dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and Context of the Study In this research, we use panel data from 25 emerging market countries for the seven year period between 2010 and 2016. The sample is taken from three secondary databases: the Global Entrepreneurship Monitor (GEM), the World Bank's World Development Indicators (WDI) and the World Bank's Doing Business publication (DB). The GEM collects its data through interviews with entrepreneurs, specialists and researchers from different countries and provides two sets of data at the national and individual levels: (a) the 'Adult Population Survey' and (b) the 'National Expert Survey' (GEM, 2019). Additionally, it utilises a tested methodology to gather various factors influencing entrepreneurship and defines the total entrepreneurial activity (TEA) as the proportion of the adult population who are engaged in entrepreneurship (i.
Findings To compare the motives of women entrepreneurs in the domestic market with those in the international market, we use two models: (i) static panel data analysis, comparing a fixed-effect and random-effect model; and (ii) dynamic panel data analysis using the system-GMM approach. Furthermore, the influence of the dependent variables, opportunity to become independent (MT1), opportunity to earn financial rewards (MT2), necessity (MT3) and multiple motives (MT4), are assessed separately to resolve multicollinearity problems. Therefore, in our analysis, Models 2-5 test hypotheses 1-4: exploring the relationship between women's entrepreneurial motives (opportunity-driven, necessity-driven and multiple) and domestic/international business venturing. Model 1 tests the control variables, and Model 6 considers all identified motivational factors in one analysis.
Thus, we extend opportunity-vs. necessity-driven entrepreneurship research in an internationalisation context. The findings of this paper also suggest that policymakers can encourage female entrepreneurs in emerging economies to engage in internationalisation processes by emphasising monetary opportunities. This will contribute to the economic growth of emerging market nations based on international incomes. In conducting this research, we faced several limitations, which future studies can address. First, to collect the data, the current research was restricted to employing secondary resources in which variables were taken from the GEM and World Bank databases. Inspired by the distinction between opportunity-and necessity-driven motives, this paper was limited to four factors, which limits the context of the research to those predefined motives.
In fact, the Records theory has been applied in several fields. Form climate change, natural gas markets to finance and sports, this method has proved its relevance in www. josis. org capturing the dynamics occurring in certain markets or through sets of data  [31]. In our case, the study will be based first on the HR satellite images demand, through the GEO-SUD 1  /Theia 2  SDI, in France. Since 2011, the GEOSUD and Theia Land data center form a unique SDI, aiming to progressively build-up an ecosystem of innovation in the field of satellite imagery for Earth observation. On the other hand, and more generally, three satellite image markets through the PEPS 3  SDI platform will be also explored [Landsat (U. S. ), Sentinel (Europe) and SPOT (France)]. Designed since 2015, the PEPS platform is managed by the French National Space Agency (CNES).
The novelty relies in offering additional processing elements in order to enrich the GIS market policy development, by bringing a new perspective on possible risks and facts that could be present while considering the geospatial markets dynamics. The cases of the GEOSUD and PEPS SDIs, for which the analysis was performed, serve as relevant illustration for the proposed approach. Through the framework presented, a grounded mathematical modeling tool is combined to to the usage of geospatial information, with a focus on the market forecasting analysis techniques. The remainder of the paper is organized as follows. We ï ň Ąrst outline the methodological protocol used in this study (description of the SDIs characteristics, satellite-images schemes, data collection process). In a second time, we present the record models used in our work (classical and Yang-Nevzorov) followed by a statistical estimation of parameters and a goodness of fit test analysis.
SDI characteristics'-GEOSUD / PEPS GEOSUD aimed since its launch at the development of the geospatial technology and data with a particular focus on the high resolution satellite images, contributing to its availability and easy access for its direct users. While facilitating the creation of integrated EO environment and supporting the geospatial communities in developing their ecosystem, it is continuously exploiting new opportunities through the steady development of its platform. It is targeting both the scientific community and the public actors based on a concept of open innovation. Through developing a set of functionalities for the accompaniment of its users, GEOSUD is lowering the barriers and reconsidering the value chain of the geospatial data, with all the economic and societal benefits that may emerge behind.
On the other hand, PEPS constitutes the missing link between the European spatial infrastructure and the French institutional users, scientists and industries. Managed by the CNES, it is conceived to offer innovative services and open access, mainly to the Sentinel satellite images and products. By redistributing geospatial data and enabling users to process them on servers close to the source, it is helping in achieving Copernicus goals in implementing and monitoring environmental and security policies. As a concrete example, the Theia scientific community is increasingly using PEPS via the CNES system to directly access to the whole Sentinel archive and more particularly Sentinel-2A data, in order to make atmospheric corrections (level 2A) and monthly syntheses (level 3A). www. josis. org.
