dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the SVM implementation provided by Scikit-learn  (Pedregosa et al. , 2011)  with the default parameters. We made the source code that reproduces our results publicly available on https: 410 //github. com/basma-b/multi_level_chatbot. The baseline system provided by the challenge organizers is an implementation of the dual encoder of  Lowe et al. (2015). We recall the differences between our system and the baseline system in the following. (1) Our system learns to match the context and the candidate response on the word-level and sequence-level whereas the baseline system is based on only the sequence similarity.
As we can see, our system outperforms the provided baseline system on all the metrics with a good margin. Also, we observe that the performance of our system in addition to the baseline system on the Advising Corpus are lower than the performance on the Ubuntu Dialogue Corpus (V3). The performance of our system on Subtask 3 are lower than Subtask 1 on all the metrics. We believe that this result is logic as the system is challenged to retrieve all the correct responses which is harder than retrieving only one correct response (as in Subtask 1). The results of subtask 4 are quite lower than expected. We analyzed the subtask datasets and found that the SVM classifier is hard to train because of the unbalanced data. As mentioned in Table  4 , the percentages of training samples where no correct response is available in the candidate set are 20% and 20.
05% in the case of Ubuntu and Advising datasets respectively. At the training step, the system will see 80% of dialogues with a correct response and thus will tend to generalize and predict a correct response most of the time. Applying some data balancing techniques may solve this problem. After incorporation of the external knowledge as required by Subtask 5, the performance of our system did not improve. The results of Subtask 1 and 5 are comparable as they use the same datasets. As we can see in Table  3 , the results of Subtask 5 are lower than Subtask 1 on both datasets. We believe that this is mainly due to the new word embeddings that we computed on the external data. When we used the word embeddings produced from the training data as in Subtask 1, we were able to find 89,284 and 4,534 word embedding vectors for the training data of the Ubuntu Dialogue Corpus (V3) and the Advising Corpus respectively.
Compared to these systems, our system is simpler and at the time of the submission, no data augmentation technique was used. We show later, that when we augment the training set with more positive samples to balance the ratio of positive and negative samples, our system would be ranked 5 th on the Ubuntu corpus. on the word-level is better than matching them on only the sequence-level. These re-475 sults mean that explicitly considering the words separately are more meaningful than considering them implicitly when encoding the sequence and provide a fine-grained representation of the context and the response. Based on these results, we can deduce two points. (1) The modification of the dual encoder with only sequence similarity results in better performance compared to the baseline (the original dual encoder).
The context is composed of multiple turns t 1 , t 2 ,. , t n. Hence, we construct new positive samples starting from the second turn by concatenating the previous turns t i and considering them as a new context and the turn t j as the response with a label 1. By applying this data augmentation approach to the datasets of Subtask 1, we were able to obtain 10,349,002 training samples for the Ubuntu Dialogue Corpus (V3) which represents an increase of 3. 49% of the total number of samples. Even if the training data remains unbalanced, we show in Table  7  that with this small increase in the number of positive samples, the performance of our system increased considerably. Ubuntu Dialogue Corpus (V3) R@1 R@10 R@50 MRR Our system 0. 469 0. 756 0. 947 0. 573 Our system + data augmentation 0. 526 0.
Evaluation on Other Benchmark Datasets To measure the efficiency of our approach and to compare it to other state-of-the-art systems, we evaluated our system on the widely used datasets in building and evaluating retrieval-based dialogue systems such as the Ubuntu Dialogue Corpus  (Lowe et al. , 2015)  and the Douban Conversations Corpus  (Wu et al. , 2017). In Table  9 , statistics of both datasets are given. The choice of these baseline systems is motivated by their novelty, the availability of their performance on one or both datasets, the availability of their source code to reproduce their results and the fact that these systems were considered as baselines in most of the recent works which helped in comparing our system to previous systems. In Table  10 , we summarize the evaluation results of our system and the following baseline systems:   (Boussaha et al.
System Ubuntu Dialogue Corpus V1 Douban Conversation Corpus R2@1 R10@1 R10@2 R10@5 R10@1 R10@2 R10@5 P@1 MAP MRR As we can see in Table  10 , compared to the single-turn systems (the first six rows), our system achieves the best results on all metrics and on both datasets. The first four systems are based on only sequence level similarity between the context and the candidate response whereas our system incorporates word level similarity in addition to the sequence similarity. Moreover, our system outperforms the SMN dynamic  (Wu et al. , 2017)  with a good margin (around 4% and 3% on Recall@1 and 2 respectively on UDC). Even if the SMN matches the response with every context turn and uses multiple convolutions and max pooling to rank the response, its performance is lower than our system's performance.
