dataesr/scientific-paragraphs-categorization huggingface dataset:
Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting we have an input X in a general space, and an output Y = f (X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S 1 and S 2 of different sizes such that only f (S 1 ) is available. We tackle the problem of selecting a subsample S 3 ∈ S 2 of smaller size on which to run the complex model f , and such that distribution of f (S 3 ) is close to that of f (S 1 ). We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self-noise data set.
P( min C k ∈Cn ∥F k -F n-k ∥ ≤ t) = 1 -P( min C k ∈Cn ∥F k -F n-k ∥ > t) ≥ 1 -P( min C k ∈Cn (∥F k -F ∥ + ∥F -F n-k ∥) > t) = 1 -P(∥F k -F ∥ > t/2) L + P(∥F n-k -F ∥ > t/2) L ≥ 1 -2e -mt 2 L/2 + 2e -(n-m)t 2 L/2. ( 8 ) Figure  1  shows the behaviour of the obtained lower bounds for simulated Gaussian datasets using m = 1000 and L = 1000, which shows the good accuracy of inequalities (7 -left) and (8 -right).
The design science research process The main goal of Design Science Research is the creation of successful IS artifacts  (Gregor and Hevner 2011; Hevner et al. 2004; Peffers et al. 2008; Riemer and Seidel 2013; vom Brocke et al. 2020). Those artifacts can be virtual (e. g. databases, programs) or physical products (e. g. , mobile device) or methods such as a prototyping methodology or an IS management strategy. As the word "design" is both a noun and a verb, a theory can be about both the principles underlying the form of the design and also about the act of implementing the design in the real world  (Gregor and Jones 2007). Gregor and Jones (2007)  also underline the relationships between the two main types of IS/IT artifacts, namely: (i) the materials artifacts, such as an instantiated product or an instantiated method: they have a physical existence in the real world; and (ii) the abstract artifacts, such as methods, constructs, and models: they usually need to be communicated in words or diagrams for having a physical existence.
From a design perspective, additional data should be collected to measure user's experience with the learning app (e. g. , conducting a survey or semi-structured interviews). The information collected will be useful to refine both the method and also the mobile app. Last, in order to better understand the organizational impact generated by the training method, it could be interesting to evaluate if the use of the app is correlated with change on individual key performance indicators (e. g. , productivity, satisfaction, etc. ). Finally, it is hoped that the advances made in this paper will stimulate further reflection about how the gradual emergence of fluid digital ecosystems can leverage the development of new forms of employees' competence acquisition processes. Funding Open access funding provided by Università degli Studi G.
The cost estimation correlations for both CAPEX and OPEX according to the selected unit are further detailed in Appendix A. Finally, the TAC is defined as: TAC [$/y] = OPEX + CRF • CAPEX (1) where the CRF is the Capital Recovery Factor and it is given as: CRF = i • (1 + i) n (1 + i) n -1 ( 2 ) where i is the discount rate and n is the unit lifetime  [53]. This coefficient accounts for capital depreciation over time. The most suitable values for those two terms can be found in the available literature and databases according to the specific process unit  [54, 55]. After that, a second design procedure is performed in order to account for the uncertain operating conditions impact on operating costs. Although the procedure does not depend on the uncertainty PDF, some specific trends should be used in this work to present some quantitative results.
So, to numerically check the accuracy of the fit between a given probability law and the corresponding statistical data produced by numerical simulations, we considered two finite element P k 1 and P k 2 (k 1 < k 2 ) and we fixed a given number of meshes to be built by the generator of meshes, each of them associated to a same given mesh size h. Then, to evaluate the relative accuracy between the two concerned finite elements, we tested for each mesh if u (k 2 ) h -u 1 is lower than u (k 1 ) h -u 1. Then, we repeated the same process for different values of h which gave us a function of h, namely, the frequency of cases when the approximation error associated to P k 2 finite element is lower than the one computed with the P k 1 one. In all cases, we use the FreeFem++ package  [13]  to compute the P k i , (i = 1, 2), finite element approximations.
An application area is the analysis of artificial lift and ESP systems (see, e. g. ,  Al-Aslawi et al. 2010; Skoczylas et al. 2018; Vandevier 2010). RT then indicates the period during which a specific item was in use, from the time when it was started (e. g. , a pump motor started), until it was stopped for whatever reason, and up to the point in time where the data were collected. The rules for when to stop the data collection could vary but should reflect the relevant "in use" time for the items and applications considered. For example, Gleirscher (2017) considers run-time for automated vehicles. For an oil and gas application,  Quinn et al. (2014)  consider the run-time of vapor recovery units (VRU), a type of equipment for flare gas utilization, and use the concept to indicate how long the equipment is running before stopping for some reason.
