Empirical techniques and algorithms to develop a resilient non-supervised touch-based authentication system

Touch dynamics (or touch based authentication) refers to a behavioral biometric for touchscreen devices wherein a user is authenticated based on his/her executed touch gestures. This work addresses two research topics. We first present a series of empirical techniques to detect habituation in the user’s touch profile, its detrimental effect on authentication accuracy and strategies to overcome these effects. Habituation here refers to changes in the user’s profile and/or noise within it due to the user’s familiarization with the device and software application. With respect to habituation, we show that habituation causes the user’s touch profile to evolve significantly and irrevocably over time even after the user is familiar with the device and software application. This phenomenon considerably degrades classifier accuracy. We demonstrate techniques that lower the error rate to 3.68% and sets the benchmark in this field for a realistic test setup. Finally, we quantify the benefits of vote-based reclassification of predicted class labels and show that this technique is vital for achieving high accuracy in realistic touch-based authentication systems. In the second half, we implement the first ever non-supervised classification algorithm in touch based continual authentication. This scheme incorporates clustering into the traditional supervised algorithm. We reduce the mis-classification rate by fusing supervised random forest algorithm and non-supervised clustering (either Bayesian learning or simple rule of combinations). Fusing with Bayesian clustering reduced the mis-classification rate by 50% while fusing with simple rule of combination reduced the mis-classification rate by as much as 59.5% averaged over all the users.Master of ScienceComputer Science & Information SystemsUniversity of Michigan-Flinthttp://deepblue.lib.umich.edu/bitstream/2027.42/134750/1/Palaskar2016.pdfDescription of Palaskar2016.pdf : Main articl

Multimodal Grounding for Sequence-to-Sequence Speech Recognition

Humans are capable of processing speech by making use of multiple sensory modalities. For example, the environment where a conversation takes place generally provides semantic and/or acoustic context that helps us to resolve ambiguities or to recall named entities. Motivated by this, there have been many works studying the integration of visual information into the speech recognition pipeline. Specifically, in our previous work, we propose a multistep visual adaptive training approach which improves the accuracy of an audio-based Automatic Speech Recognition (ASR) system. This approach, however, is not end-to-end as it requires fine-tuning the whole model with an adaptation layer. In this paper, we propose novel end-to-end multimodal ASR systems and compare them to the adaptive approach by using a range of visual representations obtained from state-of-the-art convolutional neural networks. We show that adaptive training is effective for S2S models leading to an absolute improvement of 1.4% in word error rate. As for the end-to-end systems, although they perform better than baseline, the improvements are slightly less than adaptive training, 0.8 absolute WER reduction in single-best models. Using ensemble decoding, end-to-end models reach a WER of 15% which is the lowest score among all systems.Comment: ICASSP 201

Comparative analysis of mast cell count in normal oral mucosa and oral pyogenic granuloma

Introduction: Mast cells are large granular cells that arise from a multipotent CD 34+ precursor in the bone marrow normally distributed throughout connective tissues. The most common method to study role of mast cells in any altered condition involves their identification and quantification in that condition and compare the values with that of the normal average count or number of mast cells. The present study was thus, undertaken to identify as well as quantify mast cells in oral pyogenic granuloma and compare it with the average count of mast cells in normal oral mucosa, thus aiming to assess the changes in count of mast cells in oral pyogenic granuloma. Materials and Methods: Ten cases of normal oral mucosa and thirty cases of oral pyogenic granuloma were studied for mast cell number using 1% toluidine blue. Results: An increase in mast cell number was observed in oral pyogenic granuloma. The mast cell count/high power field in pyogenic granuloma and normal oral mucosa was 10.27 and 4.58 respectively. There is a statistically significant increase in the mean of average mast cell count per high power field in oral pyogenic granuloma in comparison to normal oral mucosa. These facts may morphologically indicate a possibility of a role of mast cells in angiogenesis and recruitment of inflammatory cells which are characteristic features of oral pyogenic granulom