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According to WHO, Drowning is a very much serious and every year 3,74,000 people claiming the lives as a public health threat. Underwater human body detection through live video is a very challenging task because of movement of water currents continuously and changes the intensity of water. Detecting people from an underwater video is a very complex and challenging problem as the video can be affected by various factors such as undesired artifacts (e.g. noise), monitoring limitation of cameras, illumination variation etc. It is very important to build the appropriate model to address this problem. The major goal of this paper is to examine the possibility of detect humans in an underwater Ambient using one of the Faster R-Convolution neural network (Faster R-CNN) and data enhancement algorithms and acutely, recall and precise evaluation of the proposed model results.

Live video applications are getting popular, and content providers widely use adaptive bitrate (ABR) streaming to improve QoE while maintaining low latency. However, users' increasing preference to watch videos on mobile devices poses great challenges for ABR algorithm due to the dramatically varying cellular network. Existing learn-based ABR algorithms face difficulties to generalize to various network conditions because of their reliance on training traces, and model/rule-based ABR schemes suffer from rebuffering under low latency constraint since they cannot robustly control the buffer occupancy within a small range. To address it, this work proposes Cratus, a lightweight and robust ABR algorithm for mobile live streaming, which achieves high QoE and low latency by accurately regulating the buffer at a small level. To enhance the control ability, Cratus controls the buffer dynamic behavior rather than the buffer occupancy. By using sliding mode control approach, Cratus robustly controls the buffer dynamic and ensures that the buffer occupancy is bounded around the target level regardless of network uncertainties. Trace-driven experiments show that Cratus outperforms existing ABRs: average QoE is increased by 12.3% to 28.6%, and rebuffering time is limited within 0.8s on average, which is reduced by 53.5% to 92.3%.

Desde la existencia de los medios de comunicación masiva, esto es, prensa, radio, televisión e internet, existen líderes de opinión que influyen en la toma de decisiones de su audiencia. Con la adopción masiva de las plataformas de redes sociales, la ubicuidad que proporcionan los dispositivos móviles y los live videos, la barrera de información entre el líder de opinión y su audiencia se ha reducido significativamente (Carillo et al., 2017). Las empresas, al entender que el desplazamiento de la atención de los consumidores hacia lo digital es más permanente que transitorio, han destinado mayores recursos a la publicidad digital, particularmente, en el influencer marketing o marketing de influyentes (Insider Intelligence, 2021). Como asevera Voorveld (2019), el influencer marketing ofrece una oportunidad para llegar a diversos grupos de audiencia de manera más directa, personal e interactiva. Para entender el tema de influencer marketing, en particular el papel que juegan los influencers y líderes de opinión en la era digital, se analizará a fondo el tema en el Capítulo 1. La presente tesis doctoral buscó responder cómo las variables relativas a la relación que un seguidor experimenta con el influencer, siendo estas identificación, interacción parasocial y afinidad, anteceden la respuesta a los live videos publicados por los influencers. Además, contestar si los live videos publicados por influencers son efectivos para anunciar las marcas, esto a través de las variables engagament y dependencia hacia el video. Por último, se evaluaron las consecuencias de estas variables evaluando cómo éstas afectan el comportamiento y sentimiento hacia las marcas que anuncian los influencers a través de las variables Brand advocacy, Brand attachment e Intención de Compra. Para responder a las preguntas de investigación, se diseñó la investigación con un enfoque tipo empírico causal y se analizaron las relaciones entre las variables a través de un modelo estructural. El instrumento de medida con la medición de las variables fue un cuestionario, y se consiguió una muestra de n = 376 en Puerto Rico a través de un método de muestreo por conveniencia principalmente a través de correo electrónico y en las redes sociales Instagram y Facebook. Los resultados muestran que las variables relativas al influencer: la identificación, la interacción parasocial y la afinidad, afectan el resultado hacia el contenido publicado en un live video medido a través de las variables engagement y dependencia al medio. Esta relación con el contenido que publican en vivo los influencers tiene un efecto en las marcas que se anuncian, esos resultados siendo las variables brand advocacy, brand attachment e intención de compra. En general, los hallazgos de esta tesis doctoral presentan implicaciones teóricas y prácticas en el tema de marketing de influencers. Específicamente, los resultados aportan valor para una mejor comprensión del uso del live video en el marketing de influencers, un canal reciente en la literatura. Además, ofrece recomendaciones para la gestión de marketing en las empresas. En particular para gerentes y directores de marketing y comunicación, para utilizar los live videos en el marketing de influencers en las redes sociales Instagram y Facebook como alternativa publicitaria. Los resultados de la presente tesis sirven también como guía para los influencers, proveyendo recomendaciones para el uso de live videos en su estrategia de contenido y para manejar de manera más efectiva su relación con sus seguidores. Since the existence of the mass media, that is, the press, radio, television and the Internet, there have been opinion leaders who influence the decision-making of their audience. With the massive adoption of social networking platforms, the ubiquity provided by mobile devices and live videos, the information barrier between the opinion leader and their audience has been significantly reduced (Carillo et al., 2017). Companies, understanding that the shift in consumer attention to digital is more permanent than transitory, have allocated more resources to digital advertising, particularly in influencer marketing (Insider Intelligence, 2021). As Voorveld (2019) affirms, influencer marketing offers an opportunity to reach various audience groups in a more direct, personal and interactive way. To understand the subject of influencer marketing, in particular the role that influencers and opinion leaders play in the digital age, the subject will be analyzed in depth in chapter 1. This doctoral thesis pursued to answer how the variables related to the relationship that a follower experiences with the influencer, being these identification, parasocial interaction and affinity, precede the response to the live videos published by the influencers. Also, to answer if the live videos published by influencers are effective to advertise the brands, this through the variables engagament and dependence on the video. Finally, the consequences of these variables were analized, evaluating how they affect the behavior and sentiment towards the brands that influencers advertise through the variables Brand advocacy, Brand attachment and Purchase intention. To answer the research questions, the research was designed with a causal empirical approach and the relationships between the variables were analyzed through a structural model. The measurement instrument with the measurement of the variables was a questionnaire, and a sample of n = 376 was obtained in Puerto Rico through a convenience sampling method mainly through email and on the social networks Instagram and Facebook. The results show that the variables related to the influencer: identification, parasocial interaction and affinity, affect the result towards the content published in a live video measured through the variables engagement and dependence on the medium. This relationship with the content that influencers publish live has an effect on the brands that are advertised, these results being the variables brand advocacy, brand attachment and purchase intention. In general, the findings of this doctoral thesis present theoretical and practical implications on the subject of influencer marketing. Specifically, the results provide value for a better understanding of the use of live video in influencer marketing, a recent channel in the literature. In addition, it offers recommendations for marketing management in companies. In particular for marketing and communication managers and directors, to use live videos in influencer marketing on the social networks Instagram and Facebook as an advertising alternative. The results of this thesis also serve as a guide for influencers, providing recommendations for the use of live videos in their content strategy and to more effectively manage their relationship with their followers.

