by jsendak | Jul 16, 2024 | Computer Science
arXiv:2407.09766v1 Announce Type: new
Abstract: In the rapidly evolving field of multimedia services, video streaming has become increasingly prevalent, demanding innovative solutions to enhance user experience and system efficiency. This paper introduces a novel approach that integrates user digital twins-a dynamic digital representation of a user’s preferences and behaviors-with traditional video streaming systems. We explore the potential of this integration to dynamically adjust video preferences and optimize transcoding processes according to real-time data. The methodology leverages advanced machine learning algorithms to continuously update the user’s digital twin, which in turn informs the transcoding service to adapt video parameters for optimal quality and minimal buffering. Experimental results show that our approach not only improves the personalization of content delivery but also significantly enhances the overall efficiency of video streaming services by reducing bandwidth usage and improving video playback quality. The implications of such advancements suggest a shift towards more adaptive, user-centric multimedia services, potentially transforming how video content is consumed and delivered.
Enhancing User Experience and System Efficiency in Video Streaming Through User Digital Twins
In the fast-paced world of multimedia services, video streaming has become an integral part of our daily lives. As the demand for video streaming continues to grow, there is a need for innovative solutions that can enhance user experience and optimize system efficiency. This paper introduces a novel approach that tackles these challenges by integrating user digital twins with traditional video streaming systems.
The concept of user digital twins is an exciting development in the field of multimedia information systems. A user digital twin is a dynamic digital representation of a user’s preferences and behaviors. It captures data about the user’s video consumption habits, interests, and viewing patterns. By continuously updating the user’s digital twin, the system can gain a deeper understanding of the user’s preferences in real-time.
One of the key advantages of integrating user digital twins with video streaming systems is the ability to dynamically adjust video preferences. This means that the system can tailor the video content to match the user’s individual tastes and preferences. By analyzing the data from the user’s digital twin, the system can optimize the transcoding process to adapt video parameters and ensure optimal quality and minimal buffering.
Machine learning algorithms play a crucial role in this methodology. These algorithms continuously update the user’s digital twin based on new data, allowing the system to adapt and personalize the video content accordingly. This adaptive approach not only improves the personalization of content delivery but also enhances the overall efficiency of video streaming services.
The implications of this innovative approach are far-reaching. By leveraging user digital twins, multimedia services can become more adaptive and user-centric. This has the potential to transform how video content is consumed and delivered. Rather than a one-size-fits-all approach, video streaming services can now provide a truly personalized experience based on the user’s individual preferences and viewing habits.
From a multidisciplinary perspective, the integration of user digital twins with video streaming systems combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By seamlessly combining these diverse fields, this approach opens up new possibilities for creating immersive and engaging video streaming experiences.
In conclusion, the integration of user digital twins with video streaming systems is a groundbreaking development in multimedia services. By leveraging advanced machine learning algorithms and real-time data, this approach enhances both the user experience and system efficiency. The implications of this development are significant, and it has the potential to revolutionize how video content is consumed and delivered in the future.
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by jsendak | Jul 16, 2024 | Computer Science
Expert Commentary: Examining ChatGPT Responses on Health-Related Topics
In the era of digital information, understanding the quality and impact of artificial intelligence (AI) systems like ChatGPT is crucial, especially when it comes to sensitive and critical issues such as public health. The findings presented in this study shed light on ChatGPT’s responses regarding vaccination hesitancy in English, Spanish, and French, providing invaluable insights into its potential influence on public health decision-making.
One of the noteworthy findings is that ChatGPT responses exhibit less hesitancy compared to human respondents in previous studies. This suggests that ChatGPT has the potential to provide more confident and decisive information in the context of vaccination, potentially contributing to a more positive public perception of vaccines. However, it is important to note that caution should be exercised, as overconfidence or excessive certainty may give rise to misinformation or disregard for individual circumstances.
The variation observed across different languages is another intriguing finding. English responses, on average, tend to be more hesitant than those in Spanish and French. This disparity might be influenced by cultural, linguistic, or regional differences in perceptions of vaccines and trust in health information sources. Further exploration is needed to delve into the underlying factors that drive these language-specific variations.
Furthermore, the study reveals that ChatGPT responses remain consistent across different model parameters, indicating resilience to variations in model architecture. However, slight variations were observed when it comes to scale factors such as vaccine competency and risk. These nuances demonstrate the importance of understanding how model parameters and input specifications impact the responses generated by AI systems, as they can significantly affect the reliability and relevance of the information presented.
