by jsendak | May 23, 2025 | Computer Science
arXiv:2505.15629v1 Announce Type: new
Abstract: Social networking services (SNS) contain vast amounts of image-text posts, necessitating effective analysis of their relationships for improved information retrieval. This study addresses the classification of image-text pairs in SNS, overcoming prior limitations in distinguishing relationships beyond similarity. We propose a graph-based method to classify image-text pairs into similar and complementary relationships. Our approach first embeds images and text using CLIP, followed by clustering. Next, we construct an Image-Text Relationship Clustering Line Graph (ITRC-Line Graph), where clusters serve as nodes. Finally, edges and nodes are swapped in a pseudo-graph representation. A Graph Convolutional Network (GCN) then learns node and edge representations, which are fused with the original embeddings for final classification. Experimental results on a publicly available dataset demonstrate the effectiveness of our method.
Expert Commentary: Analyzing Image-Text Relationships in Social Networking Services
Social networking services have become an integral part of our daily lives, with users sharing vast amounts of image-text posts on platforms like Instagram, Facebook, and Twitter. Understanding the relationships between images and text in these posts is crucial for improving information retrieval and enhancing user experience. This study tackles the challenge of classifying image-text pairs in SNS by going beyond traditional similarity-based approaches.
Multi-Disciplinary Approach
This research combines concepts from computer vision, natural language processing, and graph theory to develop a novel method for classifying image-text relationships. By leveraging the CLIP model to embed images and text, the study ensures a holistic representation of multimedia content. The use of graph-based techniques, including clustering and Graph Convolutional Networks, further enriches the analysis by capturing complex relationships between image-text pairs.
Relevance to Multimedia Information Systems
The findings from this study have significant implications for multimedia information systems, as they highlight the importance of considering both visual and textual cues in content analysis. By classifying image-text pairs into similar and complementary relationships, the proposed method provides a structured approach to organizing multimedia data. This can lead to more accurate content recommendations, personalized search results, and improved user engagement on social networking platforms.
Connections to AR, VR, and Artificial Reality
While this study focuses on analyzing image-text relationships in 2D social networking services, its insights can also be applied to the realms of Augmented Reality (AR), Virtual Reality (VR), and Artificial Reality. Understanding how images and text interact can enhance the development of immersive AR/VR experiences, intelligent virtual assistants, and personalized content recommendations in artificial reality environments. By incorporating the proposed graph-based classification method, AR/VR applications can better interpret and respond to multimedia content in real-time.
Overall, this research sheds light on the intricate relationships between images and text in social networking services, paving the way for enhanced multimedia information systems and interactive experiences in augmented, virtual, and artificial realities.
Read the original article
by jsendak | May 23, 2025 | AI
arXiv:2505.14689v1 Announce Type: new
Abstract: This paper presents a novel dynamic post-shielding framework that enforces the full class of $omega$-regular correctness properties over pre-computed probabilistic policies. This constitutes a paradigm shift from the predominant setting of safety-shielding — i.e., ensuring that nothing bad ever happens — to a shielding process that additionally enforces liveness — i.e., ensures that something good eventually happens. At the core, our method uses Strategy-Template-based Adaptive Runtime Shields (STARs), which leverage permissive strategy templates to enable post-shielding with minimal interference. As its main feature, STARs introduce a mechanism to dynamically control interference, allowing a tunable enforcement parameter to balance formal obligations and task-specific behavior at runtime. This allows to trigger more aggressive enforcement when needed, while allowing for optimized policy choices otherwise. In addition, STARs support runtime adaptation to changing specifications or actuator failures, making them especially suited for cyber-physical applications. We evaluate STARs on a mobile robot benchmark to demonstrate their controllable interference when enforcing (incrementally updated) $omega$-regular correctness properties over learned probabilistic policies.
Expert Commentary on Dynamic Post-Shielding Framework
The concept of a dynamic post-shielding framework that enforces both safety and liveness properties over pre-computed probabilistic policies represents a significant advancement in the field of autonomous systems and robotics. Traditionally, safety-shielding has been the primary focus, ensuring that systems never enter into undesirable states. However, this new framework expands beyond safety to include liveness properties, guaranteeing that the system eventually reaches desired states or goals.
