by jsendak | Nov 3, 2025 | Computer Science
Expert Commentary: NanyinHGNN – A Breakthrough in Computational Ethnomusicology
In the realm of computational ethnomusicology, where cultural preservation meets cutting-edge technology, the development of NanyinHGNN represents a crucial advancement in the field. Nanyin, a UNESCO-recognized intangible cultural heritage, poses unique challenges due to its heterophonic tradition centered around the pipa. This tradition, characterized by orally transmitted ornamentations layered over notated core melodies, has long presented difficulties for preservation and innovation.
The NanyinHGNN model tackles these challenges head-on by leveraging a Pipa-Centric MIDI dataset and a specialized tokenization method, NanyinTok, to capture the nuances of Nanyin music. Through the conversion of symbolic sequences into graph structures, the model ensures the preservation of key musical features and the authenticity of the generated heterophonic ensembles.
One of the standout features of NanyinHGNN is its innovative approach to ornamentation generation. By reframing ornamentations as nodes within a heterogeneous graph, the model seamlessly integrates melodic outlines optimized for ornamentations with a rule-guided system informed by Nanyin performance practices. This unique methodology not only produces authentic ornamentations but also does so without the need for explicit ornamentation annotations during training.
The experimental results showcasing the successful generation of heterophonic ensembles featuring traditional instruments validate the efficacy of NanyinHGNN in addressing data scarcity challenges in computational ethnomusicology. By incorporating domain-specific knowledge into the model architecture, the researchers have demonstrated that a deep understanding of cultural context can enhance the effectiveness of AI models in preserving and innovating upon intangible cultural heritage.
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by jsendak | Oct 13, 2025 | Computer Science
arXiv:2510.08839v1 Announce Type: cross
Abstract: Real-time multi-view 3D reconstruction is a mission-critical application for key edge-native use cases, such as fire rescue, where timely and accurate 3D scene modeling enables situational awareness and informed decision-making. However, the dynamic and unpredictable nature of edge resource availability introduces disruptions, such as degraded image quality, unstable network links, and fluctuating server loads, which challenge the reliability of the reconstruction pipeline. In this work, we present a reinforcement learning (RL)-based edge resource management framework for reliable 3D reconstruction to ensure high quality reconstruction within a reasonable amount of time, despite the system operating under a resource-constrained and disruption-prone environment. In particular, the framework adopts two cooperative Q-learning agents, one for camera selection and one for server selection, both of which operate entirely online, learning policies through interactions with the edge environment. To support learning under realistic constraints and evaluate system performance, we implement a distributed testbed comprising lab-hosted end devices and FABRIC infrastructure-hosted edge servers to emulate smart city edge infrastructure under realistic disruption scenarios. Results show that the proposed framework improves application reliability by effectively balancing end-to-end latency and reconstruction quality in dynamic environments.
Expert Commentary: Real-time Multi-View 3D Reconstruction with Reinforcement Learning
The concept of real-time multi-view 3D reconstruction using reinforcement learning is a groundbreaking development in the field of multimedia information systems. This innovative approach addresses the challenges posed by dynamic and unpredictable edge environments, where traditional reconstruction pipelines may struggle to maintain reliability and accuracy.
By utilizing reinforcement learning agents for camera and server selection, this framework leverages AI-driven decision-making to optimize resource utilization and adapt to changing conditions in real-time. This multi-disciplinary approach combines computer vision, machine learning, and edge computing to enhance the performance of mission-critical applications such as fire rescue operations.
Furthermore, the use of a distributed testbed featuring lab-hosted end devices and FABRIC infrastructure-hosted edge servers adds a layer of realism to the evaluation process. This setup allows researchers to simulate a smart city edge infrastructure and test the framework’s effectiveness under realistic disruption scenarios, providing valuable insights into its potential real-world applications.
Overall, this research not only advances the field of real-time 3D reconstruction but also contributes to the broader fields of artificial reality, augmented reality, and virtual reality by demonstrating the potential for AI-driven optimization in dynamic and resource-constrained environments. As technology continues to evolve, we can expect to see further innovations in multimedia information systems that improve the efficiency and reliability of complex tasks across various domains.
