dataesr/scientific-paragraphs-categorization huggingface dataset:
Expected losses Our Feller-Pareto model has αγ = 1. 297. By (6), its expected value exists. We may therefore easily compute the expected losses (in million DKK) for the Danish dataset using function mfpareto. [1] 3. 977563 After  McNeil (1997) , we also compute the expected losses for an excess-of-loss reinsurance contract where the reinsurer is responsible for losses between d and d + c, where d is the deductible value and c, the cover. Let X be the original loss amount and X re , the reinsurance cost. We have X re = min(max(X -d, 0), c) = min(X, d + c) -min(X, d). The expected loss cost for the reinsurer is E  , d) ]. We used function levfpareto to compute these expected values and to draw Figure  8 , which displays the expected cost E [X re ] as a function of the deductible d for different values of covers c.
(2019). But for a few simple cases, finding the distribution of the random variable S is a difficult task. However, one may turn to simulation to get good estimates of quantities related to this distribution. Let us simulate a large sample from a compound Poisson distribution where the severity of losses is distributed according to our Feller-Pareto. This is straightforward using function rcomppois from package actuar. (See  Goulet and Pouliot (2008)  for an explanation of the syntax to specify simulation models. ) R> x <-rcomppois(1e6, lambda = 10, rfpareto(min = 0, + shape1 = par. fp["shape1"], shape2 = par. fp["shape2"], + shape3 = par. fp["shape3"], scale = par. fp["scale"])) We may now, for example, get fairly good approximations of the upper tail quantiles of the distribution of S. Since the sample size of x is one million, we can reasonably assess quantiles up to probability level p = 0.
Accessing the C routines The actual workhorses behind the R functions of actuar presented in this paper are C routines. Package developers who need one or many of these routines in their C code can take advantage of them through the new package API of actuar. The header file inst/include/actuarAPI. h in the package installation directory contains the declarations of the more than 200 routines part of the API. We direct the interested reader to the "distributions" package vignette for further instructions to access the C routines through the API. The companion package expint (Goulet 2022) ships with a complete test package implementing these instructions. See the vignette of the latter package for more information.
Conclusion This paper introduced the implementation of the Feller-Pareto distribution and related Pareto distributions with a location parameter in version 3. 0-0 of the R package actuar. The Feller-Pareto defines a large family of distributions encompassing the transformed beta family and many variants of the Pareto distribution. A large proportion of the numerical challenges discussed in the paper are related to the fact that, for most distributions of the Feller-Pareto family, we need to evaluate the incomplete beta function for a value that can jump to 1 if not carefully computed to avoid numerical overflows. By applying a simple correction to many functions present in previous versions of actuar, we were able to fix long standing discrepancies in CDF and limited moment functions, as well as to provide robust new functions for the Feller-Pareto distribution.
A faulty actuator can be switched to its sane redundant counterpart. A faulty sensor can be put aside from the data fusion process  [7]. In particular, the location of an airplane is an essential piece of information during the landing process. It is obtained from a set of sensors that present redundancies and whose values are fused. Thus, each sensor involved in the data fusion must provide measures without anomalies. Normally, the set of sensors consists of a global positioning system (GPS), an inertial reference system (IRS), an instrument landing system (ILS), and a radio-altimeter (RA). Typically, these sensors work properly with a specific accuracy and specific fusion techniques are applied to get a good estimate of the airplane's location  [7]. However, sensors can provide data with anomalies.
e. , the vertical distance between the aircraft and the ground. In order to get Z RA , one must apply a correction with respect to the relief under the aircraft, using a terrain database: Z RA = H RA + H terrain (1) where H terrain is the altitude of the terrain with respect to the runway threshold. The H terrain value can be obtained using the X GPS or X IRS values. The ILS is a ground-based system that emits signals along the vertical and lateral axis so that the aircraft can follow a line of reference named the localizer (LOC) in the lateral axis and the glideslope (GS) on the vertical axis. The ILS can be manipulated to obtain the airplane's position coordinates with respect to the runway (Y ILS , Z ILS ). These values can be calculated using Equations (  2 ) and (3). These equations provide a good approximation to the real values  [18].
NADCA for Real Landings A set of 36 landings from the same airport was selected. Each landing had the following signals: [Z ILS , Z RA , Z GPS , Z IRS ] for the Z coordinate, [Y ILS , Y GPS , Y IRS ] for the Y coordinate, and [X GPS , X IRS ] for the X coordinate. The approach phase was filtered for each landing. These 36 landings form a real data set. The data were useful to carry out the learning and validation process for the predictive model creation and to determine decision thresholds U that were used to decide if there was an anomaly or not. There was a predictive model for each coordinate. Likewise, each sensor had its U threshold for each coordinate. The algorithm NADCA-L was used for X GPS , X IRS , Y IRS , Z GPS , and Z IRS. The algorithm NADCA-O was used for Y GPS , Y ILS , Z ILS , and Z RA where L was created from the SG filter.
