dataesr/scientific-paragraphs-categorization huggingface dataset:
EEG Data Analysis EEG data analysis was performed using Cartool Software 3. 55 (URL: https://sites. google. com/site/cartoolcommunity/files, accessed on 01 February 2019). Raw EEG data were re-referenced offline to a common average reference, a band pass filter was applied between 1 to 30 Hz and a notch filter was applied at 50 Hz to remove environmental artifacts. Analyses were conducted on the interval around the reward screen for two intervals. This method has been applied by our research team in a previous study  [29]. The processing of the feedback was analyzed through extraction of epochs of 700 ms (100 ms prior to stimulus onset to 600 ms following stimulus onset) from the raw data, with a baseline correction of 100 ms applied before the feedback (100 ms to 0 ms)  [29]. Based on grand averages of ERP responses for "doubling" and "explosions" conditions, the FRN was defined as the mean voltage from 270 ms to 345 ms, and the P300 was defined as the mean voltage between 375 ms to 575 ms.
Remaining epochs were visually inspected, manually removing those containing blinks, eye movements, or other sources of transient noise from the analysis. Electrodes with an aberrant signal (excessive noise due to malfunctioning or a bad signal during data collection) were interpolated using a three-dimensional spline algorithm (average: 4. 67% interpolated electrodes  [30] ). Based on previous literature on feedback processing, six central electrodes (Fpz, Fz, FCz, Cz, CPz, and Pz) were chosen for analysis  [31] [32] [33] [34] [35] [36] [37]. In order to identify the cortical areas responsible for impulsivity, a source localization of ERPs correlated with impulsivity scales was applied  [29]  using a distributed linear inverse solution based on a Local Auto-Regressive Average (LAURA) model, comprising a solution space of 3005 nodes to estimate the brain regions in response to the different electrocortical map configurations.
The current distribution was calculated within the grey matter of the average brain provided by the Montreal Neurological Institute (MNI). To investigate whether the brain regions were correlated with impulsivity, Cartool software offers the opportunity to select a list of Talairach regions and generates a group of solution points (nodes) that fit within each of the identified regions. To allow current density measures (indicating activation strength in µA/mm 3 ) to be extracted from the region of interest, the inverse solution was estimated for the group of solution points for each time window of interest.
Conceptualization, M. N. , J. G. , E. H. , D. B. and D. G. ; software, D. G. ; investigation and data curation, J. T. M. , C. C. , M. N. , Y. G. and T. C. ; writing-original draft preparation, J. T. M. , C. C. , D. B. and D. G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by a national Clinical Research Program (Programme Hospitalier de Recherche Clinique-PHRC; EUDRA CT nb. : 2009-A00806-51) in the University Hospital of Besançon. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital of Besançon (General Health Administration-ANSM 2016-A00870-51).
Author Contributions: Conceptualization, M. N. , J. G. , E. H. , D. B. and D. G. ; software, D. G. ; investigation and data curation, J. T. M. , C. C. , M. N. , Y. G. and T. C. ; writing-original draft preparation, J. T. M. , C. C. , D. B. and D. G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by a national Clinical Research Program (Programme Hospitalier de Recherche Clinique-PHRC; EUDRA CT nb. : 2009-A00806-51) in the University Hospital of Besançon. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital of Besançon (General Health Administration-ANSM 2016-A00870-51).
The authors propose and implement a series of valuable tools to enhance security and privacy in this context as well as compliance with the GDPR. They also illustrate through a case study the use of the proposed tools for sharing health records and their integration within the reference framework. In their paper "BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks", Lamia Berkani, Lylia Betit and Louiza Belarif present a new approach to improve accuracy and coverage of clustering based recommen-dations systems for social networks. The approach is based on improving the results of multiview clustering by combining it with a bees swarm optimization algorithm. Through extensive experimentation with two real-world datasets, they are able to demonstrate the effectiveness of the proposed approach to significantly improve accuracy, outperforming others clustering-based approaches.