The use of Strategy-Template-based Adaptive Runtime Shields (STARs) is a key innovation in this framework. By leveraging permissive strategy templates, STARs are able to enforce post-shielding with minimal interference, allowing for a better balance between formal correctness guarantees and task-specific behaviors. The ability to dynamically control interference with a tunable enforcement parameter is particularly noteworthy, as it provides flexibility in how aggressively the system enforces correctness properties based on the current situation.
Furthermore, the support for runtime adaptation in STARs is a crucial feature, especially in cyber-physical applications where specifications may change or components may fail. The ability to dynamically adjust to these changes ensures the continued reliability and effectiveness of the system over time.
The evaluation of STARs on a mobile robot benchmark underscores the practical applicability of this framework. By demonstrating controllable interference when enforcing $omega$-regular correctness properties over learned probabilistic policies, the study showcases the effectiveness of STARs in real-world scenarios.
Overall, the multi-disciplinary nature of this research, combining concepts from control theory, formal methods, and robotics, highlights the importance of integrating diverse expertise to push the boundaries of autonomous systems and ensure their safety and reliability in complex environments.
Read the original article
by jsendak | May 22, 2025 | DS Articles
Amid so many different machine learning algorithms to choose from. This guide has been designed to help you navigate towards the right one for you, depending on your data and the problem to address.
Understanding the Importance of Choosing the Right Machine Learning Algorithm
As there are a multitude of machine learning algorithms available, deciding on the most appropriate one for your specific needs and data sets can appear daunting. The key is understanding that each algorithm is designed to address a unique type of problem based on the data’s characteristics. The article emphasizes the need to select a machine learning algorithm wisely and offers a guide to help navigate these decisions.
Long-term Implications
The future of machine learning and its applications is inextricably tied to the choice of the right algorithm. This choice forms the basis of successful implementation and the maximization of benefits from these applications. As machine learning technologies become more sophisticated and versatile, making the right algorithmic choice becomes critical.
To ensure precision and efficiency, machine learning algorithms must constantly evolve and adapt to new, complex datasets and scenarios. In the long term, this will spark further innovation in the development and iteration of these algorithms. Companies and researchers who actively implement and update their machine learning strategies will therefore have a competitive advantage.
Possible Future Developments
Future developments in machine learning algorithms are anticipated to focus on increasing complexities in data and the need to solve more intricate problems. Enhanced capabilities for handling unstructured data, more efficient processing, and greater adaptability to different data types and scenarios are some areas of development to watch.
There also may be a trend towards ‘self-learning’ algorithms that continually update and refine themselves based on new data. Such advancements could significantly enhance machine learning applications’ effectiveness and accuracy.
Actionable Advice
Regularly updated algorithm selection strategy
Keep your approach to machine learning algorithms dynamic. Regularly review and update your choice of algorithm, based on the changing nature of the data and the complexity of the problem at hand.
Invest in learning and development
Continuously invest in learning about new machine learning algorithms, their workings, and their unique applications. An in-depth understanding will aid in making more informed decisions.
Anticipate future development
Stay abreast of emerging trends and developments in the field. This will allow for more accurate future planning for implementing machine learning applications.
Collaborate with experts
Consider collaborations with machine learning experts or consultancies to maximize the potential of your data and ensure the appropriate algorithms are applied.
Choosing the right machine learning algorithm can form the foundation of a successful implementation plan. Hence, it’s crucial to stay updated, make informed decisions, and anticipate future trends in the field.
