dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 4: Administration A total of 344 students aged 16 to 33 years responded to F-eHEALS, two additional scales (HLS-EU-Q16 and the PAM-13), and questions on their sociodemographic characteristics. This population was relevant because the suitability of the tool for a population of young adults has already been demonstrated   [32, 33, 35, 37, 41, 42]. To target this population, the scales were shared on Facebook groups of students from several French universities. All respondents had to be students and have French as their native language. The instructions given to the participants explained the main objective of the study regarding the translation of the eHEALS scale and provided some explanations to justify the presence of the other questionnaires (ie, HLS-EU-Q16 and PAM-13): "It concerns your perceptions of your ability to search, find and process health-related information on the Internet, your involvement in your health.
Overview The results were analyzed using SPSS (version 22; IBM Corp). All the data of this study are in open access  [59]. The fidelity assessment was performed by analyzing the internal consistency of the tool, as assessed by Cronbach α. Cronbach α was >. 7, which indicates that the items in the study are consistent  [60]. Construct validity was measured by means of three statistical analyses: (1) exploratory factor analysis (principal component analysis with varimax rotation), (2) analysis of interitem correlations, and (3) analysis of construct effects using Pearson correlations among HLS-EU-Q16, PAM-13, and F-eHEALS.
Internal Consistency and Temporal Stability Of the 344 participants who responded to the questionnaires, 170 (49. 4%) participants agreed to be recontacted for the retest. Of these 170 participants, 84 (49. 4%) responded to the questionnaire. Of the 84 participants, 6 (7%) participants had to be excluded because their identification did not allow a link to be made with the database of contacts from the first test. Thus, 93% (78/84) of the participants were included in the retest. The temporal stability of our sample was (intraclass correlation coefficient=0. 84; 95% CI 0. 76-0. 9; F 77,77 =6. 416; P<. 001). We also observed a strong and positive correlation between the 2 sessions (r=0. 72; P<. 001). The internal consistency of F-eHEALS was estimated by a Cronbach α of. 89.
This is probably owing to the common Latin roots of French and Italian languages. However, these similarities are not observed in other Latin translations (eg, Portuguese and Spanish). Moreover, in an Italian validation with a population of nurses, De Caro et al  [33]  observed a unidimensionality. This shows the instability of eHEALS according to the population. Item 8 also seemed problematic in other validations observing a 2D scale  [39]. This is likely owing to the fact that in the original article, the loading of item 8 was not excellent. Item 8 loads at 0. 6 without rotation, which does not seem to be good  [62]. However, to the best of our knowledge, no study on the validation of eHEALS has removed item 8 from the questionnaire, despite its weakness. Therefore, despite the difficulties of discrimination and the ambiguous load factor of item 8, consistent with Dale et al  [39] , Gazibara et al  [37] , Richtering et al  [66] , and Shiferaw  [30] , we decided to retain it.
Considering these indications, we suggest calculating scores for each factor and an overall score in future uses of F-eHEALS. The variance explained by the 2-factor model in this study is also relatively high compared with that in other similar validation studies in other languages  [27, 32, 35]. The quality of representation of the items (ie, whether the items are well represented by the dimensions of the construct) was judged to be excellent because all the items showed a score >0. 45. Thus, we have decided to retain all items in the translation and validation of eHEALS. The means of the interitem correlations of the original eHEALS scale  [7]  are consistent with the F-eHEALS results. The effect of the F-eHEALS scale construction was acceptable. To validate its content, we formulated 3 hypotheses, which have proved to be correct.
Our comparisons were made on the basis of a wide variety of cultures and languages. We hope that translations of scales, such as F-eHEALS, will promote studies in the field. Fifth, eHEALS, HLS-EU-Q16, and PAM-13 are subjective assessments  [38] , as the level of eHealth literacy is self-reported by respondents and may be overestimated or underestimated. Therefore, it will be interesting to clarify the links between subjective and objective assessments to better understand the margins of error in these tests. Recently, since the development of interactive applications-social networks, forums, and so on (Health 2. 0)-that help people communicate about their health, eHealth literacy is required to be considered as a broader skill  [35]. To bridge the digital divide, it will be important to use instruments that measure all varieties of eHealth literacy.