Satellite-images schemes 2. 2. 1 GEOSUD high resolution satellite imagery In November 2018, more than 15,000 images, covering 55 million sq. km. , have been downloaded by GEOSUD direct users since the beginning of the project. The HR images provided through the SDI web portal vary mainly between Rapid Eye and SPOT 1-5. Lately, GEOSUD extended its image acquisition capacity by settling a new Antenna in Montpellier (south France), with the aim of accomplishing by itself the SPOT 6/7 image production process. It is responding to the needs of the scientific community, through a pooling system developed specifically to better sharing the benefits of the open innovation processes. For instance, an absence of such a system (an investment of e11 million), would have cost e110 million to GEOSUD users to acquire images separately from independent spatial data providers at preferential rates  [33].
PEPS satellite imagery: Landsat, Sentinel, and SPOT While PEPS has been designed initially to offer an access to Sentinel satellites images, other satellite images are also present on the platform: • First, the Landsat images. Landsat was the first EO system to employ a global image acquisition strategy, offering satellite imagery in a free and open manner  [68]. The USGS Landsat data archive currently contains almost 6 million images  [70] , with an actual acquisition capacity with the Landsat 7 and 8 of approximately 1200 scenes per day. • Second, the Sentinel images. Although the Sentinel images cannot be considered a replacement or an alternative for Landsat images, they offer a promising augmentation to the Landsat program  [18]. From global warming to land use change and the atmosphere, the Sentinel family of satellites offers free of charge data to encourage its maximum use.
Data collection The data collection process via the two SDIs platforms (PEPS & GEOSUD), as well as its pre-analysis activity, were performed as follows: • First, key-contact persons were identified in each of the SDIs (GEOSUD: platform operators, PEPS: download data managers), in order to respond to our various data requests. • Second, meetings were performed with the identified persons in order to agree on data types, quality and formats. After several exchanges, the desired data were collected in its row format as excel sheets (due to difficulties in cleaning data at source and lack of means and time). The collected data consisted of a compiled file, grouping various variables related to the downloaded images (including duplicated downloads, registered download failures, etc. ). • Third, in order to make the data usable, thorough analysis was performed with the help of data scientists and geomatics experts, due to the complexity encountered in terms of satellite databases sorting; and • Finally, the new database was ready to be used and tested under the several theoretical models proposed (see Section 2.
• Second, it is important to specify that these two peaks precede two important periods within the French academic year and research institutional sphere (universities, research institutes, research centers, etc. ). It includes setting up internship projects for Master students (March-April) as well as thesis and research projects for Ph. D. stuwww. josis. org dents and researchers (September-October). Both events are often accompanied by massive demands for images, and particularly when projects are based on geospatial information (management of natural resources, agriculture, land use planning, etc. ). Concerning the GEOSUD HR images, the data was recorded for a longer timeframe, going from February 2011 till May 2018, because of its availability since the beginning of the GEOSUD project.
Beyond iid-Yang-Nevzorov The Yang-Nevzorov model is considered the most popular in the non-iid context due to several reasons: 1. For having the structure of a proportional risk model in survival analysis, which has shown its usefulness in modeling many datasets  [31] ; 2. For generalizing the results of the Linear Drift Model (LDM) introduced by Ballerini and Resnick  [6]  which also represents a case beyond the iid. In this model, the observations are assumed to be independent but not identically distributed. In order to adjust a Yang-Nevzorov or LDM model to a set of data, an additional step is needed. One approach consists of testing the null hypothesis: H 0 : the data derive from a series of iid rv. Such an adequation test could be based on the sequence of the record indicators δ 1 ,.
• The SPOT series are obedient to an iid model, with a low probability of record due to the fact that the SPOT archive images, accessed through the PEPS operator, are no more active (the demand is based on the SPOT Word Heritage collection of images). • The probability of a record in the GEOSUD data is close to zero. This is in line with the iid case and indicates that GEOSUD is stable in terms of demand. The probability of large shifts, representing a high volatility in the future, is low. As mentioned before, the trend to full free and open data access is on the rise, even for high-resolution data. Although these data are usually distributed on a commercial basis, some are available for free via web services such as Google Earth and Bing  [37, 63]. The HR satellite imagery provided through the GEOSUD SDI comes as additional concrete example.
