dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with alternative methods Two different datasets have been generated in order to compare and evaluate the different algorithms: 1. Balanced dataset, where a subset of the negative dataset was randomly chosen to have the same size as positive dataset; 2. Imbalanced dataset after augmentation of PPs, where the ratio PPs ∶ non-PPs ≈ 1 ∶ 5 In this section, we compare the performances of our proposed EnsSVM model with the following methods: 1. single SVM trained on the imbalanced dataset. (Chang and Lin, 2011)  2. single SVM trained on the imbalanced dataset, weighted by the 1 : 5 unbalance ratio. Labels are weighted inversely proportional to class frequencies. 3. single SVM trained on the balanced dataset. 4. bagging SVM trained on the balanced dataset with linear and radial basis function (RBF) kernels respectively.
(Breiman, 1996)  5. random forest on the balanced dataset. (Breiman, 2001)  6. extra-trees on the balanced dataset. (Geurts et al. , 2006)  Bagging, random forests, and extra-trees are other form of ensemble learning in order to deal with imbalanced data, which is why we chose them for comparison, and random forests are a an extension of Bagging. The main difference between bagging and our ensemble learning is the way the sub-training sets are generated. Bagging is done using the balanced dataset, and sub-training sets are generated randomly from this balanced dataset (bootstrap with replacement), whereas the EnsSVM approach is drawing subsets of the majority class in order to build multiple sub-training sets that are balanced and contain the entire minority class. In our experiments, we trained and tested all the algorithms on the HCP data with a 10-fold cross-validation.
As visible, EnsSVM achieves the best results in Recall, G-mean and AGF, and the Extra-trees of 500 decision trees performs best in F-measure, whereas the single SVM trained on the imbalanced dataset was the worst. In comparison with all single SVMs, ensemble methods show improved performances. In particular, this shows the importance of a proper sampling for the majority class (negative data) that has a significant impact on prediction results. We tested the significance of these differences by performing statistical testing (non parametric signed-rank Wilcoxon) over the 10-fold cross-validation on the Recall values. As shown in Table  4 , all differences were significant with at least p<0. 05, except for the difference between SVM on the balanced dataset and SVM with Bagging.
Post-processing To further test the post-processing algorithm, we randomly selected 10 subjects with manually labelled PPs (both left and right hemispheres) and used the remaining data to train the EnsSVM model. For each detected PP region, we count the number of points in the region (one region is a set of consecutive points labelled as PP), and show the resulting histogram in Figure  11-a. For each PP region, we computed the associated mean EnsSVM probability. The EnsSVM probability of a PP point is the mean probability of all SVM base classifiers. The probability value as a function of the number of points in a PP region is shown in Figure  11-(b ). As we can see, regions containing one or two points are frequent, with 32 and 28 occurrences respectively. However, they also have the lowest associated probability, with a median probability of 0.
The average distance is 4. 53 , which corresponds on average to 3 vertices (the average distance between consecutive vertices on the fundus of the STS is 1. 57 for our dataset). This might be considered slightly high but it is not completely informative because false positives are introducing a strong bias: a false positive having no real counterpart will have a possibly large distance to another PP and will therefore bias the average towards a higher value. On the 10 subjects of the testing set, 87. 5% of the PPs are included in a detected region (which is in line with our average Recall score). For these, the mean distance is 1. 72 , which on average corresponds to a little more than one vertex away. Therefore we reach a good level of precision for features that we consider being at least 3 vertices wide.
Validation with external data Using the classifiers trained on the HCP dataset, we conducted a validation on the external TVA dataset. We applied the trained classifiers, and the post-processing of regions, on the TVA dataset and some examples are shown in Figure  13. We then quantitatively compared the distribution of PPs in the STS with the one from the manually identified PPs on the HCP data. On both hemispheres of the 92 subjects of the TVA data, 1085 PPs were detected, with 540 PPs in the left STS and 545 PPs in the right hemisphere. We detected 2 to 11 PPs in the left STS (Mean = 5. 86) against 3 to 10 in the right STS (Mean = 5. 92), and there was no significant difference between left and right STS across individuals ( = 0. 84; Wilcoxon signed-rank test), which is identical to the manual results produced on the HCP data in  (Bodin et al.
