Video Samples Research Articles

Acting real: a cross-cultural investigation of finfluencer strategic authenticity

Video content creators or vloggers have emerged as influential figures in social media marketing, emphasizing the need for them to cultivate an authentic image to leverage their persuasive power. This study investigated the authenticity strategies employed by financial influencers, also known as finfluencers, on YouTube and Bilibili platforms. By conducting a content analysis on a sample of finfluencer videos from China and the U.S. (N = 186), this study explores the strategies they employ to construct authenticity. The results support four categories of strategy authenticity: transparency, immediacy, ordinariness, and passion. In addition, quantitative analyses reveal cultural differences in the use of these strategies. These differences can be attributed to the monetization incentives of American finfluencers, who frequently incorporate brand sponsorships into their videos. They can also be understood through a cultural lens. The implications and limitations of international research on social media influencer marketing are also discussed.

Finnish inventory data of underwater marine biodiversity

Since 2004, marine biodiversity inventory data have been systematically collected with diving, video, and benthic sampling methods in Finland. To date, this collection of data consists of more than 194 000 spatially explicit observations, covering more than 280 aquatic genera, representing mainly macroalgae, vascular plants, water mosses, and invertebrates. We describe the data collection and storage methods, data extraction from national databases, and provide potential users a curated, open-access version of the inventory data. Additionally, examples of data applications and discussion on potential limitations are provided. This extensive dataset can be used in ecological and biogeographical studies to provide general descriptions of biodiversity patterns and species distributions, as well as in more applied studies to support marine management, conservation, and sustainable use of marine areas. The sampling strategy and high spatial and taxonomic resolution allow for statistical modelling, which further increases the usability of the data in research, for instance in identifying key biodiversity areas, estimating biodiversity loss, and assessing efficiency of conservation.

OPT-STVIT: Video Recognition through Optimized Spatial-Temporal Video Vision Transformers

In this paper, we address the computational chal- lenges associated with video recognition tasks, where video transformers have shown impressive results but come with high computational costs. We introduce Opt-STViT, a token selection framework that dynamically chooses a subset of informative tokens in both temporal and spatial dimensions based on the input video samples. Specifically, we frame token selection as a ranking problem, leveraging a lightweight scorer network to estimate the importance of each token. Only tokens with top scores are retained for downstream processing. In the temporal dimension, we identify and keep the frames most relevant to the action categories, while in the spatial dimension, we pinpoint the most discriminative regions in feature maps without affecting the spatial context used hierarchically in most video transformers. To enable end-to-end training despite the non-differentiable nature of token selection, we employ a perturbed-maximum-based dif- ferentiable Top-K operator. Our extensive experiments, primar- ily conducted on the Kinetics-400 and something-something-V2 datasets using the recently introduced MViT video transformer backbone, demonstrate that our framework achieves similar results while requiring 20 percent less computational resources. We also establish the versatility of our approach across different transformer architectures and video datasets.

The gift of health promotion in a celebration: A descriptive observational study of community baby showers on YouTube

Introduction: Although community baby showers have persisted as a global health promotion practice for infants and their families over the past decades, to date there is no study that has evaluated coverage and engagement of community baby showers across social media as a rising global health communication medium in this contemporary digital era. It follows that the goal of this study sought to fill this gap by examining the existing state of coverage for community baby showers on social media utilizing view count as an engagement metric. Methods: In this cross-sectional, descriptive and observational study, we conducted a content analysis of the top 100 most widely viewed videos populated on YouTube at one conceptual point in time that covered community baby showers. to determine the most prevalent sources, formats, and content represented across this sample of videos. Results: Many of the videos were published by nongovernmental / organizational sources (n=86) and among them, the majority were in the form of news reports (n=59). Content across these videos presented a diversity of community stakeholders and entities involved in the development and implementation of community baby showers, many which were both for-profit and non-profit organizations (n=72). There was substantial coverage of a wide range of resources and services for prenatal and postnatal care delineated across all of the videos in this sample. Several videos (n=39) covered increased support building for families of infants. Notably, these videos cumulatively generated a low number of views (N=73,036) which yields clinical, educational, and public health implications. Conclusion: Recommendations to partner with news organizations and utilize content that generated greater viewership as facilitators in increasing capacity to reach, engagement and impact of community baby showers are presented to optimize infant health outcomes, reduce infant mortality, and heighten access to resources and support for infants and their families worldwide.

