dataesr/scientific-paragraphs-categorization huggingface dataset:
The variance observed between languages could be partly explained by the fact that the number of URLs per language is not evenly distributed on the World Wide Web, particularly for the websites of critics who are highly dependent on different cultures. Finally, to evaluate WebT-IDC's ability to eliminate the noise data during the extraction process, we use a purity measure p that computes the number of reviews containing noisy data proportionally to the total number of extracted reviews as expressed in equation 1. For the evaluation of noise filtering we compare the automatically constructed corpus of feedbacks on airline services produced by WebT-IDC, to a manually created gold standard corpus from a subset of the crawled output URLs. We get a p-measure equals to zero, which means that our scraping tool matches exactly the target data and generates completely noiseless data.
Furthermore, we applied the WebT-IDC tool to extract reviews about beauty products. In this context, we considered web pages in different languages ( French, English, Greek, Russian, Italian,. ). Figure  6  shows an example of the extracted output information from respectively Greek and Russian websites. In addition to information extracted by our tool (the review text, the ranking score, the publishing date and location), we also pre-labelled each review with the key topics deducted automatically from the thematic queries given to the crawler component along with the language label. Hence, each dataset entry is associated with a product category, a polarity label deduced from the ranking score attribute (when the ranking score is higher than 3, we estimate that the reviewer has a positive impression, negative otherwise) and a label defining the language used to express the review.
To ensure that the output dataset suits well to machine learning tasks, we prepared a model ready to perform a multi-label and multi-class classification task. It relies on "bert-base-multilingual-uncased" BERT  [4]  (Bidirectional Encoder Representations from Transformers) pre trained model able to deal with multilingual encoding stacked to three linear layers that respectively encode three different classes: the language of the review, the product category, and the reviewer's impression (whether positive or negative). We used an input dataset that contains beauty-related reviews to feed the proposed machine learning model. It is a multilingual dataset composed of 45000 English reviews, 36000 French reviews and 24000 Italian reviews. The input dataset was split into a training set and a validation one, with a ratio of 8:2.
Conclusion In this paper, we presented WebT-IDC, an automatic web chain process tool able to gather online feedback of reviews and comments from forums and blogs. It produces a clean corpus, ready-to-use featured dataset for further machine learning tasks. We have shown that our method combining both crawling and scraping components defends itself well against state-of-the-art methods. WebT-IDC is independent of the inherent web page architecture and thus adaptable to different forums and blogs containing the pagination element. WebT-IDC generates clean thematic multilingual corpus since it matches only relevant target data. The use of multithreading processing and direct access to relevant web page part is a fundamental factor that boosts further the global performance. Future work incorporates machine learning tasks to improve the identification process for the relevant data region and automatically generate training datasets for a selected domain.
Despite those challenges, the proponents of the Smart City concepts have been relentless in their pursuit for this concept despite limited wireless communication, and this has led to expensive infrastructural costs  (Chang et al. , 2018; Trindada et al. , 2017; Saeed et al. , 2020). Such costs include network and database setups necessary for smart cities to be operational. Without these, data banks have been slim as only a few devices can effectively be relied upon. In addition, without a quality network, urban areas have not been able to benefit from the valuable data generated by residents through their activities (and social media interactions), who Weber (2019) acknowledges to be part and parcel of an effective 'smart' city. Without incorporating such data, Samad et al. (  2019 ) argues that the insights drawn from available data could be skewed, and should not be relied upon in providing accurate or quality representative knowledge.
According to Vector ITC (2019), it is conceivable that most 6G AI powered devices will possess large and complex computing architectures that will allow them to not only gather environmental resource data, but also help in building large databases that can offer real-time information and insights to different sectors like agriculture, natural resource management, architecture, city and regional planning, landscape architecture, education, science, climate and many others. In addition, 6G technology is expected to optimise and finesse location accuracy and location awareness. Hence, 6G technology will most likely assist in actualizing the possibility of autonomous cars, mass rapid transit systems and typologies, and other like applications linked to the movement of goods, people, services and even terrestrial, avian and aquatic wildlife.
