dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors want to thank  S. Gallegos  (author of tree-sitter-rst),  J. L. Cano Rodríguez  and  E. Holscher  (Read The Docs),  C. Holdgraf  ( 2i2c ),  B. Granger  and  F. Pérez  ( Jupyter Project ),  T. Allard  and  I. Presedo-Floyd  ( QuanSight ) for their useful feedback and help on this project.
2015; Alabas-Uslu and Dengiz 2020)  where the parameters are tuned during the run. The framework proposed in the present paper focuses on offline tuning. Several tools have now been developed to aid this process: among the most popular are iRace by  López-Ibáñez et al. (2016) , SMAC by  Hutter et al. (2011)  and ParamILS by  Hutter et al. (2009). These only require the end-user to implement a wrapper for their program that takes a particular algorithm configuration and returns a number representing some concept of fitness or performance. López-Ibáñez et al. (2016)  proposed iRace, which implements iterated racing for algorithm configuration and has been shown to be effective in parameter tuning for many different applications. Iterated racing involves sampling probabilistic distributions to choose algorithm parameters, then repeatedly running the configurations on different problem instances, removing configurations that perform statistically worse than at least one other.
OPIUM: The software under consideration Opium is a standalone application developed and maintained in-house by the Operational Research Department at KLM. It is implemented in Java, following a 'building block' model whereby different components of the simulation process are combined in stages. Much of Opium's code is highly-specific to the application, with the core simulation logic built on a freely available Java library DSOL developed at TU Delft by  Jacobs (2005). A single run of Opium estimates robustness for a schedule. Distributions for events such as aircraft breakdowns are specified in a set of data files. Opium stochastically samples these distributions over a number of repeat runs, computing resulting delays and knock-on effects from distributions, and computing an overall cost through a series of sanctions.
The historically measured punctuality for each schedule is used as the basis for measuring the accuracy of the simulation. Punctuality is measured by 22 metrics for each of 6 aircraft types (i. e. , 132 metrics in total), capturing statistics like number of aircraft delayed by 0, 5, or 15 minutes, broken down by whether they are inbound or outbound at KLM's hub airport, Amsterdam Schipol. We formulate the problem of fine-tuning the Opium software package as a min-max optimization problem that essentially minimizes the worst case (maximum) deviation from the real historical operational data (accuracy), or minimises the program's run time (time), or both. Accuracy: find a parameter configuration c ∈ C, where C is the configuration search space, that minimises the worst case deviation from reality (max mean square error -MSE) Acc(c) = min max c∈C m∈M (m h -m o (c)) 2 , ( 1 ) where m h and m o (c) are the historical and output metric values respectively, for each metric m in the set M of 132 schedule punctuality metrics, when running Opium with parameter configuration c.
To test the impact of this, we performed 100 repeat runs of Opium in its default configuration, measuring the wall-clock run time of each. The distribution of these times, shown in Fig. 1 , is approximately normal. To mitigate the stochastic nature of the measurements, we define as the execution time of the simulator its average execution time over k independent executions. Specifically, we calculate the execution time of a given configuration c, Time(c), according to the following equation:  (c) = 1 k k i=1 t(O P IU M(c)), ( 2 ) where t(O P IU M(c)) is the wall-clock time of an independent execution of the software under parameter optimization (Opium), which is configured with the given parameter configuration c. If the times did not follow a normal distribution, or it was not possible to confirm normality, then the median time could be used instead.
Case study and experimental results The proposed methodology has been evaluated on tuning the Opium tool using real world scheduling data from KLM Royal Dutch airlines. KLM provided us operating schedules that were run during the second part of summer 2007 and the first part of summer 2010. KLM operates a single hub-and-spoke network with dominant hubbanks. Its European fleet consists of 45 aircraft of 4 (interchangeable) sub-types. 10 different sub-schedules, having different fleet compositions and different sizes, were derived from the initial schedules.
