arXiv:2402.07924v1 Announce Type: cross
Abstract: Mixed Reality (MR) and Artificial Intelligence (AI) are increasingly becoming integral parts of our daily lives. Their applications range in fields from healthcare to education to entertainment. MR has opened a new frontier for such fields as well as new methods of enhancing user engagement. In this paper, We propose a new system one that combines the power of Large Language Models (LLMs) and mixed reality (MR) to provide a personalized companion for educational purposes. We present an overview of its structure and components as well tests to measure its performance. We found that our system is better in generating coherent information, however it’s rather limited by the documents provided to it. This interdisciplinary approach aims to provide a better user experience and enhance user engagement. The user can interact with the system through a custom-design smart watch, smart glasses and a mobile app.
Mixed Reality and Artificial Intelligence: Enhancing User Engagement
Mixed Reality (MR) and Artificial Intelligence (AI) are increasingly becoming integral parts of our daily lives, revolutionizing fields like healthcare, education, and entertainment. The combination of these two technologies opens up new frontiers and possibilities for enhancing user engagement and providing personalized experiences.
The focus of this paper is on the development of a new system that combines the power of Large Language Models (LLMs) and mixed reality (MR) to create a personalized companion for educational purposes. This interdisciplinary approach aims to provide a better user experience by leveraging the capabilities of AI and MR technologies.
Leveraging the power of AI, the system is capable of generating coherent and contextually relevant information in response to user inputs or queries. By utilizing Large Language Models, which have been trained on vast amounts of data, the system can provide accurate and helpful information to users, enhancing their learning experience.
The integration of mixed reality into the system adds another layer of immersion and interactivity. Users can interact with the system through a variety of devices, including custom-designed smart watches, smart glasses, and a mobile app. These devices serve as the window into the mixed reality environment, allowing users to see and interact with virtual objects or information seamlessly blended with their real-world surroundings.
The potential applications of this system are vast. In the education sector, it can serve as a personalized tutor or study companion, providing tailored explanations and examples based on the individual’s learning style and progress. In healthcare, it can assist medical professionals during procedures by overlaying real-time information or simulations onto the patient’s body. In entertainment, it can offer immersive experiences and interactive storytelling.
As mentioned in the paper, one limitation of the system is its reliance on the documents provided to it. The quality and diversity of the documents can impact the system’s ability to generate accurate and comprehensive responses. To improve this aspect, future developments could focus on expanding the dataset and refining the pre-training process to increase the system’s knowledge base.
The wider field of multimedia information systems encompasses various technologies and techniques, including animations, artificial reality, augmented reality, and virtual realities. This paper contributes to the advancement of this field by combining AI and MR to create a personalized educational companion. The integration of animations and visualizations within the mixed reality environment can further enhance the learning experience, making complex concepts more understandable and engaging.
In conclusion, the combination of Mixed Reality and Artificial Intelligence holds great potential for enhancing user engagement and providing personalized experiences in various domains. This interdisciplinary approach brings together the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, paving the way for exciting developments and innovations in the future.
arXiv:2402.07925v1 Announce Type: new
Abstract: Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For example, in an image with several dogs, it is difficult to select a particular dog and move it to a precise location. Doing this with text alone would require a complex prompt that disambiguates the target dog and describes the destination. However, direct manipulation is well suited to visual tasks like selecting objects and specifying locations. We introduce Point and Instruct, a system for seamlessly combining familiar direct manipulation and textual instructions to enable precise image manipulation. With our system, a user can visually mark objects and locations, and reference them in textual instructions. This allows users to benefit from both the visual descriptiveness of natural language and the spatial precision of direct manipulation.
Combining Direct Manipulation and Textual Instructions for Precise Image Manipulation
Machine learning has made significant advancements in image editing from natural language instructions. However, one common challenge users face is specifying precise image transformations using text alone. This is particularly difficult when dealing with complex scenes, such as images with multiple similar objects.
In the case of an image with several dogs, for example, it can be challenging to select a specific dog and move it to an exact location using text alone. This would require a complex prompt that distinguishes the target dog and describes the destination in great detail. However, direct manipulation, a technique commonly used in visual tasks, is better suited for selecting objects and specifying locations with precision.