In this way, this technology could then allow people to start and operate large business enterprises in remote areas many kilometres away from the actual site. Such technologies, for instance, are already occurring in many iron ore mining excavation and ore movement activities in the Pilbara region of Western Australia from Perth or Karratha. Similarly, 6G technology is envisaged as helping in reducing risky incidences within complex terrains and environments, and also the delimitation of human closeness to fragile or toxic ores and or chemicals. Already, in the energy sector, 5G technology is seen to have unlocked the potential to increase energy production by rendering 'smart' systems and improving on and offline security structures. Additionally, with 6G, Alsharif et al. (2020) note that it will be possible to create Digital Twins of existing software and the main operating systems, and this will help not only in simulation but in ensuring and attaining high performance objectives.
In urban areas and our post-COVID future cities, the emergence of a 6G environment may advance the concept of Digital Twin even further and make smart cities even smarter. For instance, Castro (2019) notes that having a digital replica of a city will allow stakeholders, including residents to visualize and interact with different virtual objects before they are implemented in the real world. Simplistic digital visualization models, enabling fly-thru's and the insertion on new proposed buildings or sets of new urban forest vegetation exist already for many cities. As examples, the City of Melbourne Council's urban forest dataset documents some 70,000 trees across the municipality, enabling the community viewer to extract data and to visually represent and map it, to be a 'citizen science' expert on Melbourne's forested urban domain (Melbourne, 2020).
After the targeted content is obtained, through mobile holographic technology, the content will be displayed as part of real-time services so that the required information or insight can be derived from. This was previously not possible, since the available wireless connections (1G, 2G, 3G and 4G), including 5G, did not have the capacity to allow the high data rate transmission required. In addition to human holographic displays, current technologies have not aided in achieving actual 3D holographic displays that could help change how people see different objects. For instance, Elgan (2018) has tabled an example where a concept design drawn in a computer is only visible to those facing the computer screen directly, while those in front of the computer will only see its back. However, with a holographic display, the image could be displayed in 3D form for all to see, reminiscent of images in the Matrix film (IMDB, 1999).
In cities, for instance, with the capability of high fidelity mobile holographic capability, it would be possible to present concepts like buildings, new infrastructural projects and others in a way that everyone is able to visualize and mentally engage with. The Australian Government (2020) notes that by using holographic technology, it will be possible to enhance decision-making as stakeholders are able to visualize the city in different scenarios, particularly when this technology is coupled with immersive XR. In addition, with holographic technology, and leveraging on availability of technologies like AI and Big Data, that are expected to help build super datasets for different urban fabrics, it would be possible to create and view unlimited 3D city models, critical in smart cities conversions and constructions.
The availability of 6G will particularly be novel in ensuring the success of the holographic displays by enabling transferability of large datasets required for visualisation to be effected. With the mobile holographic technology, cities of the future, as noted by the Australian Government (2020), will be designed having considered the inputs from all the stakeholders, as everyone will manage to have an opportunity to view the 3D design of identified projects before their approval and implementation. Conceptualisation of new projects through digitally enhanced computer renders and fly-thru's already exist but the visual 'journey' of experience through the 'place' or building is determined by the digital designer and little determined by the actual community. Digital games, and the dramatic evolution of expertise in this sector offers the platform to quickly enable dynamic visualizer-friendly access into these 3D worlds.
Through this digital avenue, areas of a project that may later raise concerns possess the opportunity to be streamlined and mediated to the satisfaction of all participants. Ensuring that the view of all stakeholders are given maximum attention, as noted by  Samad et al. (2019)  will not only help in building large databases, where individuals can voluntarily share data and information, but also allow for acceptance of projects by local populations. With holographic technology, coupled with other advanced technologies including immersive XR, it will also become easier to solve common urban challenges. An example is vehicular traffic, as it will be easier to investigate and identify the cause and possible permutations of a challenge virtually by rendering the map of the affected area into a 3D holographic image that is easier to visualize, navigate and understand the scale and complexity of issues.