Long-distance transport of caribou (Rangifer tarandus) can result in morbidities and mortalities. This case report describes the use of a long-acting tranquilizer, zuclopenthixol acetate (ZA) and live video monitoring (LVM) to transport caribou over 2300 kilometers. Two groups of captive caribou were transported from Fort St. John, British Columbia to Dryden, Ontario (n=14; 28.5 h transport) and Anchorage, Alaska (n=11; 32 h transport). The day prior to transport, caribou were administered ZA at 1 mg/kg via deep intramuscular (IM) injection. Digital video cameras allowed for live observation of caribou during transport. Still images of videos from each compartment in the Ontario transport were analyzed for percentage (%) lying versus standing over three time periods (Day 1, Night, and Day 2). Overall, caribou spent 57% of the transport lying down, with the highest percentage occurring at night (73%). As group size and animal density decreased there was a trend for caribou to spend more time lying down. Three animals developed extrapyramidal effects to the ZA and were effectively treated with midazolam at 0.2 mg/kg IM. There were no significant visible injuries or mortalities during or up to 6 weeks post-transport. Zuclopenthixol acetate and LVM were used to successfully transport caribou over long distances and should be considered in future translocations to improve animal welfare during transport.

Although deep learning has reached a higher accuracy for video content analysis, it is not satisfied with practical application demands of porn streamer recognition in live video because of multiple parameters, complex structures of deep network model. In order to improve the recognition efficiency of porn streamer in live video, a deep network model compression method based on multimodal knowledge distillation is proposed. First, the teacher model is trained with visual-speech deep network to obtain the corresponding porn video prediction score. Second, a lightweight student model constructed with MobileNetV2 and Xception transfers the knowledge from the teacher model by using multimodal knowledge distillation strategy. Finally, porn streamer in live video is recognized by combining the lightweight student model of visualspeech network with the bullet screen text recognition network. Experimental results demonstrate that the proposed method can effectively drop the computation cost and improve the recognition speed under the proper accuracy.

Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement.

Social presence is known to be important for distance education, and a common approach in online classes is to provide chat boxes and forums to provide the social presence. In such a class, however, learners must explicitly act beyond their normal learning activities, so often there is no social presence in the class even when there are several learners working on the same course material. In this paper, we develop an approach where learners can share the social presence without any explicit action; their normal learning activities would be used to provide visual cues for social presence. We present Cocode, a system designed for an online programming class that shows other learners' code editors and running output in the programming environment with minimum privacy issues. For evaluation, we ran two user studies with groups of participants who took an offline class and an online programming class from the university; results from the studies showed that learners felt less social presence in Cocode than in offline classes, but they felt significantly more social presence in Cocode than in online classes with live video lectures, forums, and chat sessions.

Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. Considering such devices, e.g., surveillance cameras or AR/VR gadgets, are resource constrained, although there has been significant work in creating lightweight deep neural networks (DNNs) for such clients, none of these can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, or requirements from the user. In this article, we introduce ApproxNet, a video object classification system for embedded or mobile clients. It enables novel dynamic approximation techniques to achieve desired inference latency and accuracy trade-off under changing runtime conditions. It achieves this by enabling two approximation knobs within a single DNN model rather than creating and maintaining an ensemble of models, e.g., MCDNN [MobiSys-16]. We show that ApproxNet can adapt seamlessly at runtime to these changes, provides low and stable latency for the image and video frame classification problems, and shows the improvement in accuracy and latency over ResNet [CVPR-16], MCDNN [MobiSys-16], MobileNets [Google-17], NestDNN [MobiCom-18], and MSDNet [ICLR-18].

Live video hosted by streamer is being sought after by more and more Internet users. A few streamers show inappropriate action in normal live video content for profit and popularity, who bring great harm to the network environment. In order to effectively regulate the streamer behavior in live video, a streamer action recognition method in live video with spatial-temporal attention and deep dictionary learning is proposed in this paper. First, deep features with spatial context are extracted by a spatial attention network to focus on action region of streamer after sampling video frames from live video. Then, deep features of video are fused by assigning weights with a temporal attention network to learn the frame attention from an action. Finally, deep dictionary learning is used to sparsely represent the deep features to further recognize streamer actions. Four experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can improve the accuracy and speed of streamer action recognition in live video.

