dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of accuracy of results 239 To assess the accuracy of PâturSTICS' developments, its predictions of grazing features were 240 compared to literature data and expertise. These features include the proportion of DMY that 241 is grazed vs. cut, the number of grazing days, expressed in livestock unit (LSU) grazing days 242 per ha of grassland per year  (Peyraud and Delaby, 2008) , and animal N returns. An LSU 243 grazing day (hereafter referred to as "grazing day") equals one day's grazing by a standard 244 dairy cow. Grazing days were calculated by dividing the total herbage removed by animals by 245 daily animal intake, which was set to 17 kg DM LSU -1 grazing day -1. We also analysed other 246 annual management variables (N fertilisation, number of grass defoliations) that were 247 influenced by the model's decision rules.
For each tree, the data are recursively split into the two most 280 homogeneous subsamples based on a threshold value of one explanatory variable identified to 281 best split the data. The split points are commonly called nodes. The source data are 282 bootstrapped to randomly generate a large number of random trees that are averaged and then 283 used to predict the continuous variable. The importance of explanatory variables in the 284 prediction is assessed by how often they were selected for the splits and how much they 285 increase the prediction error of the average tree after permutation of their values. 286 Ten continuous variables were selected to be both a priori explanatory of grassland DMY and 287 NY and easily accessible to stakeholders, while avoiding correlations between variables.
They 288 related to climate, soil, vegetation and grassland management (Table  3 ). "Age" refers to 289 grassland age during the simulation of a grassland sequence and varied from one year to the 290 maximum grassland duration). These variables were used for training RF models to predict 291 DMY and NY, using the R ranger package  (Wright and Ziegler, 2017) , which is particularly 292 suited for high-dimensional data. We used default algorithm values for the number of trees 293 (num. trees = 500), set the number of explanatory variables to one-third of all available 294 explanatory variables (mtry = 3), and restricted the minimum size of nodes to its default value 295 for regression models (min. node. size = 5). The variable importance available from the ranger 296 R package and calculated by permutation was used to rank the explanatory variables.
297 To ensure fair comparison of RF metamodels to PâturSTICS predictions, we used a random 298 two-thirds of the dataset to train RF models and the remaining one-third to evaluate 299 performances of RF metamodels. How well the metamodel predicted PâturSTICS predictions 300 was assessed by combining a graphical and statistical approach, using common indicators for 301 validation of biophysical models  (Bellocchi et al. , 2010 ; Wallach et al. , 2018). The indicators 302 were i) root mean square error (RMSE, Eq. 1), which is a quantitative measure of distance 303 between observed and simulated values; ii) its relative value (RRMSE, Eq. 2), which 304 expresses error as a percentage of the mean measured value; iii) Nash-Sutcliffe model.
Three-quarters of French 368 grasslands produced and exported at least 7. 6 t DM ha -1 yr -1 and 172 kg N ha -1 yr -1 , 369 respectively. One-quarter of French grasslands produced and exported at least 10. 12 yr -1 and 254 kg N ha -1 yr -1 , respectively (Figure  5A , Table  2 ). These grasslands benefit from 371 environmental conditions favourable for grass growth, with the oceanic climate in north-372 western regions of France: Bretagne (mainly Finistère (  29  RF metamodels successfully predicted grassland DMY and NY in the test datasets that were 387 not used to train the model, explaining 95% and 97% of variance, respectively, with good 388 agreement between predictions of RF metamodels and PâturSTICS (Table  4 ; Figure  6 ). For 389 predicted DMY, RF metamodels had RMSE of 0. 77 t DM ha -1 yr -1 and RRMSE of 21.
Implementation of simulations 429 The simulation plan was designed to consider the diversity of existing soil and climate 430 conditions, grasslands and management situations in France. It could be built due to the 431 existence of other studies (i. e. French Evaluation of Ecosystems and Ecosystem Services 432 (EFESE);  Thérond et al. , 2017)  and several detailed French databases for the definition of 433 soils (1:1,000,000-scale soil geographic database of France,  Jamagne et al. , 1995) , climates 434  (SAFRAN system, Durand et al. 1993) , management types (statistical agronomic surveys on 435 grasslands and the ISOP system,  Ruget et al. , 2006)   14 have a high spatial resolution of outputs and a reasonable time to build the simulation plan 439 and calculate results led us to reuse these databases while avoiding updating management 440 information, which would have been time-consuming.