Research questions Our overall goal is to determine how the proposed framework answers the following research questions for our case study software: -RQ1: Can we identify important variables / parameters, as a means of justifying the solutions that are eventually chosen? -RQ2: Can we find "optimal" configurations that approximate reality better (fit to historical data)? -RQ3: Can we minimise Opium's execution time? -RQ4: Can we identify any trade-off between accuracy and execution time? RQ1 and 2 specifically target whether relevant explanations are generated through the approach, while RQ3 and 4 consider whether the approach still produces results of sufficient quality. RQ1 is answered in stages 1, 3 and 4 of our framework (statistical sensitivity analysis, functional ANOVA and multi objective optimisation followed by Pareto front analysis); RQ2 and RQ3 are answered in stage 2 (single objective optimisation); and RQ4 is answered in stage 4 (multi objective optimisation).
Stage 1: Statistical parameter sensitivity We begin with a structured approach to parameter tuning based on the procedure outlined by  Petrovski et al. (2000 Petrovski et al. ( , 2005) )  and examined in more detail by  Czarn et al. (2004). This approach was implemented in version 17 of the Minitab statistical package (Minitab, Inc 2014). The procedure begins with a screening experiment: an exploration of the parameter search space using a fractional factorial (Plackett-Burman) Design of Experiments (DoE) procedure, considering low and high values for each parameter. The low value for each parameter was the minimum as specified by the company's developers. The high value was the value currently used for each parameter by the company in practice, which we refer to as the default values. Over years of manual tuning, the values in use had settled on the upper end of the ranges as anticipated by the program's developers.
For both OPs, CMO is always TRUE and GFO centres around 1. 8 in the Paretooptimal solutions. This matches the trend seen when tuning for all OPs. However, several of the parameters show differences. For OP12, SMO and HSFMO show no discernible trend. For OP34, SMO tends from TRUE to FALSE as MSE decreases. HSFMO tends from FALSE to TRUE as MSE decreases. For OP12, MLC is negatively correlated with MSE, for OP34 is positively correlated with MSE. For both OP12 and OP34, BMMO is mostly TRUE, in contrast to tuning for all OPs where BMMO was mostly FALSE. Having already done the implementation work in order to optimise Opium for the full data set, specialising to particular data sets is now trivial. Overall, then, the systematic procedure was able to find improved configurations, tailed to the specific sets of input data, with little additional cost in terms of personnel time.
Conclusion and discussion Bespoke industrial software applications often carry a legacy of incremental finetuning and configuration that leaves them performing sub-optimally. Configuration parameters are left with a "do not touch" warning attached: as with so many application areas, a radical change to the configuration must be rooted in a methodical approach backed up by evidence if it is to gain acceptance. This motivates the systematic approach to exploring and optimising such parameters that we have proposed. Each of the components of our approach is not novel in itself but the idea is that the combination of techniques brings optimisation of the software hyperparameters with explanation, and will allow practitioners to tackle similar problems for other industrial software. In a case study to prove the concept, we worked closely with partners at the international airline KLM to apply the methodology to a real-world software application, Opium.
All flight schedules were passed through Opium before implementation so the software represents a critical part of the business workflow with global impact: if you have flown with KLM, this software has impacted on you! We were able to improve both accuracy and speed of Opium, with the optimised configurations being adopted by the company until the application was superseded by new applications based on it. This led direct commercial impact by improving the robustness of schedules based on estimation models. Importantly, our approach was also able to show how various parameters influence the objectives, and how they interact, backed up up by well-accepted statistical testing. In contrast to the current parameter-tuning tools built on application of search-based optimisation, this offers some explanation as to why the approach settled on specific configurations, and adds confidence to the results.