Online video streaming is one of the channels of the current economic development of e-commerce. In this technology era, our lifestyle is gradually moving towards electronic development and the public use of e-commerce applications for their businesses. Online video streaming strategies, therefore, benefit marketers, and this is mainly due to their cost-effective properties. The main objective of this research is to investigate the influences of online video streaming on customers’ online purchase intention. Three independent variables include perceived credibility, perceived ease of use and perceived usefulness are used to study the influence of online video streaming on the consumers’ online purchase intention. A set of self-administered questionnaires was distributed to 215 respondents from three states in Malaysia which are Johor, Melaka and Selangor, who had the experience of online purchases that were affected by online video streaming. The collected data are then analysed using SPSS and Smart PLS software. The results of the analysis indicated that all three independent variables have a significant impact on consumers' online purchase intentions through online video streaming. In conclusion, this research is important to e-marketers in order to allow them to gain a better understanding of the online purchasing behaviour of their customers

In response to the COVID-19 pandemic, the emergency policy for online learning at home has been launched in China. Live video, prerecorded video, and their combination are the three main video lear...

In this paper, the authors develop an efficient face recognition algorithm from images or live video streaming for IoT systems based on K-nearest neighbor and support vector machine learning to recognize the person from the local database and extract the features of the face. Because of the complexity of the conditions, there might be some factors of facing errors like the size; the angle; the distance from the ear, nose, and eyes; etc. This sustainable machine learning-based IoT system is designed for sovereign face recognition with features extraction with improved accuracy near about 96%. The experimental study is done to test the performance of the face recognition in the changes of number of persons in video or images. Finally, this manuscript recognized persons from live video or images with accuracy approximately 96% by using the SVM and KNN classifiers and discussed with the block diagram and proposed algorithm.

The McCollough Effect is a color aftereffect produced by exposure to colored, oriented patterns. For example, following adaptation to vertical red and horizontal green stripes in alternation, vertical black and white patterns appear greenish, while horizontal black and white patterns appear reddish. The striking aspect of the McCollough Effect is that just a few minutes of adaptation can produce an aftereffect lasting days or weeks. Though this effect is easily induced, previous work has shown that stronger effects can be achieved with longer periods of adaptation. To allow especially long adaptation durations, the current work develops a novel method of induction of the McCollough Effect using live video feed, filtered by orientation, and viewed with a head-mounted display. Results showed that this “McCollough World” paradigm was as strong an inducer (per unit time) as traditional paradigms using gratings, while allowing observers to adapt comfortably for multiple hours. Two hours of McCollough World adaptation produced effects that were significantly larger than 20 min of traditional adaptation, which is close to the tolerance limits for gratings. This work provides insight into the features necessary for induction of the McCollough Effect and provides a strategy for creating especially strong and long-lasting color aftereffects.

Since the era of we‐media, live video industry has shown an explosive growth trend. For large‐scale live video streaming, especially those containing crowd events that may cause great social impact, how to identify and supervise the crowd activity in live video streaming effectively is of great value to push the healthy development of live video industry. The existing crowd activity recognition mainly uses visual information, rarely fully exploiting and utilizing the correlation or external knowledge between crowd content. Therefore, a crowd activity recognition method in live video streaming is proposed by 3D‐ResNet and regional graph convolution network (ReGCN). (1) After extracting deep spatiotemporal features from live video streaming with 3D‐ResNet, the region proposals are generated by region proposal network. (2) A weakly supervised ReGCN is constructed by making region proposals as graph nodes and their correlations as edges. (3) Crowd activity in live video streaming is recognised by combining the output of ReGCN, the deep spatiotemporal features and the crowd motion intensity as external knowledge. Four experiments are conducted on the public collective activity extended dataset and a real‐world dataset BJUT‐CAD. The competitive results demonstrate that our method can effectively recognise crowd activity in live video streaming.

It is relatively simple for a normal human to interpret and understand every banknote, but one of the major problems for visually impaired people are money recognition, especially for paper currency. Since money plays such an important role in our everyday lives and is required for every business transaction, real-time detection and recognition of banknotes become a necessity for blind or visually impaired people For that purpose, we propose a real-time object detection system to help visually impaired people in their daily business transactions. Dataset Images of the Iraqi banknote category are collected in different conditions initially and then, these images are augmented with different geometric transformations, to make the system strong. These augmented images are then annotated manually using the "LabelImg" program, from which training sets and validation image sets are prepared. We will use YOLOv3 real-time Object Detection algorithm trained on custom Iraqi banknote dataset for detection and recognition of banknotes. Then the label of the banknotes is identified and then converted into audio by using Google Text to Speech (gTTS), which will be the expected output. The performance of the trained model is evaluated on a test dataset and real-time live video. The test results demonstrate that the proposed method can detect and recognize Iraqi paper money with high mAP reaches 97.405% and a short time.

Background Shortening of hospital stay to 1 night has not affected the short-term safety of patients undergoing laparoscopic Roux-en-Y gastric bypass (RYGB). Whether the RYGB is feasible in an ambulatory setting (same-day discharge) without overnight hospital stay remains to be answered. We aimed to evaluate the feasibility of same-day discharge after laparoscopic Roux-en-Y gastric bypass (RYGB) using additional live video consultation and remote monitoring. Same-day discharge (SDD) was defined as surgery without postoperative overnight hospital stay. Methods This was a single-center prospective feasibility study in a selected group of patients undergoing a RYGB. Fifty patients undergoing a primary RYGB were selected and potentially treated following the SDD protocol. After SDD discharge patients were remotely monitored after surgery for 48 h using a medical device measuring vital signs three times a day. Video consultations were performed by a doctor twice a day for 2 postoperative days. Primary outcome was the success rate (%) of SDD. Secondary outcomes were emergency room presentations, readmissions, early complications (

