dataesr/scientific-paragraphs-categorization huggingface dataset:
Longitudinal studies typically monitor a number of groups over a period of years  (Ransbotham & Kane, 2011; Ren et al. , 2012). It is not feasible to cover a large number of groups over such a long period. The longitudinal studies we refer to above typically monitor between one and ten groups, making a few thousand observations. Table  1  gives an example of the invitation responses. As indicated in Table  1 , different social events are held during specific time slots. Each member's response is recorded with time stamps for each event. Based on the social event history, one can predict a member's participation. Given the social events held at time , where , we split the dataset to use events held at for model building, and the later social events, held at , for testing the predictive results.
Event created Time Event start A ----------- B -------------------------------------------------------------------- C --------------------------------------------------------------------------------------------- D --------------------- E -------------------------------------------------------------------------------------- F ---------------------------------------------- Figure C's response is censored. Censored information is an analytical problem that survival analysis deals with  (Allison, 2010)  since ignoring censored data might introduce bias. We developed the independent variables from the event history. It is impossible to use the network structure directly. To predict individual responses, we extracted network information for each individual in the network. This network information is formally defined as network features.
We also used individual response rates. We derived individual response rates from the number of past invitations the corresponding person accepted or declined. The survival model uses individual characteristics such as network information to predict the response outcome. To obtain an individual's network information (sometimes known as the network features), we first determined a social network from the social event log. Then, we extracted network features for each individual in the dataset. We used these network features in the survival model (eq. 9) as variables X. For this study, we defined social groups " " and " " as two -by-adjacency matrices, where is the total number of people who have attended past social events. In , we aggregated, for each pair of people, the number of their responses that agree.
Cox model with penalization Previous research suggests that a model using a subset of variables in a dataset sometimes outperforms the model with all variables  (Aytug, 2015; Mattila & Virtanen, 2015; Miyashiro & Takano, 2015). In regression analysis, stepwise model selection is often used to obtain an optimal subset of variables. Stepwise selection has been criticized  (Derksen & Keselman, 1992)  for overfitting the data and producing misleading results. In the context of survival analysis, alternative methods have been proposed to select variables. Considering the partial likelihood of the Cox proportional hazard model below: eq 10 users can respond to the invitation at different time points. For each of these time stamps, a fraction is calculated. The numerator is the weight of the individual responses at time , denoted as.
Experimental Setup and Evaluation Metric We conducted experiments to perform predictive tasks using the collected datasets. We divided the datasets into training and testing sets. We used 90% of the events to build the model, and the remaining 10% as future events for prediction. In other words, among social events held at time , where , we chose so that 90% of the events were held prior to for model training, and used the other 10% events after to test the model. To test the predictive accuracy, we used the AUC metric of the Receiver Operator Characteristic (ROC) curve  (Fawcett, 2006). AUC is a common measure used to test time-varying predictions in survival analysis  (Chen, Kodell, Cheng, & Chen, 2012; Heagerty & Zheng, 2005). To be more specific, the survival model outputs the survival score to quantify an individual's probable response outcome (accept or not accept) at the given time.
Results Table  5  shows the AUC values for each dataset, obtained by averaging the AUC values for predictions made at different times for the 10% testing data. The proposed bcure model predicts invitation responses accurately, with an average AUC above 0. 9. An AUC value higher than 0. 9 is generally considered excellent  (Pittman, Christensen, Caldow, Menza, & Monaco, 2007). We used a further statistical test procedure  (Benavoli, Corani, & Mangili, 2016; Demšar, 2006)  to test the significance of the results. A Friedman's test (p value < 0. 001) rejected the hypothesis that the predictive accuracy of all the methods are identical. We used Wilcoxon rank sum tests to compare the best performing "bcure" model with other candidate methods. The Wilcoxon rank sum tests rejected the hypotheses that the predictive accuracy of the candidate models were identical to that of "bcure" (p value < 0.
Conclusion This paper proposes a competing risk model to predict social event responses using social event data and past response rates. Based on past responses, we extract network information for each individual, and use that network information to make predictions. We collected datasets from three distinctive locations to test our research model. Historical social event data on member responses proves helpful in predicting positive responses to invitations. We observe homophily and heterophily to be predictive in this study. For privacy reasons, the proposed method does not require geographical or socialdemographic information. It is possible to generate knowledge about which people will respond to a social event, and when, using only historical data about other social events. This can contribute to effective event management and resource planning.