487 This module is based on known relationships between animal N returns during grazing, 488 animal herbage DM intake and herbage N content  (Delaby and Lucbert, 1999) , all of which 489 respond to N availability (including fertilisation)  (Delaby et al. , 1997; Delaby, 2000). 490 Predicted faecal and urinary N returns lay in the expected range of observations from the 491 literature (i. e. 30-120 and 60-350 kg N ha -1 yr -1 for 400-1000 grazing days, respectively) 492  (Delaby et al. , 1997 , Vertès et al. , 2018). 493 Like most grassland simulation models, PâturSTICS assumes uniform spatial return of faecal 494 and urinary N to the soil, as representing their spatial and temporal distribution in models 495 remains a challenge  (Hutchings et al. , 2007; Selbie et al. , 2015; Snow et al. , 2009).
Whiskers represent 1. 5 times the interquartile range. See Appendix 789 C for meanings of region abbreviations. 790 791 Figure 6. Random forest (RF) model performance for test datasets assessed by comparing RF 792 metamodel and PâturSTICS predictions of dry matter yield (DMY) and nitrogen yield (NY). 793 Dashed lines are 1:1 lines, while solid blue lines are linear regressions between RF 794 metamodelled and PâturSTICS predictions. 795.
In (A), the solid vertical line refers to the median and the 787 dashed vertical lines to the first and third quartiles. Corresponding values are indicated to the 788 right of each vertical line. Whiskers represent 1. 5 times the interquartile range. See Appendix 789 C for meanings of region abbreviations. 790 791 Figure 6. Random forest (RF) model performance for test datasets assessed by comparing RF 792 metamodel and PâturSTICS predictions of dry matter yield (DMY) and nitrogen yield (NY). 793 Dashed lines are 1:1 lines, while solid blue lines are linear regressions between RF 794 metamodelled and PâturSTICS predictions. 795.
and grassland types and ages(Graphical  436Register of Fields, last agricultural census). The present study seems difficult to transfer to 437 other European countries, where equivalent databases are often not available. The need to Comment citer ce document : Graux, A. -I. , Resmond, R. , Casellas, E. , Delaby, L. , Faverdin, P. , Le Bas, C. , Ripoche, D. , Ruget, F. , Therond, O. , Vertes, F. , Peyraud, J. -L. (2020). High-resolution assessment of French grassland dry matter and nitrogen yields. European Journal of Agronomy, 112, 125952. , DOI : 10. 1016/j. eja. 2019. 125952.
SENSOR SETUP AND SYSTEM TRAINING Two critical tasks to build a suitable vision-based system for automated image content analysis are the sensor setup and the system training. The former refers to choosing the right number of cameras, their locations, orientations, and parameters such as the field of view, to guarantee the complete coverage of the areas of interest, but also considering the economic viability. Regarding the system training, current state-of-the-art DNN-based methods need to be trained with very large datasets, with over 1M labeled images. Collecting such amount of labeled data can be challenging and even prohibitive. Training techniques that rely on the use of DNN models that have been pre-trained on generic data, such as "transfer learning" and "fine-tuning", allow mitigating the lack of labeled data.
Generic data means many kinds of objects "in the wild", not highly specialized, and coming from different contexts, like those from generalist datasets such as ImageNet  (Russakovsky et al. , 2015) ,  COCO (Lin et al. , 2014)  or Open  Images (Kuznetsova et al. , 2020). This way the pre-trained features would be adapted to the new data, potentially achieving meaningful improvements. Recently, Kolesnikov et al. (2020)  revisited this kind of techniques and proposed a simple recipe, named "Big Transfer" (BiT), which exploits large scale pre-training to yield good performance on downstream tasks of interest. Even though best results are obtained by training the system with a large dataset specifically designed to train the DNNs with the expected kind of images, considering such pre-trained model-based training techniques is highly relevant for the development of the system.