Purpose To evaluate the feasibility of telementoring for aquablation by comparing the outcomes of onsite versus telemetry proctoring. Methods The telemetry device of choice was Proximie, an innovative digital platform that uses live video stream with an augmented reality technology. Our study retrospectively reviewed outcomes from our IRB approved prospective Aquablation database from March 2018 till October 2019. Procedures were guided by a proctor either onsite or remotely through telemetry. One-way ANOVA or Chi-square was used to compare perioperative parameters and mixed model ANOVA was used to compare functional outcomes. Results Our data included 59 patients who underwent a proctored-based Aquablation of which 21 were telementor guided and 38 were onsite guided. The initial ten procedures were done with the latter approach. There was no statistical difference in age, comorbidities, prostate size, and baseline serum markers amongst the two groups. In contrast, telementor guidance was associated with increased general anaesthesia use (76.2% vs. 21.1%) and haemostatic cauterization (81.0% versus 47.4%) with a p value 0.05). Conclusion Following an initial phase of onsite proctoring, telementoring can be safely used in the adoption phase of a new robotic technology. This approach allowed more flexibility in patient scheduling and reduced travel costs with similar surgical outcomes.

The Onduo Virtual Diabetes Clinic (VDC) for people with type 2 diabetes (T2D) combines a mobile app, remote lifestyle coaching, connected devices, and live video consultations with board-certified endocrinologists. Adults with T2D (n = 594) who were evaluated by a VDC endocrinologist, remotely prescribed and mailed a real-time continuous glucose monitoring (rtCGM) device and used ≥1 sensor completed a CGM satisfaction questionnaire. The CGM satisfaction score was 4.5 ± 0.8 out of 5. Most respondents (94.7%) agreed/strongly agreed that they were comfortable inserting the sensor remotely and that rtCGM use improved understanding of the impact of eating (97.0%), increased diabetes knowledge (95.7%), and helped improve diabetes control when not wearing the sensor (79.4%). HbA1c (n = 372) decreased from 7.7% ± 1.6% to 7.1% ± 1.2% (P

The outbreak of COVID-19 has led to a sharp transition from offline to online education in many countries and areas. This transition heightens the intensity of existing challenges of online education, such as student attendance and education equality. During this time of uncertainty, the vast disparities in teachers? online experience and technical backgrounds, students' education level and their families' economic status, and schools' support, further pose new challenges to teachers and students. In this work, we study how Chinese teachers and students addressed challenges during this transition. We interviewed 15 teachers and 18 students from diverse backgrounds at varying education levels (K-12 and college). Our work makes timely and new contributions to the literature of online education. For example, our results showed that teachers applied Live Video Streaming (LVS) on multiple social media platforms and re-purposed different entertainment features to deliver online teaching for better student engagement; some teachers came to enjoy this new form of instruction after being resistant to it in the beginning, and students developed a better sense of intimacy with their teachers after experiencing certain online interactions. Our work also reveals the remaining challenges and prospects of LVS-based online education and sheds light on the future design of collaborative technologies for online education.

Now a day with the major advancements in the field of vehicle automation, several dangerous and crucial counter terrorist activities is being handled by the sophisticated machines which are not only effective but also responsible for saving human lives. “Unmanned guided vehicle” is built to reduce human loss in terrorist attacks. The robot has been designed to face this attacks in the country. It is equipped with self-powered, radio operated and controller. A Wifi camera has been installed on it, in order to monitor the enemy activities whenever required. It is capable of entering into the enemy field silently there by sending us all the information regarding the enemy such as the weapons that were hold by them etc. This robot would be armed with an automatic weapon mounted onto a turret and a remote operator would be getting a live video feed from the camera to help him manually control both the above mentioned units of the rover. This spy robot can also be used in motels, shopping areas, jewellery show rooms, etc where there is more chance to terrorist’s attacks and robberies. Since human life is always precious, these robots are the replacement of fighters against terrorist in such areas.

—The COVID-19 pandemic has prevented close engagement with students to avoid the spread of the virus Hence, the transition from the traditional classroom methods to fully online teaching mode is essential Academicians need to be equipped with the necessary devices and technical skills to conduct live video meetings (synchronous) and create recorded video (asynchronous) In this study, we have conducted a survey to assess the readiness of the academicians at the Center for Foundation Studies in Science, University of Malaya to teach online 88 academicians participated in this study which consists of 68 local staff and 20 Japanese staff The survey was conducted using Google Form, which focused on three main aspects of readiness They were hardware readiness, software readiness and behavior (which include experiences, opinions, and expectation on online teaching) The results show that necessary equipment to conduct video meetings for lectures and tutorials such as webcam, microphone and should be provided Continuous training should be provided to increase academicians’ competencies in using the applications used We hope that this research will provide some insights on the current condition that our academicians are having, what is the problem that they are facing and to plan ahead © 2021 by the authors

We address in this work security and privacy threats for connected camera glasses, for which very few investigations have been conducted so far, despite the considerable attention to understanding security concerns for other wearables, such as smartwatches and fitness trackers. To raise awareness about such threats, we present the results of a case study involving a low-cost spy camera glasses device, readily available on the market, that can be used to record and stream live video, for which we demonstrate infringement of several privacy requirements (regarding the camera glasses device itself, the data collected by the embedded video camera, the wearer of the device, and for bystanders as well) that lead to corresponding security threats (e.g., data confidentiality, integrity, availability, and access control). To foster replicability and reproducibility of our empirical results, investigation method, and implementation of attacks, we describe our case study in the form of a detailed activity log and release full C++ code implementing our approach. Furthermore, we present our findings to three IT security experts and summarize their recommendations for designing more secure connected camera glasses.