We believe that using our architecture, we were able to efficiently capture both similarity levels without any need to make the system more complicated by matching the response with every utterance of the context. Our system neither matches each context turn with the candidate response nor uses complex cross and self attention in addition to matching and accumulation mechanisms but achieves almost the same performance as the Deep Attention Matching (DAM)  (Zhou et al. , 2018)  and the Deep Utterance Aggregation system  (Zhang et al. , 2018)  on both datasets and on all metrics. The DAM system is based on multiple layers of the self attention (Transformer) and Convolutional Neural Networks  (LeCun et al. , 1998). Even if the advantages of the Transformer are related to the performance improvement and the acceleration of the learning compared to recurrent neural networks  (Vaswani et al.
Conclusion This paper describes our end-to-end retrieval-based dialog system that learns to match the context with the correct response. We evaluated our system on the track of sentence selection of the DSTC7 challenge and two widely used datasets in building and evaluating retrieval-based dialogue systems. Experimental results have shown the effectiveness of combining sequence and word level similarities by bringing significant improvements compared to the baseline systems. Throughout our work, we show the importance of having simple architectures that can work as well as complex architecture and sometimes better. Having a simple architecture, facilitates its adaptation and reuse in other domains and applications easy and costs less energy and pollutes less the environment. The DSTC7 challenge provided an excellent evaluation environment for retrieval-based dialog systems and has successfully pushed them towards dealing with more realistic constraints.
Unbalanced data was one of the challenges of the task and we show that using a simple approach of data balancing through data augmentation can help the system achieve a better performance. The advising dataset offers metadata in addition to the conversations between the teacher and the student including course description and student preference. It would be interesting to efficiently include them in our system as external knowledge to enrich the information that we extract from the conversation. provide descriptions of their approaches  (Gunasekara et al. , 2019).
These The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and then identify decelerations and accelerations. These steps can potentially be automated and made more objective by signal processing analysis. Various methods have been described in the literature but there are no open-source programs for performing these steps. The MATLAB toolbox presented here comprises a standard signal pre-processing function, 11 re-coded literature methods for fetal heart rate analysis, a signal viewer (enabling annotation by an expert) and an evaluation procedure with various criteria measuring intrarater agreement.
Trying to compute the baseline creates a circular definition: acceleration/deceleration (A/D) are defined as periods that are 15 bpm above or below the baseline, whereas the baseline is defined as the mean signal after A/D episodes have been excluded from the data. Several researchers have developed automatic analysis methods (AAMs)  [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]. Unfortunately, there are almost no open-source programmed versions of any algorithms or standard tools for displaying FHR analysis and measuring each method effectiveness. Furthermore, researchers have to perform their own evaluation, since there is no common database within which various methods can be compared and there is no common evaluation criteria. Here, we describe a MATLAB toolbox which gives some tools to tests, display and evaluates the various method of literature as well as future method.
Software architecture The toolbox has been designed as a modular software package. It includes a graphical user interface (GUI) (to be launched with fhrmorpho), which allows its use as a standalone tool. The user selects the FHR file to be opened and the AAM; the interface then displays the corresponding baseline and A/D. An expert can also use the interface to annotate and analyze the FHR recording by drawing a baseline and selecting the beginning and end of each A/D. Indices representing the level of discordance between the AAM and the expert can then be displayed (see section 2. 2. 5). The toolbox includes a dataset of 156 FHR recordings (described in  [17] ). Sixty-six of those form a training dataset accompanied by an expert consensus analysis. The other 90 recordings correspond to an evaluation dataset for which the Based on morphological filters; an opening filter removes accelerations and a closing filter removes decelerations.
Mantel et al. (1990) [9][10] The method uses the mode or another local maximum of the FHR histogram to obtain an initial baseline. The final baseline is obtained by progressively trimming the first baseline (based on Dawes et al. 's  [19]  method). This method was developed for antepartum recording. Yes aammongelli Mongelli et al. (1997)  [11]  This method computes a primary mode and a secondary mode; the two modes can be switched, depending on continuity and frequency criteria. The toolbox can also be called through a command line interface in MATLAB; it can therefore interact with other toolboxes or software.
IMPACT This toolbox was used in studies  [15]  and  [16]  to compare the various literature AAMs by taking as reference an expert consensus. Using the data described in  [17] , all the experiments can therefore be repeated. Figure  2  shows each method's performance with regard to three criteria  [16]. The main conclusion of our research is that Lu et al. 's AAM  [7]  is significantly better than the 10 other AAMs for reproducing the expert analyses with regard to almost all the criteria studied. However, as shown in Figure  2 , the Lu-expert difference appears to be greater than the inter-expert differences -suggesting that progress can and should be made in clinical practice. This toolbox will enable other researchers to (i) reproduce our experiments on the same dataset, (ii) visualize and better understand problematic patterns, and (iii) try to develop new AAMs and test them.
