Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2025
Volume
Vol. 26
Number
No. 4
Description
Journal Volume
Computer Science
Vol. 26 (2025)
Projects
Pages
Articles
PSO-WESRGAN: a novel document image super resolution
(Wydawnictwa AGH, 2025) Kezzoula, Zakia; Gaceb, Djamel
Abstract One of the major challenges of document images that can hinder readability and the analysis of information is low resolution; this is typically caused by low-pixel density scanning or excessive compression to save storage space. This results in a loss of fine detail in images, making it difficult to detect critical information. To solve these problems, super-resolution techniques are used. These techniques improve image quality by increasing the resolution while maintaining the fine detail. PSO-WESRGAN is an innovative method that combines wavelet processing, deep-transfer learning, and particle swarm optimization (PSO). Wavelet processing analyzes image detail at diverse scales and orientations, while transfer-based deep-learning advantages pre-trained models on vast image data sets. By integrating PSO, the efficiency of the method is enhanced through the optimal exploration of the solution space to identify the best parameters for the super-resolution model. The experimental results show the effectiveness of this method and open up prospects for future improvements in the super-resolution of document images.
A parallel approach for metaheuristics solving the labs problem using CPU and GPU
(Wydawnictwa AGH, 2025) Żurek, Dominik; Piętak, Kamil; Pietroń, Marcin; Kisiel-Dorohinicki, Marek
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 neighborhood 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 improves the efficiency of the search method.
The benefits of testing software in se research: lessons learned from two phd projects
(Wydawnictwa AGH, 2025) Novak, Matija; Mijač, Marko
Software engineering (SE) research often involves creating software – either as a primary research output (e.g., in design science research) or as a supporting tool for the traditional research process. Ensuring software quality is essential, as it influences both the research process and the credibility of findings. Integrating software-testing methods into SE research can streamline efforts by addressing the goals of both research and development processes simultaneously. This paper highlights the advantages of incorporating software testing in SE research – particularly for research evaluation. Through qualitative analysis of software artifacts and insights from two PhD projects, we present ten lessons learned. These experiences demonstrate that, when effectively integrated, software
Toward RAM forensics supported by machine-learning methods
(Wydawnictwa AGH, 2025) Jurczyk, Kamil; Topa, Paweł; Faber, Łukasz
In this article, we propose an enhancement to the computer forensics technique of using Machine-Learning tools to analyze the contents of RAM in order to extract information that is potentially useful during an investigation. In the specific case presented, the use of the extracted information to generate moreoptimal dictionaries for dictionary cryptanalysis is considered. Increasing user awareness is making cryptanalysis of passwords increasingly difficult for law enforcement. Long and complex passwords are impossible to crack – even when high-performance computing platforms are available. A sensible method of optimization is to look for hints to use a dictionary that contains text phrases more likely to be used in the specific case under attack. Such a hint could be an analysis of RAM taken from a suspect computer. Machine-learning methods can significantly facilitate this task. In this article, we also explore the effectiveness of such an approach and its usefulness in practical applications. We also consider applications of the proposed approach for other purposes, such as OSINT.
Optchain: an advanced optimization method for enhancing IoT Data security via blockchain
(Wydawnictwa AGH, 2025) Kokate, Shatakshi; Shrawankar, Urmila
The increased use of IoT devices in various domains generates abundant data traffic. Securing this data during its transfer and storage is essential. Blockchain is now a trending technology to provide security to the data; however, it is observed that blockchain performs poorly while managing large volume data. To mitigate this issue, an advanced Optchain method to reduce the data size before submitting it to the blockchain network is discussed in this paper. This Optchain method optimizes IoT-generated data using data-classification and compression techniques. The classification of data as relevant or irrelevant is based on predefined thresholds of critical healthcare parameters. Subsequently, the Optchain method employs the Z-standard algorithm for compressing only the relevant data, ensuring efficient storage and faster blockchain transactions. Simulation results using the iFogSim simulator and Ethereum blockchain demonstrated improved storage costs and computational times compared to traditional methods.