dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, according to data from the same authors, many famous narratives such as Little Red Riding Hood have a low centrality score (Table  5 ). Only the dissemination of tale type (rather than cultural success) seems to be a determining factor, with a high value of Pearson and Spearman correlation between the number of cultural Table  6. Correlation between each centrality measure of a set of ATU tale types (i. e. all tale types with a high centrality score and the 'most successful' stories identified by  Norenzayan et al. 2006: ATU 300, 301, 302, 303, 310, 313, 314, 327, 333, 400, 425, 425C, 440, 450, 461, 480, 510A, 531, 533, 550, 554, 555, 707, 709)  and the number of areas identified by  Uther (2011). areas identified for each folk tale and their centrality. While the Pearson correlation coefficient is a measure of the linear correlation between two variables, X and Y (Pearson 1895), the Spearman correlation assesses monotonic relationships (whether linear or not) between the ranking of the two variables  (Spearman 1904).
However, this hypothesis is impossible to check with the ATU database in its current form and case studies are essential to provide a better understanding of the relations between folk tales. Another possible limitation of this study is that it only considers fairy tale types. Taking into account the combinations of tales of different categories could help reduce or increase the centrality of some of them. In the preface to Roger Pinon's book Le Conte merveilleux comme sujet d'études  (1955) , Paul Delarue wrote that "we still lack in-depth studies on phenomena that have played a role in the life of the story: contamination for example" (ref. by Ténèze 1958: 297) , which leads Marie-Louise  Ténèze (1958: 298)  to note that the continuation of Paul Delarue's Catalogue will bring valuable elements to such a study; as would be the case now, for those who would try to collect them, many passages from his annotated editions, examples of contamination at different levels, either by junction pattern or by thematic analogy.
B. HYBRID NETWORKS AND RELATED WORKS The limitations discussed above of both conventional BC and BB networks drew attention towards a hybrid solution where a BB network and a BC network collaborate to deliver linear services. This kind of hybrid network could be seen as an offload of the data traffic from the limited BB network to the BC network where the spectral resources are mutualized. It can also be considered as an extension of the coverage range of mobile TV BC with the aid of the widely deployed BB network. The Hybrid Broadcast Broadband Network (HBBN) approach has recently driven numerous studies in the literature. The importance of the idea and the possible challenges and opportunities were discussed in  [5] ,  [10] ,  [11]. An HBBN can take different forms, like stream sharing networks where BB and BC share the communication chain while each performs certain jobs, and user sharing networks where BB and BC serve different subsets of users.
Fiscal policy was to become less accommodating after a devaluation in 1974 and can only be characterized as restrictive after the second oil shock in 1979 (see next section). Graph 1 shows the steady upward trend in inflation over the period. Source: Central Bureau of Statistics (Israel), data extracted in September 2017. Series used: Consumer Price Index, with straight-line trend added. Note: The tendency for inflation to rise over the period is also found when using annual inflation rates (same source): see table  3. The change in the pace of inflation detailed by Graph 1 can be explained by the modification in the terms of the distribution conflict. The market power of firms and the bargaining power of workers -the income targets of capitalists on one side and workers on the other -changed.
These measures forced unemployment up and damped economic growth (see Table  4 ). Sources: Bank of Israel Annual Report, various years for GDP Growth rates and Unemployment rates  (1982 for 1979-1981, 1986 for 1982-1985 and 1987 for 1986) , Central Bureau of Statistics (Israel) for inflation, data extracted in September 2017. Series used: Consumer Price Index. For Real Wages: own estimations based on nominal wages given by Bank of Israel, deflated by CPI. The economic policy conducted in 1979 and 1980 was unpopular and was not defended by the whole of the coalition government led by the Likud (see  Reuveny, 1997) ; the restrictive budget policy was implemented for barely a year (see  Fischer, 1985). Although the shekel replaced the Israeli lira in February 1980 (at the rate of 1 to 10), it was clearly the economic policy adopted in late 1982 that was to further accelerate inflation.
Source: Central Bureau of Statistics (Israel), data extracted in September 2017. Series used: Consumer Price Index, with trend curve added (moving average over 12 periods). Note: The tendency for inflation to strengthen over the period is also found when using annual inflation rates (see Table  4 ). In the course of 1984 fears of a monetary collapse through a hyperinflationary episode ran high. In the legislative elections of July 1984, the electoral campaign focused on two themes: economic issues and the essential resolution of the Lebanese crisis. Labour led by S. Peres became the country's leading party but could not govern alone; a government of national union was organized involving the two main historical political organizations, the Labour Party and the Likud, (who even agreed on a two-year rotation of Prime Minister and Finance Minister) together with six small political groups with members of the Knesset.