The authors introduce Point and Instruct, a system that seamlessly combines direct manipulation and textual instructions for precise image manipulation. With this system, users can visually mark objects and locations and reference them in textual instructions. This approach allows users to leverage the descriptive power of natural language along with the spatial precision of direct manipulation.
Point and Instruct brings together concepts from multiple disciplines, bridging the gap between natural language processing, computer vision, and human-computer interaction. By integrating these fields, the system offers a more intuitive and effective way for users to communicate their desired image edits.
This research holds promise for applications in graphic design, content creation, and image-based data analysis. By providing users with a versatile tool that combines direct manipulation and textual instructions, it becomes easier to iterate and experiment with visual designs. Moreover, this approach could enhance the accessibility of image editing tools for individuals with limited text-based communication abilities.
The multi-disciplinary nature of Point and Instruct highlights the importance of collaboration and cross-pollination between different fields. By combining expertise from machine learning, computer vision, natural language processing, and human-computer interaction, we can develop more powerful and user-friendly systems. As research continues to advance in these areas, we can expect even more sophisticated and precise image editing tools to be developed in the future.
arXiv:2402.07951v1 Announce Type: new
Abstract: The higher-curvature gravity with boundary terms i.e the $f(Q)$ theories, grounded on non-metricity as a fundamental geometric quantity, exhibit remarkable efficacy in portraying late-time universe phenomena. The aim is to delineate constraints on two prevalent models within this framework, namely the Log-square-root model and the Hyperbolic tangent-power model, by employing the framework of Big Bang Nucleosynthesis (BBN). The approach involves elucidating deviations induced by higher-curvature gravity with boundary terms in the freeze-out temperature ($T_{f}$) concerning its departure from the standard $Lambda$CDM evolution. Subsequently, constraints on pertinent model parameters are established by imposing limitations on $vert frac{delta T_{f}}{T_{f}}vert$ derived from observational bounds. This investigation employs dynamical system analysis, scrutinizing both background and perturbed equations. The study systematically explores the phase space of the models, identifying equilibrium points, evaluating their stability, and comprehending the system’s trajectory around each critical point. The principal findings of this analysis reveal the presence of a matter-dominated saddle point characterized by the appropriate matter perturbation growth rate. Subsequently, this phase transitions into a stable phase of a dark-energy-dominated, accelerating universe, marked by consistent matter perturbations. Overall, the study substantiates observational confrontations, affirming the potential of higher-curvature gravity with boundary terms as a promising alternative to the $Lambda$CDM concordance model. The methodological approach underscores the significance of dynamical systems as an independent means to validate and comprehend the cosmological implications of these theories.
Future Roadmap
1. Further Exploration of $f(Q)$ Theories
The study highlights the efficacy of $f(Q)$ theories in portraying late-time universe phenomena. Future research should continue to explore these theories and their implications for a deeper understanding of the universe.
2. Investigation of Other Models within the $f(Q)$ Framework
While this study focused on the Log-square-root model and the Hyperbolic tangent-power model, there are likely other models within the $f(Q)$ framework that could be explored. Future research should investigate these alternative models to expand our knowledge of higher-curvature gravity with boundary terms.
3. Further Constraints on Model Parameters
Constraints on model parameters were established using limitations on $vert frac{delta T_{f}}{T_{f}}vert$ derived from observational bounds. Further research should aim to refine and strengthen these constraints, potentially using additional observational data or improved techniques.
4. Exploration of Dynamical System Analysis
The study employed dynamical system analysis to explore the phase space of the models and evaluate their stability. Future research could further investigate the use of dynamical system analysis as a means to validate and comprehend the cosmological implications of $f(Q)$ theories.
5. Comparison with the $Lambda$CDM Model
The study affirms the potential of higher-curvature gravity with boundary terms as a promising alternative to the $Lambda$CDM concordance model. Future research should continue to compare and contrast these two models to better understand their similarities, differences, and implications for our understanding of the universe.
Potential Challenges and Opportunities
Challenges: One potential challenge in future research is the complexity of the mathematical models and equations involved in studying higher-curvature gravity with boundary terms. Researchers will need to develop sophisticated mathematical and computational techniques to overcome these challenges.
