dataesr/scientific-paragraphs-categorization huggingface dataset:
Algorithm 2: RANSAC: random sample consensus input :source and target images, restricted to the persistent pixels, and de-densified output :affine model parameters: a best , b best t ← 1000 // initialize while t > 0 do Select two points at random (a, b) ← compute parameters of line passing through d ← orthogonal distances from the points to the line i ← d < 20σ // inlier set if |i| > |i best | then (i best , a best , b best ) ← (i, a, b) i ← i do (a , b ) ← orthogonal regression(i ) d ← orthogonal distances to the line i ← d < 20σ // refined inlier set if |i | > |i best | then (i best , a best , b best ) ← (i , a , b ) t ← t -1 while |i | > |i best | and t > 0 t ← t -1 Noise estimation The RANSAC method requires to establish a threshold for the definition of its inlier set, which is usually related to the uncertainty or the noise of the data points.
A more sophisticated usage, demonstrated in this paper, is the fusion of time series from different satellites. Our algorithm does not need external information, and makes no assumption on the sensors. This means that our method can directly be used with images from other satellites. We expect to extend this algorithm to make an online version. Indeed, for the moment we require the full time series to find the reference images, compute the affine correction models. However, it is desirable to have a method that allows to add new images to an already stabilized time series. The code of our method is available online at https://github. com/chlsl/rrn-multisensor-multidate.
Raman Spectroscopy Analysis The burnt upper layer of the specimens obtained after the cone calorimetry runs at 40 kW/m 2 have been analysed by the Raman spectroscopy. The bands intensities and the ratios of interest are summarized in Table  2. The raw spectral data is provided as Supplementary Materials (Figure  S1 ). The D/G ratio greater than 1 indicated a predominantly disordered state of matter (a diamond-type char structure), while the ratio of less than 1 pointed out at predominantly ordered state of matter (a graphite char structure) in the carbonaceous protective layer  [27, 28]. The char residue obtained from the wood substrate protected with the coating A, containing titanium dioxide and RHA fillers, was characterized by the most ordered char structure, very similar to the one of unprotected wood, whilst the char from the sample coated with the formulation B, based on the industrial fillers only, was the most disordered.
The obtained mixtures have been mixed by magnetic stirring for 20 minutes at room temperature and subsequently degassed for 2h at room temperature. The mixture has been cured by a triple curing cycle: 1 hour at 80 • C + 20 minutes at 120 • C + 1 hour at 180 • C. A concentration of 3% of the filler was used for electric heating measurement. A filler concentration of 3% has allowed obtaining a sample with a DC volume conductivity value of about 6. 8 x 10 -2 S/m. This result was obtained by electrical tests carried out in agreement with Ref. 99. Regarding electric heating measurements, a data acquisition board (Thermocouple Data Logger supplied by Pico Technology) was used to acquire the thermocouples measurements. In order to record the local temperature evolution, a dedicated LabView software was developed.
Acknowledgements  G. Donati  and  G. Milano  gratefully acknowledge the  European Union Horizon 2020 Programme , under Grant Agreement No. 760940  ( MASTRO ) for financial support. The computing resources and related technical support used for this work have been provided by  CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing Infrastructure  is founded by  ENEA , the  Italian National Agency for New Technologies  and by Italian and European research programs, see http://www. cresco. enea. it/englishfor information. G. Donati  and  G. Milano  thank  Professor Roberto Pantani  from the  Department of Engineering at the University of Salerno  for very useful discussions. We develop a theoretical-computational protocol to simulate Joule heating on nanocomposite materials, allowing to investigate morphology, voltage and concentration effects.
In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger.
The training set is composed of more than 200. 000 300 px × 300 px tiles, but only in RGB channels. The areas covered are mainly residential areas, where buildings are homogeneous in size, background discriminative and instances clearly separated one from each other. SpaceNet. Several Space Net data sets have been released and used for benchmark challenges. The first and second challenges aim at the detection of building footprints from Worldview-II and Worldview-III high-resolution 8-band images. The building footprint annotations are initially derived semi-automatically and subsequently improved manually. Buildings are marked individually, where each street address is defined as building. However, building corners are only accurate to 5 px. DeepGlobe. This data set contains three challenges: road detection, building detection, land cover classification  (Demir et al.
, 2018). The building detection data set is based on the SpaceNet data set, and contains 24. 586 images of size 650 px × 650 px from the WorldView-III sensor with a spatial resolution of 31 cm in the panchromatic band and 1. 24 m in all other bands. Adjoining buildings are marked as single buildings, and thus the data set is more detailed than previous building data set. Since the building footprint is annotated, the appearance of the building in the image deviates from the annotation, depending on the height of the buildings and the viewing angle. The annotations are, however, not consistently performed throughout the data set. INRIA Aerial Image Labeling data set. The semantic segmentation data set contains building/non-building annotations, where training and test set are split over cities in Europe and America  (Maggiori et al.
