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<p>Document-Enhanced Ontological Visual Synthesis of Classical Chinese Poetry: Open-Source Multi-Sample Bayesian Approach<br>We introduce a framework for visualizing Classical Chinese poetry using AI, addressing limitations in existing approaches. The system uses three key innovations: (1) an ontology- Document-Enhanced Ontological Visual Synthesis of Classical Chinese Poetry: Open-Source Multi-Sample Bayesian Approach<br>We introduce a framework for visualizing Classical Chinese poetry using AI, addressing limitations in existing approaches. The system uses three key innovations: (1) an ontology-driven pipeline connecting poetic concepts to visual elements, (2) Bayesian-optimized parameters for Stable Diffusion XL, and (3) dynamic visualization with fluid transitions between poetic segments. Built on open-source models, our framework shows improved visual quality and consistency in representing both abstract and concrete poetic elements compared to current methods.<br><br><br>Project Summary:<br>Classically rich in imagery and multiple meanings, Chinese poetry has presented many unique challenges to modern AI visualization systems. While recent text-to-image generation developments have been remarkably capable, classical Chinese poetry<br>remains challenging to accurately visualize because of the sophisticated use of metaphor, cultural context, and abstract concepts that go beyond literal interpretation.<br><br>Current methods for visualization apply a direct translation approach that is unable to capture detailed relationships between the abstract poetic concepts and their concrete visual representations (happens in DALLE3, MidJourney, Flux). The inherent stochasticity of the modern diffusion models further exacerbates these issues when dealing with culturally-specific artistic elements. We utilize Stable Diffusion XL as our foundational model due to its<br>open-source nature enabling deep parameter optimization, built-in refiner for quality enhancement, and accessibility for reproducible research.<br><br>Our novel end-to-end framework combines ontological knowledge integration with optimized image generation and dynamic video synthesis. Technical contributions include a custom ontology-driven pipeline that bridges abstract concepts with visual elements (some similarity with HyDE pipeline but different), a Bayesian-optimized guidance scale parameter tuning system for SDXL (optimal w = 9.8) to sharpen and strength representations, and a dynamic visualization approach using sliding-window interpolation between poetic half-stanzas. With extensive experiments, we demonstrate significant improvements in both quantitative metrics and qualitative assessments, establishing a foundation for culturally aware AI systems capable of handling complex literary works.</p><p><br>It is currently a project being targeted for a paper under review at a high venue.<br>driven pipeline connecting poetic concepts to visual elements, (2) Bayesian-optimized parameters for Stable Diffusion XL, and (3) dynamic visualization with fluid transitions between poetic segments. Built on open-source models, our framework shows improved visual quality and consistency in representing both abstract and concrete poetic elements compared to current methods.<br><br><br>Project Summary:<br>Classically rich in imagery and multiple meanings, Chinese poetry has presented many unique challenges to modern AI visualization systems. While recent text-to-image generation developments have been remarkably capable, classical Chinese poetry<br>remains challenging to accurately visualize because of the sophisticated use of metaphor, cultural context, and abstract concepts that go beyond literal interpretation.