dataesr/scientific-paragraphs-categorization huggingface dataset:
Unsupervised methods based on anomaly detection could also be further explored, but we believe that they are not sufficient for attack detection, as an anomaly is not necessarily an attack, leading to a large number of false positives. • As justified by current literature, the representation of network-and host-based systems as graphs is especially concentrated around two graph structures, which are respectively network flows (leveraged by ∼78% of network papers studied in this survey) and provenance graphs (∼58% of host papers). Consequently, other data such as network sessions and system calls could be further explored, in an attempt to obtain more efficient graph representations for the detection of specific attacks. • Although most papers focus on the innovation of new models, we believe that important efforts should also be dedicated to the development of large datasets that represent real-life scenarios.
Indeed, we showed that a number of works achieve near-perfect detection performance on either small noise-free datasets or on private enterprise data. However, intrusion detection is a hard problem and datasets employed for their detection should be larger and contain various examples in order to achieve reliable performance on a variety of unseen attacks. We therefore think that larger datasets are required to continue the constant progress in this field. • Finally, it is important to note that there is a gap between the research implementations presented in papers and the actual applications in production environments. Scaling GNN classifiers to large graphs can present significant challenges, as the graph may no longer fit into memory, and computation time can become infeasible. To address these engineering issues, researchers have proposed graph sampling methods to divide the graph into manageable batches and distribute the training across multiple workers  [57].
VII. CONCLUSION The recent surge of interest in graph machine learning, in particular GNNs, has led to a plethora of applications across various domains. Although the use of GNNs in intrusion detection is relatively new, existing research has demonstrated that representing systems as graph structures offers properties that can enhance the accuracy of detection models. In this paper, we provide knowledge on the extraction of graph structures along with the training of GNN models for downstream classification tasks on both network-based and host-based intrusion detection. We comprehensively review and categorize the state-of-the-art approaches and the datasets used, highlighting the potential of GNNs in generating efficient embeddings for robust detection of various types of intrusions. Finally, we discuss the challenges that may arise when using GNNs in intrusion detection and propose directions for future research.
MRI acquisitions All subjects underwent MRI examination using a 3T Siemens Skyra scanner with a 32-channel head coil. The MRI protocol was harmonized within the RIN network including both diffusion weighted imaging (DWI) and resting-state fMRI (rs-fMRI)  (Nigri et al. , 2022). For DWI data a two-shell standard single-shot echo-planar imaging sequence (EPI) [voxel size = 2. 5 mm × 2. 5 mm × 2. 5 mm, TR/TE = 8,400/93 ms, two shells with 30 isotropically distributed diffusion-weighted directions, diffusion weightings of 1,000 and 2,000 s/mm 2  , 7 non-diffusion weighted b = 0 s/mm 2 images (b 0 images) interleaved with diffusion-weighted volumes] was implemented, and 3 non-diffusion weighted images with the reversed phaseencoding acquisition were additionally acquired for distortion correction.
Preprocessing of DWI and fMRI data Preprocessing of diffusion and fMRI data was performed according to  Monteverdi et al. (2022). Briefly, DWI data were denoised, and corrected for motion and eddy currents distortions (FMRIB Software Library and FSL) 1   (Andersson and Sotiropoulos, 2016) , then white matter, gray matter (GM), subcortical GM and CSF were segmented from the co-registered 3DT1 volume (MRtrix3) 2  (Patenaude et al. , 2011). 30 million streamlines whole-brain anatomically constrained tractography  (Smith et al. , 2012)  was performed within MRtrix3, estimating fibers orientation distribution with multi-shell multi-tissue constrained spherical deconvolution (CSD) and using probabilistic streamline tractography  (Tournier et al. , 2012). fMRI preprocessing was carried out combining SPM12 3  , FSL and MRtrix3 commands in a custom MATLABR2019b script.
