Video Components Research Articles

Toward comprehensive short utterances manipulations detection in videos

AbstractIn a landscape increasingly populated by convincing yet deceptive multimedia content generated through generative adversarial networks, there exists a significant challenge for both human interpretation and machine learning algorithms. This study introduces a shallow learning technique specifically tailored for analyzing visual and auditory components in videos, targeting the lower face region. Our method is optimized for ultra-short video segments (200-600 ms) and employs wavelet scattering transforms for audio and discrete cosine transforms for video. Unlike many approaches, our method excels at these short durations and scales efficiently to longer segments. Experimental results demonstrate high accuracy, achieving 96.83% for 600 ms audio segments and 99.87% for whole video sequences on the FakeAVCeleb and DeepfakeTIMIT datasets. This approach is computationally efficient, making it suitable for real-world applications with constrained resources. The paper also explores the unique challenges of detecting deepfakes in ultra-short sequences and proposes a targeted evaluation strategy for these conditions.

Supplementing an Epidemiology Course with Online Lectures

Background: Online distance education is proving to be an effective pedagogical vehicle for enhancing learning outcomes of medical students. Epidemiology is a subject area underexplored as an appropriate content area for online delivery. We sought to determine whether online epidemiology videos were an effective companion teaching tool that could result in measurable improvements in student performance. Methods: In partnership with the Lecturio Corporation in Germany, an online Epidemiology class created, featuring 11 recorded lectures tailored to the USMLE medical licensing requirements. Free access to the lectures was offered to the students of a 4th year undergraduate Epidemiology course at the University of Ottawa. Improvements in this group from the midterm examination baseline to the final examination performance were assessed relative to improvements experienced by students who did not watch the videos. Results: Students who watched the videos saw their average mark increased by 1.2%, while the mark in the control group decreased by 2.6%, though this difference was not statistically significant. Qualitative comments were universally positive with respect to the instructional usefulness of the videos. Conclusions: Augmenting traditional university epidemiology courses with an online video component is an effective strategy that can result in improved student learning outcomes, though further research is needed to determine how best to deploy such tools either in isolation or in partnership with in-person instruction.

Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills

Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.

Enhancing TikTok Account Performance with Data Pattern Identification

TikTok is a social media platform widely used to share information through short videos to achieve goals and interests in business, education, politics, government, and personal existence. Every activity on this platform is recorded and presented privately to each account owner. However, this data has not been utilized optimally to improve account performance. This research aims to offer a data analysis concept that integrates statistical and machine learning approaches to identify data patterns in each user's data collection, enabling the improvement of account performance. The approach utilizes Linear Regression, k-means, and Decision Tree methods. The results obtained show that the concept of identifying data patterns in TikTok account data has successfully developed a predictive model for video posts that can potentially increase total viewership, video plays, and audience engagement. This is achieved through optimizing video components such as captions, text, hashtags, sound genre, and video type. The outcome yielded a classification model that can predict capable component content to enhance account performance.

Analisis Framing Pemberitaan Program Makan Gratis Prabowo Subianto Di Media Online

online media is a combination of all components of text, video, audio and photos. Political news becomes a medium for the public or the press itself to convey political messages to political actors, both support and criticism. Prabowo Subianto is the Minister of Defense of the Republic of Indonesia who is running for President in 2024. One of Prabowo's programs if elected President is the Free Meal Program. This program was suddenly widely reported by the media, various reports about the program were in online media such as online media detik.com and VIVA.co.id. This research was conducted to analyze the online media detik.com and VIVA.co.id in framing news about Prabowo Subianto's Free Meal Program using the framing analysis approach model of Zhongdang Pan and Gerald M. Kosicki. This research is descriptive qualitative research. The method used in this research is the framing analysis model of Zhongdang Pan and Gerald M. Kosicki which uses a framing device consisting of four structures, namely, Syntactic Structure, Script Structure, Thematic Structure and Rhetorical Structure. The results of this research, detik.com media, in reporting on Prabowo Subianto's Free Meal Program, supports this program. This can be shown in the headline of the news. In the first news title, detik.com appears to explain Prabowo Subianto's Free Meal Program, which means that detik.com media reports on the positive side of the program. detik.com frames the news by selecting news sources, selecting quotes that support statements and information provided in the news. VIVA.co.id put the parties who gave counter statements and criticized the program, VIVA.co.id wants to show the public that is where the controversy is. Political news regarding the Prabowo Subianto Free Meal Program on the online media detik.com and VIVA.co.id has an impact on the knowledge, attitudes and perspective of the public who see the reality of the news framing of the program.

