Assessment and analysis of students skills and competences

У даній роботі розглянута задача нечіткого логічного висновку для прийняття рішень в задачах підбору контингенту студентів виходячи з вимог потенційних роботодавців, що дозволяє зменшити час на обробку інформації про кандидатів на конкретну посаду.This paper considers the problem of the fuzzy logic inference in order to support the decision making procedure of students’ selection based on the requirements of the potential employers. This approach allows reducing the time spent on the processing of the information about candidates for a certain position. The fuzzy characteristics of the input data set are implemented, the set of fuzzy logic inference rules is defined, the algorithm of fuzzy logic inference, which allows selecting students that better suite for a certain competence, is outlined. For the example we have considered the problem of selection of the best candidate for the position “Junior Front-End Developer” among the set of four sample candidates. By using the set of required skills in the field of front-end web development, we have applied the proposed approach to estimate the scores of such skills for each candidate in order to pick the candidates with the best score

Inflection-Tolerant Ontology-Based Named Entity Recognition for Real-Time Applications

A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems - just to name a few. To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast. In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task. Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied. Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines. In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER). As a first improvement step, we address word variations induced by inflection, for example present in the German language. Our approach is ontology-based and makes use of several language information sources like Wiktionary. We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour. Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing real-time capable runtime performance

Efficient kinetic Monte Carlo method for reaction-diffusion processes with spatially varying annihilation rates

We present an efficient Monte Carlo method to simulate reaction-diffusion processes with spatially varying particle annihilation or transformation rates as it occurs for instance in the context of motor-driven intracellular transport. Like Green's function reaction dynamics and first-passage time methods, our algorithm avoids small diffusive hops by propagating sufficiently distant particles in large hops to the boundaries of protective domains. Since for spatially varying annihilation or transformation rates the single particle diffusion propagator is not known analytically, we present an algorithm that generates efficiently either particle displacements or annihilations with the correct statistics, as we prove rigorously. The numerical efficiency of the algorithm is demonstrated with an illustrative example.Comment: 13 pages, 5 figure

Region-Based Template Matching Prediction for Intra Coding

Copy prediction is a renowned category of prediction techniques in video coding where the current block is predicted by copying the samples from a similar block that is present somewhere in the already decoded stream of samples. Motion-compensated prediction, intra block copy, template matching prediction etc. are examples. While the displacement information of the similar block is transmitted to the decoder in the bit-stream in the first two approaches, it is derived at the decoder in the last one by repeating the same search algorithm which was carried out at the encoder. Region-based template matching is a recently developed prediction algorithm that is an advanced form of standard template matching. In this method, the reference area is partitioned into multiple regions and the region to be searched for the similar block(s) is conveyed to the decoder in the bit-stream. Further, its final prediction signal is a linear combination of already decoded similar blocks from the given region. It was demonstrated in previous publications that region-based template matching is capable of achieving coding efficiency improvements for intra as well as inter-picture coding with considerably less decoder complexity than conventional template matching. In this paper, a theoretical justification for region-based template matching prediction subject to experimental data is presented. Additionally, the test results of the aforementioned method on the latest H.266/Versatile Video Coding (VVC) test model (version VTM-14.0) yield an average Bjøntegaard-Delta (BD) bit-rate savings of −0.75% using all intra (AI) configuration with 130% encoder run-time and 104% decoder run-time for a particular parameter selection