, 2021). It is noticeable that our automatic classification detected more PPs per hemisphere than the manual identification on the HCP data (Mean = 4. 5, Mean = 4. 3). Nevertheless, the distribution of the number of PPs across depth ranges or different types of PPs is similar, as shown in Figure  14-a. Again, PPs were subdivided into into superficial and deep ones. In the left STS, the number of superficial PPs = 125 and the number of deep PPs = 415, with a ratio of 1 ∶ 3. 32, while in the right STS, = 67 and = 478, with a ratio of 1 ∶ 7. 13. The ratios of superficial PPs to deep PPs differed significantly between left and right STS across individuals ( < 0. 05; Wilcoxon) which is consistent with manual results presented in  (Bodin et al. , 2021). This difference in distribution across depth ranges between left and right is illustrated in Figure  14(b).
, 2004). Inspired by the description of interlocking gyri  (Cunningham, 1890a (Cunningham, ,b, 1897)) , a new general three-dimensional characteristic, named "wall pinches" (WPs), was proposed by  (Bodin et al. , 2021)  that establishes a continuity between deep and superficial PPs. This local geometrical clue laid the foundation for the automatic detection of PPs. In our work, we proposed the first machine learning based method to automatically detect PPs based on these features, trained and tested it on the HCP dataset and validated it on an independent dataset (TVA). Generally, our method provides a good prediction accuracy for PPs (Recall = 81. 1%). Results on the independent TVA data also show a similar distribution of PPs across different ranges of depth or categories of PPs as the manual labels of HCP data.
To overcome these limitations, we implemented the AverSampleDis (ASD) maps introduced in  (Li et al. , 2010). Both experimental results and visualisation showed the superiority of new map in PPs identification compared to the DPF and curvature map. The comparison of several learning algorithms (section 3. 1) also stresses the importance of taking into account the imbalanced aspect of the problem. In particular it almost shows the importance of a proper sampling strategy to correct for this. Indeed, learning on the imbalanced dataset performs extremely poorly (SVM on the imbalanced dataset in Table  3 ) and learning on a balanced dataset does improve performances significantly. Nevertheless it involves subsampling the majority class by a factor 5 in our case, which can randomly result in a bad representation of this group.
PPs as local landmarks in superior temporal sulcus (STS) From the prediction results on the TVA dataset, we observe that the number of PPs between left and right STS is similar, which is consistent with the results in  (Bodin et al. , 2021)  but different from the observation in  (Le Guen et al. , 2018) , where they found more PPs in the left than in the right STS. This is caused by the fact that automatic methods applying a threshold on depth, such as the one proposed in  (Le Guen et al. , 2018) , find their limitation when PPs are buried in the depth of sulci, or simply when PPs are visible on the floor of a sulcus that is overall deeper than average. This is the case for the STS, which is notably deeper in the right hemisphere than in the left  (Leroy et al. , 2015). Such PPs do not pass the thresholds and are therefore rejected as noise  (Bodin et al.
Wall pinches and their links with connectivity Our method does not exactly provide the same number of PPs than manual labeling, in both hemispheres. Specifically, we find on the TVA dataset that we detect on average one more deep PP per hemisphere than the manual labelling performed on the HCP dataset. The exact nature of PPs and WPs need to be further explored to understand why we might have such (small) discrepancy and to detect the proper number of PPs, in particular deep ones. One limitation of our work is that we only used the local morphological information (shape features that characterize WPs) to detect PPs. Nevertheless, we showed in a previous study  (Bodin et al. , 2021)  that there is a specific relationship between superficial white matter U-shape fiber connectivity. This can also been noted in other recent studies  (Catani et al.