Stacked collaborative transformer network with contrastive learning for video moment localization

Video grounding intends to perform temporal localization in multimedia information retrieval. The temporal bounds of the target video span are determined for the given input query. A novel interactive multi-head self-attention (IMSA) transformer is proposed to localize an unseen moment in the untrimmed video for the given image. A new semantic-trained self-supervised approach is considered in this paper to perform cross-domain learning to match the image query – video segment. It normalizes the convolution function enabling efficient correlation and collecting of semantically related video segments across time based on the image query. A double hostile Contrastive learning with Gaussian distribution parameters method is advanced to learn the representations of video. The proposed approach performs dynamically on various video components to achieve exact semantic synchronization and localization among queries and video. In the proposed approach, the IMSA model localizes frames greatly compared to other approaches. Experiments on benchmark datasets show that the proposed model can significantly increase temporal grounding accuracy. The moment occurrence is identified in the video with a start and end boundary ascertains an average recall of 86.45% and a mAP of 59.3%.

Affordances, constraints and collaborative potential of e-mentoring platforms and their features for K–12 educators: a systematic literature review

PurposeThe purpose of this systematic literature review is to catalog, explore and disseminate knowledge developed related to the affordances, constraints and collaborative practices of e-mentoring in order to offer recommendations for mentoring programs.Design/methodology/approachAfter identifying studies utilizing inclusion and exclusion criteria and a variety of relevant databases, chosen studies were organized into three categories based on e-mentoring practices.FindingsResults reflect chat-room-based e-mentoring and e-mentoring with a video component both to have various affordances and collaborative features. These forms of e-mentoring can potentially be relevant to new teachers’ needs, may promote community and are disconnected from mentees’ school systems. A third form of e-mentoring, virtual reality e-mentoring, however, has the potential for accessibility issues and lack of relevancy to the mentoring process.Originality/valueChat-room-based e-mentoring and e-mentoring with a video component may benefit new teachers who do not have access to in-person mentors. However, these forms of e-mentoring may necessitate a component that ensures mentors and mentees alike remain consistently engaged in the e-mentoring process.

Experiences of Motivational Interviewing in Virtual Health-care Visits for Adults With Type 2 Diabetes Mellitus: A Qualitative Analysis

ObjectivesThe purpose of this qualitative study is to identify barriers minimizing the effectiveness of motivational interviewing during virtual clinic encounters for individuals with type 2 diabetes based on the capability, opportunity, motivation, and behaviour (COM-B) model. MethodsOne-on-one semistructured interviews were conducted from March to June 2023, with 17 adults with type 2 diabetes (64.7% female; median age 69 years [range 47 to 83 years]) followed at St. Michael’s Hospital (Toronto, Canada). Themes from transcribed interviews were identified through descriptive analysis using a grounded theory approach. ResultsThe following main themes were identified: 1) face-to-face appointments strengthen provider–patient rapport and collaboration; 2) virtual encounters reduce patient accountability and hinder health-seeking behaviour; and 3) individuals with physical disabilities and/or low technological proficiency experience decreased provider accessibility. Protective factors that can mitigate these negative impacts include establishing rapport during in-person appointments before transitioning to virtual appointments and incorporating a video component during virtual encounters. ConclusionsSeveral barriers of virtual appointments currently limit the effectiveness of motivational interviewing for individuals with type 2 diabetes and make it difficult to provide person-centred care, especially by phone. However, there are protective factors that help to maintain healthy lifestyle behaviours, even after transitioning to virtual settings, and are areas for optimization moving forward.

