dataesr/scientific-paragraphs-categorization huggingface dataset:
Add your own electrical circuits for your students The Laborem Box was designed to enable a teacher to create and use their own electrical circuits. The figure  16  details the process of Laborem integrated into Moodle 9. The teacher's part consists of the following steps: • create the material  First, draw your circuit from the blank plug available in the design files (Plug-template in Plugs. zip from section 3) using the open source software Kicad 10. You must respect the PCI Express terminal block presented in figure  7. For example, if you decide to connect channel 1 of your oscilloscope to V 4 and the output of your function generator to V 1 , you will have to route the V 1 and V 4 terminals of the PCI Express connector to the corresponding place in your electrical circuit (as done in figure  6 ).
• HMI: -refresh all values each 2. 5 seconds -send user inputs (example: voltage, frequencies and number of points of a Bode plots) -force instruments to read values or scripts to run. If the instruments have not been predefined, you must create them in the PyScada Core/Devices section. You must also create all of the variables (PyScada Core/Variables section) to be read or written on the instrument (for example a voltage, a current, a frequency). The example of the script can be found in  [24]  which enables the visualization of Bode diagrams  13. The script can be added to the PyScada Scripting/Script section. A script in PyScada is divided into 3 parts. One part is launched at startup, another at shutdown, and the main part at regular intervals as explained in figure  19. At startup, the initialization of some variables for the student interface is needed: progress bar, stop button, reset of the displayed points, message indicating the system startup.
filename File type Open source license Location of the file PowerSupplyCard. zip Kicad and Gerber files GNU GPL v3 Zenodo repository 5 3DBox. zip Solidworks and STL files GNU GPL v3 Zenodo repository 5 Motherboard. zip Kicad and Gerber files GNU GPL v3 Zenodo repository 5 Plugs. zip Kicad and Gerber files GNU GPL v3 Zenodo repository 5.
Each show is processed and linked to the previous ones to incrementally extend a database of annotations. In this scenario, we will consider that annotations of a show which has been processed will not be modified once archived. Modern diarization systems achieve decent performance depending on the type of data they process  [3, 4]  but those performances are often not good enough to deploy such systems without any human supervision  [5, 6]. Human assisted learning offers a way to achieve better performance by engaging an interaction between the automatic system and a human expert in order to correct or guide the automatic diarization process  [7, 8]. Amongst the different modes of human assisted learning, our work focuses on active learning where the automatic system, while processing an incoming sequence of audio recordings, is allowed to ask simple questions to the human expert  [9].
The ALLIES Corpus Experiments are performed on the ALLIES dataset (Database and protocols will be made publicly available after the ALLIES challenge), an extension of previously existing corpora  [43] [44] [45] , that includes a collection of 1008 French TV and Radio shows partitioned in three non-overlapping parts whose statistics are provided in Table  1. The training set is used to train the x-vector extractor and the PLDA model while the Development set is used to estimate the optimal clustering threshold. Performance is reported on the Evaluation set.
Within-Show Baseline Performance For each system and data set (Dev and Eval), performance of within-show diarization is given in Table  4. For each system, all parameters and thresholds are optimized to minimize DER on the development set and then used on the evaluation set. From this table, it is noticeable that the ResDS system strongly out-performs the SincDS system in terms of within-show DER. This is due to the highest quality of its speaker representations (x-vectors). Using an automatic segmentation degrades the within-show DER for all systems but this degradation is more important on the evaluation set than on the development set. For instance, ResDS within-show DER increases by a relative 6% on the Dev set but 57% on the Eval set. We observe that our two baseline systems perform better on Eval than on Dev when using the Reference segmentation but that they face strong degradation when using an automatic segmentation.
