dataesr/scientific-paragraphs-categorization huggingface dataset:
• The formulation of the optimal placement of SDTs as a quadratic assignment problem (QAP), which extends the preliminary formulation in  [12]  by accounting for different types of IoT devices, their social features, mobility patterns, and the limited computing resources of edge servers. • The design of an approximation scheme to find nearoptimal solutions and the application of approximation techniques addressing the challenge of the NP-hardness of the formulated optimization problem. • The evaluation of the performance of the proposed algorithm against the formulated optimal solution through joined CPLEX and Matlab simulations. • The analysis of the time-dependent behavior of the SIoT-Edge system under conditions of device mobility for the dynamic SDT placement problem, and the consequent selection of the proper time interval duration between consecutive runs of the SDT placement strategy for the scenario of interest.
B. Contributions of the Work This work aims to fill the aforementioned critical gap by contributing a framework and methodology for the dynamic placement of SDTs that build upon the SIoT, edge computing, and digital twin concepts. More specifically, SDTs should be placed as closer as possible to their physical counterparts to ensure low-latency interactions between IoT objects and corresponding SDTs. This can also help to reduce the amount of traffic traversing the edge network segment. In addition, social relationships have to be accounted for, since SDTs may likely need to interact with each other to offer SIoTbased services. SIoT objects may need to quickly interoperate and discover services with a low data footprint on the edge infrastructure by querying the social network. Hence, SDTs of friend SIoT devices should preferably be placed in close edge servers.
VII. EXPERIMENTAL EVALUATION This section aims to assess the performance of the proposed SDT placement optimization strategy. First, we describe the conducted simulation campaign, including the considered scenario, settings, benchmark schemes, and metrics of interest. Then, we compare the results achieved through the optimal solution, the approximation solutions, i. e. , the graph-based and the LB and RINS heuristics, and some benchmark placement schemes, by means of a simulator tool based on the IBM ILOG CPLEX Optimization Studio 12. 10. 0 and Matlab R2021b software. Simulation settings are reported in the remainder of the section and are summarized in Table  III. A. Simulation Settings 1) Network and Edge Deployments: Similarly to  [39] , we consider the city center of Santander (Spain), which roughly has an area of 4 km x 4 km.
Proximity constraint Physical device-SDT maximum latency: Uniformly distributed in  [1, 10]  ms  [45]  BSs. An edge server is associated with each BS, in agreement with ETSI documents  [28]. 2) SIoT Devices and Mobility: We evaluate the proposal on the basis of a realistic object behavior taken from the large dataset 2  generated in  [39]  that tracks device interactions based on real IoT objects  3  and the small world in motion (SWIM) mobility model  [40]. SWIM is based on a simple intuition on human mobility, i. e. , people go more often to places close to their home and to the most popular places. We consider two device density settings (i. e. , portions of the dataset), namely, 50 and 100 users with N = 113 and N = 328 heterogeneous IoT devices, respectively, spanning from static devices to consumer devices carried by users moving in the selected area of interest (see Table  III  for the percentages of each category of IoT devices).
4) SIoT Features: Social relationships are associated with each device in the dataset. For the extracted 113 devices the percentage of established relationships corresponds to 50%, 21%, 15%, and 14%, for OOR, C-LOR, SOR, and POR, respectively. Whereas for the 328 devices setup, the percentage of established relationships is as follows: 60% OOR, 8% C-LOR, 1% SOR, and 31% POR. Unlike all the aforementioned settings, there are no specific clues about how to set the parameter p ij (t). Hence, without loss of generality, we consider a set of representative values throughout the simulation campaign to understand their impact on the conceived SDT placement strategy compared to solutions oblivious of the social relationship information (see in the following). In particular, we fix p ij (t) = 0. 1 for SOR and POR.
First, the formulated problem could be extended to overstep a possible limitation as, in this stage, only the communication latency is considered. The computation delay experienced at the selected edge server to perform the related IoT devices augmentation tasks is not currently accounted for in the problem. The formulation is quite flexible to accommodate such a further latency contribution. However, in order to do this, either assumptions are needed concerning the workload of each SDT associated with a given IoT device (as in  [8] ) or realistic patterns should be derived from actual IoT deployments. This issue will be among the subject matters of future work. Moreover, the considered dataset assumes that social relationships are static. However, they may evolve (be created and deleted) over time  [49]  and hence, placement decisions should vary according to the relationships' lifecycle.
The Machine Learning (ML) based solutions in manufacturing industrial contexts often require skilled resources. More practical non-expert software solutions are then desired to enhance the usability of ML algorithms. The algorithm selection and configuration is one of the most difficult tasks for users like manufacturing specialists. The identification of the most appropriate algorithm in an automatic manner is among the major research challenges to achieve optimal performance of ML tools. In this paper, we present an auto-explained Automated Machine Learning tool for Big Industrial Data (AMLBID) to better cope with the prominent challenges posed by the evolution of Big Industrial Data. It is a meta-learning based decision support system for the automated selection and tuning of implied hyperparameters for ML algorithms.
