dataesr/scientific-paragraphs-categorization huggingface dataset:
To investigate if couple-related characteristics influence women's childbearing desire, we also undertook sub-analysis among women in a relationship. Missing data were included as a separate category to maintain sample size. Explanatory variables were selected on the basis of prior knowledge on associated factors. We used stepwise-descending approach to identify association. In the final model, statistical significance was reached when P-value <0. 05. Behaviors of women were also explored according to their respective pregnancy desire. Among those sexually active and wishing to delay or avoid pregnancy, we assessed the prevalence of modern contraceptive use to see if their behaviors correspond with their desire to limit pregnancies. Among women wishing to conceive immediately, we estimated the proportion of those who had discussed such childbearing intentions with health personnel.
Acknowledgments We are indebted to the women who accepted to participate in this study. We thank  Pierre Robert , advisor on HIV & AIDS at the  West and Central Africa Region UNFPA office  for his support in the implementation of this study. We also wish to thank the members of the scientific committee of the French charity SIDACTION for having supported this initiative. This study was part of the  West-Africa ,  Early Treatment & Integration of HIV and Reproductive health (WETIV-R) program , ancillary to the  IeDEA West Africa  project. The composition of the  WETIV-R program  is as follows. Principal investigators:  Renaud BECQUET  ( INSERM U1219, Bordeaux, France ),  Albert MINGA  ( CNTS, Abidjan, Cote d'Ivoire ). Associated investigators:  Eugène MESSOU  ( CePReF, Abidjan, Cote d'Ivoire ),  Apollinaire HORO  ( CHU de Yopougon, Abidjan, Cote d'Ivoire ).
The study was conducted under the International epidemiology Database to Evaluate AIDS (IeDEA) West Africa collaboration grants funded by the  National Cancer Institute (NCI) ;  Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) ;  National Institute of Allergy and Infectious Diseases (NIAID) ; Grant number:  5U01AI069919  (Primary investigator:  Francois Dabis ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health. Additional support was provided by the  French charity SIDACTION  (Aide aux equipes  15-2-AEQ-06-01 ) and the  West and Central Africa Region office of the United Nations Population Fund (UNFPA). Kiswend-Sida Thierry Tiendrebeogo  was a PhD fellow from the  French National Agency  on AIDS research,  ANRS  (grant  ANRS   12360 B92 ).
In SD, only around 4% of the profession are female  (Ratcliffe, 2015). This is a vitally important sector because all digital technology in the 21st century is enabled and controlled by software designed and constructed by developers/programmers/engineers  (Rapanotti and Hall, 2016). They design the structure and content of software applications, databases and web pages, and write computer code necessary to create and implement systems software. 1  The 'leaky pipeline' of women in STEM subjects (WISE, 2019) has a long history in SD. In 1994,  Lennon and  Whitford reported that women's participation in the IT professions was shrinking, as did  Howcroft and Trauth in 2008. In the USA, the numbers of women reading computer science rose sharply during the 1970s so that they comprised one-third (34%) of students in 1984, but then declined sharply (Quoctrung Bui/NPR).
(Torvalds, 1991 , quoted in Mackenzie, 2005: 87). This call, for volunteers who are 'all man' and enjoy 'all-nighters', 'deeply ties' the Linux kernel 'to a gendered corporeal set of practices of programming work'  (Mackenzie, 2005: 87)  that, as Linux is 'ported' onto different platforms, is carried into new systems. Fuller's (2017)  analysis of Linus's more recent works shows them to be sites of production where 'some of the conditions of software culture, derived both from computational forms and from cultural tendencies that are manifest as conventions realized in software, drive forms of work, communication and value-creation' (location 442). As these include gender, gender is inherent in the IT sector's 'complex temporalities' in which the 'past is enfolded into the present through skeuomorphs, details that were previously functional but have lost their functionality in a new technical ensemble'  (Hayles, 2012: 89).
Methodology We conducted a systematic literature review by searching several scientific databases: PsycINFO, PsycARTICLES, Psychology and Behavioral Sciences Collection, HeinOnline, ScienceDirect and PubMed. These databases were selected because they cover the main publications in our field. More pragmatically, they were also those that were most accessible. Due to the large proportion of studies in English in our field of study, the search terms were decision-making, use of force, law enforcement and police (in English). We ran the search using the following procedure: first, we looked for [police OR "law enforcement"] AND use of force AND decision making. As this yielded a large number of results, we then added a combination of two of these words in the Title, Abstract or Keywords. We would have preferred to find them only in the Title, but this is not possible in some databases.