Simulation Tool A proper simulation tool can alleviate the effort and cost of these two tasks. Furthermore, the simulation tool can resolve legal or privacy issues, which often make the use of real data hard or impossible. However, there are no general simulation tools ready to solve them for all kinds of scenarios and use cases. Normally, 'ad-hoc' tools are built. They require integrating adequate 3D graphical assets, virtual camera models, and illumination functionalities configured employing user-friendly parameters. For the camera setup task, the tool would allow visualizing directly the 3D scene from the virtual camera viewpoints, with camera-related effects, such as the geometric distortion introduced by the lens, while changing in real-time their parameters. For the labeled dataset generation, the tool would allow configuring scenes for a given camera setup, with a wide and balanced range of plausible situations of interest randomly generated.
It would involve the required 3D graphical assets, which should be quickly captured from the virtual camera viewpoints, along with the adequate annotations and noise, for training. Domain Adaptation techniques could be applied to guarantee successful training with data that combines synthetic and real images, as real-world images are more complex than simulated ones. These techniques try to solve the domain shift that exists between different groups of data, for example by learning domain-invariant features  (Pinheiro, 2018 , Chen et al. , 2018)  or translating the data from a domain to another one  (Inoue et al. , 2018). The basis of the simulation tool could be a 3D computer graphics software such as 3DS Max or Blender or a game engine such as Unreal or Unity, which not only allow managing 3D graphical assets, virtual cameras, and illumination conditions interactively but also controlling all of these utilizing programming scripts.
ALGORITHMS DEPLOYMENT To fulfill the system's requirements including those related to airworthiness certification, our criteria for the AI-processor scouting is that it should be compact, fanless, powerful enough, efficient, with the highest possible support for deploying cutting-edge DNN-based computer vision and machine learning algorithms, easily integrable in the system's architecture, low-cost and easily replaceable by upgraded versions without affecting the software development, if required. To support the AI-processor scouting process we rely on MLPerf Inference  (Reddi et al. , 2019) , which is a relevant benchmark suite in the machine learning community for measuring how fast machine learning systems can process inputs and produce results using a trained DNN model. It was designed with the involvement of more than 30 organizations as well as more than 200 machine learning engineers and practitioners to overcome the challenge of assessing machine learning-system performance in an architecture-neutral, representative, and reproducible manner, despite the machine learning ecosystem's many possible combinations of hardware, machinelearning tasks, DNN models, data sets, frameworks, toolsets, libraries, architectures, and inference engines.
The design and implementation of MLPerf Inference v0. 5 consider image classification and object detection vision tasks with heavyweight and lightweight DNN models in different scenarios such as multi-stream processing, relevant in our context. In such a scenario, a traffic generator sends a set of inferences per query periodically (between 50 and 100 ms). Among the performance results published in MLPerf Inference v0. 5, the NVIDIA Jetson AGX Xavier shows good potential for our purpose. It is an embedded system-on-module (SoM), thus, compact, with powerful and efficient GPU/CPU and connectivity capabilities, leveraged by NVIDIA's TensorRT software tool for high-performance DNN inference. More recently,  Libutti et al. (2020)  proposed some adaptations to MLPerf to handle new USB-based inference accelerators, more specifically, Intel Movidius Neural Compute Stick 2 (with an Intel Movidius Myriad X VPU) and Google Coral USB accelerator (with a Google Edge TPU).
They evaluated the ability of these USB-based devices to fulfill the requirements posed by applications in terms of inference time and presented a mechanism to measure their power consumption. Their results show good potential too for our purpose. VPUs and Edge TPUs can also be found in other kinds of hardware architectures (e. g. , AI-accelerator cards) with faster transmission speeds than USB 3. 0, and the software tools that power them, i. e. , OpenVINO for Intel chips and TensorFlow Lite supported by the Edge TPU compiler for Google chips, like in the case of TensorRT, have remarkable resources and active communities online, as well. Hence, these are examples of potentially good options to consider for the development and deployment of the algorithms and DNN models.
