dataesr/scientific-paragraphs-categorization huggingface dataset:
Formulation of the proposed model As the number of multivariate time series basically refers to the number of attributes in each data set, we formulate generally a multivariate time series by: X= {X ð1Þ ,X ð2Þ ,. ,X ðnÞ }, where n is the number of multivariate time series. Each target time series i could be represented as:X ðiÞ ¼ X , where m could refers to either a total length of time measurement sequence, or a specific input window length. Moreover, each window can cover a desired time horizon, and may be captured either as a fixed or random time frame. Depending on the way a dataset can be back-tested, a window could generally be obtained using either: a fixed train-test split, multiple traintest split, or a walk-forward validation split. Once datasets are split according to a desired prediction input and output time horizons, the predicted multivariate time series can be modeled as:P ¼ P ð1Þ ; P ð2Þ ; :::; P ðnÞ È É.
For better data structure interpretability, this study example  [32]  extracts and incorporates specific confidences rules during the encoding/ decoding processes, using an evolutionary Genetic Algorithm (GA). The ignored inner distribution of hidden layers' neurons was also handled in  [33]  through the introduction of a regularization term considering both local and nonlocal dataset information. The proposed manifold regulations term was inserted, in addition to the computed error, in the Particle Swarm Optimization's (PSO) cost function  [33]. As depicted in (Fig. ' for alli 2 1; :::; n f g , the encoding operation at the first hidden layer may be given as: E ð0Þ ¼ rðW ð0Þ X $ þb ð0Þ Þ ð 1Þ As each network's layer train separately, a layer's output forms the next layer's input. Hence, the encoding operation in the k th hidden layer can be represented as, where W ðkþ1Þ and b ðkþ1Þ respectively denote the weight matrix and its corresponding biases andk 2 1; :::; L f g : E ðkþ1Þ ¼ rðW ðkþ1Þ E ðkÞ þ b ðkþ1Þ Þ ð 2Þ Generally, the activation function r refer to the non-linear sigmoid function represented as: rðxÞ ¼ 1 1 þ expðÀxÞ ð3Þ After each batch encoding operation, an appropriate cost function is computed given a calculated loss between a layer's input and output.
Data preparation and analysis In this research paper, we extended an Amazon dataset that contains multivariate information on instance requests, collected between the years 2018 and 2019. Data was processed by structuring EC2 instances demands per instances family; removing unnecessary columns; completing missing values; and performing the appropriate string data conversion. For the sake of simplicity, we decided to work with only three various instances families namely: the compute-optimized, the memory-optimized, and the storage-optimized families. These families differ in terms of instances' resources utilization. As an example, the 'memory-optimized' family contains instances with high memory usage intensity, which requires an optimization of memory resources. Therefore, the same apply for other instances families.
To speed up the training process, we used a sample of one hundred thousand for every family. It should be noted that the instances were collected in one-minute intervals. We considered the ten attributes highlighted in (Table  2 ) with their associated significances. Furthermore, (Fig. 4 ) highlights the main time distribution of AWS resources demand and occupancies. From this distribution, we effectively detected the residing data noises and residuals as well as the existing hourly seasonality in most attributes' fluctuations. This seasonality was then taken into consideration while choosing the appropriate input and output prediction horizons.
Simulation environment and experimentation scenarios We implemented the proposed model on Python 3. 8, using TensorFlow framework, Keras, Numpy, Pandas and other libraries. The training of our models is performed using an NVIDIA GeForce RTX2070 GPU and 16 Go of RAM, on the top of a DELL G5 15 intel core 7 machine. In our experiments, AWS dataset is split into a training, validation, and a test set for every family. The training and validation sets were employed for training. Then, the evaluation metrics on predicted values were tested using the test set. Before implementing the predictor model, we created input time series windows of various sizes (300-360 À 420) to produce forecasts at a future horizon of 60 min. Experimentation scenarios were performed under various batch sizes (64 -128 -360) and 20 epochs.
