dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Service researchers and practitioners are equally interested in novel technologies and their impact on industries, society, service companies and customers. One of the most discussed and purportedly disruptive innovations is the distributed transaction and data management technology blockchain  (White 2017). Blockchain technology can be defined as "a distributed ledger technology in the form of a distributed transactional database, secured by cryptography, and governed by a consensus mechanism"  (Beck et al. 2017). This implies that the particular technology is not administered by a central server, but describes a peer-to-peer (P2P) network in which decentralized nodes keep copies of all the transactions within a network  (Önder & Treiblmaier 2018). In this kind of network, blockchain technology enforces transparency and ensures a system-wide consensus on the validity of every transaction  (Risius & Spohrer 2017).
Second, we expanded our search because research conducted in service contexts might not be published in service journals. We conducted the additional search using academic databases, namely ProQuest, Scopus and Ebsco to retrieve peer-reviewed research articles. We used the same search string as in the first step with the addition of: AND "service" or "services" or "service research" to focus the results on the service context. This search returned a total of 15,583 articles. To position the review on highquality evidence, we adapted exclusion and inclusion criteria from  Alkhudary et al. (2020). Since blockchain technology was first introduced in 2008  (Nakamoto 2008) , we set our search timeline from 2009 to 2021. Also, we selected only articles written in English, and we refined the research area to business, management, and customer behaviour.
2018, p. 4)  by enhancing the transparency of the supply chain, service organizations may lessen the risk of involving faulty services and goods in their own processes and reduce potential liability as errors may be traced back or avoided. From the above discussion, it is also clear that a service organization's financial risks may be downgraded. Parties can determine specific payment conditions in a smart contract that only will be activated when pre-specified conditions are met. To illustrate the above-mentioned lowered risks, consider the example of blockchain food traceability. Restaurants and hotels, for example, can use smart contracts to activate payments only when the ordered food was transported between a certain temperature range, ensuring their own service quality. These temperatures can be captured using IoT sensors that automatically upload the data to the blockchain (IBM 2020).
Despite the increasing complexity, the general idea remains unchanged. Each transaction corresponds to a chain of inseparable actions that will succeed if and only if the overall transaction succeeds. If the transaction fails, any changes due to the individual actions will be reverted, and the database will remain in its original state, i. e. , the state before this particular transaction has been executed. 3 Flash Loans use this atomicity principle and create something that can be best referred to as a transaction sandwich. At the beginning of the transaction, the initiator calls a function of a smart contract-based liquidity pool and is able to borrow any amount x e LP x , where x is the number of tokens the transaction initiator wants to borrow and LP x is the liquidity pool's token holdings before the call.
2. Blockchain Systems and Traditional IS Research In its generic form, a blockchain refers to a distributed system for capturing, validating, and storing a consistent and immutable linear event log of transactions by cryptographic means (Risius and Spohrer 2017). Blockchain systems intrinsically provide economic incentives for the networked actors to participate in storing and validating transactions and maintaining the consistency of the shared database with an agreed-upon history of transactions. In a wider sense, blockchains constitute digital infrastructures that can be used as both interorganizational IS and public platforms. Interorganizational blockchains typically consist of permissioned blockchain instances that are used to connect the IS and business processes of a bounded network of organizations.
Regarding pitcher areas, areoles of the pitcher body and areoles of the operculum had similar colouration and yielded similar contrasts, they were thus pooled into a common 'areoles' category. Likewise, the veins of the pitcher body and the veins of the operculum had similar colouration and yielded similar contrasts, and were pooled into a common 'veins' category. Hence the variable pitcher area had four levels: areoles, veins, peristome, and tube. All computations were performed with the R package pavo version 2  (Maia et al. , 2019).
ChIP analyses HY5 ChIPs were performed on 5-day-old seedlings grown under LD conditions. PIF3 ChIPs were performed on 5-day-old seedlings grown in darkness or LD conditions. ChIP experiments were performed as in  Fiorucci et al. (2019) , using anti-HY5 (Agrisera #AS121867; previously used for ChIP analysis in  Bellegarde et al. , 2019)  or anti-GFP (Life Technologies #11122). Library preparation, sequencing, and analysis Libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit (New England Biolabs E7645). Sequencing was performed on an Illumina HiSEq 4000 in 150-bp paired-end mode. Reads were trimmed using trim_ galore (https://github. com/FelixKrueger/TrimGalore) with options ''-phred33 -paired -q 20 -stringency 1 -length 35'' and then mapped to the TAIR10 genome using bowtie2  (Langmead and Salzberg, 2012)  with options ''-very-sensitive -I 150 -X 2000 -p 20 -no-mixed''.