A full transformation flow is presented and a case study is used to illustrate its implementation. The proposal is shown to be effective and flexible, improving the state of the art. In their paper "Model-Driven Engineering for End-Users in the Loop in Smart Ambient Systems", Sylvie Trouilhet, Jean-Paul Arcangeli, Jean-Michel Bruel and Maroun Koussaifi present a Model-Driven Engineering (MDE) approach to involve the user in the process of constructing at run time component based applications, adapted to a situation and user needs, in the context of ambient systems. The proposed solution relies on several domain-specific languages and a transformation process, based on established MDE tools (Gemoc Studio, Eclipse Modeling Framework, EcoreTools, Sirius and Acceleo). In this context, the authors describe an innovative way of reinforcing the place of the user in the engineering loop.
The authors propose an editor that allows the end user to be aware of the emerging applications resulting of this process, to understand their function and use, and to modify them if desired. From these actions, feedback data are extracted to improve the process. In their paper "An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices", Mathieu Briland and Fabrice Bouquet present a domain specific language (DSL) for generating test data for IoT devices/environments. The DSL is proposed for testing and simulating false data injection attacks (FDIA). First, the paper outlines a generic approach for FDIA and presents a list of possible sensor types and a categorization schema for data obtained from sensors. Then, the application of the DSL is illustrated using two examples; a simple one altering the data obtained from a temperature sensor and a more complex one concurrently altering the data obtained from three particle sensors.
The authors show that their approach works well in the case study of the Flowbird parking meter system and discuss how it can be adapted to different application domains. We are grateful to all authors of journal articles in this issue, who contributed to a fine collection of research in model and data engineering. We would like to express our greatest thanks to all reviewers, who put in a lot of time reading the articles and making substantial suggestions for improvement, which at the end led to the high quality. We also would like to thank J. UCS evaluation committee for the opportunity to publish this collection of research articles as a special issue of the Journal of Universal Computer Science and in particular to the publishing managers Dana Kaiser and Johanna Zeisberg for their timeless assistance during the whole process.
At present, the infrastructure for quantifying the GHG sources and sinks in Africa is inadequate, due to the diversity of land cover, climate, management intensity and investment in capacity development, making the continent one of the weakest links in the global observation system  (Ballantyne et al. 2015). More broadly, an integrated environmental research infrastructure (RI) in Africa is essential to achieve sustainable development and to guide the necessary investments. The need for such an infrastructure has been apparent for many years (see the commentary by  Mbow (2014)  on  Valentini et al. (2014) : "Other future directions of research should include the creation of an inventory of databases of information from in situ studies, as part of a collaborative framework to improve the availability of low-cost greenhouse gas data.
Capacity development In parallel to mapping of needs for a continental observational research infrastructure, the SEACRIFOG project engaged key stakeholder groups to identify capacity development needs in the context of developing, maintaining and operating suchlike. It concluded that in addition to technical and financial investments (outlined in subsequent sections), a functional RI requires the development of both human and technical capacities, in terms of skilled scientific and technical personnel as well as state-of-the-art data centres, calibration laboratories, and efficient local instrument support. The scarcity of capacity across most parts of Africa constitutes the major obstacle towards the establishment and operation of a continentally owned and coordinated RI  (Atickem et al.
Understanding the systematic relationship between period and cohort life expectancy and how the relationship evolves over time are critical issues in formulating the design of retirement income products, evaluating the actuarial balance of pension schemes, and more generally for all analyses where demographic projections are involved. In this study, estimates of the life expectancy gap at all ages are performed using data for 1960-2018 from the Human Mortality Database and projections are generated through 2050 for the 42 national populations, disaggregated by sex. Contrary to previous research that often uses a single deemed to be «best» model to forecast mortality rates, we use a novel adaptive Bayesian Model Ensemble of heterogeneous parametric generalized age-period-cohort stochastic mortality models, principal component methods, and smoothing approaches.