The dependency of porosity formation on laser process parameters and scan strategy was investigated. Moreover, hardness test was performed for all built samples. The platform developed for in-situ monitoring purposes includes an AM machine equipped with pulsed laser, camera, illumination system, and powerful industrial computer equipped with Cameral Link Adapter, FPGA, and Real-Time (RT) modules. An algorithm was designed using LabVIEW R software based on Particle Analysis (PA) to cease the process in the event of detection of defect in any fused layers. The first defect was caused by changing the laser spot diameter, which altered the energy intensity of the laser on the surface, and the second defect was created by the uneven thickness of powder on the platform. The monitoring system detected both defects and stopped the process immediately according to the designed algorithm.
In this study, different cube samples from stainless steel 316 L powder have been produced by applying various SLM process parameters. The structure and melting features of the samples were investigated through an electronmicroscope imaging machine. Also, the hardness strength of the samples was evaluated via a hardness test. Moreover, an in-situ monitoring platform has been developed to detect defects that may arise in any fused layer. The optical system has been used for this purpose. AM machine was equipped with a pulsed laser, camera, illumination system, and industrial computer PXI system equipped with Cameral Link Adapter, FPGA, and Real-Time (RT) modules. Using LabVIEW R software, an algorithm has been designed to stop the process if a defect was detected in any fused layers. The working principle of the algorithm is based on Particle Analysis (PA).
Compared with the other methods, it was shown that the SVR with both input types provides the best performance  [42]. Bartlett et al. used full-field infrared (IR) thermography to develop an in-situ monitoring system to detect defects and monitor AlSi10Mg specimens during SLM production. They performed transient thermal modeling to support their observations. Ex-situ scanning electron microscopy (SEM) was used to characterize parts to validate data identified defects. The results showed that defects could be identified effectively using the IR defect detection method with an 82% success rate for lack of fusion (LoF) defects. It was also demonstrated that the introduced method has the capability to analyze the presence of systematic process errors during SLM process  [43]. A defect detection method for the SLM process using a microphone based on a deep belief network (DBN) was introduced by Ye et al.
IV. IN-SITU MONITORING Since the occurrence of any defects in any fused layers can affect the structure of the final product and deteriorate the quality of part, it is important to monitor the process in each layer to detect the possible defects arise during the process. In this study, an in-situ monitoring platform was developed, which aimed to detect the defects that may appear during the SLM process. An algorithm was designed that stops the process immediately if a defect was detected in a layer. The algorithm works based on Particle Analysis (PA), the principle of which is based on the number of particles. The algorithm was developed using LabVIEW software. Fig. 8  shows the pattern of the process of image acquisition and analysis  [27]. Through Camera Link cables the acquired image data from the camera is delivered to the Camera Link module and transformed into pixels, which can be manipulated on a computer.
The maximum frame rate is 500 frames per second because of the speed of the image processing algorithm; however, in this process, analyzing one image at the end of each fused layer also is good enough. Camera output was set to 10-tap mode, which allows parallel acquisition with ten pixels. The pixels are written into small FIFOs. Image acquisition is controlled by data acquisition state machine with the aim of acquiring the image from the camera only when the image is ready for acquisition. The state machine sends the image information and acquisition status to the User Interface (UI). The 80-bit data is transferred from Cam Data FIFOs to packer CLIP (Computational Linguistics and Information Processing) and converted into eight different FIFOs with 8-bit words through multiple data packing units.
Also, a hardness test was performed for all constructed samples. It was concluded that scanning speed and hatch distance significantly influence the hardness of the product. An in-situ monitoring platform was developed for the SLM process, and its performance was evaluated with experimental method. The experiment was conducted by monitoring the mechanism of melting and solidifying metal powder layer by layer to ensure the defects can be detected in any fused layer in the earliest possible stage of the process before influencing the properties of the final parts. For this purpose, using Lab-VIEW system design software, an algorithm was designed based on Particle Analysis (PA) to cease the process in the event of detection of any defect and send an alarm to the UI to inform the operator about the defect.