Drag Artists in Shanghai: Balancing Local and Global Influences in Queer Cultural Production

This article discusses a case study of a group of local and expat drag artists based in Shanghai, China. Theoretical perspectives of “global gay” discourse, queer temporality and Chinese-centric queer theories were employed to question how Shanghai’s drag artists navigate drag’s growing popularity, Chinese state restrictions and Western dominance. Through analysing semi-structured interviews and performance video samples of seven drag artists and one LGBTQ+ venue owner, queer cultural productions in China were explored to ask how they offer new possibilities for queer knowledge production, and propose futurities that are simultaneously transnational and rooted in China. While the influence of Euro-American LGBTQ+ discourse was documented and acknowledged, other perspectives revealed more nuanced conceptualisations of the binary between copy/original, the frictions inherent in cultural exchange, and diverse forms of resistance. Shanghai’s drag scene is evolving in distinct ways, incorporating both global and local influences and cultural traditions. With queer knowledge still largely attributed to the West, and non-Western experiences vastly underrepresented, it is important to recognise what underground cultural productions such as those in Shanghai offer global queer cultural exchange.

Accelerated Data Engine: A faster dataset construction workflow for computer vision applications in commercial livestock farms

Large-scale, high-quality dataset was the foundation of developing advanced artificial intelligence applications. However, creating such a benchmark dataset in a professional field, such as precision management of animals, was always a challenge because of the costly and labor-intensive process of annotation and review. This study introduced a novel workflow named Accelerated Data Engine (ADE), designed to efficiently produce representative and high-quality computer vision datasets from raw animal surveillance footage. By incorporating referring and grounding models (R&G models) as auto-annotators, along with a distillation mechanism for dataset-auditors, ADE significantly speeded up the dataset construction process. The new workflow received natural language inputs as referrals to identify animal instances, delineated their body shapes, and then refined the auto-annotated data through a selection process. To demonstrate the efficacy of ADE, three 30-minute surveillance video samples featuring pigs, sheep, and cattle were discussed in this study. The results indicated the R&G models effectively annotated animals across various farms, while distillation mechanisms could identify various detection errors, balance the data representations, refine annotations, and verify the data quality. Two high-quality cattle datasets (6.5 k and 486 frames), including 26 k and 2.5 k cattle instances, were generated through the ADE workflow from 24-hour surveillance videos on a commercial cattle farm and made publicly available. The proposed dataset has achievable performance between 74.6 %∼84.1 %. The ADE workflow saved 78.4 % of manual work compared to the traditional dataset construction workflow (approximately 141 h). This pioneering approach empowered the fast creation of benchmark animal datasets and would enhance computer vision applications in the livestock production industry in the future.

A Machine Vision Method for Identifying Blade Tip Clearance in Wind Turbines.

This paper introduces a machine vision method for measuring the blade tip clearance in a wind turbine. An industrial personal computer (IPC) is installed in the nacelle of the wind turbine to continuously receive video data from a digital camera mounted at the bottom of the nacelle. Using the open-source computer vision (OpenCV) digital image processing library data base, the real-time trajectory of the turbine blades is determined from the video data. Furthermore, fast Fourier transform (FFT) analysis is performed for determining the operating frequency of the blades in the images. The amplitude analysis performed at this operating frequency reveals the pixel-based blade tip clearance, which is then used to calculate the actual clearance of the wind turbine. This value is subsequently transmitted to the main controller of the wind turbine. The main controller can enhance the operational safety of the wind turbine by implementing appropriate pitch control strategies to restrict and safeguard the blade tip clearance. The results obtained by conducting experiments on a 2.0 MW wind turbine unit validate the effectiveness of the proposed identification method. In this method, the blade tip clearance can be calculated effectively in real time, and both the video sampling rate and communication speed meet the requirements for controlling the blade pitch.

