dataesr/scientific-paragraphs-categorization huggingface dataset:
V. CONCLUSION In this paper, we proposed a novel no-reference point cloud quality assessment method to predict the visual quality of distorted 3D point clouds. We fine-tuned a pre-trained CNN model to learn the visual quality features of the 2D patches extracted from the rendered views of the 3D point cloud. The final quality score of the evaluated object is obtained by averaging the predicted quality scores of all patches. Interestingly, our proposed method does not require any reference information for evaluation, which is a promising solution for real-world applications. We conducted several ablation experiments to select the best parameters for our method. Moreover, the comparison with the Full-Reference, Reduced-Reference and No-Reference state-ofthe-art methods proved that our proposed method provides superior correlations with the subjective quality scores on the three benchmark datasets and across different degradation types.
Using Pretrained NLP Models to Support Social Media Monitoring To our knowledge, most social media studies regarding COVID-19 vaccine hesitancy in France are qualitative and do not benefit from advanced and efficient machine learning. There are now many pretrained NLP models available, depending on the languages or texts used during training. Models in English were created to facilitate research, such as COVID-Twitter-BERT  [25] , which is trained on English tweets mentioning the COVID-19 pandemic. Arnaud et al  [26]  described previous work on the French adaptation of Bidirectional Encoder Representations from Transformers (BERT): CamemBERT and FlauBERT. The objective was to perform unsupervised learning, while we focused on supervised learning. Blanc et al  [27]  compared the performances of CamemBERT and FlauBERT to build a chatbot, and Sauvayre et al  [28]  used CamemBERT to classify the opinion of Twitter users on COVID-19 vaccines.
Data Collection Data were collected before vaccines were available on the market. About 700 million unique tweets were identified by the Panacea Lab team at Georgia State University  [40]  for the period between February 2020 and August 2020 using a selection of keywords that were mainly COVID-19 designation variants, and the corresponding IDs were made available on GitHub. A new version is made available every week and collects approximately 3 million tweets per day. Once restricted to tweets in French, the database has yet to be restricted to the vaccination topic. We did not use Twitter's localization function to verify that the tweets were written in France because the information was often missing. The data from the Panacea Lab have the advantage of having been collected constantly since the start of the pandemic using inclusion criteria that can be reproduced over time.
Data Refinement Based on Word Embeddings Word embeddings are a way of representing a word in the vocabulary in a mathematical space. Words are transformed into vectors of a fixed number of dimensions to embed information about their meaning in the corpus. Thus, words that are close in meaning will be close in distance in this space. The choice of the word embeddings model is based on the properties and specificities of the data, as they influence the performance of the algorithms using them. For this purpose, a word embeddings model was trained on the whole French tweet data set to catch emerging words that would not exist in a training data set of pretrained algorithms or pretrained word embedding algorithms.
Data Set Generation Using a Fine-Tuned FastText Model Since the data are composed of short, noisy messages with uncertain spelling, the word embeddings generated by FastText offer significant advantages. Notably, this method is considered to be fast, enables words with a similar spelling to be brought together by using parameter sharing, and can provide better performance when the vocabulary contains many syntactic or orthographic variations of the same word. Most word embedding models learn a vectorial representation from the word's context, but as FastText also learns additional embeddings at the character level of the word, the decomposition of an unknown word enables it to learn more relevant embeddings for rare words. The aforementioned technique, which is named parameter sharing, is an advantage since most models do not address the diversity of morphologically rich languages such as French.
Model Building and Training After the preprocessing steps, an extended analysis to explore users' sentiments about vaccination was initiated using advanced machine learning models. Some pretrained models in French can be adapted to a supervised classification task. We chose transfer learning, which allows learning with a limited amount of data or low computational capacity. Thus, as the model already has a sufficient understanding of the French language, it is not necessary to attain a large quantity of hand-labeled tweets. Most recent language models are based on the BERT model  [41]. As resources trained on tweets mentioning the COVID-19 pandemic are not available in French, it was necessary to fine tune existing models. Several pretrained models are available, with different sizes of architecture that are specific to one or more languages or that are trained on a particular type of text.
Implementation We used the following Python packages: sklearn, fastText, pytorch, pandas, kedro, and transformers. We used SSH access and Jupyter notebooks on a server consisting of a 28 core/56 thread microprocessor (Intel x64), 256 GB of memory, 1 TB of NVMe (nonvolatile memory express) storage, and 2 Nvidia GPUs of 2432 cuda core each (GeForce GTX 1070 Ti) with a Linux Debian 9. 9 operating system (see the Acknowledgments section).