(2014)  use data collected from 2 years in operation; they then compare the average run-time of 300 days for all systems, and 258 days for pulled (failed) systems, with a much higher MTTF of 1651 days. There are different ways to illustrate the difference (e. g. , chronograms), but the point is that the selection of the RT metric might influence reliability management. However, there are also examples from literature where the RT AVG and MTTF concepts are mixed; for example, in relation to ESP reliability,  Mubarak et al. (2003)  formulate: "… it is shown that 'Mean time to failures' (Average runtime) significantly increased…" No clear distinction is then made between the metrics. For the situation where only failed systems are considered for the RT AVG , the approximation of the mean is made by censoring on items with failure, i.
The RL AVG is: "the average value of run-life of all artificial lift systems in a specific field currently pulled out or failed. " For RT AVG and RL AVG , two disjunct datasets are considered for the calculations as, according to the definitions in Rubiano et al. , RT and RT AVG capture strictly the active items. In that way, the authors make a clear distinction between the two metrics, although the definitions remain somewhat inconsistent with the typical RT definition (see Sect. 2. 1). By covering only pulled (failed) items, it also makes such an interpretation of RL AVG quite a poor estimator for the mean RL. Al-Jazzaf et al. (  2019 ) point to a method, or an adjusted RL metric, called the 'Dynamic Average Equipment Run Life' (DAERL). The starting point is the average equipment RL (ERL), which is calculated as the "total exposure time of all systems either currently operating or earlier failure with respect to the total number of systems".
g. , MTTF U , MTTF O , MTTF R ), to use the right ones to estimate the right failure rates, according to the studies that they are actually performing. Finally, from a reliability data collection and statistical estimation perspective, there are as many versions of MTTF as states where failures can occur. To avoid the current confusion, it is suggested that the different variants are expressed using the same acronym but combined with a time period index, identifying what is actually being considered. This will reduce ambiguity and enhance clarity regarding the states (or time periods) one is referring to. In the same way, when referring to average RT or RL, it must be clear which type of population is considered. A similar index system is suggested for these metrics: RT* AVG and RT* AVG , respectively, where * refers to the time period considered.
Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization's maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization's current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools.
The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data.
Based on the existing studies  [5, [11] [12] [13] [14] , there is no unified definition for Big Data between industry and academia. The Statistical Analysis System Institute (SAS) defined Big Data as a "Popular term used to describe the exponential growth, availability, and use of information, both structured and unstructured"  [15]. IBM also added a definition for Big Data, "Data is coming from everywhere; sensors that gather climate information, social media posts, digital videos and pictures, purchase transaction record, and GPS signal of mobile phone to name a few"  [4, 15]. Therefore, Big Data can be considered as both an entity and a process. BD as an entity includes a volume of data captured from various resources (internal and external) and consists of structured, semistructured, and unstructured data that cannot be processed using traditional databases and software techniques.
BD as a process refers to the organization's infrastructure and technologies used to capture, store and analyze various types of data  [5, 14, 16]. Moreover, BD is pointed out as a technology that enables the processing of unstructured data; BD technologies are the systems and tools used to process BD, such as NoSQL databases, the Hadoop Distributed File System, and MapReduce  [17, 18]. BD provides new insights to discover new values, supporting organizations to benefit from a deep understanding of the hidden values  [19]. Big Data analytics (BDA), as technologies (database and data mining tools) and techniques (analytical methods and techniques), can be employed to analyze large-scale and complex data for a variety of applications prepared to increase the performance and effectiveness of the organization  [5, 14, 16].
The degree of maturity defines a specific state of development within a range scaled and determined by an initial point (lowest development point) and an endpoint (highest development point). A particular level of maturity includes the specific characteristics of predefined objects and their requirements  [34]. Big Data maturity assessment models are new concerns that require more study  [33, 36]. Maturity models help organizations identify the prerequisites to start the Big Data journey. In addition, maturity models can quickly position businesses across several criteria that help prioritize and plan for Big Data projects  [36]. These activities should be applied in the organization by reporting and documenting the ongoing technology tools, resources, infrastructure, process, and business applications and determining how to improve their strategy  [10, 37].
