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Current Status of Objectification of Four Diagnostic Methods on Constitution Recognition of Chinese Medicine

Chinese Journal of Integrative Medicine 2022年第12期28卷 1137-1146页

作者： LI Cong-cong YAN Xin-sheng LIU Ming-hao TENG Gui-fa School of Information Science and Technology Hebei Agricultural UniversityBaodingHebei Province 071001China Hebei Key Laboratory of Agricultural Big Data BaodingHebei Province 071001China

Chinese medicine(CM)has thousands of years of experience in prevention of *** for CM,people's constitution is closely related to their health status,thus recognition of CM constitution is the fundamental and core contentof research on constitution *** development of technologies such as sensors,arificial intelligence and big data,objectification of the four diagnostic methods of CM has gradually matured,bringing changes in the mindset and innovations in technical means for recognition of CM *** paper presents a systematic review of the latest research trends in constitution recognition based on objectification of diagnostic methods in CM.

关键词： Chinese medicine constitution recognition four diagnostic methods objectification machine learning deep learning

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FSAKE: Few-shot graph learning via adaptive neighbor class knowledge embedding

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Expert Systems with applications 2025年 285卷

作者： Zou, Linhua Jin, Jie Li, Dongqing Zhao, Hong School of Computer Science Minnan Normal University Zhangzhou Fujian363000 China The Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou Fujian363000 China

Few-shot graph learning tackles the challenge of categorization with limited samples by utilizing the relational information encoded on the graph. Recent studies have acquired considerable success in directing query node inference on graphs by exploiting class knowledge. However, these methods independently assess the importance of different class knowledge and ignore the interrelatedness of neighbor class knowledge. In this paper, we propose an adaptive neighbor class knowledge embedding (FSAKE) consisting of knowledge filtering (KF) and correction (KC), aiming to exploit the interconnected neighbor class knowledge to enhance the recognition of crucial class features. KF integrates neighbor information into the node scoring process during the graph pooling phase, ensuring the preservation of inter-class similar and intra-class representative nodes to learn important class knowledge further. Meanwhile, KC introduces an additional correction loss during the graph unpooling phase, combined with the classification loss, to supervise the deep propagation of class knowledge. Experimental results on four popular benchmark few-shot datasets illustrate the effectiveness of FSAKE. For instance, under the 5-way 1-shot setting, FSAKE outperforms other models on miniImageNet and CIFAR-FS with an accuracy improvement of at least 1.32% and 2.11%. Code is available at https://***/fhqxa/FSAKE. © 2025

关键词： Flow graphs

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Review on application progress of federated learning model and security hazard protection

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Digital Communications and Networks 2023年第1期9卷 146-158页

作者： Aimin Yang Zezhong Ma Chunying Zhang Yang Han Zhibin Hu Wei Zhang Xiangdong Huang Yafeng Wu Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina College of Science North China University of Science and TechnologyTangshanHebeiChina College of Artificial Intelligence North China University of Science and TechnologyTangshanHebeiChina

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data *** data privacy becomes more important,it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data *** is forced to be stored independently between companies,creating“data silos”.With the goal of safeguarding data privacy and security,the federated learning framework greatly expands the amount of training data,effectively improving the shortcomings of traditional machine learning and deep learning,and bringing AI algorithms closer to our *** the context of the current international data security issues,federated learning is developing rapidly and has gradually moved from the theoretical to the applied *** paper first introduces the federated learning framework,analyzes its advantages,reviews the results of federated learning applications in industries such as communication and healthcare,then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications,and finally looks into the future of federated learning and the application layer.

关键词： data silos Machine learning Federated learning Privacy protection Learning framework

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Ipmmg: Information Propagation with Multi-Granularity Morphology-Guided for Nuclear Segmentation and Classification

SSRN

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SSRN 2025年

作者： Fan, Dawei Li, Jun Cai, Chengfei Lin, Lihui Chen, Riqing Chen, Yanping Wei, Lifang College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China College of Future Technology Fujian Agriculture and Forestry University Fuzhou350002 China Department of Pathology Clinical Oncology School of Fujian Medical University Fujian Cancer Hospital Fuzhou350014 China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan354300 China

Nuclear segmentation and classification play a pivotal role in pathological image analysis. However, it is often hindered by blurred nuclear boundaries and complex structures in digital pathology slides, caused by factors such as staining techniques and imaging methods, presenting a significant challenge for accurate segmentation and classification. To address this issue, we propose a novel and efficient approach for nuclear identification, termed Information Propagation with Multi-Granularity Morphology-Guided Network (IPMMG). IPMMG progressively captures edge morphology information from different network layers while simultaneously incorporating structural morphology features at multiple granularities. By explicitly propagating features related to both the edge and structure, our approach constrains semantic features to focus on the region of interest's contour, thereby alleviating the challenge of blurred morphology. Experiments on public datasets demonstrate that IPMMG achieves state-of-the-art performance in segmentation, as measured by Dice and IoU scores, while also attaining competitive results in classification with DQ, SQ, and PQ metrics. In particular, it excels in handling nuclei with blurred edges and complex structures. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Mutable hierarchy feature selection based on generalized fuzzy rough sets

