Search

Objetivou-se identificar o que emerge de vídeos que abordam a Educação Financeira na Educação Matemática, por meio do que é propagado em canais do YouTube. Para a caracterização dos vídeos, utilizou-se a ferramenta YouTube Data Tools, baseada no API do YouTube. Foi analisado os metadados e as transcrições dos vídeos mais relevantes nesta temática. Constatou-se que, aproximadamente, 42% dos vídeos pertenciam à categoria educação, associados a canais de universidades. Verificou-se um entrelaçamento entre professor e Educação Financeira, com discussões numa perspectiva de formação crítica. Além disso, identificou-se que, entre as potencialidades da Educação Financeira para a sociedade, estão: auxílio da tomada de decisão; consumo consciente; gerenciamento e planejamento adequado das finanças; e melhoria na qualidade de vida.

As a specific breastfeeding practice, the proportion of exclusive breastfeeding in Indonesia remains below the intended indicator. Education about breastfeeding techniques aims to increase success in providing exclusive breastfeeding by increasing mothers' knowledge and skills in breastfeeding their babies. The goal of this study is to find out how the Jambi City Health Center's breastfeeding education program affects moms' confidence and nursing behavior. Mothers who had just given birth or were nursing their babies participated in this study. Using a control group and a quasi-experimental design with pre- and post-test measures, the study used a quantitative methodology. Using accidental sampling, 25 respondents from the treatment and control groups made up the sample. The variables measured are maternal behavior and self-efficacy. The measuring instruments used were the breastfeeding action instrument and the BSES-FS. The treatment group's behavioral results produced a p-value of 0.001 (0.05). In a similar vein, the treatment group's p-value for self-efficacy outcomes was 0.001 (0.05). These findings imply that nursing moms' behavior and self-efficacy are significantly impacted by teaching packages on breastfeeding procedures delivered by video and booklet medium. In particular, compared to the control group that did not get the intervention, the treatment group, who did receive the intervention, exhibited improved behavior and greater levels of self-efficacy.

Abstract Background Limited Health Literacy (HL) is an obstacle to accessing and receiving optimal health care and negatively impacts patients’ quality of life, thus making it an urgent issue in the health care system. Visual-based interventions are a promising strategy to improve HL through the use of visual aids and pictorial materials to explain health-related concepts. However, a comprehensive summary of the literature on the topic is still scarce. Methods To fill this gap, we carried out a systematic review and meta-analysis with the aim to determine the effectiveness of visual-based interventions in improving comprehension of health related material in the clinical population. Independent studies evaluating the effectiveness of visual-based interventions on adults (> 18 years) and whose primary outcome was either health literacy (HL) or comprehension were eligible for the review. After a systematic literature search was carried out in five databases, 28 studies met the inclusion criteria and thus were included. Most of the studies were randomized controlled trials and they focused on HL and health knowledge as outcomes. Results The review and meta-analysis showed that visual-based interventions were most effective in enhancing the comprehension of health-related material compared to traditional methods. According to meta-analytic results, videos are more effective than traditional methods (Z = 5.45, 95% CI [0.35, 0.75], p

Videos are pivotal tools in pre-service teacher training. First-person, third-person, and 360-degree perspectives are commonly used types and the optimal type remains undetermined. In our study involving pre-service teachers (PSTs), we evaluated the efficacy of 360-degree videos against the first-person and third-person perspective videos concerning spatial presence, cognitive load, classroom quality rating accuracy, and classroom observation. Our results revealed that PSTs exposed to 360-degree videos exhibited superior performance in spatial presence, classroom quality rating accuracy, and classroom observation compared to the other two video types. This underscores that 360-degree videos enhance PSTs' skills in discerning, categorizing, and interpreting classroom events. Although there were no marked differences in cognitive load across the groups, cognitive load was found to modulate the influence of video type on classroom observation. Notably, at higher cognitive load levels, the 360-degree videos were more advantageous. The significance of this study is twofold: it empirically substantiates the superiority of 360-degree videos in refining pedagogical skills and it reveals the modulating role of cognitive load. These findings inform innovative instructional interventions and pedagogical strategies, emphasizing the need for using 360-degree videos in teacher education and encouraging higher learner engagement. [ABSTRACT FROM AUTHOR]

Effective utilization of spatiotemporal information is essential for improving the accuracy and robustness of Video Salient Object Detection (V-SOD). However, current methods have not fully utilized historical frame information, ultimately resulting in insufficient integration of complementary semantic information. To address this issue, we propose a novel Inheritance Enhancement Network (IENet) based on Transformer. The core of IENet is a Heritable Multi-Frame Attention (HMA) module, which fully exploits long-term context and frame-aware temporal modeling in feature extraction through unidirectional cross-frame enhancement. In contrast to existing methods, our heritable strategy is based on the unidirectional inheritance model using attention maps which ensure the information propagation for each frame is consistent and orderly, avoiding additional interference. Furthermore, we propose an auxiliary attention loss by using inherited attention maps to direct the network to focus more on target regions. The experimental results of our IENet reveal its effectiveness in handling challenging scenes on five popular benchmark datasets. For instance, in the cases of VOS and DAVSOD, our method achieves 0.042% and 0.070% for MAE compared to other competitive models. Particularly, IENet excels in inheriting finer details from historical frames even in complex environments. The module and predicted maps are publicly available at https://github.com/TOMMYWHY/IENet [ABSTRACT FROM AUTHOR]

Video captioning is designed to generate natural language descriptions based on video content. Traditional methods extract visual features and interactive relationship features between objects, but the problem of video feature isolation and semantic hierarchy is ignored. This paper proposes a Multi-Level Video Captioning Method based on semantic space (S-MLM) to solve the above problems. S-MLM extracts different levels of visual elements and visual relationships, and the visual information of different levels is aggregated layer by layer to complete the generation of low-level to high-level visual features. The multi-level structure semantic graph is constructed from the semantic point of view. It does not rely on external knowledge bases, and uses its own information as guidance to enhance feature representation and improve semantic understanding. We conduct experiments on MSVD and MSR-VTT datasets, and the experimental results show that the performance of video captioning is further improved. [ABSTRACT FROM AUTHOR]

