Video Items Research Articles

Comparison of the out-of-pocket costs of Medicare-funded telepsychiatry and face-to-face consultations: A descriptive study.

Telepsychiatry items in the Australian Medicare Benefits Schedule (MBS) were expanded following the COVID-19 pandemic. However, their out-of-pocket costs have not been examined. We describe and compare patient out-of-pocket payments for face-to-face and telepsychiatry (videoconferencing and telephone) MBS items for outpatient psychiatric services to understand the differential out-of-pocket cost burden for patients across these modalities. out-of-pocket cost information was obtained from the Medical Costs Finder website, which extracted data from Services Australia's Medicare claims data in 2021-2022. Cost information for corresponding face-to-face, video, and telephone MBS items for outpatient psychiatric services was compared, including (1) Median specialist fees; (2) Median out-of-pocket payments; (3) Medicare reimbursement amounts; and (4) Proportions of patients subject to out-of-pocket fees. Medicare reimbursements are identical for all comparable face-to-face and telepsychiatry items. Specialist fees for comparable items varied across face-to-face to telehealth options, with resulting differences in out-of-pocket costs. For video items, higher proportions of patients were not bulk-billed, with greater out-of-pocket costs than face-to-face items. However, the opposite was true for telephone items compared with face-to-face items. Initial cost analyses of MBS telepsychiatry items indicate that telephone consultations incur the lowest out-of-pocket costs, followed by face-to-face and video consultations.

Media events in an age of “cross-media”: a smartphone diary app study

PurposeAiming to explore how audience consume and produce media events in the digital, distributed and social era we live in, the paper analyzes the viewing patterns of video news items during a media event (the week of Donald Trump's presidential visit to Israel, the first to a country outside the US), compared to a parallel comparable “ordinary” period (two weeks later, in which no inordinacy events occurred). The comparison focused on simultaneous activities of audiences engaged with the event, with either related (i.e. second screening) or unrelated (i.e. media multitasking).Design/methodology/approachThe research is a diary study based on a dedicated mobile app in which respondents reported their news-related behavior during two periods: a media event period and comparable “ordinary” period.FindingsParticipants reported watching significantly more news video items in the first day of the media event week compared to the first day of the “ordinary” week. More than half of the viewing reports of the media event were not on TV. In the media event week, there were significantly higher percentages of viewing reports on smartphones/computers and significantly higher percentages of second-screening reports.Originality/valueThis is the first study that empirically explores the viewing patterns of video news items during a media event, compared to an “ordinary” period, focusing on media second screening of audiences engaged with the event. This comparison may reveal whether (1) media events still retain their centrality in a multi-screen era and (2) the role of the internet and online social media in the experience of media events.

Playback-centric visualizations of video usage using weighted interactions to guide where to watch in an educational context

The steady increase in use of online educational tools and services has led to a large amount of educational video materials made available for students to watch. Finding the right video content is usually supported by the overarching learning management system and its user interface that organizes various video items by course, categories and weeks, and makes them searchable. However, once a wanted video is found, students are often left without further guidance as to what parts in that video they should focus on. In this article, an additional timeline visualization to augment the conventional playback timeline is introduced which employs a novel playback weighting strategy in which the history of different video interactions generate scores based on the context of each playback. This includes whether the playback started after jumping forward or backward in the video, whether the playback was at a faster or slower speed, and whether the playback window was in focus on the student's screen or was in the background. The resultant scores are presented on the additional timeline, making it in effect a playback-centric usage graph nuanced by how each playback was executed. Students are informed by this visualization on the playback by their peers and can selectively watch those portions which the contour of the usage visualization suggests. The visualization was implemented as a fully-fledged web application and deployed in an undergraduate course at a university for two full semesters. A total of 270 students used the system throughout both semesters watching 52 videos, guided by visualizations on what to watch. Analysis of playback logs revealed that students selectively watched portions in videos corresponding to the most important portions of the videos as assessed by the instructor who created the videos. The characteristics of this method as a way of guiding students as to where to watch as well as a complementary tool for playback analysis, are discussed. Further insights into the potential values of this visualization and its underlying playback weighting strategy are also discussed.

