dataesr/scientific-paragraphs-categorization huggingface dataset:
Dagres N, Vasic D, Asanin M, Narin F, and Blomstrom-Lundqvist C have no disclosures pertinent to this work. Compliance with Ethics Guidelines. This article is based on previously conducted studies and does not involve any new studies of human or animal subjects performed by any of the authors. Data Availability. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Open Access. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4. 0 International License (http://creativecommons. org/licenses/ by-nc/4. 0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The Hong Kong AF cohort 70/1061 PY 6. 60 (5. 09-8. 29) [58] Forslund The Stockholm (Sweden) AF NR 0. 5 (NR) et al. [64] Database, n = 41,810 Chao et al. The National Health Insurance 2312/110,854 2. 09 (2. 00-2. 17) [37] research database in Taiwan Suzuki et al. The Shinken database, J-RHYTHM 6/1096 PY 0. 55 (0. 04-1. 23) [61]* and Fushimi AF Registries Olesen et al. Danish nationwide administrative 697/40,023 PY 1. 68 (1. 46-1. 94) [62] databases Chao et al. The National Health Insurance 1858/67,673 PY (m) 2. 75 (2. 62-2. 87) m [65] research database in Taiwan 1174/46,058 PY (f) 2. 55 (2. 41-2. 70) f Lip et al. Danish nationwide administrative 129/8573 1. 50 (NR) [57] databases Friberg et al. The Swedish nationwide health NR/12,298 0. 5 (NR) [66] registries van den The Clinical Practice Research 130/16,800 PY 0.
In most areas of Computer Science (CS), and in the software domain in particular, international conferences are as important as journals as a venue to disseminate research results. This has resulted in the creation of rankings to provide quality assessment of conferences (specially used for academic promotion purposes) like the well-known CORE ranking created by the Computing Research & Education Association of Australasia. In this paper we analyze 102 CORE-ranked conferences in the software area (covering all aspects of software engineering, programming languages, software architectures and the like) included in the DBLP dataset, an online reference for computers science bibliographic information. We define a suite of metrics focusing on the analysis of the co-authorship graph of the conferences, where authors are represented as nodes and co-authorship relationships as edges.
In the last years, several conference rankings have been proposed (e. g. , MAS 1 , SHINE 2 or GII-GRIN 3 ). Among them, the Computing Research and Education Association of Australasia (CORE) conference ranking mechanism 4  is the most well-known and widespread one. Created by an association of university departments of computer science in Australia and New Zealand, the CORE conference ranking is an ongoing process that provides yearly assessment of major conferences in the computing disciplines (e. g. , software, databases, artificial intelligence, etc. ) according to four quality ranks (i. e. , A*, A, B and C). Conference rankings are determined by a mix of indicators, including citation rates, paper submission and acceptance rates, and the visibility and research track record of the key people hosting the conference and managing its technical program.
In this sense, conferences can be regarded as a specific case of social networks where the authorship details on papers published at the conference represent connections between researchers in the community. By using co-authorship graphs, where authors are represented as nodes and coauthorships as edges, we can analyze conferences from a community dimension perspective. In this paper we define a suite of metrics for co-authorship graphs to study and characterize the community behind the software conferences included in the CORE ranking list. The graphs are built by mining the metadata provided by DBLP, an online reference for bibliographic information on major computer science publications. Metrics are first applied to each conference individually regardless their CORE rank and we then perform correlation analysis among the metrics.
Data Collection Our analysis has been performed on a dataset built from two data sources:  (1)  the CORE conference ranking list and (2) the DBLP database. The CORE conference ranking classifies conferences in the computing disciplines in four main categories according to their "quality" 5  : A* for flagship conferences, leading venues in a discipline area; A for excellent conferences and highly respected in a discipline area; B for good conferences, and well regarded in a discipline area; and C for other ranked conference venues that meet minimum standards. The first edition of the CORE Ranking was published in 2008 and there have been three subsequent editions in 2010, 2013 and 2014. In this work we used the CORE conference ranking list of 2014  6. The DBLP computer science bibliography on bibliographic meta data in computer science, hosting more than three million publication records ranging from conference papers to technical reports and theses.
This data is provided in two formats: (1) as a XML file 8  and (2) as a relational database  9. In this paper we used the snapshot of the relational database version of DBLP from February, 13 th 2016, which is around 220 MB big. In a nutshell, the database includes a set of relational tables to store papers and authors. Papers details include, among others, title, venue where it was published, pages and year. On the other hand, authors data allows keeping track of paper authors and their possible name aliases. To build our dataset, we defined an extraction process composed of three phases: (1) conference selector, (2) graph generator and (3) metric calculator. Figure  1  illustrates these phases and their corresponding inputs/outputs. The implementation of the process can be found at  [17].
