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  • 1.
    Adewumi, Oluwatosin
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Conversational Systems in Machine Learning from the Point of View of the Philosophy of Science—Using Alime Chat and Related Studies2019Ingår i: Philosophies, ISSN 2409-9287, Vol. 4, nr 3, artikel-id 41Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This essay discusses current research efforts in conversational systems from the philosophy of science point of view and evaluates some conversational systems research activities from the standpoint of naturalism philosophical theory. Conversational systems or chatbots have advanced over the decades and now have become mainstream applications. They are software that users can communicate with, using natural language. Particular attention is given to the Alime Chat conversational system, already in industrial use, and the related research. The competitive nature of systems in production is a result of different researchers and developers trying to produce new conversational systems that can outperform previous or state-of-the-art systems. Different factors affect the quality of the conversational systems produced, and how one system is assessed as being better than another is a function of objectivity and of the relevant experimental results. This essay examines the research practices from, among others, Longino’s view on objectivity and Popper’s stand on falsification. Furthermore, the need for qualitative and large datasets is emphasized. This is in addition to the importance of the peer-review process in scientific publishing, as a means of developing, validating, or rejecting theories, claims, or methodologies in the research community. In conclusion, open data and open scientific discussion fora should become more prominent over the mere publication-focused trend.

  • 2.
    Nilsson, Mattias
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Sandin, Fredrik
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic ProcessorManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Spiking neurons can perform spatiotemporal feature detection by nonlinear synaptic and dendritic integration of presynaptic spike patterns. Multicompartment models of nonlinear dendrites and related neuromorphic circuit designs enable faithful imitation of such dynamic integration processes, but these approaches are also associated with a relatively high computing cost or circuit size. Here, we investigate synaptic integration of spatiotemporal spike patterns with multiple dynamic synapses on point-neurons in the DYNAP-SE neuromorphic processor, which can offer a complementary resource-efficient, albeit less flexible, approach to feature detection. We investigate how previously proposed excitatory--inhibitory pairs of dynamic synapses can be combined to integrate multiple inputs, and we generalize that concept to a case in which one inhibitory synapse is combined with multiple excitatory synapses. We characterize the resulting delayed excitatory postsynaptic potentials (EPSPs) by measuring and analyzing the membrane potentials of the neuromorphic neuronal circuits. We find that biologically relevant EPSP delays, with variability of order 10 milliseconds per neuron, can be realized in the proposed manner by selecting different synapse combinations, thanks to device mismatch. Based on these results, we demonstrate that a single point-neuron with dynamic synapses in the DYNAP-SE can respond selectively to presynaptic spikes with a particular spatiotemporal structure, which enables, for instance, visual feature tuning of single neurons.

  • 3.
    Saini, Rajkumar
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Dobson, Derek
    FamilySearch, USA.
    Morrey, Jon
    FamilySearch, USA.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records2019Ingår i: ICDAR 2019: ICDAR 2019 HDRC Chinese, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we present a large historical database of Chinese family records with the aim to develop robust systems for historical document analysis. In this direction, we propose a Historical Document Reading Challenge on Large Chinese Structured Family Records (ICDAR 2019 HDRCCHINESE).The objective of the competition is to recognizeand analyze the layout, and finally detect and recognize thetextlines and characters of the large historical document image dataset containing more than 10000 pages. Cascade R-CNN, CRNN, and U-Net based architectures were trained to evaluatethe performances in these tasks. Error rate of 0.01 has been recorded for textline recognition (Task1) whereas a Jaccard Index of 99.54% has been recorded for layout analysis (Task2).The graph edit distance based total error ratio of 1.5% has been recorded for complete integrated textline detection andrecognition (Task3).

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