With the growth of data volume and increased query complexity, the need for the application of various optimisation techniques that enable faster execution and more efficient use of resources is increasingly becoming evident. Research shows that indexing, query execution optimisation, and the use of caching significantly reduce processing time and increase system responsiveness. Given that databases are constantly growing in size due to the need to store and analyse data, efficient database architecture and organisation are imperative to the business environment. This paper deals with the topic of analysing databases with large data sets and how to retrieve them most efficiently, using web applications, which are today the most common UI for databases.

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education.

Embedded systems, particularly when integrated into the Internet of Things (IoT) landscape, are critical for projects requiring robust, energy-efficient interfaces to collect real-time data from the environment. As these systems become complex, the need for dynamic reconfiguration, improved availability, and stability becomes increasingly important. This paper presents the design of a framework architecture that supports dynamic reconfiguration and “on-the-fly” code execution in IoT-enabled embedded systems, including a virtual machine capable of hot reloads, ensuring system availability even during configuration updates. A “hardware-in-the-loop” workflow manages communication between the embedded components, while low-level coding constraints are accessible through an additional abstraction layer, with examples such as MicroPython or Lua. The study results demonstrate the VM’s ability to handle serialization and deserialization with minimal impact on system performance, even under high workloads, with serialization having a median time of 160 microseconds and deserialization having a median of 964 microseconds. Both processes were fast and resource-efficient under normal conditions, supporting real-time updates with occasional outliers, suggesting room for optimization and also highlighting the advantages of VM-based firmware update methods, which outperform traditional approaches like Serial and OTA (Over-the-Air, the ability to update or configure firmware, software, or devices via wireless connection) updates by achieving lower latency and greater consistency. With these promising results, however, challenges like occasional deserialization time outliers and the need for optimization in memory management and network protocols remain for future work. This study also provides a comparative analysis of currently available commercial solutions, highlighting their strengths and weaknesses.

This paper is based on the analysis of the relationship between early education, mathematical skills, and student achievement in Bosnia & Herzegovina, using data from the trends in international mathematics and science study (TIMSS) 2019. The study involves 5,628 fourth-grade students whose average age was 10.1 years. The research specifically focuses on factors such as the age of students at the beginning of schooling, attendance at preschool institutions, and their mathematical skills before starting school. Through the application of SPSS analysis, insights into the statistical relationships and trends between these factors are provided, emphasizing the importance of early educational experiences to improve the quality of education in Bosnia & Herzegovina. The analysis results show that the length of stay in preschool institutions, attendance in preschool programs, and the early acquisition of mathematical skills (counting skills, recognition of numeral representations, numeral writing, simple addition, and subtraction) impact later mathematical achievements of students.

Students in Bosnia and Herzegovina (B&H) score very low on international assessments (TIMSS & PISA), lower than the countries in the region with which it shares similar background. In this paper the characteristics of teachers’ professional development (TPD) in the region of Western Balkan countries (Albania, B&H, Kosovo, Montenegro, North Macedonia, and Serbia) and Croatia are brought into correlation with average results in mathematics and science on TIMSS 2019. Research questions analyze teachers’ attitudes towards their profession and investigate the impact of professional development (PD) of mathematics and science teachers in the last two years, with a specific focus on the situation in B&H, on students’ achievement in mathematics and science. The findings indicate a lack of TPD as a contributing factor to lower results in B&H. The research suggests that B&H should consider reforms in the education system and enhance teachers’ attitudes towards their profession through intensified PD programs to advance education and learning.

<p>TIMMS istraživanja su izvr&scaron;ena sa ciljem unapređenja obrazovnog sistema na svjetskom nivou. Istraživanja su bazirana za oblasti iz matematike i prirodnih nauka. Pored toga, izvr&scaron;ena su testiranja nekoliko faktora koji imaju uticaj na razvoj učeničkih postignuća na polju informatičke pismenosti. Istraživanjem je dokazano da mentori ili nastavnici treba da budu dovoljno spretni kako bi uspostavili komunikaciju sa NET generacijom mladih. Upotrebom računara ili tableta za vrijeme nastave omogućava se uspostavljanje veze sa mladim rođenim u digitalnoj eri, prilagođavanjem nastavnih sredstava podiže se nivo interakcije. Istraživanjem je dokazano da posjedovanjem naučne laboratorije i upotrebom računarskih tehnologija značajno se utiče na razvoj informatičke i naučne pismenosti.&nbsp;<br />Posjedovanjem vlastitog mobitela i računara ima značajan uticaj kada su u pitanju usvajanja znanja iz prirodnih nauka i matematike. Prema tome, uvođenjem digitalnih tehnologija i inovacija u obrazovni sistem, trend slabih rezultata na međunarodnim istraživanjima bi trebao biti pro&scaron;lost.</p>

Abstract Within this paper, the possibility of using advanced LoRa technology in the field of long-range remote control was considered. For testing purposes, a one-way point-to-point LoRa remote control link was implemented based on the LoRa Ra-02 SX1278 transceiver and the corresponding 32-bit Cortex-M3 microcontroller. The remote control application software is developed in the Arduino development environment. The implemented link was tested experimentally to check the range, data transfer speed and link stability. Test results show that LoRa transceiver modules can be used in the field of long-range remote control.

This paper presents a new method that enables automatic channel change in case of intense interference on the active channel of a wireless communication point-to-point link. Channel change algorithms on the master and slave side of the communication link are presented, which enable the automatic selection of a new communication channel from a predefined set of available communication channels. By applying this method, it is possible to maintain a stable communication link in case of intense unpredictable interference on the transmitter or receiver side. The method applies to two-way master-slave communication links, regardless of the frequency spectrum and the number of used communication channels.

Maintaining and establishing transparency, security and privacy, when the data that should be included as part of documents that should serve as public educational documents in the labor market, are a challenging task, especially nowadays when we have more frequent cyber-attacks on public institutions. Setting up the security mechanisms of information systems that should store, process and show this type of data can be a very demanding job. For this reason, the introduction of new technologies in this area, such as blockchain technology, leads to considerable system and implementation relief. In this paper, private blockchain platforms are analyzed from the point of view of processing digital certificates or diplomas in the higher education system. An overview of the most popular platforms of this type is given. The most appropriate solution for these needs are discussed and proposed.

The protection and preservation of the privacy of personal data are one of the main requirements when it comes to an application dealing with the processing of such data. It is no different when it comes to information systems that store and process data about students in higher education systems. The public presentation of such data represents a serious threat to the safety of students as well as their status within the higher education system. For this reason, it is necessary to use the possibility of advanced technologies in order to raise data security to the highest level. One such technology that is able to provide transparency, security and data protection at a high level is blockchain technology. In this work, the Hyperledger Fabric distributed ledger private blockchain network was analyzed and its usability in terms of user rights management in higher education system was evaluated. Experimental analysis showed that such a platform has the ability to take advantage of private blockchain technologies in terms of user rights management and to provide security, flexibility and scalability of the system.