Therefore, in this context, the expertise of data-scientists is highly desired for the identification of the most appropriate algorithm configurations  [3, 4]. The selection of an algorithm or a family of algorithms that are more likely to perform better on a given combination of https://doi. org/10. 1016/j. softx. 2021. 100919 2352-7110/© 2021 The Authors. Published by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/bync-nd/4. 0/). datasets and their evaluation measures is a critical task  [3]. The ML algorithms generally have two kinds of parameters : • the ordinary parameters that the model learns and optimizes automatically based on its normal behavior during the learning phase, • the hyperparameters (categorical and continuous), which are usually manually set before the beginning of the model training.
In the context of the manufacturing industry, a major challenge is the selection of the feasible ML algorithm and the tuning of related hyperparameters. The algorithm selection and its configuration (tuning of hyperparameters) is a complex process because the ML algorithms are used as a ''black-box''. The performance is affected by the characteristics of the datasets and the configuration of algorithms hyperparameters  [5]. The selection and configuration of appropriate algorithm(s) is an error prone and time-consuming process due to the prevailing flaws while establishing the multiple configurations. It hence emphasizes the need to automate this process. The Automated Machine Learning (AutoML)  [6]  is a decision support system that partially or totally automates the ML pipeline. The major goal of this research field is to enable non-expert ML developers to effectively utilize ''off-the-shelf'' solutions, which would save time and effort for practitioners  [7, 8].
At its core, the AutoML strives to achieve the performance criteria (e. g. accuracy, recall, F1 score) in order to solve the respective ML tasks such as classification, regression, or clustering. In the intervening period, the AutoML optimizes a given performance criterion  [9]  to solve the particular task with respect to the dataset. Multiple approaches have been proposed to tackle the above problem  [9] [10] [11] [12] [13]  owing to the immense potential of AutoML. In this regard, several tools are available in the research community such as Auto-sklearn  [5] , TPOT  [12] , and AutoWEKA  [14]. There are also several commercial tools such as RapidMiner  [15] , H2O Driverless AI  [16] , Data Robot  [17] , and MATLAB ML toolbox  [18]. We observe that many industrial actors are competing around the goal of automating the machine learning  [19].
Software description It is often difficult to build an accurate predictive model based on ML for an industrial problem that is easy to be interpreted by non-expert ML developers  [6, 22]. The key idea behind the transparent and auto-explainable AutoML vision is to separate the recommendations from the explanations by using two modules simultaneously, as shown in Fig. 1. The Recommender module (AMLBID) for the recommendations and the Explanatory module (AMLExplainer) for explanations. The first module is used to provide the most appropriate ML configuration (s) for a given problem. It is aimed at maximizing the requested predictive metric (e. g. Accuracy, Recall, Precision). The second module is used to provide the rationale behind the recommended ML configuration (s) as well as auto-generated explanations to better understand the inner workings of the model in an interpretable manner through an interactive multi-view tool.
Recommendation module The AutoML tool for Big Industrial Data (AMLBID) is a metalearning based system in order to automate the problem of algorithm selection and its configuration. It uses a recommendation system that is bootstrapped with a knowledge base. The current knowledge base is derived from a large set of experiments conducted on 400 real-world manufacturing classification datasets which are collected from the popular repositories, such as OpenML, 1 UCI 2 and Kaggle 3. It accumulates the generation of more than 4 millions evaluated ML configurations (pipelines). Each pipeline consists of a choice of a ML model and the configuration of its hyperparameters. The system is able to identify effective pipelines without performing expensive computational analysis. For this purpose, the system explores the interactions between meta-features (characteristics) of the datasets and the pipelines topologies.
The recommendation phase is initiated with the occurrence of a dataset as a new input of the AutoML process. At this stage, the user selects a predictive analytical metric (e. g. Precision, Accuracy, Recall) to be used for the analysis. AMLBID then automatically provides a set of ML algorithms and recommended configuration of their related hyperparameters, so that the predictive performance becomes the first-rate performance. end-users to improve the predictive performances, in case of the unsatisfying results. Hence, it can increase the transparency, controllability, and reliability of AutoML DSS.
Software functionalities The current version of AMLBID is available on the PyPI package index in form of a Python-package 4 to facilitate its distribution and use. It presents a meta-learning based framework with major objective to automate the process of algorithm selection and the tuning of hyperparameters in supervised ML along with rational traceability. The available literature witness that the majority of state-of-the-art tools evaluate a set of pipelines by actually executing them on a given dataset prior to the recommendation. It is observed that such executions may require considerable computing time while consuming precious resources as per their availability  [22]. The proposed system (AMLBID) immediately produces a list of potential top-ranked pipelines using its knowledge base at an imperceptible computational time, hence it notably economizes the cost of resources and their provisional availability.
