by jsendak | Jan 4, 2024 | Computer Science
Generating vivid and diverse 3D co-speech gestures is crucial for various
applications in animating virtual avatars. While most existing methods can
generate gestures from audio directly, they usually overlook that emotion is
one of the key factors of authentic co-speech gesture generation. In this work,
we propose EmotionGesture, a novel framework for synthesizing vivid and diverse
emotional co-speech 3D gestures from audio. Considering emotion is often
entangled with the rhythmic beat in speech audio, we first develop an
Emotion-Beat Mining module (EBM) to extract the emotion and audio beat features
as well as model their correlation via a transcript-based visual-rhythm
alignment. Then, we propose an initial pose based Spatial-Temporal Prompter
(STP) to generate future gestures from the given initial poses. STP effectively
models the spatial-temporal correlations between the initial poses and the
future gestures, thus producing the spatial-temporal coherent pose prompt. Once
we obtain pose prompts, emotion, and audio beat features, we will generate 3D
co-speech gestures through a transformer architecture. However, considering the
poses of existing datasets often contain jittering effects, this would lead to
generating unstable gestures. To address this issue, we propose an effective
objective function, dubbed Motion-Smooth Loss. Specifically, we model motion
offset to compensate for jittering ground-truth by forcing gestures to be
smooth. Last, we present an emotion-conditioned VAE to sample emotion features,
enabling us to generate diverse emotional results. Extensive experiments
demonstrate that our framework outperforms the state-of-the-art, achieving
vivid and diverse emotional co-speech 3D gestures. Our code and dataset will be
released at the project page:
https://xingqunqi-lab.github.io/Emotion-Gesture-Web/
EmotionGesture: Synthesizing Vivid and Diverse Emotional Co-Speech 3D Gestures
In the field of multimedia information systems, the generation of realistic and expressive virtual avatars has become a crucial research area. One important aspect of animating virtual avatars is the generation of co-speech gestures that are synchronized with speech. The ability to generate vivid and diverse 3D co-speech gestures is essential for applications such as virtual reality, augmented reality, and artificial reality.
The article introduces EmotionGesture, a novel framework for synthesizing emotional co-speech 3D gestures from audio. Unlike existing methods, EmotionGesture takes into account the emotion in speech audio, which is often overlooked but plays a significant role in generating authentic gestures. The framework consists of several modules that work together to produce coherent and expressive gestures.
Emotion-Beat Mining Module (EBM)
The Emotion-Beat Mining module is responsible for extracting emotion and audio beat features from the speech audio. It also models the correlation between these features through a transcript-based visual-rhythm alignment. This module is crucial for capturing the emotional content of the speech and its rhythmic characteristics, which are important cues for gesture generation.
Spatial-Temporal Prompter (STP)
The Spatial-Temporal Prompter module generates future gestures based on the given initial poses. This module effectively models the spatial-temporal correlations between the initial poses and the future gestures, producing a spatial-temporal coherent pose prompt. By considering the relationships between poses over time, the STP ensures that the generated gestures are natural and coherent.
Transformer Architecture
The framework uses a transformer architecture to generate 3D co-speech gestures based on the pose prompts, emotion, and audio beat features. The transformer architecture is a powerful deep learning model that can capture complex relationships between different input modalities. In this case, it allows the framework to generate gestures that are synchronized with the speech and reflect the emotional content.
Motion-Smooth Loss
To address the issue of jittering effects in existing datasets, the framework introduces an objective function called Motion-Smooth Loss. This loss function models motion offset to compensate for jittering ground-truth data, ensuring that the generated gestures are stable and smooth. By enforcing smoothness in the gestures, the framework improves the overall quality and coherence of the animations.
Emotion-Conditioned VAE
The framework incorporates an emotion-conditioned Variational Autoencoder (VAE) to sample emotion features. This allows for the generation of diverse emotional results, as the VAE can learn and sample from a distribution of emotion features. By conditioning the generation process on emotion, the framework can produce gestures that express different emotions, adding further richness and variability to the animations.
In summary, EmotionGesture presents a comprehensive framework for synthesizing vivid and diverse emotional co-speech 3D gestures. By considering emotion, spatial-temporal correlations, and smoothness, the framework produces high-quality animations that are closely synchronized with speech. The multi-disciplinary nature of this work lies in its integration of audio analysis, computer vision, natural language processing, and deep learning techniques. This research contributes to the wider field of multimedia information systems, including applications in virtual reality, augmented reality, and artificial reality.
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by jsendak | Jan 4, 2024 | Computer Science
This article presents a method for inferring and synthetically extrapolating roughness fields from electron microscope scans of additively manufactured surfaces. The method utilizes an adaptation of Rogallo’s synthetic turbulence method, which is based on Fourier modes. The resulting synthetic roughness fields are smooth and compatible with grid generators in computational fluid dynamics or other numerical simulations.
