dataesr/scientific-paragraphs-categorization huggingface dataset:
g. accuracy) of a set of simplified learners, which became later on Landmarking  [44]. The intuitive idea behind Landmarking is that the performance of classifiers is related to the intrinsic features of the problem; thus, classifiers with similar accuracy may indicate problems with similar characteristics. Characterization with the use of Landmarkers is known as indirect characterization because it is not directly related to the attributes of the problem. The characterization of datasets using statistical and informational measures properties appeared for the first time within the framework of the STATLOG project  [45]. The authors use a set of 15 characteristics, spanning from simple ones, like the number of instances and the number of attributes, to more complex ones, such as canonical correlation between the attributes and the class.
This set of characteristics has been later applied in various studies for solving the ASP  [9, 13, 34]. This characterization approach is later extended, it is currently known as direct data characterization  [46]  and consist of extracting simple, statistical, and information-theoretic task properties that can be straightforwardly extracted from datasets by capturing information concerning data dimensionality, distribution, and the amount of information present in the data. Another characterization method is based on informations extracted by models built out on the problems  [16]. For instance, from a decision tree model constructed over a dataset, it is possible to extract structural informations about the tree itself, such as the number of leaves, nodes, and the tree depth  [47]. Similarly, in  [34] , the authors proposed AutoGRD, a meta-learning approach for algorithm recommendation through graphical dataset representation.
First, they applied the Random Forest algorithm to create a hierarchical representation of the datasets where the vertices represent the dataset's instances and the edges indicate the existence of a sufficiently high co-occurrence score among them. Then, the GCD method  [48]  has been used to generate the embedding representation of the obtained graph that is fed to train an XGBoost meta-model to predict the ranking of algorithms based on their performances. However, their approach suffers from a computational complexity of O(V4) where V is the number of vertices in the analyzed graph. It is further observed that this approach is not practical for large datasets of real world problems. Meta-features or data characteristics can be transformed to summarize the data, e. g. , by reducing data dimensionality.
For instance, in  [49] , the authors performed Principal Component Analysis (PCA)  [50]  to select relevant components, subsequently, a filter is used to extract the discriminating features and eliminate the redundant features. A different approach that has achieved popularity in recent years in learning most relevant features from data involves deep autoencoder neural network. We blieve that autoencoders provide the means to extract intrinsic meta-features from traditional ones. In this process, traditional meta-features are used by the autoencoder to learn relevant features, then, the knowledge captured in the hidden layers of autoencoder is used to extract latent meta-features. Once identified and extracted, they can be used by any meta-learning algorithm.
AekNN foundations We propose a novel approach to learn new latent meta-features by constructing new representations from traditional meta-features using a deep neural network, i. e autoencoder. An autoencoder is a type of artificial neural networks designed to learn efficient data representations (encoding) in an unsupervised manner  [51]. It has a similar structure to the feedforward neural network Multi-Layer Perceptron (MLP); however, the primary difference is that the number of neurons in the output layer is equal to the number of inputs, whereas the autoencoder tries to generate the inputs from the learnt representation (encoding) as close as possible to its original input. Consequently, in its simplest form, an autoencoder uses hidden layers to try to recreate the inputs. We can describe this algorithm in two parts : 1.
an encoding function Z = E(X) that converts X inputs to Z codings, and 2. a decoding function X ′ = D(Z) that produces a reconstruction of the inputs X ′. The goal is to create a reduced set of codings that adequately represents X by minimizing the reconstruction error L(X, X ′ ) , which measures the differences between the original input data X and the consequent reconstruction X ′. Formally, it can be shown as follows : The general architecture of an autoencoder is described by the number of hidden layers l n i and by the number of neurons per layer, where i is the index for the hidden layer and n is the total number of neurons in that layer. Each layer contains a learnt latent representation of the input data. Encoded hidden layer in the middle of the autoencoder, often called the bottleneck layer, comprises the final learnt latent features, where each latent variable is a representation of the original input in an abstract space.
