dataesr/scientific-paragraphs-categorization huggingface dataset:
d: WI-FI AND GEOMAGNETIC POSITIONING The Wi-Fi and Earth magnetic field data is utilised to determine the current position of the user. Since Wi-Fi requires longer scanning time, periodic Wi-Fi scans are used which can help to make periodic corrections in the current position of the user. The Wi-Fi positioning is based on the enhanced fingerprinting approach proposed in  [20]  and makes use of multiple features from the fingerprint vector. The magnetic field data are used to calculate the current position of the user. The fingerprinting technique presented in  [21]  is utilised to build the fingerprint database. The floor information from the floor module helps to load the magnetic database which the user is currently at. Similarly, the position from the PDR module serves to narrow down the search space for the magnetic field database and helps to get a more accurate position.
Using optical flow and IMU the system's relative 6DoF-path is computed. By combining landmark detection and relative motion, a 3D global map of detected landmarks is computed. Up on having such 3D map the system allows visual navigation by correlating the detected landmarks with those stored in the 3D map. The correlation is being performed using a localisation particle filter -which allows a continuous (∼20 Hz) localisation using visual sensors and an IMU. The system runs on an embedded Linux card such as Raspberry PI4 or Jetson nano and allows 1-5 m accuracy and a relatively robust solution for the Kidnapped Robot Problem. For the IPIN 2019 Competition the robot navigation system was converted to a handheld one, based on a laptop (Linux OS) which was connected to a tracking camera (Intel Real-Sense T265, which includes an IMU) and an outer  Wi-Ficard for RF fingerprinting localisation, as shown in figure  17.
b: SYSTEM STRUCTURE The system uses IMU sensors to acquire IMU data, barometric data and a camera to acquire real-time images. We get GNSS signals from our Android phone. The VIO system selects the position of the characteristic points as the observation, while the PDR using an inverted pendulum model selects the vertical zero velocity points as the observation. The inverted pendulum model is a simplification of the movement of the lower limbs of the human body, without considering the effects of knee flexion and the feet soles on waist movement. In order to correct the position and attitude deviation caused by the bad quality of observed feature points, the position and attitude of the VIO are replaced by the position and attitude of the PDR to obtain better performance. The image landmark is to judge whether it has come to the calibration place and obtain the calibration information by comparing the feature points distribution extracted from the real-time image and the off-line image extracted from the landmark database.
Note that due to the low data collection frequency of Wi-Fi, we do not use the Wi-Fi data to estimate the trajectory but use it to match the sub-trajectory with the Wi-Fi fingerprint database to find an approximate location. The positions of reference points provided in the training data set is also recorded as important known knowledge for the following step. c:  STEP 3  In this step, a PDR algorithm is applied to reconstruct the detailed segmented trajectory coordinates with time stamps. Also, all the parameters required by the PDR were determined in step two where we identified the step length, step detection threshold, etc. Moreover, known knowledge of the map can also be used for trajectory optimisation. Since each segmented sub-trajectory can be matched to a known position, then the start and end points of each sub-trajectory can be used for the optimisation of the trajectories estimated by PDR.
c: PRE-COMPETITION EVALUATION Before the competition, the developed solution was evaluated using only Training data to build the radio map and by plotting the trajectories estimated for each one of the Validation paths. The error was evaluated by measuring the positioning error at the provided ground truth points (POSI records in the Validation datasets). This evaluation revealed that the main errors were observed in parts of the trajectory where there is a significant disturbance in the heading information obtained from the AHRS records. A summary of the obtained results for each Validation trajectory is shown in table 3. Floor estimation was 100% correct for all data sets.
g. , 15 m. Accelerometer, gyroscope, and mobile phone 3D orientation: these data are fused together and used to detect the step count, stride length and orientation between two locations updates; combination of these three gives us the traveled distance and direction. The difference between this traveled distance and direction, and the distance and direction between a grid point and his parent grid point (i. e. , the previous grid point of the path) are used as an additional cost. This penalties paths that move when the accelerometer and gyroscope estimate that the user is standing still and the other way around when a path remains static and the user is moving or when a path takes a turn in the opposite direction. The physical layout of the building is modeled by a shapefile, constructed with QGIS, the PDF maps with georeferenced points, and a plugin to reference the raster to the projected coordinate system by creating a GeoTIFF file.