Baseline System Description For incremental cross-show speaker linking, we assume that at time T, a number N of shows, {F 1 , F 2 ,. F N }, has already been processed by the automatic system to produce a cross-show diarization hypothesis including a set of M detected speakers, referred to as known-speakers. A database of x-vectors is built by including one single x-vector per knownspeaker and per show, resulting in a collection of x-vectors: {S 1  1 , S 1 2 , S 1 3 , S 2 4 , S 2 5 , S 3 1 ,. S N M }, where S i j is the x-vector of speaker j observed in show i. Note that a single speaker k might have appeared in several shows F n , F m and thus be represented in the x-vector database by several x-vectors {S n k , S m k }. When receiving a new show, F N+1 , the within-show speaker diarization is performed.
All new-speakers, detected in this show, are then compared to the known-speakers from the existing database in order to detect recurrent speakers and link them with their previous occurrences in the collection. For each speaker α detected in the current show, all segments attributed to α in this show are processed to extract a collection of x-vectors that are then averaged to obtain one single x-vector S N+1 α representing this speaker. As a result, the current file produces a set {S N+1 α ,. , S N+1 λ } of x-vectors. For comparison of known-speakers and new-speakers, all x-vectors extracted from multiple shows for a same known-speaker are averaged to produce one single x-vector for this speaker. A pseudo-distance matrix is computed by comparing the new-speakers to the known-speakers using a PLDA model.
A verification score is computed for each pair of (known, new)-speaker. The verification score is then multiplied by -1 and shifted so that the minimum of pseudo-distances is 0. Values of this modified pseudo-distance matrix are processed in increasing order. Each value is compared to an empirical threshold (defined using a development set). If the pseudo-distance is lower than the threshold, then the corresponding couple of speakers is merged and both merged knownand new-speakers are removed from the linking process to avoid linking with others. Indeed, merging with another speaker would mean merging two known-speakers together or two new speakers together which is forbidden by our initial assumption. At the end of the process, each new speaker S N+1 j merged with a known-speaker S k i is renamed accordingly (S N+1 i ) and her x-vector from the current file is added to the database.
In the cross-show scenario, we decompose the task into two steps: 1. detection of recurrent speakers, i. e. , detect if a given speaker from the current file has been observed in the past; 2. human-assisted closed-set identification of speakers detected as seen during the first step. Speakers who have not been categorized as seen are simply added to the known speaker database. To detect the recurrent speakers we propose to use a pseudo-distance matrix based on the one described in Section 6. 1. The information conveyed in this matrix is the pseudodistance between couples of x-vectors, the lower the pseudo-distance, the more likely two speakers are the same. This matrix is depicted in Figure  4. This matrix can be computed in different manners depending on the representation chosen for known-speakers.
Similarly to the within-show human-assisted process, the human operator is offered two audio segments (one belongs to the current speaker and one belongs to a known-speaker) to listen to: the longest for each speaker. Based on those two segments, the question asked to the human expert is: "Is the speaker from this segment (a known-speaker) the same as the one speaking in this current segment?". If the operator answers "Yes", the two speakers are linked and the selected knownspeaker is not proposed anymore to link with any other current speaker. If the operator answers "No", the next x-vector per order of pseudo-distance is considered. For one current speaker, the process ends either when linked with a known-speaker or after a number of questions chosen empirically; in the latest case, the current speaker is added to the known speaker database.
Conclusions The benefit of human active correction for speaker diarization has been investigated for both within-show diarization and incremental cross-show diarization. This preliminary study has focused on an active correction of HAC clustering errors for the case of withinshow diarization and active correction of speaker linking for incremental cross-show diarization. For within-show diarization, starting from a strong automatic baseline, we proposed two criteria to ask questions to a human expert. Five methods to select samples for auditory tests have been proposed and evaluated using a large and challenging dataset that will be publicly released. Performance of our human assisted speaker diarization system have been evaluated by using a penalized DER proposed in  [36]  and shows that it can decrease DER by up to 22.