The implications of this research for evaluating and improving the quality and equity of health-related web information are substantial. Researchers and developers can leverage these findings as a starting point to refine and optimize ChatGPT’s responses on health-related topics. By addressing the hesitancy disparities across languages and considering the impact of scale factors, AI systems like ChatGPT can potentially provide more tailored and accurate information, empowering individuals to make informed decisions about their health.
Moving forward, it is crucial to expand this research to encompass a broader range of health topics and explore the potential biases that may influence ChatGPT responses. Additionally, assessing the impact of user demographics, question phrasing, and information sources on ChatGPT’s responses can further enhance our understanding of AI-based information systems and their role in public health decision-making.
In summary, the findings presented in this study fuel ongoing discussions surrounding the quality, equity, and influence of ChatGPT’s responses on health-related topics. By highlighting the need to address hesitancy disparities across languages and considering the impact of scale factors, researchers and developers can work towards improving the accuracy, trustworthiness, and relevance of AI-driven information systems, thereby amplifying their positive impact on public health.
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by jsendak | Jul 15, 2024 | Computer Science
arXiv:2407.09029v1 Announce Type: new
Abstract: Multimodal emotion recognition systems rely heavily on the full availability of modalities, suffering significant performance declines when modal data is incomplete. To tackle this issue, we present the Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) framework, an innovative approach that sequentially engages in cross-modal alignment, reconstruction, and refinement phases to handle missing modalities and enhance emotion recognition. This framework utilizes unsupervised distribution-based contrastive learning to align heterogeneous modal distributions, reducing discrepancies and modeling semantic uncertainty effectively. The reconstruction phase applies normalizing flow models to transform these aligned distributions and recover missing modalities. The refinement phase employs supervised point-based contrastive learning to disrupt semantic correlations and accentuate emotional traits, thereby enriching the affective content of the reconstructed representations. Extensive experiments on the IEMOCAP and MSP-IMPROV datasets confirm the superior performance of CM-ARR under conditions of both missing and complete modalities. Notably, averaged across six scenarios of missing modalities, CM-ARR achieves absolute improvements of 2.11% in WAR and 2.12% in UAR on the IEMOCAP dataset, and 1.71% and 1.96% in WAR and UAR, respectively, on the MSP-IMPROV dataset.
The Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) Framework: Enhancing Emotion Recognition in Multimodal Systems
In the field of multimodal emotion recognition systems, one of the major challenges is handling incomplete modal data. When modalities are missing or incomplete, the performance of such systems tends to suffer. To address this issue, a new framework called Cross-Modal Alignment, Reconstruction, and Refinement (CM-ARR) has been developed.
The CM-ARR framework involves three main phases: cross-modal alignment, reconstruction, and refinement. It leverages unsupervised distribution-based contrastive learning techniques to align modal distributions from different modalities. By reducing discrepancies and effectively modeling semantic uncertainty, CM-ARR ensures better alignment of heterogeneous modal data.
In the reconstruction phase, CM-ARR utilizes normalizing flow models to transform the aligned distributions and recover missing modalities. This step helps in restoring the multimodal information that was initially incomplete or unavailable. By leveraging the power of normalizing flow models, CM-ARR is able to generate plausible representations of missing data.
The final phase of the CM-ARR framework is refinement. In this phase, supervised point-based contrastive learning is employed to disrupt semantic correlations in the representations and emphasize emotional traits. This step enriches the affective content of the reconstructed representations, leading to improved emotion recognition.
The CM-ARR framework has been extensively evaluated using the IEMOCAP and MSP-IMPROV datasets. The results have shown the superior performance of CM-ARR in scenarios with both missing and complete modalities. Across six scenarios of missing modalities, CM-ARR achieved significant improvements in Weighted Average Recall (WAR) and Unweighted Average Recall (UAR) on both datasets.
Overall, the CM-ARR framework addresses the challenges of incomplete modal data in multimodal emotion recognition systems. By leveraging unsupervised and supervised learning techniques, it effectively aligns modal distributions, reconstructs missing modalities, and refines the emotional content. This innovative approach has the potential to enhance emotion recognition in various multimedia information systems, including animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jul 15, 2024 | Computer Science
Designing Ethical and Inclusive AI Systems
In today’s technologically driven society, Artificial Intelligence (AI) has become an integral part of our daily lives. However, the development and implementation of AI systems often fail to address important considerations such as ethics, inclusivity, and social justice. It is well known that AI algorithms can perpetuate biases and inequalities present in our existing systems, exacerbating systemic injustices.
