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1.
Outward Bound and outdoor adventure education : a scoping review, 1995-2019
Timothy J. Mateer, Joshua Pighetti, Derrick Taff, Pete Allison, 2022, original scientific article

Abstract: Outdoor adventure education (OAE) programming is often referenced as an ef-fective intervention that encourages a wide array of outcomes in participants such as increased confidence, independence, and communication skills. However, as outdoor adventure education continues to increase globally, what does the academic literature say about the outcomes related to these programs? Hattie, Marsh, Neill, and Richards (1997) conducted the last major review of program efficacy in this realm. This updated scoping review, largely following PRISMA guidelines (Tricco et al., 2018), aims to summarize the academic literature on one of the primary outdoor adventure education providers internationally, Outward Bound (OB). Fifty-four studies, published betwe-en 1995 and 2019, have been summarized in this review. Utilizing Outward Bound International’s (OBI) framework of “people”, “place”, and “process”, themes and gaps in the literature are explored. Specifically, the OB literature has progressed since 1995 in demonstrating social and emotional outcomes in a variety of settings, a better understanding of the nature of effective programming, and further documenting the role the instructor plays in the learning experience. Recommendations are provided on developing more rigorous methodologies for future research, understanding the role of the physical environment in the learning experience, and utilizing theoretical approa-ches to integrate outdoor adventure education into broader academic realms
Keywords: outdoor education, adventure education, Outward Bound, emotional learning, experiental learning, scoping review
Published in DiRROS: 15.04.2024; Views: 71; Downloads: 44
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2.
Sensitivity analysis of RF+clust for leave-one-problem-out performance prediction
Ana Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution

Keywords: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning
Published in DiRROS: 13.11.2023; Views: 314; Downloads: 187
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3.
Learning from forestry innovations for the European Green Deal : a research approach
Kathrin Böhling, 2023, published scientific conference contribution

Keywords: forests, forestry, innovation, policy learning, European Green Deal
Published in DiRROS: 06.10.2023; Views: 311; Downloads: 114
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4.
Models for forecasting the traffic flow within the city of Ljubljana
Gašper Petelin, Rok Hribar, Gregor Papa, 2023, original scientific article

Abstract: Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.
Keywords: traffic modeling, time-series forecasting, traffic-count data set, machine learning, model comparison
Published in DiRROS: 28.09.2023; Views: 314; Downloads: 132
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5.
Hierarchical learning of robotic contact policies
Mihael Simonič, Aleš Ude, Bojan Nemec, 2023, original scientific article

Abstract: The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.
Keywords: autonomous robot learning, learning, experience, compliance and impedance contro
Published in DiRROS: 21.09.2023; Views: 366; Downloads: 186
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6.
Algorithm instance footprint : separating easily solvable and challenging problem instances
Ana Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution

Keywords: black-box optimization, algorithms, problem instances, machine learning
Published in DiRROS: 15.09.2023; Views: 276; Downloads: 181
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7.
Assessing the generalizability of a performance predictive model
Ana Nikolikj, Gjorgjina Cenikj, Gordana Ispirova, Diederick Vermetten, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, published scientific conference contribution

Keywords: algorithms, predictive models, machine learning
Published in DiRROS: 15.09.2023; Views: 293; Downloads: 192
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8.
RF+clust for leave-one-problem-out performance prediction
Ana Nikolikj, Carola Doerr, Tome Eftimov, 2023, published scientific conference contribution

Keywords: algorithm performance prediction, automated machine learning, zero-shot learning, black-box optimization
Published in DiRROS: 30.08.2023; Views: 327; Downloads: 108
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9.
10.
Optimal sensor set for decoding motor imagery from EEG
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uroš Marušič, Sidney Grosprêtre, Ann Nowé, Romain Meeusen, Kevin De Pauw, 2023, original scientific article

Abstract: Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.
Keywords: brain-computer interface, motor imagery, feature reduction, electroencephalogram, machine learning
Published in DiRROS: 03.04.2023; Views: 454; Downloads: 184
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