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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Yang, Huanqi Sun, Zehua Liu, Hongbo Xia, Xianjin Zhang, Yu Gu, Tao Hancke, Gerhard Xu, Weitao City University of Hong Kong Shenzhen Research Institute Hong Kong City University of Hong Kong Department of Computer Science Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Computing Hong Kong Macquarie University School of Computing Australia

ISBN: (纸本)9798350334142

Physical-layer key generation is promising in establishing a pair of cryptographic keys for emerging LoRa networks. However, existing key generation systems may perform poorly since the channel reciprocity is critically impaired due to low data rate and long range in LoRa networks. To bridge this gap, this paper proposes a novel key generation system for LoRa networks, named ChirpKey. We reveal that the underlying limitations are coarse-grained channel measurement and inefficient quantization process. To enable fine-grained channel information, we propose a novel LoRa-specific channel measurement method that essentially analyzes the chirp-level changes in LoRa packets. Additionally, we propose a LoRa channel state estimation algorithm to eliminate the effect of asynchronous channel sampling. Instead of using quantization process, we propose a novel perturbed compressed sensing based key delivery method to achieve a high level of robustness and security. Evaluation in different real-world environments shows that ChirpKey improves the key matching rate by 11.03-26.58% and key generation rate by 27-49× compared with the state-of-the-arts. Security analysis demonstrates that ChirpKey is secure against several common attacks. Moreover, we implement a ChirpKey prototype and demonstrate that it can be executed in 0.2 s. © 2023 IEEE.

关键词： Compressed sensing

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Semantic Segmentation Network of Noisy Plant Point Cloud based on Self-Attention Feature Fusion 10

Semantic Segmentation Network of Noisy Plant Point Cloud bas...

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10th International Conference on Information Systems and Computing technology, ISCTech 2022

作者： Chen, Yang Zeng, An Pan, Dan Ji, Yuzhu School of Computer Science Guangdong University of Technology Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China

ISBN: (纸本)9798350332933

When 3D reconstruction of plant seedlings is performed to obtain plant point clouds, there are many noisy points between the leaves and stems of plant point clouds due to the influence of ambient light and the limitation of the camera vision, which affects the automatic measurement of plant *** order to achieve plant point cloud stem and leaf segmentation with a large amount of noise, We propose a network for semantic segmentation of noisy plant point clouds based on self-attentive feature fusion (abbreviated as SAFF-Net).The network first extracts shallow spatial features and higher-level semantic features between points by using correlations between pairs of points in the neighborhood through the local feature fusion module, and enhances the ability to extract plant shape features;The dual-branch attention pooling module is used to aggregate features, and one branch uses the channel attention mechanism to adaptively filter low-correlation features, avoiding redundancy of features and mitigating the bias effect of noise *** branch performs max pooling for the highest level feature map to obtain global contextual features, and finally merges local and global contextual features to learn more discriminative *** point cloud dataset for the experiment is derived from multi-angle photographs of the plant taken by a high-definition camera and reconstructed in 3D by Structure from Motion(SFM) *** experimental results indicate that SAFF-Net performs better than the mainstream semantic segmentation networks and extracts more fine-grained local features. OA and (mIoU) of this method on the plant dataset are 93.7% and 83.4%, respectively. It outperforms existing methods in the segmentation of noise points and leaves, and the segmentation results have higher precision and recall. © 2022 IEEE.

关键词： Semantic Segmentation

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Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

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science China(Physics,Mechanics & Astronomy) 2023年第5期66卷 23-72页

作者： He-Liang Huang Xiao-Yue Xu Chu Guo Guojing Tian Shi-Jie Wei Xiaoming Sun Wan-Su Bao Gui-Lu Long Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Beijing Academy of Quantum Information Sciences Beijing 100193China Department of Physics Tsinghua UniversityBeijing 100084China

Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and *** it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum *** said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing *** outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum *** address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful ***,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other *** review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field.

关键词： quantum computing noisy-intermediate scale quantum variational quantum algorithms error mitigation circuit com-pilation benchmarking protocols classical simulation

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional Networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction

TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescr...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Xingzhi Dong, Xin Li, Chunhao Bai, Yuning Xu, Yulong Cheung, Ka Chun Se, Simon Song, Xinpeng Zhang, Runshun Zho, Xuezhong Zhang, Nevin L. Hong Kong University of Science and Technology Department of Computer Science and Technology Hong Kong Hong Kong Beijing Jiaotong University Institute of Medical Intelligence Department of Artificial Intelligence Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing100044 China China Academy of Chinese Medical Sciences Guang'anmen Hospital Beijing China Henan University of Chinese Medicine School of Information Technology China Nvidia Nvaitc Hong Kong Hong Kong Nvidia Nvaitc Singapore

