dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper, we propose a model-driven design flow and platform for dynamic partially reconfigurable architectures aiming at accelerating the design of reconfigurable systems to be implemented on FPGAs by reducing design complexity, automating code generation, and facilitating design reuse. The GDF4DPR is based on high-level modeling that covers several design steps from system specification to physical implementation. As for reconfiguration decision-making modeling, the proposed design flow handles both centralized and decentralized control solutions to adapt to various reconfigurable systems constraints. It proposes an automatic transformation of high-level models into IP-XACT designs to ensure IP reuse and to target different technologies and commercial FPGAs. The proposed flow enables the automatic code generation of the whole system, including tool-specific files and scripts, to increase designer productivity.
In addition, this proposal may support the training of software developers by providing the most relevant information on code smells. This ontology can also be a means of representing the knowledge on CSs from different sources (documents in natural language, relational databases, HTML documents, etc. ). Therefore, it could be a valuable knowledge base to support the struggle of software developers and architects either to avoid CSs or to detect and remove them. The ontology was developed following a sound methodology. The wellknown tool Protégé was used to manage the ontology and it was validated by using different techniques. An experiment was conducted to demonstrate the applicability of the ontology and evaluate its impact on speeding up the analysis of CSs.
Hence, based on the benefits of adopting ontologies extensively reported in the literature, this paper aims to describe an ontology-based proposal to represent, analyze and share knowledge related to CSs. Especially, this ontology may be a valuable means to support the training of software architects and programmers. The information regarding CSs can be found in heterogeneous sources such as scientific articles  [3, 8] , websites, databases, etc. Consequently, this ontology will make it possible to examine the consistency of the information contained in diverse sources. In addition, the systematic usage of this ontology will allow the information represented to be extended and thus become a valuable knowledge base. This knowledge base could be exploited to support the work of those professionals involved in software development projects.
II. RELATED WORK Numerous authors argue the advantages of discovering CSs at an early stage in software development. Paiva and others  [9]  present a study done on two Java-developed programs: MobileMedia and Health Watcher. God Class, God Method, and Feature Envy were the three types of CS that were examined using the JSpIRIT tool. On the other hand, research was conducted to assess six tools: iPlasma, Together, JDeodorant, PMD, DÉCOR  [10]. SourceForge was used to analyze systems developed in Java. Additionally, Liu  [11]  evaluated the ability of the tools Checkstyle, Infusion, PMD, iPlasma, DECOR, and others to detect CSs. The features of SonarQube are described by Bastias and López  [12, 13]  for analyzing several quality attributes and identifying technological risks brought on by CSs  [14].
Da Silva  [6]  describes the ontology ONTOCEAN for managing, tracking CSs and assessing with the support of reasoners on semantic rules its impact on software. An ontology that collects and organizes the knowledge about design in object-oriented microarchitectures is introduced by Garzás and Piattini  [5]. They discovered several pertinent terms, including patterns, refactoring, bad smells, and good practices. Although they only consider a small subset of CSs, these approaches demonstrate the pertinence of ontology-based proposals to manage the knowledge related to CSs. The effect of CSs on software quality is also not fully examined. Furthermore, there is a lack of empirical data showing how these methods affect the professional knowledge of those working on software development projects.
V. RESULTS OF THE EVALUATION PROCESS The two main steps to evaluate an ontology are: checking the consistency and verifying that it meets the defined requirements. The consistency checking enables confirming that the ontology is free of contradictions. By using a reasoner, it is possible to evaluate the consistency and classify the individuals automatically. Specifically, reasoner Pellet was used throughout the development process of our ontology. Thus, we demonstrated that the ontology satisfies the requirements for a formal system. To verify that the ontology meets the defined requirements, we evaluated how the competence questions are answered. To illustrate how the competence questions are answered, the ontology was populated, and the reasoner Pellet was applied. The results of this evaluation are detailed below: Q1: ¿What is the classification of a code smell? First of all, we selected in Protégé the individual CSLargeClass, which is an instance of the class TypeCodeSmell.