The addition of ellipses reflects only the interpretation of the authors and is not based on any mathematical theory. Tweet filtering based on word embeddings provided a specific data set by restricting the collection to 344,000 tweets in French (around 9% of the initial French data set). Filtering based on the "vaccin" word alone would have generated a subset of 75,000 tweets, thus ignoring a large number of tweets potentially related to aspects of vaccine mistrust. The results presented in the rest of this paper concern the 344,000 French tweets related to a potential vaccine against COVID-19.
Second, we implemented a classifier based on CamemBERT, a French adaptation of BERT, a language model part of transformers with an F 1 -score of 0. 75 for predicting the opinion of Twitter users about potential vaccines against COVID-19. This allowed us to more precisely characterize tweets that presented positive or negative opinion on vaccination. We found that tweets with a negative opinion presented more polarized and political arguments than tweets with a positive opinion, and the role of treatments other than vaccines such as chloroquine was a major issue when discussing mistrust of vaccines.
Related Work Most papers that evaluated tweets for extracting insights about vaccination against COVID-19 used an unsupervised approach, mainly topic models with latent Dirichlet allocation  [24] , which means that a training set was not necessary. When sentiment analysis was performed, authors used a pretrained classifier most of the time, especially relying on Valence Aware Dictionary for Sentiment Reasoning (VADER)  [43] , which is available in the NLTK Python library. Using a pretrained classifier expedites classification but presents the inconvenience that it is not specialized for the study domain. Transformers are currently acknowledged as presenting the best performances in several NLP tasks. Tweets are more difficult to classify due to their characteristics: They are limited to 280 characters but can be much shorter; they are not necessarily written with correct syntax; and they can contain abbreviations, misspellings, and slang words  [44].
We also observed that the results of predictions are generally significantly better for correctly written texts such as PubMed abstracts, than with tweets, for example  [45]. In previous work, several custom algorithms were proposed, such as (1) deep learning with a CamemBERT model  [28] , BERT  [46, 47] , RoBERTa  [48] , FastText  [49] , convolutional neural network-long short-term memory with word2vec embeddings  [50]  or (2) machine learning with naïve Bayes  [51]  or decision tree  [52]  models. Off-the-shelf sentiment analysis models include Amazon Web Services Comprehend sentiment analysis  [53]  and VADER  [54] [55] [56] [57] [58] [59] [60] , which is a Python lexicon and rule-based sentiment analysis tool  [43]. In a recent evaluation of 11 sentiment analysis tools on 7 social media data sets, He et al  [61]  observed that these tools do not provide results that are accurate enough to aid in public health decision-making.
In some cases, multitask learning made it possible to improve the results  [45, 62, 63]. However, previous studies, such as that by Rodriguez et al  [64] , observed that multitask learning did not improve the results in general. However, it was possible to train the model on a simple task using transfer learning from the multitask model, obtaining better performance using less data. For the detection of fake news on COVID-19, Malla and Alphonse  [65]  showed how it could be interesting to use a CT-BERT (COVID-Twitter-BERT) model  [25]  but not a BERTweet model  [66]. In 2020, Lee et al  [67]  proposed BioBERT, a language model pretrained on PubMed abstracts and PubMed Central full-text articles, that largely outperformed BERT for biomedical NLP tasks. Similarly, Med-BERT, a pretrained model on a structured electronic health record data set of 28,490,650 patients  [68] , allows working with smaller training sets while obtaining better performance.
Acknowledgments This work was funded by grant number  ANR-16-CE23-0011-01  from the  French Agence nationale de la Recherche (ANR)  through the  Pharmacovigilance enrichie par des Groupements Améliorant la   detection des Signaux Emergents (PEGASE)  project. Exaion , a new subsidiary of the  Électricité de France  group, a cloud provider of blockchain and high-performance computing solutions, lent one of its servers with 2 graphical processing units free of charge for the duration of the study. We thank the representatives of  Exaion  for providing technical support for this server:  Fatih Balyeli ,  Laurent Bernou-Mazars ,  Nicolas Meaux , and  Vivien Sayve. Exaion  never intervened in the conduct nor interpretation of the study. We acknowledge the work of the  Panacea Lab  in collecting tweets related to the COVID-19 pandemic.
Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete.
That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.