Acknowledgments This project was financially supported by the  European Regional Development Fund, Evolucare , and  Programme d'Investissements d'Avenir. The authors thank their translators,  Catherine Jackson ,  Patrick Jackson , and  Guillaume Fenger  and the  Maison des Langues de l'Université de Picardie Jules Verne (Amiens). The authors would also like to thank  Yannick Gounden  for his participation in the expert committees,  Vanessa Laguette  for the diffusion of the questionnaires, and all the students who agreed to participate in the study.
Predictive maintenance aims to improve reliability, availability, and efficiency of manufacturing systems. In the manufacturing industry, data used for predictive analytics are often heterogeneous and big in volume  [6]. Within manufacturing processes, big industrial data is collected from sensors installed on machines as well as manufacturing environments. To process the collected big data, statistical AI technologies are used to extract valuable information from heterogeneous data sources (e. g. product data, operational data, environment data). These technologies aim to achieve in-depth understanding and gain insight from data for accurate and timely decision making  [7, 8]. The strong computational capability of statistical AI technologies is significantly improving the efficiency and accuracy of decision making.
0, named KSPMI. KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The execution of the software starts from the chronicle mining (a special type of sequential pattern mining approach) process on industrial data sets, after which machine degradation models are extracted from the data indicating the time of occurrence of future machinery failures. After that, symbolic AI technologies, especially domain ontologies and Semantic Web Rule Language (SWRL) rule-based reasoning, use the extracted chronicle patterns to query and reason on system input data with rich domain and contextual knowledge. In this way, KSPMI enables the detection of future machinery failures as well as the prediction of their time of occurrence. The prediction of failures is associated with a pair of numeric values, which give the upper and lower numeric bounds of their time of occurrence.
To improve the performance of failure prediction, KSPMI implements a rule pruning process for selecting a subset of best-quality SWRL rules for ontology reasoning. The rule pruning process is followed by an expert rule integration process, which enables the rule base to improve its quality progressively. During the integration process, KSPMI is programmed able to detect and avoid potential rule quality issues such as rule conflict, subsumption, and redundancy. The performance of KSPMI is evaluated and tested on both real-world and synthetic data sets. To the best of our knowledge, KSPMI is the first software that combines chronicle mining and ontology reasoning for Industry 4. 0 predictive maintenance. The remainder of this paper is structured as follows. Section 2 gives a comprehensive literature review on the existing approaches for Industry 4.
Knowledge-based approaches A knowledge-based system maintains a knowledge base that stores the symbols of a computational model in the form of statements about the domain and performs reasoning by manipulating these symbols  [21]. This type of systems measure the similarity between a new observation and a databank of previously described situations and deduce appropriate decisions  [20]. Knowledge-based approaches can be further classified into knowledge graphs and ontologies, rule-based systems, and fuzzy systems. The term knowledge graph is often used as a synonym for ontology  [22]. The concept of ontology originated in philosophy. It studies concepts that directly relate to being, in particular becoming, existence, reality, as well as the basic categories of being and their relations  [23].
In computer science, an ontology is considered as ''an explicit specification of a conceptualization for a domain of interest  [24] ''. Within this definition, specification refers to an act of describing or identifying something precisely. This requires the concepts and relationships in ontologies to be clearly defined by using formal logic. Conceptualisation stands for an intentional semantic structure that encodes implicit knowledge constraining the structure of a piece of a domain  [25, 26]. Normally, the conceptualisation within an ontology is formalised by a logic theory that is written in a certain language. Also, ontologies provide reasoning capabilities by which new knowledge can be inferred. To facilitate predictive maintenance, an ontology called OntoProg is described in  [27]. OntoProg is a generic ontology that incorporates international standards for rigorous conceptualisation.
