dataesr/scientific-paragraphs-categorization huggingface dataset:
DATASETS For our analysis we use the following two datasets of ads that users have received on their Facebook timeline: ProPublica dataset. ProPublica, an investigative journalism organization, has developed a browser extension that collects the ads users are receiving on Facebook and allows users to label whether the ads they are seeing are political or not  [27]. The extension is currently maintained by the NYU Online Political Transparency Project  [25]. While ProPublica was not able to make available all the ads it has collected, it shared with us all the ads for which at least one user has labeled it as being political, as well as all the ads that have the "Paid for by" disclaimer (i. e. , the official political ads that have been declared as such by advertisers). This dataset is valuable because it provides us with a unique view of which ads are considered political by "ordinary" people/volunteers.
To our knowledge, there are no studies of such data. For this study, we only kept ads with at least three votes (either political or non-political) and that were received between June 2018 and May 2020; resulting in a dataset of 54. 6k ads coming from 7530 advertisers. The median number of votes per ad after filtering is 5. We call the ads that have the "Paid for by" disclaimer the official political ads and the ads that do not have the disclaimer the official non-political ads. Table  1  shows the number of ads in the ProPublica dataset as well as the fraction of official political ads and official non-political ads. Note that this dataset does not contain a representative sample of political ads as they are ads received by people who answered ProPublica's call for action to install the tool.
AdAnalyst dataset. Similar to the extension provided by ProPublica, AdAnalyst collects the ads users see on their Facebook timeline  [2]. The AdAnalyst dataset contains over 500k ads from users in various countries. For this study, we keep only ads in English (detected using text-blob python library  [35] ) and that targeted users in the US between October 2018 and May 2020. For this, we use information about ad targeting available in the "Why am I seeing this ad?" button and select only ads targeted at people who live in the USA or visited places in the USA recently. The resulting dataset contains 9k unique ads (198 ads with "Paid for by" disclaimer and 8802 without). This dataset does not have votes from volunteers. Ethical review board and reproducibility. Both data collection by ProPublica and AdAnalyst were approved by the respective ethical review boards.
Disagreement among volunteers At least three volunteers have labeled each ad in the ProPublica dataset as being political or non-political. The volunteers were given no instructions for what ads they should consider as political, and users were left to decide based on their instinct. To observe to which extent volunteers agree on what ads are political, Figure  1  plots the distribution of the number of political votes divided by the number of all votes for each ad in the ProPublica dataset. We denote this fraction as fr. A fraction fr = 1 means that everyone agrees that the ad is political, while a fraction fr = 0 means that everyone agrees that the ad is not political. The plot shows that for more than 50% of the ads, at least one volunteer disagrees with the others (fr is neither 0 nor 1), which shows that deciding whether or not an ad is political is debatable for more than half of the cases.
Disagreement between volunteers and advertisers The ProPublica dataset provides data on whether an ad was labeled as political by the advertiser itself (see Section 2). Table  2  presents the overlap between ads labeled as political by volunteers and ads labeled as political by advertisers (the official political ads). The table shows that 96% of strong political ads, and 93% of political ads were also declared as political by advertisers. Hence, most ads considered political by the majority of volunteers are  also considered political by advertisers. There are, however, 4% of strong political ads and 7% of political ads that advertisers did not label as political. The more surprising finding is that advertisers label as political a large majority (74%) of marginally political ads. Looking the other way around, 83% of official political ads are labeled as political by most volunteers.
Analysis of advertiser categories To characterize advertisers we analyze their category. Advertisers need to create a Facebook Page and select from a pre-defined list a category for their page such as "Software Company" or "Political Party"  [13]. We use the advertiser's ids available in the dataset to collect their category using the Facebook Graph API. Some pages no longer exist, we were able to extract categories for 6476 ProPublica advertisers (82%). Figure  2  plots the breakdown of the corresponding advertisers categories for strong political ads, political ads, marginally political ads, official political ads and nonpolitical ads. We group similar advertiser categories (grouping details can be found in our supplementary material at http://ligmembres. imag. fr/gogao/www21. html). Figure  2  shows that most strong political ads come from political actors (58% w.
To see why for a particular social issues, some ads have higher fr than others, Tables  7 8  Limitation: There are other reasons for disagreement that we could not analyze with this dataset. For example, the background knowledge of volunteers might impact how they vote (the political nuance of an ad is only recognized by some) or the political ideology of volunteers impacts how they vote. These questions are essential for recruiting moderators, and we leave them for future work. Takeaway: Ads from NGOs, causes, news media, education, and businesses and ads on social issues lead to the highest disagreement among volunteers. To distinguish better political from non-political ads, we would need policy recommendations that clarify the perimeter of social issue ads. This raise a multitude of questions such as: Should we treat ads about more politicized issues differently than ads about less politicized issues? Should we treat social issues depending on the country? Should we treat ads that call for precise actions differently than ads that just inform citizens? Should we define social issues at a smaller granularity (in both topics and locality) than currently? How should the system adapt to emerging social issues? How much weight should be given to the advertiser's identity (as opposed to just the ad content)?.