Referring video object segmentation (R-VOS) is a fundamental vision-language task which aims to segment the target referred by language expression in all video frames. Existing query-based R-VOS methods have conducted in-depth exploration of the interaction and alignment between visual and linguistic features but fail to transfer the information of the two modalities to the query vector with balanced intensities. Furthermore, most of the traditional approaches suffer from severe information loss in the process of multi-scale feature fusion, resulting in inaccurate segmentation. In this paper, we propose DCT, an end-to-end decoupled cross-modal transformer for referring video object segmentation, to better utilize multi-modal and multi-scale information. Specifically, we first design a Language-Guided Visual Enhancement Module (LGVE) to transmit discriminative linguistic information to visual features of all levels, performing an initial filtering of irrelevant background regions. Then, we propose a decoupled transformer decoder, using a set of object queries to gather entity-related information from both visual and linguistic features independently, mitigating the attention bias caused by feature size differences. Finally, the Cross-layer Feature Pyramid Network (CFPN) is introduced to preserve more visual details by establishing direct cross-layer communication. Extensive experiments have been carried out on A2D-Sentences, JHMDB-Sentences and Ref-Youtube-VOS. The results show that DCT achieves competitive segmentation accuracy compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user's video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user's eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Within the domain of multi-label classification for micro-videos, utilizing terrestrial datasets as a foundation, researchers have embarked on profound endeavors yielding extraordinary accomplishments. The research into multi-label classification based on underwater micro-video datasets is still in the preliminary stage. There are some challenges: the severe color distortion and visual blurring in underwater visual imaging due to water molecular scattering and absorption, the difficulty in acquiring underwater short video datasets, the sparsity of underwater short video modality features, and the formidable task of achieving high-precision underwater multi-label classification. To address these issues, a bilayer graph convolution learning network based on constrained codec (BGCLN) is established in this paper. Specifically, modality-common representation is constructed to complete the representation of common information and specific information based on the constrained codec network. Then, the attention-driven double-layer graph convolutional network module is designed to mine the correlation information between labels and enhance the modality representation. Finally, the combined modality representation fusion and multi-label classification module are used to obtain the category classifier prediction. In the underwater video multi-label classification dataset (UVMCD), the effectiveness and high classification accuracy of the proposed BGCLN have been proved by numerous experiments. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: The presented solution can allow for automated video quality assessment and quick and effective modelling of the video quality experienced by the user. The classical objective methods of assessing video quality used so far, apart from their advantages, such as low costs, also have disadvantages. The need to eliminate these defects results in the search for better and better solutions. This article proposes a video quality assessment method based on machine learning using a linear regression model. A set of objective quality assessment metrics was used to train the model. The results obtained show that the prediction of video quality based on a machine learning model gives better results than the objective assessment based on individual metrics. The proposed model showed a strong correlation with the subjective user assessments but also a good fit of the regression function to the empirical data. It is an extension and improvement of the efficiency of the classical methods of objective quality assessment that have been used so far. The solution presented here will allow for a more accurate prediction of the video quality perceived by viewers based on an assessment carried out using a much cheaper, objective method. [ABSTRACT FROM AUTHOR]

Motor contagions refer to implicit effects induced by the observation of actions made by others on one's own actions. A plethora of studies conducted over the last two decades have demonstrated that both observed and predicted actions can induce various kinds of motor contagions in a human observer. However, motor contagions have always been investigated with regard to different features of an observed action, and it remains unclear whether the background environment in which an observed action takes place modulates motor contagions as well. Here, we investigated participant movements in an empirical hand steering task during which the participants were required to move a cursor through a visual channel after being presented with videos of an actor performing the same task. We manipulated the congruency between the actions shown in the video and the background channels and examined whether and how they affected the participants' own movements. We observed a clear interaction between the observed action and its background. The movement time of the participants' actions tended to increase or decrease depending on whether they observed a faster or slower movement, respectively, and these changes were amplified if the background was not congruent with the action contained within it. These results suggest that background information can modulate motor contagions in humans. [ABSTRACT FROM AUTHOR]

Video saliency prediction aims to resemble human visual attention by identifying the most relevant and significant elements in a video frame or sequence. This task becomes notably intricate in scenarios characterized by dynamic elements such as rapid motion, occlusions, blur, background variations, and nonrigid deformations. Therefore, the inherent complexity of human visual attention behavior during dynamic scenes necessitates the assessment of both temporal and spatial data. Existing video saliency frameworks often falter under such conditions, and relying solely on image saliency models neglects crucial temporal information in videos. This study presents a new Video Salient Object Detection via Multi-level Spatiotemporal Bidirectional Network using Multi-scale Transfer Learning (MSB-Net) to address the problem of identifying significant objects in videos. The proposed MSB-Net achieves notable results for a given sequence of frames by employing multi-scale transfer learning with an encoder and decoder approach to acquire knowledge and saliency map attributes spatially and temporally. The proposed MSB-Net model has bidirectional LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) components. The VGG16 (Video Geometry Group) and VGG19 architectures extract multi-scale features from the input video frames. Evaluation of diverse datasets, namely DAVIS-T, SegTrack-V2, ViSal, VOS-T, and DAVSOD-T, demonstrates the model's effectiveness, outperforming other competitive models based on parameters such as MAE, F-measure, and S-measure. [ABSTRACT FROM AUTHOR]

Semantic segmentation of UAV data has been one of the research hotspots in recent years. Many applications, such as the aerial mapping of urban–rural areas, the automatic extraction of roads/buildings, etc., require accurate and efficient segmentation algorithms. The proposed method implements a deep learning framework combining SegNet encoder-decoder architecture and convolutional long short-term memory (ConvLSTM) neural network for semantic oblique video segmentation of urban scenes. The SegNet, as a frame-based semantic segmentation method, along with the ConvLSTM, learns the spatial and temporal information and provides the final video segmentation results. The proposed method was evaluated over two UAV oblique video datasets including, UAVid and Varied Drone Dataset (VDD). The obtained results were compared with two neural networks, U-Net and FCN, in combination with Conv-RNN architecture. The proposed method significantly improved the segmentation results of two video datasets by extracting spatial–temporal features. The proposed SegNet-ConvLSTM architecture achieved the best results of both UAVid and VDD video sets with 81.72% and 87.03% accuracy, respectively. The UNet-ConvLSTM architecture, with 77.57% and 84.42% accuracy, and FCN-ConvLSTM architecture, with 73.79% and 82.51% accuracy in UAVid2020 and VDD datasets, provided weaker semantic segmentation. Moreover, the SegNet-ConvLSTM architecture, using NVIDIA Tesla T4 GPU, segments each video sequence with an average inference time of approximately 33 ms per frame, and it can be applied in real-time applications. [ABSTRACT FROM AUTHOR]

