A Perspective on Computer Assisted Assessment Techniques for Short Free-Text Answers

Computer Assisted Assessment (CAA) has been existing for several years now. While some forms of CAA do not require sophisticated text understanding (e.g., multiple choice questions), there are also student answers that consist of free text and require analysis of text in the answer. Research towards the latter till date has concentrated on two main sub-tasks: (i) grading of essays, which is done mainly by checking the style, correctness of grammar, and coherence of the essay and (ii) assessment of short free-text answers. In this paper, we present a structured view of relevant research in automated assessment techniques for short free-text answers. We review papers spanning the last 15 years of research with emphasis on recent papers. Our main objectives are two folds. First we present the survey in a structured way by segregating information on dataset, problem formulation, techniques, and evaluation measures. Second we present a discussion on some of the potential future directions in this domain which we hope would be helpful for researchers.

PolyMage: Automatic Optimization for Image Processing Pipelines

This paper presents the design and implementation of PolyMage, a domain-specific language and compiler for image processing pipelines. An image processing pipeline can be viewed as a graph of interconnected stages which process images successively. Each stage typically performs one of point-wise, stencil, reduction or data-dependent operations on image pixels. Individual stages in a pipeline typically exhibit abundant data parallelism that can be exploited with relative ease. However, the stages also require high memory bandwidth preventing effective utilization of parallelism available on modern architectures. For applications that demand high performance, the traditional options are to use optimized libraries like OpenCV or to optimize manually. While using libraries precludes optimization across library routines, manual optimization accounting for both parallelism and locality is very tedious. The focus of our system, PolyMage, is on automatically generating high-performance implementations of image processing pipelines expressed in a high-level declarative language. Our optimization approach primarily relies on the transformation and code generation capabilities of the polyhedral compiler framework. To the best of our knowledge, this is the first model-driven compiler for image processing pipelines that performs complex fusion, tiling, and storage optimization automatically. Experimental results on a modern multicore system show that the performance achieved by our automatic approach is up to 1.81x better than that achieved through manual tuning in Halide, a state-of-the-art language and compiler for image processing pipelines. For a camera raw image processing pipeline, our performance is comparable to that of a hand-tuned implementation.

Low Resolution Face Recognition Across Variations in Pose and Illumination

We propose a completely automatic approach for recognizing low resolution face images captured in uncontrolled environment. The approach uses multidimensional scaling to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low resolution and the high resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. Stereo matching cost is used to obtain the similarity of two images in the transformed space. Though this gives very good recognition performance, the time taken for computing the stereo matching cost is significant. To overcome this limitation, we propose a reference-based approach in which each face image is represented by its stereo matching cost from a few reference images. Experimental evaluation on the real world challenging databases and comparison with the state-of-the-art super-resolution, classifier based and cross modal synthesis techniques show the effectiveness of the proposed algorithm.

The Uniaxial Tensile Deformation of Ni Nanowire: Atomic-Scale Computer Simulations

The mechanical deformations of nickel nanowire subjected to uniaxial tensile strain at 300 K are simulated by using molecular dynamics with the quantum corrected Sutten-Chen many-body force field. We have used common neighbor analysis method to investigate the structural evolution of Ni nanowire during the elongation process. For the strain rate of 0.1%/ps, the elastic limit is up to about 11% strain with the yield stress of 8.6 GPa. At the elastic stage, the deformation is carried mainly through the uniform elongation of the distances between the layers (perpendicular to the Z-axis) while the atomic structure remains basically unchanged. With further strain, the slips in the {111} planes start to take place in order to accommodate the applied strain to carry the deformation partially, and subsequently the neck forms. The atomic rearrangements in the neck region result in a zigzag change in the stress-strain curve; the atomic structures beyond the region, however, have no significant changes. With the strain close to the point of the breaking, we observe the formation of a one-atom thick necklace in Ni nanowire. The strain rates have no significant effect on the deformation mechanism, but have some influence on the yield stress, the elastic limit, and the fracture strain of the nanowire.

Dependence of collision frequency on distance between two hard-sphere particles estimated by computer simulation

A Monte Carlo simulation is performed to study the dependence of collision frequency on interparticle distance for a system composed of two hard-sphere particles. The simulation quantitatively shows that the collision frequency drops down sharply as the distance between two particles increases. This characteristic provides a useful evidence for the collision-reaction dynamics of aggregation process for the two-particle system described in the other reference.