by jsendak | Oct 24, 2024 | Computer Science
arXiv:2410.16284v1 Announce Type: new
Abstract: The advent of 5G has driven the demand for high-quality, low-latency live streaming. However, challenges such as managing the increased data volume, ensuring synchronization across multiple streams, and maintaining consistent quality under varying network conditions persist, particularly in real-time video streaming. To address these issues, we propose a novel framework that leverages 3D virtual environments within game engines (eg. Unity 3D) to optimize multi-channel live streaming. Our approach consolidates multi-camera video data into a single stream using multiple virtual 3D canvases, significantly increasing channel amounts while reducing latency and enhancing user flexibility. For demonstration of our approach, we utilize the Unity 3D engine to integrate multiple video inputs into a single-channel stream, supporting one-to-many broadcasting, one-to-one video calling, and real-time control of video channels. By mapping video data onto a world-space canvas and capturing it via an in-world camera, we minimize redundant data transmission, achieving efficient, low-latency streaming. Our results demonstrate that this method outperforms existing multi-channel live streaming solutions in both latency reduction and user interaction. Our live video streaming system affiliated with this paper is also open-source at https://github.com/Aizierjiang/LiveStreaming.
The Evolution of Live Streaming: Enhancing Quality and User Experience with 3D Virtual Environments
As the demand for high-quality, low-latency live streaming continues to grow with the emergence of 5G technology, content providers and service providers face a range of challenges. These challenges include efficiently managing increased data volume, ensuring synchronization across multiple streams, and maintaining consistent quality under varying network conditions. Real-time video streaming, in particular, faces unique obstacles in meeting these requirements.
In order to address these challenges and optimize multi-channel live streaming, a novel framework has been proposed that leverages the power of 3D virtual environments within game engines, such as Unity 3D. This multi-disciplinary approach combines the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to create an innovative solution.
The core idea behind this framework is the consolidation of multi-camera video data into a single stream using multiple virtual 3D canvases. By mapping the video data onto a world-space canvas within the virtual environment and capturing it via an in-world camera, redundant data transmission can be minimized. This results in a significant increase in channel amounts, reduced latency, and enhanced user flexibility.
The use of game engines, such as Unity 3D, allows for seamless integration of multiple video inputs into a single-channel stream. This not only supports one-to-many broadcasting but also enables one-to-one video calling and real-time control of video channels. The integration of 3D virtual environments adds a new level of immersion and interactivity to the live streaming experience, enhancing user engagement and satisfaction.
The proposed framework offers several advancements over existing multi-channel live streaming solutions. Firstly, it effectively addresses the challenges of data volume management, synchronization, and quality consistency, ensuring a smooth streaming experience. Secondly, it significantly reduces latency, allowing for real-time interaction between the streamers and viewers. Lastly, it provides users with greater flexibility in terms of controlling and customizing video channels, resulting in a more personalized experience.
From a wider perspective, this framework exemplifies the multi-disciplinary nature of the concepts related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By combining knowledge and techniques from these fields, innovative solutions like this one can be developed to overcome existing challenges and push the boundaries of live streaming technology.
In conclusion, the proposed framework that leverages 3D virtual environments within game engines to optimize multi-channel live streaming represents a significant advancement in the field. Its ability to consolidate video data, reduce latency, and enhance user flexibility opens up new possibilities for high-quality, immersive live streaming experiences. As technology continues to evolve and 5G becomes more widely available, it is expected that solutions like this will become increasingly important in meeting the growing demand for real-time video streaming.
For more information and access to the open-source live video streaming system associated with this paper, visit https://github.com/Aizierjiang/LiveStreaming.
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by jsendak | Oct 24, 2024 | Computer Science
Expert Commentary: The Potential of Distributed Acoustic Sensing (DAS) for Real-Time Traffic Monitoring
Distributed Acoustic Sensing (DAS) technology has emerged as a promising solution for real-time traffic monitoring by leveraging existing fiber optic cables to detect vibrations and acoustic events. In this paper, the authors introduce a novel methodology that focuses on real-time processing through edge computing, enabling efficient vehicle detection and tracking.
The authors’ approach utilizes the Hough transform, a well-established method in computer vision, to detect straight-line segments in the spatiotemporal DAS data. By applying this algorithm, they successfully identify segments corresponding to vehicles crossing the Astfjord bridge in Norway. This initial detection is further refined using the Density-based spatial clustering of applications with noise (DBSCAN) algorithm, which consolidates multiple detections of the same vehicle and reduces noise, leading to improved accuracy.