This article introduces Lester, a novel method to automatically synthesize retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment Anything Model (SAM) and the resulting masks are tracked through subsequent frames with DeAOT, a method of hierarchical propagation for semi-supervised video object segmentation. The geometry of the masks' contours is simplified with the Douglas–Peucker algorithm. Finally, facial traits, pixelation and a basic rim light effect can be optionally added. The results show that the method exhibits an excellent temporal consistency and can correctly process videos with different poses and appearances, dynamic shots, partial shots and diverse backgrounds. The proposed method provides a more simple and deterministic approach than diffusion models based video-to-video translation pipelines, which suffer from temporal consistency problems and do not cope well with pixelated and schematic outputs. The method is also more feasible than techniques based on 3D human pose estimation, which require custom handcrafted 3D models and are very limited with respect to the type of scenes they can process. [ABSTRACT FROM AUTHOR]

By exploring the current block-based lossy video coding process and compressed videos, this paper finds two unique characteristics namely quality fluctuation and pixel deficiency. And we use 3D convolutional neural network (3D-CNN) to make full use of the limited temporal and spatial information in compressed video and build compressed video quality enhancement network (CVQENet) to improve the compressed video quality. The experimental results show that compared with the videos encoded by High Efficiency Video Coding (HEVC/H.265), the mean value of the Peak Signal-to-Noise Ratio (PSNR) of enhanced videos has been improved by 0.4652 dB under Low Delay (LD) configuration with Quantization Parameter (QP) is set to 37. [ABSTRACT FROM AUTHOR]

With the continuous advancement in Internet-of-Things and deep learning, video action recognition is gradually emerging in daily and industrial applications. Spatiotemporal and motion patterns are two crucial and complementary types of information used for action recognition. However, effectively modelling both types of information in videos remains challenging. In this paper, we propose a spatiotemporal and motion information extraction (STME) network that extracts comprehensive spatiotemporal and motion information from videos for action recognition. First, we design the STME network, which includes three efficient modules: a spatiotemporal extraction (STE) module, a short-term motion extraction (SME) module and a long-term motion extraction (LME) module. The SME and LME modules are used to model short-term and long-term motion representation, respectively. Then, we apply the STE module to capture comprehensive spatiotemporal information which can supplement the video representation for action recognition. According to our experimental results, the STME network achieves significantly better performance than existing methods on several benchmark datasets. Our codes are available at https://github.com/STME-Net/STME. [ABSTRACT FROM AUTHOR]

The present study aims to elucidate whether insight problem solving could be facilitated by the cognitive component of humour. The authors take interest in whether the logical mechanisms of humour can affect how fast insight problems are solved. To that end, the authors conducted two experiments where participants solved insight problems after watching visual humorous stimuli such as videos and slideshows. The first experiment demonstrated the overall impact of facilitation by humour on insight problem solving; however, it did not show any difference in how particular logical mechanisms of humour affect the solution time of insight problems. The second experiment showed that the cognitive component of humour could selectively affect insight problems whose difficulty stems from different sources. These results suggest that the cognitive component of humour, when operationalised as logical mechanisms and schema switching, contributes to solving insight problems. [ABSTRACT FROM AUTHOR]

With the rapid development of network and multimedia technologies, the widespread use of images in social networks, military applications, and other fields has led to substantial demands for image encryption. To protect images from potential threats, most protocols perform multiple rounds of confusion and diffusion until a satisfactory security level is attained. However, this strategy is time-consuming and unable to meet the requirements for real-time video encryption. Consequently, many works realize video encryption by simplifying the encryption process, e.g. employing a single round of confusion and diffusion to encrypt each frame. To enhance security to the level of image encryption while ensuring real-time performance, this paper proposes a real-time video encryption protocol based on multi-round confusion-diffusion architecture. It splits the frame into subframes, uses a set of threads to concurrently perform confusion and diffusion. The statistical and security analyses demonstrate the proposed protocol exhibits exceptional statistical properties and is resistant to various attacks. The encryption speed evaluation shows that our method significantly enhances computational efficiency, achieving latency-free encryption of 5 1 2 × 5 1 2 24FPS videos using the Intel Xeon Gold 6226R@2.9 GHz CPU. Even with the execution of four rounds of confusion and six rounds of diffusion operations on each frame, the average encryption time remains below 30 ms. [ABSTRACT FROM AUTHOR]

Action segmentation is vital for video understanding because it heuristically divides complex untrimmed videos into short semantic clips. Real-world human actions exhibit complex temporal dynamics, encompassing variations in duration, rhythm, and range of motions, etc. While deep networks have been successfully applied to these tasks, they face challenges in effectively adapting to these complex variations due to the inherent difficulty in capturing semantic information from a global perspective. Merely relying on distinguishing visual representations in local regions leads to the issue of over-segmentation. In an attempt to address this practical issue, we propose a novel approach named ASGSA, which aims to obtain smoother segmentation results by extracting instructive semantic information. Our core component, Global Semantic-Aware module, provides an effective way to encode the long-range temporal relation in the long untrimmed video. Specifically, we exploit a hierarchical temporal context aggregation, which is identified by a gated-mechanism selection to control the information passage at different scales. In addition, an adaptive fusion strategy is designed to guide the segmentation with the extracted semantic information. Simultaneously, to obtain higher-quality video representation without extra annotations, we resort to self-supervised training strategy and propose the Video Speed Prediction module. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on all three challenging benchmark datasets (Breakfast, 50Salads, GTEA) and significantly improves the F1 score@50, which represents the reduction of over-segmentation. The code is available at https://github.com/ten000/ASGSA. [ABSTRACT FROM AUTHOR]