The organization's current security should be assessed, such as recovery and backup systems, performance management, disaster recovery, and infrastructure monitoring processes. Organizations should complete the assessments, analyze the gaps, and improve their processes to ensure the compatibility of their technologies, people, and the organization itself and the maturity of readiness and implementation  [10, 28]. Big Data practitioners and statistics experts sometimes design maturity assessment models that can be very complex and hard to understand. Furthermore, the task may consume most of their organizational capabilities  [4, 33, 38]. It has been found that the majority of the existing literature is mainly available as white papers or reports also registered on external developers' websites as internet materials  [33, 35, 37].
This was achieved by providing answers to the three predefined research questions below: RQ1: What are the existing maturity assessment models for Big Data? RQ2: What are the assessment dimensions for Big Data maturity models? RQ3: What are the assessment tools for Big Data maturity models? Answering these predefined research questions could give managers critical insights to decide which tool fits within their organization and assess their Big Data maturity level. Moreover, the findings of these research questions could be used as a critical reference to propose a preliminary classification for the maturity assessment dimensions of Big Data that could be used to develop a new BDMM. This SLR's structure starts with the Introduction in Section 1. The review methodology is presented in Section 2, highlighting the method used to conduct this paper.
Methods This paper used the SLR method to determine the literature that focuses on Big Data maturity assessment models, their assessment dimensions, and tools. The SLR method is considered reliable; it is also suitable and accurate, making it suitable to evaluate the existing research related to a specific phenomenon of interest, research question, issue, or topic domain  [39] [40] [41]. The systematic review strategy in this article was conducted based on the instructions for performing SLR in software engineering by  [39, 40]  to answer the research questions. This systematic literature review contains the following main stages: (1) review planning, (2) review conducting (3) review reporting. The stages are presented in Figure  3.
Stage 1: Review Planning The planning stage involves several steps, including pointing out the necessity for SLR, constructing research questions, and developing a review protocol that will construct the research question and the methods used to perform the review. Identifying the need for this SLR was highlighted in the previous section (Section 1). The limitations in the current literature were addressed in this SLR, and it also contributes to the body of knowledge of researchers and practitioners about the available Big Data maturity assessment models, assessment dimensions, and tools. The findings from this paper could help organizations identify the proper tool that fits with their organization and to know how to assess their maturity level for Big Data. This SLR identified three research questions (RQ), as presented in Table  1.
RQ3 What are the assessment tools for Big Data maturity models? To identify the existing tools used to assess the maturity of Big Data. This SLR was performed through a predefined search strategy to identify the literature related to the research questions. The strategy used in this SLR aimed to pick out the main studies, including the resources and search keywords related to predefined research questions. The resources included conference proceedings, electronic search engines, gray literature, journals, and digital libraries. In this SLR, the planning stage evaluated the available research questions and findings. The conducting stage highlighted this systematic literature review's used sources and keywords. More details about the conducting stage will be found in the following sub-sections.
Stage 2: Conducting the Review To conduct a systematic review, many stages should be applied, such as: identifying the search sources and the search strategy, in addition to the selection of main studies, extracting and monitoring the data and data synthesis, and studying the quality assessment  [39, 40]. As per  [42] , the lack of related studies represents the main validity threat. Some other examples of these threats are incorrect or automatic search, incomplete search terms, incorrect search method, inaccessible databases and papers, limited time duration, errors in the identification of main studies that occur during the search process, and finally, favoritism or bias. The backward snowballing method was referenced by a list of related works that were first identified using the database search method.
Sources When constructing the review protocol, the appropriate databases and sources should be identified by determining all possible sources when conducting the stage of the review protocol  [39]. This systematic literature review began with a database search method that identified the existing literature related to this SLR's study questions. As per  [43]  recommendation, we mainly used the database search method in this SLR as the first strategy. This SLR depended on electronic search engines and digital libraries as resources; conference proceedings, journals, and gray literature were also used  [39]. After conducting a comprehensive search on several databases and search engines, seven resources were identified as the initial electronic databases for choosing the best literature related to the predefined research questions.
The Tampere University of Technology has made a popularity-of-use list of best resources from which we chose eight databases for this SLR  [28] , which were as follows: EBSCOhost, ScienceDirect, Scopus, Springer, Web of Science, Digital Library ACM, and IEEE. Publisher Elsevier owns both ScienceDirect and Scopus  [28]. In order to eliminate any biased or redundant data, we dropped the database ScienceDirect. This SLR excluded the "Web of Science" from the main databases due to the limited availability of the related information. ResearchGate, Google, Google Scholar, and USM Library Repository were also chosen in order to have a manual search and included sources by the industry that could be found using the search engine Google. Nine databases were finally selected as the list of search sources for this SLR.
