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PMC:7796058 / 59412-63261 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
429 235-241 Species denotes people Tax:9606
430 255-261 Species denotes people Tax:9606
431 353-359 Species denotes people Tax:9606
432 1021-1027 Species denotes people Tax:9606
433 1220-1226 Species denotes People Tax:9606
434 1275-1281 Species denotes People Tax:9606
435 1712-1718 Species denotes people Tax:9606
436 757-762 Disease denotes Cough MESH:D003371
437 1357-1365 Disease denotes coughing MESH:D003371
438 1490-1495 Disease denotes cough MESH:D003371
439 1650-1658 Disease denotes elevator MESH:D006973
441 3694-3700 Species denotes people Tax:9606

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T15 757-762 Phenotype denotes Cough http://purl.obolibrary.org/obo/HP_0012735
T16 1357-1365 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735
T17 1427-1432 Phenotype denotes falls http://purl.obolibrary.org/obo/HP_0002527
T18 1490-1495 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T433 0-4 Sentence denotes 5.7.
T434 5-39 Sentence denotes Risk Calculation and Visualization
T435 40-171 Sentence denotes To demonstrate risk calculation using Equation (2), we evaluated the proposed IoCT using the following cleaning use case scenarios.
T436 172-338 Sentence denotes In meeting room number 326 of the CCIT building, the number of people increased as people entered the room, and this event was detected by a smart camera in the room.
T437 339-478 Sentence denotes The number of people was shown online in the video frame and map visualization browser in green until the room capacity (five) was reached.
T438 479-611 Sentence denotes When the fourth person came in (room capacity is assumed to be three), the alarm notification for “Room exceeded capacity” is shown.
T439 612-733 Sentence denotes After that, a person coughed in the meeting room, and this event was detected by both the smart camera and audio sensors.
T440 734-773 Sentence denotes A notification showed “Cough detected”.
T441 774-867 Sentence denotes Then, the person who coughed opened the door and this event was detected by the smart camera.
T442 868-923 Sentence denotes A “High-risk behavior detected” notification was shown.
T443 924-1054 Sentence denotes The risk profile at that moment exceeded the threshold of 0.7 and a notification was sent to the people in room, and to a cleaner.
T444 1055-1219 Sentence denotes The color of the room polygon turned red indicating high risk and the room polygon was extruded (i.e., the polygon height increases) proportional to the risk value.
T445 1220-1385 Sentence denotes People started to leave the room causing the risk from People Density to go down, but the risk is higher than at the very beginning as a coughing event had occurred.
T446 1386-1508 Sentence denotes The total risk value of the meeting room falls but remains higher than before the risky behavior (i.e., cough) took place.
T447 1509-1668 Sentence denotes The cleaner closer to the room changes his activity status to cleaning (shown by an icon on the map) and moves closer towards the room (from elevator to room).
T448 1669-1779 Sentence denotes The cleaner trajectory alongside the other people trajectories extracted from BLE beacons were visualized too.
T449 1780-1916 Sentence denotes After the cleaning activity, the room’s total risk level goes back down to zero and the color of the room polygon changes back to green.
T450 1917-2030 Sentence denotes The video demo of this scene is attached in the Supplementary Materials which shows the risk profile of the room.
T451 2031-2120 Sentence denotes A sample screen shot of the Supplementary Materials demo video is presented in Figure 14.
T452 2121-2271 Sentence denotes To evaluate the impact of various weights assigned to different map layers, we used two sets of weights for map layer aggregations on the client side:
T453 2272-2376 Sentence denotes Profile 1: W1=W2=W3=W4=1; and Profile 2: W1=0.1, W2=0.4, W3=0.3, and W4=0.2 as mentioned in Section 4.1.
T454 2377-2453 Sentence denotes Figure 15 shows two risk profiles for room 326 over 40 min from 20:00 to 20:
T455 2454-2478 Sentence denotes 40 p.m. on 11 June 2020.
T456 2479-2626 Sentence denotes Evaluating precision, recall, and F-Score of video-Based and audio-Based risky behavior detection are listed in in Table 5 and Table 7 accordingly.
T457 2627-2919 Sentence denotes Table 8 includes time performance of different developed functionalities (e.g., video-based person density, video-based physical distancing, video-based risky behavior detection, and audio-based risky behavior detection) on various platforms such as Jetson NX, laptop, and android smartphone.
T458 2920-3025 Sentence denotes The performance of using a deep learning engine is highly dependent on Graphics and Computing processors.
T459 3026-3137 Sentence denotes Therefore, the performance of those functionalities is evaluated on a laptop with more robust processing units.
T460 3138-3225 Sentence denotes The laptop has NVIDIA GeForce RTX 2070 with 7.5 computation capabilities and a Core i7.
T461 3226-3294 Sentence denotes Therefore, the performance on Jetson NX is lower than on the laptop.
T462 3295-3416 Sentence denotes The best performance values are video-based risky behavior detection because they only involve the object detection task.
T463 3417-3539 Sentence denotes Audio-based risky behavior detection segments the voice in specific time frames and converts them into spectrogram images.
T464 3540-3598 Sentence denotes Voice patterns are detected in images using the VGG model.
T465 3599-3681 Sentence denotes Therefore, the time of processing for audio is higher than video object detection.
T466 3682-3849 Sentence denotes Video-based people density and video-based physical distancing give worse performance values than simple object detection regarding complexities in tracking functions.