The use of convolutional neural networks for abnormal behavior recognition in crowd scenes

This study introduces the Abnormality Converging Scene Analysis Method (ACSAM) to detect abnormal group behavior using monitored videos or CCTV images in crowded scenarios. Abnormal behavior recognition involves classifying activities and gestures in continuous scenes, which traditionally presents significant computational challenges, particularly in complex crowd scenes, leading to reduced recognition accuracy. To address these issues, ACSAM employs a convolutional neural network (CNN) enhanced with Abnormality and Crowd Behavior Training layers to accurately detect and classify abnormal activities, regardless of crowd density. The method involves extracting frames from the input scene and using CNN to perform conditional validation of abnormality factors, comparing current values with previous high values to maximize detection accuracy. As the abnormality factor increases, the identification rate improves with higher training iterations. The system was tested on 26 video samples and trained on 34 samples, demonstrating superior performance to other approaches like DeepROD, MSI-CNN, and PT-2DCNN. Specifically, ACSAM achieved a 12.55% improvement in accuracy, a 12.97% increase in recall, and a 10.23% enhancement in convergence rate. These results suggest that ACSAM effectively overcomes the computational challenges inherent in crowd scene detection, offering a robust solution for real-time abnormal behavior recognition in crowded environments.

How attentive is a student in class? A concentration evaluation method based on head Euler angle

How attentive is a student in class? It is an interesting and challenging question in the field of education research. In this paper, a concentration evaluation method based on head Euler angle is proposed. First, a fine large-small-interval video sampling strategy is advocated and concepts of dynamic threshold and action amplitude coefficient are defined, which are fused together to get the head up rate. Second, head front-facing rate is computed through analyzing the yaw angle, which is obtained from a proposed spatial physical model of camera and student seat position. Third, for any given time period, class concentration of a student is derived by fusing and normalizing the head up rate and head front-facing rate based on Euclidean-distance. Finally, accuracy experiments are conducted, where the proposed method achieves an average accuracy of 89.8% compared with the average score from the questionnaire given by 22 reviewers. Also, robust experiments on several head pose estimation models are facilitated, which achieves an average accuracy of 98.43% on recorded video dataset, indicates insensitivity to the head pose estimation model used for data collection and verifies that the proposed method is independent on any specific head pose estimation model. The experimental results show that the proposed method is accurate, effective and robust for the concentration evaluation in class.

Stress Detection through Compound Facial Expressions Using Neural Networks

Human face is the most dynamic part of the body that conveys information about the instant emotions. Facial expression analysis starts from early 1900s where later on scientists identify the six basic facial expressions as Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutral with the pioneering studies of psychologists. In the last decades, the acceleration in artificial intelligence and computer vision research makes it possible to automatically detect facial expressions through images. Furthermore, micro expressions, muscle movements and compound facial expressions; that are the combinations of the basic expressions can be also analyzed with computer vision algorithms. The main motivation in automatic facial expression analysis is to support human-computer. Furthermore, facial expression analysis can be a driver for automatic emotion analysis. In this study, we propose a novel method to detect stress indicators on the frontal face images. The detection procedure is based on compound facial expression analysis. 49 couples of 6 basic facial expressions where one is dominating, and the other is the complementary expression are employed. iCV-MEFED facial expression dataset is used in the experiments where video and image samples are provided for every compound facial expression class. The training and testing of compound facial expressions are done using a deep neural network. The robust representations of faces are achieved using a fusion method that combines deep texture features and the action units on the face. Then, through the appropriate grouping of the compound expressions, the system can detect the signs of stress. The proposed approach obtains encouraging results, and it is open to further improvements.