One of the main advantages of this method is its ability to extrapolate homogeneous synthetic roughness fields using a single physical roughness scan. This is in contrast to machine learning methods, which typically require training on multiple scans of surface roughness. The ability to generate synthetic roughness fields of any desired size and range using only one scan is a significant time and cost-saving benefit.
The study generates five types of synthetic roughness fields using an electron microscope roughness image from literature. The spectral energy and two-point correlation spectra of these synthetic fields are compared to the original scan, showing a close approximation of the roughness structures and spectral energy.
One potential application of this method is in computational fluid dynamics simulations, where accurate representation of surface roughness is crucial for predicting flow behavior. By generating synthetic roughness fields that closely resemble real-world roughness structures, researchers can improve the accuracy and reliability of their simulations.
Further research could focus on validating this method with additional roughness scans from different surfaces and manufacturing methods. It would be interesting to explore how well the synthetic roughness fields generalize to different types of surfaces and manufacturing processes.
Conclusion
The method presented in this article provides a valuable tool for inferring and extrapolating roughness fields from electron microscope scans. Its ability to generate smooth synthetic roughness fields compatible with numerical simulations using only one physical roughness scan is a significant advantage over other methods that rely on machine learning and multiple scans for training. By closely approximating the roughness structures and spectral energy of the original scan, this method has the potential to improve the accuracy of computational fluid dynamics simulations and other numerical simulations that involve surface roughness. Further research and validation will help establish the generalizability and robustness of this method across different surfaces and manufacturing processes.
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by jsendak | Jan 4, 2024 | Computer Science
With the development of social media, rumors have been spread broadly on
social media platforms, causing great harm to society. Beside textual
information, many rumors also use manipulated images or conceal textual
information within images to deceive people and avoid being detected, making
multimodal rumor detection be a critical problem. The majority of multimodal
rumor detection methods mainly concentrate on extracting features of source
claims and their corresponding images, while ignoring the comments of rumors
and their propagation structures. These comments and structures imply the
wisdom of crowds and are proved to be crucial to debunk rumors. Moreover, these
methods usually only extract visual features in a basic manner, seldom consider
tampering or textual information in images. Therefore, in this study, we
propose a novel Vision and Graph Fused Attention Network (VGA) for rumor
detection to utilize propagation structures among posts so as to obtain the
crowd opinions and further explore visual tampering features, as well as the
textual information hidden in images. We conduct extensive experiments on three
datasets, demonstrating that VGA can effectively detect multimodal rumors and
outperform state-of-the-art methods significantly.
Expert Commentary: The Significance of Multimodal Rumor Detection
Rumors have always existed, but with the advent of social media, their spread has become more rampant and harmful to society. This is because rumors can easily be disseminated and amplified through social media platforms, reaching a large number of people within a short period of time. In recent years, there has been growing concern about the impact of rumors, particularly those that use multimedia elements such as manipulated images or concealed textual information.
Dealing with these multimodal rumors requires a multidisciplinary approach that combines expertise from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The content of this article specifically focuses on the development of a novel Vision and Graph Fused Attention Network (VGA) for multimodal rumor detection.
The Importance of Considering Comments and Propagation Structures
A key limitation of existing multimodal rumor detection methods is that they primarily focus on analyzing the source claims and their corresponding images, while neglecting the invaluable insights provided by comments and propagation structures. Comments on social media platforms often represent the collective wisdom of crowds and can provide crucial information for debunking rumors. By incorporating the analysis of comments, VGA ensures that the crowd opinions are taken into account, leading to more accurate and reliable rumor detection.
Furthermore, understanding the propagation structures among posts is vital in comprehending how rumors spread and gain traction. By utilizing these propagation structures, VGA can capture the patterns and dynamics of rumor dissemination, improving its ability to identify and debunk rumors effectively.
Enhanced Visual Features and Textual Information
Another unique aspect of VGA is its ability to extract enhanced visual features and uncover textual information hidden within images. In the age of sophisticated image manipulation techniques, it is important to consider the possibility of tampering and deception in rumor-related images. VGA goes beyond basic visual feature extraction and incorporates advanced methods to detect visual tampering, ensuring that manipulations are not overlooked in the rumor detection process.
Addtionally, the textual information concealed within images can also be a vital clue in unraveling rumors. VGA employs advanced techniques to analyze and extract textual information from images, further enhancing its ability to identify and debunk multimodal rumors.
