by jsendak | Mar 1, 2024 | Computer Science
arXiv:2402.18702v1 Announce Type: new
Abstract: This study aims to investigate the comprehensive characterization of information content in multimedia (videos), particularly on YouTube. The research presents a multi-method framework for characterizing multimedia content by clustering signals from various modalities, such as audio, video, and text. With a focus on South China Sea videos as a case study, this approach aims to enhance our understanding of online content, especially on YouTube. The dataset includes 160 videos, and our findings offer insights into content themes and patterns within different modalities of a video based on clusters. Text modality analysis revealed topical themes related to geopolitical countries, strategies, and global security, while video and audio modality analysis identified distinct patterns of signals related to diverse sets of videos, including news analysis/reporting, educational content, and interviews. Furthermore, our findings uncover instances of content repurposing within video clusters, which were identified using the barcode technique and audio similarity assessments. These findings indicate potential content amplification techniques. In conclusion, this study uniquely enhances our current understanding of multimedia content information based on modality clustering techniques.
Enhancing Understanding of Multimedia Content through Modality Clustering
As the internet continues to evolve, multimedia content has become an integral part of our daily digital experience. Platforms like YouTube have contributed significantly to the growth of multimedia content, with millions of videos being uploaded and consumed every day. However, understanding the information within these videos can be challenging due to their diverse nature.
This study addresses this challenge by presenting a multi-method framework for characterizing multimedia content on YouTube. By clustering signals from different modalities, such as audio, video, and text, the researchers aim to provide a comprehensive characterization of the information present in videos.
The multi-disciplinary nature of this research is evident in the approach taken. By analyzing different modalities, the study combines techniques from fields such as audio signal processing, computer vision, and natural language processing. This integration of multiple disciplines enhances the accuracy and depth of the analysis.
The case study conducted on South China Sea videos demonstrates the effectiveness of the proposed framework. By analyzing a dataset of 160 videos, the researchers were able to gain insights into content themes and patterns. The analysis of the text modality revealed geopolitical themes related to countries, strategies, and global security. On the other hand, the analysis of video and audio modalities identified distinct patterns related to news analysis/reporting, education, and interviews.
One interesting finding of this study is the discovery of content repurposing within video clusters. The researchers used techniques such as the barcode technique and audio similarity assessments to identify instances of content amplification. This insight into content repurposing highlights the potential for future research on content manipulation techniques and their impact on the dissemination of information through multimedia platforms.
The implications of this research go beyond the specific case study of South China Sea videos. The framework presented in this study can be applied to other domains and topics, allowing for a deeper understanding of multimedia content on various platforms. Whether it’s analyzing animations, artificial reality, augmented reality, or virtual realities, the multi-method framework can provide valuable insights into the information contained within these multimedia experiences.
Overall, this study contributes to the wider field of multimedia information systems by introducing a comprehensive characterization framework for multimedia content on YouTube. By combining signals from different modalities, the researchers provide a multi-faceted analysis that enriches our understanding of online content. The findings of this study have significant implications for content creators, platform administrators, and researchers interested in studying the impact of multimedia content on society.
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by jsendak | Mar 1, 2024 | Computer Science
Decision making and planning have long relied on AI-driven forecasts, and the government and the general public are focused on minimizing risks and maximizing benefits in the face of future public health uncertainties. A recent study aimed to enhance forecasting techniques by utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique, which improves the convergence of Particle Swarm Optimization (PSO) and the accuracy of Artificial Neural Network (ANN).
The RDV IW technique takes inspiration from the motions of a golf ball and modifies the velocities of particles as they approach the solution point. By implementing a parabolically descending structure, the technique aims to optimize the convergence of the models. Simulation results demonstrated that the proposed forecasting model, with a combination of alpha and alpha_dump values set at [0.4, 0.9], exhibited significant improvements in both position error and computational time when compared to the old model.
The new model achieved a 6.36% reduction in position error, indicating better accuracy in forecasting. Additionally, it showcased an 11.75% improvement in computational time, suggesting enhanced efficiency. The model reached its optimum level with minimal steps, showcasing a 12.50% improvement compared to the old model. This improvement is attributed to better velocity averages when speed stabilization occurs at the 24th iteration.