Conclusion In this paper, we present the first machine learning based method to automatically detect plis-de-passage (PP) in the superior temporal sulcus (STS). This difficult problem is addressed using geometrical descriptors and machine learning while taking into account the imbalanced nature of the problem. Our results are very consistent with manual labeling, and show similar distributions of PPs across two different datasets. They further confirm that the number of PPs between left and right STS are similar while there are more superficial PPs in the left hemisphere than the right. We advocate for the fact that PPs are essential landmarks to understand sulcal variability across individuals  (Bodin et al. , 2021; Regis et al. , 2005) , and this work will open the way to their use for understanding cortical variability and organisation.
Acknowledgements The HCP data were provided by the  Human Connectome Project ,  WUMinn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. Tianqi Song  is supported by a grant from  China Scholarship Council (No. 201801810039 ). The project leading to this publication has received funding via the Laennec institute from the  Excellence Initiative of Aix-Marseille Universite -A*Midex , a  French "Investissements d'Avenir programme "  AMX-21-IET-017. The authors would like to thank  Sylvain Takerkart  for initial discussions of this work and  Pascal Belin  for providing the TVA dataset.
Data collection: Camera Trapping We conducted the camera trap survey between July and October 2019, and July and November 2020, using 20 no-glow camera traps (Browning Dark Ops HD Pro X 2019). We rotated the camera traps every 30-40 days at a total of 75 stations within a standard 5 × 5 km EEA grid [27], covering 36% of the Ioannina regional unit. We positioned cameras to maximize the probability of detecting wolves along forest roads. We hid cameras and locked them on trees (height: 60-250 cm) to minimize camera theft and vandalism. Nonetheless, towards the end of the survey period, three cameras were stolen, and one was disrupted by livestock, resulting in a final set of 71 camera trap stations (Figure  1 ). Cameras remained for an average of 37. 5 (±6. 5 SD) trap days per site. The average spacing between cameras was 4.
Relative Abundance Index We considered all wild mammalian species and 12 human disturbance types (related to human presence, vehicles, dogs, and livestock) (Table  1 ). We created the raw database by classifying images using the open access Wild. ID software version 0. 9. 31  [1]. We extracted the matrices of independent detection events in R software version 4. 1. 1 using scripts available online  [28]. We did not use photographs recorded within 30 min of a previous picture of the same species at the same site, as they are not considered to be independent  [29]. We used independent detections to estimate the detection rate, also called.
Data Analysis 2. 3. 1. Relative Abundance Index We considered all wild mammalian species and 12 human disturbance types (related to human presence, vehicles, dogs, and livestock) (Table  1 ). We created the raw database by classifying images using the open access Wild. ID software version 0. 9. 31  [1]. We extracted the matrices of independent detection events in R software version 4. 1. 1 using scripts available online  [28]. We did not use photographs recorded within 30 min of a previous picture of the same species at the same site, as they are not considered to be independent  [29]. We used independent detections to estimate the detection rate, also called the relative abundance index (RAI), by dividing the number of independent detections by the sampling effort (100 camera trap days)  [30].
Wolf Temporal Activity Patterns Relative to Other Mammals We estimated the temporal activity patterns of wolves and sympatric species using kernel density estimation in the R package 'overlap'  [31]. We excluded small species as well as mammals with <20 records from the analyses. We first converted the time of each observation into radians to account for the circular distribution of the time of day. We then created kernel density estimation curves for each species and calculated the non-parametric coefficient of overlap ∆ between each species pair. The minimum sample size defines the appropriate estimator (∆ 4 if n > 75, otherwise ∆ 1  [1] ), and since our sample size was >75 detections for all species, we used the ∆ 4 estimator. We estimated the mean coefficient of overlapping with 95% CIs by bootstrapping with 10,000 permutations.
Coefficient ∆ ranges between 0 (no overlap) and 1 (total overlap)  [32]  and is considered to be high if ∆ > 0. 75, intermediate if 0. 50 < ∆ < 0. 75, and low if ∆ < 0. 50  [33, 34]. Besides coefficient ∆, we employed a more refined method to test whether activity patterns significantly differed between each species pair, using the circular statistic Mardia-Watson-Wheeler test (MWW test) in the R package 'circular'  [35]. We considered differences to be significant if p < 0. 05 and marginally significant if 0. 10 < p < 0. 05.
