dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual Member Results Quantitative Data In addition to the academic institution survey, CPNP members with academic affiliations were surveyed on the topic of IPPEs in psychiatry and neurology. The survey response rate reported in the article by Dopheide et al  9  was 173/267 (64. 8%) because the article only included survey results with a completion of more than 80% in specific fields. The respondents comprised tenure-track (17%), non-tenure track (39%), and adjunct faculty (39%). Further, among faculty, most were assistant professors (31%), followed by associate professors (25%), and full (16%), and others classified themselves as clinical instructors or research faculty. Forty percent of respondents reported practicing at government-funded medical centers. 9  order to capture all available data on IPPE-related items for this manuscript, the response rate was expanded to any individual responding to these items, 182/267 (68.
Results The materials, data and code for this study are available at: https://osf. io/yktbz/. Data preparation. Incorrect responses on the lexical decision task were excluded from the analysis (2. 46%). Following recommendations for reaction time (RT) data  (Bargh & Chartrand, 2000) , responses greater than 2000 ms were replaced by 2000 ms. Then, we computed the mean RT for the 5 neutral verbs, the 2 positive verbs unrelated to self-liking, and the 3 positive verbs related to self-liking. As a method for outlier detection, we used the absolute deviation around the median (Median Absolute Deviation, MAD, Leys, Ley,  Klein, Bernard, & Licata, 2013). We report in text the means after exclusion of the outliers detected using the MAD3 method. For transparency, we also report the results obtained when outliers are defined via the standardized z-scores method for the main analyses.
Further exploratory analyses of eye-tracking data will provide insights on how text-picture integration processes differ with and without CRL. Participants reading skills will be inserted in the analyses as a potential moderator. This study will contribute to test an implementation of the coherence condition, theoretically developed in the ITPC model, when a document is designed with   Cross-Representational Signaling. The findings will provide guidelines regarding the design of commented diagrams used instructional or public awareness purposes. Open Access This chapter is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.
Background: In a time of physical inactivity pandemic, attempts to better understand the factors underlying the regulation of exercise behavior is important. The dominant neuropsychological approach to exercise behavior explains physical activity as a reward. However, the opposite of physical exertion -behaviors minimizing energy cost -may also be a reward, which activates automatic reactions favoring the engagement in behaviors associated with lower energetic costs. Objective: Our objective was to systematically review studies testing the automatic reactions triggered by stimuli associated with different types of exercise behavior (e. g. , physical activity, sedentary behaviors) and energetic cost variations (e. g. , behaviors minimizing energetic cost). Methods: Two authors systematically searched, screened, extracted, and analyzed data from articles in the MEDLINE database.
Eligibility criteria and study selection 2. 2. 1. Inclusion criteria To be included in this systematic review, the article had to 1) be published in a peer-reviewed journal and written in English, 2) report original data collected from humans, 3) test the automatic behaviors or brain activation triggered by stimuli associated with different types of exercise behavior (e. g. , physical activity, sedentary behaviors, and BMEC), and 4) the reward should be assessed during the presentation of stimuli related to exercise behavior or during the performance of this behavior.
Exclusion selection Five steps were used to select the articles meeting the inclusion criteria. If there was a doubt at any step, the article was kept for further inspection. At step 1, articles not written in English were excluded. At Step 2, articles not reporting original experimental data were excluded (e. g. , reviews, meta-analyses, commentary, technical reports, case studies). At Step 3, articles were excluded if they did not involve a human population. At step 4, articles were excluded if they did not test the automatic behaviors or brain activation triggered by rewarding stimuli. At step 5, articles exclusively using self-reported measures of these processes were excluded. We performed reference screening and forward citation tracking on the articles remaining after step 5.
Guiding Principles for the Transparent Statistics Guidelines The working draft of the transparent statistics guidelines is available at transparentstats. github. io/guidelines. The document starts by laying out general guiding principles for the guidelines themselves. The purpose of the transparent statistics guidelines is not to admonish an entire field of researchers for their existing practices or to attempt to shame them to do better. The multifaceted nature of HCI means practices will always vary, and a fixed set of DOs and DON'Ts would be both too brittle to change over time and too restrictive in the face of the various ways of generating knowledge in our field. Instead, the guidelines aim to advance a vision of transparent statistical communication for the field. Whatever the methods used, we can at least provide guidance that makes the communication of those methods more transparent, that makes reproduction and replication of work easier, and that makes evaluation of work (e.
