dataesr/scientific-paragraphs-categorization huggingface dataset:
Inputs: Data from interviews and documents Every decision making process relies on the analysis of information sourced. In our case, whether it is for the adapted Godet method or for MyChoice, information comes from different stakeholders of the value-chain as said before. It is mainly in the form of text since semi-directive interviews are conducted and then transcribed to ensure proper analysis later on. To the interviews we added documents since during the time of the study, remote work was a necessity considering the sanitary context. Each document read was considered as an interview done  (Chaib et al. , 2021). In total, for project Sentinel, 21 texts were analyzed, including 12 transcribed interviews and 9 documents (Le  Teno, 2013; Delanoue and Roguet, 2014; INAPORC, 2019; Barberis et al.
Outputs The outputs obtained following the interviews are double: on one hand we have outputs by using the adapted Godet method, and on another hand, we have the outputs obtained using the MyChoice tool for multicriteria argumentation since we think this method can help in constructing the ontologies needed. Indeed, it provides a standardized data structure to describe the opinions collected from the interviews/documents; moreover, it allows capitalizing the vocabularies built from the first interviews/documents and suggesting items for the next ones. Both methods were initially created with different objectives in mind: the Godet method aims to identify key variables which are used for the creation of scenarios, whereas the MyChoice tool originally serves to pinpoint what may be the strengths and weaknesses of the value-chain.
-For the concepts: in the adapted Godet method they are more general than the ones of MyChoice. A concept in Godet contains on average 4 criteria but can contain up to 24, whereas in MyChoice a concept contains 2 criteria on average and can have up to 12. -As for the variables, they are more specific in the MyChoice database, however, some of them can be combined and it is possible to obtain 12 variables corresponding to those in the Godet method. This is more explicit in Figure  4. The fact that MyChoice is an easy tool to use makes the process of homogenizing vocabularies and constructing ontologies of variables a bit easier: indeed, it is easier to avoid redundancies between criteria, because of the separation between values, evaluations and the denomination. We find ourselves with fewer denominations and a homogenized vocabulary.
It is thus easier to group them into concepts manually. In addition, the fact that each concept can only belong to one aim forces us to be specific in the nomenclatures. The variables eventually obtained using the MyChoice tool correspond to the ones obtained by following the Godet adapted method. MyChoice thus seems to be an adapted tool for the construction of an ontology of variables: it allows us to formalize and standardize vocabularies manually but still easily compared to the adapted Godet process. This allows a better exploitation of data in the future. The MyChoice tool however does not allow us to identify key variables, at least not for now; following the process of the adapted Godet method explicited previously and detailed in  Chaib et al. (2021)  seems inevitable. What the MyChoice tool could facilitate is the confirmation of key variables, especially since it is sometimes rather complicated to obtain responses of stakeholders when using the Delphi method.
Actually, for a given ZnO concentration, the electric field applied on the MRs is much higher than that applied on the MPs. The following COMSOL numerical simulation confirms this observation. It has been revealed that the electrical properties related to the connectivity patterns of composites play an important role in their piezoelectric and dielectric properties  [62] [63] [64]. To assess the electric field distribution within the fillers and their surrounding matrix, 2D finite-element models were built using COMSOL Multiphysics software. In order to highlight the effects of the material's structuration, as well as the filler's shape, three kinds of samples were drawn, as illustrated in Figure  4 , i. e. , comprising (a) random MRs composite, (b) structured MR composite and (c) structured MPs composite.
All samples were elaborated with 8% volume fraction of ZnO fillers. The MR composites were assimilated to rectangle-shaped ZnO (3. 9 µm length × 0. 477 µm width) embedded in a PDMS polymer matrix (23 µm length × 20 µm width). To build the randomly dispersed filler distribution (Figure  4a ), the filler's orientation and position were pro-generated using MATLAB software with a random number generator function that can be then imported into COMSOL software. For the structured composites Figure  4b ), rods were vertically aligned in one column and spaced equally at around 0. 7 µm distance. This resulted in a ratio of the average particle size to the effective inter-particle distance (denoted γ) of approximately 0. 18. The electric behavior of the composite was then evaluated via Electrostatics modulus, where the bottom was electrically grounded, and a floating potential was applied on the top to ensure an electric field of 0.
