dataesr/scientific-paragraphs-categorization huggingface dataset:
The sampling rate of each sensor varied from logfile to logfile, depending on the embedded sensor chips used by a particular phone and version of the operating system. Typical sampling frequency values for the inertial data is about 50 Hz, while pressure, sound and light sensors have a much lower update rate (<10 Hz). Wi-Fi scans are available every 4. 7 s in average according to the analysis done by the UMinho team. Wi-Fi scans depend on the device and they are generally available every 4 to 6 s (0. 25 to 0. 17 Hz). Although multiple Wi-Fi access points were registered in the logfiles, their positions were unknown to the competitors. This is to emulate real world cases, where the positions of the access points is often unavailable. Nevertheless, several geo-referenced floor-map images for each building and some videos were provided to the competitors as additional data.
Sensors 2017, 17, 557 5 of 17 The stream of sensor data generated in the phone is sequentially registered at the time it is obtained in a plain text file (called a logfile). Each line in the logfile corresponds to a single sensor reading, beginning with an initial header identifier (such as WI-FI, ACC, or MAGN, among others) that determines the kind of sensor reading, and several fields separated by semicolons with different values. A parser in Matlab code was provided to competitors (http://indoorloc. uji. es/ipin2016track3/) to facilitate data rearrangement in the preferred format. Figure  2  shows an excerpt of a logfile.
Dataset Types As mentioned earlier, logfiles provided to the competitors were divided into training (known trajectories) and evaluation (unknown trajectories) logfiles. The training logfiles contained geo-referenced positions at some landmarks, in the form of additional records with a POSI header followed by the ground truth coordinates: latitude, longitude, floor and building identifiers (see highlighted line in Figure  2 ). The latitude and longitude coordinates are in WGS84 format, the floor corresponds to the floor number, and the building identifiers were introduced in Figure  1. The competition organizers published the training logfiles in the official website, including geo-referenced positions at some landmarks. On 25 July, the organizers provided the evaluation logfiles to the competitors, which contained measurements taken following the same procedure of the training files (although possibly by different users or phone models), but without geo-referenced positions (POSI data).
The HFTS Team The HFTS system employed GNSS, Wi-Fi, accelerometer, compass, and gyroscope data  [42]. Accelerometer data was used to detect step events, and compass and gyroscope were used for heading estimation. Drift compensation and step length estimation were performed by a particle filter using the information of floor plans to detect the most likely path (see also  [43, 44] ). Floor detection was based on position and received signal strength indicator (RSSI) evaluation. For the RSSI positioning, the scalar product correlation fingerprinting algorithm  [45]  was applied. Heading detection was performed by combining gyro and magnetic information: the gyroscope is able to detect heading changes quite accurately on a short timescale, but will drift in the long-term. Therefore, the algorithm stabilized the gyro heading with the compass heading.
Compass heading is subject to strong local magnetic perturbations but shows no drift on a long-term scale. The estimated heading was calculated by summing gyro heading changes immediately, but relaxing back to magnetic heading with a configurable constant. Step detection was obtained by filtering the accelerometer data and detecting maximal and minimal values of the result. Each time a step was detected, the particle filter updated its state. The particle filter contained a constant number of particles. Besides the position, a particle state also comprised individual step length and heading offset values. On each step, all particles were moved according to the estimated heading, individually modified by the particle specific offsets. Each time a particle collided with, for example, a wall, it was replaced by a new one.
The UMinho Team The UMinho approach is mostly based on Wi-Fi fingerprinting enhanced with information extracted from the accelerometers and atmospheric pressure data. Thus, the UMinho team used only the POSI, Wi-Fi, accelerometer and atmospheric pressure records, ignoring the remaining data. The strategy involved three steps: creation of additional POSI records to increase the ground truth information density, segmentation of the data in periods of movement and immobility, and association between positions and Wi-Fi fingerprints. Building the radio map raises a few challenges. The POSI records are sparse in time and the user movement cannot be modelled by straight lines between adjacent POSI records since they involve crossing walls or floors in the provided building blueprints. Thus, intermediate POSI records were created based on the videos of the data collection process, or, in cases where video was not available, by our best guesses of the most likely path compatible with the building blueprints.