After applying the search strategy, it appeared that these databases had excellent repositories to show the available literature related to this SLR; as per the search, these databases were found to be the main sources of existing literature by other databases  [44]. Using these key resources, we found published journal papers, conference proceedings, white/gray literature, IEEE bulletins, book chapters, white papers, symposiums, technology reports, workshops, and developers' websites. Other sources for this SLR search were used based on the backward snowballing method for the reference lists in the extracted papers.
(3) Before the quality assessment, a total of 75 articles in related studies were chosen to read their abstracts, introductory sections, and conclusions. (4) A total of 33 of the returned papers were extracted in the quality assessment after a complete review of the abstract and full text of these papers. (5) Irrelevant and duplicated articles were excluded using exclusion criteria by filtering the quality assessment stage results; then, a total of 15 articles were accepted and chosen as final sources for the data synthesis. Based on the snowballing technique, the references extracted from the most related papers were checked manually to search for any other sources. Applying the full screening criteria after searching the keywords in the identified databases and removing the overlapping papers that were out of the research domain, this SLR selected 15 publications as the main sources for the existing BDMMs.
As presented in Table  4 , the analysis rated 8 articles (53%) to be very good in terms of quality and 7 articles (47%) as good, eliminating the rest as poor quality articles. Based on the predefined quality assessment questions (Q1-Q4), the outcomes (A1-A15) of the quality assessment applied to the 15 articles that were chosen for this SLR are shown in Table  5. for the data synthesis. Based on the snowballing technique, the references extracted from the most related papers were checked manually to search for any other sources. Applying the full screening criteria after searching the keywords in the identified databases and removing the overlapping papers that were out of the research domain, this SLR selected 15 publications as the main sources for the existing BDMMs. The search process, results, and paper selection are shown in Figure  4.
Quality Scale Very Poor (<1) Poor (1-<2) Good (2-<3) Very Good (3-4) Total   ID Q1 Q2 Q3 Q4 Total A1 1 1 0. 5 1 3. 5 A2 1 0. 5 0 1 2. 5 A3 1 1 0. 5 1 3. 5 A4 1 1 0 1 3 A5 1 0. 5 0 1 2. 5 A6 1 1 0 0. 5 2. 5 A7 1 1 0 0. 5 2. 5 A8 1 1 0. 5 0. 5 3 A9 1 1 0 0. 5 2. 5 A10 1 0. 5 1 1 3. 5 A11 1 1 0 1 3 A12 1 1 0. 5 0. 5 3 A13 1 1 0 0. 5 2. 5 A14 1 1 0 0. 5 2. 5 A15 1 1 1 1 4 After addressing gaps in this SLR, the following sub-sections reviewed and identified the existing BDMMs, assessment dimensions, and tools. This was done by providing answers to the following three research questions: 1. (RQ1): What are the existing maturity assessment models for Big Data? 2. (RQ2): What are the assessment dimensions for Big Data maturity models? 3. (RQ3): What are the assessment tools for Big Data maturity models? The following sections present the existing Big Data maturity models.
Conclusions As the global economy gradually recovers from the health crisis due to the COVID-19 pandemic, organizations must redefine their priorities and gain insight from their Big Data. The success of organizations that capitalize on Big Data is due to adopting mature designs before rolling out their implementation. This paper comprised a systematic literature review of the existing Big Data maturity models in the last 15 years (2007-2022) to answer three predefined research questions: RQ1: "What are the existing maturity assessment models for Big Data?", RQ2: "What are the assessment dimensions for Big Data maturity models?", and RQ3: "What are the assessment tools for Big Data maturity models?". A final list of 15 high-quality articles and models was extracted and analyzed to answer the predefined research questions and analyze the existing models' shortcomings.
Experimental procedures The experiment was conducted in the Laboratory for Experimental Economics (LEEN) in April and November 2018. It was computerized using z-Tree  [26]. Participants were recruited among those registered in the ORSEE [29] database. In total, 8 sessions were conducted, each with 24 participants. None of them participated more than once. Four sessions implemented the condition with neutral instructions and another four sessions implemented the condition with framed instructions. All 192 participants were the students in economics and between 18 and 26 years old. The absence of variation in the Field of study and Age variables reduced potential experimenter demand effects  [46]. The experiment was designed to determine whether the gender of participants was relevant information to the seller's likelihood to defraud consumers.