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In our relentless pursuit of improving healthcare and elevating wellness standards, we have unveiled a groundbreaking approach that amalgamates the cutting-edge realms of quantum optics, Channel coding, artificial intelligence (AI), and Advanced Optical Systems (optical IoT). This innovative collaboration is meticulously designed to fortify healthcare systems while enhancing cybersecurity protocols in the healthcare imaging sector. At the heart of this transformative initiative lies the cyber security threat and detecting analysis (CYSTDA), a groundbreaking framework that seamlessly integrates the strengths of AI, quantum optics, and advanced optical IoT to secure sensitive imaging data and systems. Quantum optics, known for its precision and sensitivity, offers a robust foundation for safeguarding imaging data. By leveraging quantum principles, it becomes possible to encode and protect visual information in an exceptionally secure manner. Channel coding techniques, initially designed for communication systems, are adapted to ensure the integrity and confidentiality of imaging data transmission, mitigating the risks of cyberattacks. On the other hand, AI plays a crucial role in CYSTDA by providing advanced threat detection techniques using deep learning models such as neural networks, enabling real-time monitoring and analysis of imaging data, and detecting anomalies and potential security breaches. This approach enhances the cybersecurity posture of imaging systems and safeguards the confidentiality of the visual information. Integration of advanced optical IoT is the other advantage of CYSTDA for improving cybersecurity in imaging; by deploying interconnected security devices and sensors, CYSTDA establishes a dynamic and secured ecosystem. Overall, the CYSTDA, which leverages the advanced technologies discussed earlier, was used to elevate cybersecurity protocols for imaging. This framework has the potential to significantly enhance the security of visual data, imaging devices, and infrastructure, safeguarding the confidentiality and integrity of imaging information in an increasingly digital and interconnected world. [ABSTRACT FROM AUTHOR]

Short answer scoring is a significant task in natural language processing. On datasets comprising numerous explicit or implicit symbols and quantization entities, the existing approaches continue to perform poorly. Additionally, the majority of relevant datasets contain few-shot samples, reducing model efficacy in low-resource scenarios. To solve the above issues, we propose a Multi-level Semantic Inference Model (M-Sim), which obtains features at multiple scales to fully consider the explicit or implicit entity information contained in the data. We then design a prompt-based data augmentation to construct the simulated datasets, which effectively enhance model performance in low-resource scenarios. Our M-Sim outperforms the best competitor models by an average of 1.48 percent in the F1 score. The data augmentation significantly increases all approaches' performance by an average of 0.036 in correlation coefficient scores. • M-Sim improves Chinese few-shot SAS with multi-scale similarity calculation. • New prompt-based data augmentation enhances SAS models low-resource performance. • New Chinese SAS dataset released to promote further study. [ABSTRACT FROM AUTHOR]