by jsendak | Feb 27, 2025 | Computer Science
In this article, a novel framework for automated code generation and debugging is presented. The framework aims to improve accuracy, efficiency, and scalability in software development. The system consists of three core components: LangGraph, GLM4 Flash, and ChromaDB, which are integrated within a four-step iterative workflow.
LangGraph: Orchestrating Tasks
LangGraph serves as a graph-based library for orchestrating tasks in the code generation and debugging process. It provides precise control and execution while maintaining a unified state object for dynamic updates and consistency. This makes it highly adaptable to complex software engineering workflows, supporting multi-agent, hierarchical, and sequential processes. By having a flexible and adaptable task orchestration module, developers can effectively manage and streamline their software development process.
GLM4 Flash: Advanced Code Generation
GLM4 Flash is a large language model that leverages its advanced capabilities in natural language understanding, contextual reasoning, and multilingual support to generate accurate code snippets based on user prompts. By utilizing sophisticated language processing techniques, GLM4 Flash can generate code that is contextually relevant and accurate. This can greatly speed up the code generation process and reduce errors caused by manual coding efforts.
ChromaDB: Semantic Search and Contextual Memory Storage
ChromaDB acts as a vector database for semantic search and contextual memory storage. It enables the identification of patterns and the generation of context-aware bug fixes based on historical data. By leveraging the semantic search and memory capabilities of ChromaDB, the system can provide intelligent suggestions for bug fixes and improvements based on past code analysis and debugging experiences. This can assist developers in quickly identifying and resolving common coding issues.
Four-Step Iterative Workflow
The system operates through a structured four-step process to generate and debug code:
- Code Generation: Natural language descriptions are translated into executable code using GLM4 Flash. This step provides a bridge between human-readable descriptions and machine-executable code.
- Code Execution: The generated code is validated by identifying runtime errors and inconsistencies. This step ensures that the generated code functions correctly.
- Code Repair: Buggy code is iteratively refined using ChromaDB’s memory capabilities and LangGraph’s state tracking. The system utilizes historical data and semantic search to identify patterns and generate context-aware bug fixes.
- Code Update: The code is iteratively modified to meet functional and performance requirements. This step ensures that the generated code is optimized and meets the desired specifications.
This four-step iterative workflow allows the system to continuously generate, execute, refine, and update code, improving the overall software development process. By automating code generation and debugging tasks, developers can save time and effort, resulting in faster and more efficient software development cycles.
In conclusion, the proposed framework for automated code generation and debugging shows promise in improving accuracy, efficiency, and scalability in software development. Utilizing the capabilities of LangGraph, GLM4 Flash, and ChromaDB, the system provides a comprehensive solution for code generation and debugging. By integrating these core components within a structured four-step iterative workflow, the system aims to deliver robust performance and seamless functionality. This framework has the potential to greatly assist developers in their software development efforts, reducing time spent on coding and debugging, and improving the overall quality of software products.
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by jsendak | Feb 24, 2025 | Computer Science
arXiv:2502.15290v1 Announce Type: new
Abstract: Multimodal information extraction on social media is a series of fundamental tasks to construct the multimodal knowledge graph. The tasks aim to extract the structural information in free texts with the incorporate images, including: multimodal named entity typing and multimodal relation extraction. However, the growing number of multimodal data implies a growing category set and the newly emerged entity types or relations should be recognized without additional training. To address the aforementioned challenges, we focus on the zero-shot multimodal information extraction tasks which require using textual and visual modalities for recognizing unseen categories. Compared with text-based zero-shot information extraction models, the existing multimodal ones make the textual and visual modalities aligned directly and exploit various fusion strategies to improve their performances. But the existing methods ignore the fine-grained semantic correlation of text-image pairs and samples. Therefore, we propose the multimodal graph-based variational mixture of experts network (MG-VMoE) which takes the MoE network as the backbone and exploits it for aligning multimodal representations in a fine-grained way. Considering to learn informative representations of multimodal data, we design each expert network as a variational information bottleneck to process two modalities in a uni-backbone. Moreover, we also propose the multimodal graph-based virtual adversarial training to learn the semantic correlation between the samples. The experimental results on the two benchmark datasets demonstrate the superiority of MG-VMoE over the baselines.
Multimodal Information Extraction on Social Media: A Comprehensive Approach
In this article, we explore the concept of multimodal information extraction on social media and its relevance to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The tasks involved in this process include multimodal named entity typing and multimodal relation extraction, both of which aim to extract structural information from free texts incorporating images.