One of the key advantages of the proposed workflow is its ability to count vehicles and estimate their speed with only tens of seconds latency. This real-time capability is crucial for effective traffic monitoring, allowing timely decision-making and congestion management. Furthermore, the use of edge computing ensures that the processing happens on the edge devices themselves, reducing the need for excessive data transfer and enabling immediate analysis and visualization via cloud-based platforms.
To validate the system’s accuracy, the authors compare the DAS data with simultaneous video footage, achieving high accuracy in vehicle detection. Notably, they are able to distinguish between cars and trucks based on signal strength and frequency content, illustrating the potential for more detailed traffic analysis using DAS technology.
The ability to process large volumes of data efficiently is another significant advantage of this methodology. Real-time traffic monitoring generates a vast amount of data, and the capability to handle this data effectively ensures that the system remains scalable and practical for implementation in various traffic situations.
In addition to traffic monitoring, the authors highlight the potential use of DAS for structural health monitoring. By detecting structural responses in the bridge, the system can provide valuable insights into the integrity and performance of the infrastructure. This dual functionality adds further value to the implementation of DAS technology.
Looking ahead, further research and development could explore the optimization of the proposed methodology. This could involve refining the clustering algorithms to accommodate more complex traffic scenarios, such as intersections and varying vehicle speeds. Additionally, investigating the integration of other sensor technologies, such as radar or lidar, could augment the accuracy and reliability of the system.
Overall, this paper presents a compelling case for the use of DAS technology in real-time traffic monitoring. Its ability to provide accurate vehicle detection and speed estimation, process large volumes of data efficiently, and offer insights into structural health monitoring makes it a valuable tool for traffic management and infrastructure maintenance.
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by jsendak | Oct 23, 2024 | Computer Science
arXiv:2410.16058v1 Announce Type: new
Abstract: The emerging short-form video platforms have been growing tremendously and become one of the leading social media recently. Although the expanded popularity of these platforms has attracted increasing research attention, there has been a lack of understanding of whether and how they deviate from traditional long-form video-sharing platforms such as YouTube and Bilibili. To address this, we conduct a large-scale data-driven analysis of Kuaishou, one of the largest short-form video platforms in China. Based on 248 million videos uploaded to the platform across all categories, we identify their notable differences from long-form video platforms through a comparison study with Bilibili, a leading long-form video platform in China. We find that videos are shortened by multiples on Kuaishou, with distinctive categorical distributions over-represented by life-related rather than interest-based videos. Users interact with videos less per view, but top videos can even more effectively acquire users’ collective attention. More importantly, ordinary content creators have higher probabilities of producing hit videos. Our results shed light on the uniqueness of short-form video platforms and pave the way for future research and design for better short-form video ecology.
The Rise of Short-Form Video Platforms
Short-form video platforms have become a dominant force in the world of social media, captivating the attention of millions of users. In recent years, platforms like Kuaishou have experienced tremendous growth, sparking interest among researchers who seek to understand how these platforms differ from traditional long-form video-sharing platforms such as YouTube and Bilibili.
In an effort to bridge this gap in understanding, researchers have conducted a large-scale data-driven analysis of Kuaishou, one of China’s largest short-form video platforms. By examining the vast collection of 248 million videos uploaded to the platform, they have identified several key differences that set short-form video platforms apart from their long-form counterparts.
Distinctive Characteristics
One of the most notable differences uncovered in the study is the shortened length of videos on platforms like Kuaishou. Unlike long-form platforms where videos can span several minutes or even hours, videos on short-form platforms are significantly shorter. This shift to brevity reflects the evolving preferences of users who seek concise and easily consumable content.
Furthermore, the study reveals that short-form video platforms have distinct categorical distributions. Rather than being heavily focused on interest-based videos like those found on YouTube or Bilibili, short-form platforms like Kuaishou have a greater emphasis on life-related videos. This finding suggests that users on these platforms prioritize content that is relatable, personal, and relevant to their daily lives.
Engagement and Attention
When it comes to user engagement, the study discovers that users interact with videos less frequently on short-form platforms compared to long-form platforms. However, it is important to note that top videos on short-form platforms have a unique advantage in acquiring users’ collective attention. This highlights the potential for viral content to quickly gain traction and reach a wide audience.
Moreover, the research findings indicate that short-form video platforms provide a greater opportunity for ordinary content creators to produce hit videos. Unlike long-form platforms where established creators often dominate the landscape, short-form platforms allow newcomers to have a higher probability of creating viral content. This democratized aspect of short-form platforms opens up new avenues for aspiring creators to gain recognition and success.