This ontology is designed for Prognostics Health Management (PHM) mechanical items of manufacturing systems. The main goal is for predicting the Remaining Useful Life (RUL) of mechanical components. To enable timely interventions of maintenance, SPARQL queries  [28]  are used to retrieve the stored knowledge about real-world activities. In  [29] , a domain ontology for smart condition monitoring is introduced. This ontology formalises domain knowledge relevant to condition monitoring for manufacturing processes. It is developed into three ontology modules: the Manufacturing Module, the Context Module, and the Condition Monitoring Module. The effectiveness and usability of the ontology are tested on a conditional maintenance task of bearings in rotating machinery. The domain ontology is further extended in the literature  [30] , where a domain ontology named Manufacturing Predictive Maintenance Ontology (MPMO) is developed.
In their work, MPMO is used together with sequential pattern mining techniques to enable anomaly detection and prediction on production lines. The proposed ontology is tested on a real-world data set collected from a semi-conductor manufacturing process. Rule-based systems are another type of knowledge-based approach that has been widely used in Industry 4. 0. Normally, this type of systems encodes problem-solving knowledge of domain experts in terms of a set of situation-action (IF-THEN) rules  [31]. In the literature, rule-based systems have been used for intelligent product design  [32] [33] [34]. To facilitate the use of the knowledge sources scattered within different engineering domains, rule-based reasoning are used together with domain ontologies to enable intelligent retrieval of abstract solutions for product design.
In their approach, model-based and digital twin data-driven methods are jointly used. Nevertheless, the hybrid approach does not involve a knowledge module which captures domain knowledge coming from experts, machine documents, and international standards. In  [48] , a hybrid intelligent model is developed to facilitate degradation process prediction of rotational machinery. In the model, only a selective neural network ensemble model is used. Since the neural network works as a black box and lacks semantic interoperability, a uniform knowledge representation framework is needed. For our KSPMI system, we aim to address the aforementioned challenges and limitations by combining data-driven and knowledge-driven approaches.
To address the above challenges, KSPMI implements a rule base integration module where the integrated rule base is verified and refined. During this process, Redundancy, Conflict, and Subsumption are considered as three important rule quality issues. Following Definition 8, the definition of rule Redundancy, Conflict, and Subsumption are formalised as follows  [56] : Definition 9 (Chronicle Rule Redundancy). Two chronicle rules  ∶  ∧  →  and  ′ ∶  ′ ∧  ′ →  ′ are redundant iff the two rules have the same sets of events, and the events have the same temporal constraints: ChroRedundancy(𝑅, 𝑅 ′ ) ⟺ (𝐸𝐶 = 𝐸𝐶 ′ ) ∧ (𝑇𝐸𝐶 = 𝑇𝐸𝐶 ′ ) ∧ (𝑇𝐹 = 𝑇𝐹 ′ ). ( 1 ) Definition 10 (Chronicle Rule Conflict). Two chronicle rules  ∶  ∧  →  and  ′ ∶  ′ ∧  ′ →  ′ have a conflict iff the two rules have the same sets of non-failure events and the same temporal constraints for these non-failure events, but conflicting temporal constraints of a failure: ChroConf lict(𝑅, 𝑅 ′ ) ⟺ (𝐸𝐶 = 𝐸𝐶 ′ ) ∧ (𝑇𝐸𝐶 = 𝑇𝐸𝐶 ′ ) ∧ (𝑇𝐹 ≠ 𝑇𝐹 ′ ).
Proposed approach This section gives the core approach based on which the KSPMI software is developed and programmed. As a hybrid predictive maintenance method that combines chronicle mining and semantic technologies, the proposed approach consists of two main phases. These two phases are further divided into four main processes: (i) the frequent chronicle mining process; (ii) the SWRL rule generation and pruning process; (iii) the expert rule integration process; and (iv) the failure detection and prediction process. Fig. 4  gives an overview of our approach. We describe these four processes as well as their main steps: 1. The frequent chronicle mining process. Chronicles are a type of sequential patterns that incorporates rich temporal information of different events for decision making.
