dataesr/scientific-paragraphs-categorization huggingface dataset:
search for the desired feature, this criterion no longer appears. For this criterion, a significance coefficient of 6/10 has been assigned. For this criterion Onshape received a score of 9/10 and Fusion 360 received a score of 7/10. Accessibility depends on the conditions of use of the software: does the student have to install (heavy or light)? Does he need a computer with certain specifications? Does it need to have a highperformance internet connection? This criterion is so important to ensure smooth training for students, hence, it is very important for the student to be able to access the software from his personal computer, without encountering any problem. As a result, it was assigned a coefficient of 9/10. For this criterion, Onshape is rated 6/10 and Fusion 360 is rated 9/10. The "design" criterion links the software directly to its main use: design.
We are talking here about all the features related to design, and modelling workshops. The underlying questions relate to the possibilities offered by these software via their functionalities: what features are present or absent, and does this pose a problem in the design of the system? This criterion is the average of the volume, surface designs, and of the assembly, which are considered primary objectives of TN20 formation. As a result, a 10/10 coefficient was assigned for this criterion, the main objective of this study. The rating given to Onshape, after experimentation, is 7. 7 / 10 while Fusion 360 received a rating of 8. 3 /10. The criteria "software familiarisation" takes into account the time it takes for a new user to adapt to the software: does the software operate similarly on the features, and if so, does this make it easier to use? For example, we can note that on Onshape, the interface will often be the same on the different features: surface or volume, then Boolean operations, then we select the entities on which we work.
For this criterion a coefficient of 7/10 was assigned, for which Onshape received a score of 7/10 and Fusion 360 was rated 6/10. The criterion of collaboration is important since it is a very interesting additional functionality compared to traditional CAD software. In addition, the CAD learning subject is done in pairs at the University of Technology of Compiègne. This led us to determine that collaboration is an important criterion, especially as the cloud and the collaborative system are becoming more and more important in companies today. This is why it was assigned a coefficient of 8/10, on which Onshape and Fusion 360 got the same rating of 8/10. The conversion refers to the possible export and import formats. These formats allow interoperability between different software. In addition, in the context of an evolving digitization in an industry 4.
0, it seems to be a fairly important criterion as well as collaboration. Hence, this criterion was defined at the same level as the collaboration and it was assigned a coefficient of 8/10, on which the two software tested do not seem to be mature enough receiving a similar score of 6/10. The additional functionalities are just a plus. Hence, they were assigned a coefficient of 3/10. For this criterion Onshape received a score of 7/10 while Fusion 360 received a score of 8/10. We can notice, from Table  1 , that the scores are relatively close and that each software has its peculiarities. Thus, Fusion 360 has more features and is therefore easier to use for design, but is less intuitive than Onshape, which can lose a CAD novice.
CONCLUSION As part of the implementation of a geometric modeling software for mechanical engineering training at UTC and more precisely for the TN20 teaching unit, we launched the experimental project for two software Onshape and Fusion 360 , to study their capacities to be used by students generally working in pairs or even in trinomials. This experiment focused on the evaluation of the two software programs on certain criteria (intuitiveness, handling, accessibility, collaboration, design and conversion) which are, either from the literature review that we developed in the first section, are derived from the objectives of TN20. This study showed that Onshape has a more attractive side than Fusion 360, especially since its philosophy is the same for all features, so an error is quickly spotted.
Fusion 360 is more interesting than Onshape, at the assembly level, where the Onshape system is a bit difficult to grasp. The choice of assembly by skeleton method was not good, the software already making it possible to collaborate effectively via the comments and annotations sections, as well as by the functionalities already present. In addition, Fusion 360 can be used offline, which is a significant advantage, especially in the case of unstable internet connection, where disconnections are very frequent on Onshape. Like all CAD software in SAAS mode, Onshape and Fusion 360, are not subject to heavy maintenance. Indeed, software updates in SAAS mode are done directly from the software publisher's servers, so the customer does not have to manage any software upgrades. During an update, the customer will connect to his software and will then have direct access to the latest version of the software.
