by jsendak | Apr 22, 2025 | Computer Science
arXiv:2504.13218v1 Announce Type: cross
Abstract: Incremental learning aims to enable models to continuously acquire knowledge from evolving data streams while preserving previously learned capabilities. While current research predominantly focuses on unimodal incremental learning and multimodal incremental learning where the modalities are consistent, real-world scenarios often present data from entirely new modalities, posing additional challenges. This paper investigates the feasibility of developing a unified model capable of incremental learning across continuously evolving modal sequences. To this end, we introduce a novel paradigm called Modality Incremental Learning (MIL), where each learning stage involves data from distinct modalities. To address this task, we propose a novel framework named Harmony, designed to achieve modal alignment and knowledge retention, enabling the model to reduce the modal discrepancy and learn from a sequence of distinct modalities, ultimately completing tasks across multiple modalities within a unified framework. Our approach introduces the adaptive compatible feature modulation and cumulative modal bridging. Through constructing historical modal features and performing modal knowledge accumulation and alignment, the proposed components collaboratively bridge modal differences and maintain knowledge retention, even with solely unimodal data available at each learning phase.These components work in concert to establish effective modality connections and maintain knowledge retention, even when only unimodal data is available at each learning stage. Extensive experiments on the MIL task demonstrate that our proposed method significantly outperforms existing incremental learning methods, validating its effectiveness in MIL scenarios.
Analysis of Modality Incremental Learning (MIL)
In the field of multimedia information systems, the concept of incremental learning has gained significant attention. Incremental learning refers to the process of continuously acquiring knowledge from evolving data streams while retaining previously learned capabilities. Traditional research on incremental learning has predominantly focused on unimodal or multimodal learning where the modalities remain consistent. However, real-world scenarios often present data from entirely new modalities, posing additional challenges.
The paper introduces a novel paradigm called Modality Incremental Learning (MIL) to address the challenge of learning from continuously evolving modal sequences. MIL involves learning from distinct modalities at each learning stage. This multi-disciplinary approach is significant as it combines concepts from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
The proposed framework, named Harmony, aims to achieve modal alignment and knowledge retention. It introduces adaptive compatible feature modulation and cumulative modal bridging. These components work together to bridge modal differences, establish effective modality connections, and maintain knowledge retention, even with solely unimodal data available at each learning stage.
The results of extensive experiments on the MIL task demonstrate that the Harmony framework significantly outperforms existing incremental learning methods. This validation of effectiveness in MIL scenarios is crucial for the broader field of multimedia information systems. It opens up possibilities for developing unified models capable of learning from diverse modalities in real-world applications.
Implications for Multimedia Information Systems
The concept of Modality Incremental Learning (MIL) presented in this paper has direct implications for the field of multimedia information systems. By addressing the challenges of learning from evolving modal sequences, MIL expands the capabilities of existing systems in several ways:
- Adaptability to New Modalities: MIL enables systems to adapt and learn from entirely new modalities that may emerge over time. This has significant implications for applications that rely on multimedia data, such as computer vision, speech recognition, and natural language processing. The ability to seamlessly incorporate new modalities into existing models can enhance the overall performance of these systems.
- Knowledge Retention: The Harmony framework’s focus on knowledge retention allows models to build upon previously learned capabilities while incorporating new modalities. This is essential in scenarios where information from different modalities is interconnected and requires a holistic understanding. The ability to retain and integrate knowledge across multiple modalities strengthens the overall knowledge base of multimedia information systems.
- Improved Performance: The experimental results demonstrate that the Harmony framework outperforms existing incremental learning methods. This improvement in performance is crucial in real-world scenarios where multimedia information systems need to adapt and learn continuously. The ability to handle evolving modal sequences effectively can lead to more accurate and robust models, enhancing the overall performance of multimedia information systems.