Software development environment Several software and tools are used for the development of KSPMI. They are listed as follows: • Java Platform, Standard Edition (Java SE). 2  Java SE is a computing platform for developing and deploying portable codes for desktop and server applications. This platform uses Java as programming language and belongs to the Java software-platform family. • Eclipse. 3  It is an open source and integrated development environment (IDE) for computer programming. Eclipse maintains essential tools for Java developer, which includes a Java IDE, a Concurrent Version System (CVS) client, Git client, Maven integration and WindowBuilder. It also contains a base workspace Algorithm 2 Algorithm to detect the three issues when an expert rule is integrated into the chronicle rule base, adapted from  [56].
The UCI SECOM data set In the UCI SECOM data set, 1567 data records and 590 attributes are collected. The rows in the data set stand for test points. Every test point consists of 589 attributes, where each attribute is associated with its numerical value. The values in the second last column are the timestamps when these test points are recorded. Since the timestamps are in ascending order, the test points are considered as a sequence of events that are timely ordered. A binary label is given in the last column for each row, which indicates a pass (0) or fail (1) of the recorded test point. In total, 104 number of records represent the failures of production. The data is stored in a raw text file, within which each line represents an individual example of recording with its timestamp. The features are separated by spaces.
In the input data set, events are associated with timestamps, and each test point is given a binary label (e. g. -1 corresponds to normal status, and 1 corresponds to a failed status). Fig. 5  shows an excerpt of the UCI SECOM data set as system input. However, not all the data contained in the UCI SECOM data set is relevant to the failure prediction task. Indeed, some of the data is irrelevant and is considered noise. To deal with this challenge in high-dimensional data, feature selection is a feasible way to remove irrelevant and redundant data. This can improve learning accuracy, facilitate a better understanding of data, reduce data processing time and memory consumption  [61]. In this work, the feature selection method introduced in  [62]  is used to select a relevant subset of the feature set for failure prediction.
After the feature selection step, data discretisation  [63]  is performed on the data set. The goal of data discretisation is to transform continuous variables, functions or data attribute values into a finite set of (discrete) intervals. Each interval is associated with a range of data values. For each variable, 20 bins are created to discretize continuous variables into discrete numeric intervals. We then use integers from 1 to 20 to represent these discrete numeric intervals. After that, data sequentialisation is implemented to transform the UCI SECOM data set into a sequential format. This will enable us to perform frequent chronicle mining on the data. To do this, the sequentialised data is structured in the form of pairs (event, timestamp). Within each data sequence, the last event represents a failure.
The frequent chronicle mining GUI The execution of KSPMI starts with the frequent chronicle mining phase on input data. To show the functionalities of the software, we take the pre-processed UCI SECOM data set as input. After frequent chronicle mining, we obtain a set of frequent sequential patterns which contain the order information of events. The CloSpan algorithm also allows to filter less informative patterns and retain closed frequent sequential patterns that best describe the data set. Thereafter, we execute the Clasp-CPM algorithm to obtain a set of frequent failure chronicles that describe failures events and their temporal constraints. The frequent failure chronicles are mined from similar data sequences to the one shown in Fig. 2 , where the last events are failure. For a failure chronicle, the last node also represents a failure (e.
However, due to a certain degree of imprecision in real-world data, some of the extracted chronicle rules may suffer from low quality. Also, overfitting is a problem that often arises in rule-based classification and prediction problems  [64]. In this context, these lowquality and overfitting rules may reduce the accuracy and efficiency of KSPMI. Therefore, we design a rule pruning software module to prune the chronicle rule base and select a subset of high-quality rules for failure prediction. In this context, KSPMI implements a rule pruning process applied to the rule base obtained from frequent chronicle mining. As introduced in Section 4, two measures are computed to evaluate rule quality. We identify accuracy and coverage as the two rule measures for evaluating the quality of SWRL rules, as these two measures are two basic characteristics of a rule and indicate a rule's reliability  [65].