One concerning aspect of this situation is the lack of educational opportunities for teenagers to understand the workings of AI and its socio-technical complexities. This issue is particularly pertinent for marginalized communities, including Black, Indigenous, and People of Color (BIPOC) teens. Not only are they often misrepresented throughout AI development, but they also face limited access to STEM education, exacerbating the existing disparities.
Critical Approaches to Child-Centered AI Design and Education
In response to these challenges, there is a growing need for critical approaches to child-centered AI design and education. By giving voice to marginalized youth, we can incorporate their perspectives and empower them to critique existing AI systems while envisioning more just AI futures. One approach that proves promising in this regard is co-speculative design.
Co-speculative design practices, inspired by Haraway’s Situated Knowledges and Speculative Fabulations, provide a framework for engaging youth in understanding AI’s social and ethical implications while fostering their imagination of alternative AI-driven worlds. These practices aim to dismantle the dominant techno-capitalist values that currently shape the AI landscape.
The Black-Led AI STEM Program
Our case study revolves around a black-led AI STEM program, which comprised a series of workshops over an 8-week period. Within this larger program, three 2-hour sessions were dedicated to exploring co-speculative design. Our analysis draws on various sources of data, including pre-post surveys, workshop recordings, focus group discussions, learning artifacts, and field notes.
Findings and Contributions
- Perception of AI’s Social and Ethical Implications: The workshops revealed that marginalized youth have a keen understanding of how AI systems can have far-reaching social and ethical implications. They recognize that these technologies are not neutral, but instead reflect and perpetuate systemic biases. Giving voice to these perceptions is crucial in shaping AI systems that are more inclusive and just.
- Engagement with Speculative Approaches: This case study highlights the effectiveness of speculative approaches in engaging youth with complex socio-technical issues. By encouraging them to imagine alternative AI futures, we empower them to challenge existing techno-capitalist values and envision systems that prioritize equity, fairness, and justice.
- Enabling AI Possibilities without Techno-Capitalist Values: Through co-speculative design practices, we enable youth to explore AI possibilities devoid of dominant techno-capitalist values. By fostering critical thinking and imagination, these workshops facilitate the envisioning of AI systems that align with socially just principles and serve the needs of marginalized communities.
Overall, this case study underscores the importance of including marginalized youth in the design and development of AI systems. By leveraging co-speculative design practices, we can empower BIPOC teens to understand, critique, and reimagine AI in ways that address the social and ethical implications. This approach not only promotes inclusivity and justice but also sets the stage for the creation of AI-driven futures that prioritize the well-being of all.
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by jsendak | Jul 11, 2024 | Computer Science
arXiv:2407.07111v1 Announce Type: cross
Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making “what you want is what you see” a reality. Among these, video editing has gained substantial attention and seen a swift rise in research activity, necessitating a comprehensive and systematic review of the existing literature. This paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. We begin by overviewing the mathematical formulation and image domain’s key methods. Subsequently, we categorize video editing approaches by the inherent connections of their core technologies, depicting evolutionary trajectory. This paper also dives into novel applications, including point-based editing and pose-guided human video editing. Additionally, we present a comprehensive comparison using our newly introduced V2VBench. Building on the progress achieved to date, the paper concludes with ongoing challenges and potential directions for future research.
Expert Commentary: Advances in Diffusion Model-Based Video Editing Techniques
Video editing has become a crucial component in the multimedia information systems field, enabling users to create visually appealing and informative content. The rapid development of diffusion models (DMs) has significantly enhanced the capabilities of image and video applications, allowing users to see exactly what they want. This paper provides a comprehensive and systematic review of the existing literature on diffusion model-based video editing techniques, shedding light on their theoretical foundations, practical applications, and future directions for research.
One of the key strengths of this paper is its multi-disciplinary nature. Video editing techniques in diffusion models draw upon concepts from various fields such as computer vision, image processing, and machine learning. By exploring the mathematical formulation and key methods in the image domain, the paper establishes the theoretical foundations of diffusion model-based video editing techniques. This interdisciplinary approach is crucial for understanding the complex algorithms underlying these techniques and their potential applications.
The paper categorizes video editing approaches based on the inherent connections of their core technologies, providing a comprehensive overview of the evolutionary trajectory in this field. This categorization aids in understanding the different techniques employed and their relative strengths and limitations. Furthermore, the paper goes beyond traditional video editing techniques and explores novel applications such as point-based editing and pose-guided human video editing. These innovative applications demonstrate the versatility of diffusion model-based video editing techniques and their potential impact on various domains, including entertainment, advertising, and education.
