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  • 101. Tomaszewski, Piotr
    et al.
    Lundberg, Lars
    Grahn, Håkan
    Increasing the Efficiency of Fault Detection in Modified Code2005Conference paper (Refereed)
  • 102. Tomaszewski, Piotr
    et al.
    Lundberg, Lars
    Grahn, Håkan
    The accuracy of early fault prediction in modified code2005Conference paper (Refereed)
  • 103.
    Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Efficient document image binarization using heterogeneous computing and parameter tuning2018In: International Journal on Document Analysis and Recognition, ISSN 1433-2833, E-ISSN 1433-2825, Vol. 21, no 1-2, p. 41-58Article in journal (Refereed)
    Abstract [en]

    In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times. © 2018 The Author(s)

  • 104.
    Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    User Feedback and Uncertainty in Interactive BinarizationManuscript (preprint) (Other academic)
    Abstract [en]

    A major challenge in document image binarization is the large variety in appearance of images from different document collections. This is especially challenging for parameterless, machine learning based binarization algorithms, which require additional ground truth training data to generalize or fine-tune to a new image collection. Reducing this costly labeling effort is relevant to companies and government institutions, which possess many different document image collections. One approach to address this problem is interactive machine learning, which enables a user to guide the fine-tuning process by providing feedback on the produced binarization result.

    In this paper, we evaluate the claim that user guided training requires less labeled samples to fine-tune a basic model for binarization to a new image collection. Further, we propose a way to guide user feedback by visualizing the model’s labeling uncertainty and analyze the relationship between model uncertainty and binarization quality. Our experiments show that user feedback biases the model towards favoring foreground labels, which results in less erased text and thus better readability than when training samples are chosen randomly. Additionally, we find that model uncertainty serves as a useful guide for users and explain how the Dunning-Kruger effect prevents model uncertainty from being useful for automated sample selection.

  • 105.
    Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    User Feedback and Uncertainty in User Guided Binarization2018In: International Conference on Data Mining Workshops / [ed] Tong, H; Li, Z; Zhu, F; Yu, J, IEEE Computer Society, 2018, p. 403-410, article id 8637367Conference paper (Refereed)
    Abstract [en]

    In a child’s development, the child’s inherent ability to construct knowledge from new information is as important as explicit instructional guidance. Similarly, mechanisms to produce suitable learning representations, which can be trans- ferred and allow integration of new information are important for artificial learning systems. However, equally important are modes of instructional guidance, which allow the system to learn efficiently. Thus, the challenge for efficient learning is to identify suitable guidance strategies together with suitable learning mechanisms.

    In this paper, we propose guided machine learning as source for suitable guidance strategies, we distinguish be- tween sample selection based and privileged information based strategies and evaluate three sample selection based strategies on a simple transfer learning task. The evaluated strategies are random sample selection, i.e., supervised learning, user based sample selection based on readability, and user based sample selection based on readability and uncertainty. We show that sampling based on readability and uncertainty tends to produce better learning results than the other two strategies. Furthermore, we evaluate the use of the learner’s uncertainty for self directed learning and find that effects similar to the Dunning-Kruger effect prevent this use case. The learning task in this study is document image binarization, i.e., the separation of text foreground from page background and the source domain of the transfer are texts written on paper in Latin characters, while the target domain are texts written on palm leaves in Balinese script.

  • 106.
    Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lavesson, Niklas
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    A Case for Guided Machine Learning2019In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl, Springer, 2019, Vol. 11713, p. 353-361Conference paper (Refereed)
    Abstract [en]

    Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.

  • 107.
    Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Document Image Binarization Using Recurrent Neural Networks2018In: Proceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, IEEE, 2018, p. 263-268Conference paper (Refereed)
    Abstract [en]

    In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.

  • 108.
    Wohlin, Claes
    et al.
    Blekinge Institute of Technology, School of Computing.
    Aurum, Aybueke
    Blekinge Institute of Technology, School of Computing.
    Angelis, Lefteris
    Phillips, Laura
    Dittrich, Yvonne
    Gorschek, Tony
    Blekinge Institute of Technology, School of Computing.
    Grahn, Håkan
    Blekinge Institute of Technology, School of Computing.
    Henningsson, Kennet
    Kågström, Simon
    Low, Graham
    The Success Factors Powering Industry-Academia Collaboration2012In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 29, no 2, p. 67-73Article in journal (Refereed)
  • 109.
    Zeeshan Iqbal, Syed Muhammad
    et al.
    Blekinge Institute of Technology, School of Computing.
    Grahn, Håkan
    Blekinge Institute of Technology, School of Computing.
    Törnquist Krasemann, Johanna
    Blekinge Institute of Technology, School of Computing.
    A Comparative Evaluation of Re-scheduling Strategies for Train Dispatching during Disturbances2012Conference paper (Refereed)
    Abstract [en]

    Railway traffic disturbances occur and train dispatchers make re-scheduling decisions in order to reduce the delays. In order to support the dispatchers, good rescheduling strategies are required that could reduce the delays. We propose and evaluate re-scheduling strategies based on: (i) earliest start time, (ii) earliest track release time, (iii) smallest buffer time, and (iv) shortest section runtime. A comparative evaluation is done for a busy part of the Swedish railway network. Our results indicate that strategies based on earliest start time and earliest track release time have the best average performance.

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