Currently, the major open-source deep learning frameworks are TensorFlow and PyTorch, which count with big support not only from the companies that develop and maintain them (Google and Facebook, respectively) but also from researchers and practitioners around the world. Thus, there are many pre-trained generalist DNN TensorFlow and PyTorch models available on the Internet, especially for image classification and object/person detection tasks with generalist datasets, which can also be converted to 3rdparty inference engines for the optimal deployment of DNNs in AI-processors like those relevant in our context (e. g. , for NVIDIA's TensorRT and Intel's OpenVINO). Testing pre-trained generalist DNN models for object/person detection with images such as those shown in Figure  1 , could be seen as quite simple, but it can help us clarify important aspects for the design and development of the TRL3 DNN-based approach for the system, like: ▪ How do image transformations like resizing and rotating affect the pre-trained generalist DNN responses? ▪ Considering lens distortions and that the aspect ratios of the DNN's input and the image can be quite different, is it convenient to process the full image or cropped image regions? For these preliminary tests, we have chosen the SSD-MobileNet-v1-FPN model, which has a good trade-off between accuracy and performance among those publicly available at the TensorFlow Object Detection API webpage  (Huang et al.
, 2017) , trained with the generalist COCO dataset. Nevertheless, the object detection field is constantly progressing, and newer more accurate, efficient and smaller DNN models are being proposed, such as EfficientDet  (Tan et al. , 2020) , released on March 18, YOLO-v4  (Bochkovskiy et al. , 2020) , released on April 23, and the controversial  YOLO-v5 (Jocher, 2020) , released on June 9, and will probably continue so during the following months and years. Similar tests could be done with these models too for the same purpose. We have processed images such as those shown in Figure  1  in two ways: (1) the full image with padding added to maintain the aspect ratios (the DNN expects square input sizes), and (2) the image cropped in 7 regions, also with the same kind of padding for each region.
In both cases, the object detector receives images at different sizes (640x640, 1280x1280 and 1920x1920 for full images; 640x640 and 960x960 for cropped images) and orientations (0º, 90º, 180º and -90º). Figure  2  and Figure  3  show some examples of the obtained detection results. In the COCO dataset images are not as distorted as in these images, and therefore, objects/people seen from the camera over the corridor are better detected, as they are further away and less distorted. It can also be observed that the bounding boxes in some cases are correctly placed for the object's real boundaries, but the classification is incorrect. This behavior was expected since COCO contains many classes out our context (e. g. , "car", "sink", "toilet", "stop sign", "mouse", "traffic light", "toaster", etc.
), and therefore the DNN has learned "noisy" visual features from them and has more chances to misclassify. Overall, the class that is better detected is that of "person", even when people are partially occluded and located in more distorted image regions. Another interesting behavior is that some objects are correctly detected in some image orientations but not in others. Probably this happens because the data for training was not sufficiently balanced for this factor, and thus, the learned visual features for some objects work better for some orientations compared to others. It can also be observed that the chosen input size also plays an important role as, depending on it, bigger and/or smaller objects/people can be missed. The reason for other misdetections could also be because COCO does not contain so many top view perspectives of objects/people.
CONCLUSION AND FUTURE WORK In this position paper, we have analyzed and discussed the main technological factors that system designers should consider for building a camerabased smart sensing system for digitalized ondemand aircraft cabin readiness verification. Our purpose is to make system designers be aware of the relevant technological factors for that and assist them with the TRL2 to TRL3 transition while designing their DNN-based approaches that would allow building camera-based systems that could reach TRL9. These include the sensor setup, system training, the selection of appropriate camera sensors and lenses, AI-processors, and software tools for optimal image acquisition and image content analysis with DNN-based recognition methods. For the sensor setup and system training, we consider that a proper simulation tool would be helpful, along with Domain Adaptation techniques to guarantee successful training with data that combines synthetic and real images.