Read the original article
by jsendak | May 22, 2025 | AI
arXiv:2505.13466v1 Announce Type: new
Abstract: The scarcity of data depicting dangerous situations presents a major obstacle to training AI systems for safety-critical applications, such as construction safety, where ethical and logistical barriers hinder real-world data collection. This creates an urgent need for an end-to-end framework to generate synthetic data that can bridge this gap. While existing methods can produce synthetic scenes, they often lack the semantic depth required for scene simulations, limiting their effectiveness. To address this, we propose a novel multi-agent framework that employs an iterative, in-the-loop collaboration between two agents: an Evaluator Agent, acting as an LLM-based judge to enforce semantic consistency and safety-specific constraints, and an Editor Agent, which generates and refines scenes based on this guidance. Powered by LLM’s capabilities to reasoning and common-sense knowledge, this collaborative design produces synthetic images tailored to safety-critical scenarios. Our experiments suggest this design can generate useful scenes based on realistic specifications that address the shortcomings of prior approaches, balancing safety requirements with visual semantics. This iterative process holds promise for delivering robust, aesthetically sound simulations, offering a potential solution to the data scarcity challenge in multimedia safety applications.
Expert Commentary: Bridging the Data Gap in Safety-Critical AI Systems
In the realm of AI-driven safety applications, the scarcity of real-world data depicting dangerous situations poses a significant challenge for training systems to effectively identify and respond to potential risks. The traditional approach of using real-life data for training is often limited by ethical considerations, as well as the practical difficulties of collecting diverse and representative datasets.
This article highlights the importance of developing an innovative framework for generating synthetic data that can simulate safety-critical scenarios with the necessary semantic depth. The proposed multi-agent framework, which leverages the collaboration between an Evaluator Agent and an Editor Agent, marks a significant step forward in addressing this data scarcity challenge.
One key aspect of this framework is the use of Language Model (LLM)-based reasoning to enforce semantic consistency and safety-specific constraints in the synthetic scene generation process. By integrating common-sense knowledge and safety guidelines into the AI decision-making process, the system can produce realistic and meaningful scenes that balance safety requirements with visual semantics.
The iterative nature of the collaboration between the Evaluator Agent and the Editor Agent allows for continuous refinement and improvement of the synthetic scenes, ensuring that the final output meets the desired specifications for safety-critical applications. This approach not only enhances the quality of the generated data but also opens up new possibilities for creating robust and visually accurate simulations.
Overall, this multi-disciplinary framework represents a promising solution to the data scarcity challenge in multimedia safety applications. By combining the strengths of AI reasoning, common-sense knowledge, and safety guidelines, this approach has the potential to revolutionize the training of AI systems for construction safety and other critical applications, ultimately leading to safer and more reliable outcomes in real-world scenarios.
Read the original article
by jsendak | May 20, 2025 | Art
Welcome to the World of Alex Chinneck: A Week at the Knees
British sculptor Alex Chinneck is known for his mind-bending and visually stunning installations that defy the laws of physics and challenge our perceptions of reality. His latest commission, A Week at the Knees, continues this tradition with its mesmerizing and thought-provoking design.
Chinneck’s work is reminiscent of the surrealism movement of the early 20th century, where artists like Salvador Dali and Rene Magritte sought to explore the unconscious mind and push the boundaries of conventional art. Just as these artists sought to disrupt our understanding of reality, Chinneck’s sculptures disrupt the spaces they inhabit, inviting viewers to question their surroundings and look at the world in a new light.
Exploring Themes of Transformation and Illusion
With A Week at the Knees, Chinneck delves into themes of transformation and illusion. By creating a sculpture that appears to be bending and contorting in impossible ways, he challenges our perceptions of what is possible and asks us to consider the limitations of our own minds. In a world where technology allows us to manipulate images and create virtual realities, Chinneck’s work serves as a reminder of the power of physical objects and the importance of experiencing art in person.
- Historical Context: Chinneck’s sculptures can be seen as a modern continuation of the tradition of optical illusions and trompe l’oeil techniques used by artists throughout history to create stunning visual effects.
- Contemporary Relevance: In an age where reality is increasingly filtered through screens and digital devices, Chinneck’s work challenges us to engage with the physical world around us and consider the ways in which art can transform our perceptions.
Join us on a journey into the fantastical world of Alex Chinneck, where reality is just a suggestion and imagination knows no bounds.
Marta Bogna-Drew meets British sculptor Alex Chinneck to discuss his latest commission: A Week at the Knees.
Read the original article
