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Abstract: Namen diplomskega dela je bil razvoj digitalne različice namizne igre Uno z večigralsko funkcionalnostjo. Rešitev je bila implementacija arhitekture odjemalec-strežnik, ki je obravnavala pogoste težave pri razvoju večigralskih iger, kot so sinhronizacija igralcev, upravljanje stanja in posodobitve v realnem času. Za opredelitev mehanike igre in primarnega uporabniškega vmesnika je bilo uporabljeno ogrodje libGDX. Zasnovo lokalnega strežnika je omogočalo okolje Node.js, upravljanje z zbirko podatkov pa PostgreSQL. Rezultat je bila celovita in robustna aplikacija, ki je vse svoje komponente integrirala v kohezivno celoto, hkrati pa izpolnila tehnične zahteve in zahteve glede uporabniške izkušnje.
Keywords: razvoj računalniških iger, Uno, večigralstvo, libGDX, odjemalec-strežnik
Published in DKUM: 15.10.2025; Views: 0; Downloads: 10
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Abstract: Fair comparison with state-of-the-art evolutionary algorithms is crucial, but is obstructed by differences in problems, parameters, and stopping criteria across studies. Metaheuristic frameworks can help, but often lack clarity on algorithm versions, improvements, or deviations. Some also restrict parameter configuration. We analysed source codes and identified inconsistencies between implementations. Performance comparisons across frameworks, even with identical settings, revealed significant differences, sometimes even with the authors’ own code. This questions the validity of comparisons using such frameworks. We provide guidelines to improve open-source metaheuristics, aiming to support more credible and reliable comparative studies.
Keywords: metaheuristics, evolutionary algorithm, metaheuristic optimization framework, algorithm comparison, benchmarking
Published in DKUM: 02.10.2025; Views: 0; Downloads: 2
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Abstract: Diplomsko delo se ukvarja z razvojem mobilne aplikacije, ki z uporabo tehnologij velikih jezikovnih modelov in igrifikacije uporabnikom svetuje na področju osebne rasti. Sestavljeno je iz teoretičnega in praktičnega dela. Teoretični del vsebuje pregled obstoječih aplikacij, ki izrabljajo velike jezikovne modele ter pregled uporabljenih tehnologij. V praktičnem delu je opisan proces načrtovanja in razvoja mobilne aplikacije, izzivi pri tem delu in njihovo reševanje ter primerjava razvoja mobilnih aplikacij, ki izrabljajo velike jezikovne modele in razvoja klasičnih mobilnih aplikacij.
Keywords: umetna inteligenca, veliki jezikovni model, igrifikacija, mobilna aplikacija, osebna rast
Published in DKUM: 04.09.2025; Views: 0; Downloads: 47
Full text (3,86 MB)

Abstract: Rehabilitation aids help people with temporal or permanent disabilities during the rehabilitation process. However, these solutions are usually expensive and, consequently, inaccessible outside of professional medical institutions. Rapid advances in software development, Internet of Things (IoT), robotics, and additive manufacturing open up a way to affordable rehabilitation solutions, even to the general population. Imagine a rehabilitation aid constructed from accessible software and hardware with local production. Many obstacles exist to using such technology, starting with the development of unified software for custom-made devices. In this paper, we address open issues in designing rehabilitation aids by proposing an extensive rehabilitation platform. To demonstrate our concept, we developed a unique platform, RehabHand. The main idea is to use domain-specific language and code generation techniques to enable loosely coupled software and hardware solutions. The main advantage of such separation is support for modular and a higher abstraction level by enabling therapists to write rehabilitation exercises in natural, domain-specific terminology and share them with patients. The same platform provides a hardware-independent part that facilitates the integration of new rehabilitation devices. Experience in implementing RehabHand with three different rehabilitation devices confirms that such rehabilitation technology can be developed, and shows that implementing a hardware-independent rehabilitation platform might not be as challenging as expected.
Keywords: movement observation, rehabilitation aid, assistive technology, robot-assisted rehabilitation, additive manufacturing, local production, human-computer interaction, code generation, domain-specific languages
Published in DKUM: 16.06.2025; Views: 0; Downloads: 7
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Abstract: This paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or hide global optima in isolated regions, making effective exploration particularly challenging. To address the issue of premature convergence caused by blind spots, we propose LTMA+ (Long-Term Memory Assistance Plus), a novel meta-approach that enhances the search capabilities of MAs. LTMA+ extends the original Long-Term Memory Assistance (LTMA) by introducing strategies for handling duplicate evaluations, shifting the search away from over-exploited regions and dynamically toward unexplored areas and thereby improving global search efficiency and robustness. We introduce the Blind Spot benchmark, a specialized test suite designed to expose weaknesses in exploration by embedding global optima within deceptive fitness landscapes. To validate LTMA+, we benchmark it against a diverse set of MAs selected from the EARS framework, chosen for their different exploration mechanisms and relevance to continuous optimization problems. The tested MAs include ABC, LSHADE, jDElscop, and the more recent GAOA and MRFO. The experimental results show that LTMA+ improves the success rates for all the tested MAs on the Blind Spot benchmark statistically significantly, enhances solution accuracy, and accelerates convergence to the global optima compared to standard MAs with and without LTMA. Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead.
Keywords: optimization, metaheuristics algorithm, algorithmic performance, duplicate solutions, nonrevisited solutions, blind spots, LTMA
Published in DKUM: 19.05.2025; Views: 0; Downloads: 4
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