Live video streaming platforms have attracted millions of streamers and daily active users. For profit and popularity accumulation, some streamers mix pornography content into live content to avoid online supervision. Therefore, accurate recognition of porn streamers in live video streaming has become a challenging task. Porn streamers in live video present multimodal characteristics including visual and acoustic content. Therefore, a porn streamer recognition method in live video streaming is proposed that uses attention-gated multimodal deep features. Our contribution includes the following: (1) multimodal deep features, i.e., spatial, motion and audio, are extracted from live video streaming using convolutional neural networks (CNNs), in which the temporal context of multimodal features is obtained with a bi-directional gated recurrent unit (Bi-GRU); (2) the tri-attention gated mechanism is applied to map the associations between different modalities by assigning higher weights to important features for further reduction in the redundancy of multimodal features; (3) porn streamers in live video streaming are recognized via the attention-gated multimodal deep features. Six experiments are conducted on a real-world dataset, and the competitive results demonstrate that our method can effectively recognize porn streamers in live video streaming.

Live video hosted by streamers is being sought after by an increasing number of Internet users. Some streamers mix pornographic content with live video for profit and popularity, but this greatly harms the network environment. To effectively identify porn streamers, a multilevel fusion method of multimodal deep features for porn streamer recognition in live video is proposed in this paper. (1) Visual and audio features including spatial, audio, motion, and temporal context in live video are extracted by a multimodal deep network. (2) Audio-visual attention features are obtained by fusing visual and audio features at the feature level based on a multimodal attention mechanism. (3) Text features are extracted by using the bullet screen text network based on the BERT (bidirectional encoder representations from transformers) model after collecting text information from the viewers’ bullet screen comments. (4) The prediction results of the audio-visual deep network and the bullet screen text network are fused at the decision level to improve the porn streamer recognition accuracy. We build a real-world dataset of porn streamers and conduct experiments and demonstrate that our method can improve the porn streamer recognition accuracy.

This paper summarizes a set of design considerations that survey researchers exploring the potential for live video to substitute for in-person interviewing will need to address. While the solutions appropriate for a particular study are likely to vary, researchers will need to consider (at least) which sample members have access to video and will be willing and able to participate, which video platform(s) to use, whether interviews need to be scheduled in advance or are conducted on demand, how interviewers’ screens should be configured, the interviewer’s visual background and auditory environment, and how interviewers should be trained to administer video interviews, avoid bias, and be prepared to handle technological problems as they arise.

The recent global demand for video streaming applications has paved the way for peer-to-peer streaming system (P2PSS). Strategic scheduling scheme and dynamic overlay topology are essential to maintain quality of service (QoS) and quality of experience (QoE) in P2PSS. The concept of P2PSS was tailored towards relying on active peers’ bandwidth to achieve cheap and scalable means of distribution over the Internet, such that peers with highest bandwidth serve as backbones for others. However, selecting backbone peers in low-capacity network environment is challenging due to insufficient bandwidth and poor infrastructure, thereby resulting in poor QoS and unpleasant user’s QoE. In this paper, we conducted a survey on users’ experiences with live video in selected locations in Nigeria. We designed an adaptive P2P streaming protocol and performed a packet-level simulation in Network Simulator 3(NS-3). Diverse simulation scenarios were set up to evaluate the proposed streaming protocol. Trace files data were analysed to measure end-to-end delay, start-up delay, and throughput. Furthermore, the proposed streaming protocol was benchmarked against selected existing schemes. The evaluation results revealed a 7.4% and 28% reduction in start-up and in end-to-end delays and 9% increase in throughput.

هدف البحث الحالي تحديد أنسب نمط للعرض في بيئة الواقع المعزز (الرسوم المتحرکة – الفيديو الحي) لتلاميذ المرحلة الإعدادية ذوي أسلوب التعلم (العميق - السطحي) لتنمية الانخراط في التعلم، وتم استخدام التصميم التجريبي العاملي (2×2)، وتطبيق البحث على عينة قوامها (80) من تلاميذ الصف الثالث الإعدادي. صنفت العينة إلى أربعة مجموعات تجريبية بعد تطبيق استبانة أسلوب التعلم (العميق والسطحي) کالآتي: المجموعة الأولى: عدد (20) تلميذ (عميق× رسوم)، والثانية: عدد (20) تلميذ (عميق× فيديو)، والثالثة: عدد (20) تلميذ (سطحي× رسوم)، والرابعة: عدد (20) تلميذ (سطحي× فيديو). وتمثلت أدوات القياس في مقياس الانخراط في التعلم، ودلت نتائج البحث على تفوق التلاميذ الذين درسوا بنمط الرسوم المتحرکة على الذين درسوا بنمط الفيديو في بيئة الواقع المعزز، وتفوق التلاميذ ذوي أسلوب التعلم العميق على التلاميذ ذوي أسلوب التعلم السطحي، وحدوث تفاعل بين نمط عرض المحتوى التعليمي (رسوم متحرکة/ فيديو حي) في بيئة الواقع المعزز وأسلوب التعلم (عميق/ سطحي)، لصالح المجموعة التجريبية (عميق× رسوم متحرکة). The aim of this research is to determine the most appropriate display type in the augmented reality environment (animation - live video) for preparatory school students with a (deep - surface) learning style to develop learning engagement. The global experimental design (2 × 2) was used, and the research was applied to a sample of (80) students in the third year of preparatory school, and they were divided into four experimental groups after applying the (deep and surface) learning style questionnaire, the first group: (20) students (deep x animation), the second: 20 students (deep x video), the third: 20 students (surface x animation), and the fourth: 20 students (surface x video). The measurement tools were represented in learning engagement scale, and the results of the research indicated the superiority of students who studied with the animation type over those who studied in the video type in the augmented reality environment, and the superiority of students with the deep learning style over students with a surface learning style, and there was an interaction between the educational content display type (animation / video) in augmented reality environment and learning style (deep / surface), for the experimental group (deep x animation).