The above-mentioned data sets and the many others not mentioned here, enable an objective and comprehensive comparison of different ML approaches and a thoughtful development of novel methods. Some of the data sets were offered in the form of challenges and have thus led to further progress in the development of new, more efficient methods  (Bakuła et al. , 2019 , Rottensteiner et al. , 2014). The provision of winning solutions as open-source software promotes further progress, as it was done for example in the SpaceNet Challenge 1 1. We advocate that experiments should be released as open source code to foster reproducibility and development. In this paper, we introduce the SemCity Toulouse benchmark, which aims at complementing existing benchmarks, and to foster the development of machine learning methods which address the aforementioned open research questions.
Furthermore, most of these questions can also be addressed by a multi-task approach, in which building instance segmentation is supported by land cover semantic segmentation. We further encourage the development and evaluation of methods which use both labeled and unlabeled data, or various kinds of labeled data. The latter one can be approached by using OpenStreetMap data or ancillary data, which we provide with multi-class semantic labels. A promising direction is multitask learning, where different kinds of tasks such as instance segmentation and semantic segmentation are solved jointly and both tasks regularize each other, resulting in a higher generalization ability and a higher robustness of the learned models. Another promising direction is the pre-training of models which use labels, which are easier to acquire then instances.
Evaluation SemCity Toulouse is designed for building instance segmentation, where individual building masks are compared to reference building masks. For the evaluation of the building instance segmentation, we follow the evaluation metrics of the COCO data set  (Lin et al. , 2014)  4. One essential measure is the intersection over union (IoU) score defined as: IoU = TP TP + FP + FN , ( 1 ) where TP are the true positives, FP the false positives, and FN are the false negatives. Moreover, we evaluate the result by means of precision p and recall r scores, which are defined as: p = TP TP + FP r = TP TP + FN. ( 2 ) We summarize precision and recall scores by means of the F1 score for the class "buildings": F1 = 2 • p • r p + r. ( 3 ) Precision and recall are computed with respect to the building instances.
We also report average precision (AP), average recall (AR) and average F1 (AF1), which are calculated over multiple IoU values as for the COCO benchmark. Specifically, (•)@0. 5 -0. 95 means the average of measure (•) over several IoU thresholds in the interval from 0. 5 to 0. 95 with step size 0. 05. As standard for COCO, we use P@. 5, P@. 75 and AP@. 5-. 95; but we also use recall R@. 5, R@. 75 and AR@. 5-. 95 and F1@. 5, F1@. 75 and AF1@. 5-. 95. We report these metrics for two different, complementary settings. The former relates to the detection of an instance: a counter is simply increased when the predicted object mask IoU ≥ t, where t is the IoU threshold 4 http://cocodataset. org/#detection-eval with the reference mask. In the second setting, we retrieve objects as detected based on the mask IoU, but we then accumulate, for each object, the number of correctly segmented pixels.
Experimental setup To create a first and realistic baseline, we use the benchmark in combination with the standard instance segmentation method Mask R-CNN  (He et al. , 2017). We use this method as it is widely accepted as a standard benchmark, and it provides the baseline metrics to which all consecutive developments should compare to. We use the Matterport implementation, available at https://github. com/matterport/Mask_RCNN. Before training, we re-tile original images into 512×512 patches. For training, we use a stride of 256, so every image overlaps with a direct neighbor 50%. We also use data augmentation, wrapped by the imgaug package in Matterport, available at https://github. com/aleju/imgaug. We use vertical and horizontal flipping with a probability of 0. 5, and one rotation among 7 main directions.
CONCLUSIONS AND FUTURE PLANS We introduced the SemCity Toulouse benchmark, a satellitebased data set for very high spatial resolution building instance segmentation. Beside building instances, we provide ancillary data in form of a multi-class land cover classification map, covering four times the area of the instances annotation map. This should give opportunity to take advantage of different types and levels of labels, if the number of actual labels for the intended task is limited. The data sets are available under http://rs. ipb. uni-bonn. de/data/. We also discussed pros and cons of the baselines obtained by Mask R-CNN, which is a standard for instance segmentation tasks. In addition, we make suggestions on how the baseline solution can be improved. We hope that our project will enable and foster the evaluation and development of machine learning methods and bridges the gap between different research communities.
ACKNOWLEDGMENTS We thank the  International Society for Photogrammetry and Remote Sensing for funding the scientific initiative  ' ISPRS Benchmark Challenge on Large Scale Classification of VHR Geospatial Data ', in which this benchmark was created. We like to acknowledge the annotators who ensure the quality of the data set not only by manual labeling but also by independent crosschecking. Finally, we would like to thank the  Swiss Data Science Center  for offering a GPU to train the benchmark models.
