by jsendak | Dec 30, 2024 | Computer Science
The rise of fake news has become a major concern in various domains, particularly in politics and healthcare. However, its impact extends beyond these areas, affecting a growing number of industries and sectors. As technology advances, fake news takes on different forms and continues to evolve, making it essential to tackle its spread and detect misinformation.
The Importance of Detecting Textual Misinformation
One of the most prevalent forms of fake news is textual misinformation, which spreads rapidly through social media posts and blog articles. To combat this issue, it is crucial to develop effective methods for detecting fake news in its textual form.
Novel Method for Extracting Textual Features
This thesis introduces a novel approach to extracting textual features from news articles, specifically designed for misinformation detection. By leveraging the disparities in thematic coherence between authentic and false news stories, this method identifies distinct composition of themes as the story progresses.
By analyzing these textual features, it becomes possible to differentiate between genuine news and fake news effectively. This innovative approach provides valuable insights into the structure and content of news articles, enabling more accurate detection of misinformation.
The Effectiveness of Topic Features
This research also demonstrates the effectiveness of topic features in detecting fake news. By utilizing classification and clustering techniques, topic features can be used to identify patterns and similarities among news articles.
Clustering, in particular, offers a valuable advantage as it does not require a labeled dataset, which can be time-consuming and resource-intensive to collect. Instead, it allows for the identification of groups of articles with similar themes, providing further evidence of the presence of misinformation.
Contributing to a Better Understanding of Misinformation
This thesis not only provides practical solutions for misinformation detection but also contributes to a broader understanding of the phenomenon and effective methods for combating it. By employing machine learning and natural language processing techniques, this research highlights the importance of leveraging technology to address the challenges posed by fake news.
As technology continues to advance and the landscape of fake news evolves, ongoing research and innovation will play a critical role in mitigating the harmful effects of misinformation on society. By developing accurate detection methods, we can empower individuals to make informed decisions, protect the integrity of public discourse, and promote a more trustworthy information ecosystem.
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by jsendak | Dec 25, 2024 | Computer Science
arXiv:2412.18416v1 Announce Type: new
Abstract: Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between existing research and practical applications. To address this issue, we propose Muse, the first multimodal conversational recommendation dataset. Muse comprises 83,148 utterances from 7,000 conversations centered around the Clothing domain. Each conversation contains comprehensive multimodal interactions, rich elements, and natural dialogues. Data in Muse are automatically synthesized by a multi-agent framework powered by multimodal large language models (MLLMs). It innovatively derives user profiles from real-world scenarios rather than depending on manual design and history data for better scalability, and then it fulfills conversation simulation and optimization. Both human and LLM evaluations demonstrate the high quality of conversations in Muse. Additionally, fine-tuning experiments on three MLLMs demonstrate Muse’s learnable patterns for recommendations and responses, confirming its value for multimodal conversational recommendation. Our dataset and codes are available at url{https://anonymous.4open.science/r/Muse-0086}.
Multimodal Conversational Recommendation Systems: Bridging the Gap Between Research and Practice
Current conversational recommendation systems primarily focus on text-based interactions, but real-world recommendation settings involve a fusion of various modalities such as text, images, and voice. This leads to a significant gap between existing research and practical applications. To address this challenge, the authors introduce Muse, the first multimodal conversational recommendation dataset.
Muse consists of 83,148 utterances collected from 7,000 conversations specifically centered around the Clothing domain. What sets Muse apart is the inclusion of comprehensive multimodal interactions, rich elements, and natural dialogues. The dataset is automatically synthesized using a multi-agent framework powered by multimodal large language models (MLLMs). This approach leverages real-world scenarios to derive user profiles, enabling better scalability without relying solely on manual design or historical data.
The conversations in Muse are meticulously designed to simulate and optimize conversational scenarios, making them highly relevant to real-world recommendation systems. The quality of these conversations is verified through evaluations conducted by both human experts and the MLLMs. Both evaluations demonstrate the high quality of the Muse dataset.
Furthermore, the authors conduct fine-tuning experiments on three different MLLMs, providing valuable insights into the learnable patterns for recommendations and responses within Muse. These experiments confirm the dataset’s effectiveness in training multimodal conversational recommendation models.