In the end, the radio map was built by associating fingerprints to the linearly interpolated coordinates between known positions, combined with the movement information extracted from the accelerometer data. Once the radio map is built, it is possible to estimate the trajectories in each of the evaluation dataset. For each fingerprint in the evaluation dataset, it was necessary to estimate the corresponding location. To estimate the building, a majority rule was used. To estimate the floor, the team used a filtering function combined with another majority rule  [47]. Additionally, the pressure data was also used to estimate the floor. Having estimated the building and the floor, a k-Nearest Neighbors (kNN)  [48]  based method was used to estimate the coordinates using the fingerprints contained in the radio map that are most similar to the one being processed  [49].
With the previously described method, the UMinho team was able to estimate the position (building, floor and coordinates) for the provided evaluation datasets. However, Wi-Fi fingerprints were collected with an average sampling period of 4. 7 s and, as defined in the competition rules, the final trajectory estimation must be provided at discrete time intervals of 0. 5 s. For this purpose, different strategies were considered, based on using linear interpolation and the mobility profile along each trajectory built from the accelerometer data. In the end, with these different strategies, three different solutions were built  [39].
Floor Identification and Inferring Absolute Position From the provided training data, a grouping step was applied to get the Wi-Fi fingerprinting database. Two notable characteristics of the resulting data were its sparseness in terms of the feature space (most of the APs have unseen value) and in terms of sampling points. For the learning task, three models were applied: k-Nearest Neighbour, Random Forest  [52]  and Extreme Gradient Boost  [53]. In addition, the set of features was varied: raw RSS value features, K-Filtering  [49] , and Hyperbolic Location Fingerprinting features  [54]. Each model was tested with classifier and regressor.
Path Approximation within the Floor For path reconstruction within the floor, it was necessary to estimate the moving distance and direction. By applying a threshold on the standard deviation of accelerometer values, it is possible to recognize whether the user is standing or walking. In the case of walking, the moving distance was calculated based on the average speed, assuming it did not change throughout the logfile. The user moving direction was assumed to coincide with that of the phone, as derived from accelerometer, magnetic and gyroscope information. Several ways of extracting the phone's direction were tested, including using rotation matrix from magnetic and accelerometer, using gyroscope integration, Madgwick filter  [55]  and AHRS data directly. From all of the approaches above, the value of Azimuth (or yaw) axis was used as the user's direction of movement.
A particle filter was used to reconstruct the full path. The particle only included a moving model with the direction and moving distance. A working observation model based on a Wi-Fi fingerprint model could not be created. To reduce the drifting errors, Wi-Fi data and map information were integrated into the approximating path. Instead of building an observation model for each particle, a method which adjusts the particle on the basis of a spatial probability was proposed. The WiFi feature vector at time t was denoted by X, and Y was the position estimated by the Wi-Fi fingerprinting model, a particle P then could be pulled to position P new closer to M with a constant λ as P new = P + λ × (Y -P). The position Y came from the prediction of Wi-Fi fingerprinting and the position of the three closest centers to P.
Discussion and Dataset Future Plans We now discuss how to improve the datasets for future algorithm comparisons, according to the experience of creators of the data set and the feedback from its users (the competitors). The reference logfiles included some prerecorded paths in the different buildings and the competitors had to apply their own strategies for training and validation. Some competitors suggested that including differentiated logfiles for training and validation would have been appreciated since they could have had a better overview of their systems accuracy and avoided programming errors in their systems. Moreover, they could have provided comparable results at the competition session. After the competition, using the evaluation logfiles with ground truth (POSI headers), the Marauder team discovered that their Wi-Fi model was over-fitted because of their cross-validation strategy on small Wi-Fi fingerprinting data.