The subset of nodes defining each network and their connections were extracted from whole-brain SC obtaining specific network SC matrices, used as input to TVB (as detailed below). In addition, both static and dynamic experimental FC (expFC and expFCD, respectively) were reconstructed from rs-fMRI data for each of the six brain networks, to capture not only synchronous fluctuations of BOLD signals but also their spatiotemporal-dynamics during resting-state  (Hansen et al. , 2015). The expFC matrix was created by extracting the time-course of BOLD signals for each node and computing the Pearson's correlation coefficient (PCC) of the time-course of pairs of atlas-defined brain regions. Matrix elements were converted with a Fisher's z transformation and thresholded at 0. 1206  (Palesi et al. , 2020).
Virtual brain modeling The TVB workflow [reported in  Monteverdi et al. (2022)  for the whole brain] was applied to each one of the six selected brain networks (Figure  2 ). The Wong-Wang neural mass model  (Deco et al. , 2014 ; Supplementary Figure  1 ), implemented with an optimized C code  (Schirner et al. , 2022) , was chosen to simulate local microcircuits activity, resulting from two populations of interconnected excitatory and inhibitory neurons coupled through NMDA and GABA receptor types. In our TVB simulations, this neural mass model was associated to each node of the network, while the SC matrix was used for the nodes interconnection. A set of parameters had to be tuned globally for each network: the global coupling (G), which is a scaling factor that represents the connections strength, and three synaptic parameters, i.
Statistical analysis Statistical tests were performed using SPSS software version 21. Optimal TVB parameters derived for each subject and for each network were tested for normality (Shapiro-Wilk) and then two control tests were performed to assess: (i) whether different networks presented a different E/I balance within the same clinical group (i. e. , evaluation of the inter-network E/I balance); and (ii) whether inter-networks E/I balance changed in healthy vs. pathological subjects. Two statistical tests were used: (i) univariate general linear model followed by bias-corrected accelerated Bootstrap  (Pek et al. , 2018)  to correct for age and gender differences in the groups and take into account non-Gaussian data distributions; and (ii) multivariate general linear model between the mean difference (i.
Funding The work performed at the  IRCCS Mondino Foundation  was supported by the  Italian Ministry of Health  ( RC2022-RC2024 ). The work performed at the  University of Pavia  was supported by  H2020 Research and Innovation Action Grants Human Brain  Project  785907  and  945539  (SGA2 and  SGA3 ) to ED'A, FP, and PR. Moreover, the project was supported by the  MNL Project "Local Neuronal Microcircuits" of the Centro Fermi (Rome, Italy) , # NEXTGENERATIONEU (NGEU)  and funded by the  Ministry of University and Research (MUR) , the  National Recovery and Resilience Plan (NRRP) , project  IR00011-EBRAINS- Italy  to ED'A;  Horizon2020 [Research and Innovation Action Grants Human Brain Project   945539  (SGA3)], BRC (# BRC704/CAP/CGW ),  MRC  (# MR/S026088/1 ),  Ataxia UK  to CW-K; PR acknowledges Digital Europe Grant  TEF-Health  # 101100700 ;  H2020 Research and Innovation Action Grant Human Brain Project  ( ICEI 800858 ,  EOSC VirtualBrainCloud 82642 ,  AISN 101057655 );  H2020   Research Infrastructures Grant  ( EBRAINS-PREP 101079717 ,  EBRAIN-Health 101058516 );  H2020   European Innovation Council  ( PHRASE 101058240 );  H2020   European Research Council   Grant  ( ERC BrainModes 683049 );  JPND ERA PerMed PatternCog   2522FSB904 ;  Berlin Institute of Health and Foundation Charité ;  Johanna Quandt Excellence Initiative ; and  German Research Foundation  ( SFB 1436 , project  ID 425899996 ;.
Building on these methods, we aimed to develop and validate deep learning approaches to identify land classes associated with the breeding sites of malaria mosquito vectors in West Africa. Data were assembled from multiple sites in Burkina Faso and Côte d'Ivoire to develop an approach able to generalize to different malaria-endemic landscapes. We developed a land classification system based on habitats of interest for the breeding ecology of the malaria vector An. funestus and An. gambiae, which are present in the study sites. We used RGB drone images from the study sites to build a training dataset and to implement two CNN-based frameworks using the U-Net and attention U-Net architectures to identify features of interest for Anopheles breeding. The specific objectives of this study were to (i) collect and assemble a labeled dataset of drone images in malaria-endemic areas in Côte d'Ivoire and Burkina Faso; (ii) develop a protocol to label land classes of interest based on the local vector ecology; (iii) assemble a labeled dataset for each class and (iv) train, validate and test the U-Net and attention U-Net deep learning architectures.