Limitations This systematic literature review only included studies published in the last ten years -since 2009 -from six databases and based on restrictive selection criteria. It is likely that other relevant studies on this topic exist but are not included in this review. Certain factors identified and discussed in this review are the subject of only one of the 52 studies. These include: • police officer's experience  (Boulton, & Cole, 2016)  • police officer's personality  (Brandl, & Stroshine, 2013)  • age of the suspect  (Brown, Novak, & Frank, 2009)  • gender of police officer  (Nickel, 2016)  • prior information (Johnson, D. J. 2018) • presence of colleagues  (Pickering, 2017)  • police officer's educational level  (Rydberg, & Terrill, 2010)  • police officer's working memory ability  (Kleider, et al.
Nevertheless, it can be assumed that police officers face situations that are highly emotionally charged and they could be overwhelmed by those emotions. Works on emotion regulation and cognitive abilities would help police agencies in their training process and police officers in their daily duty. In any event, these issues about the decision to use force require further research as its ins and outs could be further explored. Belonging to an ethnic minority 934 encounters in an African American neighborhood Data collected during ride-along patrols and coded on three categories: a warning is given, a citation is issued, or an arrest is made. In a highly multicultural environment, physical appearance, such as dreadlocks that enhance suspect's racial stereotype, is correlated with more frequent use of force.
To model response variables according to a variety of both quantitative and qualitative variables, we used random forests  [26]. These consist of a large number of decision trees built on varying, randomly chosen portions of the dataset and are used to model either a quantitative response (regression) or a categorical one (classification). Random forests enabled us to assess the relative importance of descriptors to model the answer to a particular question. To analyse open questions, we performed a statistical analysis of the textual data  [27]  using the open source software Iramuteq  [28]. In particular, to identify the words that were over-represented in the discourse of participants according to their user profile of the ViaRhôna, we measured specificity scores  [29]. We considered that a term was specific to a category of respondents if its specificity index was greater than 2  [30].
Sampling, recruitment and data collection We used purposive maximum variation sampling. 8 nterviewees were recruited from multiple sources, including professional contacts, networks and directly from publishers. Eligible study participants consisted of journal editors of biomedical journals including all specialties, referring to individuals who were at the time of the interview involved in the communication process between authors and peer reviewers and/or who were in a position to decide about the fate of manuscripts. Interviewees were approached via email from multiple sources, including professional contacts; attendees of the Eighth International Congress on Peer Review and Scientific Publication, and from the BioMed Central and British Medical Journal publishing groups. Prospective interviewees were provided with a study consent form and an information sheet.
The concept of Smart City has spread as a solution to ensure better access to information and services to citizens, but also as a means to reduce the environmental footprint of cities. To this end, a continuous and wide observation of the environment is necessary to analyze information that enables government bodies to act on the environment appropriately. Moreover, a diffused acquisition of information requires adequate infrastructure and proper devices, which results in relevant installation and maintenance costs. Our proposal enables reducing the number of necessary sensors to be deployed while ensuring that information is available at any time and anywhere. We present the HybridIoT system to cope with the lack of environmental information in the urban context through an estimation technique that integrates heterogeneous data acquired from some different sensors.
HybridIoT leverages two types of estimation: endogenous, in which historical data of a device is used whereas this is not capable of providing any information from the direct observation of the environment; exogenous, estimating missing information by integrating heterogeneous information perceived from different data sources. To motivate the use of exogenous estimation, consider an urban sensing platform used to monitor the environment. The platform can exploit a multitude of sensors capable of perceiving environmental information on a large scale. Sensors can be of different types, acquire heterogeneous information or data of the same type using different units of measurement. In such a context, we hypothesize that the abundance of different information is crucial for both AI and data analysis techniques to extract useful knowledge from the urban environment to achieve the smartness goal of a city.
Tomaras et al. [17]  proposed HELIoS (HEalthy LIving Smart), a framework that combines heterogeneous information such as urban traffic and pollution data to diagnose the health state of urban areas in a smart city. Data has been acquired hourly from 3402 heterogeneous fixed sensors deployed in different urban areas. The method uses three regression models to predict the health state of urban areas: • Support Vector Regression (SVR): a regression variant of the SVM classifier; • Random forest: a set of multiple decision trees where each tree is trained using a different subset of the training set; • Gaussian process: a non-linear and non parametric model that is an extension of the multivariate Gaussian distribution for an infinite collection of real value variables. The technique is not designed to be deployed at large scale and is not able to face unpredictable events such as sensors malfunction or unavailability.
