by jsendak | Jul 4, 2024 | Computer Science
arXiv:2407.02773v1 Announce Type: new
Abstract: We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at url{https://github.com/thuiar/OpenVNA}, with a demonstration video available at url{https://youtu.be/0Z9cW7RGct4}.
Expert Commentary: OpenVNA – Advancing Language Understanding Systems Evaluation
In the field of multimedia information systems, the evaluation of language understanding systems is a complex task that requires the consideration of various factors. OpenVNA, an open-source framework, presents a significant development in this area by providing researchers with a comprehensive toolkit for analyzing the behavior of multimodal language understanding systems under noisy conditions. This framework offers both batch-level robustness evaluation and on-the-fly instance-level demonstration, thereby enabling researchers to assess the system’s performance in different scenarios.
The multi-disciplinary nature of the concepts covered in OpenVNA is noteworthy. It encompasses elements from the fields of machine learning, natural language processing, and human-computer interaction. This integration illustrates the importance of considering these aspects to obtain a holistic understanding of language understanding systems.
The benchmark Python library provided by OpenVNA is a valuable resource for assessing the global model robustness of language understanding systems. With its high flexibility and extensibility, researchers can customize the library by incorporating user-defined noise types and models. This capability allows for a more comprehensive evaluation of system performance by simulating real-world scenarios where noise and variations are prevalent.
Furthermore, OpenVNA includes a GUI-based interface that simplifies the analysis of local model behavior. This feature enhances the usability of the framework by providing an intuitive way to explore and visualize the system’s response to different inputs. Researchers can easily observe and interpret how the language understanding model interacts with various noisy conditions, gaining insights into its strengths and weaknesses.
In the broader context of multimedia information systems, OpenVNA aligns with the advancements in technologies such as animations, artificial reality, augmented reality, and virtual realities. Language understanding systems are increasingly being integrated into these technologies, and evaluating their performance in realistic environments is crucial for improving user experiences. OpenVNA’s focus on robustness evaluation under noisy conditions contributes to this objective by enabling researchers to identify and address potential limitations of language understanding systems in these multimedia contexts.
Overall, OpenVNA represents a significant contribution to the field of language understanding systems evaluation. Its open-source nature, combined with the multi-disciplinary approach and the provision of both a benchmark Python library and a GUI-based interface, make it a valuable tool for researchers looking to analyze and enhance the robustness of multimodal language understanding systems.
References:
- OpenVNA. (n.d.). Retrieved from https://github.com/thuiar/OpenVNA
- OpenVNA Demo Video. (n.d.). Retrieved from https://youtu.be/0Z9cW7RGct4
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by jsendak | Jul 4, 2024 | Computer Science
Expert Commentary:
The increasing demand for dynamic behaviors in automotive use cases has led to the emergence of Software Defined Vehicles (SDVs) as a promising solution. SDVs bring dynamic onboard service management capabilities, allowing users to request a wide range of services during vehicle operation. However, this dynamic environment presents challenges in efficiently allocating onboard resources to meet mixed-criticality onboard Quality-of-Service (QoS) network requirements while ensuring an optimal user experience.
One of the key challenges in this context is the activation of on-the-fly cooperative Vehicle-to-Everything (V2X) services in response to real-time road conditions. These services require careful resource allocation to ensure they can run efficiently while not compromising the user experience. Furthermore, the ever-evolving real-time network connectivity and computational availability conditions further complicate this process.
To address these challenges, the authors propose a dynamic resource-based onboard service orchestration algorithm. This algorithm takes into account real-time in-vehicle and V2X network health, as well as onboard resource constraints, to select degraded modes for onboard applications and maximize the user experience. It introduces the concept of Automotive eXperience Integrity Level (AXIL), which expresses a runtime priority for non-safety-critical applications.
The algorithm presented in this article aims to produce near-optimal solutions while significantly reducing execution time compared to straightforward methods. The simulation results demonstrate the effectiveness of this approach in enabling efficient onboard execution for a user experience-focused service orchestration.