Similarly, in Bouaké, we conducted targeted drone surveys from June to August 2021 using a DJI Phantom 4 Pro drone to collect RGB data at a 2 cm per pixel resolution, as described by  [2] ; 77 images were used in this study. This area was also rural and dominated by small-scale agriculture; however, this site had different vector compositions, including high densities of An. funestus. For both sites, drone images were processed using Agisoft Metashape Professional (Agisoft: https://www. agisoft. com/ (accessed on 19 April 2023)). The steps performed were photo alignment (using high accuracy, selecting the generic preselection, reference preselection and adaptive camera model fitting options; the key point was set to 40,000), building a dense point cloud (high quality and moderate depth filtering), building a digital elevation model (extrapolated option) and finally performing the orthomosaic generation.
Image Labeling and Development of Labeled Dataset We identified specific land cover classes of interest associated with Anopheles breeding sites, including water bodies and irrigated agricultural land types  [2]. Within these settings, crops, roads and tillage areas were identified as land types with a high probability of containing small water bodies where An. gambiae breeds, the predominant vector in the Burkina Faso site. For An. funestus, a dominant vector in areas of the Cote d'Ivoire site, we differentiated between vegetated and non-vegetated water bodies as An. funestus is most commonly identified only in vegetated water bodies. We additionally identified habitat types associated with human activities, including roads and buildings. The final land cover classes of interest for this analysis included vegetated and non-vegetated water bodies, irrigated crops (planted vegetation with no tree cover), tillage (land cleared for planting crops), buildings and roads.
While this classification cannot specifically identify whether or not an area is an Anopheles breeding site, this provides the basis for targeting future entomological surveys and wider epidemiological studies on how landscape impacts malaria transmission. To identify these classes using a supervised deep learning approach, we first needed to assemble a dataset of harmonized labeled images. We manually labeled a total of 103 drone acquisitions to generate gold-standard (i. e. , labeled images) masks for each of the specific land classes. Trained personnel familiar with the study area validated the labels. This process was performed using two different tools: GroundWork (GroundWork: https://groundwork. azavea. com (accessed on 19 April 2023)) and QGIS (QGIS: https: //qgis. org/site/forusers/download.
html (accessed on 19 April 2023)). The former is a cloud-based licensed tool where the images are uploaded for labeling. In this cloud-based interface, a grid is overlaid in the image as illustrated in Figure  2A. The user is then able to select one of the predefined classes to assign the corresponding class over the images, as shown in Figure  2B. While this tool has a streamlined workflow to facilitate labeling, this required internet access, had a limited data allowance and was not suitable in all contexts. Therefore, we also used QGIS, which, on the other hand, is a Geographic Information System (GIS) open-source desktop application that allows one to input geo-referenced images and annotate them manually using polygons (Figure  3 ). Images labeled with each software were randomly selected.
The obtained polygons from both tools were checked to detect invalid polygons and were corrected manually. Once all the images were labeled, ground truth masks were created for the supervised image classification. As shown in Figure  4 , first, we create a subset of vector layers, one for each class. Then, we rasterized the vector layers to create a separate raster image for each class. The land class presence depended on the acquisition site's characteristics. For example, Figure  5  shows a labeled region from Burkina Faso where all the land classes are present; however, this was not the case for all the images. Images that did not contain labeled polygons were not considered in this study. We built six land class datasets: crops, tillage, roads, buildings, vegetated water bodies and non-vegetated water bodies (defined at the beginning of this section).
For each dataset, we selected only the drone images that contained labels of the corresponding land class. From each dataset, we randomly selected images for training, validation and testing. To avoid bias related to the training data, we considered using a 3-fold cross-validation scheme. (A) (B) Due to GPU memory constraints, the entire drone image and its corresponding labels were split into several patches, which were used to train, validate and test the deep learning models. In addition, because of the variable extension of each labeled polygon, splitting the image in a grid pattern resulted in a highly unbalanced dataset where the background predominated over the class of interest. Instead, we used a new approach for data patching and augmentation based on ROI shifting, described in [43], to avoid this imbalance.