PurposeThe purpose of this paper is to identify the impact factors that might influence audiences' voluntary donation to content creators on the online platforms, and to build an effective prediction model by considering both content and creator-related features.Design/methodology/approachThis study collected the real-world data of content consumption from Xueqiu.com and extracted both content and creator characteristics from the data set. The best donation prediction model based on such features was determined by evaluating four prevalent classifiers with various performance metrics. Furthermore, three feature selection methods were applied to validate the robustness of the constructed model, and then the predictability of different feature groups was examined. Finally, we conducted an interpretive analysis to identify relatively important predictors.FindingsThe experimental results show that the random classifier with all extracted features outperformed other built models and achieved excellent performance, indicating the usefulness of these factors in predicting the donations. Moreover, the predictability of content features was demonstrated to be relatively better than that of creator ones. Finally, several particularly important predictors were identified such as the number of modal particles in the article.Originality/valueThis study is among the first to investigate what factors might drive customers' voluntary donation to content contributors on social websites. Different from previous studies focusing on live video streaming, we expand the research vision by examining the donations to user-generated text content, calling for attention to other important topics in the burgeoning industry.

The recurrent deaths of sewage workers make surveillance of sewage an urgent activity. Therefore, in order to ensure the health of workers working under these harsh conditions,there is a need to develop technologies. In this paper, we intend to provide a system that can quantify and analyze the amount of harmful gasses in realtime and send an warning message when the levels are beyond the threshold. ?This project attempts to device an IoT technology using Raspberry Pi, ThingSpeak platform, and Pushover application. ?The gas levels along with humidity and temperature shall be observed. The experimental results show that whenever the gas levels exceed the threshold, the authorized individuals receive an alert on the connected mobile devices. In addition, the device offers live streaming of video to check blockages, if any.The directions for future research have been included at the end of the conclusion.

Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate and search for the alternate band by a conditional handoff. Existing handoff methods reduce the network’s efficiency. The proposed stochastic approach under hybrid Spectrum Hand-off is (SHO) employing the Invasive Weed Optimization (IWO) algorithm thus increasing the SHO efficiency in CR Networks. The proposed Centralized Cognitive Device monitors load balancing, minimizes handoff delay as well as the SU’s service time, conserves energy, speeds up the data transmission, supports the Internet of Things, multimedia applications including audio, live video streaming and still images over resource constrained WSNs. This algorithm is compared with the existing Genetic algorithm and Particle Swarm Optimization. The channel selection accuracy of the proposed IWO method is found to be 97.8% and it outperforms conventional methods. Besides, the pre-emptive resume priority M/G/1 queuing model is also utilized. This paper presents a complete system model along with a detailed study of its parameters proving the technique’s effectiveness.

The advent of Cyber-Physical Systems (CPS) has brought a revolutionary change coined as a mixture of information, communication, computation, and control. With applications in smart grid, health monitoring, automatic avionics, distributed robotics, etc., CPS is currently an area of attention among the academia and industry. The advancement of mobile communications and embedded technology has made it possible to build large scale CPS consisting of the interconnection of mobile phones. These devices collect information about the surrounding environment at any time anywhere basis through real-time video capture. Video streaming has proven to be a massive industry that is growing rapidly playing an important role in everyday life. Customer-driven approach wanting best experience with quality has to be the core offering of contemporary scenario. Video streaming is categorized into Video-On-Demand Streaming (VoDS) and Live Video Streaming (LVS) showing the current state-of-art opportunities. Many diverse applications of video streaming are military video surveillance using drones, live sports match player face recognition, on-demand video characters recognition, movie summarization like identifying parts of the movie which are viewed many times by different users, movie and series recognition, motion detection, gesture recognition, image segmentation, etc. This paper introduces an approach to develop video analysis on VoD and LVS using cloud-based services and analyzes the impact of Quality of Experience (QoE), cost, and bandwidth on the cloud. To achieve the best user experience for video streaming and video analysis, Content Delivery Network (CDN) offers the best QoE at various analyzed locations using various cloud providers like Amazon Web Services (AWS) CloudFront, Google Cloud CDN, Azure CDN, Akamai CDN, etc.

Assuming that video streaming is now responsible for the absolute majority of the Internet traffic and considering that the audience uses WebRTC-enabled web browsers and mobile devices to access and retrieve content, this work proposes the development of a peer-to-peer overlay network to assist the delivery of video streaming events that use HTTP-based protocols without the need to install additional software. Using the peer-to-peer network, the client/server model becomes hybrid, where network nodes that are watching the same event can retrieve portions of the video content directly from the server or neighboring nodes. This approach has two main objectives; decrease the client/server traffic and consequently the economic cost of delivery while improving the quality of the users' experience, given that communication between neighboring nodes can support the flow of better quality videos between the points

There is increased utilisation of cytopathology to provide a rapid onsite evaluation (ROSE) of fine needle aspiration and touch preparations of small biopsies. A well-executed ROSE procedure can significantly impact the diagnostic quality and appropriate specimen triage of procured biopsy materials. To accommodate the demand for ROSE, telecytology has been increasingly implemented to facilitate ROSE occurring remotely. Telecytology can be categorised based on camera systems including eyepiece system, camera port system and robotic microscope/whole slide image scanner system. Image sharing methods include static images, broadcast only live video streaming, teleconferencing and whole slide image management system. In this review, we will discuss the advantages and disadvantages of each of these systems and deployment considerations.