Motivation As mentioned in this study, numerous review documents are available on various fault detection methods in photovoltaic systems  [1, 37, 39, 45, 66, 69, [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93]. However, these reviews use conventional state of the art review methodologies that have certain limitations. Firstly, algorithms are mainly compared based on their accuracy value in fault detection. This presents an issue as it must be considered that not all machine learning algorithms are trained under the same conditions. In other words, different datasets, weather conditions, and photovoltaic technologies are used, among others. These conditions vary depending on the specific application for which machine learning algorithms are used. Fig. 2. Example of a bibliometric network represented graphically, consisting of various nodes and lines.
Related work and methodology As mentioned in Section 2, the methodologies have limitations due to the human capacity required to analyze numerous documents accurately and impartially. Consequently, it becomes challenging to provide a comprehensive and unbiased overview of the conceptual and intellectual framework of a scientific field, emerging research areas, or collaboration networks. Bibliometrics, on the other hand, is a valuable tool that can address the subjectivity issues encountered in conventional state of the art reviews. Additionally, this tool enables the mapping of collaboration networks, emerging research areas, and relevant technological fields. References such as  [110] [111] [112] [113]  highlight the capabilities of bibliometrics in quantitative analysis. Using computer engineering, database management, and statistical techniques, bibliometrics organizes, explores, and analyzes large volumes of information (Big Data) in a structured manner.
This approach provides more detailed insights into each topic  [138]. One of the main contributions of this research resides in its capacity to tackle the issues discussed. To achieve this, the study proposes a novel hybrid methodology that combines bibliometric analysis and expert qualitative content analysis. This methodology is implemented in several stages, as shown in Fig. 3. As seen in Fig. 3 , the proposed methodology consists of four steps or stages. The first stage is called Data Collection, where documents related to the studied areas are retrieved from databases such as Scopus and WoS. The aim of the initial phase is to construct a data corpus containing all the relevant information for automatically determining hidden relationships. In the second stage, known as Bibliometric Analysis, these documents are processed to extract relevant information, including emerging trends, highly cited documents, collaboration networks, keywords, and more.
Data collection stage To ensure dependable conclusions during the construction of a wide range of state of the art  [141] , it is essential to have a data corpus that exhibits adequate accuracy and robustness. With this consideration in mind, the data collection stage has been conducted following the framework presented in Fig. 4. In the studied area, the data collection stage was applied on May 03, 2022. Initially, a group of documents was retrieved from widely used databases for bibliometric studies, namely Scopus and Web of Science  [142, 143]. These databases provide data from studies carried out over a longer period compared to other databases  [144]. To perform this initial retrieval, a search equation was constructed using the following keywords: "PV", "Photovoltaic", "Fault", "Failure", "Anomaly", "Detection", "Diagnosis", "Classification", "Artificial intelligence", and "Machine learning".
The search equation was configured with Boolean operators, as indicated in part a of Fig. 4. Construction of the search equation using keywords is considered one of the best methods to initiate a systematic  search  [145]. The second step of the data collection stage involved performing the search for each document title, abstract, and keywords. As a result, an additional group of documents was retrieved from both Scopus (262 documents) and Web of Science  (205 documents). Duplicate documents were eliminated using Zotero software  [146]. In the third step, a set of documents from other databases was added, peer-reviewed for relevance, to the initial group, resulting in a total of 671 documents. The distribution of these documents retrieved over the years is presented in Fig. 5. The sample concludes in May 2022, so as shown in Fig.
Bibliometric analysis Bibliometric analysis allows for an objective and replicable review of a data corpus. This method is used to analyze various indicators such as authors, countries, citations, and keywords, enabling the quantitative assessment of different aspects within a specific field of knowledge  [149] [150] [151]. Consequently, Bibliometrics facilitates the identification of popular topics in the present, past, or future  [152]. Therefore, it serves as a crucial tool to review the state of the art in a research field, covering both microlevel analysis involving individual scientists and institutes, as well as macro-level analysis on national and global scales  [112, 130]. However, not every database of scientific publications can be directly used for this type of bibliometric analysis.
It is crucial that the databases are structured and devoid of duplicated publications, variations in author name spellings, and ideally include fields such as article title, authors, journal, publication year, citation count, institutions, countries, keywords, and bibliographic references. The results of Bibliometric analysis can be visualized through maps or networks. In this context, the closeness between two elements signifies the intensity of their connection. A reduced distance signifies a more robust relationship. In these maps, the elements are unevenly distributed in space, which facilitates the assignment of labels. An example of such representation is presented in Fig. 2. There are two main approaches for creating Bibliometric maps: distance-based methods and graphic-based methods.