The mean grain size of films for each substrates positions at room temperature and after annealing at 900°C are measured from the Scherrer formula with data extracted from MAUD software. The mean grain size of films obtained in the targets axis decreases from 500 nm down to 130 nm after annealing, which can explain the rise of hardness. Anyway, the difference in microstructure evolution after annealing between the two substrates positions is still not clear. Beyond this Hall-Petch type effect, hardness increase by annealing is a phenomenon frequently observed in nanocrystalline materials. It can be linked to a specific threshold grain size of the material. The reasons are still controversial in the literature, but they are linked to dislocation-grain boundaries interactions. The hardness increase by annealing is often explained by the segregation of the solutes in grain boundaries.
A variety of data collection methods were used for three studies in this research. One research question in this study concerned the relationship between attitudes and unsafe driving intention. According to research results, no relationship between attitudes and unsafe driving intention was found. The research used a bipolar scale (from À3 to +3) to measure attitudes towards driving at level crossings. Participants were asked to complete statements by rating pairs of adjectives while driving at level crossings (e. g. good/bad). The bipolar scale was then recoded using unipolar 1-7 scale. Another study from this research used a set of 19 items to measure specific risky motorist behaviour at passive and active level crossings (e. g. Trying to beat the train at a level crossing; Overtaking cars that are stopped at the crossing; Not looking at passive crossings; Going through flashing lights; etc.
). In their study,  Iversen and Rundmo (2012)  explored changes in driver behaviour in traffic and attitudes in Norway over the nine-year period from 2000 to 2008. They used self-report surveys carried out with two independent and representative samples of Norwegian drivers, with data collected in 2000 (n = 2614) and 2008 (n = 1731). The results show that although both attitudes and self-reported behaviour changed significantly, the changes were small to moderate, and all except one were in the direction of greater safety.
Participants and procedure A sample of 274 participants between 18 and 34 years old voluntarily participated in the study. From the total sample, 62. 4% were men. The participants were recruited from 11 driving schools in the Croatian capital of Zagreb and in Osijek. Another part of the sample was comprised by university students (first and second year). No participant had a driving license. This research was conducted under the supervision of master students at the Faculty of Transport and Traffic Sciences of the University of Zagreb in April 2018. The participants' data was collected in the University. The survey took place in groups of 20 students, on a regular school day, and lasted for about 30 min. The participants were randomly allocated to one of the two study conditions, the intervention group or the control group, by an experimenter blind to the purpose of the study.
Overview First, we conducted preliminary analysis in order to evaluate how the 360°video-based intervention was perceived by the participants. Independent samples t tests were also conducted to examine whether there were gender differences and difference between the experimental and the control groups in risky driving attitudes and risky driving behaviour at level crossings in our sample. Second, the associations among main variables were computed. Third, we tested the relations between attitudes towards traffic rules, impulsiveness and behavioural intentions at level crossings, as well as the moderating role of the 360°video-based intervention. To test our hypotheses, we used the PROCESS custom dialogue for IBM SPSS  (Hayes, 2013). This solution permits building bootstrap-based confidence intervals in order to test the statistical significance of moderation  (Preacher & Hayes, 2004 ).
Acknowledgments The research described in this paper was conducted within the scope of the project '' Implementacija mjera za povecanje sigurnosti najranjivijih sudionika u prometu na z ˇeljeznic ˇko-cestovnim prijelazima" (''Implementation of measures  to improve the safety of the most vulnerable traffic participants at level crossings''), carried out as part of the  National programme of Road Traffic Safety  of the Republic of Croatia 2011 -2020 of the  Croatian Ministry of the Interior. The leader of the project is the  Faculty of Transport and Traffic Sciences  at the  University of Zagreb , and HZ ˇInfrastruktura is the project partner.
Experimental bench The laboratory test bench has been designed for a 100 cm 2 single PEM fuel cell (UBzM, Germany) and set up. Figure  1  shows the flow diagram of the experimental bench with the various ancillaries and the variables measured. The membrane electrode assembly (MEA) of MEA-H500 model (UBzM, Germany) formed by a Nafion™-212 membrane and catalysts layers loaded with 0. 4 mg Pt cm -2. The gas diffusion layers (GDL) used were of Sigracet 29 BC grade comprising an MPL and treated with 5% PTFE. A Kikusui™ power unit was used as an external load to vary the current density. The bench was controlled by dSPACE ® real-time electronic card, and the user interface was provided by the Controldesk software. The setup was linked with MATLAB-Simulink 2017 ® mathematical environment for all control settings such as reference and measured inlet gas flow rates, reference and measured outlet pressure, current density, data acquisition and processing, and emergency shutdown settings.