Proposed approach AeKNN consists of two main phases i. e the learning phase and the recommendation phase. The former phase is carried out using the meta-dataset to train the autoencoder model. It allows the extraction of latent meta-features of data. Later, the recommendation phase is performed that principally uses the feed forward autoencoder model which has been generated in the learning phase to extract the latent meta-Features of the test data and, later on, the recommendation and ranking of the optimal pipeline (s) are estimated based on nearest neighbors in the meta-knowledge base. Figure  3  elaborates this process, while Algorithm 1 shows the pseudo-code of AeKNN that is thoroughly discussed in the following. The proposed methodology constructs an autoencoder which can be used as a latent features extractor.
After providing traditional meta-features as input, we train the autoencoder to learn a meaningful latent representation of the meta-dataset. Once the autoencoder is trained, the meta-dataset is forward propagated to extract the latent variables from the middle hidden layer to induce the AeKNN meta-model. The algorithm consists of two phases. The first phase corresponds to the learning of AeKNN (lines 1-5) while the second phase (lines 6-8) refers to the recommendation phase. During learning, AeKNN focuses on learning a new representation of the data to extract latent meta-features. This is done through the feed forward autoencoder model, using the training meta-data to learn the weights linking the units of autoencoder. During the recommendation phase, the optimal pipelines are generated.
Datasets, performance evaluation and meta-knowledge base construction Phase 1 of our experiments includes datasets selection, meta-features extraction, and performance evaluation. We actually collect 400 real-world classification datasets from the popular UCI 1 , OpenML 2 , Kaggle 3 , KEEL 4 repositories and from other real world scenarios. These datasets cover varied tasks with respect to their 1 https:// archi ve. ics. uci. edu. 2 https:// www. openml. org. 3 https:// www. kaggle. com. 4 https:// sci2s. ugr. es/ keel. size, number of attributes, their composition and class imbalance. Datasets characteristiques are illustrated in Table  1. Later on, in order to build the meta-knowledge base, we evaluate 08 common learning algorithms from the popular Python-based machine learning library, Scikitlearn.
These classifiers are AdaBoost, Support Vector Classifier (SVC), Extra Trees, Gradient Boosting, Decision Tree, Logistic Regression, Random Forest, Stochastic Gradient Descent (SGD) Classifier on each dataset, and recording their generalization performance in terms of Precision, Recall, Accuracy and F1score. We generate 1000 different combinations of the hyperparameters configurations for each execution of a classifier C over a dataset D. This process results in an aver- age of 8000 pipelines per dataset. In particular, for each classifier, we generate a list of all possible and reasonable combinations where we conduct, for each dataset, a random search among them. During the training phase, we use a fivefold stratified cross-validation strategy to construct the meta-datasets. As a result, the knowledge base consisted of more than 4 millions evaluated classification pipelines.
In parallel, we extract meta-features of training datasets using the PyMFE tool  [52]  for the general, statistical, info-theoretical, model-based and landmarking categories. Consequently, it generates a meta-dataset of 400 meta-instances and 60 metafeature (characteristics) that is used to train the deep autoencoder to extract the latent meta-features. The process of meta-features extraction is formalized by  [52]  as a function F : D → R k that receives a dataset D as input, and returns a features vector of k values characterizing the dataset, and that are predictive of algorithms performance when applied to the dataset. Formally, it can be detailed as follows : where D = {(x i , y i )|i ∈ {1,. , N }} is a dataset with N instances; x i and y i indicate the i- th training data and label respectively.
The measure m : D → R k ′ can extract more than one value from each data set, i. e. , k ′ can vary according to D , which can be mapped to a vector of fixed length k using a summarization function σ. In MtL, where a fixed cardi- nality is needed, the summarization functions can be, e. g. , mean, minimum, maximum, skewness and kurtosis. Thus, a meta-feature can therefore be seen as a combination of a measure and a summary function  [52]. In the recommendation phase, the combination of the meta-dataset and the results of all runs are stored in a meta-knowledge base KB where each record represents an execution of a classifier C with hyperparameters configurations H over a dataset D. In particular, each record stores the meta-features that model the dataset, the pipeline, and their interdependencies.