CLASSIFICATION AND DISAGREEMENT Traditional supervised classification algorithms create models from positive and negative examples that we feed in the training phase. The previous sections showed significant discrepancies between ads labeled as political by advertisers and ads labeled as political by volunteers. Hence, this raises the question of whether classifiers trained on one or the other would result in significantly different models. Intuitively, if the training examples are biased, the models will be different, while if the training examples are representative of political ads in general, the resulting models will be similar. This section investigates how discrepancies in positive labels from advertisers and volunteers impact the resulting classification models. For the evaluation we split the ProPublica dataset in two equalsize slices of 28k ads:  1 and  2.
We use  1 as the training and validation dataset and  2 as the holdout/test dataset. We build four models using four different sets of positive labels but the same negative labels. As negative examples, we took 7. 5k ads in English from AdAnalyst without the "Paid for by" disclaimer (see Section 2). The   model: the positive labels are a random sample of 8000 official political ads from  1. is trained with positive examples from advertisers. The   model: the positive labels are a random sample of 8000 strong political ads from  1. is trained with only positive examples where all volunteers agree they are political, fr = 1. The   model: the positive labels are a random sample of 4000 political ads and 4000 strong political ads from  1. is trained with positive examples where the majority of volunteers consider the ads as political, fr > 0.
5. The  1 model: the positive labels are a random sample of 2600 strong political ads, 2600 of political ads and 2600 marginally political ads from  1. 1 takes as positive examples all ads where there exist at least one user that labeled it as political, fr > 0. To build the different models, we used Naive Bayes. While Naive Bayes is neither new nor sophisticated, it was shown by Silva et al. [33]  that it achieves high accuracy for detecting political ads and outperforms other methods. The classifiers only take as input the ad's text, and as pre-processing, we deleted all Html tags, stop words, and punctuation. We used Count Vectorizer for text embedding  [32]. We performed 10-fold cross-validation for each classifier over its specific training-validation dataset that contains 8000 positive and The main challenge in evaluating the classifiers is that we do not have a gold standard collection of political and non political ads.
For detecting strong political ads, all models detect more than 94% of ads. For detecting political ads the   and   perform the best (detecting over 94% of ads). For marginally political ads,  1 and   , perform well (over 86% detection), while  1 has a 84% detection. For non-political ads,   and   label more than 85% as political. The detection rates of   and   are similar across different datasets, with   performing better especially on marginally political ads and non-political ads. Hypothetically if the resulting classifiers would label as political the precise same ads, the input data is representative of political ads, and who is labeling the training data (be it advertisers or volunteers) does not matter. To understand whether   and   label the same ads as political, we computed the fraction of ads labeled by both models as political over all ads for different ad groups in  2.
The average accuracy and true positive rate for a 1% false positive rate for the four models. Each classifier is evaluated over its specific training-validation dataset. Table5presents the average accuracy and true positive rate for a 1% false positive rate for the four classifiers. For systems where the fraction of positive examples (political ads) is much smaller than the fraction of negative examples (non-political ads), it is essential to limit the rate of false positives (non-political ads labeled as political); hence, we are interested in true positive rates for a 1% false positive rate. The table shows that all classifiers achieve high accuracy of over 95%, but only   ,   , and   achieve true positive rates of more than 90%. The lower true positive rate of  1 (85%) is expected as it has a more challenging task because it is trained and tested with more debatable political ads.
Within this context, our opinion is that vaccinating the entire population may be necessary after ensuring the safety and efficacy of vaccination of children (see NCT04796896 and NCT04816643 trials on clinicaltrials. gov). The present study makes the strong hypothesis of a 100% acceptance level of vaccination. Virus extinction will happen if the population is widely vaccinated. This means that more efforts should be made, in terms of communication and vaccination strategy. It would be interesting to tackle the issue of vaccine acceptance by measuring the impacts of partial acceptance levels on the course of the epidemic. Another modelling choice was ignoring the duration of vaccine protection. This might be the object of future investigations. The model presented in this paper has been calibrated on the historical strain, variants with contagiousness similar to that of the Alpha variant, virulence similar to that of the historical strain, and the absence of vaccine resistance.
