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Background: Fractures of the distal radius are among the most common bone injuries, and their frequency is constantly increasing, leading to an elevated need for subsequent rehabilitation. This growing need has led to the emergence of online content aimed at providing guidance on rehabilitation. Nonetheless, unreviewed online content raises concerns about its reliability; therefore, the objective of this study was to evaluate the quality, reliability, and comprehensiveness of online videos concerning rehabilitation following a distal radius fracture. Methods: A total of 240 YouTube videos were screened, identifying 33 videos that met the inclusion criteria. These selected videos were evaluated by five independent experts from various professional groups, using the Global Quality Scale, the DISCERN reliability tool, and the JAMA Benchmark Score, as well as a structured set of questions to assess their comprehensiveness and coverage of pertinent aspects. Results: The observers' assessment of the Global Quality Scale exhibited a broad spectrum of viewpoints, indicating considerable variability in evaluations. In most cases, therapy aligned well with the diagnosed condition, and most raters deemed the indication and instruction in the videos acceptable. A proportion of 87% of the videos was deemed suitable for home training by at least three raters. However, a concerning trend emerged, as potential risks and pitfalls were scarcely addressed. Conclusions: The moderate overall quality of the videos and the divergence in expert opinions highlight the need for a regulatory authority to ensure adherence to guidelines and maintain high-quality content. Additionally, our results raise concerns about the applicability of established assessment tools in this context. [ABSTRACT FROM AUTHOR]

Audiovisual productions currently dominate internet content, accounting for approximately 82% of online traffic in 2022. With the popularization of adaptive video streaming over the internet, transcoding has become an essential task to allow for bitrate adaptation and compatibility between servers and client devices that support various standards and formats. However, such conversion presents a high computational cost due to its cascade features, which sequentially runs the video decoding and re-encoding processes. This high cost exists especially due to the various encoding mode possibilities allowed in video codecs, mainly in recent, highly efficient standards and formats. On the other hand, the cascade transcoder model allows the possibility of inheriting information from the original bitstream, which can be used to accelerate the re-encoding process. This survey presents a systematic literature review of the main works published in the literature that propose transcoding acceleration methods for the main video coding standards and formats developed in the last decades. The majority of the works focus on both homogeneous and heterogeneous transcoding for the MPEG-2, H.264/AVC, H.265/HEVC, H.266/VVC, VP8, VP9, and AV1 formats and standards, and are based on heuristics and prediction models trained with machine learning algorithms. Based on the systematic literature review, a classification of the works according to the transcoding type and the methodology employed for acceleration is presented. The results obtained in each solution are compared in terms of complexity reduction and compression efficiency, and future trends on the field are discussed. [ABSTRACT FROM AUTHOR]

Advances in automatic speech recognition technology, increases in bandwidth availability, and the widespread use of video streaming and sharing platforms have opened new horizons for corpus phonetics. CoANZSE Audio, a searchable online version of the Corpus of Australian and New Zealand Spoken English, provides access to over 195 million words of transcribed speech from transcripts of videos uploaded to YouTube by councils and other local government entities in Australia and New Zealand. Audio and forced alignment files are also available, making the resource suitable for the investigation of a range of research questions pertaining to morphosyntax, phonetics, and discourse. The resource, which is freely available via login through CLARIN, Europe’s main language resources infrastructure network, was created through the use of open-source tools and software: yt-dlp, a Python library for collecting data from video and streaming websites; the Montreal Forced Aligner, a recent neural network alignment suite; and Parselmouth-Praat, Python bindings for the Praat acoustic analysis software. The website is powered by BlackLab, which combines a powerful search engine based on Apache Lucene with an intuitive web frontend. CoANZSE Audio may be useful for the investigation of regional differentiation of language features, and with additional annotation, differences in feature use according to social or demographic groups. Recent applications have included studies of double modals, a rare syntactic feature, and apology sequences. The nature of the audio and alignment data may make the resource especially suitable for the study of regional phonetic variation. Furthermore, the methods used to create the resource may be of interest to researchers seeking to adopt a pipeline approach for the creation of specialized corpora from publicly available online content. [ABSTRACT FROM AUTHOR]

How to exploit social networks to make internet content spread rapidly and consistently is an interesting question in marketing management. Although epidemic models have been employed to comprehend the spread dynamics of internet content, such as viral videos, the effects of advertising and individual sharing on information dissemination are difficult to distinguish. This gap forbids us to evaluate the efficiency of marketing strategies. In this paper, we modify a classic mean-field SIR (susceptible–infected–recovered) model, incorporating the influences of sharing and advertising in viral videos. We mathematically analyze the global stability of the system and propose an agent-based modeling approach to evaluate the efficiency of sharing and advertising. We further provide a case study of music videos on YouTube to show the validity of our model. [ABSTRACT FROM AUTHOR]