CONSTRUÇÃO E VALIDAÇÃO DE VÍDEO EDUCACIONAL PARA PREVENÇÃO DA VIOLÊNCIA SEXUAL DE ADOLESCENTES

ABSTRACT Objective: to evaluate the elaboration and validation process of an educational video for the prevention of sexual violence in school-age adolescents. Method: this is a methodological study, developed from September 2018 to February 2021 in Recife-PE. Production of the educational video was carried out according to the methodological path proposed by Kindem and Musberg. The video was submitted to content validation and face evaluation by expert judges and school-age adolescents, respectively. In the data analysis, absolute frequency, Content Validity Index and binomial test were used. Results: in content validation, agreement was satisfactory and the mean validity index for all video items was 0.92. In the face evaluation, the adolescents considered the video understandable, with a minimum agreement level of 92.3%. Conclusion: the educational video elaborated was considered by the judges as valid to contribute to the knowledge and attitude of school-age adolescents regarding the prevention of sexual violence.

Effect of a motivational video on flossing behaviour: A pilot study.

Motivational interviews used for health behavioural change requires several sessions and expert consultations. A practical method is needed to promote oral hygiene habits. The aim of the study was to develop a video as a new motivational intervention method combining several health behaviour change theories to improve flossing behaviour. A questionnaire about individual psychosocial characteristics was conducted to 30 volunteer dental patients. Plaque index (PI) scores were calculated and a video about flossing was shown the patients. The second PI measurements were performed after 2months. The video including flossing instructions and expressions of performers acting dental patients was produced to test as a motivational method. Cues used in the video were generated according to health behaviour change theories and models under the supervision of an expert team. Content validity ratio (CVR) and content validity index (I-CVI) of the video and reliability of the questionnaire was determined. First and second PI scores were compared to assess the effect of the video. Correlation between answers of the questionnaire and PI changes were tested. The average I-CVIs of the video was 0.93, CVR of video items was 1, I-CVIs of 5 items was 80% and of 10 was 100%. Cronbach's alpha of the questionnaire was 0.93. PI scores significantly decreased after 2months (p<0.001). A single session motivational intervention via a video based on health psychological models and theories was efficacious in improving oral hygiene and flossing behaviour in the short term.

Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines

AbstractOver the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.

CBT HOTS Assessment Instrument Model: Teacher Perception Analysis of a Role of Illustration as Well as Quiz and Question Setting According to HOTS

21st Century Learning requires HOTS learning to improve students' higher-order thinking skills, in addition to learning that promotes HOTS assessment must also promote HOTS. However, there are still many teachers who have not done HOTS who are able to stimulate students' high-level thinking skills well. This study discusses teacher perceptions about HOTS CBT assessment instrument models about the role of illustrations and quizzes and questions set in measuring HOTS. This type of research is a mixed method. The mixed-method strategy used is Sequential Explanatory Strategy. This research was conducted on five high school physics teachers in Metro City. Data collection using teacher perception respondents questionnaire conducted in April 2019 by five high school physics teachers in Metro City. The data collected was analyzed descriptively quantitatively. As many as 96% of teachers strongly agree that the Computer Based Test Model (CBT) involves video illustrations, animations, simulation questions, 80% of teachers agree that describing items can enrich thinking levels (C1, C2, C3, C4, C5, C6) and GPA for considered, as well as 96% of teachers strongly agree with the form of illustration of video items, animations, complications can be removed verbalism. In addition to questions and types of questions, quizzes and question settings are considered necessary by teachers to improve higher-order thinking skills (HOTS). Based on the data obtained the validation instrument was developed to obtain the best types of questions, stimuli, as well as quizzes and question settings in improving students' higher-order thinking skills (HOTS). Keywords: HOTS CBT assessment, stimulation, 3 types of questions, quizzes and question settings DOI: http://dx.doi.org/10.23960/jpf.v8.n1.202002

Deep Auto Encoder Model With Convolutional Text Networks for Video Recommendation