The Muse dataset addresses the multi-disciplinary nature of multimodal conversational recommendation systems. By incorporating multiple modalities, it brings together the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
To summarize, Muse is an innovative and comprehensive multimodal conversational recommendation dataset that bridges the gap between research and practical applications. Its inclusion of multimodal interactions and natural dialogues make it an invaluable resource for training and evaluating cutting-edge recommendation systems. Researchers and practitioners in the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities will greatly benefit from Muse’s insights and potential for advancements in multimodal conversational recommendation systems.
Source: https://anonymous.4open.science/r/Muse-0086
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by jsendak | Dec 25, 2024 | Computer Science
In this article, the authors discuss the evaluation of large language models (LLMs) on their linguistic reasoning capabilities, specifically in the context of abstract multilingual reasoning. The goal is to understand the limitations and gaps in these models’ skills when it comes to performing complex linguistic tasks in low-resource languages.
The authors propose a two-stage procedure to address this evaluation. The first stage involves generating analogical exemplars using a language model. Analogical reasoning is a critical aspect of human cognition, so exploring its application in language models is valuable. The generated exemplars are then used in-context along with target language exemplars to perform the reasoning tasks.
The results of their experiments on the modeLing dataset show that analogical prompting is effective in improving the models’ performance on abstract multilingual reasoning tasks. Specifically, GPT-4o’s performance improved by 8.1% and Llama-3.1-405B-Instruct’s performance improved by 5.9% over chain-of-thought approaches. These gains can be attributed to the analogical demonstrations, whether they are self-generated or produced by weaker multilingual models.
Furthermore, the authors demonstrate that their method generalizes well to other tasks present in Linguistics Olympiad competitions. They achieved sizable improvements across all problem types and difficulty levels included in the LINGOLY dataset with GPT-4o. This suggests that the proposed approach is not only effective for abstract linguistic reasoning but also applicable to a wide range of linguistic problem-solving tasks.
The authors also highlight several interesting phenomena that drive linguistic reasoning performance, which they discovered during their experiments. These findings indicate that linguistic puzzles, like the ones used in this study, can serve as valuable benchmarks for evaluating and advancing reasoning methods in language models.
Overall, this work provides valuable insights into the abilities and limitations of large language models when it comes to abstract multilingual reasoning. The proposed two-stage procedure with analogical prompting shows promising results in improving the models’ performance. Future research can build upon these findings to further enhance the reasoning capabilities of language models and address the identified gaps and limitations.
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by jsendak | Dec 24, 2024 | Computer Science
arXiv:2412.16495v1 Announce Type: cross
Abstract: Text-editable and pose-controllable character video generation is a challenging but prevailing topic with practical applications. However, existing approaches mainly focus on single-object video generation with pose guidance, ignoring the realistic situation that multi-character appear concurrently in a scenario. To tackle this, we propose a novel multi-character video generation framework in a tuning-free manner, which is based on the separated text and pose guidance. Specifically, we first extract character masks from the pose sequence to identify the spatial position for each generating character, and then single prompts for each character are obtained with LLMs for precise text guidance. Moreover, the spatial-aligned cross attention and multi-branch control module are proposed to generate fine grained controllable multi-character video. The visualized results of generating video demonstrate the precise controllability of our method for multi-character generation. We also verify the generality of our method by applying it to various personalized T2I models. Moreover, the quantitative results show that our approach achieves superior performance compared with previous works.
Multi-Character Video Generation: A Novel Approach for Realistic Scenarios
In the field of multimedia information systems, the generation of text-editable and pose-controllable character videos is a challenging but important topic. With practical applications in areas such as virtual reality and augmented reality, the ability to generate dynamic and realistic multi-character videos can greatly enhance user experiences. However, existing approaches have mainly focused on single-object video generation with pose guidance, overlooking the realistic scenario where multiple characters appear concurrently.