<br><br>Current methods for visualization apply a direct translation approach that is unable to capture detailed relationships between the abstract poetic concepts and their concrete visual representations (happens in DALLE3, MidJourney, Flux). The inherent stochasticity of the modern diffusion models further exacerbates these issues when dealing with culturally-specific artistic elements. We utilize Stable Diffusion XL as our foundational model due to its<br>open-source nature enabling deep parameter optimization, built-in refiner for quality enhancement, and accessibility for reproducible research.<br><br>Our novel end-to-end framework combines ontological knowledge integration with optimized image generation and dynamic video synthesis. Technical contributions include a custom ontology-driven pipeline that bridges abstract concepts with visual elements (some similarity with HyDE pipeline but different), a Bayesian-optimized guidance scale parameter tuning system for SDXL (optimal w = 9.8) to sharpen and strength representations, and a dynamic visualization approach using sliding-window interpolation between poetic half-stanzas. With extensive experiments, we demonstrate significant improvements in both quantitative metrics and qualitative assessments, establishing a foundation for culturally aware AI systems capable of handling complex literary works.</p><p><br>It is currently a project being targeted for a paper under review at a high venue.</p><p>&nbsp;</p>

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<h1>Introduction</h1><p>The global aging population presents significant challenges to healthcare systems, particularly in elderly care homes. The demand for caregivers is rising, yet manpower shortages hinder the ability to provide timely and efficient care. SAFERIN is an AI-powered elderly care monitoring system designed to address these critical issues by integrating intelligent safety, staffing, and healthcare solutions.</p><p>This proposal outlines how SAFERIN utilizes Artificial Intelligence (AI) to enhance elderly care, reduce response times, and alleviate the workload of caregivers.</p><p>The scholarship will provide crucial support in furthering AI research and implementation within SAFERIN, accelerating its impact on the healthcare industry.</p><p>&nbsp;</p><h2>Problem&nbsp;Statement</h2><p>Elderly&nbsp;care&nbsp;homes&nbsp;in&nbsp;Hong&nbsp;Kong&nbsp;and&nbsp;worldwide&nbsp;face&nbsp;three&nbsp;major&nbsp;challenges:</p><ul><li><strong>High Accident Rates</strong>: Falls and delayed responses contribute to severe injuries and high medical costs (HKD 100,000 per case on average).</li><li><strong>Manpower&nbsp;Shortages</strong>: With&nbsp;a&nbsp;20% vacancy rate&nbsp;and&nbsp;a&nbsp;caregiver-to-elder ratio of 1:20 (compared to the recommended 1:5), timely care is compromised.</li><li><strong>Inefficient Resource Allocation</strong>: Traditional nursing call systems fail to prioritize emergency cases effectively, leading to longer wait times and inadequate&nbsp;responses.</li></ul><p>&nbsp;</p><h1>Proposed&nbsp;Solution:&nbsp;SAFERIN</h1><p>&nbsp;</p><p>SAFERIN is a comprehensive AI-driven elderly care monitoring system that provides three core functionalities:</p><p>&nbsp;</p><h2>Smart&nbsp;Safety&nbsp;–&nbsp;AI-Powered&nbsp;Fall&nbsp;and&nbsp;Vital&nbsp;Sign&nbsp;Detection</h2><ul><li>Uses&nbsp;mmWave&nbsp;technology&nbsp;and RFID&nbsp;to&nbsp;detect&nbsp;falls,&nbsp;wandering,&nbsp;and irregular heart rates.</li><li>AI&nbsp;algorithms&nbsp;predict&nbsp;potential&nbsp;health&nbsp;risks&nbsp;and&nbsp;send&nbsp;instant&nbsp;alerts&nbsp;to caregivers.</li></ul><h2>Smart&nbsp;Staffing&nbsp;– AI-Enhanced Nurse-On-Demand&nbsp;System</h2><ul><li>AI-powered voice recognition system classifies requests based on urgency.</li><li>Automatically&nbsp;assigns&nbsp;caregivers,&nbsp;reducing&nbsp;wait&nbsp;times&nbsp;by&nbsp;over&nbsp;50%.</li></ul><p><br>&nbsp;</p><h2>Smart&nbsp;Care&nbsp;–&nbsp;Intelligent&nbsp;Health&nbsp;Predictive&nbsp;&amp;&nbsp;Analytic&nbsp;System</h2><ul><li>AI&nbsp;analyzes&nbsp;patient&nbsp;history&nbsp;and&nbsp;real-time&nbsp;data&nbsp;to&nbsp;generate&nbsp;health&nbsp;reports.