Model Calibration The parameters of the model (Figure  4 ) obtained from the literature are shown in Table  7 , and those estimated by calibration are shown in Table  6. The calibration was performed using public data from Santé Publique France (https://www. data. gouv. fr/fr/ datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/ accessed on 15 October 2020). Comparisons between those reference data and the simulation outputs, obtained from the estimated parameters, are shown in supplementary Figures  S1-S5. The left-hand part of the model was calibrated independently of the right-hand part (the dotted frame in Figure  4 ). For the left-hand part ('disease spreading'), the parameters were estimated by likelihood maximization using daily hospital admissions from 1 September 2020 to 31 January 2021.
Data Availability Statement: All data generated during this study are available from the corresponding author on reasonable request. The prevalence values of Alpha variant in France are publicly available from Santé Publique France (https://www. santepubliquefrance. fr/ (accessed on 15 October 2020)). The data used for estimation are also publicly available from Santé Publique France (https: //www. data. gouv. fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/ (accessed on 15 October 2020)). Prevalence of Removed individuals in France in 1 January 2021, are available from the publicly available data of Institut Pasteur (https://modelisation-covid19. pasteur. fr/realtime-analysis/infected-population/ (accessed on 15 October 2020)). The source code is available on gitlab: https://gitlab.
Rigour We used Lincoln and Guba's criterion to ensure trustworthiness (credibility, transferability, dependability and confirmability). 45 Conclusions of the study analysis were ensured by several techniques to try to reduce bias. Firstly, we confirmed the data source by conducting voice-recorded interview with neutral attitude, and using a full interview transcript. Secondly, data were analyzed separately by two researchers from different disciplines and places within the research laboratory. Then, researchers examined the themes without any prior information or personal bias. Finally, analysis was discussed by all of the researchers. We did not validate written transcribed interviews with the participants because we had not asked for a specific ethics approval to contact participants again after the study.
Regulatory Proceeding and Ethics This study was conducted in accordance with the Declaration of Helsinki. RC gave participants a full explanation of the research project, its objectives, the voluntary nature of participation, the possibility of halting participation at any time without any consequences, the confidentiality of their answers and the way of anonymously handling data 15 days prior to the interview so they could consider their participation. RC also explained that they should read the information and consent form before agreeing to participate, and that they could be helped if needed. Researchers scheduled the interview appointment at a time and place that was convenient to the participant. At the beginning of each interview, participants were asked to consent to being audio recorded and to the publication of anonymized responses.
Characterization of the Older People Based on data saturation, 34 older adults (20 women and 14 men) were interviewed, with a mean age of 82 years. Among those, nine were living at home with professional care services (mean age 86 years), most often with a relative, 13 were living in a foster care family in Although we did not seek to collect medical data related to the participants' health status, as requested by the Ethics Institutional Review Board of INSERM, we were given data related to the subjects' medical conditions. These data revealed that the participants with similar medical conditions, including diabetes, cardiovascular disease, stroke, depression, deafness, blindness, and physical handicaps, were included in the three groups. Therefore, we concluded that the health status of the participants was relatively comparable across the three groups.
Acknowledgements Begun early on during my posting at the efeo Centre in Jakarta from 2009 to 2014, and initially presented at the World Sanskrit Conference held in Kyoto in September 2009, the research for this article then went dormant for ten years until there were two strong reasons for its resumption in 2019: the reemergence of the inscription and the launching of the project ' The Domestication of "Hindu" Asceticism and the Religious Making of South and Southeast Asia ' (dharma), funded from 2019 to 2025 by the  European Research Council (erc)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 809994 ). On the project, see https://dharma. hypotheses. org. It is in the framework of this collective research endeavour that I have finally been able to bring this study to conclusion, and I thank my fellow project members  Andrea Acri ,  Véronique Degroot ,  Dominic Goodall , and  Annette Schmiedchen  for their valuable suggestions on a draft of this article.
Considering the users' behaviors are often impacted by the geographical location and some personalized attribute information, we set the user priority based on them to help the parking officer determine the allocation sequence. We evaluate our allocation scheme using large real-world dataset with on-street parking sensor data, and extensive experimental results reveal (i) a minimum improvement of 15. 9%, 1. 4%, 96. 9%, 160% in parking allocation time, average traveling time, I/O cost and service utility compared to the progressive methods, and (ii) a minimum improvement of 8. 9%, 11. 1%, 78. 2%, 714% in parking allocation time, average traveling time, I/O cost and service utility compared to the baseline methods.