Collaborative filtering (CF) approach has been successfully used in recommender system (RS). Sparsity and cold start are two common phenomena in the CF algorithms nearly for each data set. Hence, these drawbacks of the classical CF algorithms have limited the recommendation performance. Deep learning theory is a very useful tool to mine the latent features in many scientific areas, such as image processing, video processing, and signal processing. In this paper, a novel deep learning-based recommendation model is introduced to solve the sparsity and cold start recommendation problems by mining the auxiliary data of users' viewing behavior datasets (e.g., the user attribute features information and video item attribute features information) and to deeply mine the latent information and their correlations of the user features and item features. First of all, the user features and video item features are processed and deeply mined by the data preprocessing layer, embedding dense layer, convolution network layer, share layer, and the auto encoder layer of our proposed model. After that, the final predictive rating process is conducted in multi-layer perception by combining with the target rating vector data and the processed user and item feature data, which is deeply mined by the above-mentioned submodels of our proposed algorithm model. The extensive experiments have shown the benefits of the proposed algorithm in the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms. Besides, the impact of choices of different components and parameters of our proposed algorithm model is also studied thoroughly.

Multi-Channel and Fusion Encoding Strategy Based Auto Encoder Model for Video Recommendation

Recommendation techniques are widely used in many areas to deal with the information overload problem. However, the recommendation theory has suffered from the sparseness problem, which decreases the precision of the recommendation algorithms heavily. Deep learning theory has proven to be a very efficient tool to mine the latent information of data. In this paper, a novel scalable multi-channel and fusion encoding strategy-based auto encoder (MCFE-AE) model is introduced to make recommendations by deeply mining the latent features of users and video items of the data. The detail of the proposed algorithm is summarized as follows. First, the rating data that represent the users’ preference are sent to the input port of the proposed MCFE-AE model as raw input data. Second, the latent features of users and items have been deeply mined by the multi-channel and fusion encoding process of the proposed MCFE-AE model. Third, the final rating prediction result has been obtained by the decoding process of the proposed MCFE-AE model. The extensive experiments have shown the benefits of the proposed algorithm on the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms. Besides, the number of channels of the MCFE-AE, the $L_{1}$ and $L_{2}$ regularization method, the learning rate, and the important regularization parameter $\lambda $ have been studied thoroughly in this paper.

Health Information National Trends Survey in American Sign Language (HINTS-ASL): Protocol for the Cultural Adaptation and Linguistic Validation of a National Survey.

BackgroundThe Health Information National Trends Survey (HINTS) collects nationally representative data about the American’s public use of health-related information. This survey is available in English and Spanish, but not in American Sign Language (ASL). Thus, the exclusion of ASL users from these national health information survey studies has led to a significant gap in knowledge of Internet usage for health information access in this underserved and understudied population.ObjectiveThe objectives of this study are (1) to culturally adapt and linguistically translate the HINTS items to ASL (HINTS-ASL); and (2) to gather information about deaf people’s health information seeking behaviors across technology-mediated platforms.MethodsWe modified the standard procedures developed at the US National Center for Health Statistics Cognitive Survey Laboratory to culturally adapt and translate HINTS items to ASL. Cognitive interviews were conducted to assess clarity and delivery of these HINTS-ASL items. Final ASL video items were uploaded to a protected online survey website. The HINTS-ASL online survey has been administered to over 1350 deaf adults (ages 18 to 90 and up) who use ASL. Data collection is ongoing and includes deaf adult signers across the United States.ResultsSome items from HINTS item bank required cultural adaptation for use with deaf people who use accessible services or technology. A separate item bank for deaf-related experiences was created, reflecting deaf-specific technology such as sharing health-related ASL videos through social network sites and using video remote interpreting services in health settings. After data collection is complete, we will conduct a series of analyses on deaf people’s health information seeking behaviors across technology-mediated platforms.ConclusionsHINTS-ASL is an accessible health information national trends survey, which includes a culturally appropriate set of items that are relevant to the experiences of deaf people who use ASL. The final HINTS-ASL product will be available for public use upon completion of this study.

Techniques and Steps of Project Physical Progress Control through the Comparison of Site Images with Prototype Models

Evaluation of project physical progress through statistical data prepared by project staff and project managers may cause problems in the quality of the obtained results due to human error and approximations. Therefore, making use of image and video items of as-constructed situation of projects, and their comparison with prototype models can yield detailed reports on the status of project progress. Producing site images in the form of as-built data, image analysis through image processing techniques, and comparison with prototype models is a new and rapidly growing method for preparing project progress reports.