Data and data preparation Using the web scraping tool WebHarvey, we collected consumer reviews relating to second-generation versions of the Amazon Echo and Amazon Echo Dot, from amazon. com, posted between October 24, 2016 (first review date), and December 10, 2018 (download date). In total, we collected 31,870 reviews for the Echo and 119,805 reviews for the Echo Dot, for a total of 151,675 reviews. We used Knime Analytics's (4. 4. 2) embedded text processing tools  (Tursi & Silipo, 2018) , in line with recommendations from  Ordenes and Silipo (2021). Similar to previous analyses  (Milne et al. , 2020; Villarroel Ordenes et al. , 2019) , we removed stop words (e. g. , "on," "and," "is"), reduced terms to their stems by applying the Kuhlen stemming algorithm (e. g. , "buy" for "buying" and "buys"), erased punctuation, removed words with fewer than four characters, filtered out numerical terms (both numbers and terms that represent them), and converted all letters to lower case.
Sample and data collection We test the proposed model (Fig. 1 ) among respondents from the online Prolific platform. First, using a screening study, we identified participants likely to have an Amazon Echo (or related) device, by asking 3,000 respondents if they owned an Amazon Echo device, for how long, and if they would be willing to participate in a follow-up study. We identified 1,386 participants who indicated they had owned an Echo device for at least a month (see  McLean et al. , 2021) , which helped ensure they had some experience with the VA  (Pitardi & Marriott, 2021). Of these 1,386 respondents, 815 participated in the follow-up study, but 82 gave incorrect responses to the attention check question (i. e. , indicated the number of cars in a picture incorrectly), leaving 733 respondents (48.
Measurement model We used the statistical package SmartPLS 3. 3 for PLS-SEM, employing 10,000 bootstrap resamples to obtain robust standard errors and t-statistics for the parameters in our model  (Hair et al. , 2017a, b). Measurement models do not apply to singleitem constructs, so we excluded natural speech, task range, accuracy, age, length of ownership, and gender measures from our reliability and validity assessments  (Hair et al. , 2017a, b). For internal reliability, we considered composite reliability, which takes the outer loadings of the indicator variables into account, and Cronbach's alpha, which is a more conservative measure  (Hair et al. , 2017a, b). Both measures indicated good internal reliability, and the values for all the multi-item constructs exceeded 0. 7  (Hair et al.
10, see Table  4 ) moderated the impact of perceptions of intelligence on continued usage intentions, consistent with H 9B and H 10B. Finally, we found that the negative effects of artificiality and the positive effects of intelligence (path coefficients 0. 10 and -0. 09 respectively, see Table  4 ) were weaker amongst older consumers (H 11A and H 11B ). We produced Johnson-Neyman plots (Fig. 3 ), using latent variable scores from SmartPLS 3. 3  (Hair et al. , 2017a, b). Specifically, we used the R package interactions  (Long, 2019) , with a bootstrapped variance-covariance matrix of estimates (10,000 samples) obtained from the R package sandwich  (Zeileis, 2004; Zeileis et al. , 2020). The positive association between perceptions of VA intelligence and continued usage intentions was significant only when perceived sacrifice levels were greater than 2.
Plant phenotyping platforms generate large amounts of high-dimensional data at different scales of plant organization. The possibility to use this information as inputs of models is an opportunity to develop models that integrate new processes and genetic inputs. We assessed to what extent the phenomics and modelling communities can address the issues of interoperability and data exchange, using a science mapping approach (i. e. visualization and analysis of a broad range of scientific and technological activities as a whole). In this paper, we (i) evaluate connections, (ii) identify compatible and connectable research topics and (iii) propose strategies to facilitate connection across communities. We applied a science mapping approach based on reference and term analyses to a set of 4332 scientific papers published by the plant phenomics and modelling communities from 1980 to 2019, retrieved using the Elsevier's Scopus database and the quantitative-plant.