As a first SDI market study using the Records theory, several ways have been revealed for improvement and extension. The main challenge relies on the further development of this research in order to assess the record values in the future. For this to be done, a more detailed theoretical work should be performed. Moreover, the methodology used in this study could be reproduced on larger scales such as the large EO observation programs (Airbus data history, NASA/Copernicus data demand history, etc. ) and other types of geospatial data, where the access and demand conditions differ and the amount of data is largely greater with an impact consequently bigger. Other record models may also be included in further assessment, leading to a better examination of the demand occurring via the SDI ecosystems.
Data source The hospitalist had to complete a standardized paper form for each patient included. This form was designed for this study. It had been previously tested for one month in the same unit to ensure its validity and the understanding of the questions by 4 physicians who did not participate to the study. The rst part of the form collected the times and dates of the calls. The second part included the collection of PCPs opinion. The third part included the collection of hospitalists opinion. It was collected by means of 12 questions concerning the feasibility and interest of the telephone contact. The occurrence of a change in hospital management (medical, therapeutic or social) as a result of the telephone exchange was also reported. Patient and hospitalization characteristics were collected from the hospital EMR.
Data and methods We drew data in December 2018 from an open data site (http://observatoire-airbnb. fr/) 5  that exhaustively recorded listings in Bordeaux for 2018 (13,991 accommodations; rooms: 21. 5%; EH: 78. 5%). Data refer to the location (GPS), type of accommodation, type of dwelling, facilities, additional services, rental rules, host reputation, and price. Additional variables in SA and CTA vectors are implemented per IRIS 6  (aggregated blocks for statistical information), the smallest urban spatial scale for official statistical information. Bordeaux includes 87 IRISs with a median size of 0. 3 km 2 Figs. 1  and 2  show the listing density per IRIS respectively for EH and rooms. From the GPS coordinates, we added distances to the center and to public transport (tram and railway stations) and neighborhood profiles.
The Airbnb platform produces a neighborhood guide 7  suggesting items for profiling that match guests' perceptions (peace and quiet, dining, shopping, loved by residents, artsy, touristy, nightlife, green spaces, great transit, market, tendency). The French census produces appropriate data to describe these profiles per IRIS. The only exception concerns tourism and safety indicators, which we had to figure out. For the first indicator, Bordeaux (a World Heritage city) features >80 points of tourist interest throughout the city. We measure the tourist character of an IRIS by the sum of the photos posted on Flickr and Instagram, 8  plus the number of reviews on Tripadvisor of all the tourist attractions included in each IRIS. For the second indicator, because the IRIS safety data is confidential, we made a safety proxy with the number of papers published in the local press 9  including "unsafety" and "district name" terms from 2010 to 2018  (Faye & Le Fur, 2012).
This database is therefore more comprehensive than that of previous studies. Table  3  displays all the attributes that readers can compare with former studies detailed in  Cai et al. (2019, p. 230). For each observation (i) on each market segment (t), estimated models are T ( p t i ) = β t 0 + β t i,h T ( LA t h,i ) + β t i,j T ( SA t j,i ) + β t i,m T ( CTA t m,i ) + β t i,n T ( LR t n,i ) + β t i,l T ( HA t l,i ) + ε t i (1) where T(. ) indicates the transformation of the variable specific to each functional form, β t are the estimated coefficients in each category of variables (Table  1 ), and ε the residue. According to  Rosen (1974) , when the functional form selected is linear, β (the marginal rate of substitution between expenditure (p) and a specific attribute) estimates the implicit price paid for this attribute.
Oracles in Blockchain Systems In a blockchain system, by design, there is no proper way to add external information in a trusted manner. Either the provider or the data itself is trusted. Therefore, any interaction between the blockchain and the outside world contradicts the blockchain trustless philosophy. However, to leverage the power of the blockchain technology and, more specifically, smart contracts, such interactions are often necessary and desired. The current solution is to use oracles (Blockchain Oracles, https://blockchainhub. net/blockchain-oracles/, accessed on 2 July 2021): participants bridging the blockchain system with the outside world (i. e. , Web Services, sensor data stream, etc. ). The issue of having such trusted entities and the related vulnerabilities in public blockchain systems have already been discussed as the Oracle problem  [15, 16].
