, Sweden.
    Nowaczyk, Slawomir
    Halmstad University, Sweden.
    Fan, Yuantao
    Halmstad University, Sweden.
    Jakubowski, Jakub
    AGH University of Science and Technology, Poland.
    Paiva, Nuno
    INESC TEC, Portugal; NOS Comunicações, Portugal.
    Davari, Narjes
    INESC TEC, Portugal; University of Porto, Portugal.
    Bobek, Szymon
    Jagiellonian University, Poland.
    Jamshidi, Samaneh
    Halmstad University, Sweden.
    Sarmadi, Hamid
    Alabdallah, Abdallah
    Halmstad University, Sweden.
    Ribeiro, Rita P.
    INESC TEC, Portugal; University of Porto, Portugal.
    Veloso, Bruno
    INESC TEC, Portugal.
    Sayed-Mouchaweh, Moamar
    University of Lille, France.
    Rajaoarisoa, Lala
    University of Lille, France.
    Nalepa, Grzegorz J.
    Jagiellonian University, Poland.
    Gama, Jaoa
    INESC TEC, Portugal; University of Porto, Portugal.
    Explainable Predictive Maintenance2023In: arXivArticle in journal (Refereed)
    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.

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  • 12.
    Pena, F. J.
    et al.
    KTH Royal Institute of Technology, Sweden.
    Gonzalez, A. L.
    KTH Royal Institute of Technology, Sweden.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Al-Shishtawy, Ahmad
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Payberah, Amir H.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.
    Siambert: Siamese Bert-based Code Search2022In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference 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

  • 13.
    Soliman, Amira
    et al.
    RISE Research Institutes of Sweden, Digital Systems.
    Girdzijauskas, Sarunas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Bouguelia, Mohamed
    Halmstad University, Sweden.
    Pashami, Sepideh
    Halmstad University, Sweden.
    Nowaczyk, Slawomir
    Halmstad University, Sweden.
    Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters2020In: 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020(Lecture Notes in Computer Science book series (LNCS, volume 12084)), Springer , 2020, p. 343-355Conference paper (Refereed)
    Abstract [en]

    The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not only many privacy concerns, but also communication overhead. Therefore, in certain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or model aggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%. 

1 - 13 of 13
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