Overall, this article highlights the importance of efficient resource allocation in Software Defined Vehicles to meet mixed-criticality onboard QoS network requirements. The proposed dynamic resource-based onboard service orchestration algorithm, leveraging the concept of AXIL, addresses this challenge and paves the way for improved user experiences in SDVs.
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by jsendak | Jul 2, 2024 | Computer Science
arXiv:2407.00556v1 Announce Type: new
Abstract: Social media popularity (SMP) prediction is a complex task involving multi-modal data integration. While pre-trained vision-language models (VLMs) like CLIP have been widely adopted for this task, their effectiveness in capturing the unique characteristics of social media content remains unexplored. This paper critically examines the applicability of CLIP-based features in SMP prediction, focusing on the overlooked phenomenon of semantic inconsistency between images and text in social media posts. Through extensive analysis, we demonstrate that this inconsistency increases with post popularity, challenging the conventional use of VLM features. We provide a comprehensive investigation of semantic inconsistency across different popularity intervals and analyze the impact of VLM feature adaptation on SMP tasks. Our experiments reveal that incorporating inconsistency measures and adapted text features significantly improves model performance, achieving an SRC of 0.729 and an MAE of 1.227. These findings not only enhance SMP prediction accuracy but also provide crucial insights for developing more targeted approaches in social media analysis.
The Applicability of CLIP-based Features in Social Media Popularity (SMP) Prediction
Social media popularity (SMP) prediction is a complex task that requires integration of multi-modal data. In recent years, pre-trained vision-language models (VLMs) like CLIP have gained popularity and have been widely adopted for this task. However, the effectiveness of these models in capturing the unique characteristics of social media content has been largely unexplored.
This paper critically examines the applicability of CLIP-based features in SMP prediction, with a particular focus on the phenomenon of semantic inconsistency between images and text in social media posts. It has been observed that as post popularity increases, the semantic inconsistency also increases, thereby challenging the conventional use of VLM features.
The significance of this research lies in its comprehensive investigation of semantic inconsistency across different popularity intervals. By analyzing the impact of VLM feature adaptation on SMP tasks, the researchers uncover crucial insights for developing more targeted approaches in social media analysis.
The findings of this study demonstrate that incorporating measures of inconsistency and adapted text features significantly improve the performance of SMP prediction models. The proposed model achieves a Spearman’s Rank Correlation (SRC) of 0.729 and a Mean Absolute Error (MAE) of 1.227.
The Multi-disciplinary Nature of the Concepts
This research has a multi-disciplinary nature that spans across several fields including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The integration of vision and language models in analyzing social media content is a key area of interest in multimedia information systems. By focusing on social media popularity prediction, which heavily relies on visual and textual information, the study contributes to advancing the field of multimedia information systems.
The incorporation of CLIP-based features and the investigation of semantic inconsistency between images and text also have implications in the field of animations. As social media platforms are increasingly used to share animated content, understanding the relationship between images and text becomes crucial for accurate popularity prediction.
Furthermore, the study indirectly relates to artificial reality, augmented reality, and virtual realities. These technologies rely on the seamless integration of visual and textual information to create immersive experiences. By uncovering the challenges posed by semantic inconsistency in social media content, the research contributes to improving the accuracy and realism of these immersive technologies.
In conclusion, this research on the applicability of CLIP-based features in social media popularity prediction provides valuable insights into understanding the unique characteristics of social media content. By incorporating measures of semantic inconsistency and adapted text features, the proposed model achieves improved performance. The study’s multi-disciplinary nature contributes to the wider fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jul 2, 2024 | Computer Science
Expert Commentary: Modeling Complex Systems with Automatic Methodology
This work presents an innovative and automatic methodology for modeling complex systems, specifically focusing on modeling the power consumption of data centers in this case study. The methodology combines Grammatical Evolution and classical regression techniques to obtain an optimal set of features for a linear and convex model.