VI. ASSESSING THE IMPACT OF THE ONTOLOGICAL MODEL To demonstrate with empirical evidences the impact of our proposal, we conducted an experiment. Specifically, we are interested in assessing whether the application of this ontological approach speeds up the analysis of knowledge related to CSs. We conducted a quasi-experiment with ten software architects and developers who have been involved in different software projects. We measured the time that the participants needed to analyze information regarding CSs. Firstly, they used different means to find information about CS. Secondly, the participants used our approach. To carry out this experiment, we used the competence questions that were defined to specify the scope of the ontology: Q1: What is the classification of a code smell? Q2: What quality features are impacted by a code smell? Q3: What metrics can be used to discover a specific code smell? Q4: What tools help with the identification of a particular code smell? Specifically, to answer these questions, we selected the CS CSLargeClass and the tool VComplex.
Considering that the participants have different levels of knowledge about CSs, they searched for information from different sources such as scientific literature, work reports, databases, etc. Our preliminary hypothesis was that the application of our approach might reduce by 10 times the time necessary to analyze the knowledge regarding CSs. The second column in Table  IV  shows the time that spent each participant to answer the questions without using our approach. Whilst the third column shows the time that spent each participant to answer the questions using our approach. The last row shows the average time for each observation. In this row, it is shown that for the first observation, the average time was 778 seconds whilst for the second observation, the average time was 68 seconds. Hence, the time by using our approach was reduced by 11.
Therefore, the use of the Johnson-Cook flow law defined by Equation (2) requires the identification of 5 material parameters. The main problem that researchers are confronted with after the phase of realization of the experimental tests concerns the choice of the flow law to use according to the observations made on these test results. This choice of flow law is also restricted by the FE code used and the availability of such flow laws. Thus, a user of the Abaqus FE code will turn more particularly to a Johnson-Cook  [8]  flow law, where it is natively implemented in this software. The choice of another form of flow law, Zerilli-Armstrong, or Arrhenius, for example, obliges the user to program himself the computation of the flow stress σ y of the material through a VUMAT subroutine in FORTRAN 77 as proposed by Gao et al.
Unfortunately, and this is not part of their study, if these last two models are satisfactory from a theoretical point of view, from a practical point of view for the user of a FE code such as Abaqus, it will be necessary to carry out a numerical implementation in a FORTRAN 77 VUMAT subroutine of the modified Johnson-Cook flow law or the Arrhenius law as carried out by a few authors  [24, 27, 28]  to use these laws for numerical simulation. This requires a certain expertise in the development and implementation of flow laws, which is not available to all users of the Abaqus FE code. From this observation, and from the necessity to select a flow law for a type of material, then to identify the parameters of this flow law according to experimental tests, and finally to implement this flow law as a user subroutine in FORTRAN in the Abaqus FE code, we recently proposed in Pantalé et al.
ε and the temperature T. After a training phase based on the use of the Python library TensorFlow  [42, 43] , the weights and biases of the trained neural network are transcoded into a subroutine in FORTRAN 77, which is compiled and linked with the libraries of the Abaqus FE code to include the behavior of the material by allowing the computation of the flow stress σ y as a function of ε p ,. ε and T, and of its three derivatives ∂σ y /∂ε p , ∂σ y /∂. ε and ∂σ y /∂T. The structure of this paper is as follows: Section 2 addresses the presentation of a neural-network-based flow law and its training from the data proposed by Ji et al. [30]  and reported in Figure  1. The comparison of several neural network architectures regarding accuracy and implementation complexity will be presented and compared.
In Section 3, we will present the transposition of this neural network into a FORTRAN 77 subroutine for the Abaqus FE code. Validation is based on the numerical simulation Abaqus Explicit FE code of a compression test in the same configuration as the one proposed by Ji et al. [30]  using four different ANN flow laws. In this Section, we will present the problems of overfitting the neural network and its visible consequences on the results concerning numerical simulations. Finally, a conclusion and perspective section will conclude this paper.
