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Browsing by study line "Theoretical Computer Science"

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  • Karis, Peter (2020)
    This thesis presents a user study to evaluate the usability and effectiveness of a novel search interface as compared to a more traditional solution. InnovationMap is a novel search interface by Khalil Klouche, Tuukka Ruotsalo and Giulio Jacucci (University of Helsinki). It is a tool for aiding the user to perform ‘exploratory searching’; a type of search activity where the user is exploring an information space unknown to them and thus cannot form a specific search phrase to perform a traditional ‘lookup’ search as with the conventional search interfaces. In this user study InnovationMap is compared against TUHAT, a search portal that is currently in use at the University of Helsinki for searching for research works and research personnel from the university databases. The user evaluation is conducted as a qualitative within-subject study using volunteer users from the University of Helsinki. Each participant uses both systems in an alternating order over the course of two sessions. During the two sessions the volunteer user carries out information finding tasks defined in the experiment design, answers to a SUS (System Usability Scale) questionnaire and participates in a semi-structured interview. The answers from the assigned tasks are then evaluated and scored by field experts. The combined results from these methods are then used to formulate an educated assessment of the usability, effectiveness and future development potential of the InnovationMap search system.
  • Salmenperä, Ilmo (2021)
    Quantum Computing is a novel technology that has wide applicability in the field of machine learning. One of these applications is training Quantum Restricted Boltzmann Machines, which have been shown to have advantages over their classical counterparts. These Quantum Restricted Boltzmann Machines can be then used to pretrain more complex machine learning models, such as Deep Belief Networks, which means that quantum annealing can have applications in the field of deep learning. Main issue of Quantum Restricted Boltzmann Machines is that embedding them into quantum annealing devices will restrict their layer size and connectivity quite drastically. This thesis proposes the use of a common weight regularization method called the unit dropout method to reduce the overall size of these networks by splitting these Restricted Boltzmann Machines into smaller more manageable models, training them separately and composing them into a complete model. While this method can be shown to affect learning negatively, it is yet to be known, whether the advantages of quantum computing can outweigh the disadvantages of extreme use of the unit dropout method.