Predictive Model Using Real Landings In this section, three predictive models (PM Z , PM Y , and PM X ) for real data according to the X, Y, and Z axes are analyzed. Each predictive model only works with signals without oscillations. In this way, the convergence of the learning process is better and the anomaly detection process is more robust. On the other hand, data preparation is more laborious because signals with oscillations are smoothed using the SG filter. Each predictive model was created using 30,554 examples for training and 15,050 examples for validation. The selected LSTM network architecture has three LSTM layers and 300 cells per layer. Using this architecture, the learning process adapts the weights of network. To do this, a backpropagation algorithm was used together with the set of learning examples.
This algorithm, in addition to the number of layers and cells per layer, requires some hyperparameters to be defined. Specifically, the optimization algorithm (used to train the network) is Adam's algorithm and the loss function (used to evaluate the network that is minimized by the optimization algorithm) is mean squared error (mse). The number of epochs (an epoch is one pass through all samples in the training dataset and updating the network weights) is 70. The batch size (a batch is one pass through a subset of samples in the training dataset after which the network weights are updated) is 32. The activation function is Relu (an activation is required to allow the neural network the ability to model non-linear processes).
Z Axis Prediction errors are calculated using PM Z and data without anomalies. Figure  18  shows the prediction error for Z GPS and Z IRS. Re f Z_GPS and Re f Z_GPS represent P Z_GPS i+1 and P Z_IRS i+1 value sets (for the Z coordinate), respectively. These values are altitudes. The Z IRS threshold can be set to U IRS Z = 0. 06. This result is good to detect anomalies. On the other hand, the Z GPS threshold can be set to U GPS Z = 1. 2. This threshold is also small and acceptable to detect anomalies. However, U GPS Z is higher than U IRS Z. This means that Z GPS data may have minor anomalies. For ILS, U ILS Z is the envelope of the maximum error between Re f Z_ILS and Z ILS , where Re f Z_ILS is the set of predicted values using Z ILS L (see Figure  19 ). For RA, U RA Z is determined with the help of two envelopes, one envelope for positive differences and another for negative ones.
The number of epochs (an epoch is one pass through all samples in the training dataset and updating the network weights) is 70. The batch size (a batch is one pass through a subset of samples in the training dataset after which the network weights are updated) is 32. The activation function is Relu (an activation is required to allow the neural network the ability to model non-linear processes). The network can be trained using the learning examples and simultaneously, it can also be evaluated with the help of the validation examples. This evaluation provides an estimate of the performance of the network at making predictions for unseen data in the future. A positive evaluation means a good fit between the learning and validation sets. A good fit is a case where the performance of the model is good on both the training and validation sets.
Such MIP based exact approach has been extended to NCS 2-additive models  ( Sobrie et al. , 2015 ). However, experimental results showed that such MIP approach becomes computationally prohibitive with a large number of assignments (learning an MR-Sort model with 100 alternatives, 5 criteria and 3 categories involve a MIP with 1100 binary variables, and the computing time exceeds 100 s). To cope with the computational burden, a heuristic approach has been proposed to learn an MR-Sort model from assignment examples by  Sobrie (2016)  and  Sobrie, Mousseau, & Pirlot (2019)  which can handle large datasets, but losing optimality guaranty. More recently  Belahcene et al. (2018a)  defined a Boolean satisfiability formulation of Inv-NCS, which keeps optimality guarantee while enabling computations even for real-size datasets.
Literature related to Inv-NCS Learning preference models from preference data to faithfully represent the DM judgment has been considered since several decades in the literature. In the context of MCDA, a well-known example of such an approach is the UTA method proposed in Jacquet-Lagreze &  Siskos (1982)  in the case of an additive multicriteria value model. Learning an Electre Tri model (the initial multicriteria sorting procedure from which NCS was formalized, see  Roy (1991)  ) from assignment examples was initially formulated using non-linear programming in  Mousseau & Słowi ński (1998). A mixed-integer linear formulation was proposed by  Leroy et al. (2011)  to learn an additive majority rule sorting model (MR-Sort: additive NCS without veto) from a dataset; however, these approaches were not able to handle datasets corresponding to realworld problems.