Therefore, d is a metric. Definition 4 (Ambient Context Agent): an Ambient Context Agent (ACA) is associated with a sensing device; its goal is to provide environmental information in a local part of the environment. Thanks to cooperative behavior, ACAs are capable of providing estimates even if ad hoc sensors are unavailable. Each ACA is characterized by a knowledge base containing ACWs. Each ACW belongs to a unique ACA. Definition 5 (Sensing Device): a sensing device is any physical instrumentation that embeds sensors capable of detecting events and changes in its environment. Sensing devices can be either fixed or mobile: fixed sensing devices are installed in specific positions and enable continuous monitoring of the environment. They acquire information continuously and store these in dedicated databases.
E. SYNTHESIS This section presented the HybridIoT system for estimating missing information in urban contexts through both endogenous and exogenous estimations. Thanks to cooperative behavior, the ACAs estimate missing data using a large amount of data acquired from sensors in the urban context. HybridIoT does not use any specific data fusion technique for heterogeneous data; this is an advantage because the system can use information whose type is not known in advance. Nevertheless, heterogeneous information cannot be used directly for estimating a missing value. For example, it is not possible to estimate wind speed information using only humidity values. To estimate missing information using heterogeneous values, agents indicate the trend that the missing information should assume with respect to the last perceived value.
After discussing the proposed technique implemented by ACAs for estimating missing information, this section presents the results obtained by HybridIoT on a real weather dataset. Section IV-A discusses the dataset, describing the type of information, its size, the frequency of acquisition and where data has been acquired. Following, the section describes the k-fold technique used to validate the obtained results. Then, we provide some details of the machine used to validate the proposed solution. Two experimental cases are presented: endogenous (Section IV-C) and exogenous (Section IV-D) estimations. In the former case, we evaluate our proposal using only one type of information (for instance, the temperature). Although HybridIoT can estimate missing information from continuous data of any type, we focus on temperature values to evaluate the accuracy of the proposed technique.
A. DATASET We used a dataset containing real environmental information acquired in the Emilia Romagna region in Italy  [21]. In Emilia Romagna region, the prevailing climate is temperate subcontinental, with hot and humid summers followed by cold and harsh winters. Orographically the region is divided almost symmetrically between the Po Valley and the hills and mountains of the northern Apennines. We considered the average daily air temperatures at 2 meters of altitude, the average daily solar irradiance, the average daily wind speed at 10 meters of altitude and the average daily air humidity at 2 meters of altitude. Data have been collected in 196 days from September 8 2017 to April 25 2018. The days when some sensors were not operational are not considered. We used two versions of this dataset for the experimentations: (i) a version containing all the types of information previously listed, that consists in an array of 8112 numerical values acquired from 9 weather sensors; (ii) a version containing only temperature values, which results in an array of 15680 numerical values acquired from 80 weather sensors.
This difference in terms of the number of samples and sensors, between the two datasets versions, is because for different days not all the information has been acquired correctly by each sensor. Fig. 5  shows the distribution of mean temperature values for the first version of the dataset, obtained through a standard normalized convolution: this is a non-direct methodology widely used for filtering incomplete or uncertain data which is based on the separation of both data and operator into a signal part and a certainty part  [21]. Fig. 5  shows that temperatures are uniformly distributed over the territory except in urban areas (center, north-west) where the temperatures are more intense than in the rest of the region, especially in the south-east part, closer to the Mediterranean sea. the region is between 6.
72°C and 9. 51°C. Fig. 7  shows the distribution of radiation, relative humidity, wind speed, and temperature for the dataset obtained from 9 weather sensors. Here, the wind speed is more intense in the south-east part of the region, which is closer to the Mediterranean sea, and as in Fig. 5 , the temperatures are higher in urban areas. We do not report the histograms of the values for the dataset obtained from the 9 sensors because the information content is limited and the graphs would be superfluous.
The k-th partition is being validated by estimating its data (step ) to simulate the unavailability of the sensor. During this step, the agent pursues an endogenous estimation. Then, the ACA determines the set of the other agents with which it can cooperate to provide an accurate estimate (step ). Finally, the ACA pursues an exogenous estimation by cooperating with the available agents (step ). The steps to are repeated for each partition. The estimates provided for all the partitions are merged to compare the results to the real values (step ). The average error is calculated by comparing OpenStreetMap  [22] ). The red markers represent the position of the weather sensors. The x-axis reports the longitude, the y-axis the latitude. the estimated and the real values for each sensor of the dataset (step ).
In our experiments, we used a k value of 5, 10 and 15. For the dataset containing only homogeneous information, the values of k chosen (5, 10, 15) yield to test partitions containing respectively 20%, 9% and 6% of data with respect to the size of the dataset. No significant differences in terms of error were observed using different values of k. All the experiments were carried out on a machine equipped with i7 -7820HQ, 32GB RAM and Windows 10. With such a configuration, estimating a missing value is practically instantaneous. The proposed solution has been developed in Java language and simulates a multi-agent system distributed in a large scale environment. To evaluate the performance of the solution with respect to the quality of the estimation of missing data, we did not consider additional computational overhead such as communication costs between agents.
