Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
Background During the recent years, emergency services in several countries have integrated video streaming into medical emergency calls, and research on the topic has gained increased focus. Video streaming during medical emergency calls may change dispatcher’s perspective of the call and can be a helpful tool for supervising bystanders’ first aid. Little research exists, however, about the caller’s perspective of video streaming during a medical emergency call. With this study, we explore the caller’s experiences with video streaming. Methods The study is a qualitative interview study. During a period of five weeks, we recruited respondents from the region of Oslo who had called the medical emergency number 113 and where video streaming had been used by the dispatcher during the call. We conducted 14 semi-structured individual interviews, in-person or digitally on Zoom/Teams, from October to December 2023. The interviews were transcribed verbatim, and we analyzed them drawing on Malterud’s systematic text condensation. Results Our material was sorted into three category headings: Increased sense of safety, the unexpected option of video streaming, and emotional discomfort. Most respondents felt comforted knowing that the dispatcher could see and assess the situation visually. Several were also positively surprised that video streaming was an option during the call. Some respondents however felt increased stress during the call due to video streaming. Other respondents reflected on the societal taboo of filming ill or injured persons. Conclusion Most respondents experienced video streaming as a positive addition to the medical emergency call and felt comforted knowing that the dispatcher could see the situation. Knowledge of the integration between video streaming and basic communication in a call is nonetheless of great importance, as to not increase stress experienced by the caller. The dispatcher should be sensitive for how the caller will handle video streaming for each call....
Watermarking is widely employed to protect audio files. Previous research has focused on developing systems that balance performance criteria, including robustness, imperceptibility, and capacity. Most existing systems are designed to work with prerecorded audio signals, where the characteristics of the host signal are known in advance. In such cases, processing time is not a critical factor, as these systems generally do not account for real-time signal acquisition or report tests for real-time signal acquisition nor report the elapsed time between signal acquisition and watermarking output, known as latency. However, the increasing prevalence of audio sharing through real-time streams or video calls is a pressing issue requiring low-latency systems. This work introduces a low-latency watermarking system that utilizes a spread spectrum technique, a method that spreads the signal energy across a wide frequency band while embedding the watermark additively in the time domain to minimize latency. The system’s performance was evaluated by simulating real-time audio streams using two distinct methods. The results demonstrate that the proposed system achieves minimal latency during embedding, addressing the urgent need for such systems....
Video-text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data. Effective video frame sampling plays a crucial role in improving retrieval performance, as it determines the quality of the visual content representation. Traditional sampling methods, such as uniform sampling and optical flow-based techniques, often fail to capture the full semantic range of videos, leading to redundancy and inefficiencies. In this work, we propose CLIP4Video-Sampling: Global Semantics- Guided Multi-Granularity Frame Sampling for Video-Text Retrieval, a global semantics-guided multi-granularity frame sampling strategy designed to optimize both computational efficiency and retrieval accuracy. By integrating multi-scale global and local temporal sampling and leveraging the CLIP (Contrastive Language-Image Pre-training) model’s powerful feature extraction capabilities, our method significantly outperforms existing approaches in both zero-shot and fine-tuned video-text retrieval tasks on popular datasets. CLIP4Video-Sampling reduces redundancy, ensures keyframe coverage, and serves as an adaptable pre-processing module for multimodal models....
This study examines the impact of interactive storytelling on improving news writing skills among Communication Science students in an English broadcasting course. Integrating digital platforms with multimedia elements—including audio, video, and graphics—fosters narrative creativity and enhances students' engagement in English news broadcasting. The study involved seven Communication Science students as participants in a qualitative case study, with data collected through news script analysis and interviews. Analysis focused on narrative structure, creativity, and multimedia use, while interviews captured insights into students' experiences and challenges with digital platforms. Using Nvivo 12 software, data analysis revealed significant improvements in students' narrative creativity and multimedia integration. Interactive storytelling techniques improved news script quality and promoted student engagement, independence, and adaptability in learning. Students valued the flexibility and real-time feedback of digital platforms, which allowed experimentation with storytelling techniques and self-paced learning. Despite these benefits, some students encountered challenges related to technical skills and media adaptation. Findings emphasize the value of interactive storytelling in journalism education, with potential applications in professional training for broadcast media, journalism workshops, and multimedia content creation....
The rapid loss of biodiversity significantly impacts birds’ environments and behaviors, highlighting the importance of analyzing bird behavior for ecological insights. With the growing adoption of Machine Learning (ML) algorithms in the Internet of Things (IoT) domain, edge computing has become essential to ensure data privacy and enable real-time predictions by processing high-dimensional data, such as video streams, efficiently. This paper introduces a set of dimensionality reduction techniques tailored for video sequences based on cutting-edge methods for this data representation. These methods drastically compress video data, reducing bandwidth and storage requirements while enabling the creation of compact ML models with faster inference speeds. Comprehensive experiments on bird behavior classification in rural environments demonstrate the effectiveness of the proposed techniques. The experiments incorporate state-of-the-art deep learning techniques, including pre-trained video vision models, Autoencoders, and single-frame feature extraction. These methods demonstrated superior performance to the baseline, achieving up to a 6000-fold reduction in data size while reaching a classification accuracy of 60.7% on the Visual WetlandBirds Dataset and obtaining state-of-the-art performance on this dataset. These findings underline the potential of using dimensionality reduction to enhance the scalability and efficiency of bird behavior analysis....
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