The available version of AMLBID in its present form supports 08 different classification algorithms from the popular Pythonbased ML library Scikit-learn. The Table  1  gives the detailed description of the supported algorithms and the tuned hyperparameters. Broadly, AMLBID is an interactive tool to guide the end-users for improving the utility and usability of the AutoML process with the following salient features: • It automatically (and accurately) selects the most appropriate ML algorithm (s) with related hyperparameters configuration through the use of a collaborative knowledge-base. 4 https://pypi. org/project/AMLBID/. The knowledge-base is continuously improved by running more tasks. It makes AMLBID smarter by achieving more experience, based on the growing knowledge-base. • It provides assistance when AutoML returns unsatisfying results, in order to improve the predictive performances.
That is achieved by assessing the importance and the correlation among the algorithm hyperparameters. • The framework is equipped with an explanation module, which allows the end-user to understand the diagnostic design of the returned ML models using various explanation techniques. In particular, the explanation artifact allows the end-user to: investigate the reasoning behind the AutoML recommendation generation process, and explore the predictions of a recommendation in a trustful manner, through linked visual summaries in form of graphical, tabular, or textual information for a higher trust. Therefore, AMLBID enables the end users to ask a series of what-if scenarios while probing the opportunities to use predictive models. It can improve outcomes and reduce costs for various tasks such as the dependencies of classical collaborations of domain experts and data-scientists.
Illustrative examples AMLBID broadly has two major modules, the AMLBID_ Recommendation module and the AMLBID_Explainer module. The AMLBID_Recommendation module recommends and builds highly-tuned ML pipelines, whereas, the AMLBID_Explainer module is used to intercept the inner working of the generated pipeline (s). In the following sections we discuss the functionality of these modules in further detail.
The recommendation module Listing 1 summarizes the interactions required to use AML-BID in order to recommend a pipeline. Subsequently, AMLBID attributes a score to the chosen pipelines and export the best pipeline to a dynamically stored. py file. As shown on line 5, we define the root directory of the dataset to be loaded. The recommend function (as shown on line 8) initializes the meta-learning process to find the highest-scoring pipeline according to the desired performance criteria. Then, the recommended pipeline is trained on the train-set of the provided samples (as shown on line 10). After the execution of this code, Recommended_pipeline. py (for instance, as given in listing 2) is generated dynamically using the export function (as shown on line 15). It contains the corresponding Python code for the optimized pipeline.
1 import numpy as np 2 import pandas as pd 3 from s k l e a r n. t r e e import D e c i s i o n T r e e C l a s s i f i e r 4 from s k l e a r n. metrics import c l a s s i f i c a t i o n _ r e p o r t 5 from s k l e a r n. model_selection import t r a i n _ t e s t _ s p l i t 6 7 data = pd. read_csv ( " Dataset. csv " ) 8 9 X = data. drop ( ' c l a s s ' , a x i s =1) 10 Y = data [ ' c l a s s ' ] 11  12  X_train , X_test , Y_train , Y _ t e s t = t r a i n _ t e s t _ s p l i t (X , Y , t e s t _ s i z e = 0. 3 , random_state =42) Listing 2: Generated python file.
Impact The ML modeling process generally operates as a highly iterative exploratory process. In reality, there is no one-size-fits-all model solution, i. e, there does not exist a single model or algorithm which can be used to achieve the highest accuracy for all datasets in a certain application domain. On that account, undertaking a large number of ML algorithms with different hyperparameters configurations would not yield a practical solution, rather it would be an inefficient, tedious, and time consuming process. In this context, the application of AMLBID is twofold: primarily it makes possible for non-data science specialists (practitioners and researchers) to build robust ML pipelines without the need for specialist assistance or intervention and even having to write a single line of code.
Subsequently, the white-box specificity of the proposed AutoML tool makes it possible to interactively inspect the inner-workings of the ML predictive models without having to depend on a data scientist to generate and interpret all the extreme plots and tables. The main objective of the AMLBID has been focused towards the design of a decision support system in order to support the non-expert practitioners and researchers. Prospectively, we intend its use in the domain of industry 4. 0  [24]  to take maximum benefit of ML techniques to optimize the automated manufacturing processes. In our previous works  [25, 26] , we studied the effectiveness of the recommendation module for the selection and parameterization of ML for problems concerning the manufacturing industry. The evaluation results respond the basic research question that is how some ML oriented manufacturing works could be further improved, simply through the use of a better ML algorithm configuration using the AMLBID.