Future plausible perspectives could focus on improving our proposal's precision in terms of memory and storage usages time series being marginally less precise than obtained utilized vCPU predictions results. On the other hand, we also seek to investigate the influence of different input sizes of the captured instances families datasets, and then the influence of different input and output data window horizons on the prediction results. Again, we are looking forward to further explore the analysis of power consumption, temperature, and training time variations over several additional training cycles, then across various hardware graphics cards.
II. BACKGROUND LoRaWAN is a Long Range communication protocol  [9]  often used to create Low Power Wide Area Networks (LPWANs) with an operating range that goes from 300 meters up to 10 kilometers. Among the main LPWANs protocol, LoRaWAN is one of the most known and used, given its open architecture. LoRa modems modulate symbols into increasing and decreasing frequencies chirps, respectively called up-chirps and down-chirps. LoRa modulation has many parameters, which can partially be modified, depending also on the operating region of the system. These parameters are: carrier frequency, signal bandwidth, coding rate, spreading factor, chirp polarity, and sync word. More precisely, the spreading factor (SF) defines how many chirps are sent per second. It ranges among SF7 and SF12. In detail, a large SF increases the symbol airtime and the energy consumption, thus improving the communication range, but reducing the available data rate and messages' payload size.
In this paper, we consider that all the devices use the Adaptive Data Rate (ADR) mechanism for SF selection  [10]. The LoRaWAN ADR is a scheme that allows to optimize the network's data rates, time-on-air (ToA) and energy consumption by controlling the data rate and the transmission power for all the end nodes independently. As for the LoRaWAN end devices classes, they can be classified into three categories: Class A, B, and C. A Class A device has the lowest energy consumption mode, it can send uplink messages at any time, however downlink messages can be received only during two specific windows of time after an uplink transmission. Class B devices can send uplink messages any time, and have a time-synchronised receiving window for the downlink. Class C devices has the highest power consumption, they keep the receiving window open unless they are transmitting again which is the case of LoRaWAN gateways.
A. Simulation setup Our proposal is evaluated in a simulator written in Python. The model parameters are as follows: • Total number of devices: {200, 400, 600, 800, 1000} As for the genetic algorithm, we consider 200 generations of 20 individuals/chromosomes per population. A chromosome is composed of 9 genes. Two parents are selected from each generation using a tournament parent selection, and a single point crossover. A random mutation is used with 20% for genes mutation. We use the PyGAD Python library for building the genetic algorithm and optimizing its results  [20].
Data-driven research in phonetics and phonology relies massively on oral resources, and access thereto. We propose to explore a question in comparative linguistics using an open-source crowd-sourced corpus, Lingua Libre, Wikimedia's participatory linguistic library, to show that such corpora may offer a solution to typologists wishing to explore numerous languages at once. For the present proof of concept, we compare the realizations of Italian and Spanish vowels (sample size = 5000) to investigate whether vowel production is influenced by the size of the phonemic inventory (the Inventory Size Hypothesis), by the exact shape of the inventory (the Vowel Quality Hypothesis) or by none of the above. Results show that the size of the inventory does not seem to influence vowel production, thus supporting previous research, but also that the shape of the inventory may well be a factor determining the extent of variation in vowel production.
, 2020) , or other types of language-specific laboratory recordings such as the TIMIT database for English  (Garofolo et al. , 1993)  or NCCFr for French  (Torreira et al. , 2010). On the other hand, they can gather audio recordings from other sources such as TV or radio shows, as was done for instance in the framework of the international project OSEO Quaero (www. quaero. org/), or from audio books, as exemplified by the LibriSpeech corpus for English  (Panayotov et al. , 2015, www. openslr. org/12). Both options have the disadvantage of being overly costly, both in money and human resources, and sometimes not freely accessible to the community. A third path has been recently explored: crowd-sourced data, recorded by volunteers and therefore much less costly in time and money and generally opensource.