Comparison of EIS and EPIS data The experiments were conducted with partly humidified air (RH=55%) at various current density levels: obtained EIS and EPIS data were plotted in the form of Bode and Nyquist representation, as exemplified by Figure  5  at 0. 8 A. cm -2. In both spectroscopies, the potential of the cathode referred to the anode potential is measured, and because of low polarization of the anode in hydrogen fuel cells, the signal is principally that of the cathode. As a result, in steady state, cathode potential increases when cathode outlet pressure increases. As can be seen in EIS spectra recorded at 0. 8 A/cm 2 for comparison purpose (Fig. 5a  and 5b), little information can be extracted in the lower-frequency region below 1 Hz. As shown in Figure  5c , pressure modulation has a significant effect on the cell voltage in the whole frequency domain (1 mHz to 1 Hz): increasing the frequency results in increased modulus whereas the phase shift decreases from 0 to approx.
This approach has been initially proposed for predicting a level of the user satisfaction with a given (composite) service. Therefore, followed by our hypothesis, we assume that the results obtained for evaluating and predicting 'the feelings' of an end-user about web/multimedia/electronic services and their compositions, can be applied to the case when these services are created for the educational needs. Moreover, we provide a technique for the effective use of available tools for logic synthesis and verification that support necessary operations and process combinations of integers encoded as Boolean vectors very fast. In particular, we provide examples of using software tool ABC developed at the UC, Berkeley, for scalable prediction of the effectiveness of a composite teaching strategy.
RELATED WORK Different types of services are now developing for making life of a human being easier and more progressive. A huge domain in a service-oriented environment encompasses numerous implementations of electronic services implemented for various human needs. Internet connection allows almost everyone to use a web browser to access a service of a specific need. That is the reason why a technical field named 'Software as a Service (SaaS)' is nowadays progressing rapidly. The advantage using of such software is the absence of the need of its installation and download, i. e. a user does not have to obtain a license on the software he/she wants to utilize. Instead, he/she can 'buy' a subscription and can utilize the corresponding service from his/her web browser 'on demand'. In this case, Cloud Computing makes a good job providing an opportunity of storage for services and related applications in the Cloud.
On the other hand, EaaS becomes an issue of business and marketing, which requires finding new solutions of how to study the quality of existing services and user satisfaction. Nowadays, various education services (see for example, iTunes U, or Intel Education) offer different teaching strategies providing large-scale educational materials and include several options such as sharing lectures, organizing tests, verifying results, etc. Emerging online courses are very promising; however, there arises a problem how to evaluate the quality of these systems  (Casey, 2008). Another service is provided by Moodle software (Moodle Courses & Content) that is widely used around the world. However, Moodle has its own drawbacks such as the complexity in usage for a teacher and the impossibility to use Moodle for a wide range of tasks in various disciplines.
Moodle also does not allow to choose the most suitable teaching strategy for a certain student depending on his/her individual features and expectations. This type of software is most often used as an additional training or self-evaluating tool for homework or self-study activities supervised by the teachers. Therefore, in some cases, due to the absence of a possibility for a student to evaluate the effectiveness of a proposed teaching strategy it is reasonable and beneficial for an Institution not to buy a license for the software like this and waste IT resources on its installation and usage. Another option is to use a number of online services that support this or that teaching strategy, and to execute them whenever it is needed. It is also convenient for teachers and students when all the test results and materials to share are stored in the Cloud, and appropriate services are activated to access the necessary information on-the-fly.
If a student is awarded grades from 3 to 5 he/she is supposed to pass an exam in this discipline. The following data have been collected during a two-year period. The experimental groups were groups of students of the faculties of technical sciences. Both male (60%) and female (40%) students took part in the experiment. All the research participants were nearly of the same age from 17 to 19 years old (from the first to the third year of studies). The division into groups at this faculty is often made based on their English proficiency (their background level after a high school). The scope of the research is rather wide since the level of English varied between elementary and upper-intermediate. There were four groups of the elementary level (30% of students who took part in the experiment).
We try to answer the following questions:  What are the predominant factors influencing the target households' decision to evacuate?  What is their perception of the risk?  Do they have a means of travel and relocation?  Are they able to continue their professional activity from their temporary place of residence? The database used for this study is that of a prospective questionnaire conducted in 11 high-rise buildings in Paris. They are located in the 15 th district, in an area along the banks of the River Seine. The data is provided by 523 respondents, representing 23% of the total number of residents who received the questionnaire. There are only a few residential high-rise buildings in Paris. The presence of this type of building in the "Front de Seine" zone has made it the most densely-populated area in the immediate vicinity.