IN TROD UCTION During the past decade, plant phenotyping platforms have generated large amounts of detailed data at different spatial and temporal scales for thousands of genotypes under controlled conditions or in the field  (Tardieu et al. 2017). Information extracted from these datasets could be more widely used as variables or parameters of mathematical and computational models, thereby broadening the scope of information extracted from phenomics data  (Muller and Martre 2019; Louarn and Song 2020). Feeding such data to process-based crop models (models representing crop characteristics at the field scale;  Jones et al. 2003; Holzworth et al. 2014)  or individual-based models (models representing plants individually with various degrees of architectural realism, e. g. functional-structural plant models;  Evers et al.
For example, process-based crop models with genotype-dependent parameters will become more widespread as parameters can now be obtained for thousands of genotypes in phenotyping platforms  (Parent and Tardieu 2014; Casadebaig et al. 2020). A crucial question is to what extent phenomics and modelling communities exchange datasets and use them together. Bibliometric analysis and science mapping of scientific publications are relevant approaches to assess current connections and identify compatible and connectable research topics. They have been used to provide new insights into specific disciplines (e. g. reveal the historical evolution or identify the emerging topics of a research discipline) and provide a field overview by visualizing the main research areas of a community. For example, bibliometric science mapping has been used to evaluate the structure and evolution of Mediterranean forest research  (Nardi et al.
2016). It has also revealed the evolution of plant phenotyping research structures  (Costa et al. 2019) , with a trend toward a higher diversity of phenotyped species and research in real field conditions. However, bibliometric mapping science has never been applied to reveal and evaluate the connections between different scientific communities. Here, we carried out bibliometric analysis on a set of scientific papers related to plant phenotyping and modelling. We retrieved the papers via Elsevier's Scopus database. Then, we evaluated the connections and identified compatible research topics between the communities using the VOSviewer software (van Eck and  Waltman 2010). Finally, we present strategies to facilitate connection across scientific communities in the context of the EMPHASIS research infrastructure initiative (European Infrastructure for Multi-scale Plant Phenomics and Simulation for Food Security in a Changing Climate; https:// emphasis.
Scopus search The Scopus database (https://www. scopus. com/) was used to retrieve bibliographic records related to plant phenotyping, plant image analysis software tools (called 'image analysis tools' hereafter), process-based crop models (called 'crop models' hereafter) and individual-based models (called 'plant models' hereafter) over the period 1980-2019. To identify relevant papers and retrieve the bibliographic datasets associated with each community (image analysis tool database, crop model database or plant model database), we used the following keywords in the combined field of title, abstract and keywords for: • the plant phenotyping: phenotyping AND plant It should be noted that, due to Scopus limitations, some sources could be missing in the research. Indeed, Scopus only analyzes the citations of the journals in its index and has a bias in its coverage of European journals and Elsevier titles.
'Quantitative Plant' database The Scopus database was completed with scientific articles from the online resource quantitative-plant. org (Saint Cast and Lobet 2019), a website referencing plant image analysis software tools, crop models and plant models. 'Quantitative Plant' extends the 'Plant Image Analysis website'  (Lobet et al. 2013; Lobet 2017). One of the objectives of 'Quantitative Plant' is to develop an online portal referencing crop and plant models to raise awareness and highlight the diversity of models and their applications. This website is hand curated with image analysis tools and plant or crop models identified in a thorough review of the literature. Each tool and model is presented concisely in a consistent framework and is described by their general characteristics (e. g.
Bibliometric mapping In order to visualize connections between papers from both communities, we used the VOSviewer software version 1. 6. 13 (freely available at https://www. vosviewer. com/) which was specifically developed for creating, visualizing and exploring science's bibliometric maps  (van Eck and Waltman 2010). VOSviewer allows linking documents that reference the same set of cited documents (i. e. bibliographic coupling links). Two articles with at least one common reference are identified as articles with a bibliographic coupling relationship  (Kessler 1963; Chang and Huang 2012). The method of bibliographic coupling can help researchers to filter out a group of articles with a particular type of connection by the creation of so-called 'bibliography coupling maps'  (Boyack and Klavans 2010).
