dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , is the number of API functions in the program. g 0 (G, i ) is the probability of occurrence of label i in G, i. e. , g 0 (G , i ) = 1 |V | ∑ v∈V 1 { (v)= i }. Similarly, g 1 (G, i , j ) (resp. g 2 (G, i , j , k )) corre- sponds to the probability of occurrence of edges with labels ( i , j ) (resp. connected triplet of vertices with labels ( i , j , k )). Comparison with Well-known Antiviruses. We compare the performance of our method with different existing antiviruses including Avira, Kaspersky, Avast, Qihoo-360, McAfee, AVG, BitDefender, ESET-NOD32, F-Secure, Symantec and Panda. Since known antiviruses update their signature database as soon as a new malware is known, in order to have a fair comparision with these antiviruses, we need to consider new malwares. For this, we use three generators to create new malwares: NGVCK, RCWG and.
User detection, recognition, and tracking is at the heart of human-robot interaction, and yet, to date, no universal robust method exists for being aware of the people in a robot's surroundings. The present article imports into existing social robotic platforms different techniques, some of them classical, and other novel, for detecting, recognizing, and tracking human users. The outputs from the parallel execution of these algorithms are then merged, creating a modular, expandable, and fast architecture. This results in a local user mapping through fusion of multiple user recognition techniques. The different people detectors comply with a common interface called PeoplePoseList Publisher, while the people recognition algorithms meet an interface called PeoplePoseList Matcher. The fusion of all these different modules is based on the Unscented Kalman Filtering technique.
The robot here, drawn as the dark blue and white shape in the middle, is surrounded by three users. Figure  1 (b) illustrates the process of creating user awareness. User detection consists in detecting, in the sensor data stream, the users around the robot, and knowing where they are. Each of them can then be identified by a temporary ID. Those detections are shown in Figure  1 (b) as pink crosses. The fields of view of the different sensors are drawn as angular ranges. For each user, its track contains the user's identity, previous positions, and so on. Note that the user highlighted in blue is out of the sensors' field of view; therefore, the robot is not yet aware of that user's presence. In Figure  1 (b), an ellipse shows the position of each track, associated with the uncertainty of its position, and a dashed line shows its previous positions.
The additional knowledge in each track is depicted next to the track ellipse in the colored frame box with a solid border. User recognition aims at obtaining a long-term coherency with the way it detects the users, in other words, matching the temporary IDs to a permanent identity. User recognition is used to match detections and tracks: The higher the similarity, the more probable the detection corresponds to the track. The similarity between each user detection (a pink cross) and the set of tracks, obtained by user recognition, is in the pink rectangle with a dashed border next to this cross. After the user recognition is performed, through Kalman filtering and multimodal data fusion algorithms, each track can be updated with the matching user detection, by using the similarities computed by the user recognition.
The set of tracks constitutes the user mapping. This work has been carried out using Robot Operating System (ROS),  18  a software architecture specifically developed for use with robots. To sum-up the ROS landscape and glossary, processes are called nodes. They are independent and run in parallel. They can exchange data via messages sent on topics, data channels uniquely identified by their name. A function of a given node can be called from other nodes using a service, uniquely defined by a string name and a pair of strictly typed messages: One for the request and one for the response. ROS topics are many-to-many communication mechanisms, while ROS services are many-to-one.
A common data structure Common practice in computer science consists in standardizing the communication layer and the data that are exchanged by the different modules, more so than the particular structures of these modules. For this reason, we designed a common data structure that describes the information associated to a user detection and this will be the output of any user detector that we want to integrate: the PeoplePose (PP). To ensure the modularity of our user awareness architecture, we exploited ROS messages and services mechanisms. The PP data structure corresponds to a single user detection and contains all the information worth being shared, such as the 3-D pose of the user, the confidence of the detection, RGB and depth images of the user if they are available (for vision-based algorithms), and so on.
The PP data structure is implemented as an ROS msg, a simple message description language for describing a data structure exchanged by ROS nodes. The details of the PP message are in Code listing 1. Not all the fields of the message are necessarily filled by all the methods: For instance, a people detector based on the information of a two-dimensional (2-D) laser range finder will not use the image fields. A detector can detect several users at once with a single data input. For instance, a face detector can find several users in the same RGB image. The different PPs generated by each detection are then gathered into a single message: This collection of PPs is then called PPL and is also based on a ROS msg. An example of a PPL is provided in "Architecture output: Sample data based on multimodal fusion" section.