Fusion 360 requires a light installation on the local computer (this is what allows the software to be used offline), which can potentially require a little management of this installation on a company's computers. In addition, updates are made on the publisher's servers, the data is managed remotely (but can be imported if you want to work offline). Onshape maintenance is next to zero, as it is fully accessible through a browser. Both software have various advantages, starting with storage. Indeed, the cloud allows users to not have to manage their data locally. Investing in various expensive equipment such as servers. The amount of storage will depend on an agreement between a software publisher and the institution. This notion of storage still has some drawbacks, so a company will no longer have control over its data because it will be present on the publisher's servers.
Then, the difference between those generative and ground-truth information is encoded by Peak Signal-to-Noise Ratio (PSNR). We propose to classify those features in a classical supervised scenario by building a small training set with some abnormal samples of the original test set of the dataset. The binary Support Vector Machine (SVM) is applied for frame-level anomaly detection. Finally, we use Mask R-CNN as detector to perform object-centric anomaly localization. Our solution is largely evaluated on Avenue, Ped1, Ped2, and ShanghaiTech datasets. Our experiment results demonstrate that PSNR features combined with supervised SVM are better than error maps computed by previous methods. We achieve state-of-the-art performance for frame-level AUC on Ped1 and ShanghaiTech. Especially, for the most challenging Shanghaitech dataset, a supervised training model outperforms up to 9% the state-of-the-art an unsupervised strategy.
Most of recently successful studies  [1] [2] [3] [4] [5] [6]  have tackled this challenge in specific unsupervised ways. They only use normal samples from training sets to generate the standards for normal actions, then try to enlarge the deviation between abnormal samples in the test set and their standards. Using unsupervised strategy naturally follows the structure of popular benchmarks datasets containing only normal samples that do not require annotation task. The state of the art proposes two approaches for defining the anomalous feature that are, respectively, based on changing detection  [1, 3, 7]  and reconstruction/prediction errors  [4] [5] [6]. The first solution is a natural approach where each event is compared with its neighbors to find the most different ones. The weakness of this solution appears when an abnormal event always or never happens in a sequence.
• We demonstrate the improvement of anomaly detection by adding a supervised stage exploiting the feature extractor proposed by our unsupervised architecture. We add a SVM stage that we train on abnormal samples from the test dataset. We achieve at least competitive performances on all benchmarks: Avenue, Ped1, Ped2 in term of frame-level AUC, and a huge improvement on the most challenging datasets, such as Shanghaitech. The rest of the paper is organized as follows. We summarize related studies on anomaly detection in Section 2. Section 3 describes our architecture in detail. All the experiment implementations and results are presented in Section 4. Finally, we draw final conclusions and future work in Section 5.
Histograms of optical flow orientation (HOFO) were extracted by Wang et al. [15]  to classify abnormal events by one-class SVM or kernel PCA. More recently, Giorno et al. [7]  built a combination of HOG, HOF, and MBH to train their classifiers, then took the average classification scores to draw the output signal. Generally, most of those methods yielded just moderate performance due to the limitation of hand-crafted features in case of large datasets and complex scenarios.
All pix2pix-CGAN streams are then trained by Adam optimizer  [17]  with a learning rate of 0. 0002 and momentum parameter β 1 = 0. 5, β 2 = 0. 999 for both Generator and Discriminator. We test with different activation functions (LeakyReLU, ClippedReLU, eLU) and other hyper-parameters such as mini-batch size to find the most suitable parameter values for each stream and dataset.
Frame-Level Inference Model As mentioned in the previous section, we cast anomaly detection problem as a supervised action recognition scenario. We use the unsupervised multi-channel pix2pix CGANs described previously and trained on a dataset containing only regular events. Then we split the test set into two parts: a first part for learning binary SVM classifier and a second part for testing the performance of the trained model. Both parts have abnormal and normal samples. Thus, labeled training data are required. During the evaluation, we analyze how the performance is modified regarding the size of the dataset used to train the SVM classifier. We aim at defining a trade-off between the performance improvement and the amount of work required for the annotation task. In detail, we have two typical classes for each frame: normal and abnormal frame.