Evolution of the trends of publication A total of 4173 scientific papers were retrieved from the Scopus database, completed by 159 scientific papers from the quantitative-plant. org website. Approximately, 88. 6 % of the papers were research papers, 6. 9 % review papers, 3. 4 % book chapters and the remaining 1. 1 % were books, letters, conference papers or notes. Top journals were Frontiers in Plant Science (n = 224; 5. 2 %), Field Crop Research (n = 163; 3. 8 %), Annals of Botany (n = 144; 3. 3 %), Plant Methods (n = 132; 3. 2 %) and Journal of Experimental Botany (n = 120; 2. 8 %). These papers were then clustered manually in four categories that belong to either phenomic or modelling communities, namely plant phenotyping, image analysis tools, crop models and plant models (numbers of papers: n = 2074, n = 363, n = 1567 and n = 328, respectively).
The crop modelling community. During the period 1980-84, 13 publications were associated to the crop modelling community, but the first paper was referenced by Scopus in 1971. We acknowledge that papers indexed in the survey provides a partial representation of the crop modelling activity as our research may be biased by the Scopus database (i. e. the publications and the journals indexed) and the field (i. e. title, abstract and keywords) or the keyword used in Scopus queries. However, the above-mentioned period seems to correspond to the early stage of the crop modelling community described by  Passioura (1996) ,  Jones et al. (2017)  and  Keating and Thorburn (2018). This early establishment led to the development of a well-organized community. Several groups (e. g. APSIM, DSSAT, EPIC or STICS;  Jones et al.
The image analysis tool community. Plant scientists have produced massive datasets involving billions of images during the last decade  (Furbank and Tester 2011; Fiorani and Schurr 2013). Indeed, images provide information about the structure and the physiological status of the plant for the scientists (e. g. shape, colour, growth, transpiration or light received) at different spatial and temporal scales (e. g. leaf expansion rate of an individual organ or canopy expansion of a population;  Dhondt et al. 2013; Coppens et al. 2017). Moreover, they can be produced for a large diversity of species (e. g. annual or perennial species) in experiments performed in controlled conditions or in the field, using automatic image recording  (Tardieu et al. 2017; Neveu et al. 2019). This massive and diverse amount of images produced during the last decade called for the development of a variety of image analysis software tools dedicated to data extraction and analyse (e.
1Promote the interest and raise awareness. It is currently challenging for phenomics researchers to become aware of the diversity of models and their applications (Fig. 5A ). To solve this first challenge, we developed the 'Quantitative Plant' online portal (quantitativeplant. org) allowing the exploration of the diversity of >100 plant and crop simulation models and their applications. This web-based repository extends the 'Plant Image Analysis' website (an online database for plant image analysis software tools;  Lobet et al. 2013). It helps researchers in search for image analysis software for their phenotyping experiments to find out potential game-changing model applications on the associated crop or plant model webpage. From now on, the objectives will be to maintain and update this online portal and promote the interest for modelling approaches by advertising this website within the phenomics community (e.
g. at conferences, workshops and symposia). A second challenge is for the modelling community to become aware of the phenomics datasets and phenotyping platforms. To address this second challenge, a mapping exercise was carried out by IPPN (https://www. plant-phenotyping. org/infrastructure_map) and EMPHASIS (https://emphasis. plant-phenotyping. eu/emphasis_ infrastructure_map) thereby increasing the visibility of phenotyping platforms, in collaboration with national plant phenotyping communities. Surveys were carried out to extract detailed information on existing and upcoming infrastructures. In addition, platform characteristics were inventoried throughout workshops organized in different regions of Europe and the World. Phenotyping platforms were described by their general characteristics (e.
g. description of the phenomics variables used in the parameter estimations). In addition of the benefit to find compatibilities between phenomics datasets and models using ontologies, the PCMO would facilitate the connection between the models themselves, promoting the design of modular models  (Christensen et al. 2018; Passot et al. 2018; Benes et al. 2020; Peng et al. 2020)  or the intercomparison of models  (Athanasiadis et al. 2009; Porter et al. 2014; Schnepf et al. 2020).