User detection An algorithm capable of generating PPL messages is called a PeoplePoseList Publisher (PPLP). The integration into the robotic architecture is straightforward as long as each new detector publishes a PPL data structure. An example of a processing flow with several PPLPs is visible in Figure  2. Let us suppose that our robot is equipped with a microphone, a laser range finder, and an RGB webcam. The different input streams can be seamlessly shared between different PPLPs. In this example, PPLP3 and PPLP4 share the color stream of the camera, and as PPLP4 is computationally costly, it is chosen to run on a remote computer. The topics are relayed between the robot and the remote computer using a ROS communication layer. The integration of the user detection algorithms. The architecture integrates a series of user detection algorithms that make use of the most common sensor technologies in social robots, such as RGB images, 3-D depth, and 2-D lidar information.
NiTE-based PPLPs. The patented PrimeSense NiTE middleware,  21  freely distributed under Apache License, version 2. 0, allows detecting and tracking human shapes from depth maps. The NiTE middleware supplies a data structure that a system module converts into a PPL, the users multi-mask, and publishes it using the ROS messaging system. This structure indicates where the users are: if a pixel p of the user's multi-mask has the value 0, this means there is no user in p, whereas if it has the value 1, p corresponds to a pixel of the user 1, and so on. For a given user, a user mask is the multi-mask image where all the Code listing 1. The People Pose message. pixels that do not belong to this user are set to 0 (i. e. "erasing" the other users). The NiTE middleware already includes tracking capabilities.
Experimental results In this section, we present the robotic platform employed in the experiments in "Robotic platform" section and the acquisition of a realistic data set in "RoboticsLab People Dataset (RLPD): A realistic HRI-based people data set" section. The data are used to test the performance of the presented algorithms for user detection (see "Benchmarking of user detection algorithms with RLPD" section) and user recognition (see "Benchmarking of user recognition algorithms with RLPD" section). "Architecture output: Sample data based on multimodal fusion" section presents a sample output of the architecture and, finally, the performance of the overall user awareness architecture is evaluated in "Benchmarking of multimodal fusion configurations with RLPD" section.
RoboticsLab People Dataset (RLPD): A realistic HRI-based people data set There are academic data sets adapted for the evaluation of people detection, recognition, and tracking, such as DGait  37  and Kinect Tracking Precision (KTP). 38 They allow a faithful measurement of the performance of the user awareness system, make the comparison with other similar systems easier, and ensure that the measurements can be repeated in the same context. However, even though our system was designed in a fashion as generic as possible, it has been tested in a social robot. For this reason, we decided to acquire real data from the robot Mini, and with users that fit best the target audience: people from Spain, with a variety of genders and shapes. In addition, the actors do not wander randomly on the stage, as they do in the KTP data set: We designed scenarios that mimic a realistic HRI situation in which one or several users interact naturally with the robot: addressing the robot, using gestures, respecting the proxemics distance, and so on.
Data set summary. The data set represents three users interacting with a robot that integrates a Kinect camera. They move on the stage according to a script that was previously defined and is made of three scenarios of increasing difficulty. Their motion is challenging: They get in and out of the room, there are occlusions and partial views. The data set is meaningful if and only if the real positions of the users are known. We first thought of using markers, such as ARToolkit markers. 39 However, the imperfect detections could not guarantee an accurate ground truth concerning the users' positions in each frame. For this reason, in each of the 600þ frames, the ground truth user positions have been manually labeled. This data set is licensed under the terms of the GNU General Public License version 2 as published by the Free Software Foundation and freely available for downloading along with images and videos (https://sites.
google. com/ site/rameyarnaud/research/phd/roboticslab-peopledataset). Input frames. Each frame is labeled with a time stamp. The time stamp is expressed in milliseconds elapsed after the beginning of the recording, using six digits with leading zeros. For instance, frame 065514 was recorded 1 min and 5 s after the beginning of the recording. In total, we have 647 frames for 133 s (2 min 15 s roughly), which is, on average, 5 frames per second. Data set annotation. The ground truth user positions have been labeled manually in each frame, using both RGB and depth contents to create a "perfect" user mask. We developed a Graphical User Interface shaped as a raster graphics editor that allows the manual labeling of each pixel of a depth image. Like the data set, these tools are licensed under the terms of the GNU General Public License version 2 as published by the Free Software Foundation, and freely available for downloading at the same URL.
Data set analysis. The data set was recorded on July 2014. It is made of about 650 frame acquisitions, each of them consisting of four images, that is, about 2600 images. The data set is roughly 65 megabytes. These images can be easily imported into any programming language, such as Cþþ or Matlab. The complete data set takes 133 s and the total number of frames is 647, and 548 of them have several PPs. Some samples are visible in Figure  6.
Benchmarking of user detection algorithms with RLPD We benchmarked the different user detection algorithms on this new data set. These algorithms were all wrapped with a common interface, PPLP. The performance of the different PPLPs on the RLPD is presented in Table  1. We calculated the accuracy and the hit rate of each algorithm. Accuracy refers to the percentage of correctly evaluated frames (see equation (  1 )). The hit rate measures the number of people successfully detected considering the frames that certainly contain at least one person (see equation (2)) Accuracy ¼ true positive þ true negative total number of frames Â 100 (1) Hit rate ¼ true positive total number of frames with people Â 100 We can see first that no detector has a very good performance by itself. As explained before, the RLPD has a high level of complexity: The occlusions are frequent, the users are sometimes partially shown in the images, and in general, they are somewhat far away from the camera.