One of the key contributions of this methodology is its ability to provide both Feature Engineering and Symbolic Regression, which allows for the inference of accurate models without requiring any manual effort or expertise from designers. This is particularly valuable in the context of data centers, where accurate and fast power modeling is essential.
The Importance of Data Centers and Power Consumption
As advanced Cloud services become increasingly mainstream, the power consumption of data centers has become a significant concern for modern cities. Data centers consume a substantial amount of power, ranging from 10 to 100 times more per square foot than typical office buildings. Therefore, modeling and understanding the power consumption in these infrastructures is crucial for anticipating the effects of optimization policies.
Analyzing power consumption in data centers is challenging due to their complex nature, and traditional analytical approaches have not been able to provide accurate and fast power modeling for high-end servers. This is where the proposed methodology plays a significant role in addressing this challenge.
Testing and Results
The methodology has been tested using real Cloud applications, and the results demonstrate its effectiveness in power estimation. The average error in power estimation was found to be 3.98%, which is a significant improvement compared to existing approaches. This level of accuracy is crucial in enabling the development of energy-efficient policies for Cloud data centers.
Applicability and Future Directions
This work not only contributes to the field of data center power modeling but also has broader applicability to other computing environments with similar characteristics. The methodology’s automatic and feature-driven approach can be adapted to various domains where accurate modeling is essential.
In terms of future directions, further research could focus on expanding the scope of this methodology to model other aspects of complex systems, such as performance or reliability. Additionally, exploring the integration of machine learning techniques could enhance the methodology’s capabilities in handling more diverse and complex data.
Overall, this automatic methodology for modeling complex systems provides a valuable contribution to the field and opens up possibilities for more accurate and efficient modeling in various domains. As the demand for advanced Cloud services continues to increase, the ability to effectively model and manage the power consumption of data centers will play a critical role in building sustainable and energy-efficient infrastructures for our modern cities.
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by jsendak | Jul 1, 2024 | Computer Science
arXiv:2406.19776v1 Announce Type: new
Abstract: Fake news detection has received increasing attention from researchers in recent years, especially multi-modal fake news detection containing both text and images.However, many previous works have fed two modal features, text and image, into a binary classifier after a simple concatenation or attention mechanism, in which the features contain a large amount of noise inherent in the data,which in turn leads to intra- and inter-modal uncertainty.In addition, although many methods based on simply splicing two modalities have achieved more prominent results, these methods ignore the drawback of holding fixed weights across modalities, which would lead to some features with higher impact factors being ignored.To alleviate the above problems, we propose a new dynamic fusion framework dubbed MDF for fake news detection.As far as we know, it is the first attempt of dynamic fusion framework in the field of fake news detection.Specifically, our model consists of two main components:(1) UEM as an uncertainty modeling module employing a multi-head attention mechanism to model intra-modal uncertainty; and (2) DFN is a dynamic fusion module based on D-S evidence theory for dynamically fusing the weights of two modalities, text and image.In order to present better results for the dynamic fusion framework, we use GAT for inter-modal uncertainty and weight modeling before DFN.Extensive experiments on two benchmark datasets demonstrate the effectiveness and superior performance of the MDF framework.We also conducted a systematic ablation study to gain insight into our motivation and architectural design.We make our model publicly available to:https://github.com/CoisiniStar/MDF
Fake News Detection and the Multi-disciplinary Nature of Multimedia Information Systems
Fake news detection has become an increasingly important area of research in recent years, as the impact and spread of misinformation continues to grow. In particular, the detection of multi-modal fake news, which combines both text and images, poses a significant challenge due to the inherent noise present in the data.
Previous works have attempted to address this challenge by simply concatenating or applying attention mechanisms to the text and image features before feeding them into a binary classifier. However, this approach often leads to intra- and inter-modal uncertainty, as the noise in the features is not properly accounted for. Additionally, the fixed weights across modalities used in many methods ignore the potential impact of certain features, which can limit the accuracy of the detection.