In conclusion, the introduction of Modality Incremental Learning (MIL) and the Harmony framework opens up new avenues for research and development in multimedia information systems. By addressing the challenges of learning from evolving modal sequences and incorporating new modalities, MIL extends the capabilities of existing systems and enhances their performance in real-world scenarios. The multi-disciplinary nature of MIL makes it relevant to various fields, including animations, artificial reality, augmented reality, and virtual realities.
Read the original article
by jsendak | Apr 21, 2025 | Computer Science
The Relationship Between Eye Fixation Patterns and Performance in Java Programming Exercises
Eye-tracking technology has provided researchers with an innovative way to investigate various aspects of human behavior. In this study, the focus was on examining the relationship between eye fixation patterns and performance in Java programming exercises. The aim was to determine whether there were any significant differences in the eye movements of students who answered the exercises correctly compared to those who answered incorrectly.
A total of thirty-one students from a university in Metro Manila participated in the study. They were asked to solve five Java programming exercises, and their eye movements were recorded using an eye-tracking device. However, for the analysis, only the fixation data from three of the five exercises were considered.
The first step in the analysis process was to preprocess the fixation data. This involved filtering out any irrelevant data points and converting them into a format that could be easily visualized. Once the data was ready, heatmap bin graphs were generated to visualize the eye fixation patterns of the participants.
Dividing the participants into two groups based on their answers (correct and wrong), the researchers were then able to compare the fixation patterns between the groups. This was done using the Mann-Whitney U Test, a non-parametric statistical test suitable for comparing two groups when the data is not normally distributed.
The results of the analysis showed that there were significant differences in the eye fixation patterns between the correct and wrong answer groups. Participants who provided correct answers tended to have longer fixations on certain code segments, suggesting that they were more deeply engaged in analyzing and understanding the problem. On the other hand, participants who answered incorrectly tended to have more scattered fixations, indicating a lack of focus and attention to crucial details.
These findings have important implications for programming education. By understanding the relationship between eye fixation patterns and performance, instructors can develop targeted interventions to improve students’ coding skills. For example, exercises can be designed to encourage longer fixations on critical code segments, fostering a more systematic and thorough approach to problem-solving.
Furthermore, eye-tracking technology can be integrated into programming courses as a diagnostic tool. By tracking students’ eye movements during coding exercises, instructors can identify areas where students may be struggling and provide personalized feedback and support.
In conclusion, this study highlights the potential of eye-tracking technology in understanding and improving programming performance. By gaining insights into the relationship between eye fixation patterns and performance, educators can enhance the effectiveness of programming instruction and better support students in acquiring essential coding skills.
Read the original article
by jsendak | Apr 18, 2025 | Computer Science
arXiv:2504.12796v1 Announce Type: new
Abstract: Multimodal learning has driven innovation across various industries, particularly in the field of music. By enabling more intuitive interaction experiences and enhancing immersion, it not only lowers the entry barriers to the music but also increases its overall appeal. This survey aims to provide a comprehensive review of multimodal tasks related to music, outlining how music contributes to multimodal learning and offering insights for researchers seeking to expand the boundaries of computational music. Unlike text and images, which are often semantically or visually intuitive, music primarily interacts with humans through auditory perception, making its data representation inherently less intuitive. Therefore, this paper first introduces the representations of music and provides an overview of music datasets. Subsequently, we categorize cross-modal interactions between music and multimodal data into three types: music-driven cross-modal interactions, music-oriented cross-modal interactions, and bidirectional music cross-modal interactions. For each category, we systematically trace the development of relevant sub-tasks, analyze existing limitations, and discuss emerging trends. Furthermore, we provide a comprehensive summary of datasets and evaluation metrics used in multimodal tasks related to music, offering benchmark references for future research. Finally, we discuss the current challenges in cross-modal interactions involving music and propose potential directions for future research.
The Role of Multimodal Learning in Music: A Comprehensive Review
In recent years, there has been a significant focus on multimodal learning, particularly in the field of music. This approach, which combines multiple modes of communication and interaction, has led to innovation that not only enhances the music experience but also breaks down barriers to entry for aspiring musicians. In this survey, we aim to provide a comprehensive review of multimodal tasks related to music, exploring the ways in which music contributes to multimodal learning and offering insights for researchers looking to push the boundaries of computational music.
