Abstract:

In the modern world, the problems arising in the field of traffic are of great importance. In order to solve existing problems, various intelligent systems are being developed, one of which is the Smart City system. This work is devoted to the development of an information and analytical system (IAS) for controlling an intelligent traffic light. The presented system consists of two levels, each of which contains a set of specific operations. The first level is responsible for detecting objects, in particular pedestrians and cars at the intersection, and the second level calculates the operating time of traffic light signals for the control signal that is transmitted to the device. For comparative analysis, the combined method (HOG+SVM) Histogram of oriented gradients was chosen, based on counting the number of gradient directions on individual image areas and Support Vector Machines, which are used to construct hyperplanes in n-dimensional space in order to separate objects belonging to different classes. The results of an experimental study, during which the recognition of objects in images was carried out, showed the superiority of the developed information and analytical system over existing methods. The average accuracy of detecting pedestrians and cars through the IAS was 69.4%. In addition, according to the experiment, it was concluded that the accuracy of detecting objects in images is directly proportional to the distance from the video camera to the object.

Abstract:

This paper addresses the problem of accelerating the construction of accurate editorial annotations for handwritten archival texts within an incremental training cycle based on weak transcription. Unlike our previously published results, the present work focuses on integrating automatic post-correction of weak transcriptions using large language models (LLMs). We propose and implement a protocol for applying LLMs at the line level in a few-shot setup with carefully designed prompts and strict output format control (preservation of pre-reform orthography, protection of proper names and numerals, prohibition of structural changes to lines). Experiments are conducted on the corpus of diaries by A.V. Sukhovo-Kobylin. As the base recognition model, we use the line-level variant of the Vertical Attention Network (VAN). Results show that LLM post-correction–exemplified by the ChatGPT-4o service–substantially improves the readability of weak transcriptions and significantly reduces the word error rate (in our experiments by about −12 percentage points), without degrading the character error rate. Another service tested, DeepSeek-R1, demonstrated less stable behavior. We discuss practical prompt engineering, limitations (context length limits, risk of “hallucinations”), and provide recommendations for the safe integration of LLM post-correction into an iterative annotation pipeline to reduce expert annotators’ workload and speed up the digitization of historical archives.

Abstract: Ancient Egyptians represented each fraction as a sum of unit fractions, i.e., fractions of the type 1/n. In our previous papers, we explained that this representation makes perfect sense: e.g., it leads to an efficient way of dividing loaves of bread between people. However, one thing remained unclear: why, when representing fractions of the type 2/(2k+1), Egyptians did not use a natural representation 1/(2k+1)+1/(2k+1), but used a much more complicated representation instead. In this paper, we show that the need for such a complicated representation can be explained if we take into account that instead of cutting a rectangular-shaped loaf in one direction – as we considered earlier – we can simultaneously cut it in two orthogonal directions. For example, to cut a loaf into 6 pieces, we can cut in 2 pieces in one direction and in 3 pieces in another direction. Together, these cuts will divide the original loaf into 2 * 3 = 6 pieces. It is known that Egyptian fractions are an exciting topics for kids, helping them better understand fractions. In view of this fact, we plan to use our new explanation to further enhance this understanding.