Each floor is represented by a polygon with the floor id as a feature value and likewise, the escalator and lift are represented by a polygon with the start and end floor as a feature value. The route mapping filter uses this shapefile as input to construct a grid database with a certain grid size, e. g. , 1 m, and a reachable database, consisting of the reachable grid points, for each grid point. The reachable grid points are the neighboring grid points that do not cross walls or obstacles, and the above or below grid points if it is located within the polygon of an escalator or lift (and correct floor level). This grid and reachable database are used to select the next candidate positions in the route mapping filter, given the current last points of the paths in memory and the time that has passed since the last location update.
a: LESSONS LEARNED We assigned too much weight to the Wi-Fi and magnetic measurements in the cost function of our algorithm. This caused the reconstructions to be mapped to areas for which RSS and magnetic training data were available, which is disadvantageous for an evaluation that contains previously unvisited places. It would be better to assign more weight or make a switch to a pedestrian dead reckoning technique when the best matches between measurements and the fingerprint databases become too large. The competition is a well-organised event and a useful contribution for the indoor positioning and tracking community.
b: STEP 2 The training dataset gives the location (longitude and latitude) of some known points, i. e. , POSI, but these POSI points are sparse in the space domain. First, we artificially construct a magnetic field strength table by linear interpolation method such that each sensed magnetic data has its corresponding location tags. Second, we exploit the coarse positioning results P W as the initial point and choose the data record in the magnetic fingerprints which are within the circle with radius of 1 m. The schematic view of cooperation between Wi Fi positioning results and magnetic positioning results is illustrated in figure  31. Note that we exclude all particles outside the circle, where the blue dots are all particles that has a sensed magnetic field value. The point where similar Wi-Fi similar value obtained in the second step is the center of the circle.
Two data sets were given to competitors. Dataset n • 1 was taken on a single static location for several hours, and was meant to be used for sensor calibration, by enabling competitors to compute noise and measurement bias of inertial sensors (Allan variance). Dataset n • 2 was the data recorded on CNR Area following 6 different steps, as shown in table  4  and in figure  34. The competitors' objective was to re-build the trajectory realised by the Track chairs. The evaluation was done by comparing 2D position and floor level estimated by each team to the coordinates of 68 reference points (key points). To do so, a table containing timestamps of expected key points    was shared, and competitors had to provide the corresponding coordinates. Data Set and supplementary materials -e. g. datasheet of sensors embedded in PERSY-were provided to competitors of Track 4.
In particular, the winner team, SNU-NESL, reached a remarkable score of 3. 8 m and their accuracy exhibited an impressive 95 th percentile of 7. 3 m. It is worth noting how the 95 th percentile can be considered a metric for systems which approach the market and, generally, research-grade systems show bigger errors than the results reached by the 2019 winner. As described in section IV, competitors have developed algorithms which consider all the available sensors (inertial, magnetic, barometric, Wi-Fi). The differences in terms of final score are related mainly to the different weights that competitors gave to the real-time sensor readings, the offline fingerprint database and the algorithm for evaluating the user orientation and step length. In Track 2 we again see three scores under 15 m, with the best one at 3.
• There is some controversy about how an ideal evaluation should start. Currently, the initial position is provided to competitors at the beginning of the competition. Developers of pure PDR-based solutions feel that initial heading must be provided too, whereas competitors dealing with multiple sources of data (sensor fusion) stress the necessity of being autonomous to deal with the case when the user suddenly requests location information. Efforts are needed to reduce the importance of getting the initial position and heading from an external source. • Most competitors share the idea that combining multiple complementary technologies is the future of indoor positioning. Some are considering the inclusion of the light sensor to improve system accuracy. • Independent evaluation has been an incentive for all.
ACKNOWLEDGMENT The authors would like to thank  Siemens , the  Electronic and Telecommunications Research Institute (ETRI) , the  Korean Institute of Communications and Information Sciences (KICS) ,  Ganko Food ,  J-Power Systems , and the other sponsors who provided prizes for the winners of the competition VOLUME 8,  2020 TRACK 1 SNU NESL (Track 1 ): This work was supported by the  Institute for Information & Communications Technology Promotion (IITP)  grant funded by the  Korean Government (MSIT)  (Development of human enhancement fire helmet and fire suppression support system) under Grant  2018-0-00781. MITLab (Track 1):  MITLab Team  gratefully thanks the  Ministry of Science and Technology (MOST)  for supporting this research under Grant  MOST-108-2221-E-011-061-. YNU Team (Track 1 ): This work was supported in part by the  Ministry of Science and ICT (MSIT) ,  South Korea , through the  Information Technology Research Center (ITRC) support program  supervised by the  Institute for Information & communications Technology Planning & Evaluation (IITP)  under Grant  IITP-2019-2016-0-00313 , in part by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT , under Grant  2017R1E1A1A01074345 , and in part by the  ICT Research and Development Program of MSIP/IITP  ( Development of Precise Positioning Technology  for the Enhancement of Pedestrian Position/Spatial Cognition and Sports Competition Analysis) under Grant  2017-0-00543.