Duplicated reads were marked using picard-tools (https://github. com/ broadinstitute/picard). Reads were then filtered using samtools (https:// github. com/samtools/samtools. git) with options ''view -hb -F 1804 -L se-lected_TAIR10_genome_Chr. bed'', which corresponds to the TAIR10 genome after filtering out genomic regions with aberrant coverage or low sequence complexity  (Quadrana et al. , 2016). Browser tracks were generated using deeptools  (Ramı ´rez et al. , 2016).
, 2009)  with options ''-dna -mod anr -revcomp -maxsize 25000000 -nmotifs 10 -minw 6 -maxw 12 -maxsites 10 000 -brief 3000 -p 5''. All found motifs were then compared with the DAP-seq database of motifs  (O'Malley et al. , 2016)  using Tomtom  (Gupta et al. , 2007)  with default options. The GO enrichment analyses were performed using GO-TermFinder  (Boyle et al. , 2004)  via the Princeton GO-TermFinder interface (https://go. princeton. edu/cgi-bin/GOTermFinder), simplified using RE-VIGO  (Supek et al. , 2011; Langmead and Salzberg, 2012) , and visualized as an unclustered heatmap using pheatmap (https://cran. r-project. org/package=pheatmap).
Pigment quantification For anthocyanin quantification, the aerial parts of 15-20 5-day-old seedlings, collected from different plates, were pooled for each replicate. Anthocyanin quantification was performed as described in  Hillis and Swain (1959). Six to ten 7-day-old seedlings were pooled for chlorophyll measurements. Acetone 80% (v/v) was used for extraction, and A645 and A663 were measured in a spectrophotometer. Data analysis was done according to  Arnon (1949). Three independent replicates (seedling pools) were measured for each sample. Values represent mean ± SD.
(Van Leene et al. , 2015;Garcı ´a- Leon et al. , 2018)pension cultures, and TAP purifications were performed as described previously(Van Leene et al. , 2015;Garcı ´a- Leon et al. , 2018). For the protocols of proteolysis and peptide isolation, acquisition of mass spectra by a 4800 MALDI TOF/TOF Proteomics Analyzer (AB SCIEX), and mass spectrometry-based protein homology identification based on the TAIR10 genomic database, we referred to Van Leene et al. (2010) and Garcı ´a-Leon et al. (2018). Experimental background proteins were subtracted based on 40 TAP experiments on WT cultures and cultures expressing TAP-tagged mock proteins GUS,.
FUNDING This work was supported by a  Ramon y Cajal  ( RYC-2014-16308 ) grant funded by the  Ministerio de Economı ´a y Competitividad  to S. F. Work by S. F. in F. B. 's lab was supported by the  COST Action CA16212 INDEPTH (European Union ). Work in V. R. 's laboratory was funded by the  Agencia Estatal de Investigacion/Fondo Europeo de Desarollo Regional/European Union  ( BIO2016-80551-R  and  PID2019-105495GB-I00 ). Work in F. B. 's lab was supported by  CNRS EPIPLANT Action (France)  and funded by  Agence Nationale de la Recherche  grants  ANR-10-LABX-54 ,  ANR-18-CE13-0004-01 ,  ANR-17-CE12-0026-02  (France) and by  Velux Stiftung (Switzerland). B. G. G. is funded by  President's International Fellowship Initiative postdoctoral fellowship  (no. 2020PB0082 ) from the  Chinese Academy of Sciences , and is the recipient of a  Talent-Introduction grant  ( Chinese Postdoctoral International Exchange Program ).
Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module-a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector.
Researchers also began to explore application of machine learning to NDT  [12] [13] [14] [15]. However, at present, standard machine learning approaches have limited value: Firstly, most researchers have no access to big data such approaches require and even a few laboratory datasets used below have required a considerable effort and expense to collect. Secondly, standard approaches often lead to results that are unexplainable, and a highly regulated branch of industry, such as NDT of nuclear reactors is unlikely to adopt results of this nature. In this paper we present an alternative: a code that combines a signal processing algorithm based on a simple modification of the TFM with the well-known image processing algorithms as well as a decision tree. The latter is an AI module, which mimics thought processes followed by human inspectors in writing standard inspection reports.
The code has been designed to deal with the scatter from large planar cracks, whether specular reflections from crack surfaces or echoes from crack tips. We demonstrate the efficacy of the approach using laboratory data. To collect such data engineers manufacture test blocks to contain flaws with known characteristics and use the the NDT procedure they want to investigate to establish whether it can generate reasonable estimates of these characteristics  [1]. The paper is organized as follows: in Section 2 we describe the relevant experiments; in Section 3 we present our composite signal/image processing/AI algorithm for crack characterization and in Section 4 we present results of its training on 16 datasets and testing on two. Since it is known that in many industrial situations the main source of error is undulations in component surfaces, one of the test blocks has been deliberately chosen to have a qualitatively different surface to the test block used for the AI training.