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by jsendak | Oct 13, 2025 | Computer Science
Expert Commentary: The Rise of Open-Source Language Models
Large Language Models (LLMs) have indeed revolutionized the way users interact with technology, shifting the focus from traditional GUI-driven interfaces to intuitive language-first interactions. This paradigm shift enables users to communicate their needs and intentions in natural language, allowing LLMs to understand and execute tasks across various applications seamlessly.
However, a major drawback of current implementations is the reliance on cloud-based proprietary models, which bring concerns around privacy, autonomy, and scalability. The need for locally deployable, open-source LLMs is critical not only for convenience but also for ensuring user trust and control over their data and interactions.
This study’s exploration of open-source and open-access LLMs in facilitating user intention resolution is essential in advancing the development of next-generation operating systems. By comparing these models against proprietary systems like GPT-4, we can assess their performance in generating workflows for diverse user intentions.
Empirical insights from this study will shed light on the practicality and potential of open LLMs as locally operable components, laying the groundwork for more autonomous and privacy-conscious user-device interactions in the future. This research also contributes to the ongoing conversation about decentralizing and democratizing AI infrastructure, making advanced technology more accessible and user-centric.
Key Takeaways:
- Open-source LLMs play a vital role in enabling language-first interactions and facilitating user intention resolution.
- Local deployment of LLMs is imperative for ensuring privacy, autonomy, and scalability in AI-driven workflows.
- Comparative analysis against proprietary models helps evaluate the performance and potential of open LLMs in next-generation operating systems.
- Decentralizing AI infrastructure through open-access models contributes to more seamless, adaptive, and privacy-conscious user-device interactions.
Overall, the future of AI-driven user interfaces lies in the development and adoption of locally deployable, open-source LLMs, paving the way for more intuitive and secure interactions between users and technology.
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by jsendak | Oct 10, 2025 | Computer Science
arXiv:2510.07326v1 Announce Type: new
Abstract: Self-supervised audio-visual source separation leverages natural correlations between audio and vision modalities to separate mixed audio signals. In this work, we first systematically analyse the performance of existing multimodal fusion methods for audio-visual separation task, demonstrating that the performance of different fusion strategies is closely linked to the characteristics of the sound: middle fusion is better suited for handling short, transient sounds, while late fusion is more effective for capturing sustained and harmonically rich sounds. We thus propose a hierarchical fusion strategy that effectively integrates both fusion stages. In addition, training can be made easier by incorporating high-quality external audio representations, rather than relying solely on the audio branch to learn them independently. To explore this, we propose a representation alignment approach that aligns the latent features of the audio encoder with embeddings extracted from pre-trained audio models. Extensive experiments on MUSIC, MUSIC-21 and VGGSound datasets demonstrate that our approach achieves state-of-the-art results, surpassing existing methods under the self-supervised setting. We further analyse the impact of representation alignment on audio features, showing that it reduces modality gap between the audio and visual modalities.
Expert Commentary: Leveraging Multimodal Fusion for Audio-Visual Source Separation
In this groundbreaking work on self-supervised audio-visual source separation, the authors delve into the intricate relationships between audio and visual modalities to effectively separate mixed audio signals. The concept of multimodal fusion, where information from different sensory modalities is combined to enhance performance, plays a crucial role in this study. By systematically analyzing existing fusion methods, the authors shed light on the importance of choosing the right fusion strategy based on the characteristics of the sound being separated.
The multi-disciplinary nature of this research is evident in the fusion strategies proposed: middle fusion for short, transient sounds and late fusion for sustained and harmonically rich sounds. This demonstrates a nuanced understanding of the interaction between audio and visual cues, highlighting the need for adaptive fusion techniques in multimodal tasks.
Furthermore, the authors introduce a hierarchical fusion strategy that integrates both middle and late fusion stages, showcasing a novel approach to enhance separation performance. By incorporating high-quality external audio representations and aligning them with pre-trained audio models, the training process is streamlined, leading to state-of-the-art results on benchmark datasets.