Criticism of the different approaches These methods have a loading system and a measuring system that are often dissociated: the data must be synchronized. The deflection measurement is estimated from a theoretical model. There is a sensitivity on the identification of the position of the load and its direction. Some methods exist to identify the center of the wrist such as trilateration  [3]. Moreover, in the case of a weight, the first axis is not under load and two test campaigns must be carried out. The approach requires several hours to have sufficient data to estimate the robot parameters. In this context, a new patented system has been designed based on the ballbar system.
A human-machine interface (HMI) (Fig. 7 ) has been developed allowing the display and acquisition of measured quantities (angles, distance, force) through Labview. The interface helps deducing the positions and orientations of the upper plate center connected to the robot. The HMI allows the acquisition of static points at the request of the operator or from a trigger programmed on the robot. The information about the recorded points is displayed in a point table and can be exported in ASCII format. For each position, synchronized force and position data are recorded. The software also allows to launch continuous acquisitions with a sampling rate of one millisecond. In the case of continuous acquisition, to optimize performance, the display of the 3D device and the table of recorded points is disabled.
LiDAR is a technological concept introduced in the early 60 s, when Massachusetts Institute of Technology (MIT) scientists reported on the detection of echo signals upon sending optical radiation to the moon surface  4. Since the pioneering MIT work, LiDARs have been using laser sources to illuminate targeted objects and to collect the returning echo signals offering the possibility of reconstructing highly resolved three-dimensional (3D) images. Conventional LiDARs rely on time-of-flight (ToF) measurement, which employs a pulsed laser directed toward a distant reflective object to measure the round-trip time of light pulses propagating from the laser to the scanned scene and back to a detection module. All LiDAR components must act synchronously to tag single returning pulses for ranging imaging reconstruction.
Generally, LiDARs are classified into scanning or non-scanning (Flash LiDAR) systems depending on whether the laser sources simply illuminate  24  or scan the targeted scene. A scanning LiDAR system can be essentially described in terms of three key components, (i) the light source for illumination, (ii) the scanning module for fast beam direction at different points in the scene, and (iii) the detection system for high-speed recovering of the optical information received from the scene. Over the past decades, nanophotonics-based LiDAR systems have blossomed, and more advanced scanning and detection techniques have been proposed  25, 26. The expected massive use of LiDARs in the automotive industry for advanced driver-assistance systems (ADAS) or even full-autonomous driving brought out new challenges for the scanning systems, including low fabrication complexity, potential for scalable manufacturing, cost, lightweight, tolerance to vibrations and so on.
e. , four time series to recover the speed, the maximum event detection can increase up to a speed of 47 mega-meter/h. High-speed scanning modules for LiDAR applications have to trade-off between the maximum distance and spatial resolution (see Supplementary Information S5, S10). The frame rate of a single ToF system can be expressed as: f Rate = c 2 n d max ð5Þ where c is the speed of the light. Equation (  5 ) thus indicates that both the number of pixels in the image (n) and the maximum ambiguity distance, ðd max Þ, defines the imaging frame rate. Such echoing time can be reduced by encoding the signal sent in each scanning direction with a specific identification code namely Code-division multiple access (CDMA)  47. Multiplexed observation is realized by decorrelating the ToF signal using matched filter technique.
Acknowledgements This work was financially supported by the  European Research Council proof of concept (ERC POC)  under the European Union's  Horizon 2020 research and innovation program  ( Project i-LiDAR , grant number  874986 ), the  CNRS prématuration , and the  UCA   Innovation Program (2020 startup deepTech ) and the  French defense procurement agency  under the  ANR ASTRID Maturation program , grant agreement number  ANR-18-ASMA-0006. CK and MS acknowledge inputs of the technical staff at the  James Watt Nanofabrication Centre at Glasgow University. C. Kyrou  has been supported with a postdoctoral fellowship grant by the  Bodossaki Foundation (Athens, Greece).