[Display omitted] • MCTracker integrates motion correlation and mix-scale feature enhancement to track small, dense, and blurry objects effectively. • CDFE, IFMCM, and MSEB structures enhance contrast encoding, motion correlation, and multi-scale feature integration. • STFM resolves temporal-spatial fusion inconsistencies, boosting fusion expressive capability for temporal-spatial features. The video data captured by satellites, colloquially referred to as "gazing," holds significant value for analyzing object status and dynamically tracking movements. Such data and their associated applications play pivotal roles in disaster response, urban traffic monitoring, national defense, and security operations. The multi-object tracking task in satellite video surveillance is inherently more complex than single-object tracking, presenting challenges such as tracking dense small objects and limited capability in multi-object tracking in ambiguous environments. This study proposes a satellite video multi-object tracking (MCTracker) method that considers inter-frame motion correlation and multi-scale cascaded feature enhancement, achieving outstanding performance in multi-object tracking tasks. This study builds upon a differentially encoded backbone network incorporating both global and local information, proposing an Inter-frame Motion Correlation Module (IFMCM) to enhance inter-frame dynamic continuity. A Mixed-scale Enhancement Block (MSEB) is designed to address the challenge of detecting and tracking small objects under multiscale effects. Additionally, a Spatio-temporal Fusion Module (STFM) is introduced to improve the feature fusion representation capability among modules. Through experiments conducted on two open-source datasets, AIR-MOT and SAT-MTB, it has been demonstrated that the proposed MCTracker exhibits efficient synergistic consistency in detection and ReID tasks. On the AIR-MOT dataset, the object tracking accuracy, as measured by MOTA, reaches 59.4 %, achieving optimal performance. In the SAT-MTB dataset's four-class multiple object tracking task, particularly in tracking small-scale objects such as cars and ships, the proposed method demonstrates comprehensive optimal performance, yielding robust tracking results. The satellite video-based multiple object tracking approach proposed in this study holds significant reference value for further exploration of object motion states and trajectory tracking. [ABSTRACT FROM AUTHOR]

As a novel data source for earth observation, satellite video can provide large-scale temporal information for dynamic monitoring. However, the cloud occlusion prevents satellite video from continuous and seamless observation of the earth's surface. We propose the first satellite video cloud removal model RFE-VCR to approach this problem. In RFE-VCR, an efficient strategy of taking distant frames into training period is applied. A reference enhance block based on gated aggregation layers is proposed to explore the complementary information hidden in distant frames. A bidirectional local enhance block using deformable convolution is improved for feature refinement. Moreover, a decoupled temporal-spatial transformer is utilized for long-distance dependence modeling. Simulative and real experiments on Jilin-1 satellite videos demonstrate that our proposed network can achieve remarkable performance in video cloud removal task, as well as sensitive object hiding and high-reflection removal. More dynamic results of our experiments can be found at https://xyjin99.github.io/RFE-VCR/. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate and compare the quality of information in YouTube videos on erectile dysfunction (ED) treatment in English and Hindi language. The English and Hindi terms for erectile dysfunction treatment were searched on YouTube, and the first 100 videos were screened. Of them, 148 videos were eligible for analysis. Quality of health information was assessed using: a modified 5-item DISCERN tool; and the Patient Education Materials Assessment Tool (PEMAT) for Audio/Visual Materials. The analyzed videos had cumulative views of more than 70 million. Only 8.8% of videos had DISCERN score ≥ 4 indicating good content quality. About 46% and 40% of videos presented information in an easy-to-understand and actionable manner based on PEMAT sub-scale scores ≥ 70% respectively. The three most common treatments suggested for ED were lifestyle modification-based advice (41.2%), oral Phosphodiesterase type-5 inhibitors (37.8%), and penile prosthetic implantation (28.4%).Herbal/ over-the-counter medicines, and specific diet/ food item(s) were more frequently suggested in Hindi videos. Whereas, intra-cavernosal injections, MUSE suppositories, and shock wave therapy were more commonly suggested in English videos. A substantial number of videos suggested treatment strategies that were not concordant with the American Urological Association guidelines. Healthcare professionals need to be mindful of this while counseling patients and advocate for the creation of more reliable and accurate online sources of information in English and other vernacular languages like Hindi. [ABSTRACT FROM AUTHOR]

In teacher training, it is important to support prospective teachers as they become reflective practitioners. The aim of the study was to discover what, in the opinion of student teachers, are the advantages and disadvantages of using a video diary for learning purposes compared to a written diary. A questionnaire was used to collect data from 34 student teachers, which was then analyzed using qualitative content analysis. In the opinion of student teachers, the advantages of the video diary format were authenticity, team-building facilitation, efficiency, novelty, professionalism, and historical value. The disadvantages were related to anxiety, technical issues and digital competence, usage demands for location, text composition, and hesitation regarding possible video circulation. [ABSTRACT FROM AUTHOR]

Static and moving objects often occur in real-life videos. Most video object segmentation methods only focus on extracting and exploiting motion cues to perceive moving objects. Once faced with the frames of static objects, the moving object predictors may predict failed results caused by uncertain motion information, such as low-quality optical flow maps. Besides, different sources such as RGB, depth, optical flow and static saliency can provide useful information about the objects. However, existing approaches only consider either the RGB or RGB and optical flow. In this paper, we propose a novel adaptive multi-source predictor for zero-shot video object segmentation (ZVOS). In the static object predictor, the RGB source is converted to depth and static saliency sources, simultaneously. In the moving object predictor, we propose the multi-source fusion structure. First, the spatial importance of each source is highlighted with the help of the interoceptive spatial attention module (ISAM). Second, the motion-enhanced module (MEM) is designed to generate pure foreground motion attention for improving the representation of static and moving features in the decoder. Furthermore, we design a feature purification module (FPM) to filter the inter-source incompatible features. By using the ISAM, MEM and FPM, the multi-source features are effectively fused. In addition, we put forward an adaptive predictor fusion network (APF) to evaluate the quality of the optical flow map and fuse the predictions from the static object predictor and the moving object predictor in order to prevent over-reliance on the failed results caused by low-quality optical flow maps. Experiments show that the proposed model outperforms the state-of-the-art methods on three challenging ZVOS benchmarks. And, the static object predictor precisely predicts a high-quality depth map and static saliency map at the same time. [ABSTRACT FROM AUTHOR]

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ∼ 20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20K [ABSTRACT FROM AUTHOR]