It leads to the issue that the final performance is highly dependent on the accuracy of bounding box detector. The missing detections or confusing detections can affect the final decision. Another problem is how to pre-define the number of abnormal objects in case of hard-decision. In popular datasets, this number should be one or two object. Then we could search for two BBox candidates obtaining minimum PSNR scores. It based on the fact that the key object in our real world scenario is abnormal one, so the false positive decision is more acceptable than false negative one. We could also go beyond this limitation by searching for a soft threshold of PSNR score.
Experiments In this section, we evaluate our unsupervised backbone network with supervised classifier methods on 4 popular datasets for anomaly detection: CUHK Avenue  [10] , USCD Pedestrian 1-2  [9] , and ShanghaiTech  [4]. First of all, we briefly introduce those benchmarks and the evaluation metric that we use to measure performance. Then, we present our implementation step by step. Next, we show our promising results in Section 4. 3. We end this section by some discussions.
Datasets and Evaluation Metric CUHK Avenue: 16 training sequences with some outliers and 21 testing videos containing 47 irregular events as throwing objects, loitering, and running. The size of the people are changing because of the camera position and angle. The normal samples of the test set are more numerous than abnormal ones. USCD Ped1 & Ped2: Ped1 has 34 training and 36 testing videos with 40 abnormal events. Ped2 is smaller with 16 training and 12 testing videos. Almost abnormal events are related to moving vehicles. Ped1 seems to be more challenging than Ped2 due to the different camera angle. Both have more abnormal events than normal ones on the test set. ShanghaiTech: A very large benchmark containing 13 scenes integrating complex light conditions and camera angles. There are 130 abnormal events and over 270,000 training frames.
Implementation Details Implementation frameworks: To build each Pix2pix CGAN stream, we implement our architecture based on Justine Pinkney framework  [20]  on Matlab. Regarding optical flow extraction, we apply Full Flow  [21]  on Avenue, Ped1, and Ped2. Because Shang-haiTech is larger than the other datasets, we use the simple Lucas-Kanade [22] optical flow implemented in OpenCV to reduce time processing. After calculating PSNR score for each CGAN stream to encode feature vectors, we learn binary SVM classifier using Classification Learner Toolbox on Matlab. For the object-centric anomaly localization step, we apply the pre-trained model Mask R-CNN  [23]  as the object detector. All steps are implemented on Matlab R2020a with Nvidia GeForce GTX 1080 environment except Fullflow algorithm that is used on Matlab R2018a.
For inference phase, we split test set into two subsets: SVM-train and SVM-test. To explore the effect of supervised scenario, we start to take from 10% up to 80% from the training set to build SVM-train. We also split SVM-train into train and validation sets for 5-fold cross validation. To optimize the classifier, we run optimization process on SVM learner with different kernel types: Gaussian, linear, cubic, and quadratic. Similarly to the previous phase, on ShanghaiTech dataset, we run the optimization process only once when SVM-train equals 80%. Then we apply optimized parameters for the rest to save time. Outliers removing on Avenue: Several outlier frames exist in Avenue training set. For the frameworks that calculate abnormal scores then compare with threshold  [4] [5] [6] , this task is necessary.
Quantitative evaluations: We do the first experiments to evaluate the performance of feature encoding using PSNR and MSE. We choose Avenue benchmark as reference dataset because there are color images with acceptable dataset size (Pedestrian dataset have only grayscale frames and ShanghaiTech is too large). Results are illustrated in Table  2. The large margin about 10% on all streams and combination shows that PSNR technique is significantly better for frame level anomaly detection task. Next, we apply PSNR technique for the other benchmarks. We go further by investigating various combinations of four streams. Multi-stream is combined by concatenating PSNR features of each stream to produce a new vector. Results are shown in Table  3. On two grayscale datasets, Ped1 and Ped2, CGAN-3 and CGAN-4 produce almost the same performance.