Computation of the Derivatives of the Neural Network As introduced in Section 1, implementing a flow law in a FE code requires both the computation of the flow stress σ y as a function of the input data, performed using the previous Equations (  5 )-  (11) , but also the evaluation of the three derivatives of σ y with respect to the input data to use a Newton-Raphson algorithm within the stress integration scheme, as proposed by many authors  [24, 28, 45, 46]  based on the radial return algorithm in the Abaqus FE code. It is, therefore, necessary to perform a numerical evaluation of these three derivatives based on the ANN to obtain these quantities. It seems obvious that it is not possible to train a neural network to evaluate these values of derivatives insofar as the training data are not physically collectible data during the experimental tests.
Training of the Neural Networks In neural network learning, it is necessary to define the objective function to be minimized and the evaluation of the model error. In this study, the error evaluation is based on the mean square error (E MS ) and the root mean square error (E RMS ) given by the following equation: E RMS (MPa) = E MS = 1 N N ∑ i=1 □ e i -□ y i 2 , ( 16 ) where N is the total number of numerical training data used, □ y i is the ith value predicted by the neural network, and □ e i is the corresponding experimental value coming from the experimental tests. The accuracy and predictive ability of the models is assessed by the mean absolute relative error (E MAR ) defined by Equation (  17 ): E MAR (%) = 1 N N ∑ i=1 □ y i -□ e i □ e i × 100 (17) The numerical implementation of the learning phase of the neural network was done in Python language, using the TensorFlow library  [42, 43].
The minimization procedure of the objective function is based on the use of the adaptive moment estimation (ADAM) solver proposed by Kingma et al. [47]. The training data used in this section are taken from the publication of Ji et al. [30]. Thus, data for which compression tests were performed for the 3 strain rates. ε = 0. 001 s -1 ,. ε = 0. 01 s -1 and. ε = 0. 1 s -1 , and the 12 temperature values between 750 • C and 1300 • C in 50 • C steps are used. For each pair of data (. ε, T), we have a record of 71 values of flow stress σ e corresponding to values of deformation between 0 and 0. 7, regularly spaced of 0. 01. We use a database of 2556 quadruplets of values (ε p ,. ε, T, σ e ) for the training of the ANNs. The set of these data is used as training data for the neural network. Several neural network architectures have been studied in this work; they differ from each other by the number of neurons present in the two hidden layers.
This will be analyzed in the next section concerning the numerical simulation of the compression of a cylinder on the Abaqus Explicit software using the ANN flow law. Since, as reported in Figure  3 , the 3-7-4-1 model seems to have converged after the training phase, we are going to compare it with the 'accurate' model, the 3-15-7-1 model. From a more physical point of view, Figures  4  and 5  show the correlation between the data predicted by the neural network and the experimental data for the 3-7-4-1 and 3-15-7-1 networks, respectively. 4  and 5  shows a very good correlation between the ANN results and the experimental results, which is reflected by the very low values of the E MAR and E RMS coefficients reported in Table  1. As reported by Phaniraj  [48] , the correlation coefficient (R) is generally not a good measure in our case of study because it only shows the correlation of the model with respect to the data and not its accuracy, which is a determining factor in the qualification of a model.
ε and T. Implementing the ANN flow law identified above is realized here in a VUHARD subroutine, similarly as proposed by van Rensburg et al. [49] , used by the Abaqus Explicit FE code in order to allow a user to program the computation of the flow stress σ y and its 3 derivatives as a function of the model input data. This subroutine is used when calculating the stress tensor σ 1 at the end of an increment from the stress tensor at the beginning of the increment σ 0 , the deformations, the material parameters and the history of the deformation at each finite element integration points, according to the stress integration algorithm based on the radial return method as described in Simo et al. [46]  for the general aspects, Ming et al. [28]  for Abaqus Explicit FE code, or Pantalé et al. [1]  for the DynELA FE code.