Recent years have witnessed great progress in creating vivid audio-driven portraits from monocular videos. However, how to seamlessly adapt the created video avatars to other scenarios with different backgrounds and lighting conditions remains unsolved. On the other hand, existing relighting studies mostly rely on dynamically lighted or multi-view data, which are too expensive for creating video portraits. To bridge this gap, we propose ReliTalk, a novel framework for relightable audio-driven talking portrait generation from monocular videos. Our key insight is to decompose the portrait's reflectance from implicitly learned audio-driven facial normals and images. Specifically, we involve 3D facial priors derived from audio features to predict delicate normal maps through implicit functions. These initially predicted normals then take a crucial part in reflectance decomposition by dynamically estimating the lighting condition of the given video. Moreover, the stereoscopic face representation is refined using the identity-consistent loss under simulated multiple lighting conditions, addressing the ill-posed problem caused by limited views available from a single monocular video. Extensive experiments validate the superiority of our proposed framework on both real and synthetic datasets. Our code is released in (https://github.com/arthur-qiu/ReliTalk). [ABSTRACT FROM AUTHOR]

Automatically understanding funny moments (i.e., the moments that make people laugh) when watching comedy is challenging, as they relate to various features, such as body language, dialogues and culture. In this paper, we propose FunnyNet-W, a model that relies on cross- and self-attention for visual, audio and text data to predict funny moments in videos. Unlike most methods that rely on ground truth data in the form of subtitles, in this work we exploit modalities that come naturally with videos: (a) video frames as they contain visual information indispensable for scene understanding, (b) audio as it contains higher-level cues associated with funny moments, such as intonation, pitch and pauses and (c) text automatically extracted with a speech-to-text model as it can provide rich information when processed by a Large Language Model. To acquire labels for training, we propose an unsupervised approach that spots and labels funny audio moments. We provide experiments on five datasets: the sitcoms TBBT, MHD, MUStARD, Friends, and the TED talk UR-Funny. Extensive experiments and analysis show that FunnyNet-W successfully exploits visual, auditory and textual cues to identify funny moments, while our findings reveal FunnyNet-W's ability to predict funny moments in the wild. FunnyNet-W sets the new state of the art for funny moment detection with multimodal cues on all datasets with and without using ground truth information. [ABSTRACT FROM AUTHOR]

In recent years, many kinds of interventions have been developed that seek to reduce susceptibility to misinformation. In two preregistered longitudinal studies (N1 = 503, N2 = 673), we leverage two previously validated "inoculation" interventions (a video and a game) to address two important questions in misinformation interventions research: (1) whether displaying additional stimuli (such as videos unrelated to misinformation) alongside an intervention interferes with its effectiveness, and (2) whether administering an immediate posttest (in the form of a social media post evaluation task after the intervention) plays a role in the longevity of the intervention. We find no evidence that other stimuli interfere with intervention efficacy, but strong evidence that immediate posttests strengthen the learnings from the intervention. In study 1, we find that 48 h after watching a video, participants who received an immediate posttest continued to be significantly better at discerning untrustworthy social media posts from neutral ones than the control group (d = 0.416, p =.007), whereas participants who only received a posttest 48 h later showed no differences with a control (d = 0.010, p =.854). In study 2, we observe highly similar results for a gamified intervention, and provide evidence for a causal mechanism: immediate posttests help strengthen people's memory of the lessons learned in the intervention. We argue that the active rehearsal and application of relevant information are therefore requirements for the longevity of learning‐based misinformation interventions, which has substantial implications for their scalability. [ABSTRACT FROM AUTHOR]

Existing cost allocation methods for adaptive video steganography mainly focus on specific transform coefficients, resulting in lower capacity. Moreover, distortion drift is a significant challenge for steganography in HEVC videos. Therefore, this paper proposed a cost allocation method that combined the intra-frame and inter-frame processes of HEVC video coding to achieve high-capacity, low-distortion transmission in high-performance adaptive video steganography. Firstly, the method investigated the discrete sine transform features in HEVC video coding, analyzing the error propagation patterns of these coefficients under disturbance. During the embedding processed, it conducted a detailed analysis on the intra-block distortion, interblock distortion, and inter-frame distortion caused by modifying transform coefficients. The algorithm also took into account the differentiation in inter-block distortion resulting from steganography in different blocks, leading to the classification of blocks. This paper maximized the utilization of all non-zero transform coefficients, allocating distinct distortion costs for various carrier coefficients. The covert information was then embedded into frames that minimally impacted video quality. Experimental results indicate that, compared to existing HEVC video coefficient domain steganography methods, the proposed algorithm demonstrates advantages in terms of video bitrate, video quality, and embedding capacity. [ABSTRACT FROM AUTHOR]

Via AI video generators, it is possible to create educational videos with humanistic instructors by simply providing a script. The characteristics of video types and features of instructors in videos impact video engagement and, consequently, performance. This study aimed to compare the impact of human instructors and AI-generated instructors in video lectures on video engagement and academic performance. Additionally, the study aimed to examine students’ opinions on both types of videos. Convergent-parallel approach mixed method was used in this study. A total of 108 undergraduate students participated: 48 in the experimental group, 52 in the control group, and eight in the focus group. While the experimental group (AI-generated instructor) and control group (human instructor) watched 10 minutes of two videos each in two weeks, the students in the focus group watched both types of videos with human and AI-generated instructors. Data were collected through the Video Engagement Scale (VES) after the experimental process, and the Academic Performance Test as a pretest and posttest was administered in both groups. The findings of the experimental part revealed that learners’ video engagement was higher in the course with the human instructor compared to the course with the AI-generated instructor. However, the instructor type did not have a significant effect on academic performance. The results based on the qualitative part showed that students thought the AI-generated instructor caused distraction, discomfort, and disconnectedness. However, when the video lesson topic was interesting or when students focused on the video with the intention of learning, these feelings could be ignored. In conclusion, even in today’s conditions, there is no difference in performance between human and AI-generated instructors. As AI technology continues to develop, the difference in engagement is expected to disappear, and AI-generated instructors could be used effectively in video lectures. [ABSTRACT FROM AUTHOR]