Temporal Activity Patterns Relative to Human Disturbance We evaluated temporal segregation between the mammalian community and human disturbance, as described in Section 2. 3. 2. We first evaluated total temporal activity pattern overlap between each species and human disturbance using data from all camera trap sites. We then defined each camera trap site as either having high or low human disturbance, using the mean value of human disturbance RAI as the threshold  [16]. The mean value of the RAI for human disturbance was 125. 3 (min = 4. 8, max = 544. 4) and resulted in 24 high and 47 low human disturbance sites (Figure  1 ). We estimated overlap in the temporal activity patterns (coefficient ∆) between (a) wild mammals and human disturbance (at all sites), and (b) each species at high and low human disturbance sites (∆ 4 for >75 detections, ∆ 1 for <75 detections).
To do so, we used the Least Absolute Shrinkage and Selection Operator (LASSO), a regression analysis method that performs variable selection and regularization  [43]  with 1000 permutations, using the package 'glmnet' in R  [44]  (Figure  S1 ). We included the variables selected by this process (variables with a lambda. min coefficient > 0; see Figure  S1 ) in Generalized Linear Models (GLMs) with a quasi-Poisson distribution error to account for overdispersion  [45]. We conducted model selection with a multi-model inference approach that compares all possible combinations of variables  [46] , using the 'MuMIn' R package  [47]. We ranked models using the quasi-Akaike Information Criteria for small samples (QAICc) and made inferences on those with ∆QAICc < 2. We estimated the model-averaged coefficients (β), standard errors (SE), p-values, 95% and 90% confidence intervals (CI).
Results The dataset comprised 2698 camera days and 8659 independent detections, of which 5295 were of 14 species of wild mammals (Table  1 ). Wild boars were the most frequently detected large mammal (94% of sites, n = 598 detections), followed by wolves (79%, n = 168), roe deer (62%, n = 159), and brown bears (52%, n = 150). Red foxes, brown hares, and martens were the most frequently detected medium-sized species. Human disturbance was widespread (100% of sites, n = 3379 detections), with vehicles having the highest rate of detections, followed by dogs, humans, and livestock (Table  1 ).
For example, roe deer became more diurnal as a response to the presence of lynx  [13]. Roe deer were detected at fewer cameras than wild boars (62% of them versus 94% for the wild boar) and with an overall lower RAI (6. 04 for roe deer and 22. 52 for wild boars). Roe deer in Greece display low densities and fragmented distribution, and they are currently mostly present in mountainous forested areas with low human disturbance, after suffering significant population declines and local population extinctions due to intense hunting and deforestation in the past  [50]. Following their protection in the 1960s, there are indications of local population increases, especially during the last few decades; however, scientific data on population trends are largely lacking. Roe deer may not yet be common prey for wolves in our study area, meaning that wolves may not have adapted their temporal activity to that of roe deer.
To test for within-strain variation in Y resistance, we performed a Kurskal Wallis test between groups of F2 progenies (from the same F1 cross). All the iso-Y lines appear homogeneous (SupTable1). We performed a Wilcoxon test to determine whether those lines were significantly different from ST8. They all appear significantly more resistant than ST8 except Sey81 and New91 (SupTable1). Finally, we performed a Tukey HSD post-hoc test on the anova model to determine groups (Figure  1B ). All statistical analyses were conducted in R base (R Core Team 2022). The R script used can be found on github: Figure1/resistance. R.
Mapping and Variant calling Paired-end reads were trimmed using trimgalore (trim_galore --paired --nextera --length 75 --phred33 --no_report_file -fastqc)  (Krueger et al. 2021). Read quality was assessed using FASTQC. Reads were mapped against the reference genome with bwa (v7. 4) using the BWA-MEM algorithm (default parameters). We used the heterochromatin-enriched assembly of D. simulans  (Chang et al. 2022 ) that include 15Mb of the Y chromosome as reference genome. Resulting sam alignment files were converted into bam files and sorted using respectively samtools (v1. 11) view and sort command. PCR duplicates were removed using Markduplicates from Picardtools (v2. 12. 0) (https://broadinstitute. github. io/picard/). For our SNP calling analyses we want to keep only the uniquely mapping reads, whereas for the repeat quantification we need to keep also the multi-mapping reads.