Background and aims During COVID-19 pandemic, researchers are using innovative technologies for fast-tracking the development to end this menace. Virtual Reality (VR) also offers an imperative role for fighting this pandemic, through audiovisual-based virtual communication. Methods A brief study on Virtual Reality and its applications for the COVID-19 pandemic is carried out by employing keywords as Virtual reality or VR and COVID-19 from the databases of SCOPUS, Google Scholar, PubMed, Web of science Academia and ResearchGate. Results VR is beneficial for remote sites for exploring telemedicine, planning, treatment, and controlling of the infections by providing proper awareness to the people regarding this disease. Conclusions VR technology develops a platform to reduce the face to face interaction of doctors with the infected COVID-19 patients. Through live video streaming, it helps to improve surveillance systems on the ongoing situation., Highlights OF THE STUDY • Virtual Reality (VR) plays a vital role in effectively handling of the ongoing pandemic COVID-19 cases. • This technology is useful for patient treatment, medical marketing, disease awareness and medical training & learning. • VR is for image acquisition and proper communications to provide awareness concerning this disease. • This technology is helpful for physical rehabilitation and pain management of an infected patient during the treatment process.

In recent years, peer-to-peer (P2P) live streaming is popularised by the scalability and cost-effectiveness of P2P networks. User satisfaction in P2P live streaming systems depends on several factors, including chunk scheduling techniques and overlay construction mechanisms in these systems. P2P live streaming systems are involved with the peer and chunk selection problems to improve quality parameters of streaming, such as playback continuity, startup delay, and playback latency. In this study, WidePLive as a P2P live video streaming system is proposed. In WidePLive, the authors proposed a low-delay overlay construction mechanism and a mixed strategy based chunk scheduling scheme which are coupled together by a contribution-aware peer selection strategy as a coupling feature to improve the quality parameter. The proposed overlay construction mechanism allows new peers to have the opportunity to connect with previous peers near the server and forms a low-depth and low-delay overlay. The proposed chunk scheduling scheme uses the benefits of Rarest First and Greedy strategies to trade-off between quality parameters. The evaluation of WidePLive simulation results demonstrates an acceptable improvement in streaming performance and shows that WidePLive has lower startup delay and playback latency and higher playback continuity compared to previous works.

Despite the numerous reported benefits of implementing technology-enhanced learning (TEL) strategies in sport coaching, and advancements in the ‘user-friendliness’ of technologies available to huma...

The aim of this study was to compare the effectiveness of live-video and video demonstration methods in training dental students in orthodontic emergency applications. A total of 105 fifth-year dental students at a dental school in Turkey participated in the study in 2018. A pretest was given to the students to evaluate their level of knowledge about band cementing and re-bonding of brackets. Subsequently, two clinical applications were demonstrated with either live-video or video demonstration. During the live-video demonstration, the lecturer gave information about the steps of the procedure while performing the clinical application on the patient using a camera attached to the loupes. The students were able to see the process on the screens. During the video demonstration, previously recorded videos of those clinical applications were shown, and information was given to students in a classroom. On the next day, posttests were given to the students. The posttest also asked students to give their opinions about both methods. The results showed that the mean posttest scores on the video demonstration were significantly higher than on the live-video demonstration. However, no significant difference between the demonstration methods was found with regard to increase of scores from pre- to posttest. Most students preferred use of the two demonstrations together for education in the clinical orthodontics lecture. This study found that the two demonstration methods had comparable effects on increasing students' level of knowledge. However, from the students' perspective, the two should be used together to achieve the highest effect.

Teamwork and interdisciplinary collaboration play an import role especially in pediatric oncology. Currently, team training workshops integrate simulation-based education to a varying extent. Unfortunately, high-end equipment is costly and therefore not accessible to all educators. To overcome this financial barrier, the implementation of low-cost live video and audio equipment for simulation and debriefing in pediatric oncology was investigated. Here, an easy-to-adopt setup which proved effective in simulation-based team trainings in pediatrics is introduced.

Live video broadcasting normally requires a multitude of skills and expertise with domain knowledge to enable multi-camera productions. As the number of cameras keep increasing, directing a live sports broadcast has now become more complicated and challenging than ever before. The broadcast directors need to be much more concentrated, responsive, and knowledgeable, during the production. To relieve the directors from their intensive efforts, we develop an innovative automated sports broadcast directing system, called Smart Director, which aims at mimicking the typical human-in-the-loop broadcasting process to automatically create near-professional broadcasting programs in real-time by using a set of advanced multi-view video analysis algorithms. Inspired by the so-called "three-event" construction of sports broadcast, we build our system with an event-driven pipeline consisting of three consecutive novel components: 1) the Multi-view Event Localization to detect events by modeling multi-view correlations, 2) the Multi-view Highlight Detection to rank camera views by the visual importance for view selection, 3) the Auto-Broadcasting Scheduler to control the production of broadcasting videos. To our best knowledge, our system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events. It is also the first system to solve the novel problem of multi-view joint event detection by cross-view relation modeling. We conduct both objective and subjective evaluations on a real-world multi-camera soccer dataset, which demonstrate the quality of our auto-generated videos is comparable to that of the human-directed. Thanks to its faster response, our system is able to capture more fast-passing and short-duration events which are usually missed by human directors., Comment: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

The growth of live video streaming (LVS) technology provides new possibilities for online tutoring in that it accommodates a massive number of learners simultaneously. Questions still exist, howeve...

The Onduo Virtual Diabetes Clinic (VDC) telehealth technology/care model for adults with type 2 diabetes (T2D) combines connected devices, remote lifestyle coaching, and clinical support with a mobile App. Key differentiating program features are the availability of live video consultations with board-certified endocrinologists for medication management and real-time continuous glucose monitor use for higher-risk participants. Preliminary data ( n = 740) suggest that participation was associated with a significant improvement in HbA1c with up to 6 months follow-up in those not meeting treatment targets. HbA1c decreased by 2.3% ± 1.9%, 0.7% ± 1.0%, and 0.2% ± 0.8% across baseline categories of >9.0%, 8.0%-9.0% and 7.0% to

Many existing mobile robotic telepresence systems have equipped with two cameras, one is a forward-facing camera for video communication, and the other is a downward-facing camera for robot navigat...