As case in point have been the mad scramble by architecture schools in Australia to digitally replicate face-to-face design studios for March-June 2020 teaching tri/semester (and continuing now into their July-November tri/semester) through software like Zoom, Padlet, Issuu, Blackboard Collaborate and Mural. Such has prompted much digital and philosophical ponderings about both digital technology use in tertiary design teaching but also its legitimacy in architecture professional education accreditation regimes, as well as major investments and applied explorations of their use, possibilities and performance where previously it was 'foreign country'. It has also prompted major teaching locational re-thinking. For example; an Australian-based design studio or class can be successfully taught by an instructor in Barcelona; sessional and continuing staff can be physically located anywhere in Australia but still operate what is notionally a Melbourne or Sydney-sited design studio or class; and, that enrolled students can reside in China, Hong Kong, India, or Singapore yet dial-in through the web to access and participate in USA-based, Canada-based or Australian-based learning respectively  (Kvan, 2001).
Optimization Results and Discussion The SOS algorithm has been developed and adapted for the electromagnetic design and optimization of the ORDSPMG. The tuning parameters which were used in this study are: The maximum number of iterations: max iter = 500. A program of the SOS algorithm was developed in the MATLAB environment so that to permit its interaction with a FEA software. The program has been executed on a computer system whose processor has 6 cores/12 threads with clock speed at 3. 3 GHz, while its RAM was equal to 16 GB. Three independent sets of simulations were performed, with each set comprised of 5 trial runs, in order to test the SOS algorithm's robustness. It is noted that the optimal search variables values (obtained by the algorithm for each one of the conducted runs) were always almost identical between them.
Recent studies have shown that a more comprehensive evaluation of clinical, genomic, and epigenomic features, leveraging both coding and regulatory impact, can help improving diagnostic rates  (9, 10). However, currently available CNV annotation tools are mostly based on the assessment of the protein-coding genes targeted by the CNVs under evaluation (e. g. , SG-ADVISER CNV  (11)  and cnvScan  (12) ) or aggregate static annotations from reference databases (AnnotSV,  (13) ) without providing further filtering and analysis tools. In addition, features of interest often involve heterogeneous high-dimensional omics data, scattered across multiple databases, often difficult to access and interpret from a clinical perspective. In practice, the evaluation task may be error-prone, time-consuming, and lowly reproducible across geneticists.
CNVxplorer is publicly available at http://cnvxplorer. com. Detailed tutorials, comprehensive documentation, and a Frequently Asked Questions section are provided. In addition, a stand-alone open-source R implementation with a shiny interface is offered, allowing its deployment as a private server through a Docker image without external dependencies. Source code and instructions to locally deploy the application are available at https://github. com/RausellLab/cnvxplorer.
Genetic variants datasets CNV syndromes were obtained from DECIPHER (version 2020-01-19,  (18) ) and ClinGen  (19)  databases. Pathogenic and likely pathogenic CNVs were obtained from DECIPHER (version 2020-01-19, (18)) after filtering out CNVs of length < 50 base pairs. CNVs identified in the general population were obtained from the DECIPHER control set as well as from the DGV database (version 2020-02-25,  (20) ) and gnomAD (controls-only dataset, version 2. 1, (  21 )). DGV CNVs were restricted to those labeled as "deletions", "duplications", "loss", and "gain", while gnomAD  (21)  CNVs were reduced to those with a filter equal to PASS and SVTYPE equivalent to 'DEL' or 'DUP'. Pathogenic and likely pathogenic variants were further extracted from ClinVar (version 2021-02-21, (  22 )). Trait-associated Single Nucleotide Polymorphisms were obtained from the GWAS Catalog (version 1.