AeKNN architectures analysis AeKNN is characterized by the aforementioned l i n parameter that establishes the architecture of the model. This parameter allows the selection of different architectures in term of depth (number of hidden layers) and number of neurons per layer. Table  2  shows the considered architectures. For each model (architecture) the number of hidden layers, as well as the number of neurons in each layer, are shown. The results produced by the considered architectures using a benchmark of 20 real world datasets with different characteristics (as shown in Table  3 ) are presented as grouped by classification metrics and datasets. Tables 4, 5 and 6 summarize the evaluation results of each recommended pipeline for each architecture respectively. The evaluation results of the recommended pipelines by AeKNN with different architectures are presented in Table  4.
These results mainly consider the Accuracy classification metric. The obtained results indicate that the architectures with single hidden layer perform better on 17 out of 20 datasets, whereas the architectures with three hidden layers perform better on 2 out of 20 datasets while the five hidden layers architecture obtained best results on 1 out of 20 datasets. Therefore, it can be observed through the obtained rankings that single hidden layer architectures perform better most of the times  (17)  in given circumstances. The l i n = (32) works best for most cases (14 win), while l i n = (16) has shown disparate results as these can be simultaneously the best values for some cases and worse values for other cases. for the F1-Score classification metric. Based on the shown findings, there is no ideal architecture for all datasets.
In this case, the architecture l i n = (32) performs better most of the times  (8). Although, the single hidden layer architectures l i n = (16) and l i n = (8) are the top performers in each of the five cases. Despite being better the same number of times, the architecture l i n = (16) has more balanced results. - - 16 - - (32) AeKNN2 1 While, analyzing the classification results corresponding to the AUC metric, presented in Table  6 , it can be observed that the single hidden layer architectures obtained the best overall results in 20 out of 20 datasets. These rankings show that the single hidden layer model l i n = (32) outperformed more often  (11) , Whereas the l i n = (32, 16, 32) and l i n =  (32, 16, 8, 16, 32)  did not deliver any better results. Therefore, it is considered that l i n = (32) is the best among them.
Thus, in the fol- lowing the results of AeKNN, using the presented architecture, is compared against the classical kNN as well as other state-of-the-art meta-models. To validate and assess the competitiveness provided by the deep autoencoder-KNN based meta-model, we perform a comparative study to other state-of-the-art meta-models with an oversampling approach using the 20-benchmark datasets. We   [9, 13, 34, 53].
AeKNN vs traditional meta-models This section is focused to assess the competitiveness of the proposed meta-model. In this regard, a comparison has been made between the results obtained with AeKNN, using the l i n = 32 architecture as selected in the previous section, and the results obtained with the baseline meta-models on the same datasets. Tables  7  and 8  provide a summarized pairwise one-to-one comparison of the baseline meta-models against AeKNN indicating how often one meta-model is better than another for the three considered classification metrics. The results shown in Table  7  indicate that the proposed AeKNN works better than most of the traditional meta-models especially the classical kNN and RF for recommending well performing ML pipelines of a given classification dataset.
The AeKNN improves kNN in 19 out of 20 cases, obtaining the best overall results in 16 of them and obtains better results than the Random Forest meta-model in all cases for the Accuracy classification metric. Regarding the classification results related to the F1-score and AUC metrics, it can be seen that the l i n = 32 meta-model produces the best over- all results in 17 and 14 out of 20 cases respectively, representing an improvement of 17 times over the KNN. As shown in Table  8  and summarized in 7, the results obtained through the proposed AeKNN meta-model improve those obtained with the traditional kNN as well as the other state of the art meta-models for most of the datasets for the automated selection of ML algorithms.
