In this work, which is intended to be a Work in Progress Paper in the Research to Practice Category, a novel remote testing protocol based on Cognitive Load Theory and the Theory of Situated Cognition is described. The proposed protocol reduced students’ cognitive overload during online exams by addressing student privacy, unreliable internet connections, accessible accommodations, all-or-nothing automatic grading, and false accusations of plagiarism. The described protocol relies on finite exam times and a sophisticated question randomization system. The protocol has been used successfully for five semesters, for two courses (20 sections).

One area where on-line handwriting recognition technology is most critical is the domain of small portable platforms. Because such platforms have limited resources, it is not presently practical to consider a continuous parameterization for the hidden Markov models used in the recognition. On the other hand, discrete parameter techniques such as used in speech recognition are difficult to apply, because there is no well-understood handwriting equivalent to phonological rules. A possible solution is to extract this information directly from the data, by constructing an alphabet of sub-character, elementary handwriting units. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.

Our goal is to perform handwritten character recognition using a bank of multilayer feedforward neural networks. This paper presents both the front-end and the back-end of such a recognition system. The front-end relies on a data pre-classification scheme based on the concept of segment. A segment can be viewed as a representative building block of handwriting. The back-end hinges on a connectionist approach. Instead of a single large network, a bank of parallel networks is developed to overcome commonly encountered difficulties such as slow training process and requirement for a large amount of training data. The recognition system has been evaluated, on tasks involving (i) discrimination between similarly shaped characters and (ii) recognition of discretely written upper-case characters.>

The performance of a large alphabet handwriting recognition system based on a probabilistic framework is critically tied to the quality of the prototype distributions that are established in the relevant feature space. To better account for handwriting variability, we describe a supervised strategy for the construction of prototype distributions which are more robust to allograph deformations. The idea is to incorporate supervision to relate the allographic models to their manifestations in the feature space. This makes for a better utilization of the available training data, while at the same time allowing for a short design time turn around. The performance of this method is illustrated on a discrete handwriting recognition task with an alphabet of 81 characters.>

We consider an object buried in an inhomogeneous elastic background material. The entire medium, comprising object and background, is probed by a body force and the field scattered by the object is observed at some known receiver location. The goal is to use those measurements to determine the object position as well as the magnitude of its elastic parameters. The main difficulties arise from the nonlinearity and ill-posedness of such inverse problems. Using the Born approximation, we have previously developed a general framework based on vector integral equations. To circumvent some of the limitations of this approximation, we propose to tackle the problem iteratively. The paper presents a two-step iterative procedure alternating direct and inverse modelling to improve on the resolution and accuracy of the original reconstruction.>

The automatic recognition of online handwriting is considered from an information theoretic viewpoint. Emphasis is placed on the recognition of unconstrained handwriting, a general combination of cursively written word fragments and discretely written characters. Existing recognition algorithms, such as elastic matching, are severely challenged by the variability inherent in unconstrained handwriting. This motivates the development of a probabilistic framework suitable for the derivation of a fast statistical mixture algorithm. This algorithm exhibits about the same degree of complexity as elastic matching, while being more flexible and potentially more robust. The approach relies on a novel front-end processor that, unlike conventional character or stroke-based processing, articulates around a small elementary unit of handwriting called a frame. The algorithm is based on (1) producing feature vectors representing each frame in one (or several) feature spaces, (2) Gaussian K-means clustering in these spaces, and (3) mixture modeling, taking into account the contributions of all relevant clusters in each space. The approach is illustrated by a simple task involving an 81-character alphabet. Both writer-dependent and writer-independent recognition results are found to be competitive with their elastic matching counterparts.>

The ultrasonic probing by a time-harmonic line (point) source of a multidimensional object buried in an inhomogeneous elastic background structure is considered. The goal is the imaging and quantitative characterization of the object from the scattered field measured along a receiver array. This leads to a multidimensional continuous inverse scattering problem which is nonlinear and ill-posed. The authors linearize the problem within the Born approximation for inhomogeneous background, and seek a minimum-norm least-square solution to the (discretized) integral equations. This solution is based on a singular value decomposition of the forward operator matrix. A priori information can be incorporated into the algorithm to enhance the accuracy and improve the resolution of the recovered object characteristics. The method is illustrated on a 2-D problem where constrained least-squares inversion of the object characteristics is performed from synthetic data. A Tikhonov regularization scheme is also examined and compared to the minimum-norm least-square estimation.>