The first phase receives the full CORE ranking conference list and filters a subset of its entries to keep only international conferences in the software domain. In particular, the selection process takes all the conferences tagged as computer software in the field of research property in the CORE list and removes (1) workshops, (2) Australasian/Australian conferences, (3) conferences not included in DBLP, (4) conferences with less than five editions including one in 2014, and (5) conferences not reporting the length of their papers. The condition checking the editions allows us to select conferences that are alive and have had at least five editions which is the minimum data period we will use in our analysis. Also, even though the DBLP snapshot we are using is from February 2016, we decided to set the last edition to 2014 given that some conferences from 2015 could not have been added to DBLP yet (e.
g. , conferences taking place in the end of 2015). The condition on the number of pages is due to the fact that the DBLP database does not include the type of a publication (i. e. , full paper, short paper, demo, etc. ) in their records and therefore we had to resort to the page length to guess what papers were in fact full papers, which are the ones that are taken into account in the CORE ranking to evaluate the conference. In total, the full CORE ranking conference list for the computer software field includes 271 conferences. The selection process removed 72 workshops,  12 Australasian/Australian conferences , 17 conferences not present in DBLP (no name, alias or acronym match was possible), 62 conferences with less than five editions or that had not been hold in 2014, and 4 conferences not reporting the number of pages in DBLP.
e. , they have at least one paper published in both editions). -The metric Community Papers is the percentage of papers for which all authors are part of the community. -The metric Newcomers Papers is the percentage of papers for which all authors are new to the conference. 5-edition metrics. This group of metrics assesses a set of properties in the complete co-authorship graph of a conference (the one aggregating all data for the period of time considered in our study). In particular, we consider the following metrics: -The metric Avg Degree calculates the average degree of the nodes of the graph and can help us to individually assess the collaboration degree of the authors of a conference. -The metric Connected Components determines the number of subgraphs in the co-authorship graph. It helps us to identify sets of authors that are mutually reachable through chains of co-authorships.
Threats to Validity Our work is subjected to a number of threats to validity which we classify into: (1) internal validity, which is related to the inferences made based on the application of our research methodology; and (2) external validity, which discusses the generalization of our findings. Regarding the internal validity, our data collection process relied on the DBLP dataset where 17 ranked conferences were not present (there was not matching conference names nor acronym) and 4 more were also rejected since DBLP did not include the page numbers, required as a filter in our process. On the positive side, being DBLP a curated dataset where authors, publications and venues are well-identified, there was no need to apply identity matching and merging techniques. Our rule of thumb for restricting the analysis to full papers is also a thread to validity since different conferences propose different lengths for full papers (also depending on the paper template they use) so our analysis may include papers that were in fact short papers which may slightly pollute the results.
The works presented in  [6, 10]  study several metrics on the co-authorship graph representing the full set of computer science conferences (the former using DBLP and the latter Citeseer datasets), however, they do not include any metric similar to our set of growth metrics nor any rank analysis. In  [1]  authors provide an study of communities in DBLP where individual researcher behavior is analyzed (e. g. , productivity or collaboration trends). Instead, we focus on the conference community behavior. Other works have studied specific metrics in collaboration networks. In  [4]  authors analyze the co-authorship graph built out of a subset of conferences included in DBLP to assess several metrics with the aim of checking the socalled six degrees of separation phenomenon. The work presented in  [16]  studies the relation between the centrality of authors in the co-authorship network and the future success of their publications.
An alternative perspective for measuring author impact by applying PageRank algorithm to a co-authorship network is presented in  [24]. The work presented in  [20]  measures 11 software engineering conferences over a period of more than 10 years by means of a suite of metrics assessing dimensions such as community, openness to new authors or introversion (all similar to those included in our study) Additional works have studied the differences between publishing in conferences versus journals in the field of computer science  [5, 18, 7, 15]  assessing, for instance, the impact of the publications. Besides research publications, several datasets have been recently made available with the aim to facilitate analyzing conference data. Apart from existing online databases like DBLP, Microsoft Research, IEEE Explore, ACM Digital Library and others, authors have proposed curated datasets such as  [19] , which complements the publication data from a reduced set of conferences with information on their program committee members; or  [21] , which provides a dataset merging information from DBLP and the ACM digital library.