Conclusion In this paper, a novel meta-model based latent features extraction method, namely AeKNN, is proposed. This model is based on kNN to recommend the optimal pipelines while its major objective is to mitigate the data characterization limitations. In this regard, AeKNN internally incorporates a model-building phase which is aimed at an extraction of latent meta-features, using a feed forward autoencoder. The main reason that has led to the design of AeKNN is the good results that have been obtained  by autoencoder when they are used to generate higher-level features and those of KNN for performing pipelines recommendation in meta-learning systems. AeKNN relies on a feed forward autoencoder to extract latent representations of a higher level that replaces the original meta-features. In order to assess the competitiveness of the proposed approach, a series of experiments are carried out.
Acknowledgements The authors thank the  University of the Littoral Cote d'Opale, School of engineering's and business' sciences and technics (HESTIM) Morocco , and  CNRST Morocco  for the financial support, and the CALCULCO computing platform, supported by  SCoSI/ULCO  ( Service COmmun du Système d'Information de l'Université du Littoral Côte d'Opale)  for the computational facilities.
6719 0. 9570 0. 7188 0. 8552 0. 8803 0. 8005 0. 4433 0. 9681 0. 7307 0. 9500 0. 9905 0. 9739 AUC 0. 9191 0. 7283 0. 9654 0. 7724 0. 7631 0. 8627 0. 9610 0. 8550 0. 7338 0. 8007 0. 9381 0. 7333 0. 8941 0. 9124 0. 6302 0. 8900 0. 7204 0. 9660 0. 9727 0. 8748 F1-score 0. 9823 0. 8743 0. 9250 0. 9635 0. 9246 0. 5887 0. 8515 0. 9715 0. 7229 0. 9343 0. 9381 0. 9321 0. 7998 0. 8394 0. 3747 0. 9130 0. 6886 0. 9571 0. 9653 0. 6161 AeKNN Acc 0. 9921 0. 7283 0. 8155 0. 9999 0. 9976 0. 6982 0. 888 0. 9645 0. 7229 0. 9708 0. 7921 0. 8793 0. 9671 0. 8759 0. 5207 0. 9735 0. 8344 0. 9740 0. 9362 0. 9843 Dataset APSFailure Higgs CustSat car kr-vs-kp airlines vehicle MiniBooNE jannis nomao Credi-g Kc1 Cnae-9 albert Numerai28. 6 segment Covertype KDDCup shuttle Gas_Sens-uci.
[29]  using Hartree-Fock-Bogoliubov (HFB) calculations with the BSk14 effective Skyrme interaction  [30]. These densities are tabulated (total angular momentum, parity, energy) for a large number of nuclei in the RIPL-3 database, together with correction parameters. These parameters, denoted c and p, were introduced to improve the reproduction of the discrete levels and the mean resonance spacing of the nuclei, when these quantities are available  [18, 29] : ρ HFB (E, J , π) = e c √ E-p × ρ tab (E -p, J , π) , ( 9 ) where ρ tab is the tabulated value of the level density from the RIPL-3 database. The main identified issue with the use of these level densities in Fifrelin is the definition of the cut-off energy, E cutoff. Indeed, the one provided in RIPL comes from a fitting procedure between the CTM model and the discrete level schemes of nuclei  [18].
[3] , we confronted the Fifrelin calculations of type F-3 to this experimental data. The results are depicted in Fig. 5. In this figure, some deviations can be observed for the calculated total γ multiplicity as a function of TKE, with respect to the experimental data. First, there is an overestimation of the calculated Mγ (TKE) for TKE 180 MeV. The calculation also presents a dip for TKE values between 200 and 210 MeV, which does not appear in the VESPA data. In order to better understand this behavior, we separated Mγ (TKE) for light (L) and heavy (H) fragments, as depicted in Fig. 5. It appears that both the experimental data and the calculation show a rather flat distribution as a function of TKE for the light fragments. We observe that the low-energy overestimation seems to be caused by the light fragments, while the dip around 200 MeV comes from the heavy ones.