Implications and Future Research
These findings shed light on the distinctiveness of short-form video platforms and lay the groundwork for further research and design improvements within this burgeoning field. Understanding the specific characteristics and preferences of users on short-form platforms is crucial for developers and content creators alike.
From a multi-disciplinary perspective, the study aligns with the field of multimedia information systems, where the interplay between technology, content, and user behaviors is carefully examined. The analysis of video length, categorical distributions, user engagement, and content creation sheds light on the complex dynamics that underpin short-form video platforms.
Furthermore, the study’s insights are relevant to the wider domains of animations, artificial reality, augmented reality, and virtual realities. As short-form video platforms continue to evolve, there is a growing need to explore how these platforms can integrate with emerging technologies to further enhance user experiences and create immersive content.
In conclusion, the research conducted on Kuaishou provides valuable insights into the unique nature of short-form video platforms. By unraveling the differences between short-form and long-form platforms, researchers can better understand user preferences, improve platform design, and foster the growth of a vibrant short-form video ecology.
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by jsendak | Oct 23, 2024 | Computer Science
Abstract:
Causal networks are often incomplete with missing causal links. This is due to various issues, such as missing observation data. Recent approaches to the issue of incomplete causal networks have used knowledge graph link prediction methods to find the missing links.
In the causal link A causes B causes C, the influence of A to C is influenced by B which is known as a mediator. Existing approaches using knowledge graph link prediction do not consider these mediated causal links.
This paper presents HyperCausalLP, an approach designed to find missing causal links within a causal network with the help of mediator links. The problem of missing links is formulated as a hyper-relational knowledge graph completion. The approach uses a knowledge graph link prediction model trained on a hyper-relational knowledge graph with the mediators.
The approach is evaluated on a causal benchmark dataset, CLEVRER-Humans. Results show that the inclusion of knowledge about mediators in causal link prediction using hyper-relational knowledge graph improves the performance on an average by 5.94% mean reciprocal rank.
Expert Commentary:
Causal networks are essential for understanding complex systems and their dynamics. However, incomplete causal networks pose a challenge as they limit our ability to fully comprehend the underlying causal relationships. This limitation can arise from various factors, such as missing observation data.
Recent approaches have focused on utilizing knowledge graph link prediction methods to address the problem of missing causal links. These methods aim to leverage the existing information in the causal network to predict the missing links accurately.
One aspect that has been often overlooked in previous approaches is the role of mediators in the causal network. In a causal chain where A causes B causes C, the influence of A on C is mediated by B. Understanding these mediator links is crucial for developing a more comprehensive understanding of causal relationships.
The HyperCausalLP approach presented in this paper takes into account the mediator links to find missing causal links within a causal network. By formulating the problem as a hyper-relational knowledge graph completion, the approach combines the knowledge of mediators with the existing causal network information.
To enable the prediction of missing links, the approach utilizes a knowledge graph link prediction model trained on a hyper-relational knowledge graph that includes mediators. This training enhances the ability of the model to capture and leverage the mediator links effectively.
The evaluation of HyperCausalLP on the CLEVRER-Humans benchmark dataset demonstrates promising results. The inclusion of mediator knowledge in the causal link prediction improves the performance, as indicated by the average 5.94% mean reciprocal rank improvement.
Overall, this approach fills an important gap in existing methods for incomplete causal networks by considering the mediated causal links. By incorporating knowledge about mediators in the prediction process, the HyperCausalLP approach provides a more accurate and comprehensive understanding of causal relationships within a network.
Future research in this area could explore the application of HyperCausalLP to larger and more complex causal networks, as well as investigate the impact of different types of mediators on the performance of the approach. Additionally, considering the uncertainty and confidence levels associated with predicted causal links could be a valuable direction for further enhancements in the field.
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by jsendak | Oct 22, 2024 | Computer Science
arXiv:2410.14154v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. MLLMs involve significant external knowledge within their parameters; however, it is challenging to continually update these models with the latest knowledge, which involves huge computational costs and poor interpretability. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs. Considering the redundant information within vision modality, we first leverage the question to instruct the extraction of visual information through interactions with one set of learnable queries, minimizing irrelevant interference during retrieval and generation. Besides, we introduce a pre-trained multimodal adaptive fusion module to achieve question text-to-multimodal retrieval and integration of multimodal knowledge by projecting visual and language modalities into a unified semantic space. Furthermore, we present an Adaptive Selection Knowledge Generation (ASKG) strategy to train the generator to autonomously discern the relevance of retrieved knowledge, which realizes excellent denoising performance. Extensive experiments on open multimodal question-answering datasets demonstrate that RA-BLIP achieves significant performance and surpasses the state-of-the-art retrieval-augmented models.