Driver fatigue detection method based on temporal–spatial adaptive networks and adaptive temporal fusion module

The reduction of traffic accidents by determining the driver’s state through fatigue detection is a worthy research issue. Most of the current fatigue driving detection method fails to fully utilize the temporal features of fatigue. To address this problem, this paper proposes a driver fatigue detection method combined with temporal–spatial adaptive networks (TSNet) and adaptive temporal fusion module (ATFM). First, a frame sequence of T frames is obtained by strided sampling of the input video and data enhancement. Subsequently, the temporal–spatial adaptive module (TSAM) is used as the core module and incorporated into Efficientnet-v2 to construct TSNet, which adaptively extracts temporal features according to different videos, adds attention weights to discriminative spatial and channel features, and fully extracts fatiguing temporal–spatial features of videos. Finally, ATFM is utilized to learn the weights between the fatigue classification scores of each frame in the frame sequence and adaptively fuses the fatigue classification scores of individual frames to obtain fatigue prediction results, increasing the extent of the influence of keyframes on the fatigue prediction results. In this paper, the proposed method achieves an accuracy of 89.42% on the NTHU-DDD dataset, which is better than other state-of-the-art methods, and the number of parameters of the proposed method is 24.70M, which is smaller than most of the methods. Through a series of comparative experiments, TSNet and ATFM alone also outperform models and modules with similar functionality.

A fast open data reduction workflow for the electron microprobe flank method to determine Fe3+/ΣFe contents in minerals

Abstract Knowing the Fe3+/ΣFe ratio in minerals is important to understand the formation and evolution of minerals and their host rocks. Variable Fe3+/ΣFe in, e.g., garnet is accompanied by a change of their characteristic FeLα and FeLβ X-ray emission lines, which can be quantified with electron microprobe measurements using the flank method. Previously, the required data reduction process to determine the Fe3+/ΣFe was complex. Here, we present a new data reduction tool taking garnet data as an example. This new Flank Reduction app is a freely available, graphical user interface (GUI)-driven, web-based application to reduce flank method data quickly and easily. The entire data reduction process is achieved in minutes compared to hours or days, as it was before. Flank Reduction provides comprehensive insights into, e.g., the flank method standards employed, data obtained, and errors through a wide array of controls and visualization tools. Documentation with comprehensive information on the flank method, data reduction, as well as training material such as video tutorials or sample data sets, are available on a dedicated webpage. Flank Reduction emphasizes the high value of Findable, Accessible, Interoperable, Reproducible (FAIR) and open research software and demonstrates how current developments in coding and app implementation can facilitate the development of powerful and expandable research software.

Efficient and reliable methods for estimating the abundance of keystone coastal macrofauna over large spatial scales.

Coastal bivalves are important ecosystem engineers, and identifying critical habitats can enhance conservation outcomes for threated keystone species as well as determining hotspots for invasive species. As early action is more efficient in both conservation and mitigation of species invasions, efficient and reliable tools for mapping and monitoring species over large scales are essential. We assessed the reliability and efficiency of towed video and quadrat sampling for estimating the abundance of three keystone macrofaunal bivalve species. To assess reliability, we compared the measured density based on each of the two methods to the "true" density estimated by manually surveying an entire transect. We found that both the video and quadrat method caused underestimation of the density of bivalves, but that the amount of underestimation was comparable, and further that both methods took substantially less time than surveying an entire transect manually. The video method underestimated the abundance of Pacific oysters (Magallana gigas), European flat oysters (Ostrea edulis), and blue mussels (Mytilus spp.) by 23%, 24%, and 16%, respectively. The causes of underestimation for the two oyster species were bivalves grouped in clusters, large amounts of small individuals, and generally higher abundances. While Mytilus spp. were underestimated overall, here observer experience was important, with inexperienced observers overestimating and experienced observers underestimating. Our study found both methods to be reliable and efficient for estimating the abundance of three keystone macrofaunal species, suggesting their potential applicability to other sessile or slow-moving species. We propose that these methods, due to their efficiency, can advance scientific knowledge and enhance conservation outcomes by establishing population baselines, assessing trends over time, and identifying and protecting critical habitats.

TikTok Videos Related to Rhinoplasty in China: Characteristics and the Quality of the Information.