On this basis, we can narrow the previously stated research question and define the main research question addressed in this work: How can Digital Twin-enhanced smart software systems be designed that support the continuous and reliable adaptation of high-availability mission-critical CPS in their physical space and cyberspace to emergent needs and uncertainties in their environmental and operational context? Our approach to addressing the two stated research questions is by transferring De-vOps principles to the domain of high-availability mission-critical CPSs. DevOps (short for Development Operation) is a set of principles to integrate and streamline the development and operation of modern software systems. DevOps originates from the IT domain and is considered the most effective way to provide value with IT systems through the creation of cross-functional product teams that manage software services throughout their lifecycle, along with automating engineering processes such as the building, testing, and deployment of IT systems  [23] [24] [25].
In recent decades, several engineering processes, design patterns, tools, frameworks, libraries, and middleware technologies have been developed to accelerate software engineering for the consumer market. These IT tools can also be used and integrated for engineering and managing software that is deployed in non-critical industrial cloud and edge environments  [26] [27] [28] [29]. However, transferring DevOps to mission-critical industrial and embedded real-time environments is a challenging problem  [19, 30, 31]. Nevertheless, the transfer of DevOps principles to mission-critical embedded CPS encompasses the core of our design and research methodology to enable system adaptation and evolution for smart CPS. 1. 1. Toward Resilient, Sustainable, and Human-Centric Smart CPSs: The Potential of DevOps Although the ongoing Industrial Revolution is not over (Industry 4.
These processes entail downtime, during which costly equipment stops generating revenue. In addition, the primarily manual and laborious interactions with the system are error-prone, time-consuming, and costly. These manual onsite interventions will eventually become unmanageable in the increasingly dynamic industrial environments and ecosystems. Hence, the industry calls for solutions that effectively support the industrial use cases listed in Table  1  (see U1 to U4)  [47] [48] [49] [50] [51]. With the introduction of DevOps for CPS, we aim to streamline the offsite developmentcentric and the onsite operation-centric processes. To that aim, we seek to transfer the following DevOps practices: continuous integration (CI), continuous deployment (CD), and continuous experimentation (CE). CICD comprises automated software testing, integration, building, and deployment to reduce manual work and streamline software deployment  [52, 53].
In particular, Digital Twins have the potential to serve as a unified means to describe, document, abstract, monitor, access, manage, and transfer runtime and engineering knowledge between humans and machines and across CPS layers and lifecycle phases  [55]. In order to unfold this potential, developing a software system that supports the integration and interconnection of Digital Twins throughout all CPS layers (i. e. , cloud, edge, and embedded control layers) becomes necessary. The transfer of the Digital Twin principle to the embedded and mission-critical CPS layers is among the main challenges toward realizing DevOps for CPS. As shown in Figure  1a , we have identified three design dimensions required to provide the outlined smarter CPS capabilities: • Adaptability denotes the ability of a system to adjust its properties to new conditions.
[59]. Besides defining a structured research agenda/scope and experiment design, one of their key recommendations is to establish an explicit mapping between software architecture components and their corresponding MAPE-K elements. We establish this explicit mapping in Section 4. 2. Research Scope: To the best of the authors' knowledge, this work is the first to investigate DT-enhanced self-adaptative software design models for embedded OT control units targeting sub-millisecond real-time closed-loop control. Hence, this work's case study has the characteristics of a feasibility study, intending to assess the proposed SWF's (and its underlying design models) effectiveness and applicability. Therefore, this work's experimental goals are to assess the SWF's MAPE-K characteristics alongside the execution of mission-critical closed-loop control services.
The Challenge of Runtime and Development Time Convergence In Section 1, we explained the need for adaptation and evolution in software systems and showed that the realization of these properties results in blurring the traditional segregation between runtime and development time, which we denote as the convergence of runtime and development time. This section builds on our initial arguments and discusses industry practices to address emerging needs. Agile software practices, including DevOps, are increasingly adopted by regulated industries, where safety and standard compliance are cornerstones in software development for mission-critical CPSs. These industries rely on rigorous engineering processes to deliver certifiable or certification-ready software. As concluded in  [71] , agile practices and CICD pipelines produce high-quality and certifiable software ready to be delivered more frequently to the end-user.