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Abstract: Federated learning is a distributed learning framework designed to facilitate collaborative training of deep learning models at the network edge, ensuring the privacy and security of users while effectively responding to the challenges posed by data silos within the healthcare domain. However, deploying intricate medical segmentation network models in realistic settings often leads to substantial communication overhead for federated learning clients, thereby severely undermining the operational efficiency of federated systems. In light of these issues, we propose an innovative federated learning approach known as FedSKD, which is intended to alleviate the significant communication overhead incurred during medical image segmentation tasks in federated learning. This method strikes a delicate balance between communication costs and model training accuracy. A key feature of the FedSKD framework is incorporating a self-knowledge distillation mechanism, which encompasses both prediction map distillation loss and feature map distillation loss. This integration plays a key role in enhancing model accuracy without the need for training an additional resource-intensive mentor model, thereby optimizing computational efficiency. Additionally, we introduce parameter difference dynamic compression technology to compress communications, resulting in a reduction of communication costs by more than 30% within the federated system. Furthermore, extensive experiments utilizing FedSKD were conducted on widely recognized public medical segmentation datasets such as KiTS19 and COVID-19. The experimental findings unequivocally demonstrate that our FedSKD not only significantly diminishes the expenses associated with federated communication but also enhances overall model accuracy.Abstract: Federated learning is a distributed learning framework designed to facilitate collaborative training of deep learning models at the network edge, ensuring the privacy and security of users while effectively responding to the challenges posed by data silos within the healthcare domain. However, deploying intricate medical segmentation network models...Learn More
Abstract: Taking the water resource management system in the Huangbai River Basin as an example, considering the many drawbacks of ArcIMS compared to ArcGIS Server, as well as the weak spatial analysis intelligence function and limited data format support types of the original system, software engineering UML use case modeling technology and ArcGIS Server and JavaScript network development technology were applied to study the improvement and optimization of the original water resource comprehensive management WebGIS system: The system retains the original mode of unified management of spatial and attribute data using the ArcSDE spatial data engine and SQL Server relational database; Upgrades and updates have been made to the software versions of the database server and business logic processing; Mainly, the configuration of the map service software has been updated and replaced, abandoning the original ArcIMS map network publishing technology and applying the more powerful and advanced ArcGIS Server technology, completing seamless integration of the new system with other surveying and mapping systems, GIS systems, and remote sensing software systems; The replacement and upgrading of the comprehensive water resource management system in the Huangbai River Basin has been achieved.Abstract: Taking the water resource management system in the Huangbai River Basin as an example, considering the many drawbacks of ArcIMS compared to ArcGIS Server, as well as the weak spatial analysis intelligence function and limited data format support types of the original system, software engineering UML use case modeling technology and ArcGIS Server an...Learn More
Abstract: This study proposes a method based on the CLSTM model for detecting misconduct among laboratory personnel. The model integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Initially, the CNN perceives image features and performs feature extraction through convolutional and pooling operations. These extracted features are subsequently fed into the LSTM network, which leverages its memory capability to capture and understand patterns of misconduct in the temporal sequence. This structure enables the CLSTM model not only to effectively process and analyze complex image data but also to strike a balance between long-term memory and short-term variations, thereby enhancing the accuracy and robustness of misconduct detection. The model enhances the representation capability of image features through the convolutional network, further optimizing the LSTM model's performance in computational efficiency and training effectiveness. Compared to traditional LSTM models, CLSTM demonstrates faster convergence during training. To validate the model's effectiveness, experiments were conducted on two different datasets. The results indicate that the proposed CLSTM model significantly outperforms traditional LSTM methods, achieving performance improvements ranging from 7% to 11%. These findings underscore the superiority of the CLSTM model in laboratory personnel misconduct detection tasks.Abstract: This study proposes a method based on the CLSTM model for detecting misconduct among laboratory personnel. The model integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Initially, the CNN perceives image features and performs feature extraction through convolutional and pooling operations. These extracted fea...Learn More
Abstract: This paper focuses on generative AI, a typical representative of contemporary artificial intelligence (AI) and artificial general intelligence (AGI), aiming to delve into the latest research progress in its basic theory. The research method involves a comparative analysis of the differences in underlying logic and formal understanding between traditional AI and Current AI, further exploring the distinctions between the three core viewpoints of traditional AI (symbolism, connectionism, behaviorism) and the three major schools of Current AI (generative AI/AGI based on large language models (LLMs) such as ChatGPT; new quality productive force AGI characterized by small models, such as I3DNA; and twin Turing machines based on dual formal understanding models that are compatible with both large and small models). The research reveals the core components of the basic theory of AI and AGI: bit-list logic, linkage functions, followed by generalized bilingualism or generalized translation based on digital and intelligent text with the three fundamental laws. The significance of this research lies in not only enhancing the interpretability of generative AI/AGI based on LLMs represented by ChatGPT but also providing generalized translations for the new quality productive force AGI characterized by small models and its complex theories of cosmic intelligence and the universal model series. At the same time, it demonstrates the potential of twin Turing machines as inclusive intelligent agents in integrating data, knowledge, computing power, algorithms, and human-computer mutual assistance in the new era of cognitive paradigms, laying the foundation for constructing super intelligent systems.Abstract: This paper focuses on generative AI, a typical representative of contemporary artificial intelligence (AI) and artificial general intelligence (AGI), aiming to delve into the latest research progress in its basic theory. The research method involves a comparative analysis of the differences in underlying logic and formal understanding between tradi...Learn More