The data used in  [15]  and  [16]  (described on  [17] ) are available at http://utsb. univcatholille. fr/fhr-review. Researchers who wish to compare their results with others can submit their analysis of the evaluation dataset, and the leaderboard will be displayed on this website. The toolbox might also be useful for researchers wishing to automatically analyze FHR signals and quantify pathologic features (the number and surface of A/D, the occurrence of tachycardia, etc. ). This process might be very important for objectively measuring the effect of treatments during the labor (amnioinfusion, the administration of oxytocin, etc. ). Lastly, the final objective of AAMs for the FHR is to best predict the risk of fetal acidosis and thus provide decision support (caesarean section, operative delivery, etc.
The BASECOL database has been created and scientifically enriched since 2004. It contains collisional excitation rate coefficients of molecules for application to the interstellar medium and to cometary atmospheres. Recently, major technical updates have been performed in order to be compliant with international standards for management of data and in order to provide a more friendly environment to query and to present the data. The current paper aims at presenting the key features of the technical updates and to underline the compatibility of BASECOL database with the Virtual Atomic and Molecular Data Center. This latter aims to interconnect atomic and molecular databases, thus providing a single location where users can access atomic and molecular data.
Introduction to BASECOL The current publication aims at providing a description of the latest technical developments performed on the BASECOL database. The intention is to provide a paper that conveys the technical quality of the BASECOL service in relation with the Virtual Atomic and Molecular Data Center (VAMDC) [1-3], which is supported by the VAMDC consortium  1. The current VAMDC e-infrastructure interconnects about 38 atomic and molecular databases that cover atomic and molecular spectroscopy and processes. About 90% of the inter-connected databases handle data that are used for the interpretation of astronomical spectra and for the modeling in media of many fields of astrophysics. VAMDC offers a common entry point to all encorporated databases through the VAMDC portal  2  and it develops central services such as the species database  3  or the Query Store service  4.
VAMDC also develops standalone tools in order to retrieve and handle the data such as the SPECTCOL tool [4]   5. VAMDC provides software and support in order to include new databases within the VAMDC e-infrastructure  6. One current feature of VAMDC e-infrastructure is the constrained environment for the description of data, in particular the XSAMS schema and other standardized protocols  7  that ensure a higher quality for the distribution of data. The genesis of this database was prompted by the need of collisional inelastic rate coefficients for the analysis of astrophysical spectra obtained with the latest spatial and ground heterodyne instruments. The first publication [5] on BASECOL introduced the status of BASECOL in 2012 and had an exhaustive table about the 2012 scientific content.
The database has been partially updated since 2013, and, once completed, further communication will provide the scientific status. An excellent review of the scientific needs and issues related to such collisional data can be found in [6]. The BASECOL database collects from the refereed literature the state-to-state rate coefficients for the inelastic excitation of rotational, vibrational and ro-vibrational levels of molecules by atoms, molecules, and electrons. Currently, the collisional rate coefficients come from theoretical calculations. The collisional processes are described in the temperature range relevant to the interstellar medium, to circumstellar atmospheres and to cometary atmospheres. In addition, the database includes the energy tables that contain identification of the levels involved in the collisional transitions, some fitted collisional data sets to the numerical data and the references attached to the collisional data sets.
Indeed, one aspect of the BASECOL database is the quality, completeness, and safety of the provided information, as BASECOL is managed by scientists having a deep knowledge of the type of data ingested in the database. This means that the data are checked with respect to the methodologies used to calculate the data and with respect to the consistency of the data sets. The data policy of BASECOL is to keep a record of all available and published data sets. Other important scientific aspects of BASECOL are the versioning of the collisional data sets and the references attached to all sets of data. This allows traceability of the information. From a technical point of view, BASECOL is a sophisticated product that follows international rules for management of FAIR data [7] (cf. Section 6) and that is compliant with the VAMDC infrastructure.
• The last component is a REST-like API that provides a software interface to the database. It is a set of web services that perform the data-extraction from the central database. This API may be used not only by the website, but also by any other client. This API relies on the the Spring Java framework. The endpoint of each web service composing the API, reachable through the HTTP protocol, returns JSON 9  formatted data. As shown in Figure  1 , the BASECOL system is composed of a private "ingestion instance" and of a public "production instance". The latter is interfaced with VAMDC. As shown in Figure  2 , a BASECOL "instance" is composed of its relational database, its API, and its web user interface. The "ingestion instance" allows the database maintainer to verify and to validate the imported data.