The proposed approach is called CP-spline model. Consider a mortality dataset comprising deaths and exposureto-risk arranged in two m × n matrices, Y = ( d ij ) and E = ( E ij ) , respectively, with rows and columns classified by single age at death, x, m × 1 and single year of death, t, n × 1, respectively. The approach assumes that the number of deaths d ij at age i in year j is Poisson-distributed with mean µ ij E ij , i. e. , d ij ∼ P ( µ ij E ij ). The goal is to model and forecast mortality over both age and time combining (fixed knot) B-splines with a roughness penalty to achieve a compromise between fitting accuracy and smoothness. Let B x , m × k x and B t , n × k t be the B-splines over ages and years, respectively. The log mortality is described as a linear combination of B-splines and associated coefficients (α): ln[E(Y )] = ln(E) + Bα (21) where ln(E) is the offset and η = Bα is the linear predictor.
Data The paper uses historical mortality D x,t and population E x,t data for 42 homogeneous national populations (countries or areas) in different regions of world obtained from the Human Mortality Database (2019). The set of procedures applied by HMD to input data from vital statistics, census counts, birth counts, and population estimates from various sources, particularly national statistical offices, facilitates the cross-national comparability of the information. Table  2  lists the countries considered in this study together with details about data availability in the defined historical ''lookback window''. The analysis covers medium -and high-income countries in North America, central and western Europe and Asia and the Pacific, as well as Chile in Latin America. We can observe that data availability is not the same for all populations investigated.
Discussion and policy implications The paper confirms very affirmatively the deficiency of period estimates of life expectancy compared to cohort life expectancy. If mortality across ages improves, period life expectancy substantially underestimates the cohort life expectancy that differs for each birth cohort and over ages. The degree of underestimation increases with improvement in mortality rates. To make this point in a convincing manner the paper (i) uses a global database (the Human Mortality database) for 42 countries -the most extensive illustration of the phenomenon yet produced; (ii) proposes a promising "model-ensemble" approach to estimate the period and cohort life expectancy based on the weighted outcomes of several projection models that generally speaking performs better than any of them do if applied individually; and (iii) translates the mis-estimation of the period vs cohort life expectancy into a statistical measure of the life expectancy gap that serves as a direct measure of the impact of the gap on the pension scheme liabilities and consequently the financial status of a pension scheme.
Acknowledgements The authors would like to express his gratitude to the editors and the anonymous referees for his or her careful review and insightful comments, that helped strengthen the quality of the paper. We thank also the suggestions and remarks from participants at the  Fifteenth International Longevity Risk and Capital Markets Solutions Conference, Washington , September  2019. Jorge M. Bravo  was supported by  Portuguese national funds through FCT  under the project  UIDB/04152/2020  - Centro de Investigação em Gestão de Informação (MagIC). Mercedes Ayuso is grateful to the  Spanish Ministry of Science and Innovation for funding received  under grant  PID2019-105986GB-C21.
Background Though randomized control trials (RCTs) are considered to provide the best evidence in comparative effectiveness research (CER), they also have some limitations  [1, 2]. They can often be resource-intensive and timeconsuming. As such, RCTs may not be able to detect effects on long-term outcomes or rare events  [3] [4] [5]. Observational studies using routinely collected data have been used to complement RCTs  [5] [6] [7] [8]. Routinely collected health data (RCD) are generated from the daily operations of healthcare systems, recorded without a priori research question  [6]. A broad range of sources (e. g. , disease registries, health administrative data, quality/safety surveillance databases, electronic health records, and pharmacy data) hosts such routinely collected data and contains both drug exposure and clinical outcomes to be used to provide evidence on treatment effectiveness.
