Closed-loop primitives: A method to generate and recognize reaching actions from demonstration

The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activates neural circuits that are also responsible for generating the very same actions in the animal. The mirror neuron hypothesis argues that such an overlap between action generation and recognition can provide a shared worldview among individuals and be a key pillar for communication. Inspired by these findings, this paper extends a learning by demonstration method for online recognition of observed actions. The proposed method is shown to recognize and generate different reaching actions demonstrated by a human on a humanoid robot platform. Experiments show that the proposed method is robust to both occlusions during the observed actions as well as variances in the speed of the observed actions. The results are successfully demonstrated in an interactive game with the iCub humanoid robot platform

Event Extraction from Turkish Football Web-casting Texts Using Hand-crafted Templates

In this paper, we present a domain specific information extraction approach We use manually formed templates to extract information from unstructured documents where grammatical and syntactical errors occur frequently We applied our approach to primarily Turkish unstructured soccer web-casting texts Compared to automated approaches we achieve high precision-recall rates (97% - 85%). In addition to that, unlike automated approaches we do not use part-of-speech taggers, parsers, phrase chunkers or that kind of a linguistic tool. As a result, our approach can be applied to any domain or any language without the necessity of successful linguistic tools. The drawback of our approach is the time spent on crafting the templates. We also propose the means to decrease that time

Machine learning aided diagnosis of hepatic malignancies through in vivo dielectric measurements with microwaves

In the past decade, extensive research on dielectric properties of biological tissues led to characterization of dielectric property discrepancy between the malignant and healthy tissues. Such discrepancy enabled the development of microwave therapeutic and diagnostic technologies. Traditionally, dielectric property measurements of biological tissues is performed with the well-known contact probe (open-ended coaxial probe) technique. However, the technique suffers from limited accuracy and low loss resolution for permittivity and conductivity measurements, respectively. Therefore, despite the inherent dielectric property discrepancy, a rigorous measurement routine with open-ended coaxial probes is required for accurate differentiation of malignant and healthy tissues. In this paper, we propose to eliminate the need for multiple measurements with open-ended coaxial probe for malignant and healthy tissue differentiation by applying support vector machine (SVM) classification algorithm to the dielectric measurement data. To do so, first, in vivo malignant and healthy rat liver tissue dielectric property measurements are collected with open-ended coaxial probe technique between 500 MHz to 6 GHz. Cole-Cole functions are fitted to the measured dielectric properties and measurement data is verified with the literature. Malign tissue classification is realized by applying SVM to the open-ended coaxial probe measurements where as high as 99.2% accuracy (F1 Score) is obtained