The Experimental Set-Up This paper builds on the original feasibility study reported in  [16] , with the experimental set-up presented in Figure  1. The RF (radio-frequency) data used there were collected by DPS (Doosan Power Systems) engineers with a demonstrator multiplexed to an 128 element IMASONIC linear transducer array with the pitch D e = 0. 8 mm, the central pulse frequency f = 5 MHz and sampling frequency f s = 50 MHz. The specimen probed was a steel block, 30 mm thick, 200 mm wide and 350 mm long, with four surface-breaking notches and four further notches buried underneath the notched surface. Four notches out of eight were non-tilted and four, tilted at 110 • to the surface. The longitudinal speed in steel varies with composition. In the steel used in this experiment it was c l = 5.
Signal Processing The submodules of the Signal Processing module are used to create 2D images of the tested specimen: 1. SurfaceProfiling effects profiling by (1) locating for each array element the surface point directly underneath and (2) interpolating the acquired surface points using polynomial regression. The first step is performed by convolving h(A)(N∆tt), the Hilbert transform of the time inverse of the input pulse with the corresponding pulse scattered by the surface. Only A-scans received by the same transducers that transmitted them are utilized. Hence the maximum number of surface points collected during the first step is K. The regression model used in interpolation is z = X b + e, (2) where the response vector z, the parameter vector b, the design matrix X and the error vector e are given, respectively, by z =     z 1 z 2.
• In the latest version of AutoNDE the degree p is selected automatically. There is a number of approaches recommended for this purpose in the literature on machine learning. We have found that the most common of those, the bias-variance trade-off leads to ill-conditioned the Vandermonde matrix X T X and overfitting of the DPS data. Since for all DPS datasets SurfaceProfiling acquires surface points whose location error is random it is reasonable to assume that their underlying error distribution is normal. Therefore, we attempted and found satisfactory a method that involves the Wald test  [18]  based on the t-statistic of the leading coefficient.
• In order to apply this method we first estimate p max and q max , where p max is the highest polynomial degree that can be reliably estimated from the available data and q max is the maximum number of digits of accuracy on top of what would be lost to the numerical method due to loss of precision from arithmetic methods  [19]. A well-known rule-of-thumb suggests that p max = J/5 and the training of AutoNDE on the DPS data suggests that for realistic random surface undulations used in this experiment q max = 6.
3. RayTracing starts by issuing a fan of rays from each array element. The central angle of each fan is -90 • to the x-axis, the optimal vertex angle has been found to be 60 • and the optimal difference between the angles of neighboring rays, 0. 057 •. These values effect a trade-off between the code accuracy and speed. For each ray the RayTracing submodule locates the point where it hits the upper surface, finds the refracted ray issuing from this point (in the current version no shadowing is accounted for) and calculates the time it takes the ray to reach each row in the region of interest. In the present version of AutoNDE mode conversion is allowed as well as one reflection from the backwall.
Image Processing The image processing module is used to select those MTFM images, which lend themselves to easy interpretation. The basis for selection is a priori knowledge that the crack to be characterized is large and plane. Therefore the crack image is expected to contain a straight segment, which is a specular reflection from the crack, or else two diffraction spots surrounding the crack tips. Sometimes only one crack tip can be picked up. AutoNDE differentiates the possible diffraction spots from the possible specular features by size, allowing for some overlap. The ImageProcessing module of AutoNDE uses a variety of intensity thresholds. As mentioned above, the maximum intensity is 256. Thresholding is a standard tool in image processing, which is used to filter out noise. During the AutoNDE training, in most cases 125 has been found to produce the best results.
Explainable AI The AI module of AutoNDE is a decision tree, which selects images that appear to contain defects, characterizes these defects and then groups similar images. Note that by their nature, decision trees produce explainable results: all the reasoning can be traced. The decision tree comprises the following submodules: 1. ImageSelection submodule selects images containing one or two blobs (bright spots), two small contour selected features, one large feature or maybe, one blob and one small feature. If a blob and a contour selected feature are detected at the same location it is the feature parameters that are used to characterize the potential diffraction spot. If one of the contour selected features is slightly bigger than 7 mm it is still treated as a possible diffraction spot.
Similarly, it has been found that enough information could be collected with 25 images, D m varying between 0 to 24 array elements. No interpretable images were produced for larger values of D m. Finally, whatever the dataset, quality results have been obtained for the same region of interest. This has been chosen as the central region, roughly 20% of the area underneath the probe, symmetrical with respect to the probe center. The resulting estimates of notch characteristics are compared to their known experimental values in Table  1. The experimental values were established using standard approaches used in experiments of this nature, see, e. g. ,  [1]. Table  1  shows that depths of the notches in DPS data could be estimated with the error of up to 2 mm (in one instance, 3 mm), and orientations-with the error of 5-10 •.
