This study is devoted to the analysis of hand-drawn sketches made by surveyed individuals. The survey aimed to connect the given points in the coordinate system with a line to create a function graph. We distinguish two cases: the graph resembles a straight line segment (corresponding to so-called linear thinking), or it is a smooth function (corresponding to so-called nonlinear thinking). We have shown the relationship between the thinking style and the field of study.

SUMMARY: Finding Maximum Exact Matches, i.e. matches between two strings that cannot be further extended to the left or right, is a classic string problem with applications in genome-to-genome comparisons. The existing tools rarely explicitly address the problem of MEM finding for a pair of very similar genomes, which may be computationally challenging. We present copMEM2, a multithreaded implementation of its predecessor. Together with a few optimizations, including a carefully built predecessor query data structure and sort procedure selection, and taking care for highly similar data, copMEM2 allows to compute all MEMs of minimum length 50 between the human and mouse genomes in 59 s, using 10.40 GB of RAM and 12 threads, being at least a few times faster than its main contenders. On a pair of human genomes, hg18 and hg19, the results are 324 s and 16.57 GB, respectively. AVAILABILITY AND IMPLEMENTATION: copMEM2 is available at https://github.com/wbieniec/copmem2.

MOTIVATION: Genome-to-genome comparisons require designating anchor points, which are given by Maximum Exact Matches (MEMs) between their sequences. For large genomes this is a challenging problem and the performance of existing solutions, even in parallel regimes, is not quite satisfactory. RESULTS: We present a new algorithm, copMEM, that allows to sparsely sample both input genomes, with sampling steps being coprime. Despite being a single-threaded implementation, copMEM computes all MEMs of minimum length 100 between the human and mouse genomes in less than 2 minutes, using 7 GB of RAM memory. AVAILABILITY AND IMPLEMENTATION: https://github.com/wbieniec/copmem. SUPPLEMENTARY DATA: Supplementary data are available at Bioinformatics online.

The paper is focused on the concept of using open questions in an interactive computer system for assessing students' knowledge. The E-test System works as a web system in the local network in the Institute of Applied Computer Science, Lodz University of Technology. The system is used in practice for several years and supports academic teachers in the laborious process of tests' marking. The paper presents a new concept of open questions automatic evaluation by the use of Artificial Neural Networks (ANN). The system was tested experimentally on the basis of tests carried out on a large group of students. The paper reports on the results obtained by the use of new ANN based algorithm which is compared with Authors' previous solution.

The paper presents the conceptual design and the implementation of an interactive computer system for assessing students' knowledge. The system works as a web system in the local network in the Institute of Applied Computer Science, Lodz University of Technology. The system is used in practice and supports academic teachers in the laborious process of tests' marking. The paper presents a new concept of open questions scoring by the use of approximate text matching. The system was tested experimentally on the basis of tests carried out on a large group of students. The paper reports on the results and statistical analysis of the evaluations.

In this paper an image processing algorithm for automatic evaluation of scanned examination sheets is described.The discussed image contains selected function graphs sketched on a prepared sheets.This type of task is characteristic of final high school exams of natural sciences.Our challenge was to develop an evaluation algorithm, which works with a precision comparable to the teacher.If the image contains the correct solution, the algorithm should husk it from a set of random lines, deletions, amendments, drafts, bearing in mind, that lines were drawn by hand.In addition, the algorithm should calculate scores for partially correct solutions.An essential part of our proposal, which is image segmentation and identification, is based on leastsquares fitting combined with 1-NN classification.The proposed solution is flexible and can be extended to other types of tasks such as drawing geometrical figures.