The objective of this article is to bring together the two perspectives, to contribute to the exploration of the deep, complex and tangled relationship between what we say and what we do, and the outcomes of this. Specifically, we are interested in the form this triangle takes when applied to children engaged in a collaborative learning activity, in which what they say and do is strongly tied to how they perform, and subsequently what they will ultimately learn from the activity. To investigate the above question, we transcribe a subset of previously collected data (containing audio and action logs) generated by teams of two children engaged in a collaborative learning activity. This activity aimed at providing an intuitive understanding of networks/graphs and spanning trees, in order to improve the Computational Thinking (CT) skills of children  (Nasir et al.
Though our results are on a small dataset of 10 transcribed dialogues, the proposed method automatically captures some patterns of alignment on completely spontaneous children's dialogues, and its relation to task success, without timeconsuming manual annotations. The rest of the article is organised as follows. Sec. 2 describes our context: the activity and the dataset we obtained to study our research questions. Sec. 3 presents our methodology with our hypotheses and how we investigate them. Sec. 4 presents our results and our interpretations that address RQ1 and RQ2. Sec. 5 concludes the findings of the paper.
The Dataset Dataset description. We collected the JUSThink dataset 7  , which consists of 76 children in teams of two (41 females: M = 10. 3, SD = 0. 75 years old; and 35 males: M = 10. 4, SD = 0. 61). We have one problem solving session, i. e. task, per team. 8 out of the 38 teams (≈ 21%) found an optimal solution to the activity. The teams were formed randomly, without considering the gender, nationality, or the mother tongue. They include mixed and same gender pairs, and this information is available but not used in our analyses. The study was conducted in multiple international schools in Switzerland, where the medium of education is in English, and hence students are proficient in English. The dataset contains: 1. The recorded audio files: Audio was recorded as two mono audio channels synchronised to each other, with one lavalier microphone per channel.
Transcribing a representative subset. In order to study referring and alignment of the interlocutors, we selected a subset of 10 teams of the dataset 9. The teams were chosen to be a representative sample of the overall dataset (see Fig. 3 ), keeping in mind the task success distribution; which we measure through performance in the task and learning outcomes observed from the pre-test and the post-test 10. Table  1  provides further details about the transcribed subset. As shown, the mean duration of the task is ≈ 23 minutes, and the transcriptions account for ≈ 4 hours of data. The transcripts report which interlocutor is speaking (either A or B) and the start and end timestamps for each utterance, beside the utterance content. Utterance segmentation is based on  Koiso et al. (1998) 's definition of an Inter Pausal Unit (IPU), defined as "a stretch of a single interlocutor's speech bounded by pauses longer than 100 ms".
We also annotated punctuation markers, such as commas, full stops, exclamation points and question marks. Fillers, such as 'uh  ' and 'um', (using Meteer et al. (1995)  for reference) were transcribed, as well as the discourse marker 'oh' 11. The above 3 spontaneous speech phenomena occur frequently in the dataset {'um': 236, 'uh': 173, 'oh': 333}. Other phenomena, such as 'ew' or 'oops!' were also transcribed, however, their frequency is too low for analysis. Transcription included incomplete elements, such as "Mount Neuchat-" in "Mount Neuchat-um Mount Interlaken". Pronunciation differed among and within interlocutors (for example, for the word 'Montreux', pronouncing the ending as /ks/ or /ø/), due to the unfamiliarity of the interlocutors with the referents, and individual accents. As our methodology is dependent on matching surface forms (refer to Sec.
