dataesr/scientific-paragraphs-categorization huggingface dataset:
B. COGNITIVE SECURITY PERFORMANCE In this section we describe the C4IIoT framework performance with regards to the BACS evaluation (anomaly detection). We evaluate both use casesm Smart Logistics and Smart Factory, in order to validate our approach. For the Smart Logistics use case we operate in an unsupervised fashion, where we only measure the system response time and not anomaly detection accuracy. Here, we report that all our models successfully converge and behave normally during training. For the Smart Factory use case, we employ our synthetic anomaly generation process described in the previous section. In addition to the system response time, we also measure validation metrics (such as accuracy and F1-score) obtained in testing with the synthetic dataset. It is important to emphasize that BACS was designed to be compatible with any tabular dataset, and to work with both unsupervised and supervised data.
This was achieved through the definition of abstractions in the code which help with unknown data sources and making BACS configurable to adapt to these situations. All hyperparameters (such as epochs of training, learning rate, neural network architectures and so on) were decided though a trial-and-error process. A hyperparameter optimization process such as grid search would surely benefit the system, but it has not been performed due to time constraints. In the results ahead, there are a few notation specifics that we now explain for clarity. If there are several models with the same name, we also annotate them with the architecture layer they belong to (e. g. , TFAutoAD [Edge]). The TFAutoAD (TensorFlow autoencoder) has the same configuration in all the three architecture layers (edge, field gateway, cloud), but the layers themselves are different by design in operating with varying window lengths.
This means that the input to the models is not the same across the architecture layers, and that is why we add the layer designation in the naming conventions. The window lenghts used are 1, 5, and 10 for the Edge, Field Gateway and Cloud layers, respectively. The models having ''TF'' in their name are implemented in TensorFlow 2 (neural networks), ''SKLAD'' are implemented with scikit-learn library, ''PyOD'' with the PyOD library, and ''DPL'' with the diffprivlib library. In Table  2  we present performance for all the supported BACS models. Column Inference Time represents total time to perform inference for the entire dataset, while the column Average Inference Time represents the average response time for a specific model. In the Smart Logistics use case, and in similar systems in general, we expect the models to have low response times (e.
g. , less than 100ms), which is achieved here. In Table  3  and Table  4 , we present similar results for the Smart Factory use case. The only exception is the Facebook Prophet (ProphetAD) model. As Prophet is a tool for univariate time series modelling, we had to create multiple   synthetic anomalies, with respectable accuracy and F1scores. It is important to mention that the F1-score is the main metric we use to compare models here, as accuracy can often be misleading in anomaly detection scenarios when working with imbalanced data (less anomalies compared to normal data). In the results table we can also see that the Principal Component Analysis (PCA_DPLAD) with differential privacy included performs well while the K-Means algorithm from the same family of algorithms fails to converge, mostly due to it being more suitable for unsupervised analysis (e.
C. SYSTEM RESPONSE EXAMPLE: DATA MANIPULATION ATTACK A data manipulation event corresponds to the scenario when the data is changed either in transit or at rest. This event is detected by comparing the hash of the data against the hash stored on the blockchain element of DAC. The hash of the data is stored on DAC at the Field Gateway for the Smart Logistics data and at the Edge for the Smart Factory use case. This hash is checked before the data are stored, by the storage connector, and if there is a mismatch a data manipulation event is reported, which is the case where the data have been somehow changed in between the different layers and components of C4IIoT (in transit). An event like this would suggest that a certificate which allows access to the DFB (Data Fusion Bus) has been compromised and therefore has to be invalidated.
On the other hand, for the singular anomalies, both model types performed similarly well. The Apache Kafka message queue as one part of the data fusion bus (DFB) provided a solution for communication between modules which was easily adopted by the other modules using high level programming languages but it is more challenging for other technologies. Kafka Mirror makers are a good solution where Kafka cannot be deployed as a cluster, but only if the flow of the data is one way. The SDN Controller uses the OpenFlow protocol that is not flexible enough and contains some ambiguities. However, the SDN switches have to implement the standard following their interpretations of it. This leads to incompatibilities between vendors. The SDN switches must be controlled by the SDN Controller. In order to do that, they listen for instructions on a port.