A key challenge in this domain is the growing number of multimodal data, which leads to an expanding category set. This means that new entity types or relations need to be recognized without additional training. To tackle this issue, the article focuses on zero-shot multimodal information extraction tasks. These tasks require the use of both textual and visual modalities to recognize unseen categories.
Existing multimodal information extraction models align the textual and visual modalities directly and leverage fusion strategies to enhance their performance. However, these methods overlook the fine-grained semantic correlation of text-image pairs and samples. To address this limitation, the article proposes the multimodal graph-based variational mixture of experts network (MG-VMoE).
The MG-VMoE utilizes the MoE network as its backbone to align multimodal representations in a fine-grained manner. Each expert network in the MG-VMoE is designed as a variational information bottleneck, allowing it to effectively process two modalities in a unified backbone. Additionally, the article introduces the multimodal graph-based virtual adversarial training to learn the semantic correlation between samples.
The experimental results on two benchmark datasets validate the superiority of MG-VMoE over baseline methods. This approach not only improves the performance of multimodal information extraction but also highlights the importance of considering the fine-grained semantic correlation of text-image pairs and samples in the process.
From a multi-disciplinary perspective, this article intersects various fields including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The extraction of multimodal information from social media has implications for these domains, as it enables a deeper understanding of user-generated content and the integration of textual and visual data sources. Moreover, the proposed MG-VMoE framework incorporates concepts from machine learning, natural language processing, computer vision, and graph theory, showcasing the intersectionality of these disciplines in solving complex multimodal information extraction problems.
Overall, this article sheds light on the importance of multimodal information extraction on social media and presents a comprehensive approach that addresses the challenges posed by the growing category set of multimodal data. The proposed MG-VMoE framework demonstrates its efficacy through experimental results, emphasizing the need to consider the fine-grained semantic correlation of text-image pairs and samples in multimodal information extraction tasks.
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by jsendak | Feb 24, 2025 | Computer Science
Exploring the Need for Explainable AI (XAI)
Artificial Intelligence (AI) has become increasingly prevalent in various industries, but its lack of explainability poses a significant challenge. In order to mitigate the risks associated with AI technology, the industry and regulators must focus on developing eXplainable AI (XAI) techniques. Fields that require accountability, ethics, and fairness, such as healthcare, credit scoring, policing, and the criminal justice system, particularly necessitate the implementation of XAI.
The European Union (EU) recognizes the importance of explainability and has incorporated it as one of the fundamental principles in the AI Act. However, the specific XAI techniques and requirements are yet to be determined and tested in practice. This paper delves into various approaches and techniques that show promise in advancing XAI. These include model-agnostic methods, interpretability tools, algorithm transparency, and interpretable machine learning models.
One of the key challenges in implementing the principle of explainability in AI governance and policies is striking a balance between transparency and protecting proprietary information. Companies may be reluctant to disclose their AI algorithms or trade secrets due to intellectual property concerns. Finding a middle ground where transparency is maintained without compromising competitiveness is crucial for successful XAI implementation.
The Integration of XAI into EU Law
The integration of XAI into EU law requires careful consideration of various factors, including standard setting, oversight, and enforcement. Standard setting plays a crucial role in establishing the benchmark for XAI requirements. The EU can collaborate with experts and stakeholders to define industry standards that ensure transparency, interpretability, and fairness in AI systems.
Oversight is an essential component of implementing XAI in EU law. Regulatory bodies must have the authority and resources to monitor AI systems effectively. This includes conducting audits, assessing the impact of AI on individuals and society, and ensuring compliance with XAI standards. Additionally, regular reviews and updates of XAI guidelines should be conducted to keep up with evolving technological advancements.
Enforcement mechanisms are vital for ensuring compliance with XAI regulations. Penalties and sanctions for non-compliance should be clearly defined to promote adherence to the established XAI standards. Additionally, a system for reporting concerns and violations should be put in place to encourage accountability and transparency.
What to Expect Next
The journey towards implementing XAI in EU law is still in its early stages. As the EU Act on AI progresses, it is expected that further research and experimentation will be conducted to determine the most effective XAI techniques for different sectors. Collaboration between academia, industry experts, and regulators will be vital in this process.
Additionally, the EU is likely to focus on international cooperation. Given the global nature of AI technology, harmonization of XAI standards and regulations across countries can maximize the benefits of explainability while minimizing its challenges. Encouraging dialogue and collaboration with other regions will be essential for creating a unified approach to XAI governance.