Opportunities: The study highlights the potential of higher-curvature gravity with boundary terms as an alternative framework for understanding the universe. This opens up opportunities for interdisciplinary collaborations between cosmologists, mathematicians, and physicists to further explore and develop these theories.
Conclusion
The study demonstrates the efficacy of $f(Q)$ theories, grounded on non-metricity, in portraying late-time universe phenomena. It establishes constraints on two prevalent models within this framework and highlights the potential of higher-curvature gravity with boundary terms as a promising alternative to the $Lambda$CDM concordance model. Future research should continue to explore these theories, investigate alternative models, refine constraints, and compare with the $Lambda$CDM model. Challenges in complexity can be overcome through interdisciplinary collaborations, presenting exciting opportunities for further advancements in our understanding of the cosmos. Read the original article
The Importance of Understanding Individuals’ Perception and Interaction with Data Protection Practices
In today’s digital age, where technology is an integral part of our lives and personal data is readily available, it is crucial to understand how individuals perceive and interact with data protection practices. This research takes a game theoretical approach to uncover the psychological factors that influence individuals’ awareness and comprehension of data protection measures.
By viewing data protection from the perspective of a game, researchers are able to gain comprehensive insights into the cognitive processes that shape individuals’ decision-making regarding data protection. By studying strategies, moves, rewards, and observations within the game, the research provides a deeper understanding of the psychological factors at play.
The Role of Knowledge and Attitudes in Data Protection Awareness
The findings of this study highlight the significance of knowledge and attitudes in shaping individuals’ awareness of data protection. Individuals who possess a higher level of knowledge about data protection practices have a greater likelihood of making informed decisions and taking appropriate measures to protect their personal data.
Moreover, individuals’ attitudes towards data protection play a crucial role in determining their behavior. Those who perceive data protection as important are more likely to engage in protective behaviors and be proactive in safeguarding their personal information.
Perceived Risks as a Motivator for Data Protection
The research also emphasizes the influence of perceived risks on individuals’ data protection awareness. When individuals perceive the potential risks associated with the misuse or unauthorized access of their personal data, they are more likely to be vigilant and take proactive measures to protect their information.
This finding highlights the need for organizations and policymakers to adequately communicate and educate individuals about the potential risks and consequences of inadequate data protection practices. By raising awareness about these risks, individuals are more likely to take data protection seriously and adopt appropriate measures.
Individual Differences in Data Protection Awareness
The study also recognizes the role of individual differences in shaping data protection awareness. It is evident that individuals’ cognitive abilities, socio-demographic factors, and previous experiences influence their comprehension and behavior regarding data protection practices.
Understanding these individual differences is essential for designing effective awareness games and educational initiatives. Tailoring interventions to cater to the specific needs and characteristics of different individuals can significantly enhance their understanding and engagement with data protection practices.
Implications for Developing Effective Awareness Games and Educational Initiatives
The findings of this research have profound implications for developing effective awareness games and educational initiatives in the domain of data protection. By identifying the psychological factors that impact individuals’ awareness, these insights can shape the design and implementation of initiatives that effectively educate and engage individuals in protecting their personal data.
For instance, educational games could be designed to enhance individuals’ knowledge about data protection practices and raise their awareness of potential risks. By gamifying the learning experience, individuals are more likely to be actively engaged and motivated to learn. Furthermore, these games can enable individuals to practice decision-making in a safe environment, allowing them to understand the consequences of their choices related to data protection.
While this research sheds light on the intricate nature of human cognition and behavior concerning data protection, it is important to note that technology and threats in the digital landscape continue to evolve rapidly. Therefore, ongoing research and development are crucial to ensure that awareness games and educational initiatives remain effective and up-to-date in addressing the evolving challenges of data protection.
In conclusion, understanding how individuals perceive and interact with data protection practices is a vital aspect of ensuring the privacy and security of personal information in an increasingly digital world. By employing a game theoretical approach and identifying key psychological factors, this research provides valuable insights that can inform the development of effective awareness games and educational initiatives to promote data protection.