This has been identified as a potential security hole. There are several attack types which we left somewhat unexplored as they are not relevant to our concrete system and use cases. For example, real time monitoring and detection of ransomware attacks remains to be explored in our future work. Techniques such as auto-quarantine of malware or buggy software, full network communication halt of potentially compromised components can be used to somewhat reduce risks of data loss in such scenarios. To conclude, through our system definition, implementation and validation, we display our vision of a secure connected platform in smart factory and smart logistics environments. We considered concepts important in IIoT scenarios through procedures such as mitigation strategies, encryption, deep learning based anomaly detection, data validity, etc.
All patients were managed in accordance with the amended Declaration of Helsinki (http://www. wma. net/en/30publications/10policies/b3/) and gave their informed consent for the processing of personal data for scientific research purposes. The study was approved by the hospital's institutional review board. Demographic data, personal medical history, neurological and cardiovascular assessment, ongoing treatments, and follow-up events were collected in a centralized anonymized database. Patient ethnicity was determined via self-reported information collected at the time of hospital registration. Patients' vital status was controlled till May 10 th 2021 (time of statistical analysis).
Univariate and multivariate Cox regression models were fitted to assess the independent effect of predictors on time to CIE occurrence. Variables with significant association in univariate analysis (p < 0. 25) were retained for backward stepwise multivariate regression analysis. Associations were quantified using hazard ratios (HR) and 95% confidence intervals. All statistical analyses were conducted using SAS software 9. 4 for Windows (SAS Institute, Cary North Carolina, USA).
However, these approaches are not properly suited for severe class imbalance, which is precisely the case of crack detection. In fact, the problem of inaccurate annotations for crack detection is barely discussed in the literature beyond the use of tolerance margins for evaluation. However, a common practice to avoid overfitting in deep learning is data augmentation. This is particularly useful when available labeled data is scarce. Motivated by the difficulty to obtain annotated images for crack detection, offline data augmentation has been used too  (Mazzini et al. , 2020; Kanaeva and Ivanova, 2021). However, these approaches rely on inaccurate annotations, carrying the bias introduced by manual annotation. Unlike those approaches, our proposed tool -Syncrack-provides a way to create datasets with accurate labels.
Background Generation Our method (illustrated in Fig. 1 ) consists of 5 main steps: 1. Creating a 2D Perlin Noise. By using the noise module  (Duncan, 2018) , we create a noise map based on Perlin noise  (Perlin, 1985). The 'scale' parameter for the Perlin noise follows a normal law defined by the background average smoothness and standard deviation provided as user parameters to run the program. 2. Converting to color image. Public crack datasets contain RGB images; even though the material generally looks gray, the hue is not strictly zero. To emulate the appearance of real materials, two random, low saturated, colors are chosen to represent the darkest and the lightest regions in the Perlin noise. We merge these colors together with a weighted average using the Perlin noise as weight. 3.
Synthetic Datasets In this paper, we use 3 datasets (200 images each) obtained with the Syncrack generator using different user parameter values. Particularly, we modified 2 parameters: the background average smoothness (referred to as bas) and the crack average contrast (referred to as cac). The background smoothness intervenes both in the Perlin noise generation and the amount of added noise (see subsection 3. 1). The crack contrast modifies the relative contrast between the background and the added crack (see subsection 3. 1). By modifying these two parameters, we create Syncrack datasets with 3 difficulty levels as described in Table  1.
Difficulty level Easy Medium Hard Background smoothness (bas) 6. 0 3. 0 1. 5 Crack contrast (cac) 0. 5 0. 7 0. 7 The easy difficulty has a smooth background and well-contrasted cracks. The medium difficulty (default values) has rough textures with low-contrast cracks. The hard example has the same contrast as the medium one, but the background is rougher. The 3 datasets share the same cracks (see Fig. 4  for example). To study the effect of different label noise levels in annotations used for training, we create noisy versions of the 3 above mentioned datasets. Particularly, we introduce 5 label noise levels from 0 (no noise) to 4 (the maximum amount of noise) by varying the values of the np parameter (see subsection 3. 4). An analysis of the generated noise is presented in Table  2. The crack-to-background and background-to-crack mislabeling percentages are calculated with respect to the total number of crack pixels.