3296-3297 ). https://github. com/tensorflow/models/tree/master/research/object_detection Inoue, N. , Furuta,  R. , Yamasaki , T. and Aizawa, K. , 2018. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the  IEEE/CVF Conference on Computer Vision and Pattern Recognition  (pp. 5001-5009 ). Jocher, G. , 2020. YOLOv5. https://github. com/ultralytics/yolov5Kolesnikov, A. , Beyer, L. , Zhai, X. , Puigcerver, J. , Yung, J. , Gelly, S. and Houlsby N. , 2020. Big Transfer  (BiT): General visual representation learning. arXiv preprint arXiv: 1912. 11370. Kuznetsova, A. , Rom, H. , Alldrin, N. , Uijlings, J. , Krasin, I. , Pont-Tuset, J. , Kamali, S. , Popov, S. , Malloci, M. , Kolesnikov , A. , Duerig, T. and Ferrari, V. , 2020. The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale.
Eclipse Steady, the open source implementation of our code-centric and usage-based approach is the tool recommended to scan Java software products at SAP; it has been successfully used to perform more than one million scans of about 1500 applications. In this paper we report on the lessons learned when maturing the tool from a research prototype to an industrial-grade solution. To evaluate Eclipse Steady, we conducted an empirical study to compare its detection capabilities with those of OWASP Dependency Check (OWASP DC), scanning 300 large enterprise applications under development with a total of 78165 dependencies. Reviewing a sample of the findings reported only by one of the two tools revealed that all Steady findings are true positives, while 88. 8% of the findings of OWASP DC for vulnerabilities covered by our code-centric approach are false positives.
Introduction The use of OSS libraries as well as the number of available libraries are ever-increasing: Synopsys Black Duck (2019) reports that over 96% of the applications they analyzed include OSS libraries, whose code often weighs more than 50% of the average code-base;  Snyk (2019)  reports a growth of 102% in the number Java OSS libraries available in Maven Central and of 40% for Python libraries in the Python package index  (Pypi). Though the use of OSS libraries speeds up development, it comes at a cost, as vulnerabilities discovered in OSS libraries may affect the dependent applications. While using OSS components with known vulnerabilities is included in the OWASP Top 10 Application Security Risks since 2013  (OWASP Foundation 2013 , 2017) , the problem is still far from being solved.
To overcome the before-mentioned issues, the providers of commercial tools and services usually maintain proprietary vulnerability databases that map vulnerabilities onto the affected artifacts; however, the creation and maintenance such mappings requires substantial human effort that is multiplied across vendors and whose results are error-prone. Furthermore, merely detecting the inclusion of vulnerable libraries does not cater for the needs of the entire software development life cycle. In the early phases of development, updating a library to a more recent release is relatively unproblematic, because the necessary adaptations in the application code can be performed as part of the normal development activities. On the other hand, as soon as a project gets closer to the date of release to customers, and during the entire operational lifetime, all updates need to be carefully pondered, because they can impact the release schedule, require additional effort, cause system downtime, or introduce new defects.
This result shows the advantages of our codebased approach. Eclipse Steady can also use manually-provided annotations to specific library versions, based on which it can detect vulnerabilities that are fixed without changing the source code (e. g. , erroneous default configuration). For those we obtained 843 findings reported by both tools, 27 only by Steady (all true positives), and 1728 only by OWASP DC. A sample review of the latter showed that 63. 3% are true positives. Most of them are not found by Steady as they affect libraries re-bundling the affected one. Though Steady did not report any false positive, the presence of false negatives shows the limitations of relying on manual annotations. The remainder of the paper is organized as follows: Section 2 describes the technical approach, Section 3 defines the update metrics, Section 4 illustrates our approach in practice, and Section 5 discusses the comparative empirical study.
Their vulnerability detection suffers from false positives and false negatives, and the maintenance of corresponding vulnerability databases is costly and error-prone. Besides precise vulnerability detection (cf. Section 2. 2), the code-centric approach also supports functionalities out of reach for approaches based on meta-data, especially the analysis whether vulnerable code can be executed in the context of a given application (cf. Sections 2. 3 to 2. 5), which is needed in order to prioritize findings, as well as update metrics considering the actual library use with the goal to reduce regressions (cf. Section 3). Compared to previous works, Sections 2. 1, 2. 2 and 2. 3 generalize  (Plate et al. 2015) , and Sections 2. 2, 2. 4, 2. 5 extend it with unique novel contributions. In particular Sections 2.