ISBN: (纸本)9798350386226

Traditional Chinese medicine (TCM) has relied on specific combinations of herbs in prescriptions to treat various symptoms and signs for thousands of years. Predicting TCM prescriptions poses a fascinating technical challenge with significant practical implications. However, this task faces limitations due to the scarcity of high-quality clinical datasets and the complex relationship between symptoms and herbs. To address these issues, we introduce DigestDS, a novel dataset comprising practical medical records from experienced experts in digestive system diseases. We also propose a method, TCM-FTP (TCM Fine-Tuning Pre-trained), to leverage pre-trained large language models (LLMs) via supervised fine-tuning on DigestDS. Additionally, we enhance computational efficiency using a low-rank adaptation technique. Moreover, TCM-FTP incorporates data augmentation by permuting herbs within prescriptions, exploiting their order-agnostic nature. Impressively, TCM-FTP achieves an F1-score of 0.8031, significantly outperforming previous methods. Furthermore, it demonstrates remarkable accuracy in dosage prediction, achieving a normalized mean square error of 0.0604. In contrast, LLMs without fine-tuning exhibit poor performance. Although LLMs have demonstrated wide-ranging capabilities, our work underscores the necessity of fine-tuning for TCM prescription prediction and presents an effective way to accomplish this. © 2024 IEEE.

关键词： Medicinal chemistry

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Geneto-Neuro-Fuzzy Solution for Detection of Early Prostate Cancer 10

Geneto-Neuro-Fuzzy Solution for Detection of Early Prostate ...

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Oghorodi, Duke Kingsley, Igulu Singh, Thipendra P Atajeromavwo, Edafe J. Nwankwo, Wilson Delta State University of Science & Technology Department of Comp. Sc. Ozoro Nigeria Kenule Beeson Saro-Wiwa Polytechnic Bori Department of Computer Science Bori Nigeria School of Computer Science Engineering & Technology Bennett University Greater Noida India Delta State University of Science & Technology Department of Cyber Security Ozoro Nigeria

ISBN: (纸本)9798331511890

This study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating genetic algorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, the system achieves a marked improvement in sensitivity and specificity-up to a 12% increase over conventional diagnostic methods-based on extensive simulations and a controlled clinical dataset. The neuro-fuzzy component adaptively merges genetic, clinical, and historical patient data, refining its predictive accuracy through continuous learning. A decentralized computation strategy enhances scalability and lowers device-level computational overhead, supporting widespread implementation in diverse healthcare environments. While initial results underscore its promise, validation through large-scale clinical trials remains a priority. This approach offers a practical, high-accuracy solution that could significantly reduce late-stage prostate cancer diagnoses, particularly in resourcelimited settings, and stands as a step forward in precision oncology diagnostics. The developed approach can be helpful in diagnosis of the relevant healthcare problem. © 2025 IEEE.

关键词： Fuzzy neural networks

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Harnessing large language models to auto-evaluate the student project reports

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computers and Education: Artificial Intelligence 2024年 7卷

作者： Du, Haoze Jia, Qinjin Gehringer, Edward Wang, Xianfang Department of Computer Science North Carolina State University RaleighNC27695 United States School of Computer Science & Technology Henan Institute of Technology Xinxiang453003 China

Addressing the problem of the difficulty in providing timely and reasonable feedback evaluation for student project reports, this paper proposes a method based on LLMs (Large Language Models) that can automatically generate instant feedback evaluations for student project reports. Three LLMs, namely BART (Bidirectional and Auto-Regressive Transformer), CPTB (chatgpt_paraphraser_on_T5_base), and CGP-BLCS (chatgpt-gpt4-prompts-bart-large-cnn-samsum), were designed to generate instant text feedback pre-training models for student project reports. The effectiveness of the feedback was evaluated using ROUGE Metrics, BERT Scores, and human expert evaluations. Experiments showed that the lightweight, fine-tuned BART model, when trained on a larger dataset of 80%, generated effective feedback evaluations for student project reports. When trained on a smaller dataset of 20%, both the BART and CPTB models had unsatisfactory overall performance, while the fine-tuned CGP-BLCS model was able to generate feedback evaluations that approached human-level evaluations. The detailed descriptions of the methods used with the LLMs for generating effective text feedback evaluations for student project reports will be useful to AI computer programmers, researchers, and computer science instructional designers for improving their courses and future research. © 2024

关键词： Students

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Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis

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International Journal of Cognitive Computing in Engineering 2021年 2卷 93-103页

作者： Pi, Pengpeng Lima, Dimas School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo 454000 China Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil

Background: Chest CT is considered to be a more accurate method for diagnosing suspected patients. However, with the spread of the epidemic, traditional diagnostic methods have been unable to meet the requirements of efficiency and speed. Therefore, it is necessary to use artificial intelligence to help people make efficient and accurate judgments. A number of studies have shown that it is feasible to use deep learning methods to help people diagnose COVID-19. However, most of the existing methods are single-layer neural network structures, and their accuracy and efficiency need to be improved. Method: In this scheme, a hybrid model is adopted. Firstly, the gray co-occurrence matrix is used to extract the features of the images, and then the extreme learning machine is used for classification. Results: The experimental results show that the model proposed in this paper is feasible and can help medical staff to accurately determine suspected patients for subsequent isolation and treatment. © 2021

关键词： COVID-19 Extreme learning machine Gray level co-occurrence matrix

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Domain Adaptation with Sample Relation Reinforcement 1

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24th International Conference on Web Information Systems Engineering, WISE 2023

作者： Teng, Shaohua Liu, Wenjie Guo, Ruixi Zhang, Wei Zheng, Zefeng Teng, Luyao Chen, Tongbao School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China School of Electrical Engineering Xi’an Jiaotong University Xi’an710049 China School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou511483 China School of Advanced Manufacturing Guangdong University of Technology Guangzhou510006 China

ISBN: (数字)9789819972548

ISBN: (纸本)9789819972531

Domain adaptation transfers knowledge from the source domain into the target domain by minimizing the distribution discrepancy between two domains. Recently, existing approaches ignore the position differences between samples within the same class. However, there are still two problems that need to be resolved. Firstly, marginal samples and central samples may interact with each other, which leads to samples within the same class aggregate toward the margin (loss common knowledge) or toward the center (loss specific knowledge). Secondly, large distribution differences may result in degraded performance in classification. To solve these two problems, we propose a domain adaptation method called Sample Relation Reinforcement (SRR). To mitigate the interaction between marginal samples and central samples, marginal samples detection (MSD) and central samples detection (CSD) are proposed. Specifically, MSD propose a triple distances metric among the sample pairs and class centers to detect the marginal samples and learn specific knowledge. CSD utilizes the nearest and second-nearest centroid of samples to detect the central samples and learn common knowledge. In addition, dual centroid detection (DCD) is proposed to alleviate the negative effects of distribution differences, which utilizes the class centers of the source and target domains to classify the target samples. Finally, the experimental results on four datasets demonstrate that the proposed method outperforms other competing approaches significantly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement

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Spatial distribution information extraction of winter wheat in henan Province based on multi-source sentinel remote sensing data

Spatial distribution information extraction of winter wheat ...

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2024 International Conference on Remote Sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Zhang, Ziqian Lv, Junye Cui, Tengfei Zhou, Qiongbo Jiang, Chuande Liu, Zhiyang Si, Qiliang Henan University of Science and Technology Software School No. 263 Kaiyuan Avenue Luolong District Henan Province Luoyang City471000 China School of Surveying and Land Information Engineering Henan Polytechnic University No. 2001 Shiji Road Jiaozuo454000 China Boston University Metropolitan College BostonMA02215 United States University of Southern California Gould School of Law CA90089 United States School of Water Conservancy North China University of Water Resources and Electric Power Zhengzhou450046 China College of Science Weihai264209 China

ISBN: (纸本)9781510685826

Timely and accurately acquiring spatial distribution information for winter wheat is crucial for formulating agricultural policies. However, the information extraction for traditional spatial distribution for winter wheat relies primarily on manual field surveys. This method is labour-intensive and subjective and less accurate. With that remote sensing technology continues to develop, new avenues for extracting distribution information for winter wheat have emerged. Nevertheless, previous studies often did not fully leverage the advantages of remote sensing data from multiple sources, resulting in lower extraction accuracy. To address this, this study focuses on henan Province, where Sentinel-2 optical imagery and Sentinel-1 radar imagery. Then support vector machine and random forest algorithms were used to construct winter wheat extraction model. The results indicate that the extraction accuracy of Sentinel-2 optical imaging surpasses Sentinel-1 radar imaging, with an overall accuracy of 82.04%, the producer's accuracy for winter wheat reaches 86.25%, the accuracy of user is 85.19%, the Kappa coefficient is 0.770. Further integration within the Sentinel-2 optical imagery with Sentinel-1 radar imagery significantly enhances extraction's accuracy. The overall accuracy improves to 90.19%, the accuracy for producers increases to 96.25%, user's accuracy rises to 95.06%, and the Kappa coefficient increases to 0.874. Additionally, by comparing different machine learning algorithms, it is observed that the algorithm random forest consistently outperforms the algorithm support vector machine in extraction accuracy regardless of the dataset. In this way, this study provides valuable guidance for obtaining information on the spatial distribution of winter wheat in henan Province. © 2024 SPIE.

关键词： Radar imaging

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