Numerical Simulations and Benchmarks Tests To validate the proposed approach and to compare the different neural network architectures proposed in Section 2. 3, we propose here to simulate on the Abaqus Explicit FE code the high-temperature compression of a cylinder on a Gleeble-type thermomechanical device. We consider a cylinder in compression with an initial diameter d 0 = 8 mm and an initial height h 0 = 12 mm made of GCr15 material, for which the final height after compression is h = 6 mm, which is a reduction of 50% of its total height. The compression of the sample is done in 10 s so that the strain rate. ε is in the corresponding range of the characterization of the material behavior defined by Ji et al. [30]. Concerning the flow laws, the 4 models presented in Section 2. 3 will be used and compared between them.
Unfortunately, it is not possible here to compare these results with the experimental results, even if the initial shape of the specimen is the same, as these are not available in the references of the work of Zhou et al. [14]  or Ji et al. [30]. The mesh of the sample is made with 850 axis-symmetric quadrilateral finite elements with 4 nodes and reduced integration (named CAX4R in the Abaqus software) with 50 elements in the vertical direction and 17 elements in the radial direction, respectively. The cylinder is between two rigid surfaces, and the Coulomb friction law with a friction coefficient at the contact surfaces was set to µ = 0. 15. The simulation time being fixed at 10 s in order to reduce the simulation time, considering that an explicit integration scheme is used, a global mass scaling is used for all simulations.
Conclusions and Future Work In this paper, a flow law based on an artificial neural network capable of predicting the flow stress of a material as a function of input data, such as the plastic strain ε p , the strain rate. ε and the temperature T, for a metallic material subjected to high thermomechanical loading is presented. From the equations that govern the writing of the neural network, the expressions of the derivatives of the flow stress of the model as a function of the plastic strain ε p , the strain rate. ε and the temperature T were established. These expressions allow the transfer of the neural network behavior into a VUHARD subroutine written in FORTRAN 77 language to allow the use of this network for the computation of the material flow stresses within the Abaqus FE code. In a first step, data from the works of Ji et al.
It is therefore important to always adjust the structure and size of the neural network to the experimental data that we wish to approximate to avoid this over-fitting phenomenon. This work thus allowed to highlight the significant contributions of flow laws based on neural networks in numerical simulation by finite elements on a commercial FE code, such as the Abaqus software. The quality of the results obtained allows us to go further in the use of the simulation results and in particular to consider that the results of these finite element simulations can predict the phase transformations and the dynamic recrystallization within the material during the thermomechanical transformation at high temperature. Funding: This research received no external funding. The biases of the first hidden layer -→ b 1 is a 7-component vector: -→ b 1 =           1.
Physical function was assessed using the HAQ-DI  19  ; normalised physical function was defined as HAQ-DI score ≤0. 5. Minimal disease activity (MDA) and very low disease activity (VLDA) were defined as meeting at least five or all seven, respectively, of the following criteria: TJC ≤1, SJC ≤1, Psoriasis Area and Severity Index (PASI)  20  ≤1, patient pain VAS (0-100) ≤15, PtGA (arthritis and psoriasis; VAS 0-100) ≤20, HAQ-DI ≤0. 5 and tender entheseal points ≤1. 21 ISCOVER-2 ( ClinicalTrials. gov: NCT03158285) was conducted in accordance with Declaration of Helsinki and Good Clinical Practice guidelines.