Eligibility criteria We included cohort studies which evaluated a therapeutic intervention by using RCD  [6]. Studies were eligible for inclusion if they (1) evaluated a therapeutic intervention, defined as a treatment-related to healing a disease, i. e. , pharmaceuticals, surgery; (2) used RCD as the data source; and (3) answered a comparative effectiveness question, i. e. , research aiming to identify which interventions work best for improving health. Studies that did not answer CER questions, studies without an abstract, and retracted papers were excluded. The inclusion and exclusion criteria for study selection are provided in Additional file 1: Table  S2.
Immortal time bias and misclassification of treatment Table S4explanation of data items). Any disagreement was discussed with senior researchers (RP, IB) to reach a consensus. The following data were extracted from the selected papers: treat effect, per-protocol effect). (e) Time points of eligibility (i. e. , when individuals were evaluated regarding their eligibility), treatment assignment (i. e. , when individuals were classified to one of the treatment groups), and the start of follow-up (i. e. , when individuals started outcome assessment). 4. After determining the time points of eligibility, treatment assignment, and the start of follow-up, we assessed if these time points were aligned to avoid bias. We identified the type of bias that might arise when they were not aligned (Table  1 ) and whether the authors described a solution to address bias.
Reporting essential information of the target trial Only 18% (n = 14/77) reported a diagram to illustrate the study design and reported the three essential time points (i. e. , eligibility, treatment initiation, start of follow-up). Eighteen percent (n = 14/77) did not report completely essential time points, i. e. , the start of followup, when individuals completed the eligibility criteria and when patients started the treatments of interest. Regarding the inclusion criteria, only 12% (n = 9/77) reported the exclusion of patients with contraindication to one of the evaluated interventions. Only one article explained the reason for not excluding patients with such a contraindication, due to the inability to identify these patients from the dataset. Sixty-five percent of articles (n = 50/77) did not specify the type of causal contrast estimated (Table  3 ).
31 Sun et al. predicted ED demands by fitting ARMA models. 32 They concluded that the time-series models represent a good tool for predicting ED demands and workload of medical staff. Aboagye-Sarfo et al. developed multivariate vector-ARMA forecast models for predicting emergency department (ED) demand in Western Australia. 33 They collected data of the number of monthly visiting patients for 7 years (2006-2012) from public hospital ED in Western Australia.
RMSE y y n t t t n = ( ) , 2 =1 - ∑  (3) MAPE y y y n t n i i i = 100[ ( | | | | )] / =1 ∑ - (4) where y t is the number of PED visits, y t  is its corresponding predicted values, and n is the number of data points. R 2 , also called the coefficient of determination, is a statistical metric that quantifies the ratio of the explained variance by the constructed model to the total variance in the dataset. 37 It is always between 0 and 1; the higher the value, the better the model. For instance, R 2 = 0. 99 means that the model captures 99% of the variance in the data, and only 1% is in the residuals. Of course, a more accurate model is characterized by higher R 2 and lower RMSE and MAPE values.
Case study This study is accomplished using data for arrivals to the PED in CHRU-Lille. The CHRU-Lille hospital assists around four million inhabitants in Nord-Pas-de-Calais in France, and its PED admits on average 23,900 patients per year. Also, the PED shares with other hospital departments access to its resources (e. g. clinical laboratory, scanner, and X-rays). The daily attendance to the PED from January 2012 to December 2012 is used in this study. The second co-author has full access to these data following a collaboration with the PED in CHRU-Lille during a HOST research project.
Data analysis In this study, a time series model has been adopted to construct a descriptive model for patient attendance at the PED dataset. Firstly, we perform a descriptive study of the data to summarize their main characteristic features. A key preliminary step in the time-series analysis is plotting the original observations over time to get simple descriptive measures of the principal characteristics of the series. This enables to preliminarily evaluate data patterns and behavior over time and may reveal relevant features of the series, such as trends, outliers, discontinuity, and seasonality. Figure  3  indicates that daily visits are almost stable during the period of the data set. we can observe the absence of a long-term trend or seasonality in this time-series data (Figure  3 ).
