1. A case study on the design and implementation of a platform for hand rehabilitationTomaž Kosar, Lu Zhenli, Marjan Mernik, Marjan Horvat, Matej Črepinšek, 2021, izvirni znanstveni članek Opis: 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.
Ključne besede: movement observation, rehabilitation aid, assistive technology, robot-assisted rehabilitation, additive manufacturing, local production, human-computer interaction, code generation, domain-specific languages
Objavljeno v DKUM: 16.06.2025; Ogledov: 0; Prenosov: 0
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2. Attraction basins in metaheuristics: a systematic mapping studyMihael Baketarić, Marjan Mernik, Tomaž Kosar, 2021, pregledni znanstveni članek Opis: Context: In this study, we report on a Systematic Mapping Study (SMS) for attraction
basins in the domain of metaheuristics. Objective: To identify research trends, potential issues,
and proposed solutions on attraction basins in the field of metaheuristics. Research goals were
inspired by the previous paper, published in 2021, where attraction basins were used to measure
exploration and exploitation. Method: We conducted the SMS in the following steps: Defining research
questions, conducting the search in the ISI Web of Science and Scopus databases, full-text screening,
iterative forward and backward snowballing (with ongoing full-text screening), classifying, and data
extraction. Results: Attraction basins within discrete domains are understood far better than those
within continuous domains. Attraction basins on dynamic problems have hardly been investigated.
Multi-objective problems are investigated poorly in both domains, although slightly more often
within a continuous domain. There is a lack of parallel and scalable algorithms to compute attraction
basins and a general framework that would unite all different definitions/implementations used
for attraction basins. Conclusions: Findings regarding attraction basins in the field of metaheuristics
reveal that the concept alone is poorly exploited, as well as identify open issues where researchers
may improve their research.
Ključne besede: attraction basin, systematic mapping study, systematic review, metaheuristics
Objavljeno v DKUM: 16.06.2025; Ogledov: 0; Prenosov: 0
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3. Overcoming stagnation in metaheuristic algorithms with MsMA’s adaptive meta-level partitioningMatej Črepinšek, Marjan Mernik, Miloš Beković, Matej Pintarič, Matej Moravec, Miha Ravber, 2025, izvirni znanstveni članek Opis: Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces , a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques.
Ključne besede: optimization, metaheuristics, stagnation, meta-level strategy, algorithmic performance, duplicate solutions
Objavljeno v DKUM: 30.05.2025; Ogledov: 0; Prenosov: 3
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4. Tackling blind spot challenges in metaheuristics algorithms through exploration and exploitationMatej Črepinšek, Miha Ravber, Luka Mernik, Marjan Mernik, 2025, izvirni znanstveni članek Opis: 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.
Ključne besede: optimization, metaheuristics algorithm, algorithmic performance, duplicate solutions, nonrevisited solutions, blind spots, LTMA
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 2
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5. Leveraging grammarware for active video game developmentMatej Črepinšek, Tomaž Kosar, Matej Moravec, Miha Ravber, Marjan Mernik, 2025, izvirni znanstveni članek Opis: This paper presents a grammarware-based approach to developing active video games (AVGs) for sensor-driven training systems. The GCGame domain-specific language (DSL) is introduced to define game logic, sensor interactions, and timing behavior formally. This approach ensures cross-platform consistency, supports real-time configurability, and simplifies the integration of optimization and visualization tools. The presented system, called GCBLE, serves as a case study, demonstrating how grammarware enhances modularity, maintainability, and adaptability in real-world physical interaction applications. The results highlight the potential of a DSL-driven design to bridge the gap between developers and domain experts in embedded interactive systems
Ključne besede: active video games, grammarware, internet of things, DSL, procedural level generation, evolutionary computation, game controllers
Objavljeno v DKUM: 23.04.2025; Ogledov: 0; Prenosov: 3
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6. RNGSGLR: generalization of the context-aware scanning architecture for all character-level context-free languagesŽiga Leber, Matej Črepinšek, Marjan Mernik, Tomaž Kosar, 2022, izvirni znanstveni članek Opis: The limitations of traditional parsing architecture are well known. Even when paired with
parsing methods that accept all context-free grammars (CFGs), the resulting combination for any
given CFG accepts only a limited subset of corresponding character-level context-free languages
(CFL). We present a novel scanner-based architecture that for any given CFG accepts all corresponding
character-level CFLs. It can directly parse all possible specifications consisting of a grammar and
regular definitions. The architecture is based on right-nulled generalized LR (RNGLR) parsing and
is a generalization of the context-aware scanning architecture. Our architecture does not require
any disambiguation rules to resolve lexical conflicts, it conceptually has an unbounded parser and
scanner lookahead and it is streaming. The added robustness and flexibility allow for easier grammar
development and modification.
Ključne besede: context-aware scanning, pseudo-scannerless parsing, scanner conflict resolution, generalized LR (GLR), right-nulled GLR (RNGLR), scannerless GLR (SGLR)
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 6
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7. Maximum number of generations as a stopping criterion considered harmfulMiha Ravber, Shih-Hsi Liu, Marjan Mernik, Matej Črepinšek, 2022, izvirni znanstveni članek Opis: Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Ključne besede: evolutionary algorithms, stopping criteria, benchmarking, algorithm termination, algorithm comparison
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 2
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8. Evolution of domain-specific modeling language: an example of an industrial case study on an RT-sequencerTomaž Kos, Marjan Mernik, Tomaž Kosar, 2022, izvirni znanstveni članek Opis: Model-driven engineering is a well-established software development methodology that
uses models to develop applications where the end-users with visual elements model abstractions
from a specific domain. These models are based on domain-specific modeling language (DSML),
which is particular to the problem domain. During DSML use, new ideas emerge and DSMLs evolve.
However, reports on DSML evolution are rare. This study presents a new DSML called RT-Sequencer
that evolved from our DSML Sequencer to support, in addition to the Data Acquisition domain,
also a new domain—Real-Time Control (RTC) systems. The process of defining models with a new
language RT-Sequencer has changed in a way that new end-users were introduced—advanced endusers, which use general-purpose language (GPL) and advanced programming concepts to define
modeling environments for the RT-Sequencer end-users. More specifically, an industrial experience
with the RT-Sequencer is presented, where DSML was opened for extension so that a GPL code
could be inserted into the model to create new visual blocks for the end-user, and the possibility to
adapt and optimize the execution code for a particular task. Our experience shows the specific case
of DSML evolution supporting another problem domain, and the implementation effort needed to
extend domain-specific modeling language with GPL support.
Ključne besede: model-driven engineering, domain-specific modeling languages, measurement systems, Real-Time Control systems, data acquisition, language evolution, experience report
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 5
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10. Computer science education in ChatGPT Era: experiences from an experiment in a programming course for novice programmersTomaž Kosar, Dragana Ostojić, Yu David Liu, Marjan Mernik, 2024, izvirni znanstveni članek Opis: The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.
Ključne besede: large language models, ChatGPT, artificial intelligence, controlled experiment, object-oriented programming, software engineering education
Objavljeno v DKUM: 12.08.2024; Ogledov: 59; Prenosov: 9
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