Expert Commentary: The Future of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have been gaining considerable attention in recent years, and their potential as versatile models for vision-language tasks is becoming increasingly evident. However, one of the major challenges with these models is the constant update of external knowledge, as it involves significant computational costs and lacks interpretability. This is where retrieval augmentation techniques come into play, offering effective solutions for enhancing both LLMs and MLLMs.
In this study, a novel framework called multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP) is proposed. The framework takes advantage of the question to guide the extraction of visual information, minimizing irrelevant interference and allowing for more accurate retrieval and generation. Additionally, a pre-trained multimodal adaptive fusion module is introduced to achieve text-to-multimodal retrieval and integration of knowledge across different modalities.
One of the key features of the proposed framework is the Adaptive Selection Knowledge Generation (ASKG) strategy, which enables the generator to autonomously discern the relevance of retrieved knowledge. This strategy ensures excellent denoising performance and enhances the overall effectiveness of the model.
The results of extensive experiments conducted on multimodal question-answering datasets show that RA-BLIP outperforms existing retrieval-augmented models, demonstrating its potential as a state-of-the-art solution in the field.
Multi-disciplinary Nature and Relation to Multimedia Information Systems and AR/VR
The concepts explored in this study are highly multi-disciplinary and have strong connections to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
By combining language and vision modalities, multimodal large language models bridge the gap between textual and visual information, enabling more effective communication and understanding. This has direct implications for multimedia information systems, where the integration of various media types (such as text, images, videos, etc.) is crucial for efficient information retrieval and processing.
Furthermore, the use of retrieval augmentation techniques, as demonstrated in RA-BLIP, can significantly enhance the performance of multimedia information systems. By incorporating external knowledge and allowing for dynamic updates, these techniques enable better retrieval of relevant information and improve the overall user experience.
In the context of artificial reality, augmented reality, and virtual realities, multimodal large language models play a vital role in bridging the gap between virtual and real worlds. By understanding and generating both textual and visual content, these models can enable more immersive and interactive experiences in these virtual environments. This has implications for various applications, such as virtual reality gaming, education, and training simulations.
Overall, the findings of this study highlight the potential of multimodal large language models and retrieval augmentation techniques in advancing the field of multimedia information systems, as well as their relevance to the broader domains of artificial reality, augmented reality, and virtual realities.
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by jsendak | Oct 22, 2024 | Computer Science
Expert Commentary: Unveiling Insights of Peace in Global Media through RAG Model and PIR/NIR
In today’s interconnected world, understanding the dynamics of peace and conflict is crucial for societies and policymakers alike. Traditional methods of analyzing intergroup relations through media articles often lack accuracy and meaningful insights. However, this paper presents a groundbreaking approach by utilizing the Retrieval Augmented Generation (RAG) model and redefining Positive and Negative Intergroup Reciprocity (PIR/NIR) to identify key insights of peace in global media.
The introduction of the RAG model brings a new level of sophistication to the analysis of media representation. By combining retrieval and generation techniques, this approach harnesses the power of both understanding existing knowledge and generating relevant insights. This empowers researchers to delve deeper into the nuances of intergroup relations exhibited in media articles, ultimately leading to a richer understanding of the dynamics at play.
Furthermore, the paper’s highlight lies in its refinement of the definitions of PIR and NIR. The authors recognize the importance of accurate categorization and offer a more precise framework to differentiate positive and negative intergroup reciprocity. This refinement serves as a valuable contribution to the field, ensuring a more accurate and meaningful analysis of media representations of intergroup relations.
With this novel methodology, researchers can now uncover insights into the factors that contribute to or detract from peace at a national level. By analyzing media articles, which often play a significant role in shaping public opinion, this approach provides a window into societal dynamics and the potential challenges to fostering peace.
As we look to the future, this innovative research opens up exciting opportunities for further studies and applications. By expanding the dataset and incorporating multi-modal analysis techniques, researchers can enhance the precision and scope of their analysis. Additionally, future research could explore the application of the RAG model and refined PIR/NIR framework to other domains, such as social media, thereby capturing a more comprehensive understanding of intergroup relations.
In conclusion, this paper unveils a groundbreaking approach to identifying insights of peace in global media. The utilization of the RAG model and the refinement of PIR/NIR definitions offers a powerful methodology for researchers and policymakers to gain a deeper understanding of intergroup dynamics and contribute towards peacebuilding efforts. This innovative research paves the way for future studies that can harness the potential of advanced techniques to analyze media and foster positive societal change.
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