This underlines the fact the we cannot only use one PPLP and hope it will work in all situations. The NiTE PPLP has a high accuracy and hit rate (both above 90%). This benchmark confirms that the NiTE-based PPLP is a useful method for detecting users in a social robot, even in challenging conditions. The tabletop PPLP has an average performance: It has a detection rate around 40%. Indeed, as can be seen in the sample images, the ground is hardly visible in the images acquired by the camera, which generates a poor estimation of the ground Figure  6. Some samples of the RLPD. From left to right: column 1: the RGB image; column 2: the depth image; column 3: the manually labeled user map; and column 4: the NiTE (Kinect API) user map. The data set has some challenging features: partial (second row) or complete occlusions (third row), user not fully visible (fourth row).
Architecture output: Sample data based on multimodal fusion A sample PPL message obtained by the multimodal fusion based on both the Euclidean distance PPLM and the face recognition-based PPLM is shown in Code listing 2. It contains three users, labeled 1¼"irene", 2¼"jc", and 3¼"david". Sample images of the data supplied by some PPLM configurations are shown in Figure  7. Note on user labeling. Depending on the PPLMs used in a configuration, this will either perform user recognition against known users or not. Figure  7  illustrates both cases with two different configurations, at two different moments of time, illustrated by the RGB frames shown in Figure  7 (a) and (c). Figure  7 (b) and (d) presents the different user tracks (colored lines), along with their user mask and names. As user recognition between frames is performed by the PPLM configuration, we can know where the user has been and so display the trail.
Benchmarking of multimodal fusion configurations with RLPD The performance of the data fusion node according to its set of PPLMs was also benchmarked on the RLPD. Over 10 different configurations were benchmarked. The five configurations using a single PPLM for track-to-detection matching (face detection, HOG, height detection, NiTE, tabletop) were assessed. On top of these, other configurations based on combinations of these five available PPLMs were also used. The results of the benchmark are gathered in Table  3. First, the table confirms the conclusions of the benchmarking of the PPLMs made in "Benchmarking of user recognition algorithms with RLPD" section. Among the configurations relying on a single PPLMs, some perform better than others. Namely, the Euclidean distance-based, the NiTE-based, and the face recognition-based PPLMs perform less than 20 ID swaps during the whole sequence.
The strength of the proposed architecture lies in its modularity: It is easy to add or remove modules, and more generally to design a configuration that fits both the robot's hardware and software requirements, as well as the properties of its environment. This modularity allows the use of the architecture on a variety of platforms that differ in both their hardware capabilities and the way in which they interact with users. A specific data set of images was created, called RLPD, which corresponds to scenarios of user detection and recognition in a realistic HRI context, acquired on a real robotic platform. The performance of all modules was assessed by using this data set. We also demonstrated that the performance of the mapping is improved by using several algorithms in parallel.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research leading to these results has received funding from several projects: from the project called: Development of social robots to help seniors with cognitive impairment-ROBSEN, funded by the  Ministerio de Economı ´a y Competitividad  ( DPI2014-57684-R ); and from the  RoboCity2030-III-CM  project ( S2013/MIT-2748 ), funded by  Programas de Actividades IþD en la Comunidad de Madrid  and cofunded by  Structural Funds of the EU.
First, we have numerically studied the 2D leaf geometry using MATLAB in order to detect its contour and nervure network. Following this, we have exported the processed image to the electromagnetic software Ansys-HFSS to simulate the radiating elements of the patch antenna. We have considered a total of four configurations with different levels of complexity of the nervure network. Finally, we have simulated the designed structures and have also characterized them experimentally. The bioinspired designed antenna which is characterized by complex fractal shape and unique electromagnetic signature can be used to secure chipless RFID systems  [11]. In fact, RFID tags based on irregular fractal shapes inspired from nature are difficult to replicate by an unauthorized reader, especially when they are printed with invisible conductors which is important for authentication, anticounterfeiting, and security applications.
Antenna Design. The contour of a radiating patch is an important parameter which affects the radiation properties of the antenna. Since the radiating patch is obtained from a 2D complex fractal image, MATLAB was used for processing the image as well as for reproducing the fractal patch, which was simulated by using the software Ansoft-HFSS (version 2015). We have used MATLAB to detect automatically the complex contour of the leaf image, then by using an appropriate code we have determined the coordinates of all points defining the boundaries of the image. Finally, a visual basic script (vbs) was developed to export final data into HFSS and reproduce the fractal image  [12]. We have investigated the structure numerically by determining its multiple resonant frequencies, surface currents, and the radiation properties such as radiation patterns and gain values.