This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors' experience, a framework proposal for creating valuable and aggregated knowledge is depicted.
It is possible to identify "ambient assisted living" (AAL) aiming to help individuals to continue to carry out their daily activities in their homes, for as long as possible and with the least amount of assistance. Although there are research works aimed at tracking the movements of individuals with high precision, in order to gain knowledge regarding how the interactions develop for different tasks, as a wide variety of Internet of Things (IoT) devices are used  [2, 3] , this paper aims to discuss how to exploit the advantages of the knowledge gathered in much more real contexts with non-intrusive sensors. The ambition of this work is to accept high levels of noise in the dataset, which is not very rich in terms of detail, and to try to derive useful behavioural rules to be applied in the same or other environments.
For this reason, this paper has the goal of promoting the development of useful behavioural models by learning from non-intrusive general-purpose sensors that are already in place to cover other functions. Additionally, because of the intrinsic uncertainties, it becomes relevant to analyse to what extent the uncertainty in the underlying datasets impacts the quality of the models. A non-invasive environment for tracking the behaviour of individuals is particularly useful for the living environments of the ageing population, although some concerns regarding usability and user friendliness have been raised  [6]. There is a wide range of solutions for monitoring the mobility of this population, but many proposals are based on the installation of new dedicated sensors  [7]. However, it is also possible to take advantage of the already-existing infrastructure, designed mainly for other purposes, to infer information associated with people's behaviour  [6, 8, 9].
Secondly, Wi-Fi signal perturbations are being used to infer presence and movement, as stated in  [31, 32]. Finally, environmental data-fusion refers to the use of environmental values such as temperature, relative humidity, CO 2 , etc. , to infer additional information by using data-fusion and other data mining techniques, as shown in  [33, 34]. AAL technologies have also been studied to develop alarm-based monitoring systems to notify users of non-ordinary actions and behaviours in older people and people with physical disabilities. In the Italian project HDOMO, they developed an automated domestic environment that made it possible for people to continue being autonomous in their private environment. The behaviour of home inhabitants was monitored by using low-cost passive sensors alongside depth cameras.
The three monitored rooms were chosen because the residents with major behaviour problems resided there and, therefore, they were in need of more assistance and attention. Among the incidences that sometimes occur are agitation, alteration, screams, verbal aggression, physical aggression and inappropriate behaviour. The site layout plan with the monitored rooms depicted on a grey background is shown in Figure  2. Thirteen residents were living in the cohabitation unit during the year in which data were collected. The residents were seven men and six women with an average age of 84 years. A predetermined timetable of the care home provided clues about the residents' normal location, making abnormalities stand out more clearly. Residents were in bed between 22:00 and 08:00. From 8:00 on, residents were woken up, cleaned and dressed by the nursing assistants.
At night or in low light, backup batteries provided additional power to ensure the proper operation of the sensors. All sensors support the EnOcean TM standard protocol (ISO/IEC 14543-3-10) for low-power wireless communications. The data acquisition was carried out with respect to the national laws regarding non-intrusiveness, privacy and confidentiality over the period of one year. The multisensor devices collected environmental data regarding the temperature, relative humidity and CO 2 levels at a regular frequency that could decrease during extended periods of low light due to low battery charge. However, the ceiling-mounted PIR motion sensors operated as room occupancy gauges by detecting when a room was occupied or vacant. These PIR sensors did not send the motion detection data periodically but when the room occupation status changed with minimum pre-configured delays.
Careful labelling process was carried out for 16,900 patterns to be used for the presence data model, and about 560 patterns for the window opening process, as it is a less frequent event and limited unbalanced datasets were used. The patterns used for the presence model were randomly segmented into 11,323 for training and 5577 for testing. In a similar way, the 560 patterns for the opening window model were segmented into 392 for training and 168 for testing. It became clear that specific effort was required for labelling the patterns, therefore it was indicated that the chosen case study had a partially labelled dataset. After the description of the site, the sensing strategy adopted, and the data collection carried out, the method used for data preparation is presented here. Note that we were interested in developing software-based sensors that were able to determine and formalise new knowledge.
Two different models were proposed, the first one was regarding the presence or absence of people inside a room during a significant period of time, only taking into account the patterns of CO 2 , temperature and humidity evolution. The second model was to detect when the window in the room was opened or closed. As described above, the environment was very noisy, not only because of the occasional failure of the sensors but due to additional unforeseen behaviours such as nurses leaving doors opened, forced ventilation rules, diffusion of air (as well as the related physical properties) between spaces, which all occurred without any clear pattern. To be able to support the conclusions found in an unbiased context, data preparation adopted the removal of sequences of days from the datasets for learning purposes.