Copyright of Anatolian Journal of General Medical Research is the property of Galenos Yayinevi Tic. LTD. STI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a limited range of real-world scenarios, and they have several obvious limitations: limited camera viewing angles, tiny variations of the shooting scene, and even errors in manual labels. These disadvantages prevent video person ReID from being widely used in real-life scenarios. In this work, a new high-quality multi-situation video person ReID dataset, named MSA-BUPT, is built to promote the video person ReID task in large-scale urban surveillance. Specifically, MSA-BUPT contains 684 identities, 2,665 trajectories, and nearly 250,000 frames from 200-h videos across various complex scenarios. Person attribute annotations and unannotated video data are also provided for other research perspectives, such as cross-modality ReID, cross-domain ReID, and so on. Furthermore, two plug-and-play components are used to improve retrieval capabilities: a new scenario-based data augmentation method is proposed to alleviate the person misalignment problem; a re-ranking strategy based on person attribute is applied to make secondary adjustments to the content to the results of the model. The extensive experimental results show that the above methods improve the performance of some representative state-of-the-art models on the new dataset. [ABSTRACT FROM AUTHOR]

Nowadays, video is a common social media in our lives. Video summarisation has become an interesting task for information extraction, where the challenge of high redundancy of key scenes leads to difficulties in retrieving important messages. To address this challenge, this work presents a novel approach called the Graph Attention (GAT)-based bi-directional content-adaptive recurrent unit model for video summarisation. The model makes use of the graph attention approach to transform the visual features of interesting scene(s) from a video. This transformation is achieved by a mechanism called Adaptive Feature-based Transformation (AFT), which extracts the visual features and elevates them to a higher-level representation. We also introduce a new GAT-based attention model that extracts major features from weight features for information extraction, taking into account the tendency of humans to pay attention to transformations and moving objects. Additionally, we integrate the higher-level visual features obtained from the attention layer with the semantic features processed by Bi-CARU. By combining both visual and semantic information, the proposed work enhances the accuracy of key-scene determination. By addressing the issue of high redundancy among major information and using advanced techniques, our method provides a competitive and efficient way to summarise videos. Experimental results show that our approach outperforms existing state-of-the-art methods in video summarisation. [ABSTRACT FROM AUTHOR]

This study aims to determine the effectiveness of digital technology and learning videos in the process of writing inspirational stories. The research was conducted with the experimental method of preexperimental design (nondesign), namely one group pretest-posttest design (one class only) with the research subject of class IX-A students of SMP Negeri 1 Idanogawo totaling 32 people. The results showed that the percentage of effectiveness of digital technology and learning videos was 80.5% with a decent category and an average score of 76.5. The benefits of digital technology and learning videos include: (1) the emergence of innovative learning methods/strategies, (2) encouraging teachers to be more creative, (3) making it easier to access learning resources, (4) making it easier for teachers to deliver material, (5) making it easier for students to understand the topics discussed, (6) saving time. [ABSTRACT FROM AUTHOR]

Digital video coding aims to reduce the bitrate and keep the integrity of visual presentation. High-Efficiency Video Coding (HEVC) can effectively compress video content to be suitable for delivery over various networks and platforms. Finding the optimal coding configuration is challenging as the compression performance highly depends on the complexity of the encoded video sequence. This paper evaluates the effects of motion content on coding performance and suggests an adaptive encoding scheme based on the motion content of encoded video. To evaluate the effects of motion content on the compression performance of HEVC, we tested three coding configurations with different Group of Pictures (GOP) structures and intra refresh mechanisms. Namely, open GOP IPPP, open GOP Periodic-I, and closed GOP periodic-IDR coding structures were tested using several test sequences with a range of resolutions and motion activity. All sequences were first tested to check their motion activity. The rate–distortion curves were produced for all the test sequences and coding configurations. Our results show that the performance of IPPP coding configuration is significantly better (up to 4 dB) than periodic-I and periodic-IDR configurations for sequences with low motion activity. For test sequences with intermediate motion activity, IPPP configuration can still achieve a reasonable quality improvement over periodic-I and periodic-IDR configurations. However, for test sequences with high motion activity, IPPP configuration has a very small performance advantage over periodic-I and periodic-IDR configurations. Our results indicate the importance of selecting the appropriate coding structure according to the motion activity of the video being encoded. [ABSTRACT FROM AUTHOR]

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

Current video face-swapping technologies face challenges such as poor facial fitting and the inability to handle obstructions. This paper introduces Amazing FaceSwap (AmazingFS), a novel framework for producing cinematic quality and realistic face swaps. Key innovations include the development of a Source-Target Attention Mechanism (STAM) to improve face-swap quality while preserving target face expressions and poses. We also enhanced the AdaIN style transfer module to better retain the identity features of the source face. To address obstructions like hair and glasses during face-swap synthesis, we created the AmazingSeg network and a small dataset AST. Extensive qualitative and quantitative experiments demonstrate that AmazingFS significantly outperforms other SOTA networks, achieving amazing face swap results. [ABSTRACT FROM AUTHOR]

Research Summary: Multimodal data, comprising interdependent unstructured text, image, and audio data that collectively characterize the same source, with video being a prominent example, offer a wealth of information for strategy researchers. We emphasize the theoretical importance of capturing the interdependencies between different modalities when evaluating multimodal data. To automate the analysis of video data, we introduce advanced deep machine learning and data fusion methods that comprehensively account for all intra‐ and inter‐modality interdependencies. Through an empirical demonstration focused on measuring the trustworthiness of grassroots sellers in live streaming commerce on Tik Tok, we highlight the crucial role of interpersonal interactions in the business success of microenterprises. We provide access to our data and algorithms to facilitate data fusion in strategy research that relies on multimodal data. Managerial Summary: Our study highlights the vital role of both verbal and nonverbal communication in attaining strategic objectives. Through the analysis of multimodal data—incorporating text, images, and audio—we demonstrate the essential nature of interpersonal interactions in bolstering trustworthiness, thus facilitating the success of microenterprises. Leveraging advanced machine learning techniques, such as data fusion for multimodal data and explainable artificial intelligence, we notably enhance predictive accuracy and theoretical interpretability in assessing trustworthiness. By bridging strategic research with cutting‐edge computational techniques, we provide practitioners with actionable strategies for enhancing communication effectiveness and fostering trust‐based relationships. Access our data and code for further exploration. [ABSTRACT FROM AUTHOR]

