EG 2019 - Short Papers

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https://diglib.eg.org/handle/10.2312/2632750

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Browsing EG 2019 - Short Papers by Subject "centered computing"

Now showing 1 - 4 of 4
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    Asteroid Escape: A Serious Game to Foster Teamwork Abilities
    (The Eurographics Association, 2019) Pratticò, Filippo Gabriele; Strada, Francesco; Lamberti, Fabrizio; Bottino, Andrea; Cignoni, Paolo and Miguel, Eder
    Teamwork skills have become a fundamental asset in the labor market. Modern organizations are increasingly implementing team building activities, aimed to improve or assess their employees' skills. Research suggests that serious games could be promising tools capable to support the creation of engaging and effective team building experiences. However, the design and development of serious games targeting these activities is still sparse and requires further investigation. This work introduces Asteroid Escape, an immersive serious game for team building, whose design was based on theoretical models on teamwork effectiveness. Although conducted on a restricted user sample, preliminary experiments suggest that tools like the devised one could positively contribute to ongoing research and implementation efforts targeting the exploitation of technology-enhanced learning methods for the development of teamwork skills and, more in general, of so-called soft skills.
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    Font Specificity
    (The Eurographics Association, 2019) Power, Luther; Lau, Manfred; Cignoni, Paolo and Miguel, Eder
    We explore the concept of ''image specificity'' for fonts and introduce the notion of ''font specificity''. The idea is that a font that elicits consistent descriptions from different people are more ''specific''. We collect specificity-based data for fonts where participants are given each font and asked to describe it with words. We then analyze the data and characterize the qualitative features that make a font ''specific''. Finally, we show that the notion of font specificity can be learned and demonstrate some specificity-guided applications.
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    Investigating Different Augmented Reality Approaches in Circuit Assembly: a User Study
    (The Eurographics Association, 2019) Marques, Bernardo; Esteves, Rafael; Alves, João; Ferreira, Carlos; Dias, Paulo; Santos, Beatriz Sousa; Cignoni, Paolo and Miguel, Eder
    Augmented Reality (AR) has been considered as having great potential in assisting performance and training of complex tasks. Assembling electronic circuits is such a task, since many errors may occur, as wrong choice or positioning of components or incorrect wiring and thus using AR approaches may be beneficial. This paper describes a controlled experiment aimed at comparing usability and acceptance of two AR-based approaches (one based on a single device and another approach using two interconnected devices), with a traditional approach using a paper manual in the assembly of an electronic circuit. Participants were significantly faster and made fewer errors while using the AR approaches, and most preferred the multi-device approach.
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    A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures
    (The Eurographics Association, 2019) Pavoni, Gaia; Corsini, Massimiliano; Palma, Marco; Scopigno, Roberto; Cignoni, Paolo and Miguel, Eder
    The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network.