INDORA Team (Track 1 ): This work was supported in part by the  Slovak Grant Agency, Ministry of Education and Academy of Science , Slovakia, through  INDORA Team , under Grant  1/0056/18 , and in part by the  Slovak Research and Development Agency through INDORA Team  under Contract  APVV-15-0091. TRACK 2 Track 2  organizer (ETRI): This work was supported by the  ICT Research and Development Program of MSIP/IITP  ( Development of Precise Positioning Technology  for the Enhancement of Pedestrians Position/Spatial Cognition and Sports Competition Analysis) under Grant  2017-0-00543. XMU team (Track 2 ): We gratefully thank the  ATR National Defense Key Laboratory of Information and Communication Engineering, Xiamen University  for the Experimental environment and equipment. Kyushu team (Track 2): This work was supported in part by the  JSPS KAKENHI  under Grant  JP18H04125.
TRACK 3 Track 3 organizers ( CAR/CSIC-UPM, UBIK, UJI, UOC ): This work was supported in part by  MICROCEBUS  under Grant  RTI2018-095168-B-C55  ( MCIU/AEI/FEDER, UE) , in part by  REPNIN PLUS  under Grant  TEC2017-90808-REDT , and in part by  TECHNOFUSION(III)CM  under Grant  P2018/EMT-4437. UMinho team (Track 3): This work was supported in part by the  Fundação para a Ciência e Tecnologia (FCT)  within the  Research and Development Units Project Scope  under Grant  UIDB/00319/2020 , and in part by the  Ph. D. Fellowship  under Grant  PD/BD/137401/2018. INDORA Team  (Tracks 3): This work was supported in part by the  Slovak Grant Agency, Ministry of Education and Academy of Science , Slovakia, through  INDORA Team , under Grant  1/0056/18 , and in part by the  Slovak Research and Development Agency through INDORA Team  under Contract  APVV-15-0091.
Conclusion The results of the searches for several rare or forbidden Higgs boson decays have been presented: (i) H → Zγ, (ii) H → Mγ, M = (q q), (iii) ZH, H → c c, (iv) H → eτ and µτ, (v) H → µ + µ -. These searches are potentially sensitive to BSM physics affecting the Yukawa couplings to the fermions of the first two generations or leading to non-flavour-diagonal couplings. The searches have been performed on 13 TeV pp collision data recorded by the ATLAS detector at the LHC. No significant signal was observed. For all final states but the dimuon one, three times more data already recorded by ATLAS before the two-year long shutdown started at the beginning of 2019 are available for analysis. For the H → µ µ decay, the full dataset collected so far was used, and the sensitivity of the analysis (1.
In  [7] , the authors introduced an advanced occupancy grid approach to enable the robust separation of moving and stationary objects. In  [8] , a generic architecture for perception in dynamic outdoor environment by applying grid-based SLAM (Simultaneous Localization and Mapping) is presented. In  [9] , a grid-based Information Systems (GISs). The priors are obtained by building a road map using information such as the type of the road and the number of lanes, retrieved from a database and then by projecting the road map onto the vehicle frame. Although geo-referenced maps provide valuable prior semantic information, a real difficulty remains in tackling the uncertainties coming from estimation errors on the pose of the vehicle and the errors on the map features arising from the mapping process.
The system has been implemented on C++ with a Linux computer. Figure  10  shows the system and its 9 components. The inputs are a GPS receiver, a CAN bus gateway of the vehicle to access to the speeds, a LiDAR driver and a camera for visualization. The output of a GPS receiver and CAN bus data (wheel speed and yaw rate) are used in the ego-localization process to estimate the absolute pose. The semantic lane grid is constructed in the Lane Grid Application component, where the estimated pose and the prior map are taken as inputs. The evaluation process takes place in the Grid projection application component. The lane grid is projected on the image coming from the scene camera. The component LiDAR acquires the data from the SICK LiDAR which is sent to the "Occupancy grid Application" to construct the occupancy grid in real time.
"NVHSKL-201708: Research on Object detection and behavior model based on multi-modality data", in part by the  National Key Research and Development Program of China  ( 2018YFB0105000 ), in part by the  National Natural Science Foundation of China  ( U1864203 ), in part by the  Project of Tsinghua University  and  Toyota Joint Research Center for AI Technology of Automated Vehicle  ( TT2018-02 ). Acknowledgments: This work has been carried out within the framework of the  Equipex ROBOTEX  ( ANR-10-EQPX-44-01 ).
On Interpretation and Understanding To describe the complex interpretation machinery, some authors talk about a "mental model"  [23]  or a "neural apparatus"  [25] , a place of chemical reactions that can be analysed. Others believe that interpretation involves above all the socio-individual  [67] , resulting from our history, a place for expressing a form of intellectual creativity specific to each person. We all act as interpretative agents, information processors interacting with the world that surrounds us through a filter. In fact, this indescribable filter through which we interact with the world may be called an "interpretative framework "  [63]. Information is transmitted by talking, writing or acting during a sense-giving process. We collect data from this information by listening, reading or watching during a sense-reading process.