We thus performed two different filtering based on mapping quality using samtools view  (Li et al. 2009 ). To keep multi-mapping reads we use the following parameters -b -h -f 3 -F 4 -F 8 -F 256 -F 2048, to keep only the uniquely mapping reads we use the following parameters -b -h -f 3 -F 4 -F 8 -F 256 -F 2048 -q30. Mean coverage was determined using the genomecov tool of bedtools  (Quinlan et Hall 2010). The pipeline used (Mapping. sh) can be found on the github repository. Variant calling was done on the uniquely mapping bam files using mpileup and call commands from bcftools (v1. 14)  (Li 2011). We then performed different filtering steps. First isolate the SNPs associated to the Y chromosome using vcftools. We use vcftools  (Danecek et al. 2011)  to analyze the quality of the SNP calling on the Y chromosome based on variant mean depth, variant quality, proportion of missing data per site, mean depth per individual, missing data per individual.
This allowed us to determine the threshold for the quality filtering : --remove-indels --max-missing 0. 9 --minQ 30 --min-meanDP 10 --max-meanDP 50 --minDP 10 --maxDP 50. Because each iso-Y line originates from a single male and the Y chromosome is supposed to be haploid, heterozygote sites are likely to be duplicated sequences and thus difficult to take into account. We removed the heterozygotes sites using the filter command (-e 'GT="het"' option) of bcftools. Note that because Tun83 and Sey81 were sequenced twice, after verification that they were identical, we keep only the data from batch2 to avoid redundancy in the analyses. The pipeline used (VariantCalling. sh) can be found on the github repository. We calculate the nucleotide diversity along Y chromosomes per 10kb windows using the vcftool--window-pi 10000 option.
To reconstruct the phylogenetic tree, we first created a fasta sequence with only the SNP for each individual. The SNP for which we were not able to identify a corresponding allele in the sister species were coded as gaps. We constructed the maximum likelihood tree using the GTR model and ultrafast bootstrap (1000 replicates) using iqTREE web server  (Nguyen et al. 2015). D. melanogaster was used as the outgroup. The scripts used can be found in github : Figure3/Ancestry. sh and Figure3/Ancestry. R.
, 2016]. However, it is not enough, concerning user participation, to put him/her close to the designer or conduct interviews or questionnaires. Instead, user participation facilitates communication between engineers, designers, and users  [Muller and Druin, 2003]. In this sense, Participatory Design (PD) has techniques that can help participation and, consequently, potentialize communication  [Muller and Druin, 2003 ]. Participatory techniques have been widely used in HCI for interaction design. However, few participatory techniques are used to elicit requirements  [Pacheco et al. , 2018]. This may be due to the existence of many techniques, and choosing one, or a set of them, can be a complicated process for designers and engineers  [Ogunyemi and Lamas, 2014]. In order to favor the communication between designers and users during interaction (re)design (initially for educational software) and provide an effective methodological process, we conceived SPIDe  [Rosa and Matos, 2016].
SPIDe is composed of the association of PD and is theoretically grounded in Semiotic Engineering (SemEng)  [Rosa and Matos, 2016]. This association between PD and Se-mEng gives SPIDe a semio-participatory design characteristic  [Baranauskas, 2013; Rosa and Matos, 2016]. Some studies  [Rosa et al. , 2017 [Rosa et al. , , 2018] ]  have indicated that SPIDe could also provide requirements elicitation. Therefore, this paper presents an exploratory case study to investigate the feasibility of SPIDe when applied to requirements elicitation integrated with interaction design. The following question was used to guide this research: is SPIDe effective for integrated interaction design and requirements elicitation? The presented case study occurred during the software design for a music band. We collected the data through logbooks  [McAlpine et al.