Popularity-based caching for IPTV services over P2P networks

This study suggests to use popularity based caching for IP-based TV (IPTV) services over peer-to-peer (P2P) networks. Each peer in a P2P network can use two levels of cache hierarchy: an internal cache and a neighboring peer cache. Using this property, our main focus is on caching the globally most popular video files nearby the clients, in order to reduce the IPTV service delay, increase the quality of service provided to the clients, and reduce the traffic over the Internet backbone. The proposed framework was applied on real data traces from live P2P networks. The results demonstrate a significant improvement over the Least Recently Used (LRU) and the Least Frequently Used (LFU) cache management schemes. This study is motivated by the vision of large P2P networks consisting of many volunteers serving as peers, each of which has a relatively small cache size, in terms of the number of video items it can store. Since the performance of both the LRU and LFU schemes is very poor for small cache, there is a need for another cache management scheme, which outperforms these schemes, especially for small cache size. The proposed distributed popularity-based caching scheme can significantly increase the performance of P2P networks used for video streaming, with respect to the existing networks, that use the LRU or LFU schemes. The performance metric used for comparison is the cache hit ratio and the expected delay for content delivery. In both parameters a significant improvement is demonstrated.

Lyapunov stability and performance of user-assisted Video-on-Demand services

Currently, most video on-demand services offered over the Internet do not exploit the idle resources available from end-users, including YouTube. We present a taxonomic analysis of user-assistance in video on-demand systems, where users are both clients and servers, helping with the task of video distribution. From a theoretical perspective, we develop a deterministic fluid model suitable for sequential systems. We mathematically prove the Peer-to-Peer Sequential Fluid Model is globally stable in the Lyapunov sense, no matter the network parameters of the cooperative system. We theoretically prove that cooperative systems always outperform non-cooperative solutions. From a practical point of view, a caching problem is proposed and discussed in order to tackle technological concerns to massively distribute popular videos on-demand. The goal is to distribute video items into repositories minimizing the waiting times of end-users. The caching problem is inside the class of NP-Complete computational problems, and heuristically solved with a GRASP methodology enriched with a path-relinking technique. Predictions inspired in a statistical analysis of real-life YouTube traces suggest the introduction of cooperation is both robust and economically attractive. These results highlight the harmony between our theoretical development and practice.

Scalable storyboards in handheld devices: applications and evaluation metrics

Summaries are an essential component of video retrieval and browsing systems. Most research in video summarization has focused on content analysis to obtain compact yet comprehensive representations of video items. However, important aspects such as how they can be effectively integrated in mobile interfaces and how to predict the quality and usability of the summaries have not been investigated. Conventional summaries are limited to a single instance with certain length (i.e. a single scale). In contrast, scalable summaries target representations with multiple scales, that is, a set of summaries with increasing length in which longer summaries include more information about the video. Thus, scalability provides high flexibility that can be exploited in devices such as smartphones or tablets to provide versions of the summary adapted to the limited visualization area. In this paper, we explore the application of scalable storyboards to summary adaptation and zoomable video navigation in handheld devices. By introducing a new adaptation dimension related with the summarization scale, we can formulate navigation and adaptation in a two-dimensional adaptation space, where different navigation actions modify the trajectory in that space. We also describe the challenges to evaluate scalable summaries and some usability issues that arise from having multiple scales, proposing some objective metrics that can provide useful insight about their potential quality and usability without requiring very costly user studies. Experimental results show a reasonable agreement with the trends shown in subjective evaluations. Experiments also show that content-based scalable storyboards are less redundant and useful than the content-blind baselines.

Editing, Publishing and Aggregating Video Articles: Do We Need a Scholarly Approach?

The article supports the idea of providing infrastructure and training for preparing and publishing quality video articles. Properly edited, formatted, and verified video items can present graphic contents of interest to the global scientific community. It is suggested to apply traditional attributes of scholarly articles to video items and aggregate them on a specifically designed editing, publishing, and indexing platform, called PubTube. As a mega platform, PubTube may provide space for a variety of open-access sources of information, ranging from short audio-video presentations to research protocols and educational lectures. Video articles on the platform have to pass quality checks by skilled reviewers. Global editorial associations should be prepared to improving the whole process of publishing and aggregating video articles.