The feature selection methods, such as the penalized Cox regression models and the stepwise model, provide less accurate predictions. The penalized Cox regression models, such as "glmnet" and "fastcox," rely on parameter tuning. As the number of responses changes over time, it is difficult to select optimal parameters to find the best subset of variables to predict responses. Nor does the stepwise model increase predictive accuracy. The datasets have a higher number of observations than variables (n>>p), while feature selection models generally work better when n<< p. Although feature selection could be helpful, the added value was limited in this study when using a limited number of variables to make predictions. Additionally, high correlations among network features make feature selection difficult.
Table2, we permuted each single variable. The variable importance is the average decrease in AUC value obtained by comparing the permuted model with the original model across all datasets. This method quantifies the importance of each variable in predicting responses. We assessed the relative variable importance by scaling all variable importance values sum up to 1. The higher the variable importance score, the more important a variable is in predicting responses. Variable importance 6% 7% 21% 7% 9% 13% 6% 6% 25% rate_y rate_n event_nr ccfs_hete bc_hete degree_hete ccfs_homo bc_homo degree_homo Figure 4 Variable importance scores for the proposed model.
In the present study, when analyses were performed using other thresholds for walking (the non-practice of walking being defined by 0 h/week or 1 h/week), similar results were observed (data not shown). For household physical activity, a question was asked about the time spent per week usually doing moderate to vigorous activities such as cleaning the floor or using vacuum. Based on the median value, this variable was dichotomized as ±7 h/week (i. e. , 1 h/day). Leisure time physical activity was obtained by summing weekly durations of each activity reported in the leisure section of the questionnaire (except for walking for leisure and cycling for leisure that they were part of the walking and cycling variables). The resulting leisure time physical activity variable was categorized based on WHO guidelines for physical activity  [22]  , resulting in a three-class variable (0-0.
Anthropometry and Body Composition Clinical examination was performed by a trained technician and included standardized measures of weight, height, waist circumference, and body fat. Height was measured with a calibrated wall-mounted stadiometer to the nearest 0. 5 cm  [23]. Waist circumference was measured using an inelastic tape as the circumference midway between the lower ribs and iliac crests on the midaxillary line. Weight (to the nearest 0. 1 kg) and percent body fat were measured using a bioimpedance analyzer (BC-418MA, TANITA © , Tokyo, Japan), with participants wearing indoor clothes, barefoot. Covariates Sociodemographic variables were assessed by a self-administered questionnaire completed by participants at inclusion. Data included age, gender, educational level (classified as <2 years of university, ≥ 2 years of university), and home address.
The sum of all the mean durations per week of these activities was categorized as less than 2 h/day, between 2 and 4 h/day and more than 4 h/day. Urban density (number of inhabitants/surface) of the residential neighborhood was obtained from the French Census databases ( www. insee. fr ) and categorized as follows: 0-300 people/km² (rural area), 300-2,000 people/km² (urban density), and more than 2,000 people/km² (high urban density). The perception of presence of destinations/amenities around participant residence was assessed by a separate questionnaire (administered at the same period as the STAQ) on perception of residential environment. The question included 9 amenities (grocery store, supermarket, bank, post office, school, bakery, restaurant, coffee shop, and pharmacy) and a binary variable was created ( ≤ 6 and >6 unique destinations, based on the median value).
Models were also adjusted on the time period between physical activity reporting and clinical examination. Among all studied covariates, only sex was found to be a significant interaction factor (data not tabulated). Consequently, we also performed analyses stratified on sex. For all analyses, the significance level was set at 0. 05, and all tests were two-tailed. All statistical analyses were performed using SAS software (version 9. 3; SAS Institute Inc. , Cary, NC, USA).
Acknowledgements This work is part of the  ACTI-Cités  project (coordinator: JM Oppert) carried out with financial support from the  French National Cancer Institute (Institut National du Cancer, INCa)  through the  Social sciences and humanities and public health program  ( 2011-1-PL-SHS-10 ). The  NutriNet-Santé cohort study  is funded by the following public institutions:  Ministère de la Santé, Institut de Veille Sanitaire (InVS) ,  Institut National de la Prévention et de l'Education pour la Santé (INPES) ,  Fondation pour la Recherche Médicale (FRM) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Institut National de la Recherche Agronomique (INRA) ,  Conservatoire National des Arts et Métiers  (CNAM) and  Paris 13 University. Menai Mehdi  was funded by a grant from  Fondation Le Roch -Les Mousquetaires.
