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A parallel approach for metaheuristics solving the LABS problem using CPU and GPU

Żurek Dominik, Piętak Kamil, Pietroń Marcin, Kisiel-Dorohinicki Marek

Computer Science

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2025

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T. 26 (4)

33--49

EN

This paper contributes to solving the low autocorrelation binary sequence (LABS) problem that remains an open hard-optimization problem with many applications. The current direction of research is focused on developing algorithms dedicated to parallel architectures such as GPGPU or multi-core CPUs. The paper follows this direction and proposes new heuristics developed from the steepest-descent local search algorithm that extends the notion of a neighbor-hood of a given sequence. The introduced algorithms utilize the parallel nature of multicore CPUs and provide an effective method for solving the LABS problem. The eﬀiciency levels of SDSL and the new algorithm are presented; to ensure an effective comparison, they were both implemented in the same manner. The comparison shows that exploring the larger neighborhood improvesthe eﬀiciency of the search method.

2

Canine age classification using Deep Learning as a step towards preventive medicine in animals

Mazurek Szymon, Wielgosz Maciej, Caputa Jakub, Frączek Rafał, Karwatowski Michał, Grzeszczyk Jakub, Łukasik Daria, Śmiech Anna, Russek Paweł, Jamro Ernest, Dąbrowska-Boruch Agnieszka, Pietroń Marcin, Sebastian Koryciak, Wiatr Kazimierz

Annals of Computer Science and Information Systems

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2022

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Vol. 30

169--172

EN

The main goal of this work was to implement a reliable machine learning algorithm that can classify the age of a dog, given only a photograph of its face. The problem, which seems simple for humans, presents itself as very difficult for the machine learning algorithms due to differences of facial features among the dogs population. As convolutional neural networks (CNNs) performed poorly in this problem, authors took other approach of creating novel architecture consisting of combination of CNN and vision transformer (ViT) and examining the age of the dogs separately for every breed. Authors were able to achieve better results than those in initial works covering the problem.

3

Compressing sentiment analysis CNN models for efficient hardware processing

Wróbel Krzysztof, Karwatowski Michał, Wielgosz Maciej, Pietroń Marcin, Wiatr Kazimierz

Computer Science

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2020

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T. 21 (1)

25--41

EN

Convolutional neural networks (CNNs) were created for image classification tasks. Shortly after their creation, they were applied to other domains, including natural language processing (NLP). Nowadays, solutions based on artificial intelligence appear on mobile devices and embedded systems, which places constraints on memory and power consumption, among others. Due to CNN memory and computing requirements, it is necessary to compress them in order to be mapped to the hardware. This paper presents the results of the compression of efficient CNNs for sentiment analysis. The main steps involve pruning and quantization. The process of mapping the compressed network to an FPGA and the results of this implementation are described. The conducted simulations showed that the 5-bit width is enough to ensure no drop in accuracy when compared to the floating-point version of the network. Additionally, the memory footprint was significantly reduced (between 85 and 93% as compared to the original model).

4

Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

Pietroń Marcin

Polish Cartographical Review

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2019

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Vol. 51, No. 4

167--179

EN

Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.