In order to ease the use of this data for competitors, more tools (such as parsers in common languages such as Python) will be developed and provided in our repository. An improved version of the Android Application used for data collection will be publicly available so the competitors can record information in their own labs and improve the training stages. We, the organizers, believe that the size of the community has the power to push innovation in the IPS field, so we plan to let people upload new generated logfiles in order to enrich the dataset with recordings collected from all over the world with their own devices (smartphones and tablets, mainly). This is our medium-term goal, which we think is the convenient way for creating a de-facto evaluation standard for smartphone-based indoor localization.
Conclusions This paper shows an analysis of the off-site 2016 IPIN Competition and some important conclusions have been reached. First, current smartphone technology provides an excellent tool for developing IPS based on the processing of signals from RF beacons, signals of opportunity and inertial data. Moreover, movement of user crowds may enable challenging applications as well as the creation of a huge de facto database for fair comparison of smartphone based IPS. Although the competition is over, the databases can be used to evaluate new developments from competitors or, even, new participants. Second, diversity has been the key to the success of the competition. Using different environments, contexts, routes, and devices has led to a fair comparison that has not been attached to a particular environment, context or device.
Acknowledgments: We would like to thank  Tecnalia Research & Innovation Foundation  for sponsoring the competition track with an award for the winning team. We are also grateful to  Francesco Potortì ,  Sangjoon Park ,  Jesús Ureña  and  Kyle O'Keefe  for their invaluable help in promoting the IPIN competition and conference. Parts of this work was carried out with the financial support received from projects and grants:  LORIS  ( TIN2012-38080-C04-04 ),  TARSIUS  ( TIN2015-71564-C4-2-R (MINECO/FEDER) ),  SmartLoc  ( CSIC-PIE Ref. 201450E011 ), " Metodologías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores " ( TIN2015-70202-P ),  REPNIN network  ( TEC2015-71426-REDT ) and the  José Castillejo  mobility grant ( CAS16/00072 ). The HFTS team has been supported in the frame of the  German Federal Ministry of Education and Research  programme "FHprofUnt2013" under contract  03FH035PB3  (Project  SPIRIT ).
Appropriating Ideas Personally A second mark of school indicated instances where personal appropriations of the literacy of the school were occurring. This was particularly evident in relation to culturally prestigious genres such as poetry or literature, and it usually happened through music, since a large proportion of the boys' Facebook postings dealt with songs. Music gained presence in their Facebook walls through the uploading of lyrics and other related items (e. g. clips, interviews, news). Sometimes, they even posted phrases of their favorite songs that would encourage Facebook friends to continue the song in the comment section. Since intertextuality is a fundamental condition of texts-as literary theorists such as Julia Kristeva, Roland Barthes and Jacques Derrida have argued-so it is also of music  (Hatten, 1985).
However, if we think more deeply about the typical teenager's life and culture, it makes more sense to see the internet and social networks as forming a continuum with their academic lives. The hours spent at school, and all that goes on there, form a large part of the life of a teenager. At secondary school, students develop strong relationships with their classmates and these include shared experiences, shared anecdotes, shared goals, shared tasks, shared language and shared frustrations or thrills. Together they build, negotiate, resist and share the identity of being a High School Student as well as the endeavor such a position implies. Social networks-Facebook, blogs and suchlike-afford a means for students to keep in touch easily outside school and thereby maintain their shared world and mutual understanding, which involves both socializing and learning.
Research in audio/visual feedback in the form of video files (i. e. , screencast feedback) is relatively new, as screen-capture software (e. g. , Camtasia) prices continue to be relatively high for the average person-available at http://www. techsmith. com for $299 (Windows) or $99 (Mac). Free alternatives have become more popular (e. g. , Jing and Screenr); however, the 5-minute limit can deter some from considering these alternatives. Furthermore, limited Internet speed and storage space for large media files are everyday constraints for many instructors. Screen-capture software, such as Camtasia, allows users to record audio as well as all screen movement and activities, such as scrolling, typing, clicking, and web surfing. Early adopters of these technologies, however, have reported positive results regarding student attitudes and perceptions about screencast feedback  (Brick & Holmes, 2008; Marriott & Teoh, 2012; Silva, 2012; Stannard, 2008; Thompson & Lee, 2012; Vincelette & Bostic, 2013).