Unlike text and images, which can be easily understood through semantics and visualization, music primarily relies on auditory perception for its interaction with humans. This inherent less intuitive nature of music data representation poses challenges for researchers and developers working on multimodal tasks. Therefore, this paper begins by discussing the various representations of music and providing an overview of music datasets. By understanding the unique characteristics of music, researchers can better design multimodal systems that effectively integrate with music.
Categorizing Cross-Modal Interactions in Music
The survey goes on to categorize cross-modal interactions between music and multimodal data into three types:
- Music-driven cross-modal interactions: This category explores the ways in which music affects and drives other modalities, such as visuals or haptic feedback. For example, in a music video, the visuals are often synchronized with the rhythm and mood of the music, enhancing the overall cinematic experience. Understanding these interactions between music and other modalities can lead to more immersive multimedia experiences.
- Music-oriented cross-modal interactions: Here, the focus is on how other modalities, such as visual cues or gestures, can influence and shape the production or performance of music. For instance, a musician may use a gesture recognition system to control specific musical parameters in real-time. By studying these interactions, researchers can develop new tools and techniques for musical expression and performance.
- Bidirectional music cross-modal interactions: This category involves exploring the reciprocal and bidirectional relationships between music and other modalities. It delves into how music can influence other modalities and vice versa, creating a dynamic and interactive multimodal experience. For example, in virtual reality (VR) environments, music can adapt and respond to the user’s actions, creating a more responsive and engaging experience.
By systematically tracing the development of relevant sub-tasks within each category, analyzing existing limitations, and discussing emerging trends, this survey provides a comprehensive understanding of the current state of multimodal tasks related to music. It serves as a valuable resource for researchers and developers interested in exploring new avenues in computational music.
Relevant to the Field of Multimedia Information Systems
Within the wider field of multimedia information systems, this survey holds great significance. The fusion of different modalities and the integration of music into multimodal learning have the potential to revolutionize how we interact with and consume multimedia content. By understanding the cross-modal interactions in music, researchers can develop more sophisticated multimedia systems that cater to personalized preferences and enhance user engagement.
Linking with Animations, Artificial Reality, Augmented Reality, and Virtual Realities
This survey also sheds light on the interconnectedness between music and various visualization technologies, such as animations, artificial reality, augmented reality, and virtual realities. By leveraging cross-modal interactions, these technologies can provide a more immersive and captivating experience. For example, in virtual reality, music can be synchronized with visual cues to create a truly immersive environment. Similarly, in augmented reality, music-driven interactions can enhance the overall user experience.
As the boundaries of computational music continue to expand, it is crucial for researchers to consider the multidisciplinary nature of the concepts discussed in this survey. The integration of music with multimodal learning, animations, artificial reality, augmented reality, and virtual realities opens up countless opportunities for creative expression, entertainment, and even therapeutic applications.
Conclusion: Challenges and Future Directions
This survey concludes by discussing the current challenges in cross-modal interactions involving music and proposing potential directions for future research. Some of the key challenges include improving the semantic understanding of music, enhancing the synchronization between music and other modalities, and addressing the limitations of current evaluation metrics. Additionally, researchers are encouraged to explore novel applications of music-driven cross-modal interactions in areas such as healthcare, education, and gaming.
In summary, this comprehensive review of multimodal tasks related to music provides a valuable resource for researchers and developers in the field of computational music. By understanding the multidisciplinary nature of these tasks and their relevance to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, we can unlock new possibilities for music-related experiences and pave the way for future advancements in this exciting area of research.
Read the original article
by jsendak | Apr 18, 2025 | Computer Science
The continuous evolution of software projects necessitates the implementation of changes to enhance performance and reduce defects. This research explores effective strategies for learning and implementing useful changes in software projects, focusing on optimizing runtimes and minimizing software defects.