The "ingestion database" is dumped into the "production database" at regular intervals, and a specific procedure is put into place in order to ensure that only the validated data are visible in the "production instance" website. Contrary to the previous version of the BASECOL environment, the new software organization follows the multitier architecture: the user interface and the database access are now completely decoupled, making future evolution of the system easier. The structure of the database can be modified without any impact on the user interface layer, as long as the REST API remains the same.
Removed Features Now, the bibliographic section of the database only includes the publications that are referenced from the collisional data sets (cf. Section 4. 2) and from the energy tables. This reduces the bibliographic entries to about 300 entries and this is justified by the overall coherence of the ingestion and versioning procedures. The VAMDC portal and the SPECTCOL tool [4] implement the IVOA-SAMP protocol [8], created to connect client-side tools to improve productivity when working with multiple data types. VAMDC data can be directly piped into any tool implementing the SAMP protocol, e. g. , TOPCAT  10. Since the TOPCAT tool may be used to compare (or cross-match) different rate coefficients, we removed the cross-matching feature from the BASECOL web-interface. We also removed the visualization of output files in a format compatible with the RADEX code  11  (the format of those files is explained in Van der Tak et al.
[6]) as users can build their own RADEX files using the SPECTCOL tool. In addition, at least two websites already provide this kind of information: the LAMDA database [6] and the CASSIS database [9]   12. The BASECOL database is rather a reference repository for collisional inelastic rate coefficients and the advice would be to take collisional data from the BASECOL database in order to build the RADEX files on those known websites [6,9] or in any other internal or public secondary display of collisional rate coefficients. This would ensure a proper versioning of the collisional data sets as a specific effort on versioning is made in BASECOL (see Section 3 and Appendix B), and a similar versioning effort is available in the molecular spectroscopic databases connected through VAMDC: CDMS, JPL, and HITRAN [10-13].
Technical Evolution for Improved Data Integrity and for VAMDC Content Requirements We designed a new data import system that improves data integrity and authenticity (R7 of CoretrustSeal recommendation  13  ), and that introduces rigorous procedures in managing archival long lasting storage of data. The data import system is composed of an import ASCII file, of a Java application that parses and loads the import file into the database and of a git repository. We first describe the import ASCII file and then the import procedure.
Description of the Import ASCII File The import ASCII file is composed of several "Data" sections, and the concatenation of these sections constitutes what is called a "collisional data set". Each section of the import file corresponds to an "object" that follows the versioning rules (cf. Appendix B). In this paper, we give the same name both to the section of the import ASCII file and to the "object" which is the concept to which rules of versioning are applied. An "object" may include metadata and numerical data or just metadata. Some of the metadata are aimed to allow the compatibility of BASECOL with the VAMDC architecture, some metadata are either mandatory or optional, and some medatata are related to the uniqueness of an "object". The concept of uniqueness is attached to the numerical data as well, and is used in the versioning process handled by the import script (cf.
Element Section The "Element" section is compulsory and corresponds to the object "Element" of Appendix B which is composed of a list of metadata given in Table  1. An example of an "Element" section input for N 2 H + is given in Table  A1. The metadata of the "Element" object allowing compatibility with VAMDC concern the identification of the species. The VAMDC standards provide standardization of the species through the metadata "stoichiometric formula" (the atoms are ordered in alphabetic order and the number of occurences of the atom is given after the atomic symbol), and through the "inchikey". When a species is already present in BASECOL it is possible to retrieve the species metadata from BASECOL through a web user interface (cf. Appendix C); otherwise, information can be retrieved from the species database  14  when the metadata are linked to VAMDC, with the exception of "mass".
If neither BASECOL nor the species database contain the species information, the BASECOL data provider can contact the VAMDC team to get advice (support@vamdc. eu). Table  1. List of properties/metadata for the object "Element". The symbols [M] and [O] indicate that the metadata are either mandatory or optional in the import file. In the column "Uniq. ", we indicate whether the metadata defines the uniqueness of the object. In the column "Purpose", we indicate whether the information is meant for the BASECOL (B) public WebUI display and/or for VAMDC (V) interoperability. In "V(parameter)", "parameter" indicates the name of column in the species database. The mass unit is atomic mass unit (a. m. u) and the default value for mass is zero.
Energy Table Section The "Energy Table " section is compulsory and corresponds to the object "Energy Table " of Appendix B, which is composed of a list of metadata given in Table  2  and of a numerical table. The imported numerical energy table identifies the labels of the collisional transition in the rate coefficients table. The administrator can import the energy table used in the rate coefficients calculations, or an energy table created with data from spectroscopic databases such as CDMS, JPL, and HITRAN [10-13]. A template of an "Energy table" section input is given in Table  A2. The numerical energy table is composed of labels, values of the quantum numbers, and numerical values for the energy. The energy tables are currently being harmonized so that the default energy unit be "cm -1 ".