To visualise the results, a subset of best-quality rules are displayed on the left side, and the objective function values of them can be plotted by clicking the button ''Plot the Best Rules''. For UCI SECOM dataset, the rule pruning results are shown on the right side of Fig. 8. We select those rules within the first Pareto Front and consider them as best quality. After rule pruning, we use the best-quality rules to construct a new chronicle rule base.
The failure detection and prediction GUI After the expert rule integration process, we proceed to the failure prediction step. To enable failure prediction, we use Drools rule engine to perform ontology reasoning on the individuals that are populated in the MPMO ontology (this ontology is introduced in our previous paper  [30] ). Within this step, ontology reasoning is performed on the object and data properties of the individuals in MPMO. After ontology reasoning, a SQWRL query is created to retrieve the prediction results. SQWRL is built on the SWRL rule language that treats a SWRL rule antecedent as a pattern specification for a query. In the consequent part, a SQWRL query replaces a rule consequent with a retrieval specification. By matching the individuals within the ontology, the proposed SQWRL aims to retrieve the temporal information of failures.
Evaluation of rule quality We then conduct another experimentation to simulate the expert rule integration process and to evaluate the overall quality of the rule base. To do this, the UCI SECOM data set is separated into a training set (80%) and a test set (20%). We apply frequent chronicle mining on the training set to extract chronicles and rules. The rules are then applied to the test set for failure prediction. Within the training set, a subset of chronicle rules that have the highest fitness values are treated as expert rules. The reason we do this is that these high-fitness chronicle rules have a higher chance to validate the data sequences in the test set. Thus they are considered as expert rules. The remaining rules are regarded as chronicle rules. To obtain expert rules, the fast non-dominated sorting algorithm  [59]  is used to select a subset of best-quality chronicle rules.
Same as the experimentation performed on the UCI SECOM data set, the first step in this experimentation is to apply frequent chronicle mining one synthetic data sets. Fig. 14  shows a screenshot of the frequent chronicle mining GUI we obtained from on synthetic data set. Within this data set, the number of data sequences is 1000, and the chronicle support is set to a randomly-selected number as 0. 73 for the Clasp-CPM algorithm. As a result, the maximum frequency we obtained from chronicles is 893, which indicates that this chronicle appeared in 893 data sequences within the whole data set. There are in total 20 chronicles extracted from this data set. For this case study, the integer 0 in chronicles also stands for a failure event. After frequent chronicle mining, we proceed to SWRL rule generation and pruning.
Fig. 16  presents the expert rule base for this synthetic data set. In the end, after pushing all 8 expert rules into KSPMI, the integrated rule base is launched for failure prediction. For this experimentation, the integrated rule base consists of 5 SWRL rules. After launching the SQWRL query, we obtain 28 rows of results. Fig. 17  shows the prediction results for this synthetic data set. Similar to the experimentation conducted on the UCI SECOM data set, the two table columns on the right side together with the events pairs (eventsfailures) give the temporal constraints for failure occurrence. Up to this step, we have accomplished the failure prediction task for this synthetic data set.
KSPMI has been tested and validated on both real-world and synthetic data sets. The contributions proposed in this paper may induce potential future research. We describe them as follows: • The first future work is the consideration of more rule quality measures. We aim to involve more rule quality measures for rule pruning, such as Agreement, Information, and Logical Sufficiency  [58]. Among them, Agreement evaluates the association     between the elements of a contingency table with its main diagonal. Information measures the amount of entities a  needs to encode for correctly classifying an instance into a certain class. An information score can be derived as   = - 𝑁 𝑎 𝑁 + 𝑙𝑜𝑔 𝑁 𝑎𝑓 𝑁 𝑎 , where the values of   ,   ,  are computed from Table  1. Logical Sufficiency is a standard likelihood ratio statistics that evaluates whether a rule  is encouraging for the classification of class.