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Applied Soft Computing 2025年 177卷

作者： Lin, Zilong Lin, Yaojin Wang, Chenxi Chen, Jinkun School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou Fujian363000 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan Fujian354300 China School of Mathematics and Statistics Minnan Normal University Zhangzhou Fujian363000 China

Hierarchical classification divides data into correlated sub-tasks from coarse to fine. Compared to flat classification, it is more complex and suffers from the curse of dimensionality. Existing hierarchical Fuzzy Rough Sets (FRS) methods only stay at the fine-grained to select the features, which is a fine-grained search strategy. Thus, we propose a coarse-grained search strategy and use Generalized Fuzzy Rough Sets (GFRS) to enhance its robustness. Furthermore, we introduce the concept of tree fragmentation. Though assessing the degree of fragmentation in the tree structure, it selects an appropriate granularity search strategy for hierarchical tree-structured datasets. Finally, we combine both granularity search strategies and collectively named - Mutable Hierarchy Search Strategy (MHSS);the entire proposed algorithm is named - Mutable Hierarchy Feature Selection Based on Generalized Fuzzy Rough Sets (MHFS). We compare two FRS-based feature selection algorithms, three hierarchical optimization methods, and six flat feature selection methods. Extensive experiments demonstrate the performance of our method. © 2025 Elsevier B.V.

关键词： Feature extraction

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Mining Repetitive Negative Sequential Patterns with Gap Constraints

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ACM Transactions on Knowledge Discovery from data 2025年第4期19卷

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hebei Key Laboratory of Big Data Computing Tianjin China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this article explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： gap constraint key-value pair negative sequential pattern sequential pattern mining

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A Review of Terahertz Sources Based on Planar Schottky Diodes

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Chinese Journal of Electronics 2022年第3期31卷 467-487页

作者： KOU Wei LIANG Shixiong ZHOU Hongji DONG Yazhou GONG Sen YANG Ziqiang ZENG Hongxin University of Electronic Science and Technology of China Yangtze Delta Region Institute (Huzhou) National Key Laboratory of Application Specific Integrated Circuit Hebei Semiconductor Research Institute

The special position of terahertz wave in the electromagnetic spectrum makes it possess the characteristics of orientation, broadband, penetration and low energy, which promotes the extensive research of terahertz wave in the fields of communication, radar, imaging,sensing, security inspection and so on. The solid-state terahertz sources based on semiconductor devices have attracted extensive attention in the field of terahertz information technology due to their characteristics such as being able to work at room temperature, being small in size, being easy to integrate and having good frequency stability. Terahertz planar Schottky diode is a kind of low parasitic semiconductor device. Its high cutoff frequency makes it work well in the terahertz range. The frequency multiplier based on planar Schottky diode is an important part of terahertz solid state source. In this review, the development of Schottky diodes technology in recent years have been introduced, including the structures and preparation of Schottky diodes. In addition, the current situation and performance of different types of terahertz sources based on Schottky diodes are further introduced,and the future development trend is discussed.

关键词： frequency multiplier terahertz range terahertz wave terahertz planar Schottky diode terahertz information technology terahertz solid state source semiconductor devices Schottky diodes good frequency stability security inspection frequency multipliers terahertz wave devices special position solid-state terahertz sources high cutoff frequency low parasitic semiconductor device Schottky diodes technology electromagnetic spectrum

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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Remote preparation for single-photon state in two degrees of freedom with hyper-entangled states

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Frontiers of physics 2021年第4期16卷 65-73页

作者： Mei-Yu Wang Fengli Yan Ting Gao College of Physics Hebei Normal UniversityShijiazhuang 050024China School of Mathematics Science Hebei Normal UniversityShijiazhuang 050024China Hebei Key Laboratory of Photophysics Research and Application Shijiazhuang 050024China

Remote state preparation(RSP)provides a useful way of transferring quantum information between two distant nodes based on the previously shared *** this paper,we study RSP of an arbitrary single-photon state in two degrees of freedom(DoFs).Using hyper-entanglement as a shared resource,our first goal is to remotely prepare the single-photon state in polarization and frequency DoFs and the second one is to reconstruct the single-photon state in polarization and time-bin *** the RSP process,the sender will rotate the quantum state in each DoF of the photon according to the knowledge of the state to be *** performing a projective measurement on the polarization of the sender’s photon,the original single-photon state in two DoFs can be remotely reconstructed at the receiver’s quantum *** work demonstrates a novel capability for longdistance quantum communication.

关键词： remote state preparation hyper-entanglement

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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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