To address this limitation, the authors propose a novel multi-character video generation framework that allows for the simultaneous generation of multiple characters in a tuning-free manner. The framework is based on the separation of text and pose guidance, enabling precise control over each character’s appearance and movements. The key contributions of the proposed framework lay in the extraction of character masks from pose sequences to identify spatial positions, the use of Language Latent Models (LLMs) for precise text guidance, and the introduction of spatial-aligned cross attention and multi-branch control modules to generate fine-grained controllable multi-character videos.
The interdisciplinary nature of this research is evident as it combines concepts from various fields such as computer vision, natural language processing, and graphics. By integrating these different disciplines, the framework is able to generate highly realistic multi-character videos that can be tailored to specific scenarios and personalized preferences.
In the wider field of multimedia information systems, this research contributes to the advancement of animation techniques, artificial reality, augmented reality, and virtual realities. The ability to generate multi-character videos with precise controllability opens up new possibilities for immersive storytelling, virtual training environments, and interactive applications. This research also aligns with the growing demand for dynamic and realistic multimedia content in entertainment, education, and virtual simulations.
The results of the proposed approach are visually impressive, showcasing the precise controllability and realism of the generated multi-character videos. Additionally, the quantitative results demonstrate that this approach outperforms previous works in terms of performance. This is a significant achievement, as it indicates the effectiveness and generalizability of the proposed framework.
In conclusion, the proposed multi-character video generation framework represents a significant advancement in the field of multimedia information systems. By addressing the challenge of generating realistic multi-character videos, this research opens up new possibilities for immersive and interactive multimedia experiences in various domains. The interdisciplinary nature of the concepts involved further highlights the importance of integrating different fields to achieve groundbreaking results. Moving forward, further research can explore the application of this framework in real-world scenarios and investigate its potential in areas such as gaming, virtual reality storytelling, and virtual training simulations.
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by jsendak | Dec 24, 2024 | Computer Science
Evitaicossa: Exploring Antiassociative Algebras in R
Antiassociative algebras are a fascinating area of study within algebraic structures, and can be applied to various fields such as physics, computer science, and engineering. In this short article, I am excited to introduce the evitaicossa package, a powerful tool that brings the exploration of antiassociative algebras into the R programming language.
With the evitaicossa package, researchers and practitioners can now easily perform various operations and calculations on antiassociative algebras, enabling deeper analysis and insights into these complex mathematical structures.
Key Features of the evitaicossa Package
- Representation of Antiassociative Algebras: The evitaicossa package provides a convenient way to represent and manipulate antiassociative algebras in R. It offers a simple and intuitive syntax for creating and working with these algebras, making it accessible to both beginners and experts.
- Operations on Antiassociative Algebras: With the evitaicossa package, users can perform various operations on antiassociative algebras, including addition, subtraction, multiplication, and division. These operations are optimized for efficiency, ensuring fast computations even for large algebras.
- Algebraic Properties: The evitaicossa package enables the exploration of important algebraic properties of antiassociative algebras, such as associativity, commutativity, and distributivity. Users can easily verify these properties and gain a deeper understanding of the behavior of antiassociative algebras.
- Visualization and Plotting: Visualization is an essential aspect of understanding complex mathematical structures. The evitaicossa package includes functions for visualizing antiassociative algebras, providing users with graphical representations that aid in their analysis and interpretation.
- Integration with Other R Packages: The evitaicossa package seamlessly integrates with other popular R packages, providing users with an extensive ecosystem of tools for further analysis and exploration. Whether you need statistical analysis, data visualization, or machine learning algorithms, the evitaicossa package can easily integrate with your existing workflow.
What’s Next for evitaicossa?
The introduction of the evitaicossa package opens up exciting possibilities for researchers and practitioners working with antiassociative algebras. However, the development and growth of the package do not stop here. In the future, we can expect to see the following enhancements and additions:
- Advanced Functionality: The evitaicossa package will continue to expand its functionality, offering advanced features such as higher-dimensional antiassociative algebras, support for specific algebraic structures, and advanced algorithms for efficient computations.
- Integration with External Libraries: The integration of the evitaicossa package with external libraries, such as numerical computing libraries or symbolic computation systems, will further enhance its capabilities and enable more comprehensive analysis and calculations.