Recently,  Kadzinski & Martyn (2020)  proposed an enriched framework to elicit and Electre Tri B model and analyze its results. To cope with the computational burden, a heuristic approach has been proposed to learn an MR-Sort model from assignment examples by  ( Sobrie, 2016; Sobrie et al. , 2019 )  which can handle large datasets, but losing optimality guaranty. More recently  Belahcene et al. (2018a)  defined a Boolean satisfiability formulation of Inv-NCS, which keeps optimality guarantee while enabling computations even for real-size datasets. In this paper, we continue and extend this work.
Instance generation Each instance consists of a set of alternatives X * (described by tuples of evaluations on a set of criteria N ), a set of categories C 1 ≺. ≺ C p , and the assignment of the former to the latter. We set the number of categories p to three The assignment of alternatives to categories depends on the type of model sought and the problem description. In order to ensure that preference data represents a real decision problem, we use a decision model to generate it, and, in particular, a model compatible to the Non-Compensatory stance we are postulating: • In the case of U C -NCS, we use an MR-Sort model for generating the learning set, a model that particularizes U C by postulating the set of sufficient coalitions has an additive structure (see Section 2 ). It is randomly generated using the following procedure: a set of limit profiles b is generated by uniformly sampling p -1 numbers in the interval [0,1] and sorting them in ascending order, for all criteria; the voting powers w are Once the ground truth model is generated, which is by design compatible to the hypothesis class we are working with, we consider two ways of assigning alternatives to categories, depending on the problem formulation we are considering.
Performance indicators The performance indicators of interest are the computing time, the restoration rate (the proportion of the learning set correctly represented by the output model), and the generalization index measuring the alignment between the output model with the ground truth. So as to monitor the learning process, we control the level of noise in the input data through the parameter μ, and we measure the proportion of reference assignments that are correctly restored by the learning process. This restoration rate should be equal to one in the case of approaches addressing the decision problem (as there is no noise), and at least equal to 1 -μ for approaches addressing the optimization problem. The computing performance is measured in practice, by solving actual instances of the problem and reporting the computation time required by the solver.
The set of alternatives is governed by two parameters -the number of criteria |N | and the number of reference alternatives | X | -that we consider exogenous and we try to assess their respective influence on the performance indicators. Note that this design is similar to a supervised classification context, where | X | and |N | are respectively the number of rows and columns of the dataset. Instances are sampled uniformly from the cube [0 , 1] |N | : we have considered the least favourable case where all the criteria take continuous values.
Blockchain-IOT security was evaluated by Banarjee et al. [18] , who also found a variety of IOT dataset problems that need to be resolved. The authors investigated the current IOT security approaches in depth in terms of intrusion detection and prevention, application classification, network structure classification, predictive security, and self-healing before discussing the blockchain's potential use in securely exchanging data in IOT databases  [19]. All modifications to patient information would be validated, merged with other transactions, and added as blocks to longer chains under this blockchain-based standard  [20]. The blockchain can help combat dangers such as integrity-based assaults that are overgrowing. Although there is a movement to build a full blockchain node onto IOT devices  [21, 22] , this is currently difficult because of the IOT's restrictions.
If policymakers can convince citizens to participate in the discussion, governments are the best testing grounds for actual, practical, and useful IOT applications  [24]. Understanding when and how these technologies will assist the government economically and socially is necessary in order to comprehend the significance of IOT in public services. The actual benefit of the IOT for the public sector is the potential for data aggregation from several sources  [25] , allowing the government to gather information on citizens' living conditions. The Internet of Things (IOT) lends itself naturally to applications in smart buildings, smart transportation, and smart water management, as well as the capacity to monitor programs that can increase public safety. In densely populated cities, traffic congestion is a serious issue.
Data Collection A questionnaire was designed to collect the data. Our questionnaire aimed to collect data to research the impact of the Internet of Things (IOT) in improving people's continuous usage intention of the IOT through blockchain adoption. A total of 1500 people were contacted by mail or phone and prompted to fill out the questionnaire. The final sample consisted of 1008 participants, eliminating unengaged respondents and questionnaires with missing values The participants were randomly chosen from a list on the Credamo platform (Credamo is a professional research and modelling integrated Chinese data platform). The Credamo platform is an online Chinese survey platform with more than 3 million registered participants. Data were collected from June to September 2022. The construct was divided into: A five-point Likert scale was used to measure the participants' opinions regarding the different variables under study: 1 (totally disagree), 2 (disagree), 3 (neither agree nor disagree), 4 (agree), and 5 (strongly agree).
Smart-PLS 3 software with a bootstrapping of 5000 samples was used to conduct the statistical analysis  [57]. Partial least squares structural equation modelling (PLS-SEM) is an alternative to the covariance-based structural equation modelling (CB-SEM) commonly used in Lisrel, AMOS, and EQS applications. PLS-SEM is advocated when the research objective is to explore theoretical extensions of established theories, where latent variable models contain formative structures, and where sample sizes are relatively small but structural models are complex. It does not make distributional assumptions. It is argued that the highly statistical power of PLS-SEM enables better identification of the relationships between latent variables compared to CB-SEM  [58].