In particular, we define the utterance at which a referring expression becomes routine as the establishment of that routine. We extract routines and the utterances at which the routines are primed and established from the transcripts as in  (Dubuisson Duplessis et al. , 2017 , 2021). Then, we filter for the routines that contain a task-specific referent  16. 14 We process logs (folder logs/ in the dataset at DOI: 10. 5281/zenodo. 4627104) with a script (tools/1 extract performance and other features from logs. ipynb in the tools at DOI: 10. 5281/zenodo. 4675070) to compute the error for each team (in table processed data/log features/justhink19 log features task level. csv, available with the tools). 15  Studying the temporality of the establishment of the routines (H1. 1). To investigate when the routine expressions become established, we study i) the establishment time of a routine, i.
Match times have mean of the medians = 62. 6% (combined SD = 21. 9%), and mismatches have mean of medians = 67. 6% (combined SD = 26. 0%). By inspecting the outputs of the automatically inferred instructions in Table  3 , occasionally, we see that the traditional roles of IF and IG are not maintained, even for the brief fixed time of views 28 As described in Algorithm 3. We process this combined dialogue and actions corpus via a script (tools/6 recognise instructions detect follow-up actions. ipynb in the tools) to generate an annotated corpus (processed data/annotated corpus, available with the tools). The annotated corpus contains the inferred instructions, their pending lists used for matching, matches, mismatches, and nonmatches if there were no instructions to be (mis)matched. The teams' match and mismatch times in green and red respectively, as normalised by the duration for each team separately.
Conclusion In this article, we are interested in how children collaborate as they solve a problem together, in which what they say and do is strongly tied to how they perform, and subsequently what they will ultimately learn from the situated activity. To investigate this relationship, we consider the corpus of data (dialogue transcripts from audio files and action logs) generated by teams of two children engaged in a collaborative learning activity, called JUSThink, which aims at providing an intuitive understanding of graphs and spanning trees. Collaborative learning activities are a particularly interesting type of collaborative task, due to their "multi-layered goals", typically including immediate, performance goals (e. g. , finding the solution to a math problem) and deeper, learning goals (e.
Specifically we consider in the situated environment the interlocutor's i) use of expressions related to the task, and the ii) follow-up actions of such expressions. There is a crucial relationship between the two in this task, as interlocutors could simply be stuck saying. For this purpose, we investigate in RQ1 what interlocutors "say" at various phases in the dialogue (in H1. 1), and how such expressions are surrounded by hesitation phenomena, i. e. the use of fillers like 'um' and 'uh' (H1. 2). Then, RQ2 focuses on the relationship between expression and follow-up actions, at various phases in the dialogue (H2. 1), and how the follow-ups are surrounded by information management phenomena, i. e. the use of 'oh'. We also contribute a dataset of anonymised, transcribed children dialogues, event logs of their task progression, and code, which are all made publicly available to either reproduce our results or to use for further research.
, , 2021)) ), other expressions ("I don't understand") etc. Since there are many ways to learn, and hence different behaviours that could result in learning, it is unsurprising that such patterns are difficult to capture. Working with dialogues is complicated, due to i) the lack of automatic evaluation criteria (e. g. human annotators might instinctively be able to say that a particular team collaborated well after observing a dialogue, but it is hard to empirically pinpoint the exact reasons such a judgement was taken), and ii) the nature of spontaneous speech (variable turn taking, disfluencies, etc. ). Our results, albeit limited to a small dataset, highlight that in a situated activity, focusing simply on task-specific expressions and certain surrounding spontaneous speech phenomena can still give good insights into the nuances of collaboration between the interlocutors, and its ultimate links to task success (with the awareness that there are several other aspects that remain to be observed in the dialogue such as studying the gaze patterns, prosodic features and so on).
Data Availability All relevant data (transcripts, logs, and responses to the pre-test and the post-test, as well as the description of the network in the activity) are available from the Zenodo Repository, DOI: 10. 5281/zen-odo. 4627104. The code that reproduces all the results and figures given in this paper are available from the Zenodo Repository, DOI: 10. 5281/zenodo. 4675070.