For example, with 100 crack pixels in the dataset: if 10 crack pixels were labeled as background, Crack→Background mislabeling (%) = 10;  if 5 background pixels were labeled as crack, Back-ground→Crack mislabeling (%) = 5. Mislabeling (%) is the sum of both percentages. To understand better the behavior of these percentages, we show the DSC, precision and recall with respect to the clean annotations too. Fig. 5  illustrates the different label noise levels for Fig. 4.
Real Road Images To validate the performance of models trained with Syncrack on real-life data, we use the CrackForest Dataset (CFD). This public dataset contains 118 images collected from urban roads containing perturbations such as shadows, oil spots, and water stains in Beijing, China  (Shi et al. , 2016). Similarly to the default size of the Syncrack generator, the original image size is 480×320. The cracks in this dataset have a width around 3 pixels, again similarly to the default Syncrack generator. This dataset provides two annotations: borders and segmentation. As suggested by  (Sun et al. , 2020) , we removed some images with clear severe annotation errors; we kept 108 images.
Training Setup We implemented the proposed architecture using Tensorflow 2. 1. 0. For training, input images are cropped to 256×256 patches and fed as 4-patch batches. We used the Adam optimizer with a 10 -4 learning rate and default parameters. The initial weights from the encoder are the weights from VGG19 pre-trained on ImageNet; from this starting point, the whole U-VGG19 is trained together. Each dataset is randomly split into 50% training and 50% validation. Our preliminary models trained on synthetic images failed to detect cracks within color outlier pictures. For example, bluish-looking pavement in contrast with the normal grayish colors. Since learning models are sensitive to color information, we wish to neglect the effect of color saturation due to the acquisition process. To do this, all images are converted to grayscale and converted back to 3 channels by concatenation.
To avoid overfitting to the distributions used by the Syncrack generator, we trained using data augmentation. Our data augmentation consisted of randomly transforming an image (and its corresponding annotation) immediately before feeding it to the neural network for training. To do this, a random value for each of the 6 following operations is chosen: adding noise, changing illumination, flipping, zooming, rotating and shearing. Every image undergoes the 6 operations in the given order. To refine the results at late epochs, we reduce the learning rate on validation loss plateau (by 2, with 5 epochs tolerance). To avoid overfitting, we add an early stop if the validation loss does not decrease during 20 consecutive epochs. We report the scores calculated on the validation split using the network weights with the minimum validation loss.
IMPROVING REAL-LIFE CRACK DETECTION WITH SYNCRACK To have a baseline for real data, we trained U-VGG19 with our CFD training split. Method F-score U-net  (Gao et al. , 2019)  60. 48% Table  6  shows the prediction scores of the models trained on CFD and on the 3 difficulty versions of Syncrack. For these experiments, we trained on the training split of each respective dataset and calculated the scores on the CFD validation split. The models trained on Syncrack were trained using accurate annotations. A plot of the supervised scores is shown in Fig. 10. We can see that the models trained with Syncrack datasets exhibit a higher precision than the model trained on real images. However, we see a decrease of recall. Since the decrease in recall is higher than the precision increase, the DSC of the models trained with synthetic data are lower than the one of the model trained with CFD.
Particularly, the model trained on the easy Syncrack has both the higher precision and the lower recall of all the datasets. As we increase the Syncrack difficulty, the precision decreases a bit but the recall sees a great increase. Specifically, the recall increases as we increase the Syncrack difficulty level. Since the easy Syncrack has only easy-to-detect cracks, a model trained with this dataset will struggle to detect cracks with low contrast or rough backgrounds. Thus, there is a high confidence in the pixels predicted as cracks but the model will miss hard-todetect cracks. As we increase the Syncrack difficulty level, the learned models will detect harder cracks (increasing recall) but will incur in more false positives (decreasing precision). The model trained with the hard Syncrack has a better trade-off between precision and recall, obtaining a DSC of 58.