There are numerous videos related to rhinoplasty on TikTok in China. The objective is to examine videos on Chinese TikTok regarding rhinoplasty surgery and evaluate the content quality of rhinoplasty-related videos on TikTok. We collected 184 Chinese video samples related to rhinoplasty and extracted basic information. We used the PEMAT-A/V tool to evaluate each video. A total of 184 videos were included in the study. The sampled videos received approximately 4.8 million likes, 900,000 comments, and 300,000 favorites. Most of the videos (n = 130, 70.65%) were posted by certified plastic surgeons on the TikTok platform. This study found that 6 (3.26%) videos contained obvious errors or misleading information. Regarding the PEMAT-A/V, the median total score was 85.71 (n = 184, IQR = 78.57-92.45%). Videos created by plastic surgeons had the highest comprehension score (n = 130, median = 100%, IQR = 91.67-100%), while videos from news media (n = 16, median = 75%, IQR = 70.76-77.34%) had the lowest usability and PEMAT-A/V total scores. In terms of video content, the PEMAT-A/V total score for surgical risks/contraindications related content (n = 15, median = 92.86%, IQR = 83.48-92.86%) was the highest, while scores for complaint-related content (n = 20, median = 75, IQR = 71.43-78.57%) were the lowest. This study is the first report in China to evaluate the quality of information in nose plastic surgery-related videos on TikTok. We found that the quality of videos created by plastic surgeons on the TikTok platform is generally high, while videos provided by news media have lower quality. We encourage healthcare professionals to share more high-quality information on TikTok and educate patients on how to properly handle misinformation. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The Positive Effect of Technology-Integrated Teaching on Students’ Grammar Learning

The primary aim of this study is to examine and assess the impact of technology-integrated education on students’ acquisition of grammatical skills. In this context, thirty students participated in this study; the stratified sampling method allocated 15 intermediate-level students to the control group, while another 15 students were assigned to the experimental groups. To assess the grammar proficiency of both groups at the start of the study, a pre-test was administered. The control group received traditional grammatical education centered around books, while the experimental group received grammar education utilizing mobile phones, smart boards, and technological applications. Although the pre-test analyses conducted at the start of the study did not reveal any notable distinction between the two groups, the post-test analyses using SPSS-7 and t-statistics calculations indicated a significant difference of .001 between the two groups. The control group experienced a 12.17% gain in grammatical level, while the experimental group showed a higher increase of 27.25%. Upon concluding the study, during the interview analysis conducted with the students, they expressed that technology-integrated grammar education is beneficial due to its flexibility, immediate feedback, and inclusion of video clips, quizzes, and sample essays pertaining to grammar.

Analysis of Supraglottic Activity during Vocalization in Flamenco Singers.

Flamenco is a traditional music originally from Andalusia in southern Spain. Some of the vocal resources used in Flamenco have components of voice distortion and other voice qualities that could sound like hyperfunctional voice productions. The present study aimed at observing supraglottic activity in flamenco singers while engaged in singing at various degrees of pitch and loudness and while engaged in realizing phonatory tasks. A total of eighteen flamenco singers with at least 5 years of voice training were recruited. Flexible endoscopic voice evaluations were recorded and edited to provide samples of different pitches, loudness levels, and phonatory tasks. Sound was removed from video samples. Two blinded laryngologists were asked to assess antero-posterior compression, medial compression, pharyngeal compression, and VLP for every sample, using a visual analog scale. Significantly higher values were found for medial compression, anterior-posterior compression, VLP and pharyngeal compression during high loudness levels when compared to medium and low loudness. Overall, medial compression was lower than anterior-posterior compression. Supraglottic activity is present in flamenco singing in the four laryngoscopic variables. It seems to be that supraglottic activity increases with loudness level and pitch. This behavior could be a natural and necessary aspect of flamenco singing present during both sustained vowels and song.