The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280×720 or 720×480).
Edney-Browne  [10]  wondered how the capacity of UAV capturing the external reality (visuality) is related to perceptual and cognitive vision in humans. Variations in UAV characteristics such as perspective view and object size may change viewers' behavior towards content. Consequently, new visual attention processes may be triggered for this specific imaging. This means that studying UAV imagery in light of human visual attention not only opens the door to plenty of applications but could also enable to gather further knowledge on perceptual vision and cognition. In the context of UAV content, there are very few eye-tracking datasets. This is the reason why we propose and present in this paper a new large-scale eye-tracking dataset, freely downloadable from the internet. This dataset aims to strengthen our knowledge on human perception and could play a fundamental role for designing new computational models of visual attention.
The paper is organized as follows. In Section 2, we first justify and elaborate on the need for large-scale eye-tracking databases for UAV videos. Then, we introduce the entire process of dataset creation in Section 3. It describes the content selection, the experiment set up, and the implementation of fixations, saccades, and gaze density maps. Section 4 presents an in-depth analysis of the dataset. The study is two-fold: it explores what ground truth should be used for salience studies, and brings to light the fading of conventional visual biases UAV stimuli. Finally, conclusions are provided in Section 5.
It would be beneficial to evaluate qualitatively and quantitatively the center bias on a larger dataset of UAV videos to support our assumption. While it is now rather easy to find eye tracking data on typical images  [35, [37] [38] [39] [40] [41] [42] [43] [44] [45]  or videos  [46] [47] [48] [49] [50] , and that there are many UAV content datasets  [7, [51] [52] [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] , it turns out to be extremely difficult to find eye-tracking data on UAV content. This is even truer when we consider dynamic salience, which refers to salience for video content. To the best of our knowledge, EyeTrackUAV1 dataset released in 2018  [11]  is the only public dataset available for studying the visual deployment over UAV video. There exists another dataset AVS1K  [63].
However, AVS1K is, to the present day, not publicly available. We thus focus here on EyeTrackUAV1, with the awareness that all points below but the last apply to AVS1K. EyeTrackUAV1 consists of 19 sequences (1280×720 and 30 fps) extracted from the UAV123 database  [55]. The sequence selection relied on content characteristics, which are the diversity of environment, distance and angle to the scene, size of the principal object, and the presence of sky. Precise binocular gaze data (1000 Hz) of 14 observers were recorded under free viewing condition, for every content. Overall, the dataset comprises eye-tracking information on 26,599 frames, which represents 887 seconds of video. In spite of a number of merits, this dataset presents several limitations for saliency prediction applications. These limitations have been listed in  [27].
• UAV may embed multi-modal sensors during the capture of scenes. Besides conventional RGB cameras, to name but a few thermal, multi-spectral, and infrared cameras consist of typical UAV sensors. Unfortunately, EyeTrackUAV1 lacks non-natural content, which is of great interest for the dynamic field of salience. As already mentioned, combining content from various imagery in datasets is advantageous for numerous reasons. It is necessary to continue efforts toward the inclusion of more non-natural content in databases.
EyeTrackUAV2 Dataset This section introduces the new dataset EyeTrackUAV2 aiming at tackling issues mentioned above. EyeTrackUAV2 includes more video content than its predecessor EyeTrackUAV1. It involves more participants, and considers both free and task-based viewing. In the following subsections, we first elaborate on the selection of video content, followed by a description of the eye-tracking experiment. It includes the presentation of the eye-tracking apparatus, the experiment procedure and setup, and the characterization of population samples. Finally, we describe the generation of the human ground truth, i. e. , algorithms for fixation and saccade detection as well as gaze density map computation.
Content Selection Before collecting eye-tracking information, experimental stimuli were selected from multiple UAV video datasets. We paid specific attention to select videos suitable for both free and task-based viewing as experimental conditions. Also, the set of selected videos had to cover multiple UAV flight altitudes, main surrounding environments, main sizes of observed objects and angles between the aerial vehicle and the scene, as well as the presence or not of sky. We consider these characteristics favor the construction of a representative dataset of typical UAV videos, as suggested in  [11]. We examined the following UAV datasets: UCF's dataset (http://crcv. ucf. edu/data/UCF_Aerial_ Action. php), VIRAT  [51] , MRP  [52] , the privacy-based mini-drones dataset  [53] , the aerial videos dataset described in  [54] , UAV123  [55] , DTB70  [57] , Okutama-Action  [58] , VisDrone  [64] , CARPK  [59] , SEAGULL  [60] , DroneFace  [61] , and the aerial video dataset described in  [56] ).