In conclusion, the implementation of XAI is crucial for ensuring transparency, accountability, and fairness in AI systems. The EU’s emphasis on explainability in the AI Act reflects a commitment to addressing these concerns. The challenges of implementing XAI in governance and policies must be navigated thoughtfully, considering factors such as intellectual property protection and enforcement mechanisms. Collaboration and research will pave the way for successful integration of XAI into EU law.
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by jsendak | Feb 21, 2025 | Computer Science
arXiv:2502.14439v1 Announce Type: new
Abstract: Research on how humans perceive aesthetics in shapes, colours, and music has predominantly focused on Western populations, limiting our understanding of how cultural environments shape aesthetic preferences. We present a large-scale cross-cultural study examining aesthetic preferences across five distinct modalities extensively explored in the literature: shape, curvature, colour, musical harmony and melody. Our investigation gathers 401,403 preference judgements from 4,835 participants across 10 countries, systematically sampling two-dimensional parameter spaces for each modality. The findings reveal both universal patterns and cultural variations. Preferences for shape and curvature cross-culturally demonstrate a consistent preference for symmetrical forms. While colour preferences are categorically consistent, relational preferences vary across cultures. Musical harmony shows strong agreement in interval relationships despite differing regions of preference within the broad frequency spectrum, while melody shows the highest cross-cultural variation. These results suggest that aesthetic preferences emerge from an interplay between shared perceptual mechanisms and cultural learning.
Aesthetic Preferences Across Cultures: Insights from a Cross-Cultural Study
In a new research article, a large-scale cross-cultural study explores how cultural environments shape aesthetic preferences in five different modalities: shape, curvature, colour, musical harmony, and melody. This study sheds light on the multi-disciplinary nature of aesthetics and provides valuable insights into the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
Shape and Curvature
The study reveals that preferences for shape and curvature are consistent across cultures. Regardless of cultural background, participants consistently expressed a preference for symmetrical forms. This finding has significant implications for the design of visual content and animations in multimedia information systems. Symmetry can be considered a universal principle of aesthetics, which can captivate viewers across different cultural backgrounds and enhance their engagement with virtual and augmented reality experiences.
Colour
While colour preferences are categorically consistent, the study identifies cultural variations in relational preferences for colour. This suggests that different cultural environments shape how individuals perceive the relationship between colours. Understanding these cultural variations is crucial for the design of visually appealing multimedia content, animations, and artificial reality experiences. By aligning colour choices with cultural preferences, designers can effectively engage users and create immersive experiences.
Musical Harmony and Melody
The study finds strong agreement in interval relationships for musical harmony across cultures. Despite varying preferences within the broad frequency spectrum, participants from different cultural backgrounds showcased similarity in their preferences for harmonic intervals. On the other hand, melody exhibited the highest cross-cultural variation. This emphasizes the complexity of designing audio content for virtual and augmented reality experiences. Designers must consider the cultural background of their target audience to create captivating soundscapes that can resonate with users.
Implications for Multimedia Information Systems, Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The findings of this cross-cultural study have significant implications for the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By understanding the interplay between shared perceptual mechanisms and cultural learning, designers and developers can create content that appeals to a diverse range of users.
For multimedia information systems, the study highlights the importance of incorporating symmetrical forms in visual designs to capture the attention of different cultural backgrounds. Animators can utilize the universal preference for symmetry to enhance the aesthetic appeal of their creations.
In the realm of artificial reality, augmented reality, and virtual realities, designers can leverage the cultural variations in colour preferences and relational perceptions to create immersive experiences that align with specific cultural contexts. By tailoring the visual and auditory aspects of these experiences to different cultural backgrounds, developers can enhance user engagement and provide more meaningful interactions.
This cross-cultural study provides valuable insights into the interconnectedness of aesthetics, culture, and perception. It emphasizes the need for a multidisciplinary approach in the design and development of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By considering cultural variations in aesthetic preferences, designers and developers can create content that resonates with users on a global scale.
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by jsendak | Feb 21, 2025 | Computer Science
Personalized Age Transformation Using Diffusion Model
Age transformation of facial images is a task that involves modifying a person’s appearance to make them look older or younger while maintaining their identity. While deep learning methods have been successful in creating natural age transformations, they often fail to capture the individual-specific features influenced by a person’s life history. In this paper, the authors propose a novel approach for personalized age transformation using a diffusion model.
The authors’ diffusion model takes a facial image and a target age as input and generates an age-edited face image as output. This model is able to capture not only the average age transitions but also the individual-specific appearances influenced by their life histories. To achieve this, the authors incorporate additional supervision using self-reference images, which are facial images of the same person at different ages.