The project Common Voice  (Ardila et al. , 2020 , https://commonvoice. mozilla. org) for instance was launched in 2017 by Mozilla for the intended purpose of creating a free database for the development of speech recognition software. In March 2022, it contains ∼18,000 hours of speech, 14,000 of which have been validated by other speakers, in 87 languages. In the present paper, we explore a similar project: Lingua Libre, a participatory linguistic media library developed by Wikimedia France (https: //lingualibre. org). It was launched in 2015, and, in March 2022, it counts ∼700,000 recordings in 148 languages across 775 speakers. This database is interesting to explore because it differs from Common Voice in the fact that its aim is not primarily the development of new technologies, or even linguistic inquiry in general, but patrimonial conservation of languages.
Lingua Libre was used only once for academic purposes, i. e. , to automatically estimate the transparency of orthographies in 17 languages  (Marjou, 2021). With this study, we aim to show that such data can be easily processed and useful to answer phonological questions in linguistic typology. In this proof of concept, we explore the realization of vowels by comparing two Romance languages: Italian and Spanish. The outline of the paper is as follows. In Section 2, we describe our research question to justify our choice of languages. In Section 3, we present our corpus and methodology. In Section 4, we provide an analysis of the vowels in Italian and Spanish. Section 5 concludes and discusses the results.
The Inventory Size Hypothesis vs the Vowel Quality Hypothesis In this paper, we offer to use Lingua Libre to tackle the question of vowel production with regards to vowel inventory. Our research question stems from various theories regarding the shape of vowel inventories in the world's languages. Our study however focuses on synchronic phonetic variation with regards to phonological systems (on the phylogeny of vowel systems in the languages of the world, see  Zhang and Gong (2022)  and references therein). The original Vowel Dispersion Theory (Liljencrants and  Lindblom, 1972; Lindblom, 1986)  and a few years later the Adaptive Dispersion Theory  (Lindblom, 1990) , stem from the H&H ("Hypoand Hyperspeech") model of communication, that assumes that speakers tend toward minimal and sufficient perceptual contrast, i.
Materials and Methodology As a crowd-sourcing tool, Lingua Libre allows any speaker to log in, fill in a profile with basic metadata for themselves of for other speakers, and record themselves or their guests reading lists of words in their language. The device detects pauses, which allows for the recording to end when the word has been read and the next recording to start automatically after, therefore effortlessly generating relatively short audio files for each word. Each audio file is supposed to be titled on the same template of 'Language -Speaker -Item'. For example, for the recording 'spa. -Marreromarcosolucionar. wav', the language is Spanish ('spa'), the speaker ID is 'Marreromarco', and the recorded item is 'solucionar', 'solve'. All audio files are under a Creative Commons licence, i.
e. , open-source. First, the recordings are scrapped from the Lingua Libre database. In the present study, we extract a subsample of 500 items for /a, e, i, o, u/ in each language, to counter the fact that both languages have different amounts of data points and to also control for number of speakers (5) in each language. In total, we have 500 occurrences for each of the 5 vowels in both Italian and Spanish, which results in 5000 tokens. To avoid a potential sample bias, the sampling of tokens is conducted 10 times. We also took care to limit our investigation to the European variety of Spanish, to avoid any mismatch with the more limited geographical expansion of Italian. Second, the recordings are segmented and aligned using WebMAUS  (Kisler et al. , 2017) , the online open-access version of the MAUS software  (Schiel, 2004).
MAUS creates a pronunciation hypothesis graph based on the orthographic transcript of the recording (extracted from the name of the audio file) using a grapheme-to-phoneme converter. During this process, the orthographic transcription is converted to the Speech Assessment Methods Phonetic Alphabet (SAMPA). The signal is then aligned with the hypothesis graph and the alignment with the highest probability is chosen. Experiments have shown that the MAUS-based alignment is 95% accurate compared to human-based alignments  (Kipp et al. , 1997). Third, the selected vowels are extracted from the recordings and analyzed in terms of formants. For each recording of each vowel, the mean F1 and F2 of the entire sound are calculated. The mean formants are considered to attenuate the effect of co-articulation with the left and right contexts.