This definition puts emphasis on several characteristics: sharing presupposes ownership of something. Belk stresses that "[f]or there to be sharing, there must first be feelings of possession, if not ownership. Otherwise we have nothing to share. " (2007: 131). Equally important is the notion of "use". He argues that sharing is a form of distribution and hence located in the realm of consumption. Accordingly ownership precedes sharing because only what a person owns in the first place can be shared. This makes intuitive sense -as long as sharing is reduced to the sharing of material goods such as a drill or a flat; in these examples, sharing describes an organizational mechanism of making something (excess capacity) available to someone. But does this understanding of sharing account for the difference between sleeping in an Airbnb bed or a Hilton hotel room? Wouldn't both cases be examples of making something available to somebody for a certain period of time? Perhaps the distinctive feature of sharing is to be found elsewhere: what is crucial is that on Airbnb preferences are shared, pictures of one's private home are shared, location data about one's whereabouts are shared, evaluations about the other person's behaviors are shared and so on.
Data collection: media analysis and semi-structured interviews Our data collection started with a detailed media analysis in order to sensitize the research team for the specifics of the empirical setting and to reconstruct the sequence of events and decisions. It provided the context for the next step: a series of in-depth semi-structured interviews with key decision-makers of all organizations centrally involved in the collective of actors handling the crisis in Vienna. In more detail, we interviewed 22 individuals and collected more than 20 hours of interview data. Eight of our interviewees were activist-managers of Train of Hope, all of them taking on leadership roles at the beginning and/or throughout the crisis. Other interview partners included top-level politicians and senior managers in the City of Vienna administration, managers of the Austrian Railways, senior decision-makers in the military and police force, as well as prominent NGO leaders.
In more detail, we collected topics and themes related to the questions above, and aggregated them into insights on (a) the operation of Train of Hope and, more abstractly, the characteristics of its organizational form; and (b) the different aspects and nature of sharing itself. We compared and discussed these themes and topics to arrive at a shared understanding of our case. Hence our narrative is a result of an iterative process in which we interpreted our data through a 'sharing of a crisis' lens, which was grinded through detailed study of the literature on the sharing economy and finally polished into the narrative presented below.
Understanding the construction of the social gradient in health is a major challenge in the field of social epidemiology, a branch of epidemiology that seeks to understand how society and its different forms of organization influence health at a population level. Attempting to answer these questions involves large datasets of varied heterogeneous data suggesting that Big Data approaches could be then particularly relevant to the study of social inequalities in health. Nevertheless, real challenges have to be addressed in order to make the best use of the development of Big Data in health for the benefit of all. The main purpose of this perspective is to discuss some of these challenges, in particular: (i) the perimeter and the particularity of Big Data in health, which must be broader than a vision centerd solely on care, the individual and his or her biological characteristics; (ii) the need for clarification regarding the notion of data, the validity of data and the question of causal inference for various actors involved in health, such data as researchers, health professionals and the civilian population; (iii) the need for regulation and control of data and their uses by public authorities for the common good and the fight against social inequalities in health.
Research using a life course framework to study embodiment processes has shown that social inequalities in health are built up from an early stage. A seminal work often used in social epidemiology to illustrate this early construction of social inequalities in health is called the "Barker's hypothesis. " In the early 1990s,  Barker et al. observed  a relationship between intrauterine growth retardation and an increased risk of cardiovascular and metabolic diseases in adulthood  (7) , introducing the concept of Developmental Origins of Health and Disease (DOHaD). Since then, a large body of research has shown associations between a variety of exposures in early life and different health outcomes. Fundamental questions remain: through which mechanisms or pathways does the social environment become biological? How does the social environment alter normal biological functioning to promote the development of pathologies over time? Attempting to answer these questions involves large datasets of varied heterogeneous data.
Life course epidemiology is largely based on longitudinal data, particularly birth cohorts, in which social, behavioral, environmental, and biological data are collected prospectively. Such databases nevertheless remain expensive and rare. Given these issues the contribution of Big Data could be particularly relevant. Notably, France, like other countries in Europe with a national health care system, has "large" representative databases, i. e. , data coming from tax or health care systems or administrative data from reimbursement of health care consumption that may be used to study social inequalities in health. However, the use of big data to study health requires prior consideration that health has specificities that raise particular issues and problems that must be taken into account.