26% in contrast with the 69. 32% obtained by training with CFD. This DSC difference is caused only by a decreased recall. Assuming that manual annotations tend to be wider than the actual cracks, a more precise segmentation will lead indeed to a lower recall. Fig. 11  shows the unsupervised scores from the models trained with the different datasets. We can observe that the models trained with Syncrack have better scores than the one trained on CFD. As we increase the Syncrack difficulty level, the entropies in the predicted crack regions increase and the K-S decreases. Even for the hard difficulty, these scores are better than for CFD. The hypothesis is that the decrease in recall is caused mainly by missing some pixels in the excessively wide annotations. A qualitative analysis of the predictions confirmed this: as illustrated in Fig.
Using these unsupervised scores as a basis, we evaluated on real-life images the performance of models trained on Syncrack-generated datasets. Our results show competitive scores in terms of precision with respect to real-data training. Although there is an apparent decrease of recall, this behavior is due mainly to the excessive width of manual annotations. This is shown by the unsupervised scores as well as the qualitative analysis. Thus, the models trained solely with our synthetically generated data are competitive with the model trained on real images; furthermore, they are more precise in terms of crack width. The Syncrack generator is available at: https:// github. com/Sutadasuto/syncrack generator. The code used to obtain our results is available at: https:// github. com/Sutadasuto/syncrack crack detection.
Global population diversity of Salmonella Livingstone Recently, unexpected cases of SL invasive infection were observed in clinical settings in mainland China (Table  EV1 ). Notably, five intractable cases (extraintestinal infections) were associated with the failure of empiric antimicrobial therapy. The causative agents were confirmed as SL (L39, L42, L43, L44 and 18B003) by whole genome sequencing (WGS). To investigate the genetic relationship of these problematic isolates, phylogenomic analyses were conducted using WGS for Chinese isolates, including 53 from this study and 11 from public domains, contextualised with 210 global isolates  (Fig 1A; Dataset EV1). Two hundred seventy-four isolates were from different sources (human, animal and food) and different geographical regions, mainly the U.
Notably, multiple Chinese SL isolates in C-1 and C-5 were identified in imported animal feed, i. e. , fish meal and rapeseed meal, from the U. S. , U. K. and Pakistan (Dataset EV1), indicating that international trade of animal feed is a source to introduce this bacterium into China. Besides, C-5 contained Chinese isolates from the blood and stools of patients with food poisoning and samples from different stages along the meat production chain, including animal feeds, animal carcasses at slaughterhouses and animal products in markets. The presence of phylogenetically related isolates along the meat production chain suggests farm-to-fork dissemination of SL, with animal feed as a critical vehicle being the initial source. Altogether these data suggest that animal feeds served as vehicles to introduce SL into China, likely for further dissemination via the food chain.
The sufficiency of the temporal signal was evaluated by a root-to-tip regression, resulting in removing the isolates from C-3 (n = 11) and C-4 (n = 4) with incongruent sampling date. Therefore, the remaining 258 strains were used to build the maximum clade credibility tree. Within C-5, strains were estimated to diverge into Clade-5-I (further as C-5-I-a, C-5-I-b, C-5-Ic) and  Clade-5-II in ~1980 (Fig 2B; Dataset EV2). Most Chinese strains belong to C-5-I-a and C-5-I-b, which mainly comprised MDR isolates and emerged in ~2005 and ~2000, respectively. Notably, five iNTS isolates belong to the emerging C-5-I-a and C-5-I-b (Table  EV1 ). We further investigated and compared antimicrobial-resistant genes (ARGs). Notably, Chinese isolates harbour more ARGs than isolates of other countries, with 46.
Clade-5 subgroups are regionally and phenotypically distinct To provide comparative contextual analysis, we studied SL isolates from the French surveillance system in  1951, 1959 and 2016-2022. We found that the majority (55. 0%) of French isolates belonged to C-5 (n = 107) and C-5-II (n = 89) in particular  (Fig EV4A; Dataset EV3). From 2016 to 2021, C-5 and C-2 are leading clones circulating in French population, which were independently detected in China. To investigate the relatedness of C-5 isolates between France and China, we projected the phylogenetic tree by including all ST543 isolates (Fig  EV4B ). We detected that Chinese isolates mainly come from C-5-I-a and C-5-I-b, while French isolates mainly come from C-5-II and C-5-1-c. Additionally, the European isolates, including French and U.