Guidelines on Specific Topics In addition to the general guiding principles, the transparent statistics guidelines are designed around particular topics in statistics that are relevant to both authors and reviewers. At the CHI 2017 workshop, we developed a list of topics (such as experiment and analysis planning, effect sizes, p-values, Likert-scale data, data transformation, and Bayesian inference). At and after the workshop, we focused on developing a single topic-effect sizes-in order to work out issues of how to effectively structure guidelines for a particular topic. After working out the bugs and format with that first topic, we will develop chapters on the remaining topics. The current draft of the guidelines is available at transparentstats. github. io/guidelines. For each topic, we settled on a structure that consists of a general FAQ (frequently asked questions) followed by a set of exemplars, which demonstrate analyses on hypothetical datasets and include analysis code where appropriate.
Contributing to the Guidelines The transparent statistics guidelines are meant to be a collaborative artefact that enjoys the consensus support of the CHI community. As such, it welcomes contributions and comments from anyone within the community. The guidelines are written in RMarkdown and hosted on Github. To lower the entry barrier and allow anyone to easily comment and contribute, the initial version of each chapter is developed on Google Docs, allowing writing and commenting directly with a WYSIWYG interface. This first stage typically consists of developing the FAQ part, and delineating an initial structure for exemplars.
working draft of the guide for contributors is available at github. com/transparentstats/guidelines/wiki. The guidelines are developed under the CC-BY 4. 0 license (for the text) and the MIT license (for the code). These licenses are flexible enough to allow any researcher to, e. g. , use exemplar code to analyze their data and release their code as supplementary material. CHI 2018 SIG CHI 2018, April 21-26, 2018, Montréal, QC, Canada chapter through Github's in-line reviewing/commenting interface. Then, to ensure usability, the chapter will be tested with other two members without subject matter expertise. Once a chapter is released, any member of the CHI community will have the possibility of endorsing it, even if they did not contribute. Releases are never final: the transparent statistics guidelines are meant to evolve over time and reflect evolutions in the methodological debate within and outside CHI.
After the content is adequately stable, it is ported to RMarkdown and version-tracked on Github. At this stage, major contributions can be done by forking and creating pull requests, as in typical open source software development. Contributors with less technical background can also comment on the content by creating Github issues. This stage mostly focuses in developing exemplars, although the FAQ can keep being refined. Before the release of a chapter, two reviewers with subject matter expertise from the CHI community will review the SIG08, Page3 A.
Social media platforms are rich sources of information not only for personality prediction. Sentiment analysis has attracted increasing interest in recent years  [45] [46] [47] [48]. It consists of classifying the sentiment polarity of a sentence, as positive, negative or neutral. Dai et al. [49]  explored numerous feature engineering techniques for detecting adverse drug reactions from Twitter posts. Studies on demographics are also worth of note, in particular for those sites (e. g. , Twitter) where demographic information is mostly unavailable. Chamberlain et al. [50]  and Zhang et al. [51]  successfully inferred users' age based on their interaction on the social media platform. Other studies on enhancing social data with demographic attributes include gender  [52] , location  [53] , ethnicity  [54] , and political affiliation  [55].
Gold Standards In order to quantitatively test our approach, we rely on the dataset created by the myPersonality project  [1]. The dataset has been collected for research purposes by David Stillwell and Michael Kosinski using a Facebook application that administered a personality test and collected a wide range of personal and activity information from Facebook's profile of users mainly from US and UK under their consent. Participants answered a variable length (20-100 or 300 items) proxy test derived from Costa and McCrae NEO-PI-R  [63]  and elaborated by the International Personality Item Pool (IPIP) project  [81]. In practice, users were asked to answer to a set of questions that were positively or negatively correlated with certain personality traits, such as "Do you have frequent mood swings?", using a  [1, 5]  Likert scale and scores were then computed averaging the results corresponding to a specific personality trait.
The myPersonality application has been active from 2007 to 2012 and collected a huge amount of data, which is available upon request. We base our study on a sample of the original myPersonality dataset that has been made publicly available  [82]. The gold standard contains 9913 English status updates of 250 users (anonymized), annotated with personality traits scores and basic statistics describing the user social network, such as number of friends and betweenness, that is, a measure of the centrality of a node within a graph  [83]. In this work, we use as input for our predictive models the raw text coming from status updates. Tables  1  and 2 , and Figure  1  illustrate some statistics of the dataset. It contains approximately 15,000 distinct terms and 300 stopwords. A natural language word is generally considered a stopword when it has very little meaning for the sentence.