An important aspect of forecasting models is their accuracy performance. The computed p-values for various metrics, such as NRMSE, MAE, MAPE, WAPE, and R2, were found to be lower than the set level of significance (0.05). This indicates that the proposed algorithm demonstrated significant accuracy performance. Hence, the modified ANN-PSO using the RDV IW technique exhibited substantial enhancements in the new HIV/AIDS forecasting model when compared to the two previous models.
These findings suggest that the incorporation of the RDV IW technique can greatly improve the accuracy and efficiency of AI-driven forecasts. The optimization of convergence in models allows for better decision making and planning, especially in the context of public health uncertainties like HIV/AIDS. This study opens up possibilities for further research and applications of the RDV IW technique in other forecasting domains.
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by jsendak | Feb 29, 2024 | Computer Science
arXiv:2402.18107v1 Announce Type: new
Abstract: In line with the latest research, the task of identifying helpful reviews from a vast pool of user-generated textual and visual data has become a prominent area of study. Effective modal representations are expected to possess two key attributes: consistency and differentiation. Current methods designed for Multimodal Review Helpfulness Prediction (MRHP) face limitations in capturing distinctive information due to their reliance on uniform multimodal annotation. The process of adding varied multimodal annotations is not only time-consuming but also labor-intensive. To tackle these challenges, we propose an auto-generated scheme based on multi-task learning to generate pseudo labels. This approach allows us to simultaneously train for the global multimodal interaction task and the separate cross-modal interaction subtasks, enabling us to learn and leverage both consistency and differentiation effectively. Subsequently, experimental results validate the effectiveness of pseudo labels, and our approach surpasses previous textual and multimodal baseline models on two widely accessible benchmark datasets, providing a solution to the MRHP problem.
Expert Commentary: Enhancing Multimodal Review Helpfulness Prediction Using Pseudo Labels
With the rapid growth of user-generated content, identifying helpful reviews from a vast pool of textual and visual data has become a challenging task. In this research paper, the authors address the limitations of current methods for Multimodal Review Helpfulness Prediction (MRHP) by proposing a novel approach based on multi-task learning and pseudo labels.
The authors highlight two key attributes that effective modal representations should possess: consistency and differentiation. Consistency ensures that the multimodal annotations capture reliable and recurring information, while differentiation allows for the identification of unique and diverse aspects of the reviews.
One major limitation in existing methods is the reliance on uniform multimodal annotation, which fails to capture distinctive information. Moreover, the process of adding varied annotations manually is time-consuming and labor-intensive. To overcome these challenges, the authors introduce an auto-generated scheme based on multi-task learning.
The proposed approach leverages pseudo labels, which are automatically generated during training. This enables the model to simultaneously learn the global multimodal interaction task and the separate cross-modal interaction subtasks, effectively capturing both consistency and differentiation in the data.
The experiments conducted by the authors demonstrate the effectiveness of the pseudo labels and the proposed approach. The results show that the method outperforms previous textual and multimodal baseline models on two widely accessible benchmark datasets, offering a solution to the MRHP problem.
This research contributes to the field of multimedia information systems by addressing the challenges of identifying helpful reviews from multimodal data. By incorporating both textual and visual information, the proposed approach takes into account the multi-disciplinary nature of the content. This is particularly relevant in the context of multimedia information systems, where different modalities such as text, images, and videos need to be analyzed and interpreted.
The concepts presented in this paper also have implications for other related fields such as animations, artificial reality, augmented reality, and virtual realities. In these domains, the ability to accurately assess user-generated content and determine its helpfulness can greatly enhance user experiences. For example, in virtual reality applications, knowing which reviews provide valuable insights can assist developers in improving their virtual environments or applications.