Non-coding versions of RVIS (ncRVIS) and the Genomic Evolutionary Rate Profiling rejected substitutions (ncGERP), based on proximal regulatory regions (5'UTR, 3'UTR, 250bp upstream of TSS), were further considered  (39). In addition, the gene predicted probability of haploinsuficency score was retrieved (HI,  (40). Gene essentiality labels were obtained from the FUSIL classification  (41). Imprinted genes were obtained from publicly available imprinting gene database (http://www. geneimprint. com/) and ohnolog genes from the OHNOLOGS database (version 2, (42)) considering only pairs labeled as "strict" (highly reliable). Ohnologs are gene duplicates originated and retained from the two rounds of whole-genome duplication (WGD) that occurred in early vertebrates ~500 million years ago  (43).
Gene expression data for 54 human tissues was obtained from the Genotype-Tissue Expression Project (GTEx; version 8, (44)). Protein expression levels for 32 human tissues was obtained from the Human Protein Atlas (HPA; version 20. 1, (45); https://www. proteinatlas. org/download/normal_tissue. tsv. zip). The HPA project provides a curated reliability score based on the results' reproducibility and data availability in other databases. Thus, only protein expression levels labeled with "Enhanced" or "Supported" reliability were considered, while those labeled as "Not detected" are not reported in the application. The list of protein-protein interactions was obtained from the STRING database (version 11,  (46) ). STRING provides a set of experimental and predicted genes and protein interactions extracted from diverse sources.
Regulatory elements and associated genes. Enhancer regions and their associations with target-genes were obtained from the GeneHancer database (version 4. 11, (  47 )). Predicted enhancers and enhancer-gene associations with a score < 1 were filtered out, resulting in a total of 107,168 enhancer-target pairs finally retained. Topologically Associating Domains (TAD) genomic coordinates were obtained from  (48) , corresponding to H1 human embryonic stem cells (hESCs) and generated using a bin size of 40 kilobases and a window size of 2 Mb. Long non-coding RNAs (lncRNAs) and their associated genes -characterized by low throughput experiments-were obtained from LncRNA2Target (version 2, (  49 )). Transcription factors and their associated genes were retrieved from TRRUST (version 2, (  50 )). miRNAs coordinates were retrieved from miRbase  (51)  and miRNA-gene associations with strong experimental evidence were obtained from miRTarBase (version 8, (  52 )).
Functional analysis Functional and pathway enrichment analysis were implemented with the R/Bioconductor packages ClusterProfiler (version 3. 14. 3; (60)), and reactomePA (version 1. 30. 0, (61)). KEGG  (62)  and REACTOME  (63)  pathway annotation databases were used. Disease ontology enrichment analysis and visualization are implemented with the R/Bioconductor package DOSE (version 3. 12. 0, (64)). Tissue enrichment analysis is implemented with the R/Bioconductor TissueEnrich package (version 1. 6. 0, (65)).
Implementation of CNVxplorer web application and docker image CNVxplorer was developed in R (version 3. 6. 0, (66) ). The interactive web application was implemented with the Shiny R package (http//shiny. rstudio. com). This framework allows the fast implementation of R code in an easy-to-use frontend. The interface offers a reactive environment where the user can interact with the data (e. g. , add/remove input) and the results are instantly updated accordingly. CNVxplorer was additionally implemented as a Docker image (https://www. docker. com/). This facilitates the installation of the application in a private server mode without installing additional external dependencies or prerequisites. All plots and tables presented in CNVxplorer were rendered with ggplot2 and DT packages, respectively.
Tab 1: Overview. This section provides a quick overview of the major clinically relevant associated with the query CNV, summarizing the results presented in full detail in the next tabs. Thus, it provides summaries of the CNV's length, gene content, overlap with reference CNV databases, as well as with disease-associated genes and single nucleotide variants together with the number of disrupted regulatory elements. In addition, the number of orthologous mouse genes associated with mortality, aging phenotype or embryonic lethality is reported. A popup warning is displayed if any input CNVs overlap with a segmental duplication region or a lowmappability region (Methods). Complementary information includes the number of PubMed articles where the CNV associated cytobands are mentioned together with the keywords duplication or deletion.