Researchers are actively recommending optimal modes of usage for techno-pedagogical tools to ensure that athletes reap the benefits of technology in their training. In this study, we investigated the most effective video feedback viewing speed among normal-speed (NS), half-speed (HS), and quarter-speed (QS), for technical error correction in young weightlifters during the snatch movement. A total of 37 weightlifters (25 boys and 12 girls), aged 11–12 years, were randomly divided into three groups, namely the normal-speed (control group), the half-speed, and the quarter-speed group. Participants completed a pretest (T0), a seven-week snatch learning unit, and a posttest (T1), as well as a retention test one week later (T2). Video-mediated technical analysis of the snatch movement was conducted using the Kinovea motion analysis software. The main results showed that weightlifters who viewed their feedback videos at a quarter of the normal speed (QS) achieved significantly greater improvements in the displacement of the bar in the first pull Dx2 (T1 vs. T0: 27.41 ± 26.10%, Hedges' g = 0.87, p < 0.05; T2 vs. T0: 24.34 ± 27.11%, Hedges' g = 1.07, p < 0.05) and the vertical displacement VTR (T1 vs. T0: 22.89 ± 14.16%, Hedges' g = 1.16, p < 0.05) compared to those who viewed the videos at NS or HS. These findings suggest that slowing down video feedback playback to a quarter of the normal speed can be an effective strategy for enhancing technical error correction in young weightlifters. [ABSTRACT FROM AUTHOR]

Objective Method Results Conclusions Implications for nursing practice To create and evaluate the content validity of educational videos on self‐management of fluid restriction and thirst for individuals with heart failure (HF).A psychometric study was conducted in three stages: (1) an integrative literature review to identify strategies for self‐management of fluid restriction and thirst by individuals with HF; (2) creation of animated videos about strategies for self‐management of fluid restriction and thirst; and (3) analysis of the content validity of the educational videos by 11 experts. For each criterion, the content validity ratio (CVR) was calculated; it was considered adequate when <0.636. Suggestions were evaluated by the researchers, and the videos were modified.Ten scripts and videos were created, averaging 30 s each. The videos’ contents were based on 11 studies retrieved from the review and one specialized website. The videos were evaluated by experts, and adjustments were made when CVR < 0.636 and when allowed by the video production platform.Educational videos were developed and have satisfactory content validity evidence according to expert opinions.These videos are expected to be used as educational strategies in clinical practice to prevent episodes of decompensation due to excessive fluid volume. [ABSTRACT FROM AUTHOR]

Distance video learning, especially with how-to videos, has become extremely popular. Whereas previous studies revealed note-taking as a prominent and promising support measure for video learning in the lab, we focus on note-taking while learning from a how-to video in a distance learning setting. Meanwhile, learners' digital distraction and smartphone usage have become widespread and excessive, potentially harming learning. We thus also aimed to analyze potential risk and protective factors associated with learning with how-to videos, such as smartphone usage time and intellectual values. We conducted an online field experiment involving 59 psychology students, who learned with a short how-to video on plumbing. We found that note-takers outperformed non-note-takers in a posttest about the video content. Furthermore, this note-taking effect on learning outcomes was mediated by learning engagement. Besides note-taking and intellectual values as contributing positively to learning outcomes, we also identified the mean daily smartphone usage time as a risk factor to learning. Overall, our results show how beneficial it is for students to take notes while learning with how-to videos in a distance learning setting. Conversely, learners should avoid digital distractions, in particular through smartphones. [ABSTRACT FROM AUTHOR]

A method of communication for the deaf and the dumb is sign language (SL). Knowing SL allows speakers and listeners to converse effectively with deaf and dumb people. Yet, while they can acquire sign language (SL) to communicate with the dumb and the deaf, untrained persons are unable to speak with them. For such individuals, an SL to text system is more beneficial in enabling them to communicate more effectively with everyday people. To interact with the deaf and dumb, SL is a tactile gesture that employs the hands and eyes. This proposes a new model for translating SL video to text conversion or generation. Frame Conversion is the initial step, where the given input video is converted into frames. Subsequently, features considered are hierarchy of skeleton and Improved Motion estimation (ME). For generating the text, this work uses an optimised LSTM model, where training of weights is done by hybrid Combined Pelican and BES algorithm (CP-BES). Moreover, the mean value for dataset ISL-CSLTR has achieved the proposed method is 27.3%, 31.6%, 28.7%, 33.4%, 29.8%, 24.5%, 36.2%, and 34.8% greater than the conventional schemes like CNN, FSU-CNN, ASL – CNN, RNN, LSTM +POA, LSTM +ARCHOA, LSTM +HGS, LSTM +PRO and LSTM +BES. [ABSTRACT FROM AUTHOR]

To select videos to compose a change detection dataset, we can consider the videos' difficulty level. We need to use difficulty maps, which store values representing the pixels' difficulty level, to estimate these levels. The problem is that ground truth is needed to generate a difficulty map, and generating the ground truth requires manual attribution of labels to the pixels of the frames. Identifying the difficulty level of a video before producing its ground truth allows researchers to obtain the difficulty level, select the videos considering this information, and, subsequently, generate ground truths only for the videos with different difficulty levels. Datasets containing videos with different difficulty levels can evaluate an algorithm more adequately. In this research, we developed a method to generate difficulty maps of a video without using its ground truth. Our method uses the videos and the ground truths from the CDNet 2014 dataset to generate difficulty maps to train a pix2pix neural network. The results showed that the trained network could generate difficulty maps similar to those generated by the traditional approach. [ABSTRACT FROM AUTHOR]

A codec is the most important element of any streaming application like Netflix or YouTube, but its complexity is increasing with its advancement. The codec, High Efficiency Video Coding (HEVC), selects the intra mode out of a total of 35 intra modes using the brute-force method. Therefore, the Spiral Optimization Algorithm (SOA) is utilized in this article to overcome the aforementioned limitation of HEVC. Firstly, the elements of SOA are efficiently mapped to the elements of HEVC. Secondly, the current best intra mode becomes the center of the spiral of SOA. Thirdly, the current best intra mode is considered optimal if the spiral completes three spiral-loops and the current intra mode still remains the best. Otherwise, the new best intra modes becomes the center of the spiral. Results show that SOA based fast intra-mode selection reduces the time complexity of HEVC by 34.09% while BD-BR (Bjontegaard Delta - Bit Rate) overhead is only 1.56%. [ABSTRACT FROM AUTHOR]