From a broader perspective, this study contributes to the field of multimedia information systems by advancing the state-of-the-art in audio-visual source separation. The findings are relevant not only to multimedia researchers but also to practitioners working in fields such as Animations, Artificial Reality, Augmented Reality, and Virtual Realities. The ability to extract and separate audio sources from mixed signals has far-reaching implications for immersive multimedia experiences, interactive entertainment, and virtual environments.
Overall, this research underscores the importance of leveraging multimodal fusion techniques in audio-visual tasks and sets a new standard for self-supervised source separation methodologies. The insights gained from this study pave the way for future advancements in multimedia research and open up exciting possibilities for integrating audio-visual processing in various applications.
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by jsendak | Oct 10, 2025 | Computer Science
Expert Commentary
As an expert commentator in the field of autism research and education, I find this study to be a crucial step in addressing the significant challenges faced by autistic individuals, particularly in the realm of Information Technology education. The emphasis on understanding and mapping nuanced behavioral patterns and emotional identification is a key component in developing effective interventions tailored to the unique needs of autistic students.
By taking a longitudinal approach to monitoring emotions and behaviors, this research offers valuable insights into the individualized support required for successful skill development. It is essential to recognize that each autistic child has a distinct behavioral and emotional landscape, which must be comprehensively understood before effective interventions can be implemented.
The proposed targeted framework for developing applications and technical aids based on behavioral trends is a promising strategy for enhancing learning outcomes in autistic students. By aligning these interventions with the identified needs of each child, educators and specialists can create a more inclusive and supportive learning environment that fosters growth and development.
Ultimately, this research highlights the importance of prioritizing early identification of behavioral patterns in autistic children to pave the way for improved educational and developmental outcomes. By shifting the focus towards a sequential and evidence-based intervention approach, we can empower autistic individuals to reach their full potential and lead fulfilling lives in an increasingly digital world.
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by jsendak | Oct 9, 2025 | Computer Science
arXiv:2209.12164v2 Announce Type: replace-cross
Abstract: Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers. It mainly contains two stages: video segmentation and segment assemblage. The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage. To address these problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can perform efficient and coherent segment assemblage task end-to-end. It utilizes multi-modal representation extracted from the segments and follows the Encoder-Decoder Ptr-Net framework with the Attention mechanism. Importance-coherence reward is designed for training M-SAN. We experiment on the Ads-1k dataset with 1000+ videos under rich ad scenarios collected from advertisers. To evaluate the methods, we propose a unified metric, Imp-Coh@Time, which comprehensively assesses the importance, coherence, and duration of the outputs at the same time. Experimental results show that our method achieves better performance than random selection and the previous method on the metric. Ablation experiments further verify that multi-modal representation and importance-coherence reward significantly improve the performance. Ads-1k dataset is available at: https://github.com/yunlong10/Ads-1k
Expert Commentary: M-SAN for Advertisement Video Editing
Advertising video editing is a crucial aspect of multimedia information systems, where the goal is to efficiently distill important information from longer advertisements into shorter videos. This process involves complex tasks such as video segmentation and segment assemblage, which require a multi-disciplinary approach drawing from fields like Artificial Reality, Augmented Reality, and Virtual Realities.
The proposed M-SAN (Multi-modal Segment Assemblage Network) in this research is a significant innovation that tackles the challenges faced in the segment assemblage stage of video editing. By leveraging multi-modal representations and incorporating an Encoder-Decoder Ptr-Net framework with an Attention mechanism, M-SAN shows promise in achieving coherent and efficient segment assemblage end-to-end.
One key aspect of the M-SAN approach is the design of an importance-coherence reward for training the network. This reward mechanism plays a critical role in ensuring that the edited videos not only retain crucial content but also maintain coherence in the narrative flow. This emphasis on importance and coherence aligns well with the objectives of modern multimedia content creation.
The experimental evaluation of M-SAN on the Ads-1k dataset demonstrates its superiority over random selection and previous methods in terms of the proposed metric Imp-Coh@Time. This unified metric, which evaluates importance, coherence, and duration of the edited videos simultaneously, provides a comprehensive understanding of the performance of the method.
Overall, the M-SAN approach represents a significant advancement in the field of advertisement video editing within multimedia information systems. Its utilization of multi-modal representations and emphasis on importance-coherence reward showcase the potential for future developments in automated video editing technologies.
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