Declaration of Interest There is no conflict of interest. Algorithm 1: RECOGNISE-INSTRUCTIONS finds the edit instructions in an utterance. It is implemented in a script (tools/6 recognise instructions detect follow-up actions. ipynb in the tools) that generates an annotated corpus (processed data/annotated corpus available with the tools), where the column instructions gives the list of instructions that are inferred for that row. Input: A sequence of tokens U = t 1 ,t 2 ,. ,t n that make up an utterance U Output: A sequence of instructions I = i 1 , i 2 ,. , i k 1 E ← RECOGNISE-ENTITIES(U) 2 I ← an empty sequence.
In the network used in the JUSThink activity, there exist 10 nodes: { 'Luzern', 'Interlaken', 'Montreux', 'Davos', 'Zermatt', 'Neuchatel', 'Gallen', 'Bern', 'Zurich', 'Basel' } and 20 edges. A description of the network, with the node labels (e. g. "Mount Luzern"), x, y position of a node, possible edges between the nodes, and edge costs, is publicly available online with the dataset (JUSThink Dialogue and Actions Corpus), from the Zenodo Repository DOI: 10. 5281/zenodo.
: 10. 5281/zenodo. 4627104) 10 See App. A for details on the selection. 11 'Oh' can also be treated as a filler in Meteer et al. (1995) , but we consider the usage of 'oh' more aligned with (Schiffrin, 1987). 12 We standardise variations of pronunciation in the transcriptions, and we do not account for e. g. variations in accent.
We process the transcripts (folder transcripts/ in the dataset at DOI: 10. 5281/zenodo. 4627104) with a script (tools/4 extract routines from transcripts. ipynb in the tools at DOI: 10. 5281/zenodo. 4675070) to extract routines (in tables in folder processed data/routines/, available with the tools). We use the dialign package v1. 0, available at https://github. com/GuillaumeDD/dialign, by (Dubuisson Duplessis et al. , 2017 , 2021).
In our implementation, we combine the transcripts and the logs (from the dataset at DOI: 10. 5281/zenodo. 4627104) via a script (tools/5 construct the corpus by combining transcripts with logs. ipynb in the tools at DOI: 10. 5281/zenodo. 4675070) to generate a combined corpus (processed data/corpus/ available with the tools). Then, we process this corpus via a script (tools/6 recognise instructions detect follow-up actions. ipynb in the tools) to generate an annotated corpus (processed data/annotated corpus, available with the tools). The annotated corpus contains the inferred instructions, matches, mismatches, and nonmatches if there were no instructions to be matched.
In order to better formalize the notorious Inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process 1,2,3 including (i) finding "seed" descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the "seed" vectors. The main development effort here was focused on the latter stage, proposing a new Attentionbased Conditional Variational AutoEncoder (ACoVAE) neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity, while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility.
g. "virtual hits" were found. Virtual screening (VS), however, is limited by the choice of candidate structures, either from public/commercial databases or from userdesigned virtual libraries. In contrast to systematic VS, sampling techniques of chemical structures consider molecular structure as evolvable  [13] [14] [15]. This is de novo design  [16] [17] [18] [19] [20] [21] [22] [23]  , which fundamentally differs from VS by the fact that structures are not a predefined library, but are generated and/or modified "on the fly" by some automated molecular structure editor. The recent advent of Deep Neural Networks (DNN), able to extract information from arbitrary "brute" data and herewith learn to recognize patterns, had a major impact on the field of QSAR  [24] [25] [26] [27] [28]. The idea of DNN is mimicking a human brain in which neurons communicate by generating and passing signals.
Both works can be considered as examples of "hard" inverse QSAR approaches and were successfully used to generate structures in the neighborhood of known actives. However, they stopped short of coupling "easy" and "hard" QSAR problems, in order to investigate how their approaches would cope with input vectors corresponding to optima of the QSAR landscape, not to already known molecules. We have used two types of in-house generated QSAR models of ABL Tyrosine kinase 1 (CHEMBL1862) activity: 1. Support Vector Regression (SVR) models for the thermodynamic instability constant (pKi) using  ⃗ ⃗ =ISIDA  37, 38  circular fragment counts. Seed vectors prepared with the help of a Genetic Algorithm used to sample  ⃗ ⃗ space with predicted pKi value as fitness. Additionally, the descriptor vector of the molecule possessing the highest affinity ("lead molecule", LM) from the CHEMBL1862 set was also used as a seed vector.