This method also prevented potential bias caused by the network focusing on the background instead of the class of each patch. The augmentation aimed to train the models robustly in the presence of variable neighborhood context information. Thus, we identified the ROIs and framed them in rectangles, which could contain one or more polygons, as illustrated in Figure  6. Patches of 256 × 256 and 512 × 512 pixels were extracted from these rectangles and assigned to each training, validation and testing dataset. A summary of the number of images and patches in each dataset is reported in Table  2. For each patch size, we report in Table  3  the average percentage of the class present in the dataset. This was computed, per patch, as the ratio of the pixels belonging to each class and the background pixels.
Patches with classes representing less than 10% (256 × 256) or 20% (512 × 512) of the patch size were eliminated from the datasets. The total number of patches obtained for each class differed by study site; while Burkina Faso contained more tillage areas, Côte d'Ivoire had more irrigated crops. Considering both sites, water body datasets contained the least number of patches. On average, non-vegetated water bodies, tillage and crops covered greater percentages of patches as these classes were more likely to be larger.
Algorithm Development We developed a multi-step approach to classifying multiple land classes from patches, as shown in Figure  6. Following the dataset preparation, two deep learning segmentation models were selected: U-Net and attention U-Net. U-Net is a widely used architecture for semantic segmentation tasks. This method relies on the upsampling technique, which increases an image's dimensions (i. e. , the number of rows and/or columns). Thus, the present method builds on a conventional network with successive layers by using upsampling operators to replace pooling operations, which implies using contraction and expansion paths (i. e. , encoder and decoder, respectively). The contraction part reduces the spatial dimensions in every layer and increases the channels. Meanwhile, the expansive part increases the spatial dimensions while reducing the channels.
Finally, using also skip connections between the encoder and decoder, the spatial dimensions are restored to predict each pixel in the input image. An important modification in U-Net is that many feature bands are in the upper sampling part, allowing the network to propagate context information to higher-resolution layers. Consequently, the expanding trajectory is more or less symmetric to the contracted part and produces a U-shaped architecture  [44]. Attention U-Net adds a self-attention gating module in every skip connection of the U-Net architecture, without increasing the computation overhead. These modules are incorporated to improve the sensitivity and accuracy and add visual explainability to the network. The improvement is performed by focusing on the features of the regions of interest rather than the background  [45, 46].
The quantitative results to evaluate the performance of the attention U-Net using patches of 256 × 256 pixels are reported in Table  5. Comparing these results with the ones obtained with the 256 × 256 U-Net model, we observe that the vegetated and nonvegetated water body classes had improved performance. Despite this, the first fold of the non-vegetated water body class showed a low Dice value (below 5%), meaning that the training was unstable across the three folds and may have impacted the inference process due to the different data distributions present in the datasets. Therefore, cross-validation provided insights into the robustness and stability of the trained models.
Importantly, this approach is generalizable and could be used in a range of vector-borne disease-endemic contexts to identify the presence of habitats of interest that are relevant to the local land cover and local vector ecology. One of the key advantages of drone data is that they allow user-defined time points to characterize features over time. This study was predominantly cross-sectional, aiming to classify land types from labeled drone images from specific points in time. As one of the key aims was to categorize potential breeding sites for the malaria vector An. funestus, which breeds in large, semipermanent to permanent water bodies, breeding sites are less likely to vary throughout the seasons. However, these methods could be repeated to reclassify potential habitats for specific seasons or time points.
While we used data from multiple sites in West Africa, these do not indicate the full range of habitats within this region or seasonal changes. Future studies could integrate data from other sources to develop more representative datasets. Additionally, limited amounts of ground-truthed data were available from these study sites, and there were insufficient data on larvae presence or absence to predict whether specific land classes contained Anopheles larvae. If data were available, this framework could be extended to predict the presence or absence of specific species. Despite these limitations, this study developed a methodology to automatically detect potential mosquito breeding sites. Although data labeling is highly labor-intensive, this classifier can rapidly analyze RGB drone images collected using small, low-cost drones.