These removed data became the elements that were used to test the knowledge gathered after the training processes had ended. For the remaining data in the datasets, as several configurations required testing, a cross-validation folder approach was adopted to decide the most suitable modelling configuration. Indeed, regarding the context awareness discussed in the state of the art AAL approaches, it was decided to incorporate some degree of semantic description of the spaces, making it possible to be responsive not only to the indoor variations of parameters, but also to those from the next room, thus adding information about the building. The usage of Web Ontology technology enables to setup a clear relationship between the sensor data and the home context towards defining the activity context.
Mainly, the rules that were found were related to the room occupancy for users, where the aggregation of data from sensors over time must be significant in order to deal with the previously highlighted local variations. Therefore, we were interested in building models that inferred the use of the spaces, by considering the level of CO 2 , the slope of its variation, the values from the neighbouring rooms, and the RH variations. To adequately learn from the data, a smooth data function was required, and therefore a moving average of five values was used. Then, a model for presence in bedrooms was trained by preparing a dataset that involved the following variables: • CO 2 concentration level in the room. • CO 2 concentration level in the contiguous room.
• Slope of the regression linear model for CO 2 level in the room considering three points before the present time and three points after. As already indicated in the last part of the Materials and Methods Section, to preserve the independence for validating the learned model, the information from January 2018, after the 10 January 2018, was removed from the dataset and reserved for testing purposes. The remaining part of the dataset that was used for learning purposes involved 11,323 samples with an unbalanced ratio between classes of 66%/33% (i. e. , presence/absence from the bedroom). The goal when addressing artificial intelligence techniques is not just to find models that can produce accurate predictions, but also can be used to extract knowledge in an intelligible way, i. e. , can be understood as being able to explain the reasoning logic behind the provided outcome.
Results The two selected models were trialled under a strategy of 10-fold cross-validation with minimal adjustments for the selected variables as per the split of the standard recursive partitioning algorithm used, which starts with all the data and performs an exhaustive search looking for possible split points to find the one that best explains the entire data, or, in formal terms, that reduces the node impurity the most. Cross-validation ensures that the averaged performance being considered is independent from the set of data. The results show that, for this particular case, it was best to only consider two variables, as depicted in Table  1 , where the accuracy represents the percentage of correctly classified instances out of all the instances, and Cohen's Kappa essentially depicts the classification accuracy, except it is normalised at the baseline of random chance on the dataset.
Validation of the trained models was carried out using data from the same room but measured during a period after the one used for training. Therefore, full independence of the data was adopted and the results are presented in Table  2. From the results in this table, obtained from data not previously seen as the data came from the part of the dataset that was omitted for validation, it becomes clear that the accuracy of the classifier was high (F1-score: 0. 984). After validating the coherence of the model, it was then necessary to determine which variables the created knowledge was based upon, which is presented in Table  3. To this end, it becomes clear that the presence model has a high accuracy level, and the highest impact for the decision-making process regarding the presence/absence of the user in the bedroom was the CO 2 concentration (with a significant contribution from the variation rate (i.
, 2016). Faute de mobilisation politique, les États africains demeurent dans l'impossibilité d'assurer des soins en santé mentale de qualité à destination de l'ensemble des personnes affectées et de mettre en oeuvre les recommandations internationales concernant la décentralisation, l'inclusion dans la santé primaire et le respect des droits des malades. Faute de données épidémiologiques concernant les situations de base, les ressources, les évolutions des indicateurs 1 , il leur est difficile d'élaborer une politique de santé mentale efficace  (Wone, 2018). Différentes raisons expliquent ce déficit d'informations statistiques en Afrique. Tout d'abord, la santé mentale n'est pas incluse dans les soins primaires. Les malades sont référés dans des centres spécialisés qui développent chacun leur base de données, ce qui complique le travail de compilation et de standardisation nécessaire à l'élaboration de séries statistiques cohérentes.