In summary, this research paper provides a valuable contribution to the field of multimodal review analysis by proposing a novel approach based on pseudo labels and multi-task learning. By addressing the limitations of current methods and leveraging both consistency and differentiation, the proposed approach offers a promising solution to the MRHP problem. The findings of this study have implications for a wide range of domains, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Feb 29, 2024 | Computer Science
An Expert Commentary on ByteComposer: A Step Towards Human-Aligned Melody Composition
The development of Large Language Models (LLMs) has shown significant progress in various multimodal understanding and generation tasks. However, the field of melody composition has not received as much attention when it comes to designing human-aligned and interpretable systems. In this article, the authors introduce ByteComposer, an agent framework that aims to emulate the creative pipeline of a human composer in order to generate melodies comparable to those created by human creators.
The core idea behind ByteComposer is to combine the interactive and knowledge-understanding capabilities of LLMs with existing symbolic music generation models. This integration allows the agent to go through a series of distinct steps that resemble a human composer’s creative process. These steps include “Conception Analysis”, “Draft Composition”, “Self-Evaluation and Modification”, and “Aesthetic Selection”. By following these steps, ByteComposer aims to produce melodies that align with human aesthetic preferences.
The authors of the article conducted extensive experiments using GPT4 and several open-source large language models to validate the effectiveness of the ByteComposer framework. These experiments demonstrate that the agent is capable of generating melodies that are comparable to what a novice human composer would produce.
To obtain a comprehensive evaluation, professional music composers were engaged in multi-dimensional assessments of the output generated by ByteComposer. This evaluation allowed the authors to understand the strengths and weaknesses of the agent across various facets of music composition. The results indicate that the agent has reached a level where it can be considered on par with novice human melody composers.
This research has several implications for the field of music composition. By combining the power of large language models with symbolic music generation models, ByteComposer represents a significant step forward in the quest to create machine-generated melodies that align with human preferences and artistic sensibilities. This could have broad applications ranging from assisting composers in their creative process to generating background scores for various media productions. Moreover, the human-aligned and interpretable nature of the ByteComposer framework makes it a valuable tool for composers to explore new ideas and expand their creative boundaries.
However, there are still challenges to address in the future. While ByteComposer demonstrates promising results, the evaluation primarily focuses on novice-level composition. Future research should explore its capabilities in generating melodies at an advanced level with a more nuanced understanding of musical theory and style. Additionally, enhancing the transparency and interpretability of the generated compositions will be crucial for ByteComposer’s wider acceptance among professional composers.
In conclusion, ByteComposer represents a significant advancement in the field of machine-generated music composition. By combining the strengths of large language models and symbolic music generation, this agent framework shows great potential in emulating the creative process of human composers. As further improvements are made, we can expect ByteComposer to become a valuable tool for composers seeking inspiration and assistance in their musical endeavors.
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by jsendak | Feb 28, 2024 | Computer Science
arXiv:2310.06958v4 Announce Type: replace-cross
Abstract: Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods. However, they also became more vulnerable to adversarial attacks that increase metrics’ scores without improving visual quality. The existing benchmarks of quality metrics compare their performance in terms of correlation with subjective quality and calculation time. Nonetheless, the adversarial robustness of image-quality metrics is also an area worth researching. This paper analyses modern metrics’ robustness to different adversarial attacks. We adapted adversarial attacks from computer vision tasks and compared attacks’ efficiency against 15 no-reference image- and video-quality metrics. Some metrics showed high resistance to adversarial attacks, which makes their usage in benchmarks safer than vulnerable metrics. The benchmark accepts submissions of new metrics for researchers who want to make their metrics more robust to attacks or to find such metrics for their needs. The latest results can be found online: https://videoprocessing.ai/benchmarks/metrics-robustness.html.
Analysis of Modern Image- and Video-Quality Metrics’ Robustness to Adversarial Attacks
Image- and video-quality metrics play a crucial role in assessing the visual quality of multimedia content. With the advancements in neural-network-based metrics, the performance of these metrics has significantly improved. However, these advancements have also introduced a new vulnerability – adversarial attacks.
Adversarial attacks manipulate certain features of an image or video in a way that increases the quality metric scores without actually improving the visual quality. This poses a significant threat to the integrity of quality assessment systems and calls for research into adversarial robustness.