The longer temporal evolution of CGAN-4 brings us a small improvement 1∼2% with respect to CGAN-3. On the other RGB datasets, CGAN-3 surpasses CGAN-4 with a huge difference of 10%. Generally, CGAN-2 achieves the best performance among the four streams. It shows that learning both appearance and motion evolution can help us generating better features. Obviously, the combination of CGAN-1 (flow) and CGAN-4 (grayscale) produce similar or slightly better performance than CGAN-2 (combines flow with grayscale). This result also proves that the longer study of temporal evolution at CGAN-4 improves performance. Besides, when we combine several streams, we usually achieve better performance than single stream up to combination of two, three, and all four streams. The fact that the four streams combination always produces the best results is a strong experimental proof of the relevance of our proposed idea about multi-channel framework.
Generally, it is difficult to comparing the methods in different scenarios, especially in the second group, because the number of testing samples is not the same. Our solution performs almost uniformly and produces promising results on all four datasets. We achieve moderate improvement on Ped1 by about 2% while producing a competitive result on Avenue and Ped2. Particularly, we obtain a large margin of about 9% compared to the state-of-the-art method  [6]  on the challenging ShanghaiTech dataset. Considering the effect of the supervised scenario, we show that from 50% of the original test set size, we surpass state-of-the-art performance on Ped1 and ShanghaiTech. For the abnormal object localization models, we evaluate our quantitative performances in Avenue dataset using AUC and ERR metrics at pixel-level in Table  5.
30. 21 Qualitative evaluation: To understand the distinction of our features on each stream, we explore the distribution of test samples for each CGAN on each dataset. Figure  7  shows us the distribution of test samples in Ped2 dataset. Obviously, almost normal samples situate in higher values than abnormal ones. This distribution corresponds to the fact that PSNR techniques produce high scores if predicted images tend to similar source images. Besides, because Ped2 contains only grayscale images, all three channels of CGAN-3 must be equal so we observe a straight line distribution. Figure  8  present several examples selected from Avenue, Ped1, Ped2, and Shang-haiTech to demonstrate true positive and failure cases of object-centric anomaly localization. Obviously, we can strongly detect various types of abnormal events even in crowed and occlusive background with multiple camera angle, involving not only single, but also multiple objects.
Strengths: Our experiments show that we achieve very promising results, although we have not optimized all of the steps. Suppose that we replace Full-Flow  [21]  and  by recent state-of-the-art methods, we might improve our performance. Besides, our framework is quite flexible, so we can add more streams with more channels representing various types of data to learn richer features. Those features are also appropriated for many classifiers. Limitations: On one hand, the mix of unsupervised CGAN streams with supervised SVM help us obtaining impressive performance on the offline case, where all test data are prepared and well pre-processed. For online works, we get trouble when brand new abnormal actions suddenly occur without existing in the previous learned database. On the other hand, the anomaly localization is significantly affected by the detector we use (Figure  9 ).
Conclusions and Future Work In this paper, we present a flexible multi-channel generative framework for supervised anomaly detection in surveillance videos. Various types of input images are passed into 4 CGAN streams to predict future information. Then we apply PSNR technique to encode prediction error into feature vectors. A supervised scenario is built based on a part of the test set to train a SVM classifier. Our solution is evaluated on 4 datasets: Avenue, Ped1, Ped2, ShanghaiTech, and surpass state-of-the-art performance on three of them, especially producing a huge improvement on ShanghaiTech. For future work, we first plan to replace optical flow estimation methods as discussed above. The USCD Ped2 is relatively easy, so we will evaluate our methods with more datasets  [27] [28] [29].