Bias-Conflict Sample Synthesis and Adversarial Removal Debias Strategy for Temporal Sentence Grounding in Video

Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias issue, which is caused by the uneven temporal distribution of the target moments for samples with similar semantic components in input videos or query texts. Existing methods resort to utilizing prior knowledge about bias to artificially break this uneven distribution, which only removes a limited amount of significant language biases. In this work, we propose the bias-conflict sample synthesis and adversarial removal debias strategy (BSSARD), which dynamically generates bias-conflict samples by explicitly leveraging potentially spurious correlations between single-modality features and the temporal position of the target moments. Through adversarial training, its bias generators continuously introduce biases and generate bias-conflict samples to deceive its grounding model. Meanwhile, the grounding model continuously eliminates the introduced biases, which requires it to model multi-modality alignment information. BSSARD will cover most kinds of coupling relationships and disrupt language and visual biases simultaneously. Extensive experiments on Charades-CD and ActivityNet-CD demonstrate the promising debiasing capability of BSSARD. Source codes are available at https://github.com/qzhb/BSSARD.

Development of Learning Management with Animated Video to Increase Motivation and Learning Outcomes

The aim of this research is to analyse the development of learning management using thematic animated video media to increase motivation and learning outcomes for fourth grade elementary school students in Theme 9. The method used is research and development according to Borg and Gall. The data sources in this research were fourth grade elementary school students in Grobogan and Kudus Regencies, Central Java, with data collection tools used including questionnaires, tests, observations and interviews. The data analysis used was the validity and reliability test of the instrument, normality test, homogeneity test, n-Gain test and Independent Sample t-test. The research results show that the design begins with planning with components of thematic learning analysis, analysis of motivation and learning outcomes in online learning, planning consists of curriculum analysis - formulation of objectives - analysis of sources and materials - analysis of learning models, designing media animation videos by making storyboards, preparing assets – complementary assets and video animation tools. In the process with PowerPoint video components and Camtasia 2019 specifications. Animated video content with "My Country's Wealth Theme" in online learning with initial stage components (basic competencies (KD), objectives, learning materials, core activities and evaluation. Material feasibility test results get a score 3.3 with very good criteria, and media expert eligibility received a score of 3.4 with very good criteria. The results of the effectiveness test in 2 regions obtained an n-Gain value of 0.659 in the Hake n-gain table in the good category. In conclusion, thematic learning management with animated videos effectively increases learning motivation and learning outcomes in thematic learning.

Learning Experience of University Music Course Based on Emotional Computing

Identification and analysis of piano performance music are essential activities in the study of music knowledge and enjoyment. The goal of this study is to employ recurrent neural networks (RNNs) to create a multimedia identification and analysis method for piano performance music. RNNs excel at capturing the temporal relationships and dynamics seen in musical performances. The project entails gathering a large dataset of piano performance recordings that spans a variety of genres, performers, and playing techniques. The audio and video components of the performances are pre-processed in order to extract pertinent information. Long Short-Term Memory (LSTM), an RNN architecture, is used to mimic the sequential nature of the performances. The RNN is trained on the features that were retrieved in order to discover the patterns and traits connected to various piano performances. The similarity between the performance representations may be measured using similarity measures using Euclidean distance. The RNN-based system may be further developed to do tasks like score following, expressive performance analysis, and stylistic variation creation to aid in performance analysis. The system may provide insights into timing accuracy, dynamics, phrasing, and other expressive qualities of the piano performances by matching performance data with related musical scores. The proposed method RNN-LSTM provides accuracy about 99%, precision about 97% , recall about 98.9% and F1 Score of 97.6%.

Development of Digital Comic Interactive Media Toward Primary Students' Understanding of Concepts

Objective: The use of interactive media to understand the concept of material is a stimulation of the integration of all the senses in students, where the media will provide freedom to explore the material that is available in it freely, utilizing several unique features in Microsoft PowerPoint which the researchers then turned into an interactive learning tool for students, in its implementation students will be presented with learning media that integrates audio, video and visual components. Method: This research uses quantitative research with experimental methods. The population in this study was class VI students as experimental class research and VI B students as control class research, with a sample of class VI C students totaling 30 students for each sample on geographical characteristics and life in the ASEAN region. Results: This research produced media products in the form of interactive comics, which are proven to be able to increase students' conceptual understanding of material on geographical characteristics and life in the ASEAN region, as proven by the results of data processing using which proves that there is a significant influence. Novelty: There is a significant impact of the development of Digital Comic Interactive Media on Students' Conceptual Understanding of Elementary school is seen from the pretest and post-test results, which show that Ho is rejected and Hα is accepted. By rejecting Ho, it means that in this research, it is proven that the conceptual understanding of students who are given action is significantly different.