Mangenot, F. (1996). Gammes d'écriture. Logiciel Windows. Paris, CNDP. Mangenot, F. (1998)  The "childhood memories" workshop on the Marmiton website gave the idea for the task. In order to provide a "model," each of the two tutors wrote a personal note for a specially created blog. Productions are available on the blog that was created, but who will read them? 2 Share knowledge about restaurants in Riga TripAdvisor. fr.
Procedure Participants were tested individually. The experiment was conducted with the software package Ductus  (Guinet & Kandel, 2010). Each word was present-ed in uppercase Times New Roman size 18 in the centre of a 17-in screen of an IBM-compatible computer. Participants were asked to copy the words in uppercase letters on a WACOM Intuos A4 graphic tablet, using a WACOM inking pen. They were instructed to lift the pen slightly above the answering sheet to get ready to copy words, and to write the words at a natural speed. As in previous studies (e. g. ,  Bogaerts et al. , 1996; Kandel et al. , 2006) , participants were required to carry out an articulatory suppression task while they were writing their responses: they were instructed to count out aloud, starting from one, as soon as a target stimulus appeared on the screen, and to continue counting until they had written their response.
They used a software program called Keytrap. For her part,  Sasaki (2000 Sasaki ( , 2004) )  considered a three second minimal level of inactivity as a pause because of her corpus collection method, namely, videotaped sessions of writing processes. She used a stimulated recall verbalization, and asked the participants to comment on all the pauses that were longer than three seconds on the tape. The mean pause duration is often even longer  ( Janssen et al. , 1996; Spelman Miller, 2000) , and thus the stipulated criterion is set at the five second level in some studies. Ransdell, Arecco, and Levy (2001)  used a five second criterion in their two experiments studying the effects of working memory load (i. e. irrelevant speech and concurrent 6-digit load) in bilingual writers. They used a program called FauxWord.
Data Collection and Participants The participants were eleven Finnish students of French language and six French ERASMUS students. All the Finnish students were female, with ages varying between 20 and 23 (mean age = 21. 6 years). Ten of the students studied French as their major subject, while one had Swedish as her major and French as a minor subject. They were mainly third-year students. The French ERASMUS students included four female and two male students between 20 and 24 years of age (mean age = 21. 7 years). Their main subjects varied from political science to SLA studies so the group was very heterogeneous and they were recruited from several departments in the university. All students participated in the test voluntarily, and furthermore, the Finnish students received feedback on their end product from native French teachers.
The data were collected through two sessions: a writing session which was carried out by means of the ScriptLog computer program which allows one to carry out research on the on-line process of writing  (Strömqvist & Ahlsén 1999) ; and a stimulated recall verbal protocol. This means that the writing activity was recorded by the tool so that afterwards we could analyze the writers' pausal behavior, as well as the end product. The recorded activity was used as a stimulus during the audiotaped and transcribed verbalization sessions. The data collection is presented in Figure  28   Each participant wrote one French essay (L2 or L1) and in addition to this, five of the Finnish students wrote one essay in Finnish (L1). Moreover, these Finnish students and all the French students verbalized retrospectively on the task they had just completed.
The median lengths of the fluent state pauses ranged from 2. 65 to 4. 24 seconds, the thinking state pauses from 6. 41 to 12. 59 seconds, and finally, the reflective state pauses from 13. 07 to 46. 83 seconds. This means that the stipulated pause lengths of two, three, or five seconds related to the used software program (e. g. ScriptLog, Keytrap, or FauxWord) cannot reveal an exact picture of a writer's pausal behavior and profile, even if they contribute in another way to the field by allowing comparisons between studies. Furthermore, the obtained values did not follow language boundaries (i. e. L2 vs. L1), but instead varied individually, thus supporting the idea of defining individual pause lengths for each writer. It would be interesting to examine this finding in a replicated study with a similar study design.