1) Comparison to the State of the Art The results obtained by the endogenous estimation have been compared to state of the art techniques using the KNIME analytic platform. This free software provides a graphical drag-and-drop environment where pipelines can be assembled by connecting nodes that perform data analysis tasks  [23]. In KNIME, nodes are components represented as boxes having input and output ports. Each node transforms and processes data according to specific functionalities. Input/output connections ports allow data to flow through the pipeline. The KNIME platform was chosen for the experiments because of its availability, ease of use and easy reproducibility of experiments that do not require any programming languages. Moreover, in KNIME a large number of regression techniques are available, which enables a more exhaustive comparison with our proposal.
The following regression techniques have been used for estimating missing information, the nodes being available on KNIME: linear regression, polynomial regression, random forest regression  [24] , fuzzy rules  [25] , gradient boost trees regression  [26] , Autoregressive Integrated Moving Average (ARIMA)  [27] , Pace regression  [28] , Radial Basis Function (RBF)  [29]  and isotonic regression  [30]. Pace regression, RBF and isotonic regression nodes are available through the Weka data mining framework  [31] , which can be integrated with KNIME. The comparison with the state of the art does not consider the methods described in Section II as authors do not provide any tool to realize a comparison with the dataset used in this paper. The evaluation using the state of art techniques has been carried out using the default configuration for each node.
Fig. 11  shows the box plots that depict the absolute error obtained by the state of art techniques, calculated as the average among the considered folds  (5, 10 and 15). The results in Fig. 11  are obtained from the dataset containing only temperature values. Although the average error obtained for each sensor is less than 1°C, the proposed cooperative approach outperforms the state-of-the-art techniques. In fact, cooperation enables ACAs to use not only information in their historic, but also information coming from a multitude of agents in the environment. We compared the results of the endogenous estimation scheme to a pipeline of standard techniques using the same dataset and cross-validation  [21]. This pipeline includes Voronoi tessellation to determine the geographical area to which sensors belong, hierarchical clustering to group the sensors that perceive similar information and normalized convolution to estimate missing information.
The areas of two or more sensors are merged if their Voronoi areas are adjacent and they are grouped by the clustering process. Finally, for a given point, a normalized convolution is used to estimate missing environmental information using the sensors in the corresponding Voronoi region. Normalized convolution is a standard method used to reconstruct incomplete or uncertain data from a spatio-temporal signal widely used in geo-statistical applications  [34]. We used the dataset of only temperature values for this evaluation. Fig. 12  shows the comparison of results obtained by this pipeline and HybridIoT using respectively the nearest agents and most confident agents criteria for the cooperative case. HybridIoT shows better results with respect to the pipeline using both agents' selection criteria (near agents and most confident agents).
D. EXOGENOUS ESTIMATION As far as we know, nowadays no solution is available to estimate missing information by integrating heterogeneous information from different data sources. Because we cannot compare the results obtained through the exogenous estimation to any specific technique, we show that the obtained are on a par with those obtained using the endogenous scheme. Exogenous estimation is addressed through cooperation between ACAs that perceive different types of information. In our experiments, we used the proposed technique to estimate temperature values using ACWs containing solar irradiance, humidity and wind speed. As described in the previous paragraph, an ACA that encountered a NCS evaluates different estimations for each weight obtained from a cooperative process with other agents.
Then, the ACA evaluates a histogram of the estimation values to exclude those that are not relevant. In our experiments the number of bins has been set to 10: this value was obtained by using the Freedman-Diaconis rule  [35] , which allows calculating the size of the classes of a histogram. The number of bins was calculated as the median of the overall numbers of bins obtained by applying the Freedman-Diaconis rule for all the sensors. Fig. 13  shows the average error for the regional temperature dataset using heterogeneous ACWs. As for endogenous estimation, the estimation of a missing value using the exogenous technique is almost instantaneous. Table  IV    different from the temperature values. The information used in this estimation scheme does not have significant variation compared to the temperature values.
This can be overcome by using lightweight application protocols that improve smartphone performance in terms of bandwidth consumption, battery lifetime, and communication latency  [38] ; • managing communication delays due to the transmission of remote information: the communication on smartphones is bandwidth-limited and relatively expensive, especially when access to the network is achieved via cellular connection  [39] ; • verifying that the data acquired by the devices are correct and that there are no anomalies that could prevent the proper functioning of the system. In this way, having data been perceived from devices functioning properly, it is possible to validate the results obtained by the proposed technique; • building a consistent database for evaluating the results obtained from the system deployed in a real context: this requires continuous monitoring of the environment from both ad hoc devices and smartphones.