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  • 1.
    Bouguelia, Mohamed-Rafik
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Karlsson, Alexander
    University of Skövde, Skövde, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Holst, Anders
    Swedish Institute of Computer Science, Kista, Sweden.
    Mode tracking using multiple data streams2018Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 43, s. 33-46Artikel i tidskrift (Refereegranskat)
    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. 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. © 2017 Elsevier B.V. All rights reserved.

  • 2.
    Bouguelia, Mohamed-Rafik
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Multi-Task Representation Learning2017Ingår i: 30th Annual Workshop ofthe Swedish Artificial Intelligence Society SAIS 2017: May 15–16, 2017, Karlskrona, Sweden / [ed] Niklas Lavesson, Linköping: Linköping University Electronic Press, 2017, s. 53-59Konferensbidrag (Refereegranskat)
    Abstract [en]

    The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.

  • 3.
    Chen, Kunru
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Predicting Air Compressor Failures Using Long Short Term Memory Networks2019Ingår i: Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings, Part I / [ed] Paulo Moura Oliveira, Paulo Novais, Luís Paulo Reis, Cham: Springer, 2019, s. 596-609Konferensbidrag (Refereegranskat)
    Abstract [en]

    We introduce an LSTM-based method for predicting compressor failures using aggregated sensory data, and evaluate it using historical information from over 1000 heavy duty vehicles during 2015 and 2016. The goal is to proactively identify trucks that will require maintenance in the near future, so that component replacement can be scheduled before the failure happens, translating into improved uptime. The problem is formulated as a classification task of whether a compressor failure will happen within the specified prediction horizon. A recurrent neural network using Long Short-Term Memory (LSTM) architecture is employed as the prediction model, and compared against Random Forest (RF), the solution used in industrial deployment at the moment. Experimental results show that while Random Forest slightly outperforms LSTM in terms of AUC score, the predictions of LSTM stay significantly more stable over time, showing a consistent trend from healthy to faulty class. Additionally, LSTM is also better at detecting the switch from faulty class to the healthy one after a repair. We demonstrate that this stability is important for making repair decisions, especially in questionable cases, and therefore LSTM model is likely to lead to better results in practice. © Springer Nature Switzerland AG 2019

  • 4.
    Cooney, Martin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ong, Linda
    I+ srl, Florence, Italy.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Avoiding Improper Treatment of Dementia Patients by Care Robots2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.

  • 5.
    Cooney, Martin
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?2018Ingår i: WWW '18 Companion Proceedings of the The Web Conference 2018, New York, NY: ACM Publications, 2018, s. 1563-1566Konferensbidrag (Refereegranskat)
    Abstract [en]

    What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.

  • 6.
    Dahl, Oskar
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Johansson, Fredrik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Khoshkangini, Reza
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pihl, Claes
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters -- Using Clustering and Rule-Based Machine Learning2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks.

    In this paper we propose a framework that aims to extract costumers' vehicle behaviours from LVD in order to evaluate whether they align with vehicle configurations, so-called GTA parameters. GMMs are employed to cluster and classify various vehicle behaviors from the LVD. RBML was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis.

  • 7.
    Helldin, Tove
    et al.
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    University of Skövde, Skövde, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Falkman, Göran
    University of Skövde, Skövde, Sweden.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Slawomir, Nowaczyk
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Supporting Analytical Reasoning: A Study from the Automotive Industry2016Ingår i: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Cham: Springer, 2016, Vol. 9735, s. 20-31Konferensbidrag (Refereegranskat)
    Abstract [en]

    In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area. © Springer International Publishing Switzerland 2016.

  • 8.
    Khoshkangini, Reza
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Warranty Claim Rate Prediction using Logged Vehicle Data2019Ingår i: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 11804, s. 663-674Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.

  • 9.
    Pashami, Sepideh
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Holst, Anders
    RISE SICS, Stockholm, Sweden.
    Bae, Juhee
    School of Informatics, University of Skövde, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Causal discovery using clusters from observational data2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data.

    One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.

  • 10.
    Pirasteh, Parivash
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Löwenadler, Magnus
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Thunberg, Klas
    Service Market Products, Volvo Buses, Gothenburg, Sweden.
    Ydreskog, Henrik
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Berck, Peter
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain2019Ingår i: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, artikel-id 4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.

  • 11.
    Sheikholharam Mashhadi, Peyman
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life2020Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 10, nr 1Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers. © 2019 MDPI

  • 12.
    Vaiciukynas, Evaldas
    et al.
    Department of Information Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Uličný, Matej
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Learning Low-Dimensional Representation of Bivariate Histogram Data2018Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, nr 11, s. 3723-3735Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types--turbocharger and engine--considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by $t$-distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, $t$-distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation.

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