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Epistemic logic is the logic of knowledge: how do you reason about the question whether your silent admirer knows that you know that (s)he sent you an anonymous Valentine card? Is it harmful if, at a literature-exam you don't know the contents of a chapter? No, as long as you know that the examiner does not know that you do not know it. Knowing whether your… (More)

- Vadim Mazalov, Stephen M. Watt
- Document Analysis Systems
- 2012

We present a method to compress digital ink based on piecewise-linear approximation within a given error threshold. The objective is to achieve good compression ratio with very fast execution. The method is especially effective on types of handwriting that have large portions with nearly linear parts, e.g. hand drawn geometric objects. We compare this… (More)

- Vadim Mazalov, Stephen M. Watt
- ICFHR
- 2010

Representing digital ink traces as points in a function space has proven useful for online recognition. Ink trace coordinates or their integral invariants are written as parametric functions and approximated by truncated orthogonal series. This representation captures the shape of the ink traces with a small number of coefficients in a form quite compact… (More)

We present an approach to recognize handwritten characters independently of their orientation. The method is based on the theory of integral invariants and yields good results in classifying rotated samples. We propose two recognition techniques taking advantage of integral invariants up to second order. Truncated Legendre-Sobolev series are used to… (More)

- Oleg Golubitsky, Vadim Mazalov, Stephen M. Watt
- Document Analysis Systems
- 2010

We address the problem of handwritten symbol classification in the presence of distortions modeled by affine transformations. We consider shear, rotation, scaling and translation, since these types of transformations occur most often in practice, and focus most on shear within this framework. We present a distance-based classification method, in which… (More)

- Vadim Mazalov, Stephen M. Watt
- DRR
- 2012

Earlier work has shown how to recognize handwritten characters by representing coordinate functions or integral invariants as truncated orthogonal series. The series basis functions are orthogonal polynomials defined by a Legendre-Sobolev inner product. It has been shown that the free parameter in the inner product, the “jet scale”, has an impact on… (More)

- Vadim Mazalov, Stephen M. Watt
- AISC/MKM/Calculemus
- 2012

We are interested in achieving the best possible recognition rates for hand-written mathematics. This problem has challenges that go beyond the usual natural language handwriting recognition: multiple alphabets are used, the number of single-stroke and few-stroke symbols numbers in the hundreds, writers form symbols from some of the alphabets in… (More)

- Rui Hu, Vadim Mazalov, Stephen M. Watt
- AISC/MKM/Calculemus
- 2012

We present a framework for pen-based, multi-user, online collaboration in mathematical domains. This environment provides participants, who may be in the same room or across the planet, with a shared whiteboard and voice channel. The digital ink stream is transmitted as InkML, allowing special recognizers for different content types, such as mathematics and… (More)

- Vadim Mazalov, Stephen M. Watt
- ICFHR
- 2012

We present an adaptive approach to the recognition of handwritten mathematical symbols, in which a recognition weight is associated with each training sample. The weight is computed from the distance to a test character in the space of coefficients of functional approximation of symbols. To determine the average size of the training set to achieve certain… (More)

- Travis Felker, Vadim Mazalov, Stephen M. Watt
- ICCS
- 2014

The present paper approaches high-frequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the feature-weighted Euclidean distance to the centroid of a training cluster. A set of micro technical indicators, traditionally employed by… (More)