Multimedia Service using CSMA/GPS in Vehicular Communication Network

This paper 1 deals with the seamless multimedia service mechanism in vehicle communication network(VCN). The proposed mechanism uses caching/switching/multicasting agent(CSMA), a partial of GPS information and multicast transmission in VCN network. The CSMA controls some smart access points(SAP) with buffer, and may generate multicast group address for live-video in local-vehicle-network(LVN). And it manages serviced video items, switches multimedia packets to SAPs requesting service, and performs smooth handover for vehicles using table. The SAP generates modified virtual address(IP address) for vehicles that enter the VCN network. The proposed virtual IP address consists of LVN network ID, vehicle ID, GPS information(driving distance) and a driving direction mode bit. And the CSMA and SAP provide smooth handover using their service tables and a seamless multimedia service. Also, this proposed mechanism may provide promptly multicast service for live-video because multicast mechanism is performed within the VCN network, and reduce the possibility of cutoff service and packet loss because it uses GPS in-formation.

Supporting exploratory video retrieval tasks with grouping and recommendation

In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks.

Analysis and design of peer‐assisted video‐on‐demand services

AbstractWe propose a deterministic fluid model to understand the trade‐offs in the design of peer‐assisted video‐on‐demand (VoD) services. There are three entities in this model: peers (or end users), seeders (altruistic users that own one or several complete video items), and cache servers that store and forward videos with a limited capacity. Peers join the network, download one or multiple concurrent video streams (possibly different video items), and abort the system when they wish. Peers are assumed to cooperate in a BitTorrent‐based fashion, governed by tit‐for‐tat and fair availability. The issue is to minimize the expected downloading times, choosing the set of video items that should be stored in each cache server. We first prove that the deterministic model is globally stable, and find closed expressions for the expected waiting times. Then, we introduce a combinatorial optimization problem (COP), whose nature is similar to the multiknapsack problem (where the items are videos, and knapsacks are related with the storage of cache servers). The problem turns out to be NP‐complete. Therefore, we heuristically address the problem following a GRASP (greedy randomized adaptive search procedure) methodology. Finally, we simulate the new caching methodology with real‐life traces taken from YouTube logs. The results suggest that the peer‐to‐peer philosophy is both stable and cost‐effective for on‐demand streaming purposes.

The impact of hesitation, a social signal, on a user’s quality of experience in multimedia content retrieval

The social signal (SS) of hesitation is commonly manifested through a multiplicity of nonverbal behavioural cues when a user is faced with a variety of decision choices. The aim of this study is to show that the utilization of the SS of hesitation in a conversational recommender system (RS) can improve the user quality of experience (QoE) when interacting with a video-on-demand system. An appropriate experimental design was modelled to detect the impact of the SS. The experimental scenario was a manual video-on-demand system with a conversational RS where the user selected one video clip among several presented on the screen. The system adjusted the list of the video items to be recommended according to the extracted SS class {hesitation, no hesitation}. To detect if the user was hesitating, we used hand movements, eye behaviour and time between two selections. Two user groups were tested to allow realistic estimation of the impact of the SS. In the user test group, the SS of hesitation was considered, while in the control group it was not. The evaluation of impact of the SS on QoE was based on pre- and post-interaction questionnaires. Our results showed a significant difference in user satisfaction with the system between those two groups, indicating that the use of SS of hesitation in conversational RS improves the QoE when the user interacts with a video-on-demand system.

Simple models of the content duration and the popularity of television content

This paper presents parametric statistical models for the content duration (running length) and the popularity of TV programs. The parameters of the models are obtained by curve fitting data obtained from the Internet Movie DataBase (IMDB). Later, the accuracy of the models is numerically assessed, providing an estimation of the error, together with a use case example. The content duration model has been obtained exclusively from the actual duration of TV programs, which is intrinsic to the content, instead of the file size of encoded videos which is application dependent. It is also worth emphasizing that, unlike several previous studies, the model does not account for User Generated Content. The model can be used to obtain the file sizes of video items in video delivery services for different formats or resolutions, by applying their characteristic video encoding rates. On the other hand, this paper shows parametric popularity models adjusted to fit the distribution of user ratings in the IMDB database. The popularity models can be used to estimate the probability of access of programs in video on demand services. Therefore, the content duration and popularity models hereby presented are useful to evaluate the performance of video delivery services dealing specifically with TV content.