Examples of stopwords are articles and conjunctions. We used the following list of stopwords as reference  [84]. However, stopwords account for almost half of the total, lowering the predictive power of the dataset. We then created an in-house gold standard of Twitter users, English posts, and personality trait scores, to empirically validate our transfer learning approach. In the remainder of this paper, we refer to this gold standard as Twitter sample. We asked twenty four panelists to provide their Twitter handle and to perform the Big Five Inventory (BFI)  [85]  personality test. The BFI test consists of forty four short phrases based on the trait adjectives that represent the prototype of each Big5 trait. For example, the adjective original is associated with the trait of Openness and its corresponding BFI item is the phrase "is original, comes up with new ideas".
Approach Using the myPersonality gold standard described in Section 4, we derive a predictive model that is able to infer personality traits of Twitter users. Since the dataset we intend to use consists of status updates extracted from Facebook, our approach is based on transfer learning, namely modeling an algorithm for a context and applying it on a (slightly) different, yet related, context. In our case, we derive a machine learning model for predicting personality of Facebook users based on their status updates, then we test it on Twitter users' tweets. In the next subsections, we show how data is processed in order to derive a predictive model, and how the model is tested using data from Twitter.
Punctuation removal: "today #sunny day!" → "today sunny day". Tokenization: "today sunny day" → [today] [sunny] [day]. Short post removal: it removes status updates with n tokens < 3. For stop-words removal, we use the list of stop words provided by the module CountVectorizer from scikit-learn  [84]. We remove status updates that result in less than three tokens, which, in total, are 15. 78% of the initial set. We do not learn word embeddings but instead we test some pre-trained models available in literature. In Section 6, we describe those models and report their statistics and performances with the dataset. All those are based on feature word vectors of dimension 300. Landauer and Dumais  [87]  showed, with empirical experiments, that 300 is the optimal number of features for distributed word representations, although they based their approach on LSA and word co-occurrence rather than neural networks.
, p n ) are the predicted values and A = (a 1 , a 2 ,. , a n ) are actual values of the training samples that we are evaluating the model on. MSE is a positive score in the range [0, (max(p i )min(a i )) 2 ] where max is a function that selects the maximum value of the list and min a function that selects the minimum value of the list. Smaller values of MSE indicate better accuracy. Since we do not have a test set, we estimate the predictive power of our models using a 10-fold cross-validation strategy over the gold standard. We repeat the process ten times with different cross-validation splits to evaluate the model on the entire dataset and for different configurations of the hyper-parameter values for a total of twenty values chosen randomly. The number of values has been defined empirically when minimizing the MSE while keeping the task feasible at computational level.
Model We test and evaluate our models using pre-trained word vectors. We explore different embeddings and how they affect the predictive power of the models in terms of mean squared error. FastText is an open source library from Facebook AI research  [92]. There are numerous pre-trained word embeddings in many languages, though we are only interested in English for this experimentation. We use a model with one million word vectors trained on Wikipedia 2017, UMBC webbase corpus and statmt. org news dataset. Li et al. [86]  explored different sources and text processing combinations to train word embeddings. They learned word vectors both from tweets and from general text data available on the Web, including "spam" tweets (less informative ones), and considered both words and words plus phrases for the models.
The combination of those approaches led to ten different datasets. Although learning embeddings from tweets should improve the overall performance of the model, this highly depends on the context of the application and on the data it is tested on, and we observe that in our case the model from FastText performs better. Table  11  shows some statistics of the datasets we explored. We notice that despite being trained on 2. 8 times more words on average, those datasets are able to achieve only up to 3. 14% more word coverage than FastText.
Transfer Learning Assessment We evaluate the predictive power of our approach using the best performing models assessed with the myPersonality dataset over the gold standard of Twitter users. We download and process the tweets as described in Section 5. 4, and consider each of them as a single test sample that is then fed to the five SVM models. We compute the final personality trait score as the mean of the predicted values for all the tweets of a given user, and compare the results with the actual values derived from the questionnaire and reported in the Twitter sample. In Table  12 , we compare the mean squared error values of the actual (expressed by the panelists though the survey) and predicted values (computed by our approach) with the myPersonality dataset as the mean value over the 10 cross-validation iterations, reported in Table  7.
Our approach achieves significantly better results when applied to these newly created in-house gold standard for Conscientiousness, Extraversion, Agreeableness, Neuroticism; it only performs lower for Openness with a delta difference that is rather small. Anyway, our approach tested with the myPersonality and Twitter gold standards outperforms the one of Quercia et al. [22] , which we use as a reference for assessing the performance of our model. Nevertheless, the modest size of the Twitter sample cannot account for enough variation in the personality traits, whose distribution is rather biased, and this may affect the interpretation of the predictions; this will be further investigated in future work. Table  12. Comparison of mean squared error for myPersonality dataset and for the Twitter sample.