In this work we used the DBLP dataset as it offers the most updated information of the papers and authors we need to analyze. Finally, there are also tools specially tailored to the analysis of individual conferences. For instance, the work presented in  [21]  describes ReaSoN, a comprehensive set of tools for visualizing and exploring social networks resulting from academic research. MetaScience 15  is a website which provides some metrics for conferences, authors and journals. Part of this work has reused the backend of this MetaScience infrastructure.
Conclusion and Future Work In this paper we have studied the co-authorship graphs of all software conferences included in the CORE ranking list of 2014 from different dimensions. To build the graphs we have used the DBLP dataset. For replicability purposes, the extraction process, co-authorship graphs and metrics results are made available at  [17]. We have analyzed each conference individually and then grouped according to their CORE ranking. We believe our results raise some interesting findings related to how the SE conference communities grow (e. g. , island-based and scarce collaboration) and behave (e. g. , latent structures), and that only conferences with the highest CORE rank behave significantly differently from the rest; which can be useful to rediscuss the factors that CORE takes into account when evaluating conferences.
Experiment Methods Observers were six members of Harvard Vision Sciences Laboratory, all naïve to the purposes of the experiment. All provided written informed consent. All experimental procedures consent forms, and debriefing materials were approved by the Harvard Committee on Human Subjects in accordance with the Declaration of Helsinki. One observer (TC) performed the experiment without eye tracking. The remaining five observers had their eye movements monitored throughout their experiments. Eye movement data for one observer had to be discarded because time markers for experimental events were unreliably synced. Thus, results regarding the observers' reports are based on six observers, and results regarding eye movements are based on four observers.
An Eyelink II head-mounted infrared eye tracker sampled the right eye at 250 Hz, and monitored head movements based on infrared markers mounted in the periphery of the stimulus field. The calibration routine was set to a fixed sequence of markers (all computer monitors were kept off during the experiment, making the typical randomized calibration method impractical). The Eyelink hardware and PC were controlled from a Macintosh G4 desktop using the Psychophysics Toolbox and Eyelink Toolboxes in the Matlab programming environment, under the OS 9 operating system. The G4 received gaze data from the Eyelink PC, and received time markers from the experimenter and observer via USB keyboards.
Analysis Reports for successful subjective fixation and perceived afterimage motion were recorded for each fixation target, per observer and in aggregate. Eyetracking data for fixation periods were identified using time markers entered by the experimenter and observer. If the observer indicated a subjective fixation longer than two seconds, only the first two seconds of data were used. Otherwise, data were truncated at the next time marker (when the observer indicated cessation of afterimage, beginning of next instructed fixation, or diagonal saccade). Data from these fixation periods were divided into 100 ms sequential segments, and net direction of movement was calculated for each segment. Magnitude information was discarded to minimize the influence of large saccadic eye movements, which were infrequent.
Nonfoveal fixation attempts also showed changes in the drift component of eye movements. During the trial, there was often an added bias to the drift, in the direction of the selected target (Figure  3 ). To quantify this tendency, we selected trials in which observers reported that they had subjectively fixated the requested diamond corner in a nonmoving afterimage. We divided the ''fixation'' intervals into 100 ms epochs, and then subtracted each epoch's start point from its end point. The angle of each epoch's directional vector was then taken as a data point. Overall, the data shows that the direction of drift was biased in the direction of the target the observer was attempting to look at. Figure  4  plots the polar frequency histogram for the directional data, first for each subjective fixation direction separately, then for data pooled across directions.
Depending on the assumptions on the power corrections to the exclusive b → s + -decays, the latest data of the LHCb collaboration -based on the 3 fb -1 data set and on two different experimental analysis methods -still shows some tensions with the SM predictions. We present a detailed analysis of the theoretical inputs and various global fits to all the available b → s + - data. This constitutes the first global analysis of the new data of the LHCb collaboration based on the hypothesis that these tensions can be at least partially explained by new physics contributions. In our model-independent analysis we present one-, two-, four-, and also fivedimensional global fits in the space of Wilson coefficients to all available b → s + -data. We also compare the two different experimental LHCb analyses of the angular observables in B → K * µ + µ -.
Unless otherwise specified, for our analysis we have considered the experimental results obtained by the method of moments and used the recent results of the LHCb collaboration based on 3 fb -1 of data [1] for the S i observables. Alternatively, the experimental results of the optimised observables (P i ) can be used. The latter are determined by reparametrising the S i observables. The F L (1 -F L ) dependence of the optimised observables results in large and asymmetrical experimental errors in the bins where F L is close to zero or one, specially for the results which have been obtained using the method of moments (see Table  9  of Ref. [1] or Table  8  in Appendix A of the present paper). • Some of the relevant updated input parameters of this work have been given in Table  1. • The full list of observables that we have used as well as their SM predictions and experimental measurements can be found in Appendix A.