This paper focuses on analyzing the robustness of 15 prominent no-reference image- and video-quality metrics to different adversarial attacks. By adapting adversarial attacks commonly used in computer vision tasks, the authors were able to evaluate the efficiency of these attacks against the metrics under consideration.
The results of the analysis showcased varying degrees of resistance to adversarial attacks among the different metrics. Some metrics demonstrated a high level of robustness, indicating their reliability in real-world scenarios and making them safer options for benchmarking purposes. On the other hand, certain metrics showed vulnerabilities to the attacks, raising concerns about their suitability for quality assessment.
This multi-disciplinary study bridges the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. It highlights the importance of considering the robustness of image- and video-quality metrics in these domains, where accurate quality assessment is crucial for user experience and content optimization.
The research also addresses the need for a benchmark that includes adversarial robustness as a criterion to evaluate and compare different metrics. By providing a platform for researchers to submit their metrics, this benchmark fosters the development of more robust quality metrics and aids in finding suitable metrics for specific needs.
The topic of adversarial attacks and robustness has gained significant attention in recent years, and this paper adds valuable insights to the ongoing discourse. Researchers and practitioners can refer to the online platform mentioned in the article to access the latest benchmark results and stay updated with the advancements in this field.
Conclusion
As the reliance on neural-network-based image- and video-quality metrics continues to grow, understanding their vulnerabilities to adversarial attacks is crucial. This paper’s analysis of modern metrics’ robustness provides valuable insights into the effectiveness of various attacks on different metrics. It emphasizes the importance of considering robustness in benchmarking and highlights the need for more research in this area.
Furthermore, the integration of multiple disciplines such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities demonstrates the wide applicability and impact of this research. It encourages collaboration across these fields to develop more robust quality assessment techniques that can enhance user experience and optimize multimedia content.
Overall, this study contributes to the ongoing efforts in ensuring the reliability and security of image- and video-quality assessment systems, paving the way for advancements in the field and fostering innovation in research and development.
Reference: Announce Type: replace-cross (arXiv:2310.06958v4)
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by jsendak | Feb 28, 2024 | Computer Science
Abstract: PyRQA is a software package that revolutionizes the field of non-linear time series analysis by offering a highly efficient method for conducting recurrence quantification analysis (RQA) on time series consisting of more than one million data points. RQA is a widely used method for quantifying the recurrent behavior of systems, and existing implementations are unable to analyze such long time series or require excessive amounts of time to compute the quantitative measures. PyRQA addresses these limitations by leveraging the parallel computing capabilities of a variety of hardware architectures, such as GPUs, through the OpenCL framework.
Introduction: The field of non-linear time series analysis has faced challenges when dealing with long time series data. Traditional RQA implementations are either incapable of handling time series with more than a certain number of data points or are incredibly time-consuming. However, PyRQA introduces a cutting-edge solution that enables efficient RQA analysis on large-scale time series datasets.
Parallel Computing in PyRQA: PyRQA utilizes the OpenCL framework, which allows for the efficient utilization of parallel computing capabilities across various hardware architectures. By partitioning the RQA computations, PyRQA can leverage multiple compute devices simultaneously, such as GPUs, significantly improving the runtime efficiency of the analysis.
Real-world Example: To showcase the capabilities of PyRQA, the publication presents a real-world example comparing the dynamics of two climatological time series. By employing PyRQA, the analysis of a series consisting of over one million data points is completed in just 69 seconds, a remarkable improvement compared to state-of-the-art RQA software which required almost eight hours to process the same dataset.
Synthetic Example: Additionally, a synthetic example is used to highlight the speed and efficiency of PyRQA. The analysis of a time series with over one million data points is shown to be completed in a mere 69 seconds using PyRQA, demonstrating its superior runtime efficiency compared to existing implementations.
Conclusion: PyRQA represents a groundbreaking advancement in the field of non-linear time series analysis. By leveraging parallel computing capabilities through the OpenCL framework, PyRQA allows for the efficient analysis of large-scale time series datasets. The demonstrated examples highlight the significant improvement in runtime efficiency compared to existing implementations, making PyRQA an invaluable tool for researchers and practitioners in various domains where RQA analysis is crucial for understanding complex systems.
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