Data Playwright: Authoring Data Videos With Annotated Narration.

Creating data videos that effectively narrate stories with animated visuals requires substantial effort and expertise. A promising research trend is leveraging the easy-to-use natural language (NL) interaction to automatically synthesize data video components from narrative content like text narrations, or NL commands that specify user-required designs. Nevertheless, previous research has overlooked the integration of narrative content and specific design authoring commands, leading to generated results that lack customization or fail to seamlessly fit into the narrative context. To address these issues, we introduce a novel paradigm for creating data videos, which seamlessly integrates users' authoring and narrative intents in a unified format called annotated narration, allowing users to incorporate NL commands for design authoring as inline annotations within the narration text. Informed by a formative study on users' preference for annotated narration, we develop a prototype system named Data Playwright that embodies this paradigm for effective creation of data videos. Within Data Playwright, users can write annotated narration based on uploaded visualizations. The system's interpreter automatically understands users' inputs and synthesizes data videos with narration-animation interplay, powered by large language models. Finally, users can preview and fine-tune the video. A user study demonstrated that participants can effectively create data videos with Data Playwright by effortlessly articulating their desired outcomes through annotated narration.

Verification of artillery fire under the influence of random disturbances for the computer game ARMA 3

Computer gaming occupies a firm place in today's media culture and media business. Computer games are widely used both as a means of entertainment and as an educational tool. Military games traditionally hold a place in computer gaming. However, it is in this sector of gaming that a sharp contradiction is observed between the super-realistic quality of the video component and the limited scope of the game's script component. In particular, in the development of military-themed games, the introduction of modern weapon models is rapidly evolving. At the same time, the experience of military conflicts over the last decade and the new tactical techniques developed are difficult to implement in games. This work demonstrates the possibility of improving the artillery component of ARMA 3. In artillery fire, random disturbances are always present. These disturbances cannot be eliminated during the preparation of the firing. In practice, they are compensated by consecutive ranging shots. Modern artillery firing tactics require the maximum reduction of the firing time of the artillery unit. In this regard, methods for verifying each artillery shot are very relevant. Verification is understood as confirming the effectiveness of a shot immediately after it is made. A method for verifying a shot by recording the flight time of a projectile through three control points is proposed and studied. Based on the recorded flight times by sensors, a system of approximating parabolas is constructed. The solution of the system allows determining the expected point of the projectile's burst before it lands. The deviation of the burst point from the aiming point verifies the quality of the artillery shot. Simulation modeling of the proposed method has been conducted. It is demonstrated that parabolic approximation effectively compensates for random disturbances of the shot. A comparison of the proposed method with the method of compensating disturbances through consecutive ranging shots is made. It is shown that the proposed method significantly reduces the firing time of the weapon and the ammunition expenditure for hitting the target for the ARMA 3 player.

The Effect of Motivated Knowledge Type on Viewing Satisfaction in the Corporate Micro-Learning :Mediating Effect of Perceived Benefits and Moderating Effect of Video Components

The Effect of Motivated Knowledge Type on Viewing Satisfaction in the Corporate Micro-Learning :Mediating Effect of Perceived Benefits and Moderating Effect of Video Components

Artificial Intelligence in the Interpretation of Videofluoroscopic Swallow Studies: Implications and Advances for Speech–Language Pathologists

Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), especially deep learning (DL) models, has enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently, it has important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallow studies (VFSS) is still emerging. This review showcases the recent literature on the use of AI to interpret VFSS and highlights clinical implications for speech–language pathologists (SLPs). With a surge in AI research, there have been advances in dysphagia assessments. Several studies have demonstrated the successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs), which involve training a multi-layered model to recognize specific image or video components, have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable the precise interpretation of an instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing the precision, speed, and depth of VFSS interpretation, SLPs can obtain a more comprehensive understanding of swallow physiology and deliver a targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation will likely transform swallow evaluations and ultimately improve outcomes for individuals with dysphagia. However, despite AI’s potential to streamline imaging interpretation, practitioners still need to consider the challenges and limitations of AI implementation, including the need for large training datasets, interpretability and adaptability issues, and the potential for bias.