The efficiency of simulation-driven design optimization based on surrogate models, depends strongly on the suitability of the surrogate model for the simulation data on which it is based. We investigate adaptive surrogate modelling methods that maximize the efficiency and the robustness for any optimization problem. Specific techniques include: adaptive sampling, noise filtering by metamodel tuning, and small initial datasets to give maximum freedom to the adaptation. These methodological advancements are demonstrated for an analytical test problem, as well as the shape optimization of the DTMB 5415 ship model for calm-water resistance.
The resulting MF surrogate is then explored by the optimization algorithm. The efficiency and the robustness of a multi-fidelity active learning approach depend strongly on its suitability for the specific problem being modeled. Since surrogate models have many free parameters and modelling choices, a practically useable method requires automated, adaptive tuning and selection of these parameters for each specific design optimization, see e. g. [12, 7]. The present authors' work on MF active learning and model auto-tuning includes adaptive sampling of the datapoints  [10] , noise reduction through tuned least-squares fitting of the surrogate models  [15] , the use of more than two fidelity levels  [14] , and reduced initial datasets to provide maximum freedom to the adaptation  [5]. The objective of the paper is to present the authors' current adaptive multi-fidelity active learning approach.
HYDRODYNAMIC FLOW SOLVER Simulations to create the input data are performed with the Navier-Stokes solver ISIS-CFD developed at ECN -CNRS, available in the FINE™/Marine computing suite from Cadence Design Systems. ISIS-CFD is an incompressible unstructured finite-volume solver for multifluid flow. The velocity field is obtained from the momentum conservation equations and the pressure field is extracted from the mass conservation constraint transformed into a pressure equation. Free-surface flow is simulated with a conservation equation for the volume fraction of water, discretized with specific compressive discretization schemes. A detailed description of the solver is given by  [6]. The computational grids are created through adaptive grid refinement  [13]. For the MF optimization, grid adaptation is used to take into account the need for several fidelities.
DTMB 5415 SDDO problem Since this optimization of 3D free-surface flows is characterized by high computational costs, the problem is solved only with the RS-MSE approach. The comparison with the FS approach is provided taking the results presented in  [15]. Since both the adaptive metamodelling strategy and the CFD simulations have changed, this does not provide a detailed assessment of one topic; rather, the comparison globally shows the progress that has been achieved in the last two years. Figure  6a -b present the multi-fidelity surrogate models at the last iteration of the active learning approach, the MF datasets, and the predicted optima. The sampling strategies for the two approaches are radically different: RS-MSE performed an exploration of the domain using only low-fidelity samples, correctly identifying the minimum region.
This early exploration leads to a reliable identification of the minimum region. Later on, thanks to the limits imposed on the extrapolated high-fidelity uncertainty, sampling of high-fidelity data in the observed minimum region is encouraged, which efficiently increases the precision in the minimum region. For the analytical test case, the RS-MSE approach achieves even better results than the RS approach, since its formulation is effective in forcing the fidelity selection towards the highfidelity after a large number of low-fidelity evaluations have been performed. This is necessary for this case, since the low-and high-fidelity optima are in different locations. Finally, the DTMB 5415 SDDO problem shows that the RS-MSE method is highly effective in identifying the region of the minimum, performing high-and medium-fidelity evaluations almost exclusively in this region.
ACKNOWLEDGMENTS CNR-INM is partially supported by the  Office of Naval Research  through  NICOP  grant  N62909-21-1-2042 , administered by  Dr. Elena McCarthy  and by  Dr. Woei-Min Lin  of the  Office of Naval Research Gobal  and the  Office of Naval Research , respectively. The work is conducted in collaboration with NATO STO AVT task group on "Goal-driven, multi-fidelity approaches for military vehicle system-level design" ( AVT-331 ).
The purpose ONERA's OAD platform, named MYSTIC  [16] , is to provide a software environment in which main aircraft disciplines can contribute, and able to capture the interaction effects between them and the corresponding snowball effects in the aircraft figures of merit such as fuel burn. Each major discipline can be integrated through a dedicated expert tool, to improve the accuracy of the aircraft performance prediction at OAD level. Depending on their level of fidelity to the physics, tools are classified as Level 0 (values from charts, abacus, semi-empirical formulae), Level 1 (basic physical equations are solved), and Level 2 (advanced physical equations are solved, using expert software such as CFD tools or FEM for example). The time of response must remain acceptable with respect to the OAD process.
Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i. e. , datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.