- Visualization Enhancements: The evitaicossa package will aim to improve its visualization capabilities, providing users with even more options for visually representing and interpreting antiassociative algebras. This includes the addition of interactive visualizations and more sophisticated plotting techniques.
- Community Contributions: As the evitaicossa package gains popularity, we anticipate a growing community of users and contributors. This community will play a crucial role in enhancing the package by providing valuable feedback, reporting bugs, and contributing new features and functionalities.
Overall, the evitaicossa package is an important addition to the R ecosystem for working with antiassociative algebras. Its user-friendly interface, powerful features, and potential future enhancements make it a valuable tool for researchers, educators, and practitioners in various fields. With the evitaicossa package, the exploration and analysis of antiassociative algebras becomes more accessible, opening up new avenues for study and application in diverse domains.
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by jsendak | Dec 23, 2024 | Computer Science
arXiv:2412.15220v1 Announce Type: new
Abstract: Video and audio are closely correlated modalities that humans naturally perceive together. While recent advancements have enabled the generation of audio or video from text, producing both modalities simultaneously still typically relies on either a cascaded process or multi-modal contrastive encoders. These approaches, however, often lead to suboptimal results due to inherent information losses during inference and conditioning. In this paper, we introduce SyncFlow, a system that is capable of simultaneously generating temporally synchronized audio and video from text. The core of SyncFlow is the proposed dual-diffusion-transformer (d-DiT) architecture, which enables joint video and audio modelling with proper information fusion. To efficiently manage the computational cost of joint audio and video modelling, SyncFlow utilizes a multi-stage training strategy that separates video and audio learning before joint fine-tuning. Our empirical evaluations demonstrate that SyncFlow produces audio and video outputs that are more correlated than baseline methods with significantly enhanced audio quality and audio-visual correspondence. Moreover, we demonstrate strong zero-shot capabilities of SyncFlow, including zero-shot video-to-audio generation and adaptation to novel video resolutions without further training.
SyncFlow: Simultaneously Generating Audio and Video from Text
In the field of multimedia information systems, the generation of both audio and video from text has been a challenging task. While advancements have been made in generating either audio or video separately, producing both modalities simultaneously has often resulted in suboptimal outcomes. Existing approaches rely on cascaded processes or multi-modal contrastive encoders, which suffer from information losses during inference and conditioning. In this study, the authors introduce SyncFlow, a system that can generate temporally synchronized audio and video from text in a more efficient and effective way.
The core of SyncFlow is the proposed dual-diffusion-transformer (d-DiT) architecture. This architecture enables joint video and audio modeling while ensuring proper fusion of information. By incorporating the d-DiT architecture, SyncFlow overcomes the limitations of previous methods and produces audio and video outputs that are more correlated than baseline systems. This improvement is demonstrated through empirical evaluations, where SyncFlow achieves significantly enhanced audio quality and audio-visual correspondence.
SyncFlow also addresses the computational cost of joint audio and video modeling by employing a multi-stage training strategy. This strategy separates video and audio learning before joint fine-tuning, allowing for efficient management of computational resources. This approach is crucial in real-time applications, where generating audio and video in a synchronized manner is essential.
The authors further highlight the strong zero-shot capabilities of SyncFlow. This includes zero-shot video-to-audio generation, where audio can be generated without explicitly training on specific video inputs. Additionally, SyncFlow can adapt to novel video resolutions without the need for further training, showcasing its flexibility and versatility in handling different multimedia scenarios.
From a multi-disciplinary standpoint, SyncFlow merges concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By enabling the simultaneous generation of audio and video, SyncFlow improves the overall user experience in various multimedia applications. It bridges the gap between text-based content and immersive multimedia experiences, opening up new possibilities for interactive storytelling, virtual simulations, and entertainment platforms.
In conclusion, SyncFlow presents a significant advancement in the field of multimedia information systems by introducing a novel architecture for generating synchronized audio and video from text. Its ability to produce high-quality outputs, efficient computational management, and strong zero-shot capabilities make it a promising tool for various applications in multimedia content creation and consumption.
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