Finally, the size of each of the query CNVs is represented together with the size distribution of CNVs found on the reference databases (i. e. , gnomAD, DGV, Decipher Control set, and Decipher pathogenic set; Methods). Tab 2: Clinical genetic evidence. This section provides an exhaustive report of disease-associated CNVs, genes, and SNVs that partially or totally overlap with the query CNV(s). First, a list of CNVs previously reported in reference pathogenic and non-pathogenic CNV sets is provided, covering (i) CNVs associated with a phenotype, including CNV syndromes as well as pathogenic or likely pathogenic CNVs, as reported in the CinGen or Decipher pathogenic sets (Methods), and (ii) CNVs classified as benign, likely benign or present in the general population, as reported in Decipher control, DGV and gnomAD datasets (Methods).
These analyses are: Tab 5: KO mouse phenotypes. The tab presents a histogram of the phenotypic consequences observed in knockout and knock-down mouse experiments for mouse orthologs of the genes under evaluation, collectively considered (Methods). Results are displayed separately according to whether the genes map within the query CNV(s) boundaries or outside them (i. e. , indirect implication though a targeted regulatory element). Tab 6: Functional enrichment analysis. CNVxplorer provides gene-set enrichment analyses using (i) Gene Ontology (GO) terms (performed by each GO ontology: biological process, molecular function, and cellular compartment), (ii) reference pathway databases such Reactome and KEGG, and (iii) Human Disease Ontology (HDO) terms (Methods). Here, the list of functional terms and pathways that are statistically overrepresented in CNV-associated genes in reported together with their hypergeometic test p-value, corrected by Benjamini-Hochberg False Discovery Rate.
In such network, nodes represent genes/proteins are links among them represent a relation or interaction among them. For this, CNVxplorer mines high-confident protein-protein interactions reported in the STRING database ((46), Methods). CNVxplorer provides an interactive graphical interface, allowing users to zoom in/out and drag nodes from specific network regions. To ease visualization, gene-centered representations with 1-neighbor nodes are also offered. Finally, a bar plot representing the number of interactors per gene is displayed. Tab 9: Biomedical literature. CNVxplorer eases the exploration of biomedical literature associated with the query CNV(s). First, a comprehensive search across all fields of Pubmed articles is performed (Methods), and articles found are displayed as an interactive table allowing: (i) to filter by event type (deletion or duplication) and whether the article is associated with an OMIM entry, (ii) to sort by column content, e.
) from Ensembl Gene (version 103, (29)) based on the reference human genome GRCh37. p13. Disease gene lists were collected from five sources: Online Mendelian Inheritance in Man (OMIM) database (version 2021-03-04, (30)), Orphanet (version 2021-03-04, (31)), DECIPHER (version 2020-01-19, (18)), Genomics England PanelAPP (2021-03-04, (32)), and ClinGen (version 2021-03-03, (.
FUNDING The  Laboratory of Clinical Bioinformatics  was partly supported by the  French National Research Agency (ANR)  " Investissements d'Avenir" Program  (Grant  ANR-10-IAHU-01  and  ANR-17-RHUS-0002 -C ' IL-LICO  project), by the  MSD Avenir fund (Devo-Decode  project) and by  Christian Dior Couture, Dior. FR is supported by a PhD fellowship from the  Fondation Bettencourt-Schueller.
AVAILABILITY CNVxplorer is publicly available at http://cnvxplorer. com. Detailed tutorials, comprehensive documentation, and a Frequently Asked Questions section are provided. The tool is free and open to all users and there is no login requirement. CNVxplorer code and instructions to deploy a local Docker image are available at https://github. com/RausellLab/cnvxplorer , under a GNU General Public License v3. 0.