Implications and Future Directions
The development of the Vision and Graph Fused Attention Network (VGA) for multimodal rumor detection is a significant step towards combating the spread of harmful rumors on social media platforms. The multi-disciplinary nature of this approach highlights the importance of synergizing expertise from various fields such as multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
In terms of future directions, it would be interesting to explore the application of VGA in real-time rumor detection and develop strategies to counteract the harmful effects of rumors more efficiently. Additionally, incorporating natural language processing techniques to analyze text-based rumors alongside multimodal rumors could further enhance the overall accuracy of rumor detection systems.
Overall, the proposed VGA method holds great promise for addressing the critical problem of multimodal rumor detection, and its success in outperforming state-of-the-art methods in extensive experiments demonstrates its effectiveness. By leveraging the wisdom of crowds, analyzing propagation structures, and considering both visual and textual features, VGA has proven to be a valuable tool in debunking rumors and mitigating their harmful impact on individuals and society.
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by jsendak | Jan 4, 2024 | Computer Science
Analysis of SD-WAN over MPLS in the Housing Bank
In this paper, the authors provide an in-depth analysis of the implementation of Software-defined wide area network (SD-WAN) over Multiprotocol Label Switching (MPLS) in the Housing Bank, a major financial institution in Algeria. The comparison is made with traditional MPLS and direct internet access, focusing on various metrics such as bandwidth, latency, jitter, packet loss, throughput, and quality of service (QoS).
The deployment of FortiGate is considered as the SD-WAN solution for the Housing Bank. One of the key advantages of SD-WAN is its ability to enhance network traffic management, allowing for more efficient data transmission compared to traditional MPLS. This is achieved through the dynamic routing capabilities of SD-WAN controllers, which optimize traffic flows based on real-time network conditions.
Security measures have also been taken into account in this analysis. The implementation of SD-WAN over MPLS includes encryption, firewall, intrusion prevention, web filtering, antivirus, and other measures to address various threats such as spoofing, Denial of Service (DoS) attacks, and unauthorized access. This ensures that sensitive financial data in the Housing Bank is well-protected.
The paper also provides insights into future trends in the field of SD-WAN. It highlights the emerging concept of Secure Access Service Edge (SASE) architecture, which combines networking and security functions in a unified framework. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into SD-WAN is also mentioned as a key trend to watch out for. These advancements are expected to further enhance performance and security in SD-WAN deployments.
Another important topic discussed in the paper is the exploration of emerging transport methods for SD-WAN. While MPLS has been the traditional choice for reliable and predictable data transmission, new alternatives such as Internet Protocol Security (IPSec) tunnels and even direct internet access are gaining popularity due to their cost-effectiveness and flexibility.
The overall analysis concludes that SD-WAN over MPLS provides significant advantages for the Housing Bank, including enhanced performance, security, and flexibility. The dynamic traffic management capabilities of SD-WAN, combined with the security measures implemented, ensure efficient and safe data transmission for the financial institution.
Recommendations
Based on the findings of this analysis, there are several recommendations for the Housing Bank and other financial institutions considering SD-WAN deployments.
- Regular performance monitoring: Continuous monitoring of the SD-WAN deployment is crucial to identify any issues or bottlenecks that may arise. This will help ensure optimal network performance and address any potential security vulnerabilities.
- Ongoing research: The field of SD-WAN is evolving rapidly, with new technologies and best practices emerging. It is important for financial institutions to stay updated on the latest trends and conduct research to identify opportunities for improvement in their SD-WAN deployments.
Overall, this analysis provides valuable insights into the implementation of SD-WAN over MPLS in a major financial institution. The findings highlight the benefits of SD-WAN in terms of performance, security, and flexibility, while also shedding light on future trends in the field. As more organizations embrace SD-WAN as a key networking solution, it is imperative to understand its potential and continuously adapt to optimize its implementation.
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by jsendak | Jan 4, 2024 | Computer Science
This paper introduces the WordArt Designer API, a novel framework for
user-driven artistic typography synthesis utilizing Large Language Models
(LLMs) on ModelScope. We address the challenge of simplifying artistic
typography for non-professionals by offering a dynamic, adaptive, and
computationally efficient alternative to traditional rigid templates. Our
approach leverages the power of LLMs to understand and interpret user input,
facilitating a more intuitive design process. We demonstrate through various
case studies how users can articulate their aesthetic preferences and
functional requirements, which the system then translates into unique and
creative typographic designs. Our evaluations indicate significant improvements
in user satisfaction, design flexibility, and creative expression over existing
systems. The WordArt Designer API not only democratizes the art of typography
but also opens up new possibilities for personalized digital communication and
design.