"Visualizing digital discourse: interactional, institutional and ideological perspectives" is a book edited by Crispin Thurlow, Christa Dürscheid, and Federica Diémoz. The book explores the roles of visuals as an aspect of multimodality in digital communication. It consists of 12 chapters categorized into three parts: emoji-centered communication, digital visuality from interpersonal and institutional perspectives, and multimodality in organizational and commercial contexts. The book offers diverse methodologies, extensive topics, and practical principles for digital discourse research, making it valuable for researchers and postgraduate students interested in multimodal approaches to digital discourse and the roles of visuality in digital media. [Extracted from the article]

Copyright of Journal of Ege University School of Dentistry / Ege Üniversitesi Dis Hekimligi Fakültesi Dergisi is the property of Ege University Faculty of Dentistry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spanish television is increasingly subject to transmedia consumption. Faced with traditional models in which journalists limited themselves to rigorously transmitting information to strengthen their audiences, the networks' commitment to infotainment, as well as the competition with platforms or with figures from YouTube and Twitch, forces professionals to reinvent themselves, often by resorting to entertainment and show business resources. TV programs such as El intermedio (La Sexta, 2006) and El hormiguero (Antena3, 2006) have been working for years to bring journalists and comedians together in the same space, and others such as Todo es mentira (Cuatro, 2019) interact with audiences through social networks such as Twitter and Instagram. The question is whether these efforts are sufficient or whether we are witnessing a transition period in which interviews conducted by content creators such as Ibai Llanos or Jordi Wild will end up being more highly valued than those of a journalist. To answer these questions, six professionals from the current audiovisual scene were interviewed: Vicente Vallés, Antonio Castelo, Cristina Pardo, Rocío Vidal (La Gata de Schrödinger), José Antonio Lavado, and Jorge Gallardo Camacho, and their reflections were contrasted with those of three focus groups with audiences of different ages. It was concluded that the professionals recognize the necessity of adapting to the digital era and to the new needs of their audiences, but prioritize traditional journalistic rigor and do not fear competition from figures such as Ibai Llanos. However, the audience focus groups consulted, especially the younger ones, admit that they consume more audiovisual content on the Internet than on television. [ABSTRACT FROM AUTHOR]

Video content on the internet commands a substantial portion of online activities and holds immense value in driving effective marketing campaigns. The question is, how can we measure the potential of video content to capture user attention and how to improve video effectiveness without techniques that increase cognitive load and result in avoidance and skipping of advertisements. This study investigated how video dynamics represented by dedicated metrics is related to eye-tracking patterns and whether video dynamics can be a predictor of user engagement represented by the number of fixations and the duration of gaze on video content. The results showed that selected metrics can serve as predictors of eye movement patterns and that video dynamics have a real impact on user engagement with the viewed video content. [ABSTRACT FROM AUTHOR]

Children today are surrounded by both traditional and cutting-edge digital media worldwide. Children's daily life now includes screen time starting as early as infancy. Preschoolers exposed to interactive and screen media may benefit in a variety of ways, but excessive or unsuitable screen time may offer health and developmental hazards. The environment, including parents, siblings, and classmates, has a major effect on children's development. More than ever before, children are absorbing video and internet gaming content. Approximately one hour every day is spent engaging with and observing digital screens. Children are interacting with screens more frequently every day as virtual learning approaches are adopted more frequently in classrooms. Even while screen time is a necessary part of life at home and at school, keeping track of how much time your child spends using screens is crucial for their general development. To encourage healthy development in this digital age, In order to promote in-person play and physical exercise among preschoolers, parents and caregivers must endeavor to reduce screen-based activities. [ABSTRACT FROM AUTHOR]