The authors propose a semantic and smart assistance framework for handling linguistic knowledge, called LingFramework. It targets both expert and novice users. It aims to assist the user in understanding the different aspects of the linguistic domain and ease the process of proposing lingware applications. LingFramework is based on a multilingual linguistic domain ontology called LingOnto. It allows (1) representing linguistic data, linguistic processing functionalities and linguistic processing features, and (2) reasoning, via a SWRL-based reasoning engine, about the linguistic knowledge. Currently, it covers English, French, and Arabic languages. To facilitate the interaction with LingOnto, an ontology visualization tool called LingGraph is proposed. It offers an easy-to-use interface for users not familiar with ontologies.
LINGoNTo oNToLoGy: REPRESENTING ANd REASoNING ABoUT LINGUISTIC KNoWLEdGE The authors propose a multilingual linguistic domain ontology called LingOnto. It is freely available online 4. The current version of LingOnto covers the English, French and Arabic languages. Compared to related work, it does not only handle linguistic data, but also linguistic processing functionalities and linguistic processing features. Besides, it allows via a reasoning engine, inferring new linguistic knowledge from those initially entered and assisting in the process of proposing lingware applications.
4. 1. 2. Linguistic Processing Functionalities Classification Referring to well-known NLP toolkits such as Apache OpenNLP  (Mohanan and Samuel, 2016) , StandfordCoreNlP  (Manning et al. , 2014) , FreeLing  (Atserias et al. , 2006)  and LingPipe  (Konchady, 2008)  and two language processing platforms which are Language Grid  (Ishida, 2006)  and Gate  (Fairen Jimenez et al. , 2011) , the authors identify a set of linguistic processors such as "POS Tagger", "Lemmatizer", "Morphological Analyzer" and "Chunker". Some of these linguistic processors often implements one or two linguistic processing functionalities. For instance, a "Morphological Analyzer" processor for French and English languages usually implements "Paragraph splitting", "Sentence splitting", "Tokenization", "POS tagging" and "Lemmatization" functionalities.
Nerveless, a "Morphological Analyzer" processor for Arabic language and especially for analyzing diacritic Arabic texts implements "Paragraph splitting", "Sentence splitting", "Tokenization", "POS tagging", "Diacritization" and "Lemmatization" functionalities. Therefore, the automatic diacritization is an essential processing functionality for many Arabic lingware applications. Moreover, Arabic sentence components can be swapped without affecting the structure or meaning. For this reason, it leads to a more syntactic and semantic ambiguity in contrast to the English and French languages. According to  (Hayashi and Narawa, 2012) , it exits an hierarchical inter-dependencies between the linguistic processing functionalities. Indeed, a linguistic processing functionality used to perform a given analysis at one level may require, as input, the results of others analysis related to a lower level.
4. 1. 3. Linguistic Features Classification The linguistic processing functionalities are characterized by several linguistic features. LingOnto models these features to ease the process of proposing lingware applications as they identify the incoherence between linguistic processing functionalities. The English, French and Arabic languages are based on the same linguistic processing features. Indeed, according to  (Haddar and Hamadou, 2009) , a comparative study of English, French and Arabic sentences shows that it is possible, from the linguistic viewpoint, to adopt the same typology of ellipses (i. e. , Gapping, Right-node Raising, Coordination Reduction) for the Arabic language as the one proposed for the English and French languages. Figure  4  shows the proposed classification of the linguistic processing features.
LINGGRAPH: oNToLoGy VISUALISATIoN TooL oF LINGoNTo The LingOnto domain ontology is designed to be used by users, who are not necessary ontology experts. To overcome this issue, the authors propose a user friendly ontology visualization tool called LingGraph. It is freely available online 5. It offers an understandable visualization to both ontology and non-ontology expert users. Moreover, it is based on a smart search functionality. This latter relies on a SPARQL pattern-based approach. The aim is to extract and visualize an excerpt ontological view, from LingOnto, that contains only components (i. e. , concepts and object properties) corresponding to a specific user need. This latter is materialized by a set of predefined search criteria C = (C 1 ,. , C n ). Currently, the authors identified three search criteria which are: ("Abstraction Level"), ("Processing Level") and ("Language").