In response to these limitations, the authors propose a new dynamic fusion framework called MDF for fake news detection. This framework consists of two main components: an uncertainty modeling module called UEM, which uses a multi-head attention mechanism to model intra-modal uncertainty, and a dynamic fusion module called DFN, which utilizes D-S evidence theory to dynamically fuse the weights of the text and image modalities.
To further improve the performance of the dynamic fusion framework, the authors incorporate the Graph Attention Network (GAT) for inter-modal uncertainty and weight modeling before the DFN stage. This multi-disciplinary approach, combining techniques from deep learning (attention mechanisms, GAT), uncertainty modeling, and evidence theory, allows for a more comprehensive and robust detection of fake news.
The proposed MDF framework was evaluated on two benchmark datasets, and the results demonstrate its effectiveness and superior performance compared to previous methods. Additionally, a systematic ablation study was conducted to gain insight into the motivation and design of the framework, further reinforcing its potential applicability in real-world scenarios.
The concepts and methodologies presented in this article have direct implications for the wider field of multimedia information systems. Multimedia information systems deal with the processing, organization, and retrieval of multimedia data, which includes text, images, audio, and video. Fake news detection, as a specific application of multimedia information systems, demonstrates the importance of considering multiple modalities and the challenges in dealing with noisy and uncertain data.
Furthermore, the MDF framework and its incorporation of techniques such as attention mechanisms, GAT, and uncertainty modeling align with the advancements in technologies like animations, artificial reality, augmented reality, and virtual realities. These technologies often rely on a fusion of different modalities, such as combining virtual objects with real-world images or integrating virtual elements into physical environments. The MDF framework’s dynamic fusion approach can potentially contribute to the development of more robust and immersive multimedia experiences in these domains.
In conclusion, the proposed MDF framework represents a novel and multi-disciplinary approach to fake news detection, addressing the challenges of noisy and uncertain multi-modal data. Its integration of uncertainty modeling, evidence theory, and advanced deep learning techniques showcases the potential of applying multimedia information systems concepts to real-world problems. As the field of multimedia information systems continues to evolve, the lessons learned from fake news detection can contribute to the advancement of technologies such as animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jul 1, 2024 | Computer Science
Digitizing Woven Fabrics: Advancing Fabric Parameter Recovery with Reflection and Transmission Images
Woven fabrics play a significant role in various applications, such as fashion design, interior design, and digital humans. The ability to accurately capture and digitize the appearance of woven fabrics would be invaluable in these fields. Previous research has introduced a lightweight method for acquiring woven fabrics using a single reflection image and estimating fabric parameters using a differentiable geometric and shading model.
However, a single reflection image is often insufficient to fully characterize the reflectance properties of a fabric sample. Fabrics with different thicknesses, for example, may produce similar reflection images but have significantly different transmission properties. To address this limitation, a new approach has been proposed – recovering fabric parameters from two captured images: reflection and transmission.
The core of this method is a differentiable bidirectional scattering distribution function (BSDF) model that can model both reflection and transmission, including single and multiple scattering. The proposed two-layer model utilizes an SGGX phase function for single scattering, as introduced in previous work, and introduces a new azimuthally-invariant microflake definition called ASGGX for multiple scattering.
By capturing reflection and transmission photos using a basic setup consisting of a cell phone camera and two point lights, the fabric parameters can be estimated using a lightweight network and a differentiable optimization. The method also addresses out-of-focus effects explicitly, providing a more accurate representation of fabrics when using a thin-lens camera.
The results of this research demonstrate that the renderings of the estimated fabric parameters closely match the input images on both reflection and transmission. This represents a significant advancement in the field of fabric parameter recovery and paves the way for more realistic digital representations of woven fabrics.
Overall, this research opens up new possibilities for applications such as virtual fashion design, realistic interior design simulations, and even the creation of lifelike digital humans. By accurately capturing the appearance of woven fabrics, designers and developers can create more immersive and realistic virtual experiences.
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