The researchers conducted an extensive examination of winning student videos from the California Student Media Festival (CSMF) spanning the years 2009–2023. This content analysis aimed to gauge the quality of these videos and shed light on the diverse participation of schools in the festival. By scrutinizing high-quality videos across different school levels and years, the researchers sought to identify common technical and content factors, particularly those that reflect media and information literacy. The analysis also examined the patterns of participating schools. The analyzed videos not only provided insights into the evolving interests and agendas of students but also showcased their adeptness in embracing emerging technologies and employing a variety of digital tools. Notably, the presence of media assignments and teacher guidance underscored the vital role of school support and instruction in nurturing students’ media production skills. However, the study also revealed that there remains untapped potential for growth in the realm of media literacy education. [ABSTRACT FROM AUTHOR]

Electrodermal Activity (EDA), which primarily indicates arousal through sympathetic nervous system activity, serves as a tool to measure constructs like engagement, cognitive load, performance, and stress. Despite its potential, empirical studies have often yielded mixed results and found it of limited use. To better understand EDA, we conducted a mixed-methods study in which quantitative EDA profiles and survey data were investigated using qualitative interviews. This study furnishes an EDA dataset measuring the engagement levels of seven participants who watched three videos for 4–10 min. The subsequent interviews revealed five EDA morphologies with varying short-term signatures and long-term trends. We used this dataset to demonstrate the moving average crossover, a novel metric for EDA analysis, in predicting engagement–disengagement dynamics in such data. Our contributions include the creation of the detailed dataset, comprising EDA profiles annotated with qualitative data, the identification of five distinct EDA morphologies, and the proposition of the moving average crossover as an indicator of the beginning of engagement or disengagement in an individual. [ABSTRACT FROM AUTHOR]

Visual tracking has become widespread in swarm robots for intelligent video surveillance, navigation, and autonomous vehicles due to the development of machine learning algorithms. Discriminative correlation filter (DCF)-based trackers have gained increasing attention owing to their efficiency. This study proposes "context-aware environmental residual correlation filter tracking via deep convolution features (CAERDCF)" to enhance the performance of the tracker under ambiguous environmental changes. The objective is to address the challenges posed by intensive environment variations that confound DCF-based trackers, resulting in undesirable tracking drift. We present a selective spatial regularizer in the DCF to suppress boundary effects and use the target's context information to improve tracking performance. Specifically, a regularization term comprehends the environmental residual among video sequences, enhancing the filter's discrimination and robustness in unpredictable tracking conditions. Additionally, we propose an efficient method for acquiring environmental data using the current observation without additional computation. A multi-feature integration method is also introduced to enhance the target's presence by combining multiple metrics. We demonstrate the efficiency and feasibility of our proposed CAERDCF approach by comparing it with existing methods using the OTB2015, TempleColor128, UAV123, LASOT, and GOT10K benchmark datasets. Specifically, our method increased the precision score by 12.9% in OTB2015 and 16.1% in TempleColor128 compared to BACF. [ABSTRACT FROM AUTHOR]

Short-form video SNS such as TikTok has gained popularity worldwide. However, there remains a limited understanding of why individuals watch and submit short videos on these platforms, as well as how the motives for short-form video SNS use relate to their well-being. To catch up with the development of SNS context, TikTok (Douyin), one of the most popular short-form videos SNS in China, was taken as an example to explore users' motivation and well-being. Integrating the uses and gratifications theory with the in-depth interview (N = 238), the initial TikTok Use Motive Scale (including the TikTok Watching Motive Sub-Scale, and TikTok Shooting and Submitting Motive Sub-Scale) was developed firstly, and secondly exploratory factor analysis for watching (N = 2,263), and shooting and submitting (N = 3,217) short-form videos, and finally confirmatory factory analysis (N = 2,092). Six motives for watching (seeking novelty, habit, relationship maintenance, releasing pressure, killing time, and escapism) and three motives for shooting and submitting (pursuing fame, recording and sharing, and satisfying curiosity) short-form videos were identified. Moreover, the relationship between the TikTok use motives and well-being was explored, and the implications of the findings were discussed. [ABSTRACT FROM AUTHOR]

Tourism short videos are increasingly being utilized to promote tourism development and differentiate destinations through electronic word-of-mouth. This study explores the association between the perception of destination distinctiveness through tourism short videos (PDD) and electronic word-of-mouth on destination image (eWOM-DI), as online short videos gain more popularity. Drawing on the emotional appraisal theory, the study investigates how PDD affects eWOM-DI through concentration as a mediator and enjoyability and authenticity as moderators. The findings indicate that PDD has a direct and indirect effect on eWOM-DI through concentration. Additionally, the study reveals that higher levels of enjoyability or authenticity can strengthen the positive effects of PDD on concentration and eWOM-DI. These results provide valuable insights for effectively promoting destination distinctness through electronic word-of-mouth elicited by tourism short videos with enjoyability and authenticity. [ABSTRACT FROM AUTHOR]

Some research suggests that job applicants tend to express negative perceptions of asynchronous video interviews (AVIs). Drawing from basic psychological needs theory, we proposed that these negative perceptions arise partly from the lack of human interaction between applicants and the organization during an AVI, which fails to satisfy applicants' need for relatedness. Recruiting participants through Prolific, we conducted two experimental studies that aimed to manipulate the level of relatedness support through a relatedness‐need supportive introductory video containing empathetic messaging and humor. Using a vignette approach, participants in study 1 (N = 100) evaluated a hypothetical AVI that included one of two introductory videos: relatedness‐supportive versus neutral messaging. The relatedness‐supportive video yielded higher relatedness need satisfaction (d = 0.53) and organizational attraction ratings (d = 0.49) than the neutral video. In study 2, participants (N = 231) completed an AVI that included one of the two videos and evaluated their AVI experience. In contrast to the vignette study, we observed no significant differences between groups for relatedness need satisfaction, organizational attraction, nor other outcomes. Our findings provided little evidence that humor and empathic video messaging improves reactions to an AVI and illustrated the limitations on the external validity of vignette designs. [ABSTRACT FROM AUTHOR]