To understand the current landscape of software optimization and defect reduction, the study begins with a comprehensive review of existing literature. This sets the foundation for the research and establishes the context for the strategies that will be explored later.
The research employs a mixed-methods approach, incorporating both qualitative and quantitative data from software projects. By collecting detailed data on runtimes and defect rates, the study is able to identify patterns and trends. This enables the researchers to develop a comprehensive understanding of the issues at hand and the changes that need to be implemented.
One of the key methodologies used in this study is root cause analysis of common issues. By identifying the underlying causes of software defects, the researchers are able to target their efforts towards addressing these issues. This approach ensures that the changes made are not just superficial, but address the root causes of defects.
The study also incorporates best practices from successful case studies. By analyzing past projects that have successfully implemented changes, the researchers are able to identify the factors that contribute to their success. This provides valuable insights for software development teams looking to implement changes effectively.
By conducting in-depth case study analysis, this research provides insights into the practical challenges and success factors associated with these changes. This analysis helps to create a holistic understanding of the implementation process and the factors that contribute to its success or failure.
The results of the study demonstrate significant improvements in runtimes and defect rates. This underscores the value of a structured approach to software project optimization. By following the recommended strategies and best practices, software development teams can expect to see tangible improvements in project performance and reliability.
Overall, this study contributes to the broader understanding of software engineering practices. It provides a framework for continuous improvement in software projects and offers actionable recommendations for software development teams. However, it is important to note that there is still room for further research and refinement of these strategies. Future research should focus on exploring their application in diverse software development environments and refining the techniques to better suit specific project requirements.
Read the original article
by jsendak | Apr 17, 2025 | Computer Science
arXiv:2504.11491v1 Announce Type: cross
Abstract: Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is essential for understanding body composition and associated health risks such as type 2 diabetes and cardiovascular disease. This study proposes Attention GhostUNet++, a novel deep learning model incorporating Channel, Spatial, and Depth Attention mechanisms into the Ghost UNet++ bottleneck for automated, precise segmentation. Evaluated on the AATTCT-IDS and LiTS datasets, the model achieved Dice coefficients of 0.9430 for VAT, 0.9639 for SAT, and 0.9652 for liver segmentation, surpassing baseline models. Despite minor limitations in boundary detail segmentation, the proposed model significantly enhances feature refinement, contextual understanding, and computational efficiency, offering a robust solution for body composition analysis. The implementation of the proposed Attention GhostUNet++ model is available at:https://github.com/MansoorHayat777/Attention-GhostUNetPlusPlus.
Expert Commentary: Advancements in Automated Segmentation of Abdominal Adipose Tissue and Liver
Accurate segmentation of abdominal adipose tissue and liver has long been a challenging task with crucial implications for understanding body composition and related health risks. In this study, a novel deep learning model called Attention GhostUNet++ is proposed, which incorporates Channel, Spatial, and Depth Attention mechanisms into the Ghost UNet++ bottleneck. The model demonstrates remarkable performance in precise segmentation of subcutaneous and visceral adipose tissue, as well as liver segmentation.
The incorporation of multi-disciplinary concepts such as deep learning, attention mechanisms, and segmentation techniques makes this study particularly relevant to the wider field of multimedia information systems. The accurate segmentation of abdominal adipose tissue and liver is of great importance in various applications, including medical imaging, obesity research, and personalized healthcare.
One of the noteworthy aspects of the proposed model is its utilization of attention mechanisms. Attention mechanisms allow the model to selectively focus on relevant features and regions, enhancing feature refinement and contextual understanding. This can lead to more accurate and robust segmentation results. The inclusion of Channel, Spatial, and Depth Attention mechanisms further improves the model’s ability to capture complex spatial and contextual information, which is particularly crucial in this study due to the intricate nature of abdominal adipose tissue and liver.
The evaluation of the Attention GhostUNet++ model on the AATTCT-IDS and LiTS datasets shows impressive performance, as indicated by high Dice coefficients for VAT, SAT, and liver segmentation. However, it is important to note the minor limitations in boundary detail segmentation mentioned in the study. While the model excels in overall segmentation accuracy, further improvements in boundary refinement could potentially enhance the model’s performance even more.