As suggested by the con dence intervals in all our plots, this leads to su ciently small variance on the instances tested. All experiments were executed on a compute server running Ubuntu 16. 04. 5 LTS with 24 Intel(R) Xeon(R) CPU E5-2643 3. 40GHz cores and a total of 128 GB RAM. The code was executed with python version 3. 7. 6 and C++ implementations were compiled with g++ 7. 5. 0. For the random generation of the graphs and the random choices of the live-edge graphs, we do not explicitly set the random seeds used by the random number generator. This does not prevent reproducability of our results as all the reported results are averages that are robust and independent of the random seeds chosen as indicated by the con dence intervals reported. Evaluation Details. For random and synthetic instances, each datapoint in our plots is the result of averaging over 25 experiments, 5 runs on each of 5 graphs generated according to the respective graph model.
We use the following networks: (1) Random instances generated according to the Barabási-Albert model  [45 45 ] that yields scale-free networks -a property frequently observed in social networks. We use the parameter modeling the preferential attachment to be 2, i. e. , every newly introduced node is connected to two previously existing nodes. (2) Random instances generated according to the block-stochastic model that is a natural choice in our setting as the instances come along with a community structure. (3) The publicly available synthetic instances from the work of Tsang et al. [25 25 ]. (4) Real world instances from the SNAP database  [46 46 ] and a paper by Guimerà et al  [47 47] , some of which were used also by Fish et al. We describe the real-world instances in detail below in the corresponding paragraph.
Even more, in the right plots, we can see that the achieved values in expected spread by tim and our methods are very close to each other. Real World Instances. We proceed by describing the used real world instances. Arenas  [47 47]  This dataset represents an email communication network at the University Rovira i Virgili (Spain). Each user is represented by a node and there is a directed edge from a node  to a node  if  sent at least one email to. q q q q q q q q q q q q q q q q q q q q 0. 1 set_based_ea set_based_ep node_based_ea node_based_ep uf_node_based_ea uf_node_based_ep tsang_ea tsang_ep greedy_maximin myopic_fish tim q q q q q q q q q q q q q q q q q q q q 40 80 120 160 10 20 30 40 50 k expected spread q q set_based_ea set_based_ep node_based_ea node_based_ep uf_node_based_ea uf_node_based_ep tsang_ea tsang_ep greedy_maximin myopic_fish tim q q q q q q q q q q q q q q q q q q q q 0.
0 set_based_ea set_based_ep node_based_ea node_based_ep uf_node_based_ea uf_node_based_ep tsang_ea tsang_ep greedy_maximin myopic_fish tim q q q q q q q q q q q q q q q q q q q q 40 80 120 160 10  email-Eu-core  [49 49 ] Also this dataset is an email network, this time from a large European research institution. Each member of the research institution belongs to one of 42 departments, which prede nes a community structure. Nodes and edges have the same interpretation as in Arenas. ca-GrQc and ca-HepTh  [49 49 ] These datasets are co-authorship networks for two di erent categories of arXiv (General Relativity and Quantum Cosmology and High Energy Physics -Theory). The nodes in the networks correspond to authors and there is an undirected edge between two nodes if the authors co-authored at least one arXiv paper in this category.
Facebook  [50 50]  This dataset represents a part of the Facebook network, where nodes are users and edges indicate friendships. Irvine  [51 51]  This dataset is a network created from an online community at the University of California, Irvine. Nodes here represent students and each directed edge represents that at least one online message was sent among the students. com-Youtube  [52 52]  This dataset consists of a snapshot of the social network included in Youtube. The network has 1134890 nodes and 2987624 edges. Nodes correspond to users, edges to friendships between users. Also this network contains a prede ned community structure that is given by the so-called Youtube groups. On Youtube, users can open groups that others can join. As the complete network is very large, we use a connected sub-network.