Self-supervised spatial–temporal feature enhancement for one-shot video object detection

One-shot video object detection is a task that aims to locate and identify objects in video sequences given only a single video sample for each class. Exploration in this field is still in its infancy and previous few-shot video object detection methods have limitation in this task. In this paper, we propose the Self-supervised Feature Enhancement (SFE) framework to address one-shot video object detection task. SFE includes two important modules: Hybrid Spatial Self-supervised Feature Enhancement (HSSFE) and Dynamic Temporal Self-supervised Feature Enhancement (DTSFE). HSSFE enhances features from a spatial perspective with spatial self-supervised auxiliary tasks at frame and instance levels. DTSFE on the other hand enhances features from a temporal perspective with memory-based self-supervised constraint for the same object across different frames. We have conducted experiments on multiple benchmarks, and the results demonstrate that our method achieves state-of-the-art performance.

Pemodelan Identifikasi Objek Kendaraan Bermotor Menggunakan Faster Region based Convolutional Neural Network (R-CNN) Berbasis Python

The vehicles are currently experiencing a surge in number and variation. This is evident from the kinds of vehicles that are passing through the highway area. The rise in the number of motorized vehicles will surely give a squeeze to the traffic density. The increase in the number of motor vehicles is one of the biggest factors in the impact of the congestion. The congestion can also cause damage to the highway. It's supposed to be the focus of the local government in dealing with the problem. Each road point has its own potential, so it is necessary to have a calculation in identifying the number of vehicles and the type of vehicles that are slipped on the road. Motor vehicle identification can be solved using the Faster Region based Convolutional Neural Network approach. Faster R-CNN is a deep learning architecture used to detect inside computers. Research will run at several highway points to take samples of video at a certain time, for identified the type of vehicle. Vehicle labelling will facilitate the calculation of the number of vehicles crossing the road in a given unit of time. The vehicle identification needs are used to see the density of the highway so that it can help the local government in making the right decision or solution to reduce the traffic density. The results of research such as quantitative data can be easily used to give the right picture and decision.

Micro-expression recognition based on euler video magnification and 3D residual network under imbalanced sample

A student’s verbal behavior plays a crucial role in education, while nonverbal behavior, such as micro-expressions, significantly improves teaching quality. To address the problem of small facial expression movements, imbalanced data categories, and lack of temporal information in static expressions, a micro-expression recognition method is proposed based on Eulerian Video Magnification (EVM) and a 3D Residual Network (3D ResNet) under imbalanced samples. Firstly, face detection in the Dlib library is used to locate the face in the micro-expression video sample and crop it. Secondly, the EVM is used to magnify the motion features in micro-expressions. Then, the 3D ResNet is used to extract spatio-temporal information from micro-expression video samples, and the Cyclical Focal Loss (CFL) function is introduced in the network training process to solve the class imbalance problem in micro-expression datasets. Finally, the roles of the EVM and the CFL function in recognizing micro-expressions by the 3D ResNet are analyzed. The experimental results on the Spontaneous Micro-expression Database (SMIC) and Chinese Academy of Sciences Micro-expression Database II (CASME II) demonstrate the effectiveness and superiority of this method. The proposed method can assist in teaching evaluation and promote the development of smart classrooms, and further research is needed on the storage and computing of the proposed method on devices.

Thermal preference prediction through infrared thermography technology: Recognizing adaptive behaviors

The recognition of thermal adaptation behaviors is a crucial and noninvasive method for accurately and swiftly predicting the thermal preferences of occupants. This is instrumental in enhancing the energy efficiency, indoor personnel comfort, and overall productivity of office buildings. This study employed deep-learning models to analyze a thermal adaptive behavior video dataset captured by Infrared thermography (IRT) cameras to predict the thermal preferences of occupants. A large-scale infrared thermography dataset was developed for recognizing thermal adaptive behaviors using 9650 video samples. Two adaptive behavior recognition models for infrared videos were developed by leveraging neural network models: Two-Stream Inflated 3D ConvNet (I3D) and SlowFast (SF) networks. These models achieved the average prediction accuracies of 83.53 % and 88.56 %, respectively. Notably, both models exhibited a recognition time of milliseconds, facilitating the real-time recognition of thermal adaptive behaviors. This study offers vital technical and theoretical underpinnings for developing decision-making solutions for smart buildings based on noninvasive thermal preference prediction.