Application of a GLT transformation layer  49  better translates the descriptor vector into the internal representation used by the decoder network than multilayer perceptron. Finally, inspired by the GELU approximation  50  , new activation function FTSwishG resulted from some modifications of the previously reported FTSwish  51  was used throughout the ACoVAE network: 𝐹𝑇𝑆𝑤𝑖𝑠ℎ𝐺 = 𝑅𝐸𝐿𝑈(𝑥) * 𝑠𝑖𝑔𝑚𝑜𝑖𝑑(1. 702𝑥) -0. 2 (1) According to our tests it gives better results compared to the ReLU, GeLU and FTSwish activation functions. In such a way, our ACoVAE transformer architecture is a novel one, having only few in common with the one proposed by Lin et al. 46  The designed architecture is implemented using the TensorFlow framework, can be readily retrained for other descriptor types. It is available on our GitHub storage https://github.
com/Laboratoire-de-Chemoinformatique/ACoVAE Figure  1 : General scheme of the ACoVAE architecture used in this study. The GRU-based encoder (top left) parametrizes SMILES into latent vectors following a hyperspherical distribution, which is used upon inference for random sampling. The descriptor vector which is used as a condition in the generation is embedded by a GLT layer (top right). Autoregressive Transformer is used to decode random latent vectors and combined conditions into SMILES strings. A detailed representation of all three networks is given in Supplementary Information.
Support Vector Regression Models A series of ligands for ABL Tyrosine kinase (CHEMBL1862) from the ChEMBL v. 23 database was standardized using a protocol reported by Sidorov et al  39. Support Vector Regression (SVR) models for thermodynamic instability constants of protein-ligand complexes (pKi) were generated using the evolutionary libsvm model tuner  52  , which supports selection of the best suited descriptor space yielding to best performance models as a key hyperparameter. The best-suited ISIDA fragmentation schemes were defined together with the SVR-specific parameters (kernel type, cost, gamma, etc. ) optimizing model quality. The models were built on a training set containing 739 and validated on a test set of 82 molecules. The best model relies on IIRAB-1-3 ISIDA fragment count descriptors (7372 atom-centered fragments with a radius of 1 to 3 atoms with restricted fragmentation) and the Gaussian kernel option.
Resulting property "landscapes" are nothing but NB-driven QSAR models: the property of any external item can be predicted from the "local color" of the landscape zone onto which it is projected. In this work, the fuzzy class landscapes (monitoring the likelihood to classify as "active" with respect to a target) were employed. They were based on the previously published  56  Universal Map #1 (UM1)the first of a series of GTMs parameterized (using ChEMBL data) such as to maximize their "polypharmacological competence", i. e. their ability to host a large battery of highly predictive fuzzy class landscapes associated to diverse biological targets. Note that landscape-based QSAR models are parameter-free (the landscapes are built by projection of existing structure-activity data on the given manifold, in an unsupervised manner).
Therefore, landscape-based QSAR models are implicitly available as soon as the supporting structure-activity data is available. The structure-activity data set associated to the CHEMBL1862 target was projected on the manifold of the first universal map UM1  56  and was seen to "spontaneously" segregate into zones populated predominantly by "actives" and "inactives" respectively. This map was built based on ISIDA  40  atom sequence counts with a length of 2-3 atoms labeled by CVFF force field types and formal charge status (IA-FF-2-3-FC). Recall that construction of activity landscapes on a given GTM manifold is not supervised, but a purely deterministic procedure. The separation proficiency of the considered manifold was obtained by repeated leave-1/3-out cross-validation, in which iteratively 2/3 of the items are projected on the map in order to "color" the activity class landscape, whereas the remaining 1/3 of compounds a posteriori projected onto that landscape and have their activity classes assigned on basis of their residential zones in the landscape.