Wormhole Attack In the wormhole attack, two or more attackers located at different points in the network establish an overlay network to transfer information faster than on the main P2P network. They do so through alteration/addition to the Propose Transaction and Propose Block roles and their related behaviors. This attack aims at getting an unfair advantage over other participants, relying solely on the longer propagation time of the P2P network. Furthermore, the attackers may also choose to relay only relevant information according to their criteria, such as highly valuable transactions or a new block proposal. As shown in Figures  29  and 30 , we model this attack through an Attacker role overloading the generic diffuse behavior. Every data received will be handled by the Filter Data behavior to assess of its relevance for the attackers before being transmitted through the wormhole using Diffuse Data through Wormhole.
Conclusions and Future Work As of today, blockchain technology is used as a basis for a wide range of applications ranging from mere cryptocurrencies to decentralized applications. However, we face highly competitive and complex cases that have technical problems (e. g. , data reliability, confidentiality, archiving) which are being constantly reshaped by user (e. g. , performance (# of transactions/minute), fees), technology (e. g. , consensus protocol, parameters, cost) and regulatory (e. g. , standards, laws, GDPR) requirements. Moreover, blockchain applications are intended to be deployed into various environments such as computers, smartphones, vehicles, drones, IoT devices and so on. Furthermore, the blockchain ecosystem is very active, dynamic and rich (e. g. , Bitcoin, Ethereum, Tendermint, Hyperledger, Sycomore).
Background: While controlling the outbreak of communicable diseases (CDs) remained a priority, non-communicable diseases (NCDs) are placing an unavoidable burden on the health and social security system. India, a developing nation in South Asia, has seen an unprecedented economic growth in the past few years; however, it struggled to ght the burden of communicable and non-communicable diseases. Therefore, this study aimed at examining the burden of CDs and NCDs among elderly in India. Methods: Data from Longitudinal Ageing Study in India (LASI Wave-I, 2017-18)  were drawn to conduct this study. Response variables were the occurrence of CDs and NCDs. The bi-variate and binary logistic regression were used to predict the association between communicable and non-communicable diseases by various socio-demographic and health parameters.
Statistical Measures The analyses were carried out with statistical software STATA version 16th. The bi-variate technique was used to understand the prevalence of communicable diseases and non-communicable diseases by socio-demographic and health parameters and across the states in India. Further, binary logistic regression was used to predict the association between communicable and non-communicable diseases and socio-demographic and health parameters. The equation for binary logistic regression is given below, In the above regression equation, is the probability of being perceived as communicable or non-communicable diseases, , … are the predictors, is the intercept and , … are the coe cients. Furthermore, to understand the inequalities of communicable and non-communicable diseases in urban and rural areas, the Fairlie decomposition technique was used to predict the contribution toward rural-urban inequalities in CDs and NCDs.
In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches.
In complex environments, such as in the presence of multiple channels or multiple product categories, it can be quite challenging indeed for an analyst to capture all useful inter-sequence and inter-temporal interactions. In this paper, we explore whether Long-Short Term Memory neural networks (LSTM), a special kind of Recurrent Neural Networks (RNN), which rely on raw sequential data and do away with feature engineering, can offer the promise of a solution to this general class of modeling problems in marketing. In customer response models, the data are often in the form of panel data, where the firm's actions (e. g. , solicitations) and customers' behavior (e. g. , purchases) are observed repeatedly over time and along multiple dimensions (e. g. , multiple channels or product categories).
However, as we will demonstrate, RNN models in general, and LSTM models in particular, seem particularly suited for panel data analysis. We organize the paper as follows. In the first section, we introduce the LSTM model as a special class of recurrent neural networks. Given the newness of the method to social scientists in general, and to marketing analysts in particular, we dedicate significant space to explain its inner working. While LSTM models take raw behavioral data as input and therefore do not rely on feature engineering or domain knowledge, our experience taught us that some fine-tuning is required to achieve optimal LSTM performance; in the second section, we pay special attention to the proper calibration of an LSTM Fig. 1. Four customers with markedly different purchase patterns but identical features in terms of recency (last purchase), frequency (number of purchases), and seniority (first purchase).
Bayesian optimization for hyperparameter tuning provides hyperparameters (step 1) iteratively based on previous performance  (Shahriari, Swersky, Wang, Adams, & De Freitas, 2015). We use Bayesian optimization to search the hyperparameter space for our model extensively. In our experience, this approach proves far superior to the alternatives Introduction LSTM neural networks do not rely on feature engineering. In complex settings (e. g. , multidimensional data with intersequence and inter-temporal interactions), this is a significant advantage over hand-crafted, traditional models. In contrast to the second application, which will offer a more realistic context, the first application presents the most straightforward proof of concept imaginable. The data tracks only two variables over time: whether a charity has solicited its contacts during a specific period (0/1), and whether they donated (0/1).