We then tested our five learning models and observed lower MSE values than those obtained with the Facebook international benchmark dataset. The source code is publicly available and accessible at  [94]. In the approach we proposed, Twitter posts are treated individually. An experiment we are running aggregates the posts from the same user by computing the average for each one of the 900 dimensions. In this way, a single training sample would carry a lot more information and we expect to observe an increase of the accuracy. However, the relatively small size of the dataset we are using does not allow us to conduct such analysis. We thus plan to experiment with the larger myPersonality dataset and to extend, meanwhile, the Twitter sample by acquiring more panelists running our questionnaire addressing the overspecialization introduced by analyzing a homogeneous network of people.
We are also studying the effect of the interaction context in our approach. Having a larger gold standard, we plan to compute word representations using a Convolutional Neural Network to capture the context of a set of tweets written by the same person. The goal is to tune the embedding generation according to the chosen optimization function and thus minimizing the mean square error over the actual personality trait values and being able to update the initial FastText embeddings. We aim to capture also a richer semantic representation of words so that we can correlate vectors to personality dimensions in a continuous learning fashion. The current approach has been tested on English content only, we also plan to extend it to other languages exploiting the similarity of word meanings in the vector space.
Statistics of the pre-trained word embeddings models we use in our experiments. Although some of the other datasets achieve lower MSE for one or more personality traits, the mean value across all traits is still higher than the one relative to FastText. Mean squared error is computed on the models trained with configuration reported in Table7. Datasets 1-10 differ in terms of text source (general data vs. tweets), tweets filtering (spam tweets included or not) and text processing (words vs. words and phrases).
Procedure This study employed a cross over design and required the participants to visit the laboratory on three occasions (one training and two experimental sessions) at least 48 h apart and around the same time of day. In the training session, participants provided their informed consent and initial assessments (age, height, and body mass) were recorded. Participants were seated on an upright cycle ergometer (Wattbike, Wattbike Ltd. , Nottingham, United Kingdom), previously validated for power output ranging from 50 to 300 watts (W) at a cadence of 70-90 repetitions per min (rpm)  (Hopker et al. , 2010) , and fitted with a facemask to measure metabolic data (Fitmate Pro, COSMED, Miami, United States) and a heart rate monitor. Participants completed an incremental cycling exercise test to exhaustion with an increase of 15 to 25 W per min and a cycling cadence ranging from 70 to 90 rpm (in line with the American College of Sports Medicine, 2013).
In addition, entering a variable as a covariate assumes that the effect is linear and constant over time. However, our hypotheses related to fatigue suggest that the effects should be localized in the last blocks. All statistics were performed using SPSS (version 23, IBM statistics, Armonk, NY, United States). Significant main and interaction effects were reported (p < 0. 05) and followed-up using Sidak-adjusted multiple comparisons tests. To report descriptive statistics, we provided the means ± standard errors estimated by the models to represent the effect of interest after controlling for other factors and covariates.
DISCUSSION The objective of the present research was to offer a thorough analysis of Tempest et al. 's data which tracked changes of higher-order cognitive performance during 60-min of prolonged cycling-exercise. The aim of the present study was to assess the impact of prolonged high intensity exercise on cognitive control (i. e. , Eriksen task) and to dissociate the effect of physical fatigue induced by exercise from cognitive fatigue induced by the repetition of the cognitive tasks over time. By investigating changes in cognitive functioning through trial-by-trial and distributional analyses, the present study confirmed a faster RT of RT in both CO and IN trials in the high compared to the low exercise intensity condition. This finding is supported in the current literature on exercise and cognition and could be explained by an increase in the brain activation level globally improving the information processing at both the sensory and motor levels  (Davranche et al.
OpenWorm represents an international, interdisciplinary online community committed to creating a virtual worm. On pages 323-343,  Olivares, Izquierdo, and Beer (2018)  demonstrate, through computational simulations, that neurons in the ventral nerve cord are capable of producing a central pattern generator that drives locomotion. Modern datasets are arriving with unprecedented, exquisite detail, and are often multimodal in nature, presenting opportunities to study scales and levels concurrently. There is naturally a demand for tools to study and integrate them. A "Computational Approaches" session included a hands-on data showcase demonstrating the Allen Institute for Brain Science's latest data and tool releases. On pages 344-361,  Keiriz et al. (2018)  introduce a web-based visualization platform with which to explore brain networks for research and clinical purposes; the authors demonstrated their platform at the satellite with virtual reality headsets.