Acknowledgments This work was funded by the  Deanship of Scientific Research (DSR) ,  King Abdulaziz University, Jeddah ,  Saudi Arabia  under Grant no. 135-420-D1435. The authors, therefore, acknowledge with thanks the technical and financial support provided by  DSR. The authors are also grateful for the help of  Dr. B. Blonder  from the  Department of Ecology and Evolutionary Biology, University of Arizona, USA  for his helpful data dealing with the image acquisition and processing of the leaf image.
Acknowledgements We thank CJHE editors, external reviewers, and  Dr. Chelsea Jones  and  Dr. Mark Sherry  for their constructive feedback. We also thank the organizers of the Disability Disclosure in/and  Higher Education conference, University of Delaware , where we presented an early version of this paper in 2013. Funding from the  Social Sciences and Humanities Research Council  and  Canadian Institutes of Health Research  supported the development of this paper. (Dis)Embodying Disclosure in Higher Education / K. Aubrecht & N. La Monica Wilton , R. (2006). Disability disclosure in the workplace. Just Labour, (8) 24 ,  24-39. Retrieved from http://www. justlabour. yorku. ca/volume8/pdfs/02%20Wilton. pdf.
Variables to Assess Now the structure of HRV experiments has been finalized, it is important to consider any confounding variables influencing HRV that can be controlled. In the Supplementary Materials (Data Sheet 1) we provide the reader with an example of a demographic form that can be adapted and used within experiments. The content of which recommends collecting the following information and potentially controlling for them, according to the research question. Therefore we suggest the researcher considers the following stable and transient participant's variables. Stable variables: • Age and gender  (Umetani et al. , 1998)  • Smoking  (Hayano et al. , 1990; Sjoberg and Saint, 2011)  • Habitual levels of alcohol consumption  (Quintana et al. , 2013a ,b) • Weight, height and waist-to-hip ratio  (Yi et al.
Researchers should aim to obtain the ECG recording to allow for precise editing of the signal for artifact correction. Importantly, in case the device used does not allow for the use of time markers, a very precise time protocol of every experimental event should be kept in order to allow for later analysis (see an example in Supplementary Material, Data Sheet 2). At the applied level for practitioners, with the growing impulse to quantify psychology and with the growth of biofeedback, it is not surprising that many practitioners look to HRV smartphone apps to meet this need. The development of smartphone apps connected to either a chest belt or photoplethysmography to measure HRV, such as Ithlete, have been shown to provide reliable HRV measurement  (Flatt and Esco, 2013). However, this was based on the RR interval and for research purposes we recommend the use of devices able to record the ECG signal, as mentioned earlier.
ECG Sampling Together with the choice of HRV recording technique it is also important to make an informed choice of ECG sampling. Temporal accuracy is crucial to calculate successfully the variance of a time series  (Quintana et al. , 2016) , to identify as precisely as possible the fiducial points (i. e. , landmarks) of the ECG complex. The accuracy of HRV measurements is primarily determined by the sampling rate of the data acquisition system, which should be set at a minimum of 200 Hz according to the Task Force  (Malik, 1996). More conservative guidelines advise between 500 and 1000 Hz  (Riniolo and Porges, 1997; Berntson et al. , 2007) , which may be particularly relevant in case of lower amplitude RSA  (Riniolo and Porges, 1997). However, recent development on this topic, relying on Monte Carlo-based analysis of false positive rates, showed that when R-peak interpolation (i.
HRV Software The analysis of HRV data has been made very accessible through a free popular software, Kubios  (Tarvainen et al. , 2014) , which is currently the most used by researchers. New softwares like gHRV  (Rodriguez-Linares et al. , 2014) , a package for the R statistical environment, and ARTiiFACT  (Kaufmann et al. , 2011)  are in development and offer other analysis tools such as different analysis options, visualize and export the HRV data, as well as the possibility to edit the source code for gHRV. Having access to the original source code is of value when comparing it to proprietary software, as the source code can be used to interpret what is being measured.
Artifact Correction Any HRV data sets requires a signal pre-processing before proceeding to the analysis, with the objective to identify the fiducial points (typically the R peak) from a normal ECG QRS complex. Hence, all abnormal beats not generated by sinus node depolarisations should be eliminated from the record. HRV data from ambulatory recordings generally contain more artifacts that can be either of physiological or technical origins. Technical artifacts may result from poorly attached electrodes or to excessive motion from the individual. Physiological artifacts may include ectopic beats, atrial fibrillations sighs and coughs. When recording the IBI this only allows you the possibility to carry out an automatic artifact correction, given the fiducial points of an ECG are not recorded.