Therefore, these tensions might stay unexplained until Belle II will clarify the situation by measuring the corresponding inclusive b → s + - observables as we have demonstrated previously  [16, 31, 32]. The present situation motivated a recent theory analysis in which the unknown power corrections were just fitted to the data using an ansatz with 18 additional real parameters in the fit  [34]. However, we have shown that this fit to the data needs very large power corrections from 20% up to 50%, and even larger, in the critical bins of the angular observables. The existence of such large hadronic corrections cannot be ruled out in principle, but they somehow question the validity of the QCD factorisation approach for such observables. We have analysed how the tensions in the present LHCb data depend on the input parameters like form factor calculations and corresponding correlations and have shown that they are rather insensitive to these inputs.
e. , PHY and MAC). Finally, WSNet  [25]  is another event-based packet-oriented network simulator, which is used for node and environment simulation. The simulated nodes are built with a hardware component, a software component or the behavior/resource of the node. One of the key advantages of using the WSNet simulator is that there is an associated node platform simulator, WSIM  [33] , which allows simulating different components of the sensor nodes. Regarding BAN-specific works, traffic-aware dynamic MAC for BAN is implemented in WSNet; further, convergence behavior is heuristically modeled and simulated in WSNet  [34, 35]. However, the protocol is evaluated under unrealistic channel and mobility models. Please note, similar studies in the future can be evaluated more realistically after having all of the modules presented in this work.
In  [43, 44] , the authors propose a simulation platform to study interference between BANs and propose to take advantage of possible inter-body channel correlations to judiciously select the slot in the superframe. This platform is interesting, but it needs a more accurate inter-BAN channel model (scenario-based instead of statistical). In  [45, 46] , Dong and Smith evaluate the co-channel interference of multiple BANs using a TDMA MAC using a guaranteed time slot with a realistic environment model. Their simulations are based on large sets of empirical everyday activity channel data, but no network simulators are linked to this database. Finally, context-aware body area networks (CABAN)  [47, 48]  introduce channel fading characteristics for an interactive environment. For example,  [47]  proposed a context-aware measurement system, which is capable of simultaneously characterizing both off-body and on-body channels.
Network-Oriented Frameworks The authors in  [49, 50]  present a System-C-based fast simulator that can provide accurate timing and energy estimation for health monitoring applications for BAN. This simulator is able to obtain timing and energy measurements for each function in the program, as well as for each module in the hardware, which is then used for the optimization towards critical functions and/or components. The authors in  [51, 52]  present another System-C simulator using a joint mobility and semi-deterministic BAN channel model with a spatial and temporal dependency based on ray tracing techniques and benchmarked with a measurement campaign. This simulator and its BAN dedicated protocol in  [53]  have also been validated through the implementation and tests on a real platform. To create a complete simulation tool for BANs, PyLayers  [9]  has been interfaced in real time with WSNet.
The idea is to simulate in real time the PyLayers mobility feature with statistical radio channel models (PHY layer) and WSNet (upper layers), calculating the performance of the simulated scenario. The simulator embeds several modules, including: an agent mobility simulator based on a discrete event simulator to which a multi-cylinder body model can be associated; a deterministic channel propagation simulator based on a ray tracing approach for body-to-body and off-body communications; a statistical model for on-body communications taking into account the environment and the body shadowing effects. Nevertheless, in this co-simulation, the resultant models are highly complex and difficult to develop. In this regard, the realistic channel, mobility and radio link models introduced later in Section 3 can provide a realistic platform for BAN-specific performance evaluation and comparative studies for novel algorithms and protocols.
Summary To summarize, there are a number of general simulators; however, BAN and BBN require specific channels, radio links, mobility models, protocols and algorithms, which are not completely covered by most of these simulators. With regards to BAN-specific simulators, there are a number of specific contributions as highlighted (in Section 2. 2. 2): the frameworks of OMNET++ and the WSNet extensions through physical simulator PyLayers and co-simulation platform HarvWSNet. All of these contributions are in bits and pieces, and there is no single all-in-one package that can provide a complete and realistic BAN simulator. One of the main limitations with these simulators is that BAN-specific standards' (i. e. , IEEE 802. 15. 6 or IEEE 802. 15. 4j) compliance is not available, and consequently, there is no BAN-specific benchmark.