</li><li>Enables personalized care plans for elderly residents, reducing hospitalization&nbsp;rates.</li></ul><h1>&nbsp;</h1><h1>AI&nbsp;Methodology&nbsp;and&nbsp;Innovation</h1><p>&nbsp;</p><p>SAFERIN&nbsp;employs&nbsp;cutting-edge&nbsp;AI&nbsp;technologies,&nbsp;including:</p><p>&nbsp;</p><ul><li><strong>Natural&nbsp;Language&nbsp;Processing&nbsp;(NLP)</strong>:&nbsp;Enables&nbsp;elderly&nbsp;residents&nbsp;to&nbsp;communicate&nbsp;requests&nbsp;efficiently,&nbsp;even&nbsp;with&nbsp;varied&nbsp;accents.</li><li><strong>Machine Learning (ML) for Risk Prediction</strong>: Identifies high-risk individuals and prioritizes emergency cases.</li><li><strong>Computer&nbsp;Vision&nbsp;and&nbsp;Sensor&nbsp;Fusion</strong>:&nbsp;Enhances&nbsp;fall&nbsp;detection&nbsp;accuracy beyond&nbsp;traditional&nbsp;motion&nbsp;sensors.</li><li><strong>Cloud-Based AI Analytics</strong>: Provides predictive insights to optimize caregiving&nbsp;workflows.</li></ul><h1>Expected&nbsp;Outcomes&nbsp;and&nbsp;Impact</h1><p>&nbsp;</p><p>Through pilot trials conducted in elderly care homes, SAFERIN has demonstrated:</p><ul><li><strong>50%&nbsp;Reduction&nbsp;in&nbsp;Wait&nbsp;Times</strong>:&nbsp;Faster&nbsp;responses&nbsp;to&nbsp;emergencies,&nbsp;improving survival rates.</li><li><strong>55% Cost Savings</strong>: Lower operational costs for elderly care homes through optimized staffing.</li><li><strong>Enhanced Caregiver Productivity</strong>: Handling capacity increased from 14 to 25 requests per hour.</li><li><strong>Improved&nbsp;Elderly&nbsp;Well-being</strong>:&nbsp;Increased&nbsp;safety,&nbsp;reducing&nbsp;fall-related&nbsp;injuries.</li></ul><h1>&nbsp;</h1><h1>Implementation&nbsp;Plan&nbsp;and&nbsp;Timeline</h1><p>&nbsp;</p><h2>Phase&nbsp;1&nbsp;(2024-2025):&nbsp;AI&nbsp;Model&nbsp;Enhancement&nbsp;&amp;&nbsp;Pilot&nbsp;Expansion</h2><p>&nbsp;</p><ul><li>Optimize&nbsp;AI&nbsp;accuracy in&nbsp;fall&nbsp;detection and&nbsp;voice&nbsp;recognition.</li><li>Expand&nbsp;pilot&nbsp;testing&nbsp;to&nbsp;additional&nbsp;residential&nbsp;care&nbsp;homes.</li></ul><p><br>&nbsp;</p><h2>Phase&nbsp;2&nbsp;(2025-2026):&nbsp;Full-Scale&nbsp;Deployment&nbsp;&amp;&nbsp;International&nbsp;Expansion</h2><p>&nbsp;</p><ul><li>Launch&nbsp;SAFERIN&nbsp;as&nbsp;a&nbsp;SaaS-based&nbsp;platform&nbsp;for&nbsp;elderly&nbsp;care&nbsp;homes&nbsp;and hospitals.</li><li>Explore&nbsp;expansion&nbsp;to&nbsp;other&nbsp;regions&nbsp;facing&nbsp;elderly&nbsp;care&nbsp;challenges.</li></ul><p>&nbsp;</p><p><strong>Phase&nbsp;3&nbsp;(2026+):&nbsp;Continuous&nbsp;AI&nbsp;Research&nbsp;&amp;&nbsp;Product&nbsp;Diversification</strong></p><p>&nbsp;</p><ul><li>Develop&nbsp;AI-driven predictive&nbsp;analytics&nbsp;for&nbsp;chronic&nbsp;disease&nbsp;management.</li><li>Expand&nbsp;applications&nbsp;to&nbsp;smart&nbsp;home&nbsp;and&nbsp;assisted&nbsp;living&nbsp;environments.</li></ul><p>&nbsp;</p><h1>Conclusion</h1><p>&nbsp;</p><p>SAFERIN embodies the transformative power of AI in healthcare, addressing critical elderly care challenges with intelligent automation and predictive analytics.&nbsp;</p><p>With the support of this scholarship, SAFERIN will continue to drive innovation&nbsp;in&nbsp;AI powered healthcare, improving&nbsp;the&nbsp;quality&nbsp;of&nbsp;life&nbsp;for&nbsp;the&nbsp;elderly while optimizing caregiving resources.</p>

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<p>n April 2024, US Congress passed a bill - signed into law by former President Joe Biden - giving ByteDance nine months to find a US-approved buyer or see TikTok shut down.</p><p>TikTok called the law "unconstitutional" and claimed that <a href="https://www.bbc.co.uk/news/articles/c87zp82247yo">censoring its US users</a> would have a "staggering" impact on free speech. It launched several unsuccessful legal challenges.</p><p>ByteDance insisted it had no plans to sell.</p>

Technical Corner

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