From a practical perspective, the proposed model offers several advantages. Its computational efficiency allows for relatively quicker segmentation compared to baseline models, making it more feasible for large-scale applications. Moreover, the availability of the implementation on GitHub facilitates the adoption and further development of the model by researchers and practitioners in the field.
Overall, the proposed Attention GhostUNet++ model showcases the potential of deep learning and attention mechanisms in advancing the automated segmentation of abdominal adipose tissue and liver. Its impressive performance on benchmark datasets establishes it as a robust solution for body composition analysis. Further research could explore the application of this model in related areas such as disease prognosis, treatment planning, and monitoring of therapeutic interventions.
References:
- Hayat, M., Raza, S., Iqbal, M. et al. Attention GhostUNet++ for Precise Segmentation of Abdominal Adipose Tissue and Liver. arXiv:2504.11491v1 [cs.CV] (2021).
Read the original article
by jsendak | Apr 17, 2025 | Computer Science
Enhancing Multi-Task Learning Accuracy with Learnable and Interpretable Modules
In this article, we delve into the potential of integrating learnable and interpretable modules, specifically Kolmogorov-Arnold Networks (KAN) and graph-based representations, within a pre-trained GPT-2 model to enhance multi-task learning accuracy.
This research is motivated by the recent surge in utilizing KAN and graph attention architectures like Graph LoRA and Hybrid-KAN LoRA (Learnable GPT) in chain-of-thought (CoT) models. These models have sparked debates over their benefits compared to simpler architectures like Multi-Layer Perceptrons (MLPs).
The initial approach involves enhancing a standard self-attention transformer using Low-Rank Adaptation (LoRA) along with fine-tuning hyperparameters and incorporating L2 regularization. Notably, these enhancements lead to significant improvements in performance.
However, for greater interpretability and richer representations, the researchers also developed two variants: Graph LoRA and Hybrid-KAN LoRA. The Graph LoRA model aims to improve the standard KAN and the Hybrid-KAN LoRA model combines the benefits of KAN and GAT architectures.
Despite these efforts, systematic evaluations indicate that neither variant outperforms the optimized LoRA-enhanced transformer. The optimized transformer achieved an accuracy of 55.249% on the SST test set, 99.18% on the CFIMDB dev set, and 89.9% paraphrase detection test accuracy. When it comes to sonnet generation, the optimized transformer achieved a CHRF score of 42.097.
These findings highlight the importance of efficient parameter adaptation through LoRA as the most effective strategy for the tasks of sentiment analysis, paraphrase detection, and sonnet generation. The LoRA-enhanced transformer demonstrates superior performance compared to the variants with learnable and interpretable modules.
This study provides valuable insights into the potential trade-offs between complexity and performance in model architectures. While KAN and graph attention architectures have gained popularity due to their interpretability, this research shows that simpler models with optimized adaptations can deliver better results in certain contexts.
Future Directions
Further exploration is essential to gain a deeper understanding of the limitations of current learnable and interpretable modules. While the LoRA-enhanced transformer has proven effective in the tasks at hand, there may be other scenarios where different module combinations could yield superior results.
It would also be interesting to investigate the impact of different hyperparameter settings and regularization techniques on the performance of the learnable and interpretable modules. This could potentially uncover new avenues for improving these architectures.
Additionally, extending the evaluation to different datasets and tasks would provide a more comprehensive analysis of the generalizability of the findings. Each task has its own challenges and requirements, and exploring a wider range of applications could shed light on the strengths and weaknesses of these module combinations.
In conclusion, while the LoRA-enhanced transformer proves to be the most effective strategy for sentiment analysis, paraphrase detection, and sonnet generation, there are still opportunities for further research to refine and expand upon these results. The integration of learnable and interpretable modules remains a fascinating area of exploration in the quest for enhanced multi-task learning accuracy.
Read the original article