Videos are an engaging medium for learning as they provide affordances beyond text or audio-only, thereby allowing the creator more flexibility for content generation. Easy access to videos on the Web and their popularity within everyday discourse has made them an accepted medium for teaching and learning. However, this increase in the availability of videos makes it challenging for an instructor, teacher, or learner to assess their viability for learning. Each search can lead to thousands of videos on a given topic. Although content platforms and search engines use a range of data and algorithms for the recommendation, these are not tailored to recommend video content specifically for learning. In this paper, we present and test a theoretically motivated model to rate videos on their potential to support learning goals. Visual Appeal, Understanding of Content, Engagement with Topic, and Recommendation Preference (VUER) are the four components of our proposed model. We assess the model on two content areas, Big Data and Augmented Reality, using videos from the TED (Technology, Entertainment, and Design) platform. Using crowdsourcing, we collect ratings for items in the VUER model and assess effectiveness through the correlation between 1) model elements and self-reported learning and 2) the rank of a video in the TED search and self-reported learning. Results show that the VUER model ratings of videos strongly correlate with the expected learning gain of the users for videos on both topics. The expected learner ranking and observed TED ranking of videos do not necessarily align, suggesting that the ranking on platforms such as TED is not a direct fit to judge the teachability of the video content. Thus, there is a need to create personalized systems to support online video curation tasks for learners and teachers. [ABSTRACT FROM AUTHOR]

Objective. To identify the cultural knowledge of suicide that children have in middle childhood, how they know about it, what causes may they attribute to it, the associated emotions and their beliefs for its prevention. Methodology. A total of 59 urban boys and girls between 8 and 11 years old from Guadalajara, Mexico, were interviewed. Results. Most of the participants knew the meaning of the word suicide. They obtained knowledge of suicide through content on the Internet and television. Likewise, the emotional problems in children, emotional abuse in the family and bullying were placed as causes of child suicide. The associated emotions were sadness, anger, and fear. They considered that suicide prevention could be through greater understanding towards the child, greater coexistence, monitoring those at risk of suicide and physically preventing it. Conclusions. It is possible to propose culturally appropriate prevention strategies. [ABSTRACT FROM AUTHOR]

Children are more than ever exposed to all kinds of video contents on the Internet. Consequently, several studies have proposed different techniques to detect videos that can be harmful for children. However, we note so far, that no attention has been given to the cartoon characters and the underlying language used. To address this gap, we propose to evaluate the effectiveness of using actual images of cartoon characters and the language used in cartoons in categorising videos as being appropriate or inappropriate for children. We do so through the development of a multi modal classifier, which makes use of the output from two deep learning networks: LSTM for text analysis and VGGNet for image analysis. More specifically, the LSTM network is used to process user comments and closed captions associated with a video and the VGGNet network is used to recognize cartoon image characters. The LSTM model was trained and tested on a dataset comprising about 290,000 labelled text records, while the VGGNet model was trained and tested on a manually annotated image dataset of 6000 cartoon characters. A testing accuracy of 94% was obtained for the LSTM network while a testing accuracy of 99% was obtained for the VGGNet network. Our proposed approach was further evaluated using 50 actual videos intended for children from YouTube. Here also, a good accuracy of 72% was obtained using LSTM alone, while a better accuracy of 78% was obtained using VGGNet alone and an accuracy of 76% was obtained using the combined output from the LSTM and VGGNet networks. We conclude that closed captions, user comments and images of cartoon characters are all useful in detecting unsafe videos for children and can be considered as essential parameters to include when developing multimedia filtering tools. [ABSTRACT FROM AUTHOR]

Affective computing has suffered by the precise annotation because the emotions are highly subjective and vague. The music video emotion is complex due to the diverse textual, acoustic, and visual information which can take the form of lyrics, singer voice, sounds from the different instruments, and visual representations. This can be one reason why there has been a limited study in this domain and no standard dataset has been produced before now. In this study, we proposed an unsupervised method for music video emotion analysis using music video contents on the Internet. We also produced a labelled dataset and compared the supervised and unsupervised methods for emotion classification. The music and video information are processed through a multimodal architecture with audio–video information exchange and boosting method. The general 2D and 3D convolution networks compared with the slow–fast network with filter and channel separable convolution in multimodal architecture. Several supervised and unsupervised networks were trained in an end-to-end manner and results were evaluated using various evaluation metrics. The proposed method used a large dataset for unsupervised emotion classification and interpreted the results quantitatively and qualitatively in the music video that had never been applied in the past. The result shows a large increment in classification score using unsupervised features and information sharing techniques on audio and video network. Our best classifier attained 77% accuracy, an f1-score of 0.77, and an area under the curve score of 0.94 with minimum computational cost. [ABSTRACT FROM AUTHOR]