Notably, none of the isolates from the two clades killed mice after an oral challenge, whereas a dramatic difference in bacterial loads within their organs was detected. The bacterial load in the liver and cecum of infected mice was significantly higher for C-5 than that of C-1, both on day three and five postinfection, while no significant difference was observed in the spleen (Fig  3F ). Together, we found that the emerging Chinese C-5-I-a/b might be more virulent for vertebrates. To understand the genetic factors contributing to the virulence of C-5 isolates, we defined and compared the virulent genes (VGs) of isolates from each clade (Dataset EV4). Differences in VG profiles were observed between isolates from C-5 to C-1. Compared with the C-1 isolates, C-5 isolates, including iNTS isolates, contained sopD2 (a type III secretion system effector) and ratB (a colonisation factor) but lacked the lpf operon (bacterial fimbriae).
Except for the imported animal feed, the swine supply chain is a plausibly additional niche for MDR development, supported by MDR isolates from swine carcasses and the environment of slaughterhouses, as shown by previous studies  (Bonardi et al, 2016; Colello et al, 2018). The largest swine supply chain in China amplifies foodborne transmission, while fish meals are frequently supplemented as feed in the modern swine industry  (Kim & Easter, 2001). Notably, aquatic animals were also found to carry this pathogen (Dataset EV2;  Guerin et al, 2004). Extensive evidence documented this pathogen in seafood, fish, fish meals or feedstuffs from Northern Europe  (Katouli et al, 1994; Nesse et al, 2005) , Western Asia  (Nabbut et al, 1982)  and Southeast Asia  (Heinitz et al, 2000). Phenotypic and genotypic analyses indicate emerging C-5 isolates from China have evolved in a manner of enhancing pathogenicity.
Notably, the C-5 isolates displayed significantly elevated virulence in chicken embryos and mice compared with C-1 (Fig 3C  -E ). This is in concordance with the presence of C-5 specific VGs, including sopD2 and ratB (Dataset EV4), suggesting they may contribute to the invasive feature of C-5 isolates. Interestingly, deletion of sopD2 in S. Typhimurium showed decreasing bacterial replication within mouse macrophages and the spleen of infected mice  (Jiang et al, 2004). The presence of sopD2 in SL C-5 isolates might explain the higher bacterial load in the spleen of mice compared with C-1 (Fig  3F ). Besides,  Kingsley et al (2003) , demonstrated the ratB deletion mutant of S. Typhimurium showed a defect for colonisation of the mice cecum. Further study is needed for understanding clade-or subclade-specific virulence.
Bacterial strains and whole genome sequencing The primary dataset contains 274 SL genomes, including 221 publicly available genome sequences from  Enterobase (www. enterobase. warwick. ac. uk; Zhou et al, 2020)  and 53 isolates from China, which were obtained in the course of the current study. These 53 SL isolates were collected in mainland China between 1999 and 2018 from various locations, including hospitals, slaughterhouses, farms and markets (Dataset EV1). The cohort SL isolates (n = 211), with complete metadata, were provided by Institute Pasteur, France (Dataset EV3). One hundred ninety-six of these human isolates were recruited as part of routine surveillance by the French-national Salmonella reference laboratory between Jan 2016 and Jan 2022. The serum agglutination tests were performed on all examined isolates according to the Kaufmann-White classification to confirm the Livingstone serovar, as described previously  (Liu et al, 2021; Wu et al, 2021).
Statistical analysis GraphPad Prism 9. 0 software was used for statistical analyses of in vivo and in vitro experiments (i. e. , "Phenotypic comparison of growth rate, biofilm formation and resistance to desiccation and hydrogen peroxide" and "Pathogenicity assays in four animal models"). Details of statistical tests performed for each experiment are mentioned in the "Materials and Methods" and "Figure legends" sections. Expanded View for this article is available online.