REAL OBJECT DETECTION SYSTEM FOR IRAQ TRAFFIC SIGNS BASED ON MASK R-CNN

Traffic signs object detection has gained great interest in recent years, as one of the most important object detector applications. Traffic signs detection is based on deep learning, which gives it the benefit of high detection precision and timely response to condition changes of the traffic. Therefore, this paper shows an efficient method for detecting traffic signs in real-time. Hence, it implements a new Iraqi Traffic Sign Detection Benchmark (IQTSDB) dataset based on Mask Region-based Convolutional Neural Network (Mask R-CNN). The results show that the implementation of IQTSDB dataset with Mask R-CNN has a great efficiency in different conditions such as sunny, cloudy, weak light, and rainy conditions. In addition, the real video captured for traffic signs in Baghdad has been taken and compared to the German Traffic Signs Detection Benchmark (GTSDB) dataset. The IQTSDB dataset has a better performance than GTSDB dataset based on the performance parameters training loss and mean Average Precision (mAP).

Influence of On-Sight and Flash Climbing Styles on Advanced Climbers’ Route Completion for Bouldering

Route previewing has been established as a critical parameter in indoor climbing performance, as it could determine the success or failure in ascending the route. We addressed the effect of different types of previews on output climbing performance. Twenty-one advanced climbers (7b and 7c+ climbing grade) were required to complete 18 routes, rated at 6c, according to the French Rating Scale of Difficulty. Each climber previewed the route under three conditions: “No-previewing”, “video-model previewing”, and “real-model previewing”. Output climbing performance was assessed in terms of route completion. The results showed differences on output climbing performance between types of preview. Specifically, the climbers achieved more successful attempts at climbing to the “Top” of the wall when inspecting the route with the “real-model previewing” condition, compared to the other conditions of preview. On the contrary, the climbers displayed more failed attempts in climbing the route with the “on-sight” condition, compared to the “flash” styles (“video-model” and “real-model”). The preview of the route, including performance of a real/video-projected model manipulating climbing holds, seems to increase the opportunities to climb the boulder successfully, attuning climbers to information specifying ascending actions. Climbing coaches should reinforce the design of representative training, using flash styles, to promote movement solutions for route completion.

Crisis communication in audiovisual format: the information of Spain’s National Health System on YouTube in 2020

Throughout 2020 it has been seen that the Covid-19 pandemic has been an unprecedented health crisis that has involved a major change at both level informative and media. Audiovisual content has increased considerably and there is interest on the part of health institutions to demonstrate their ability as a reliable and first-order source of information. In fact, since the declaration of the pandemic, in just 2 months the number of content disseminated throughout 2019 was exceeded. This study seeks to describe the response given to this health crisis by the Spanish National Health System on YouTube. For this purpose, we analyze the 1035 audiovisual content that the official channels published in 2020. To carry out the analysis, the contents have been classified into 7 types, from press conferences to tutorials, recommendations, informative pieces or testimonies; the topic covered in each of them has been identified, the format, as well as the number of views and user feedback.The period of greatest production corresponds to the first wave and the de-scalding wave, and the health institution that has disseminated the most messages has been the Ministry of Health: 603 vídeos, 170 animations and 263 hybrid pieces –combining real vídeo, text and icons– have been identified. Animations and hybrid pieces are the most successful messages, and are primarily used on an appealing nature to disseminate healthy recommendations and habits. This study values the use of animations as an appropriate information resource for education and health promotion. In addition, it has allowed us to identify the main weaknesses, threats, strengths and opportunities presented by audiovisual health information on YouTube.

FPLP3D: Security robot for face recognition in the workplace environment using face pose detection assisted controlled FACE++ tool position: A three-dimensional robot

BACKGROUND: In recent years, several tracker systems have been developed to monitor a 3-dimensional skull position for facial action whereas, various tracker systems simultaneously analyze the single sequence of video, which can be provided with low-quality cameras and less security. Initially, implementing a 2-D face detector and an unrepentance system has been suggested; furthermore, it has been improved using an integrated 3-D face initialized scheme for the real-time tracker in the present face recognition systems. OBJECTIVES: To overcome the present setbacks of the conventional systems, Face Pose Detection assisted controlled FACE++ tool position of Three-Dimensional Robot (FPLF3D) has been proposed in this article. Furthermore, the suggested proposed configuration has a high-end monitoring approach, which is used to improve the reliability of the robot’s human-machine contact in the workplace environment for security assistance. Additionally, the robot’s direction can be controlled by the operator’s head position assessment of the camera (or any active viewing system) using a three-dimensional robot. RESULTS: Besides, the applications that are imitated by headers like telepresence, computer-generated reality, and video competitions will directly take advantage of the strategies introduced in this paper. CONCLUSION: Finally, real video tests at the lab-scale level show the accuracy and usefulness of the approaches proposed in this research outperform the existing methods used for tracking.

Deepfake Warnings for Political Videos Increase Disbelief but Do Not Improve Discernment: Evidence from Two Experiments

Recent advances in machine learning have led to the development of the “deepfake,” a convincingly realistic, computer-generated video of a public figure saying something they have not actually said. Policymakers have expressed concern that deepfakes could mislead voters and affect election outcomes, but existing research has found minimal persuasive effects. In this paper, we explore a downstream consequence of deepfakes: if voters are repeatedly warned of the existence and dangers of deepfakes, they may simply begin to distrust all political video footage – whether real or fake. Through two online survey experiments, we found that voters were unable to discriminate between a real video and a deepfake. Statements warning about the existence of deepfakes did not enhance participants’ ability to successfully spot manipulated video content. Instead, these warnings consistently induced participants to believe that the videos they watched were fake, even when the videos were real. The warnings were not specific to the video participants were watching; simply stating that deepfakes exist increased distrust of any accompanying video. Our findings suggest that even if deepfakes are not themselves persuasive, rhetoric about deepfakes can nevertheless be weaponized by politicians and campaigns to dismiss and disown real videos.

Models of group poisson flows in telecommunica-tion traffic control

The lack of effectiveness of the use of models of self-similar processes to the analysis of queues telecommunications systems is presented. The evolution of the flow models managed by Markovs chain is considered. The specifics of the use of Markovs flows as models of telecommunications traffic systems are considered. Models of single-channel queueing systems with input flows that have an arbitrary correlation are presented. Generalizations of the Khinchin-Pollaczek formula are given for these systems. The perspective of the application of interval methods developed by the author for queue analysis in queueing systems with correlated input flows is shown. It is suggested to use the group Poisson extraordinary flow as a model of telecommunication traffic. Interval characteristics of the given flows are reviewed and the prospects of their application are shown. The issues of multiplexing these flows during processing in queueing systems are considered. It is demonstrated that the resulting flow is also a group Poisson flow when summing up several group Poisson flows. The conclusions are confirmed by the simulation modeling results. The examples show the validity of such models to the characteristics of real video traffic flows.

The study of skeleton description reduction in the human fall-detection task

Accurate and reliable real-time fall detection is a key aspect of any intelligent elderly people care system. A lot of modern RGB-D cameras can provide a skeleton description of a human figure as a compact pose presentation. This makes it possible to use this description for further analysis without access to real video and, thus, to increase the privacy of the whole system. The skeleton description reduction based on the anthropometrical characteristics of a human body is proposed. The experimental study on the TST Fall Detection dataset v2 by the Leave-One-Person-Out method shows that the proposed skeleton description reduction technique provides better recognition quality and increases the overall performance of a Fall-Detection System.