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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by jsendak | Feb 27, 2024 | Computer Science
arXiv:2402.15513v1 Announce Type: new
Abstract: Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological features that have been shown to be correlated with anxiety and stress to high-arousal emotions. Specifically, we examine features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from the following three datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. We aim to understand whether these features are specific to anxiety or general to other high-arousal emotions through a statistical regression analysis, in addition to a within-corpus, cross-corpus, and leave-one-corpus-out cross-validation across instances of stress and arousal. We used the following classifiers: Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of the aforementioned models. We found that models trained on an arousal dataset perform relatively well on a previously unseen stress dataset, and vice versa. Our experimental results suggest that the evaluated models may be identifying emotional arousal instead of stress. This work is the first cross-corpus evaluation across stress and arousal from ECG and EDA signals, contributing new findings about the generalizability of stress detection.
Expert Commentary: Evaluating the Generalizability of Physiological Features in Stress Detection
In recent years, machine learning (ML) techniques have shown promise in detecting anxiety and stress using physiological signals. However, it is important to determine whether these ML models are truly learning features specific to stress or if they are detecting a more general state of high arousal. This article presents a study that aims to address this ambiguity by evaluating the generalizability of physiological features associated with anxiety and stress to other high-arousal emotions.
The study examines features extracted from electrocardiogram (ECG) and electrodermal (EDA) signals from three different datasets: Anxiety Phases Dataset (APD), Wearable Stress and Affect Detection (WESAD), and the Continuously Annotated Signals of Emotion (CASE) dataset. By analyzing these features, the researchers seek to understand whether they are specific to anxiety or applicable to other high-arousal emotions.
To evaluate the generalizability of these features, the researchers conducted a statistical regression analysis in addition to various cross-validation techniques. They used several classifiers, including Support Vector Machines, LightGBM, Random Forest, XGBoost, and an ensemble of these models to train and test their models on different combinations of stress and arousal datasets.
The findings from this study provide valuable insights into the nature of stress detection through physiological signals. The results indicate that models trained on datasets related to arousal perform well on stress datasets, and vice versa. This suggests that the evaluated models may be identifying emotional arousal rather than specifically detecting stress.
This is a significant contribution to the field as it is the first cross-corpus evaluation that explores the relationship between stress and arousal using ECG and EDA signals. By highlighting the generalizability of stress detection methods, this work advances our understanding of the broader implications of physiological signal analysis in the field of multimedia information systems.
The concepts explored in this study have significant interdisciplinary relevance. The field of multimedia information systems encompasses various disciplines such as computer science, psychology, and human-computer interaction. By applying machine learning techniques to physiological signals, researchers bridge the gap between these disciplines, paving the way for innovative applications in areas like augmented reality, virtual realities, and artificial reality.
Animations in virtual and augmented reality environments can be intelligently adjusted based on the user’s stress or arousal levels. For example, if a user is becoming overly stressed, the virtual environment can adapt by providing calming visuals or sounds to alleviate their anxiety. Similarly, in artificial reality applications such as medical simulations, the system can respond to the user’s stress levels to provide personalized feedback and guidance.
Overall, this study contributes to the broader field of multimedia information systems by providing insights into the generalizability of stress detection methods and highlighting the interdisciplinary nature of the concepts explored. It opens up possibilities for integrating physiological signal analysis into various multimedia applications, paving the way for more immersive and personalized experiences in virtual, augmented, and artificial realities.
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by jsendak | Feb 27, 2024 | Computer Science
The Sixth International Workshop on Languages for Modelling Variability (MODEVAR 2024) was recently held in Bern, Switzerland on February 6th, 2024. This workshop is a significant event for researchers and practitioners in the field of variability modelling, as it provides a platform for exchanging ideas, discussing challenges, and exploring new advancements in the area.
Importance of Variability Modelling
Variability modelling plays a crucial role in various domains, including software development, product line engineering, and system design. It enables organizations to manage and represent the diverse features and options that can be configured or customized in a system or product.
Having a well-defined and robust variability modelling approach helps organizations to efficiently handle the complexity of variability, thereby enhancing product quality, reducing development time, and increasing customer satisfaction. Therefore, it is imperative to have a deep understanding of the challenges and opportunities in this field.
Higlights from MODEVAR 2024
The MODEVAR 2024 workshop provided a platform for researchers and industry experts to present their latest findings and share their experiences in variability modelling. The workshop featured several informative sessions and discussions on a range of topics.
New Approaches and Techniques
A key highlight of MODEVAR 2024 was the presentation of new approaches and techniques in variability modelling. Researchers showcased innovative techniques for representing, managing, and reasoning about variability in complex systems and products. These advancements have the potential to revolutionize the way organizations handle variability and improve their product development processes.
Industry Case Studies
The workshop also featured insightful industry case studies that demonstrated the practical application of variability modelling in real-world scenarios. These case studies provided valuable insights into the challenges faced by organizations and how they successfully implemented variability modelling techniques to overcome these challenges.
Open Discussion and Future Directions
Furthermore, MODEVAR 2024 included open discussions and brainstorming sessions on the future directions of variability modelling. Experts from academia and industry shared their visions and perspectives on emerging trends, research priorities, and potential collaborations. This collaborative approach ensures that the research in this field aligns with the practical needs of the industry.
What’s Next for Variability Modelling?
As we look ahead, there are several potential future developments in variability modelling that may arise from the discussions and insights shared at the MODEVAR 2024 workshop. One important direction could be the integration of artificial intelligence and machine learning techniques in variability modelling to automate and optimize the modelling process.
Another potential advancement could be the development of standardized modelling languages and tools that enable seamless integration of variability modelling across different phases of the software development lifecycle. This would enhance communication and collaboration among stakeholders, leading to more efficient and effective variability management.
Overall, the MODEVAR 2024 workshop has played a pivotal role in advancing the field of variability modelling. The exchange of knowledge and ideas among researchers and industry professionals has paved the way for exciting developments in the years to come, and it will be fascinating to witness the impact of these advancements on various domains.
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