The branch node regions that are within the walking time constraint to the users' destinations are first positioned. Then, the Lower Bounding Driving Time and parking capacity placed on each index node are used to prune the nodes that cannot satisfy the driving time constraint or have no available parking locations. • We propose User Priority setting based on the personalized attribute and distance, combined with the estimation of geographical location proximity developed for each user, to maximize the benefit of all users. As for a set of users having the similar personalized attributes and closer departing locations, they can be combined into one group for further allocation, especially in peak hours when a large number of users request for parking. • We evaluate our proposed method by the allocation time, traveling time, I/O cost and service utility metrics using a large public dataset provided by the local city council.
In general, all these studies have not considered any time and personalized attribute constraint to help the officer manage on-street parking allocation. In our work, we investigate the parking allocation problem in the context of a centralized model, where a centralized parking officer assigns parking locations by satisfying user priorities and maximizing the benefit of all users. Pair Queries in Spatial Database. The pair queries in spatial database aim to discover some pairs of spatial objects with certain time or distance constraint. Corral et al. [14]  address the problem of finding the K pairs of spatial objects formed from two data sets that have the K smallest distances between them, where each set is stored in a structure belonging to the R-tree family. However, due to ties of distances, the result of K-Closest Pair of these two point sets may not be unique for a specific pair.
U et al. [16]  also propose an efficient algorithm for optimal capacity constrained assignment. Each customer is assigned to at most one provider. Every provider serves no more customers than its capacity. Meanwhile, the sum of Euclidean distances within the assigned providercustomer pairs is minimized. Different from the problem resolved above, our parking allocation focuses on point-topoint departing-parking location matching to maximize the service utility of all users. Chen et al. [17]  retrieve the pairs with top-k maximal probabilities of being the closest pair given two uncertain datasets in which each spatial object is modeled by a set of sample points. Unlike this dataset, our parking allocation is schemed based on certain departing and parking locations. Related Spatial and Spatial-textual Index.
The concept of smart cities has gained significant momentum in science and policy circles over the past decade. This study aims to provide an overview of the structure and trends in the literature on smart cities. Bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace are used to identify the thematic focus of over 5000 articles indexed in the Web of Science since 1991. In addition to providing insights into the thematic evolution of the field, the three-decade study period is divided into two sub-periods (1991-2015 and 2016-2021). While splitting the dataset into more sub-periods would have been desirable, we decided to only examine two sub-periods as only very few papers have been published until 2010. The annual number of publications has progressively increased since then, with a surge in the annual number of publications observable from 2015 onwards.
Materials and Methods Data for bibliometric analysis was obtained from the Web of Science (WoS). Several other databases, such as Scopus, exist that index and archive academic publication. We selected WoS for three main reasons: first, its reputation for indexing quality peer reviewed research; second, it provides detailed bibliometric information which allows researchers to obtain more accurate results using the bibliometric analysis software tools (i. e. , VOSviewer and CiteSpace); third, a large number of publications on smart cities exist in the WoS, and this is enough for meeting our objective of understanding the overall structure and tends (in other words, even if some studies are missing, it will not affect the overall structure and trends). In order to include as many relevant papers as possible in the analysis, we used a broad-based search string that includes two keywords: smart city and smart cities.
The specific search string was: TS = ("smart city" OR "smart cities") AND LANGUAGE:(English) Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI Timespan = 1900-2021. In other words, we searched for articles written in the English language that have mentioned 'smart city' and/or 'smart cities' in either the title, abstract, or keywords. The literature search was conducted on 24 April 2021, in all citation indexes of the WoS (i. e. , SCI-EXPANDED, SSCI, A&HCI, and ESCI). All types of publications archived in the WoS until the search date were included in the analysis. The search returned 7228 documents, and after excluding studies that were out of scope (i. e. , focused on fields such as physics, material sciences, and mathematics), 5722 articles were selected for analysis. The 'Full Record and Cited References' of these studies were downloaded from the WoS, in formats compatible with VOSviewer and CiteSpace.
Several software tools exist for science mapping and bibliometric analysis  [56]. While these tools sometimes adopt different analysis and illustration approaches, they all contribute to understanding thematic focus and trends by providing details on the complex interactions between different components of academic publications (i. e. , keywords, references, authors, journals, etc. ). Further details about unique features of different tools can be found in Cobo, López-Herrera, Herrera-Viedma, and Herrera  [56]. In this study, VOSviewer and CiteSpace were selected considering their abilities to meet the study objectives. Both tools are freely available Java applications (VOSviewer at: https://www. vosviewer. com (accessed on 10 May 2021); and CiteSpace at: http://cluster. cis. drexel. edu/~cchen/citespace/ (accessed on 10 May 2021)).