7,595 research outputs found
Cognitive evaluation of computer-drawn sketches
CISRG discussion paper ; 1
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Investigation of the use of navigation tools in web-based learning: A data mining approach
Web-based learning is widespread in educational settings. The popularity of Web-based learning is in great measure because of its flexibility. Multiple navigation tools provided some of this flexibility. Different navigation tools offer different functions. Therefore, it is important to understand how the navigation tools are used by learners with different backgrounds, knowledge, and skills. This article presents two empirical studies in which data-mining approaches were used to analyze learners' navigation behavior. The results indicate that prior knowledge and subject content are two potential factors influencing the use of navigation tools. In addition, the lack of appropriate use of navigation tools may adversely influence learning performance. The results have been integrated into a model that can help designers develop Web-based learning programs and other Web-based applications that can be tailored to learners' needs
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Unsupervised Classification of Neolithic Pottery From the Northern Alpine Space Using t-SNE and HDBSCAN
Terms of “Neolithic cultures” are still used to describe spatial and temporal differences in pottery styles across central Europe. These terms date back to research periods when absolute dating methods were lacking and typological classification was used to establish chronologies. Those terms are charged with problematic, biasing notions of social configurations: cultural homogeneity, spatial boundedness, and immobility. In this article, we present an alternative approach to pottery classification by using ceramics from dendrochronologically and C14-dated sites of the 40th–38th c. BC located in the northern Alpine Foreland. The newly developed methodology uses a computational unsupervised classification based on profile shape and additional nominal characteristics using t-Distributed Stochastic Neighbour Embedding and Hierarchical Density-Based Spatial Clustering of Applications with Noise for cluster analyses. Its role in our project was to provide a quantitative, algorithm-based approach to classify large datasets of pottery while simultaneously account for a large number of variables. This enabled us to find similarity structures that would escape human cognitive capacities on which typological classification is based on. It formed one pilar of a mixed method research approach combining qualitative and quantitative methods of pottery classification. Our results show that the premises of cultural homogeneity are untenable but can be methodologically overcome by using the proposed classification approaches
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