Conclusion The machine learning based applications are increasingly desired due to their robustness for the large data analysis. Also, they can rapidly integrate ''off-the-shelf" solutions in multiple areas. However, the non-expert data analysts are more inclined to adapt the ML based solutions that are more easily persuadable, among diverse algorithms, with the help of their rational traceability. We argue that the adaptability of the powerful decision support systems based on the ML based solutions can be further enhanced with the help of comprehensive instructions regarding the recommended pipelines and their insights. Thus, In this work, we present AMLBID, a novel transparent and autoexplained AutoML support system. It also includes an interactive visualization module (AMLBID_Explainer) that allows ML experts and neophytes to easily inspect and analyze the automatic results of an AutoML decision support system.
We propose to use the general explanation methods of Au-toML systems; the advantage of AMLBID on account of performance comes from the remarkable time gain and prevention of resource bottlenecks. In this context, the contemporary ML based solutions are required to train multiple ML models from scratch for the fresh datasets prior to generate the list of recommended pipelines. It competes favorably to the state-of-the-art AutoML systems in the fields that are less tolerant to the delays such as the manufacturing industry. Whilst, the AMLBID particularly addresses this challenge to promptly produce a list of potential top-ranked ML configurations using its collaborative knowledge base. In practice, the confidentiality of the analyzed datasets is respected by the fact that the knowledge-base of AMLBID consists of the meta-features of datasets and not the actual data.
Acknowledgments The authors thank the  Université du Littoral Côte d'Opale (ULCO), France , School of engineering's and business' sciences and technics (HESTIM), Morocco and  CNRST Morocco  for the partial 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.
In the Gaia era, understanding the effects of the perturbations of the Galactic disc is of major importance in the context of dynamical modelling. In this theoretical paper we extend previous work in which, making use of the epicyclic approximation, the linearized Boltzmann equation had been used to explicitly compute, away from resonances, the perturbed distribution function of a Galactic thin-disc population in the presence of a non-axisymmetric perturbation of constant amplitude. Here we improve this theoretical framework in two distinct ways in the new code that we present. First, we use better estimates for the action-angle variables away from quasi-circular orbits, computed from the AGAMA software, and we present an efficient routine to numerically re-express any perturbing potential in these coordinates with a typical accuracy at the per cent level.
The use of more accurate action estimates allows us to identify resonances such as the outer 1:1 bar resonance at higher azimuthal velocities than the outer Lindblad resonance, and to extend our previous theoretical results well above the Galactic plane, where we explicitly show how they differ from the epicyclic approximation. In particular, the displacement of resonances in velocity space as a function of height can in principle constrain the 3D structure of the Galactic potential. Second, we allow the perturbation to be time dependent, thereby allowing us to model the effect of transient spiral arms or a growing bar. The theoretical framework and tools presented here will be useful for a thorough analytical dynamical modelling of the complex velocity distribution of disc stars as measured by past and upcoming Gaia data releases.
Hereafter the transformations from angles and actions to positions and velocities (and reciprocally) are instead evaluated numerically with AGAMA  (Vasiliev 2018). The code makes use of torus mapping to go from action-angle to position-velocity, and uses the Stäckel fudge for the inverse transformation. Our goal now is to obtain the Fourier coefficients of a known perturbing potential using these numerically computed actions (instead of epicyclic). We proceed in the following way to evaluate Fourier coefficients of the perturbing potential in Eq. (  5 ). The first step is to choose a set of actions within a range representing all the orbits of interest in the axisymmetric background configuration, each triplet of actions representing one of the orbits. For instance, in the solar neighbourhood we consider radial actions ranging from 0 to 220 kpc km s -1 , azimuthal actions ranging from 1200 to 2160 kpc km s -1 , and vertical actions ranging from 0 to 26 kpc km s -1 depending on the height above the Galactic plane.
This is computationally more intensive but should not, a priori, pose any conceptual problem. The tools presented in this paper will be useful for a thorough analytical dynamical modelling of the complex velocity distribution of Milky Way disc stars as measured by past and upcoming Gaia data releases. These tools will also be useful for fully self-consistent treatments of the response of the disc to external perturbations. The ultimate goal is to adjust models to the exquisite data from Gaia, which cannot be done properly with N-body simulations due to the vast parameter space to explore. The theoretical tools and the new code presented in this paper consequently represent a useful step in this direction.
Acknowledgements. We thank the referee,  Dr Paul McMillan , for a thoughtful and constructive report which has helped improving the paper and correcting a small bug in the first version. A. S. , B. F. , G. M. , S. R. , P. R. and R. I. acknowledge funding from the  Agence Nationale de la Recherche  ( ANR  project  ANR-18-CE31-0006  and  ANR-19-CE31-0017 ) and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 834148 ).