Acknowledgements The authors are grateful to  Ciska Girault  and  Marie Legain  who helped during the study, and to  Dr. Monique Udell  who reviewed our manuscript. We also thank the owners who volunteered for the study. This work was funded by the  Association Nationale de la Recherche et de la Technologie , the  Association Aide aux Vieux Animaux , the  Centre National de la Recherche Scientifique, Aix-Marseille Université ,  LABX-0036  ( BLRI ) and  Institut Convergences for Programme d'Investissements d' Avenir (ILCB).
The BMDMs were fixed with PFA 4%. All experiments were performed in at least triplicate. Immunoblot analysis. Western blot analysis was performed as previously described  9, 10  with antibodies against the following proteins: BECN1, 1:1000, (Abcam, Cambridge, UK Ab55878), β-actin 1:1000, (Cell Signaling Inc. , Danvers, MA, USA, 4970), SQSTM1/p62 1:1000, (Sigma Aldrich, MABN130). The densitometric analysis was performed by Image J software and each data point was expressed as the mean ± S. D. of independent experiments. Real-time PCR. Total RNA was extracted with the RNeasy Mini Kit (Qiagen, Milan, Italy, 74104). The mRNA was reverse transcribed with a SuperScriptTM III First Strand Synthesis System (Promega, Milan, Italy, A5001). Quantitative RT-PCR was performed with an iCycler iQ Multicolor Real-Time PCR Detector (BioRad) with iQ TM SYBR Green supermix (Five Prime, 2900217).
The relative amounts of mRNA were calculated by using the comparative Ct method. Real-time RT-PCR analyses were executed for evaluating the efficiency of expression. Thermocycling consisted of an initial polymerase activation step at 98 °C for 5 min, and amplification was performed with 35 cycles of 95 °C for 15 s, 68 °C for 10 s, and 72 °C for 20 s with data acquisition at this stage and the reaction finished by the built in melt curve. Expression levels of genes were normalized to the housekeeping gene proC in the same sample. The sequence of PAO1 was: lasB forward 5′-ATGAACGAGGCGTTCTCCG-3′and reverse 5′-GTTGTACACGCCGCTGGAGT-3′. Housekeeping gene proC forward 5′-CAGGCCGGGCAGTTGCTGTC-3′ and reverse 5′-GGTCAGGCGCGAGGCTGTCT-3′. Cytokine assessment. Levels of IL-1β and TNF-α in culture supernatants from BMDMs collected 24 h following infection were assessed by means of standard ELISA kits (R&D Systems, Minneapolis, MN, USA), according to the manufacturer's instructions, as reported.
33, 34 Samples were read in triplicate at 450 nm in Microplate Reader (BioRad) using Microplate Manager 5. 2. 1 software. Values were normalized to protein concentration evaluated by Bradford analysis. Statistical analysis. Data are reported as arithmetic mean ± S. D. Data distribution was analyzed for normality and comparisons between groups for statistical difference were done using Student's two tailed t-test or the one-way ANOVA, as indicated. Significant differences are indicated in the figures. All experiments were performed at least three independent times with similar results. Data were analyzed using SPSS 20 software. Statistical significance was defined as P-value of o0. 05.
Acknowledgements. We thank  Dr Bob Scholte ,  Erasmus Medical Center Rotterdam, The Netherlands , who provided Cftr tm1EUR (F508del ( FVB/129 ) mice ( European Economic Community European Coordination Action for Research in Cystic Fibrosis program  EU  FP6 SHM-CT-2005-018932 ),  Dr Beth Levine , Center for Autophagy Research,  Department of Internal Medicine, UT Southwestern Medical Center, Dallas, USA , and  Dr Francesco Cecconi ,  University of Tor Vergata, Rome, Italy , who provided C57BL/6J background Becn1 +/-mice,  Dr EM Bruscia ,  Yale University School of Medicine , who provided GFP-labelled P. aeruginosa. This study was supported by The  European Institute for Research in Cystic Fibrosis (IERFC)  non-profit foundation,  Regional Cystic Fibrosis Associations of Campania, Sicilia, Lazio, Puglia, Piemonte  (to  Dr L Maiuri ,  Dr Raia ),  E-Rare (Rescue CFTR preclinic)  (to  Dr L Maiuri  and  Dr.