BIG DATA AND THE STUDY SOCIAL INEQUALITIES IN HEALTH: HIGH POTENTIAL BUT UNDERUSED IN PRACTICE Big Data could contribute to highlighting, documenting and analyzing the role of social determinants of health. This may be especially true through its potential to match social, occupational and environmental data with data of a clinical, biological, behavioral nature as well as health services data in original and innovative ways. This merging of data sources from medical systems, health insurance providers, clinical/ mortality registries, and hospitals with administrative and sociodemographic datasets is happening across countries, and may breath a new lease of life into epidemiological research. This may be especially true for the emerging use of social media in the landscape of health care. For one, it will allow for an increasingly wide variety of health determinants to be analyzed, and will facilitate exploratory analyses to identify new ones.
Big datasets used for such analyses rarely contain information on the social context and environment  (10). Consequently this usage of "Big Data" maintains that health is largely about disease and biological reductionism  (11). To move health research beyond such an outdated and limited perspective the environmental, socioeconomic, psychological, and biological determinants of health, which make health research a complex, interdisciplinary and trans-dimensional field, need to be taken into account. Hypothesis generation on the multiple determinants of health involves merging a wide variety of data sources, including social media, from a diversity of databases. This means grappling with problems like different data structures, missing data, data validity, varied measurement and collection methods, including natural language and different disciplinary traditions.
BIG DATA IN HEALTH: SOME KEY SCIENTIFIC ISSUES How Valid Are the Data? At a time when health decision making could be guided by data and algorithms, and more largely where some argue for a Big Data-driven science in which "the data speaks for itself "  (12) , it is becoming crucial to question Big Data validity for health research. One of the reason a lot of weight is attributed to Big Data analyses resides in flawed assumptions about massive datasets being quasi-complete, as opposed to being a sample from a population. But the extent to which the data being used are representative of a population is just as important a question in Big Data approaches as in any other quantitative field. The question arises in practice about population coverage and its representativeness with regard to the technology/tool used to generate data, with all the potential limitations that such work carried out on selected populations could have in terms of public health.
Which Control and Regulation? Merging different databases naturally raises the issue of data confidentiality, privacy and the respect for people's rights. These rights can no longer be guaranteed in the era of database interoperability, which can technically make previously anonymous data identifiable. This is even truer with social media data where we can interrogate what privacy means in this field? The consequences on individual liberties and privacy, commercial or discriminatory practices are not merely marginal side effects. This danger is already widely apparent in various sectors of the economy, such as the finance or insurance sector, where companies use algorithms to adapt and tailor their products to our individual situations, without our knowledge  (18). The question of data control is thus one major issue.
This is a real challenge both to protect people's rights and at the same time to offer a sufficient and simple access to public data and not to favor the emergence of a competing offer from private databases that are potentially less sensitive to the protection of individuals' rights. These issues raise the question of the public control of the development of Big Data in health. The form of this regulation remains to be defined as do its missions, but they could include anticipating relevant uses ("prospective reflection"), defining needs to encourage the emergence of ideas, best practice guidelines (for research in particular). The modus operandi of this regulation will undoubtedly have to distinguish uses for the general interest from uses for the individual interest, as distinguishing private/commercial use from public/academic use.
In this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. We aim to recognize eight modes of transportation (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from smartphone inertial sensors: accelerometer, gyroscope and magnetometer. The classical classifiers include naive Bayesian, decision tree, random forest, Knearest neighbour and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain.
Introduction Transportation mode is an important type of context information that denotes users' mobility status durig travel, such as walking, cycling, driving car or taking a bus  [3]. Analyzing such multimodal data enables context-aware applications in fields such as intelligent service adaptation, individual environmental impact monitoring, humancentered activity monitoring, and so on. In recent years, there have been numerous studies that perform transportation mode recognition from smartphone sensors  [5]. However, it is difficult to fairly compare the performance from various research groups due to their different choice of dataset and classification task, sensor modality and window size. To fill this gap and to promote the advance of the research in the field, the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge was organized with an unified recognition task, dataset and sensor modalities  [10].
However, the deep network does not extract features from the raw data, and instead relies on handcrafted features. In this paper we, as part of the challenge organizing team, establish reference performance for the challenge by applying various recognition pipelines to the challenge dataset  1. We consider classical pipelines using classifiers including naive Bayesian (NB), decision tree (DT), random forest (RF), K-nearest neighbour (KNN) and support vector machine (SVM), and also consider the emerging deeplearning pipelines, including fully-connected deep neural network (FC) and convolutional neural network (CNN). We combine three inertial sensors (accelerometer, gyroscope and magnetometer), and feed various input to the the classifiers, including hand-crafted features, time-domain raw data and frequency-domain raw data.