Learning Analytics on YouTube Educational Videos: Exploring Sentiment Analysis Methods and Topic Clustering

The popularity of social media is continuously growing, as it endeavors to bridge the gap in communication between individuals. YouTube, one of the most well-known social media platforms with millions of users, stands out due to its remarkable ability to facilitate communication through the exchange of video content. Despite its primary purpose being entertainment, YouTube also offers individuals the valuable opportunity to learn from its vast array of educational content. The primary objective of this study is to explore the sentiments of YouTube learners by analyzing their comments on educational YouTube videos. A total of 167,987 comments were extracted and processed from educational YouTube channels through the YouTube Data API and Google Sheets. Lexicon-based sentiment analysis was conducted using two different methods, VADER and TextBlob, with the aim of detecting the prevailing sentiment. The sentiment analysis results revealed that the dominant sentiment expressed in the comments was neutral, followed by positive sentiment, while negative sentiment was the least common. VADER and TextBlob algorithms produced comparable results. Nevertheless, TextBlob yielded higher scores in both positive and negative sentiments, whereas VADER detected a greater number of neutral statements. Furthermore, the Latent Dirichlet Allocation (LDA) topic clustering outcomes shed light on various video attributes that potentially influence viewers’ experiences. These attributes included animation, music, and the conveyed messages within the videos. These findings make a significant contribution to ongoing research efforts aimed at understanding the educational advantages of YouTube and discerning viewers’ preferences regarding video components and educational topics.

Improving Students' Speaking Ability by Creating a Story Telling Recorded in a Video

Technology helps a lot on the education field. By implementing technology, it would influence on the atmosphere either on students' motivation or on language itself. Oral communication is an essential skill which could help students' life in the future. The most students’ problem in studying English is expressing ideas. Some experts said that storytelling can be used to improve students' learning to express oral communication. Connecting with this idea, the researches decided the purpose of the study by improving the students’ speaking ability with producing a storytelling created in the form of a video component. The researchers used an Action Research, The students were required to create a cartoon story, voiceover narration, and background music that produced in a video presentation. The object of the research was 30 students of the Accounting Department of 2 nd Semester 2023. The result of the study was significantly improved which could be seen from the mean of the first project which amounted as big as 77,33, while on the second project, the amount was 83,16. The evaluation kept increased in every evaluation. The conclusion stated that the model was successfully implemented.
 Key words: Improving, Speaking Ability, Storytelling, Video

Adaptive sparse modeling in spectral & spatial domain for compressed image restoration

Block discrete cosine transform (BDCT) is an indispensable component of modern image and video coding standards, specifically for its decorrelation and superior energy compaction aspects. BDCT typically employs block-specific quantization, which results in unpleasant compression-blocking artifacts which predominates at low bit rates. The proposed method aims to minimize these blocking artifacts to generate high-quality images under the framework of the alternating direction method of multipliers (ADMM) optimization. The proposed method exploits the local structures identified and extracted via wavelet-patch-based sparse representation and non-local self-similarity identified and extracted via group-based sparse representation, which are subsequently combined optimally employing ADMM. Moreover, the method uses a Gaussian quantization noise model, which allows a more precise and reliable assessment. An adaptive regularization parameter is used, which integrates spectral and spatial domain sparse representations with multi-resolution dictionaries and PCA-based dictionary. The proposed algorithm improves the overall practicality of the process and outperforms existing methods in terms of objective measures like structural similarity index measure, peak signal-to-noise ratio and visual perception.

Multimedia Technologies as a Component of the Modern Educational Environment

The article demonstrates the visualization of educational information by means of multimedia technologies included in the educational environment as a vivid trend. Its importance in the actively developing modern information space is revealed. Being a high-tech work of art, realized by means of hardware and software, multimedia has quickly become an effective component of educational activities, including text, graphics, animation, static and dynamic video, sound, etc. Among many means of visualizing educational information, multimedia is an effective media tool with complementary image (visual) and audio (auditory) components. Strengthening didactic capabilities of multimedia technology is carried out at the expense of dynamic visual information and various audio effects, contributing to the perception and assimilation of educational material. All this testifies to the fact that multimedia meets one of the leading didactic principles of learning which is visualization. This includes animated films, scientific or documentary videos, which, with proper methodological use in the modern educational environment, allow you to achieve the best learning outcomes. To a certain extent, the article notes the interactivity of multimedia and its importance in enhancing cognitive activity of students