Conclusion and discussion We used crowd-sourced data to test two competing hypotheses in language typology: The production of vowels is influenced either by the size of the inventory, or by its shape. Our proof-of-concept on Italian and Spanish shows that the size of the inventory does not influence the realization of vowels, but the exact quality of the vowels at hand does. Our study also points to several caveats. First, all audio files were not properly labeled and were thus unusable. Moreover, from a human point of view, it should be noted that crowd-sourced data heavily rely on the participants' good will and that researchers have no choice but to trust the provided metadata. One possible solution to that last problem would be for Lingua Libre to propose a verification tool, as does Common Voice, to improve the reliability of the data and metadata.
Acknowledgments This research was partially supported by  Institut DATAIA  and the  MSH Paris-Saclay  in the framework of the  Excellency Award  for the project  OTELO -OnTologies pour l'Enrichissement de l'analyse Linguistique de l'Oral (PI Ioana Vasilescu  and  Fabian Suchanek ), and by the  French National Research Agency  in the framework of the grant EVOGRAM: The role of linguistic and nonlinguistic factors in the evolution of nominal classification systems,  ANR-20-CE27-0021  ( PI Marc Allassonnière-Tang ). The authors would also like to thank the  Wikimedia community  for their interest in the project, and in particular  Lucas Lévêque  for his help on the Lingua Libre tool.
Tools and the software development approach GROOV'Scape software is available as a standalone command line application or as a simulator for the OpenFLUID software environment for spatial modeling  (Fabre et al. 2010 ). The command line application allows either or both of the preparation and scenario procedures of the GROOV'Scape algorithm to be launched. It can be run in a command line terminal with the following usage: groovscape ,procedure. ,datapath. where the ,procedure. command can be discretization or scenario depending on the procedure to be executed, or complete to chain the execution of both procedures. The ,datapath. command is the path where the dataset to be processed is located on a disk. The dataset must contain the needed input vector and raster GIS files, and also an usersetparams.
json file that contains the run configuration for the GROOV'Scape algorithm. GROOV'Scape software mainly consists of a library containing the algorithm parts. It has been developed in C þþ language using the C þþ11 standard. The C þþ language was chosen for its performance and its ability to handle complex software design with an object-oriented approach. Note that the first six attributes are defined or calculated by GROOV'Scape, while the last, named with the type of infrastructure and corresponding to the coverage rate of the infrastructure on the LI, can be computed by GROOV'Scape or can be derived from the coverage of the linear infrastructure, in this case the hedge. ID, LI unique identifier; ID_TO, identifier of down unit (here SU or RS units); Xc and Yc, coordinates of the center of the LI segment; flowdist: the distance along the flow direction; type, type of anthropic infrastructure (hedge, ditch, etc.
); CoverRate, the ratio of the PLO covered by the linear anthropic infrastructure. The GROOV'Scape library relies on reference libraries and tools used in the field of spatial processing: GDAL for handling spatial information, and GEOS and GRASS (as an external command) for spatial processing. It also relies on RapidJSON and Qt libraries for utility functions, and on the OpenFLUID platform for framework facilities and for spatial coupling with other models. The GROOV'Scape source code is composed of approximately 12,000 lines of C þþ and is available as open-source software under the terms of the GPLv3 license. At present, it has only been built and tested on the Linux system using the Ubuntu distribution.
Basic data As described above, GROOV'Scape uses a DEM and a vector coverage of parcels as input. A 5 m DEM resolution covering the Doazit perimeter was obtained from the RGE ALTI ® database of the French National Institute of Geography (IGN 2018). Parcel coverage was elaborated from a field survey performed in 2014. The survey also identified the hydraulic connections between parcels and the parcel boundaries that corresponded to elements of the catchment drainage network. The former was used to evaluate the connections estimated by GROOV'Scape. The latter was used to derive a drainage network which was used as an example of GROOV'Scape application.