The authors fine-tune a pretrained diffusion model for personalized adaptation using approximately 3 to 5 self-reference images. This allows the model to learn and understand the unique characteristics of the individual’s aging process. By incorporating self-reference images, the model is able to better preserve the identity of the person while performing age editing.
In addition to using self-reference images, the authors also design an effective prompt to further enhance the performance of age editing and identity preservation. The prompt serves as a guiding signal for the diffusion model, helping it generate more accurate and visually pleasing age-edited face images.
The experiments conducted by the authors demonstrate that their proposed method outperforms existing methods both quantitatively and qualitatively. The personalized age transformation achieved by the diffusion model is superior in terms of preserving individual-specific appearances and maintaining identity.
This research has significant implications in various domains including entertainment, forensics, and cosmetic industries. The ability to accurately and realistically age-transform facial images can be used in applications such as creating age-progressed images of missing persons or simulating the effects of aging for entertainment purposes.
The availability of the code and pretrained models further enhances the practicality of this research. By making these resources accessible to the public, researchers and developers can easily implement and build upon the proposed method.
In conclusion, the authors’ personalized age transformation method using a diffusion model and self-reference images is a significant advancement in the field. This approach not only achieves superior performance in age editing and identity preservation but also opens up new possibilities for personalized image transformation.
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by jsendak | Feb 20, 2025 | Computer Science
arXiv:2502.13196v1 Announce Type: new
Abstract: Gaussian Splatting (GS) offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. Using a set of 3D Gaussians to represent complex geometry and appearance, GS achieves faster rendering times and reduced memory consumption compared to the neural network approach used in NeRF. However, quality assessment of GS-generated static content is not yet explored in-depth. This paper describes a subjective quality assessment study that aims to evaluate synthesized videos obtained with several static GS state-of-the-art methods. The methods were applied to diverse visual scenes, covering both 360-degree and forward-facing (FF) camera trajectories. Moreover, the performance of 18 objective quality metrics was analyzed using the scores resulting from the subjective study, providing insights into their strengths, limitations, and alignment with human perception. All videos and scores are made available providing a comprehensive database that can be used as benchmark on GS view synthesis and objective quality metrics.
Gaussian Splatting: Exploring Quality Assessment of Synthesized Videos
Gaussian Splatting (GS) is a technique that offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. While NeRF uses neural networks to represent complex geometry and appearance, GS utilizes a set of 3D Gaussians. The advantage of GS over NeRF lies in its faster rendering times and reduced memory consumption.
However, despite the advantages of GS, its quality assessment for generating static content has not been extensively explored. This paper addresses this gap by conducting a subjective quality assessment study to evaluate synthesized videos obtained using state-of-the-art GS methods.
The study considers diverse visual scenes, including both 360-degree and forward-facing (FF) camera trajectories. By using different scenes, the researchers aim to assess the effectiveness of GS across a range of scenarios and camera movements.
In addition to the subjective evaluation, the researchers also analyze the performance of 18 objective quality metrics. These metrics provide quantifiable measures that can be used to assess the quality of GS-generated videos. By comparing the objective metrics against the subjective scores obtained from the study, the researchers aim to gain insights into the strengths, limitations, and alignment of these metrics with human perception.
This study is significant for the wider field of multimedia information systems as it contributes to the ongoing development of techniques for real-time 3D scene rendering. GS, with its faster rendering times and reduced memory consumption, opens up possibilities for more efficient and practical applications of 3D visualization in various domains.
Furthermore, this study highlights the multidisciplinary nature of the concepts involved. It combines elements of computer graphics, virtual realities, and human perception to provide a comprehensive assessment of GS-generated content. Through the use of subjective evaluation and objective metrics, the researchers bridge the gap between technical performance and human experience, ultimately contributing to the advancement of 3D rendering technologies.
The availability of the synthesized videos and the scores obtained in the study also adds value by providing a comprehensive database that can be used as a benchmark for future research. Researchers can leverage this dataset to compare and validate their own GS methods, as well as objectively evaluate the performance of alternative rendering techniques.
In conclusion, this paper presents a subjective quality assessment study of synthesized videos generated using state-of-the-art GS methods. By evaluating the videos across diverse visual scenes and analyzing performance with objective quality metrics, the researchers provide valuable insights into the strengths and limitations of GS and its alignment with human perception. This study contributes to the wider field of multimedia information systems and advances the development of efficient 3D rendering techniques.
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