1. NutriGreen image dataset : a collection of annotated nutrition, organic, and vegan food productsJan Drole, Igor Pravst, Tome Eftimov, Barbara Koroušić-Seljak, 2024, original scientific article Abstract: In this research, we introduce the NutriGreen dataset, which is a collection of images representing branded food products aimed for training segmentation models for detecting various labels on food packaging. Each image in the dataset comes with three distinct labels: one indicating its nutritional quality using the Nutri-Score, another denoting whether it is vegan or vegetarian origin with the V-label, and a third displaying the EU organic certification (BIO) logo.
Published in DiRROS: 23.04.2024; Views: 17; Downloads: 2
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2. Towards deploying highly quantized neural networks on FPGA using chiselJure Vreča, Anton Biasizzo, 2023, published scientific conference contribution Abstract: We present chisel4ml, a Chisel-based tool that generates hardware for highly quantized neural networks described in QKeras. Such networks typically use parameters with bitwidths less than 8 bits and may have pruned connections. Chisel4ml can generate the highly quantized neural network as a single combinational circuit with pipeline registers in between the different layers. It supports heterogeneous quantization where each layer can have a different precision. The full parallelization enables very low-latency and high throughput inference, that are required for certain tasks. We illustrate this on the triggering system for the CERN Large Hadron Collider, which filters out events of interest and sends them on for further processing. We compare our tool against hls4ml, a high-level synthesis based approach for deploying similar neural networks. Chisel4ml is still under development. However, it already achieves comparable results to hls4ml for some neural network architectures. Chisel4ml is available on https://github.com/cs-jsi/chisel4ml.
Keywords: neural networks, QKeras, Chisel4ml
Published in DiRROS: 23.04.2024; Views: 7; Downloads: 0
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3. How does day-to-day stress appraisal relate to coping among office workers in academia? : an ecological momentary assessment studyStephanie Hulin, Larissa Bolliger, Junoš Lukan, Anneleen Caluwaerts, Rosalie De Neve, Mitja Luštrek, Dirk De Bacquer, Els Clays, 2023, original scientific article Abstract: Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day-to-day threat and challenge appraisal and day-to-day problem-focused coping, emotion-focused coping, and seeking social support among academic office workers. This study is based on an Ecological Momentary Assessment (EMA) design with a 15-working day data collection period utilising our self-developed STRAW smartphone application. A total of 55 office workers from academic institutions in Belgium (n = 29) and Slovenia (n = 26) were included and 3665 item measurements were analysed. Participants were asked approximately every 90 min about their appraisal of stressful events (experienced during the working day) and their coping styles. For data analysis, we used an unstructured covariance matrix in our linear mixed models. Challenge appraisal predicted problem-focused coping and threat appraisal predicted emotion-focused coping. Our findings suggest an association between threat appraisal as well as challenge appraisal and seeking social support. Younger and female workers chose social support more often as a coping style. While working from home, participants were less likely to seek social support. The findings of our EMA study confirm previous research on the relationship between stress appraisal and coping with stress. Participants reported seeking social support less while working from home compared to working at the office, making the work location an aspect that deserves further research.
Keywords: academic setting, coping, work stress
Published in DiRROS: 25.03.2024; Views: 85; Downloads: 20
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4. Hardware–software co-design of an audio feature extraction pipeline for machine learning applicationsJure Vreča, Ratko Pilipović, Anton Biasizzo, 2024, original scientific article Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
Keywords: FPGA, MFCC, keyword spotting, chisel
Published in DiRROS: 25.03.2024; Views: 86; Downloads: 43
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6. Resolving a structural issue in cerium-nickel-based oxide : single compound or a two-phase system?Jelena Kojčinović, Dalibor Tatar, Stjepan Šarić, Cora Pravda Bartus, Andraž Mavrič, Iztok Arčon, Zvonko Jagličić, Maximilian Mellin, Marcus Einert, Angela Altomare, Rocco Caliandro, 2024, original scientific article Keywords: cerium oxide, nickel oxide, crystallography
Published in DiRROS: 13.03.2024; Views: 132; Downloads: 63
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9. High temperature friction and galling properties of nanolayered (Cr,V)N coatings and effect of V contentBojan Podgornik, Marko Sedlaček, Barbara Šetina, Miha Čekada, 2023, original scientific article Keywords: (Cr, V)N coating, galling, high-temperature tribology, hot forming, self-lubrication
Published in DiRROS: 26.01.2024; Views: 188; Downloads: 121
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10. Impact assessment of the Gajke and Brstje landfills on groundwater status using stable and radioactive isotopesSonja Cerar, Luka Serianz, Polona Vreča, Marko Štrok, Tjaša Kanduč, 2023, original scientific article Abstract: Waste disposal in landfills represents a severe threat to aquatic environments on the local, regional, and global levels. In Slovenia, there are 69 registered landfills where groundwater is regularly monitored. However, isotope techniques are not regularly employed. Therefore, we employed isotope analysis of hydrogen, carbon, and oxygen in combination with total alkalinity to assess the impact of the selected landfill on groundwater and to evaluate the biogeochemical processes at work. The δ18O, δ2H, δ13CDIC, 3H activity and total alkalinity were determined in October 2020 at 12 sampling points from the surrounding area of the Gajke and Brstje landfills and leachate from the Gajke landfill. The δ18O (-9.24 ± 0.3 ‰) and δ2H (-64.9 ± 2.7 ‰) in groundwater indicate that the main water source consists in direct infiltration of precipitation, with no significant isotopic fractionation. Total alkalinity in the investigated area ranges from 5.45 to 73 mM and δ13CDIC from –14.9 to +6.1 ‰, respectively. Higher values of total alkalinity (up to 73 mM), δ13CDIC (up to +6.1 ‰), δ18O (-7.64 ‰) and 3H (209.8 TU) are detected in the leachate, indicating biogeochemical process related to CO2 reduction or methanogenesis. Methanogenesis could be present at locations GAP-10/13 (Brstje landfill) and G-2 (Gajke landfill) with δ13CDIC values ranging from –8.2 to –7.6 ‰ and with dissolved oxygen values around 0 % and elevated 3H values (from 16 to 18 TU). This study demonstrates the effectiveness of isotopic analysis as a valuable tool for monitoring landfills, revealing shifts in biogeochemical processes within the groundwater there.
Keywords: groundwater, monitoring, landfill, stable isotopes, tritium, Gajke, Brstje
Published in DiRROS: 16.01.2024; Views: 168; Downloads: 61
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