Funding: This work was supported by a  Sir Henry Dale fellowship  awarded to K. M. F. and jointly funded by the  Wellcome Trust and Royal Society  (Grant No. 221963/Z/20/Z ). Additional funding was provided by the  BBSRC  and  EPSRC   Impact Accelerator Accounts  ( BB/X511110/1 ) and the  CGIAR Research Program on Agriculture for Nutrition and Health  ( A4NH ). K. C. and J. L. are also partly supported by  UK  aid from the  UK government (Foreign, Commonwealth & Development Office-Funded RAFT [Resilience Against Future Threats] Research Programme Consortium).
Results And Discussion Genetic differentiation between wild and cheese strains of Geotrichum candidum We collected and sequenced the genomes of 88 G. candidum strains with Illumina technology and included in our analyses ten available genomes. Our dataset included 61 strains isolated from different kinds of cheeses (semi-hard, mold-ripened, smeared soft and fresh goat cheeses), 16 industrial strains used for cheese-making, seven strains from dairy products, four strains from other food substrates (e. g. , sausage or vegetables) and 10 wild strains (e. g. from soil or plant) (Table  S1 ). We identi ed 699,755 SNPs across the 98 strains by mapping against the CLIB 918 reference genome (cheese strain). The maximum likelihood tree, principal component analysis (PCA) and neighbor-net (SplitsTree) analyses all identi ed the same three clades (Fig.
We observed reticulations in the neighbor-net within and, to a lesser extent, between populations; only the Cheese_2 population lacked reticulations (Fig. 1A ). As previously mentioned, the 16 commercial starter strains in our G. candidum dataset were scattered in the maximum likelihood tree (in yellow, Fig. 1B. a. ) and we detected above footprints of recombination in the Cheese_1 and Cheese_3 populations (Fig. 1A , Figure  S3 ); the situation in G. candidum is thus very different from the use of a single clonal lineage in commercial cheeses as in both P. camemberti and P. roqueforti. The bottlenecks in G. candidum have been milder, similar to those reported in P. biforme and the Roquefort P. roqueforti population  (Dumas et al. , 2020; Ropars et al. , 2020). We nevertheless detected a few groups of clonemates, by the lack of branches in the trees and the presence of fewer than 1,200 SNPs between strains (Fig.
83 nM and the P6 v2 polymerase was bound at 0. 50 nM while the sequencing primer was annealed with sequencing primer v4 at a nal concentration of 1 nM and the Sequel 3. 0 polymerase was bound at 0. 5 nM. The libraries were sequenced on a PacBio RS II instrument at a loading concentration (on-plate) of 160 pM using the MagBead OneCellPerWell loading protocol, DNA sequencing kit 4. 0 v2, SMRT cells v3 and 4 hours movies. Raw PacBio reads were corrected using Illumina reads already available and described in a previous article  (Perkins et al. , 2020) , with the default parameters of the LoRDEC software and trimmed with Canu v1. 6  (Koren et al. , 2017; Salmela and Rivals, 2014). Corrected and trimmed PacBio reads were then assembled using Canu v1. 6. Illumina polishing of the Canu assembly was performed using Pilon v1.
22  (Walker et al. , 2014). A nal assembly step was then performed with the hybrid assembler SPAdes v3. 11. 1 using the trimmed PacBio reads, the Illumina reads and the Pilon corrected assembly as trusted contigs  (Antipov et al. , 2016; Prjibelski et al. , 2020). Additionally, the CLIB 918 assembly (Bioproject PRJEB5752) was used as a reference in the SPAdes script for the assembly of each G. candidum genome  (Morel et al. , 2015). Scaffolds were ltered using the khmer software with a length cut-off of 1,000 bp  (Crusoe et al. , 2015). The LMA-244 PacBio assembly and reads have been deposited in GenBank: nbPROJECT. To annotate short read assemblies and the LMA-244 genome, gene prediction was performed using Augustus v3. 4. 0  (Stanke et al. , 2008). The training annotation le "saccharomyces" was used, with parameters as follows: "--gff3 = on", "--protein = on", "--codingseq = on", "--exonnames = on", "--cds = on" and "--uniqueGeneId = true".