Crowdsourced live video broadcasting services, such as Twitch and YouTube Live, are becoming increasingly popular. In such a service, viewers are allowed to perform rich interactions, such as posting comments and donating monetary virtual gifts, while watching videos. Understanding viewer interactions is essential for people to comprehend the production and consumption of the crowdsourced live video content and improve the service. However, the basic characteristics of the viewer interactions are still unknown. In this paper, we present a comprehensive measurement study of the viewer interactions on Douyu, a popular crowdsourced live video broadcasting website in China. Our measurement spans four months and contains comment posting and virtual gift donating interactions from tens of millions of viewers in hundreds of thousands of channels. Based on the measurement data, we carry out a content analysis on danmu comments and characterize the patterns of the viewer interactions. We build a suite of models for capturing the gift donating process, viewer activity, and channel popularity. We further analyze the influences of the broadcaster’s behavioral factors on a channel’s popularity and present methodologies for popularity predicting. Our measurement and analysis have important implications on the design and business policy of the crowdsourced live video broadcasting services.

Autistic adults experience barriers to primary care, including distant/inaccessible clinics, sensory stressors, and communication barriers with providers. Synchronous telehealth visits, termed “virtual visits,” may be a way to minimize these barriers. We investigated the advantages and disadvantages of primary care virtual visits using a phenomenological approach. We interviewed autistic adults ( n = 7) and caregivers of autistic adults ( n = 12) from one primary care clinic. Using thematic analysis, we identified three advantages to virtual visits: (1) increased patient comfort from avoiding travel to the clinic, crowded waiting rooms, and other sensory stressors; (2) increased safety by avoiding physical contact with others who may be sick; and (3) similar or better patient–provider communication than in-person visits. Disadvantages included the following: (1) Internet instability and other technological issues, (2) the inability to receive hands-on care from the provider, and (3) reduced patient engagement due to environmental distractions. Virtual visits may minimize barriers to care for autistic adults by reducing travel time, exposure to sensory stressors, and barriers to communication with the provider. While we recognize that virtual visits may not be appropriate for all patients or in all situations, they may be a promising model of health care delivery for autistic adults. Lay abstract Autistic adults face many barriers to receiving quality primary health care like clinics that are far away and sensory sensitivities. Real-time telehealth visits, called “virtual visits,” are live video chats between the patient and provider. Virtual visits may minimize barriers to care for autistic adults. We wanted to describe advantages and disadvantages of using virtual visits for delivering primary health care for autistic adults. We interviewed 7 autistic adults and 12 caregivers of autistic adults who receive primary care through one clinic. Autistic adults and caregivers said advantages to virtual visits were that (1) patients were more comfortable at home, (2) patients could get health care while avoiding physical contact with other people during the pandemic, and (3) virtual visits were similar to or better than in-person visits. The disadvantages included that (1) there could be technology problems like grainy video, (2) the doctor could not physically examine the patient (e.g. look in ears), and (3) patients sometimes participated less in the virtual visit than they would in person. Virtual visits may be beneficial for autistic adults by eliminating travel to the clinic and avoiding stressful sensory stimuli. We recognize that virtual visits may not work for all patients or in all situations. However, our study shows that primary care virtual visits may be beneficial for autistic adults during and beyond the pandemic.

Aim To investigate whether live video streaming from the bystander’s smartphone to a medical dispatcher can improve the quality of bystander cardiopulmonary resuscitation (CPR) in out-of-hospital cardiac arrest (OHCA). Methods After CPR was initiated, live video was added to the communication by the medical dispatcher using smartphone technology. From the video recordings, we subjectively evaluated changes in CPR quality after the medical dispatcher had used live video to dispatcher-assisted CPR (DA-CPR). CPR quality was registered for each bystander and compared with CPR quality after video-instructed DA-CPR. Data were analysed using logistic regression adjusted for bystander’s relation to the patient and whether the arrest was witnessed. Results CPR was provided with live video streaming in 52 OHCA calls, with 90 bystanders who performed chest compressions. Hand position was incorrect for 38 bystanders (42.2%) and improved for 23 bystanders (60.5%) after video-instructed DA-CPR. The compression rate was incorrect for 36 bystanders (40.0%) and improved for 27 bystanders (75.0%). Compression depth was incorrect for 57 bystanders (63.3%) and improved for 33 bystanders (57.9%). The adjusted odds ratios for improved CPR after video-instructed DA-CPR were; hand position 5.8 (95% CI: 2.8–12.1), compression rate 7.7 (95% CI: 3.4–17.3), and compression depth 7.1 (95% CI: 3.9–12.9). Hands-off time was reduced for 34 (37.8%) bystanders. Conclusions Live video streaming from the scene of a cardiac arrest to medical dispatchers is feasible. It allowed an opportunity for dispatchers to coach those providing CPR which was associated with a subjectively evaluated improvement in CPR performance.

Recent natural and man-made devastations have prompted researchers to look into rescue robot systems. A novel shape-shifting remotely controlled mobile robot system rescue framework with live video telecasting is presented in this paper. Currently, the rescue system is under-trained and under-equipped. Since the majority of rescue operations are conducted there, it takes time and is often difficult to reach the accident site with such a large device. As a result, we present a paper in which these considerations are the primary focus. This paper describes a robotic device that is powered by a microcontroller and a motor driver, all of which are processed by the Arduino IDE, and which can be controlled from a remote location.