Cette pluralité de références est révélatrice des influences qui traversent la psychiatrie sénégalaise. La Direction du centre de santé mentale de Ziguinchor a mis à notre disposition ses fichiers de suivi de consultations, ceux-ci ont été anonymisés et analysés. Bien qu'ils n'offrent que peu de variables et qu'elles impliquent de nombreux de recodages en raison des circonstances de la saisie, ils procurent des informations relatives à l'origine des patients. Si nous avons pu consulter le fichier du centre catholique Dalal Xel de Thiès, nous n'avons pas été autorisés à développer une analyse statistique à partir de cette base de données alors qu'elle est d'une profondeur temporelle significative et d'une grande richesse sur les itinéraires thérapeutiques et les ressources des patients. Offre psychiatrique et parcours thérapeutiques 16 Dans cette partie nous allons examiner comment l'offre psychiatrique se positionne dans les parcours thérapeutiques et comment elle contribue à structurer les circulations des personnes malades.
This research is based on a corpus of TV segments drawn from the national audiovisual archive institute (Institut National de l'Audiovisuel, INA), the repository of all French television audiovisual archives. We selected TV items that directly referred to or spoke of heart transplants. 8 This corpus was complemented by an inventory of articles on heart transplantation in two newspapers: the daily newspaper of record Le Monde (LM) and the regional daily Sud Ouest (SO) over the 1966 to 1974 period. This data is drawn from the Europresse database. 9 The early days of heart transplantation were marked by two peaks of media coverage (Figures  1  and 2 ). The fi rst took place in 1968-1969, as the fi rst transplants were performed in France and abroad. (1966-1974) in the regional daily paper Sud-Ouest and the national Le Monde.
Storytelling and Heart Transplantation in Newspaper and on TV The newspapers and TV told the story of heart transplantation in different ways: their respective technologies and organizational forms framed their ways of reporting and developing narrative on the subject. In the 1960s and 1970s, reading the newspaper was a daily habit for a large portion of the French population. 13 The data for our analysis comes from a regional daily paper and a national one, both cited above, with different editorial approaches. 14 SO, which boasted the 'highest circulation in the Southwest' of France, 15  covers Bordeaux and the surrounding region. This means it reports primarily on local news: for instance, the tenth French transplant recipient of 1968, Bernard Marion, who underwent surgery in Bordeaux, received particularly extensive coverage in SO.
We hypothesized that performance in the real world in both cities will significantly correlate with performance in the virtual environment. Based on our original mobile-based results  [13]  and on previous gender differences found in navigational studies in the real world  [28] , we hypothesized that males will perform better than females in both environments. Finally, we predicted that familiarity with video games will not influence performance in either environment, in line with a previous study showing no effect of computer experience on spatial memory errors  [7]. In particular, the correlation between the real-world wayfinding performance and the performance at the first training Sea Hero Quest level-where no spatial ability is required-should be null. Comparing this study to the original large dataset would enable testing whether our results hold true not simply in small cohorts but on a population level.
Participants In London-We tested a total of 30 participants (15 males) but data from 1 participant was missing due to a technical problem and real-world wayfinding data from 6 participants (3 males and 3 females) were discarded due to GPS recording issues. Subsequent analyses hence include 23 participants (11 males), aged 18-30 y. o. (M = 21. 52, s. d. = 1. 81). Participants had normal or corrected to normal vision and gave their written consent to participate. Path integration data was not collected for the first 11 participants as this task was not yet implemented. Participants received 3 class credits or £20 for their participation. In Paris-We tested a total of 30 participants (15 males), but real-world wayfinding data from four participants (1 male and 3 females) were discarded due to GPS recording issues.
If the participant takes more than a set time, an arrow indicates the direction along the Euclidean line to the goal to aid navigation. On basis of the data from the mobile video game, we selected a subset of 5 of the total 75 levels in the game that varied in difficulty. In order to compare the data recorded in this study to the population-level dataset, we chose three levels of increasing but moderate difficulty appearing quite early in the game (levels 6, 11 and 16). Indeed, the sample size of the mobile video game logically dropped rapidly across levels (see  [13] ). We also included a training level (level 1) and a level of great difficulty (level43), see S1 Fig Performance was quantified with the Euclidean distance travelled in each level (in pixels). The coordinates of participants' trajectories were sampled at Fs = 2 Hz.
The coordinates of participants' trajectories were sampled at Fs = 1 Hz with the experimenter's smartphone GPS via the Beeline app. We visually inspected all recorded GPS trajectories to deal with potential losses of signal. For losses of signal where the participant did not make any turn, we linearly interpolated between the first and the last missing points. When we couldn't reconstruct the trajectory because the participant changed direction during the loss of signal, we discarded the data (5 trials out of 180 in Paris, 6 out of 180 in London). Performance was quantified with the Euclidean distance travelled in each route (in meters). 2-Path Integration. The real-world path integration task consisted of 4 path integration trials which varied in difficulty in terms of the number of turns they featured (1, 2, 3 and 4 turns, see S4B Fig).