Different distance-based map construction techniques can be found in the literature. One of the most widely utilized methods within the field of bibliometrics is introduced in  [153]. This technique suggests using multidimensional scaling entities to represent proximities between objects within a geometric space of a few dimensions  [154]. Alternatively, techniques such as visualization of similarities (VOS) viewer are also proposed  [155] [156] [157] , which have shown better results compared to multidimensional scaling  [158]. Another distance-based technique, called 𝑉 𝑥𝑂𝑟𝑑 is proposed in  [159, 160] , and has demonstrated high performance in the construction of maps with large numbers of elements (7000 or more). Finally, a new method implemented in a software called BibTechMon is proposed in  [161].
Some authors  [164, 165]  combine the work presented in  [162]  with the pathfinder network technique  [166]. There are also software versions such as CiteSpace  [167] , specialized in this type of maps, and software such as Pajek from  [168] , which combines distance-based and graph-based approaches. For the bibliometric analysis conducted in the photovoltaic research area, a distance-based bibliometric mapping was performed using VOSviewer, a bibliometric visualization and mapping software developed by Nees Jan Van Eck and Ludo Waltman. 1 VOSviewer was chosen for its interesting distance-based bibliometric results  [158]  and its widespread use in the field of bibliometrics  [169] [170] [171] [172]. Bibliometric maps generated using VOSviewer offer a comprehensive means of assessing relationships and their strengths among distinct entities (authors, laboratories, countries, etc.
In these cases, the similarity 𝛼 𝑚𝑛 between two elements 𝑚 and 𝑛 is defined in Equation (1): 𝛼 𝑚𝑛 = 𝜑 𝑚𝑛 Ω 𝑚 Ω 𝑛 (1) where 𝜑 𝑚𝑛 denotes the count of occurrences where items 𝑚 and 𝑛 appear together. Meanwhile, Ω 𝑚 and Ω 𝑛 stand for the overall frequency of individual occurrences or the combined occurrences of items 𝑚 and 𝑛, assuming that the occurrences of these items are statistically uncorrelated. After the creation of the similarity matrix, the VOS mapping technique is implemented. It is worth mentioning that the VOS technique can yield one or multiple solutions of global optimum. In cases where multiple solutions exist, it is important to use translation, rotation, or reflection operations to ensure that consistent results are generated by VOSviewer. The procedure for creating a network entails using input data in the format of bibliographic database files, such as those from sources such as Web of Science, Scopus, Dimensions, Lens, and PubMed  [179].
The size of every node is dictated by the quantity of documents authored by the individual author. In this review, all analyses were conducted using the complete counting method. However, it is important to note that the VOSviewer software provides both complete and fractional counting methods  [153]. After analyzing the data corpus, a total of 804 distinct authors who have published at least one article related to the topic of interest were identified, following the extraction of documents involving 12 authors or more. The information presented in Table  2  outlines the correlation between the quantity of authors and the quantity of research papers published in the domain of fault detection employing artificial intelligence in photovoltaic power generation systems. As illustrated in the co-authoring bibliometric analysis shown in Table  2 , there is a notable decline in the number of authors.
Furthermore, in Taiwan, research has also been conducted on modifications of conventional methods such as Random Forest and the independent component analysis (ICA) method  [230]. These modified methods achieved performances of 99. 88% and 99. 43% in the proposed scenarios. These situations arise from instances involving imbalanced datasets, where one class contains a significantly larger number of observations than the other classes. After analyzing the co-authorship patterns in publications in this field, an analysis of citations based on documents and sources (journals) is carried out.
In the VOSviewer-generated citation visualization map, each node is equipped with a hyperlink leading to the corresponding online repository of the article. An examination of the data reveals that the articles with the highest number of citations, in decreasing  A machine learning framework to identify the hotspot in photovoltaic module using infrared thermography 2020 Solar Energy order, are Zhao (2015)  [59]  with 186 citations, Chen (2017)  [231]  with 180 citations, Dhimish (2018)  [221]  with 127 citations, and Chen (2018)  [232]  with 110 citations. For a comprehensive list of the top 13 most cited articles derived from the dataset, please consult Table  9. In a complementary manner, it is also interesting to conduct an analysis based on sources. These sources refer to indexed journals obtained from the corpus of data under study, resulting in a total of 170 different sources.