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19 results on '"Pashami, Sepideh"'

1. The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models.

Author

Alabdallah A, Ohlsson M, Pashami S, and Rögnvaldsson T

Subjects
Machine Learning, Neural Networks, Computer, Survival Analysis
Abstract

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Published
2024
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2. Warranty Claim Rate Prediction Using Logged Vehicle Data

Author

Khoshkangini, Reza, Pashami, Sepideh, Nowaczyk, Slawomir, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moura Oliveira, Paulo, editor, Novais, Paulo, editor, and Reis, Luís Paulo, editor

Published
2019
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3. Multi-Task Representation Learning

Author

Bouguelia, Mohamed-Rafik, Pashami, Sepideh, and Nowaczyk, Sławomir

Subjects
Machine Learning, Feature Learning, Signal Processing, Representation Learning, Signalbehandling, Supervised Learning, Multi-Task Learning
Abstract

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.

Published
2017

4. Learning Low-Dimensional Representation of Bivariate Histogram Data.

Author

Vaiciukynas, Evaldas, Ulicny, Matej, Pashami, Sepideh, and Nowaczyk, Slawomir

Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2018
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5. Early Prediction of Quality Issues in Automotive Modern Industry.

Author

Khoshkangini, Reza, Sheikholharam Mashhadi, Peyman, Berck, Peter, Gholami Shahbandi, Saeed, Pashami, Sepideh, Nowaczyk, Sławomir, and Niklasson, Tobias

Subjects
QUALITY function deployment, FORECASTING, ORIGINAL equipment manufacturers, AUTOMOBILE industry, INDUSTRIAL costs, PRODUCT design
Abstract

Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently. [ABSTRACT FROM AUTHOR]

Published
2020
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6. Italian Debate on Measles Vaccination: How Twitter Data Highlight Communities and Polarity

Author

Ugwu, Cynthia Ifeyinwa, Casarin, Sofia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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7. A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients

Author

Rugolon, Franco, Bampa, Maria, Papapetrou, Panagiotis, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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8. Towards a General Model for Intrusion Detection: An Exploratory Study

Author

Zoppi, Tommaso, Ceccarelli, Andrea, Bondavalli, Andrea, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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9. Predicting Drug Treatment for Hospitalized Patients with Heart Failure

Author

Zhou, Linyi, Miliou, Ioanna, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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10. Auto-clustering of Financial Reports Based on Formatting Style and Author’s Fingerprint

Author

Blanco Lambruschini, Braulio C., Brorsson, Mats, Zurad, Maciej, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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11. Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA

Author

Wasielewska, Katarzyna, Soukup, Dominik, Čejka, Tomáš, Camacho, José, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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12. Financial Distress Model Prediction Using Machine Learning: A Case Study on Indonesia’s Consumers Cyclical Companies

Author

Martono, Niken Prasasti, Ohwada, Hayato, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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13. Privacy-Preserving Machine Learning in Life Insurance Risk Prediction

Author

Pereira, Klismam, Vinagre, João, Alonso, Ana Nunes, Coelho, Fábio, Carvalho, Melânia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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14. Evaluation of Group Fairness Measures in Student Performance Prediction Problems

Author

Le Quy, Tai, Nguyen, Thi Huyen, Friege, Gunnar, Ntoutsi, Eirini, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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15. A Temporal Fusion Transformer for Long-Term Explainable Prediction of Emergency Department Overcrowding

Author

Caldas, Francisco M., Soares, Cláudia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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16. Intelligently Detecting Information Online-Weaponisation Trends (IDIOT)

Author

Kara-Isitt, Fawzia Zehra, Swift, Stephen, Tucker, Allan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor

Published
2023
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17. hxtorch: PyTorch for BrainScaleS-2 : Perceptrons on Analog Neuromorphic Hardware

Author

Spilger, Philipp, Müller, Eric, Emmel, Arne, Leibfried, Aron, Mauch, Christian, Pehle, Christian, Weis, Johannes, Breitwieser, Oliver, Billaudelle, Sebastian, Schmitt, Sebastian, Wunderlich, Timo C., Stradmann, Yannik, Schemmel, Johannes, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gama, Joao, editor, Pashami, Sepideh, editor, Bifet, Albert, editor, Sayed-Mouchawe, Moamar, editor, Fröning, Holger, editor, Pernkopf, Franz, editor, Schiele, Gregor, editor, and Blott, Michaela, editor

Published
2020
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18. Inference with Artificial Neural Networks on Analog Neuromorphic Hardware

Author

Weis, Johannes, Spilger, Philipp, Billaudelle, Sebastian, Stradmann, Yannik, Emmel, Arne, Müller, Eric, Breitwieser, Oliver, Grübl, Andreas, Ilmberger, Joscha, Karasenko, Vitali, Kleider, Mitja, Mauch, Christian, Schreiber, Korbinian, Schemmel, Johannes, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gama, Joao, editor, Pashami, Sepideh, editor, Bifet, Albert, editor, Sayed-Mouchawe, Moamar, editor, Fröning, Holger, editor, Pernkopf, Franz, editor, Schiele, Gregor, editor, and Blott, Michaela, editor

Published
2020
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19. Material handling machine activity recognition by context ensemble with gated recurrent units.

Author

Chen, Kunru, Rögnvaldsson, Thorsteinn, Nowaczyk, Sławomir, Pashami, Sepideh, Klang, Jonas, and Sternelöv, Gustav

Subjects
*MATERIALS handling, *MACHINING, *MACHINE learning, *FORKLIFT trucks, *PRODUCT improvement, *PRODUCT design
Abstract

Research on machine activity recognition (MAR) is drawing more attention because MAR can provide productivity monitoring for efficiency optimization, better maintenance scheduling, product design improvement, and potential material savings. A particular challenge of MAR for human-operated machines is the overlap when transiting from one activity to another: during transitions, operators often perform two activities simultaneously, e.g., lifting the fork already while approaching a rack, so the exact time when one activity ends and another begins is uncertain. Machine learning models are often uncertain during such activity transitions, and we propose a novel ensemble-based method adapted to fuzzy transitions in a forklift MAR problem. Unlike traditional ensembles, where models in the ensemble are trained on different subsets of data, or with costs that force them to be diverse in their responses, our approach is to train a single model that predicts several activity labels, each under a different context. These individual predictions are not made by independent networks but are made using a structure that allows for sharing important features, i.e., a context ensemble. The results show that the gated recurrent unit network can provide medium or strong confident context ensembles for 95% of the cases in the test set, and the final forklift MAR result achieves accuracies of 97% for driving and 90% for load-handling activities. This study is the first to highlight the overlapping activity issue in MAR problems and to demonstrate that the recognition results can be significantly improved by designing a machine learning framework that addresses this issue. [ABSTRACT FROM AUTHOR]

Published
2023
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