We note here that assuming the inspection surface plane would produce similar estimates for most of the notches but the second entry would be 7 mm in extent, located at 1 mm depth, oriented at 75 • and the fourth entry would be 7 mm in extent, located at 0 mm depth, oriented at 125 •. It follows that results are more reliable when small surface undulations are taken into account. Typical MTFM images are presented in Figure  7 : both Figure  7a ,b contain three bright spots, with the two brightest ones joined by a thin yellow line. However, while the top spot is bright in both images, in Figure  7a  the brightest lower spot is found at a distance of 4 mm from the backwall, while in Figure  7b  the brightest spot lies on the backwall. We know that both bright spots in Figure  7a  represent diffraction spots surrounding tips of the planar notch, while the lowest bright spot in Figure  7b  is spurious, probably due to a defect in the backwall: The noise is similar to the signal and inside any given image the code cannot always distinguish between the two.
However, in this case most MTFM images allow it to make the correct choice. This leads to a reasonable entry for the correponding notch in Table  1. The accompanying AutoNDE inspection report is presented in Figure  8. Note that unlike MTFM images in Figure  7 , the TFM image in Figure  8  contains bright reflections from both top surface and backwall and the portion of the image to the left of the region of interest is not cut off. Unlike with MTFM images the diffraction spots surrounding notch tips are very faint. The presence of the TFM image in the report allows a human inspector to make an immediate assessment of the validity of the AI conclusions. Note too that the upper surface points presented in the second figure of this report have been obtained using the Profiling submodule and solid line is the interpolating polynomial.
Testing AutoNDE on AMEC Data AutoNDE has been tested on a data set collected by AMEC engineers using a 64 element phased transducer array with the pitch D e = 0. 63 mm and sampling frequency f s = 25 MHz, placed in direct contact with a 55. 5 mm deep steel specimen. The geometry of the experiment, input pulse and typical A-scans are similar to the ones in the DPS experiment and are not reproduced. The standard TFM image of the AMEC specimen is presented in Figure  12a. It contains two diffraction spots, but they are too faint to be identified by the current version of AutoNDE. The code picks the diffraction spots up only when we cut off minimum 20% of the specimen thickness from the bottom of the image, see Figure  12b. The latter displays the following estimates of the crack characteristics: extent-4 mm, depth-16 mm, orientation -100 •.
The necessity to reduce the region of interest appears to be due to the defect in the backwall, which produces a response that is too bright. After the region of interest is cropped as described, AutoNDE produces the report reproduced in Figure  13. The parameters of the manufactured notch are as follows: extent-5 mm, depth-16 mm, orientation-101 •. We can see that the AutoNDE estimates are of the same quality as estimates obtained with the DPS data. The set-up is more challenging than the one used in the DPS experiments: both the top surface and blackwall are more indulated and there is a backwall breaking flaw, creating additional response, see Figures  15b  and 16a. The numerical experiments conducted with CIVA [22] (a commercial package for analyzing and simulating NDT  [23] [24] [25] [26] [27] [28] , in particular, NDT of components with irregular surfaces  [7, 8, 28, 29] ) showed that the notch fabricated for the purposes of this experiment was best imaged using the half-skip LTT mode, with the L (longitudinal) transmitted pulse converting at the backwall to the T (transverse) pulse and then reflecting from the notch, so that the received pulse is also T, see Figure  16a.
Note that the CEA experiment was designed to investigate the effect of highly undulated surfaces, with the distribution of undulations different to normal. Both types of surfaces, those reproduced in the CEA experiment and those reproduced in the DPS experiment are realistic, but our analysis confirmed that they have to be modeled differently, The CIVA code was provided with precise descriptions of both the inspection surface and backwall obtained with a flexible probe. AutoNDE relied instead on a rather crude Profiling submodule described above. Moreover, the offset of 2095 samples in A-scans obtained with the 64 element transducer array was too high, eliminating reflections from the higher portions of the inspection surface. For this reason, the quality of the Profiling output was very low.
Conclusions A novel code containing a decision tree, that is, an explainable AI has been designed and developed for characterizing single large planar cracks. For the component surfaces whose undulation errors can be described using a normal distribution, we developed a method for automatic estimation of the degree of the interpolating polynomial. The code has been trained on 16 experimental data sets and tested on two. The inspection surface and backwall used in training had realistic small undulations whose distribuation could be considered normal. One test dataset was collected using a specimen with plane surfaces and another, a specimen with surfaces whose undulations were smooth and large and could not be described using a normal distribution. It has been demonstrated that every type of material and inspection configuration requires preliminary investigation to establish not only how to model the surfaces but also most appropriate values of such hyperparameters as the component thickness, distance to the probe and portion of the image to be analyzed.