Objective: Malaria is an important mosquito-borne disease that affects half of the world's population (mainly in tropical and subtropical countries). The contents of internet videos, though popular sources of public health information, are often unverified and questionable. Methods: In this study, the contents of the most relevantly viewed 150 videos (58 videos in English and 39 videos for Turkish were selected for further analysis) regarding malaria were analyzed on YouTube which is a popular source of health information. Results: All videos had a total of 14 373 885 views, with 281 199 likes, 4810 dislikes, and 24 339 comments. The health information websites (n=38, 39.2%) were the major source of all videos. According to the source, there was a significant difference between Turkish and English videos (p<0.001). Health information websites are dominant sources for English videos (53.4%) and news agencies are dominant sources for Turkish videos (48.7%). 66 videos were classified as useful (68.0%, English (n=54), Turkish (n=12)) and 8 were classified as misleading (8.24%, English (n=2), Turkish (n=6)). The number of views, likes, views/day, and video power index (VPI) in the useful videos were higher than misleading videos. The length was positively correlated with the number of views, dislikes, likes, comments, views/day, and VPI. The DISCERN score of videos was positively correlated with length, the number of views, dislikes, likes, comments, views/day, and VPI. Conclusion: In today's globalizing world, it is more important to create contents that will provide accurate information about malaria for the people living or traveling in endemic areas on the internet platforms like YouTube. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of the College Of Basic Education is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Analisi: Quaderns de Comunicacio i Cultura is the property of Universitat Autonoma de Barcelona and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, as interest in non-face-to-face experiences and services increases, the demand for web video contents that can be easily consumed using mobile devices such as smartphones or tablets is rapidly increasing. To cope with these requirements, in this paper we propose a technique to efficiently produce video contents that can provide experience of visiting famous places (i.e., stage tour) in animation or movies. To this end, an image dataset was established by collecting images of stage areas using Google Maps and Google Street View APIs. Afterwards, a deep learning-based style transfer method to apply the unique style of animation videos to the collected street view images and generate the video contents from the style-transferred images was presented. Finally, we showed that the proposed method could produce more interesting stage-tour video contents through various experiments. [ABSTRACT FROM AUTHOR]

A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotating the highlights from a soccer video. The proposed framework identifies the interesting clips containing possible scenes of interest, such as goals, penalty kicks, etc. by parsing and processing the audio/video components. The framework analyzes, separates and annotates the individual scenes inside the video clips and saves using kernel support vector machine. The results show that semantic analysis of videos using kernel support vector machines is a reliable method to separate and annotate events of interest in a soccer game. [ABSTRACT FROM AUTHOR]

Summary: The fast growth of video streaming is responsible for a huge amount of traffic over the past few years. Because of the variety of video content on the Internet, a potential market is emerging for video providers. However, a rapid increase in video traffic and users poses challenges for network operators maintaining user expectation. To address this problem, network operators need a mechanism to monitor the quality of experience (QoE) perceived by the user. This allows reacting to quality degradation to improve the service. With the paradigm of network function virtualization, network operators are able to deploy such a video monitoring function in the cloud. In this work, we investigate the feasibility of deploying a virtual network function (VNF) for video buffer and QoE monitoring on the Amazon Web Service cloud. To this end, we implement a VNF to analyze video flows in the network by using deep packet inspection. We investigate the influence of different points of presence (PoP) in the cloud and mobile network on the performance of the VNF for monitoring video buffer and QoE. Our findings show that the accuracy of the QoE monitoring decreases with the distance of the PoP to the client. This is not only due to the delay and bottleneck between the monitoring point and the client but also due to the client mobile access network. [ABSTRACT FROM AUTHOR]

• The multimedia community has been active in finding ways to stream immersive video content under limited bandwidth. • Degrading the quality is not the only choice to lower the data rate, and not necessarily the best for the users. • Two new types of impairments, Virtual Walls and Slow Downs, exploit the human attentional process to this aim. • Experiments confirm the potential of these alternative impairments, and indicate in which situation should they be employed. • Network simulations with viewport-based adaptations confirm their potential to improve various streaming performance metrics. Virtual reality videos are an important element in the range of immersive contents as they open new perspectives for story-telling, journalism or education. Accessing these immersive contents through Internet streaming is however much more difficult owing to the required data rates much higher than for regular videos. While current streaming strategies rely on video compression, in this paper we investigate a radically new stance: we posit that degrading the visual quality is not the only choice to reduce the required data rate, and not necessarily the best. Instead, we propose two new impairments, Virtual Walls (VWs) and Slow Downs (SDs), that change the way the user can interact with the 360∘ video in an environment with insufficient available bandwidth. User experiments with a double-stimulus approach show that, when triggered in proper time periods, these impairments are better perceived than visual quality degradation from video compression. We confirm with network simulations the usefulness of these new types of impairments: incorporated into a FoV-based adaptation, they can enable reduction in stalls and startup delay, and increase quality in FoV, even in the presence of substantial playback buffers. [ABSTRACT FROM AUTHOR]