Comparison of Frame level AUC on 4 datasets between ours solution and recent state-of-theart methods that reported their performance on at least 2 similar datasets. The best performance on each dataset is highlighted by bold number. We split those methods in two groups. The upper group contains the fully unsupervised methods without adding extra abnormal samples from original test set to training set. The second group contains the semi-supervised and supervised methods that insert abnormal samples into training set. Generally, it is difficult to comparing the methods in different scenarios, especially in second group, because the number of testing samples is not the same. For our case, all the output channels of our model are used. 80% samples of test set are used for training SVM. SHT = ShanghaiTech.
e. a GEV distribution, whose parameters are allowed to vary as a function of some timevarying covariates, like the month-of-year to account for seasonality (denoted moy) or some CIs to account for large-scale climate variability. The focus of the current study is the modelling of the influence of CIs on the GEV parameters. To do so, the use of the linear model is a common practice. For instance,  Méndez et al. (2007)  focused on the linear link between sea level and Southern Oscillation indices (SOI) at San Francisco;  Menéndez and Woodworth (2010)  analyzed the linear relationships with different CIs using tide gauge datasets from the GESLA tide gauge data repository  (Woodworth et al. , 2016) ;  Marcos and Woodworth (2017)  examined spatiotemporal changes in extreme sea levels along the coasts of the North Atlantic and the Gulf of Mexico with similar techniques;  Grinsted et al.
Non-stationary GEV The GEV distribution is assumed to be non-stationary in the sense that the GEV parameters θ=(μ,σ,ξ) vary as a function of the covariates moy, YY, and x = CI (described in Sect. 2). Since the scale parameter satisfies σ > 0, we preferably work with its log-transformation. Computing non-stationary GEV models with varying shape parameter is known to be difficult, and might lead to unstable results. To minimize this problem, we work with a modified logit transformation of ξ (restricting its range to -1 to 0. 5) by following the implementation provided in the mgcv R package  (Wood, 2017: chapter 7). We assume that the model η θ (. ) linking θ to the covariates can be decomposed into three functional terms as follows: η θ (YY, moy, x) = η LT θ (YY) + η seasonal θ (moy) + η CI θ (x) (2) where η LT θ (.
Application to Cuxhaven tide gauge We apply the procedure described in Sect. 3. 1 on the rMM data at Cuxhaven tide gauge. We consider the three GEV model's formulations described in Sect. 3. 1. The corresponding AIC criterion values reach 1320, 1102, and 1107 for GEV1, 2, and 3 respectively. This indicates that GEV2 and GEV3 model are both identified as appropriate (the AIC difference is small). Thus, we complement this analysis with the LRT test, which shows a high p-value of 40% (i. e. well above the significance threshold of 1%) hence favouring the selection of the simpler model GEV2. In addition, the residual quantile plot (Gumbel scale) in Fig. 4a  shows a better agreement for the large Gumbel theoretical quantiles (here above 4) when considering GEV2. This provides a second element in favour of the GEV2 model.
Summary and further works Using a spline-based non-stationary formulation of GEV, we provide new insight into the links between extreme local sea levels and teleconnection patterns. First, the links between the climate indices and different parameters of a GEV distribution fitted to extreme sea-level data are shown to be most of the time linear (research question Q1), but some of them present significant non-linear forms, as shown for instance in Halifax, San Francisco, Stockholm, and Honolulu, as well as at Cuxhaven to a lesser extent. To our best knowledge, taking into account these non-linear effects has rarely been done in previous studies at the exception of  Wong (2018)  but by assuming a second-order polynomial relation between the GEV parameters and the CIs. Including more complex functional relationships can have strong impacts for probabilistic flooding risk assessments and preparedness to flood events, since the time evolution of GEV parameters directly affects the one of return levels, and potentially its future projections.
Acknowledgements We acknowledge financial support of  ARN   Storisk  project ( ANR-15-CE03-0003 ) and of  INSeaPTION  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI  Climate and co-funded by the  European Union  (Grant # 690462 ). The first author also acknowledges financial support of  EUPHEME  project, which is also part of  ERA4CS. We are grateful to  Prof. Philip Jonathan  ( Lancaster uni. ) for his inspiring talk at EVAN 2019 conference which motivated the finalisation of this study. The implementation was carried out using the R package mgcv (https://cra n. r-project. org/web/packages/mgcv/index. html). The R scripts are available from the author upon request.