by jsendak | Jan 4, 2024 | Computer Science
The rise of online education, particularly Massive Open Online Courses (MOOCs), has greatly expanded access to educational content for students around the world. One of the key components of these online courses are video lectures, which provide a rich and engaging way to deliver educational material. As the demand for online classroom teaching continues to grow, so does the need to efficiently organize and maintain these video lectures.
In order to effectively organize these video lectures, it is important to have the relevant metadata associated with each video. This metadata typically includes attributes such as the Institute Name, Publisher Name, Department Name, Professor Name, Subject Name, and Topic Name. Having this information readily available allows students to easily search for and find videos on specific topics and subjects.
Organizing video lectures based on their metadata has numerous benefits. Firstly, it allows for better categorization and organization of the videos, making it easier for students to locate the videos they need. Additionally, it enables educators and administrators to analyze usage patterns and trends, allowing them to make informed decisions about course content and delivery.
In this project, the goal is to extract the metadata information from the video lectures. This can be achieved through various techniques, such as utilizing speech recognition algorithms to transcribe and extract relevant information from the video. Machine learning algorithms can also be employed to recognize and extract specific attributes from the video, such as identifying the Institute Name or Professor Name.
Furthermore, advancements in natural language processing (NLP) can enhance the automated extraction process by accurately identifying and extracting specific metadata attributes from the video lectures. By combining these technologies, we can create a robust system that efficiently organizes and indexes video lectures based on their metadata.
Ultimately, the successful extraction and organization of metadata from video lectures will greatly benefit students by providing them with a comprehensive and easily searchable repository of educational content. It will also alleviate the burden on educators and administrators by streamlining the process of maintaining and managing these videos. As online education continues to evolve, the ability to effectively organize and utilize video lectures will play a crucial role in shaping the future of education.
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by jsendak | Jan 4, 2024 | Computer Science
Given a text query, partially relevant video retrieval (PRVR) seeks to find
untrimmed videos containing pertinent moments in a database. For PRVR, clip
modeling is essential to capture the partial relationship between texts and
videos. Current PRVR methods adopt scanning-based clip construction to achieve
explicit clip modeling, which is information-redundant and requires a large
storage overhead. To solve the efficiency problem of PRVR methods, this paper
proposes GMMFormer, a Gaussian-Mixture-Model based Transformer which models
clip representations implicitly. During frame interactions, we incorporate
Gaussian-Mixture-Model constraints to focus each frame on its adjacent frames
instead of the whole video. Then generated representations will contain
multi-scale clip information, achieving implicit clip modeling. In addition,
PRVR methods ignore semantic differences between text queries relevant to the
same video, leading to a sparse embedding space. We propose a query diverse
loss to distinguish these text queries, making the embedding space more
intensive and contain more semantic information. Extensive experiments on three
large-scale video datasets (i.e., TVR, ActivityNet Captions, and Charades-STA)
demonstrate the superiority and efficiency of GMMFormer. Code is available at
url{https://github.com/huangmozhi9527/GMMFormer}.
Expert Commentary: The Multi-Disciplinary Nature of Partially Relevant Video Retrieval (PRVR)
Partially Relevant Video Retrieval (PRVR) is a complex task that combines concepts from various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This multi-disciplinary nature arises from the need to capture and understand the relationship between textual queries and untrimmed videos. In this expert commentary, we dive deeper into the concepts and discuss how PRVR methods like GMMFormer address challenges in the field.
The Importance of Clip Modeling in PRVR
In PRVR, clip modeling plays a crucial role in capturing the partial relationship between texts and videos. By constructing meaningful clips from untrimmed videos, the retrieval system can focus on specific moments that are pertinent to the query. Traditional PRVR methods often adopt scanning-based clip construction, which explicitly models the relationship. However, this approach suffers from information redundancy and requires a large storage overhead.
GMMFormer, a novel approach proposed in this paper, tackles the efficiency problem of PRVR methods by leveraging the power of Gaussian-Mixture-Model (GMM) based Transformers. Instead of explicitly constructing clips, GMMFormer models clip representations implicitly. By incorporating GMM constraints during frame interactions, the model focuses on adjacent frames rather than the entire video. This approach allows for multi-scale clip information to be encoded in the generated representations, achieving efficient and implicit clip modeling.
Tackling Semantic Differences in Text Queries
Another challenge in PRVR methods is handling semantic differences between text queries that are relevant to the same video. Existing methods often overlook these semantic differences, resulting in a sparse embedding space. To address this, the paper proposes a query diverse loss that distinguishes between text queries, making the embedding space more intensive and containing more semantic information.
Experiments and Results
The proposed GMMFormer approach is evaluated through extensive experiments on three large-scale video datasets: TVR, ActivityNet Captions, and Charades-STA. The results demonstrate the superiority and efficiency of GMMFormer in comparison to existing PRVR methods. The inclusion of multi-scale clip modeling and query diverse loss significantly enhances the retrieval performance and addresses the efficiency challenges faced by traditional methods.
Conclusion
Partially Relevant Video Retrieval (PRVR) is a fascinating field that involves concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The GMMFormer approach proposed in this paper showcases the multi-disciplinary nature of PRVR and its impact on clip modeling, semantic differences in text queries, and retrieval efficiency. Future research in this domain will likely explore more advanced techniques for implicit clip modeling and further focus on enhancing the embedding space to better capture semantic information.
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by jsendak | Jan 4, 2024 | Computer Science
In the era of data-driven economies, incentive systems and loyalty programs have become widespread across various sectors such as advertising, retail, travel, and financial services. These systems offer benefits for both users and companies, but they also require the transfer and analysis of large amounts of sensitive data. As a result, privacy concerns have become increasingly important, leading to the need for privacy-preserving incentive protocols.
Despite the growing demand for secure and decentralized systems, there is currently a lack of comprehensive solutions. That’s why the Boomerang protocol comes as a promising innovation in this field. This novel decentralised privacy-preserving incentive protocol utilizes cryptographic black box accumulators to securely store user interactions within the incentive system. By leveraging these accumulators, the Boomerang protocol ensures that sensitive user data is protected while still enabling the transparent computation of rewards for users.
To achieve this transparency and verifiability, the Boomerang protocol incorporates zero-knowledge proofs based on BulletProofs. These proofs allow for the computation of rewards without revealing any sensitive user information. Additionally, to enhance public verifiability and transparency, the protocol utilizes a smart contract on a Layer 1 blockchain to verify these zero-knowledge proofs.
The combination of black box accumulators with selected elliptic curves in the zero-knowledge proofs makes the Boomerang protocol highly efficient. A proof of concept implementation of the protocol demonstrates its ability to handle up to 23.6 million users per day on a single-threaded backend server, with financial costs of approximately 2 US$. Furthermore, by utilizing the Solana blockchain, the protocol can handle up to 15.5 million users per day with approximate costs of only 0.00011 US$ per user.
The Boomerang protocol not only offers a significant advancement in privacy-preserving incentive protocols but also paves the way for a more secure and privacy-centric future. By addressing the privacy concerns surrounding incentive systems, this protocol provides a framework for companies to offer incentives while maintaining the privacy of their users. As the demand for privacy and data protection continues to grow, solutions like the Boomerang protocol will likely become essential in various industries.
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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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