For random selection, the PLO coverage percentage by the infrastructure was randomly set between 60 and 100%. Each selection was repeated 100 times to obtain robust statistics. An R script was built to perform these selections. Figure  4  shows that the unbalanced stratified sampling of the infrastructures based on the relative positions of the PLO generated more variability than pure random sampling (Figure  4 , first white box on left) with regard to water disconnection. The closer to the hydrographic network the infrastructure was clustered (different boxplot colors in Figure  4 ), the greater the chance of finding an effective infrastructure configuration that limited direct flow connection. This is an expected effect since the clustering of infrastructure in the upper part of the hillslope prevents the interception of runoff in lower parts of the hillslope while using infrastructure that is too long to intercept the flow of areas that are too small.
DISCUSSION GROOV'Scape is an open-source software that implements a new spatial discretization of cultivated landscapes based on two main procedures. An analysis of a first application to a catchment in France showed that the resulting spatial discretization from the connection between plots is relevant. Its relevance requires further analysis through the application of GROOV'Scape to other case studies, covering a wider range of parcel configurations, soil variability, topography, and infrastructure patterns. In the following, we emphasize the three key characteristics of the new form of processing implemented using GROOV'Scape: (i) processing genericity, (ii) automation and speed, and (iii) discretization sensitivity to users' choice. Finally, we discuss steps toward moving to a fully operational tool.
Genericity A procedure dedicated to agricultural landscape discretization would qualify as generic if it could be applied to a large variety of landscape types. In the case of GROOV'Scape, genericity comes first from spatial discretization based on real landscape  objects (agricultural parcels and, optionally, soil units) to be considered irrespective of the nature of the agricultural landscape. The diversity of anthropic infrastructure considered in the building of landscape scenarios, such as grass strips, ditches, and hedges, also contribute to GROOV'Scape's genericity. Such infrastructure creates water and matter flux discontinuities in the landscape, which must be taken into account in hydrological modeling. Accounting for these discontinuities gives GROOV'Scape an advantage over other processing software (e.
Depending on the scientific or operational objective, the exploration may seek to determine the optimum density of infrastructure (Figure  3 ) or, for a given infrastructure density, the optimal spatial location of that infrastructure (Figures  4  and 5 ). In this respect, automation and speed are major assets of GROOV'Scape. GROOV'Scape's fully automated processing relies on the user's choice of minimum PAO size area. Automation allows the production of a large quantity of discretization (Figures  4  and 5 ). Non-automated ad hoc processing and discretization, involving successive manual and GIS-software-based operations, remain a widespread practice among hydrological modeling scientists and engineers. A tool that automates the processing considerably reduces subjectivity and increases the traceability of the processing conducted.
CONCLUSION GROOV'Scape is a software that implements a new landscape discretization procedure that can account for a wide range of human-made landscape elements. It is fully automated and fast enough to enable sensitivity analysis and the interactive production of mediated modeling landscape management scenarios. The first application to a real catchment (Doazit catchment, France) showed that the spatial discretization analyzed from the connection between plots is relevant. This relevance needs to be further analyzed by applying GROOV'Scpae to other case studies, covering a wider range of parcel configuration, soil variability, topography, and infrastructure patterns. This first application also analyzed the sensitivity of GROOV'Scape to input DEM resolution and the minimum size parameter needed to guide users toward appropriate modeling practices.
ACKNOWLEDGEMENTS This project was granted by  OFB (French Office of Biodiversity)  projects  'BV Service' and 'Retenues'. We thank  Armel Thöni  for his contribution to the software development of GROOV'Scape. We thank  Martin André  for his contribution to the test of GROOV'Scape on the Doazit Catchment. We thank  Adrien Chassan  ( Coopérative MaïsAdour ) for providing the geographical data needed for testing GROOV'Scape on the Doazit Catchment.