Statistical analysis Mean changes through week 100 in total PsA-modified vdH-S scores, as well as component erosion and joint space narrowing (JSN) scores, were determined using observed data derived from radiographic reading session 3. 14  In post hoc analyses, mean changes in scores were summarised for clinical responders and non-responders, with clinical response defined as ≥20% improvement in ACR criteria (ACR20 response),  16  ACR50 response, ACR70 response, PASDAS low disease activity (LDA; score ≤3. 2),  22  DAPSA LDA (score ≤14),  23  MDA, VLDA and normalised HAQ-DI (score ≤0. 5; among patients with HAQ-DI >0. 5 at baseline). In addition, the proportions of patients achieving MDA and VLDA as well as the individual MDA/VLDA components at weeks 24, 52 and 100 were determined for patients with and without radiographic progression at the same time points, with progression defined as a change from baseline in total vdH-S score >0.
54 Inhibiting the IL-23p19 subunit with guselkumab therapy demonstrated robust efficacy in treating both of these aspects of PsA with over half of patients in DISCOV-ER-2 achieving complete skin clearance and over 65% of patients achieving resolution of enthesitis at week 100. 14 hese analyses were conducted post hoc, and DISCOV-ER-2 was not powered to assess radiographic progression in the various subgroups evaluated, some of which comprised relatively small numbers of patients and, thus, may have been susceptible to a high degree of variability in radiographic scores. Owing to these limitations, formal hypothesis testing was not performed. Of note, the retention rate in this study was high, with nearly all patients enrolled and treated in DISCOVER-2 completing study treatment through 2 years, providing a robust dataset.
All patients in DISCOVER-2 were biologic-naïve and were selected using inclusion criteria designed to enrich the population for patients at higher risk of radiographic progression. These results may not be generalisable to all patients with PsA; however, it should be noted that the treatment effect with guselkumab has been observed across subgroups from DISCOVER-1 and DISCOVER-2 defined by various baseline demographic and disease characteristics. 55 In addition, 2 years may be a relatively short follow-up time for observing radiographic progression in PsA. To that end, a phase 3b study (APEX; NCT04882098) is being conducted to further evaluate the effects of guselkumab on radiographic progression in at-risk biologic-naïve patients with PsA. In APEX, an elevated risk of radiographic progression is defined by the presence of ≥2 joint erosions of the hands and feet and CRP ≥0.
3. We propose an innovative reconstruction module where we succeed in using a generalized formula to evaluate points on different primitive types, enabling our training process to be fully differential and compatible with batch operations. 4. Experiments demonstrate that our method works on the free-form point clouds and real-scan data even if we only train our model on the ABC dataset. Furthermore, we present one application of Bézier primitive segmentation to reconstruct the full Bézier model while preserving the sharp features. The code is available at: https://github. com/bizerfr/BPNet.
Dataset Pre-Processing We evaluate our approach on the ABC dataset  [Koch et al. , 2019]. However, the ABC dataset does not have the annotations to learn Bézier decomposition on point clouds. Therefore, we do a pre-processing step. Specifically, we utilize the CGAL library  [CGAL, 2009]  and OpenCascade library  [OpenCascade, 2018]  to perform Bézier decomposition on STEP files directly and perform random sampling on the surface to obtain the following labels: point coordinates, point normals, point uv parameters, surface patch indices of the corresponding points, surface patch degrees, and surface patch control points. Finally, we use 5,200 CAD models for training and 1,300 CAD models for testing. Each CAD model contains randomly sampled 8, 192 points (non-uniform) with annotations.
Comparisons We compare our algorithm with SPFN, ParSeNet, and HPNet  [Li et al. , 2019; Sharma et al. , 2020; Yan and Yang, 2021]. We use both points and normals for training all the algorithms. Since SPFN only supports four types of canonical primitives (plane, sphere, cone, and cylinder), we consider points belonging to other primitives falling out of the supported canonical primitive types as the "unknown" type. To make fair comparisons, we modify SPFN to let the network take point coordinates and normals as input for training. For ParSeNet, we only train the segmentation module on the ABC dataset. We use their pre-trained fitting model (SplineNet) directly. For HPNet, we also use the pre-trained fitting model directly, which is the same as ParSeNet. We observed that the output of HPNet is very sensitive to the number of points.