The Multidisciplinary Nature of Artistic Typography Synthesis
In this article, we explore the WordArt Designer API, a framework that brings together various fields such as art, design, linguistics, and computer science to create an innovative approach to artistic typography synthesis. By leveraging Large Language Models (LLMs) on ModelScope, the WordArt Designer API offers a user-driven design process that simplifies typographic design for non-professionals.
This framework addresses the challenge of rigid templates in traditional typographic design by providing a dynamic and adaptive alternative. It utilizes LLMs to understand and interpret user input, allowing for a more intuitive and personalized design experience. This multidisciplinary approach allows users to articulate their aesthetic preferences and functional requirements, resulting in unique and creative typographic designs.
Relations to Multimedia Information Systems
The WordArt Designer API is closely related to the field of Multimedia Information Systems (MIS), which focuses on the organization, retrieval, and presentation of multimedia data. Typography is considered an essential element in multimedia systems, as it plays a crucial role in enhancing user experience and conveying information effectively.
By combining natural language processing with artistic typography synthesis, the WordArt Designer API expands the capabilities of MIS by allowing users to dynamically generate typographic designs based on their specific needs. This integration of design principles with computational techniques demonstrates the potential for incorporating intelligent systems within multimedia information systems.
Connections to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The WordArt Designer API has implications beyond traditional typography. It aligns with the evolving landscape of animations, artificial reality, augmented reality, and virtual realities. These technologies rely heavily on visual communication and user interaction.
By providing a more flexible and creative approach to typography synthesis, the WordArt Designer API can be utilized in these domains to enhance visual storytelling, user interfaces, and immersive experiences. Whether it involves creating unique typographic animations, overlaying augmented reality elements with customized typography, or designing virtual reality environments with personalized text, this framework opens up new possibilities for digital communication and design.
The Future of Personalized Typography and Design
As the WordArt Designer API democratizes the art of typography, it empowers individuals with limited design expertise to express their creativity and communicate effectively. The framework’s evaluations indicate improvements in user satisfaction, design flexibility, and creative expression compared to existing systems.
Looking ahead, the integration of large language models, advancements in artificial intelligence, and evolving technologies in multimedia systems will continue to shape the future of personalized typography and design. Further research can explore deeper user interactions, adaptive design recommendations, and seamless integration within existing design tools.
The WordArt Designer API sets a strong foundation for the exploration and advancement of user-driven artistic typography synthesis, revolutionizing how we approach digital communication and design within the multimedia landscape.
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by jsendak | Jan 4, 2024 | Computer Science
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized many aspects of our lives in recent years. However, with these technological advancements come significant challenges, and one of the most pressing is cybercrime. Cybercriminals have capitalized on the pervasive nature of digital technologies, exploiting vulnerabilities in governments, businesses, and civil societies around the world. As a result, there has been a surge in the demand for intelligent threat detection systems that rely on AI and ML to combat this global threat.
This article delves into the topic of AI-based cyber threat detection and explores its importance in protecting our modern digital ecosystems. It specifically focuses on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection. By investigating these models and their integration into network security, mobile security, and IoT security, we can better understand the challenges that arise when deploying AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures.
One of the key takeaways from this discussion is the need for a comprehensive approach to cybersecurity. Traditional methods of threat detection, which rely heavily on human intervention, are no longer sufficient in the face of rapidly evolving cyber threats. Instead, AI and ML offer a more proactive and adaptive solution, capable of analyzing vast amounts of data in real-time to detect anomalies and potentially malicious activity. This shift towards intelligent threat detection systems is crucial for staying one step ahead of cybercriminals.
However, integrating AI-enabled cybersecurity solutions into existing IT infrastructures poses its own set of challenges. Legacy systems may not be compatible with the advanced algorithms and models that power AI-based threat detection systems. Additionally, issues of data privacy, ethics, and explainability arise when relying on AI to make critical security decisions. Overcoming these hurdles requires careful planning, collaboration between different stakeholders, and a commitment to ongoing monitoring and evaluation.
Looking towards the future, this paper suggests several research directions to further enhance the security and resilience of our modern digital industries, infrastructures, and ecosystems. This includes the exploration of advanced AI techniques, such as deep learning and reinforcement learning, to improve threat detection accuracy and response time. Additionally, research is needed to address the challenges of securing mobile devices and IoT devices, which are increasingly interconnected and vulnerable to cyber attacks.
In conclusion, AI-based cyber threat detection is an essential tool in safeguarding our digital ecosystems. The advancements in AI and ML have paved the way for more sophisticated and proactive security measures. However, implementing these solutions requires careful consideration of the challenges and limitations associated with integrating AI into existing IT systems. By addressing these issues and investing in continued research, we can strengthen the security posture of our digital world and mitigate the threats posed by cybercrime.
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