In addition, the paper introduces V2VBench, a comprehensive comparison framework that allows for a quantitative evaluation of different diffusion model-based video editing techniques. This framework enables researchers and practitioners to objectively assess the performance of these techniques, facilitating benchmarking and further advancements in the field.
When considering the wider field of multimedia information systems, diffusion model-based video editing techniques play a significant role in enhancing the user experience. These techniques contribute to the creation of visually stunning animations, artificial realities, augmented realities, and virtual realities. By utilizing diffusion models, video editors can manipulate videos in a way that seamlessly integrates with these multimedia systems. This integration opens up new avenues for immersive storytelling, interactive experiences, and realistic simulations.
However, despite the progress achieved so far, several challenges remain in diffusion model-based video editing. These include improving the efficiency and scalability of existing algorithms, developing techniques for handling complex video scenes, and addressing the ethical considerations surrounding the manipulation of video content. These challenges present exciting opportunities for future research, as they push the boundaries of current techniques and pave the way for innovative solutions.
In conclusion, this paper provides a comprehensive review of diffusion model-based video editing techniques, highlighting their theoretical foundations, practical applications, and future directions. With its multi-disciplinary approach and emphasis on novel applications, the paper significantly contributes to the wider field of multimedia information systems, making it a valuable resource for researchers, practitioners, and enthusiasts in this field.
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by jsendak | Jul 11, 2024 | Computer Science
Abstract:
Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient graph algorithms. This technical report presents the newly implemented component on locality sensitive hashing, kernel density estimation, and fast spectral clustering. The report includes a user’s guide to the newly implemented algorithms, experiments and demonstrations of the new functionality, and several technical considerations behind our development.
Introduction:
The Spectral Toolkit of Algorithms for Graphs (STAG) has been a valuable resource for researchers and practitioners working with graph algorithms. With the newly implemented component on locality sensitive hashing, kernel density estimation, and fast spectral clustering, STAG further expands its capabilities and offers a wider range of tools for analyzing and processing graphs. This technical report provides an in-depth analysis of the newly incorporated algorithms and their potential applications.
Locality Sensitive Hashing:
Locality sensitive hashing is a technique that allows nearest neighbor search in high-dimensional spaces efficiently. The inclusion of this algorithm in STAG brings enhanced capabilities for graph similarity and clustering tasks. By utilizing hashing functions to map nodes or subgraphs to buckets, it becomes possible to efficiently identify similar nodes or subgraphs within a large graph. This optimization can greatly speed up search and allow for faster similarity analysis and clustering.
Kernel Density Estimation:
Kernel density estimation is a fundamental statistical technique used for estimating the probability density function of a random variable. In the context of graph algorithms, it can be applied to measure the density of nodes or subgraphs within a graph. With this newly implemented component, STAG enables the estimation of graph local densities, aiding tasks such as anomaly detection, outlier identification, and community detection. Researchers and practitioners can leverage this feature to gain insights into the structural characteristics of a given graph.
Fast Spectral Clustering:
Spectral clustering is a popular technique for clustering data points based on the spectral properties of their affinity matrix. By incorporating fast spectral clustering in STAG, the library now offers an efficient solution for graph clustering tasks. The algorithm utilizes the eigenvectors of the graph Laplacian to uncover clusters by partitioning the graph. With this addition, STAG enables users to perform scalable and accurate graph clustering, benefiting exploratory analysis, pattern recognition, and network decomposition.
User’s Guide and Technical Considerations:
This technical report also includes a user’s guide that provides comprehensive documentation on how to use the newly implemented algorithms in STAG. It outlines the required input format, provides detailed explanations of the algorithmic steps, and offers guidance on parameter tuning for optimal results. Additionally, the report dives into technical considerations behind the development of these algorithms, discussing the trade-offs, optimizations, and potential limitations. This information equips users with the necessary knowledge for effectively applying these algorithms to their specific graph analysis tasks.
Conclusion:
The inclusion of locality sensitive hashing, kernel density estimation, and fast spectral clustering in STAG significantly enhances the capabilities of the library. These newly implemented algorithms empower researchers and practitioners to tackle a wide range of graph analysis tasks more efficiently and accurately. By providing a user’s guide and discussing the technical considerations, this technical report serves as a valuable resource for effectively utilizing the newly incorporated algorithms. Going forward, it will be interesting to see how the STAG library evolves and continues to contribute to the field of graph algorithms.
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