Adaptive bitrate streaming is a crucial element in the implementation of high-quality streaming of media content over the Internet. Dynamic adaptive streaming over HTTP (MPEG-DASH) has lately emerged as the de facto solution for over-the-top (OTT) video streaming services. In this paper, we perform macro-level analysis on a real-world MPEG-DASH-based Internet live broadcasting service to gain insight into its behavior throughout the end-to-end service-provision chain, from broadcasters to viewers. Based on this analysis, we propose methods to improve the quality-of-experience (QoE) of MPEG-DASH-based services, particularly with regard to reducing broadcasting delays between recording and viewing of videos, as well as synchronizing client terminals. [ABSTRACT FROM AUTHOR]

Copyright of Doxa Comunicación is the property of Fundacion Universitaria San Pablo - CEU and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents a report on the video contents published on the online platform Youtube. It states that digital video platform mentioned above is a place for communication between the viewers and the content creators. It mentions that the video platform has become a medium for visual and scriptual exchange between users.

How do audiences in the Middle East and North Africa respond to the dramatic expansion of content offered with the advent of online video? Rapid internet adoption in the region signifies the latest expansion of content menus available to audiences since television. In this article, we determine who—as a consequence of this expansion—diversifies their content preferences online and on traditional television, and who maintains the same preferences, regardless of platform. To answer these questions, this study applies Everett Rogers' diffusion of innovations theory, using data from a large-scale survey on media use in the Middle East and North Africa. The results reveal: In the Middle East and North Africa in 2016, classic characteristics of innovators and early adopters are no longer significant predictors that one will be receptive to different genres of content online versus on television. Instead, more significant predictors are the television landscape in one’s country, being interested in new content of all kinds, and the characteristics of the genres themselves. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the use of public video from internet user-generated content as a means of collecting naturalistic driving data. A convenience sample of 38 videos comprised of 203 events was extracted from publicly available channels on the YouTube™ platform. Each event was manually reviewed and pseudo-coded according to a subset of current CRSS variables. Pre-crash scenarios were coded using categories developed for prior NHTSA analysis. Crashes represented 67% of the reviewed cases. Collisions with motor vehicles accounted for 84% of all crashes in the sample. Pre-crash scenarios were able to be determined for all crashes and near-crashes. The most prevalent pre-crash scenario types in the video data were Crossing Paths (41%), Rear End (21%), and Lane Change (17%). The top pre-crash scenarios from Swanson et al., were Rear End (31%), Crossing Paths (21%), and Lane Change (12%). The most prevalent pre-near crash scenario types in the video data were Crossing Paths (32%), Lane Change (30%), and Pedestrian (12%). The most prevalent pre-crash scenarios in the video data were similar to those in data from FARS and NASS-GES. Though not nationally representative, this preliminary study demonstrated that user-generated content may be useful as a source of inexpensive naturalistic data and provides sufficient detail to capture important pre-crash, near-crash and crash information. [ABSTRACT FROM AUTHOR]

Key points Convergence of international standards has made requirements for accessible multimedia content clearer. Legal and commercial pressure for accessible content is mounting for higher education institutions (HEIs) and publishers as highlighted by recent high‐profile settlements in North America. Accessibility requirements for multimedia can be met by setting realistic, progressive goals and focusing resources on key content. Multimedia accessibility benefits range from wider audiences and increased user engagement to greater discoverability and improved search engine optimization. Vendors are ramping up their offerings for accessible production, and costs are going down. [ABSTRACT FROM AUTHOR]

Web content nowadays can also be accessed through new generation of Internet connected TVs. However, these products failed to change users' behavior when consuming online content. Users still prefer personal computers to access Web content. Certainly, most of the online content is still designed to be accessed by personal computers or mobile devices. In order to overcome the usability problem of Web content consumption on TVs, this paper presents a knowledge graph based video generation system that automatically converts textual Web content into videos using semantic Web and computer graphics based technologies. As a use case, Wikipedia articles are automatically converted into videos. The effectiveness of the proposed system is validated empirically via opinion surveys. Fifty percent of survey users indicated that they found generated videos enjoyable and 42 % of them indicated that they would like to use our system to consume Web content on their TVs. [ABSTRACT FROM AUTHOR]