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A High-Precision Generality Method for Chinese Nested Named Entity Recognition 18th

A High-Precision Generality Method for Chinese Nested Named...

引用

18th international conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Ji, Xiayan Chen, Lina Gao, Hong Shen, Fangyao Guo, Hongjie College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China School of Computer Science and Technology Zhejiang Normal University Jinhua China

ISBN: (纸本)9783031714696

Chinese Named Entity Recognition (CNNER) faces numerous challenges, including the diversity of the Chinese language, the complex representation of mixed Chinese and English characters and symbols in texts, the complexity of the Chinese language itself with long sentences containing multiple entities, and the uneven distribution of named entity classes in actual Chinese scenarios. To address these challenges, we propose a method called CPMFA (Character Pair-based method with Multi-feature representation and Attention mechanism). the CPMFA method predicts predetermined relations between character pairs, facilitating the identification of nested named entities based on these relations. Firstly, the method leverages the pre-trained language model LERT (Linguistically-motivated Bidirectional Encoder Representation from Transformer) and BiLSTM (Bidirectional Long Short-Term Memory) to generate comprehensive and accurate character embeddings. Secondly, it incorporates multi-feature representation to capture complex semantic information and introduces the Pyramid Squeeze Attention (PSA) module to emphasize key features. Finally, the PolyLoss function is integrated into the model training process to tackle the challenge of an imbalanced distribution of entity classes. We employed the DiaKG, Yidu-S4K and Weibo datasets to validate and evaluate the efficacy and adaptability of our method. the F1 obtained by the CPMFA on these three datasets is 83.79%, 72.03%, and 70.39%, in that order. the experimental results illustrate the outstanding performance of the proposed CPMFA method in both general knowledge and Chinese medical domains. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. this study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. the results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels

Adaptive Prescribed Time Path Following Controller for Under...

引用

international conference on intelligent Control and information Processing (ICICIP)

作者： Jawhar Ghommam Maarouf Saad Quanmin Zhu Mohammed H. Rahman Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol UK Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin USA

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Heading control of marine vehicles is an important issue in navigation and control engineering. this paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. the establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. the feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. the Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy.

关键词： Uncertainty Control design Numerical simulation Regulation Stability analysis Parametric statistics Numerical models Underactuated surface vessels Surface treatment Numerical stability

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Probabilistic Offloading Algorithm for Opportunistic Networks Integrating Node Influence Prediction 18th

Probabilistic Offloading Algorithm for Opportunistic Networ...

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18th international conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Tang, Qi Xu, Xiaodong Li, Shuai Seah, Winston Xu, Gang College of Computer Science Inner Mongolia University Hohhot010021 China The Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot010021 China Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9783031714634

In Opportunistic Network based mobile data diversion algorithms, the need for multi-hop transmission makes the selection of the next hop node critical. the traditional Prophet algorithm calculates the encounter delivery probability of a node based on historical encounter information as a forwarding condition. However, algorithms for data diversion are more concerned with the issue of nodes’ propagation influence in the network. therefore, in this paper, we propose a metric to measure the dynamic influence of a node (sir) and use the CNN-BiLSTM-Attention model to predict the sir value of a node during the current time period in the route. After that, a data diversion algorithm based on deep learning prediction of node influence is proposed in combination with encounter delivery probability. the experimental results show that the NS-Prophet algorithm performs well in terms of performance metrics in opportunistic networks, and the triage efficiency in cellular networks is also improved. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data diversion deep learning prediction node influence opportunistic networks

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Decentralized Federated Learning with Auto-switchable Local Optimizers

Decentralized Federated Learning with Auto-switchable Local ...

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international conference on intelligent Control and information Processing (ICICIP)

作者： Ting Li Zhongyi Chang Zhiyao Pan Shaofu Yang Wenying Xu Zhongying Chen School of Computer Science and Engineering Southeast University Nanjing China School of Mathematics Southeast University Nanjing China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In this paper, we propose a novel decentralized learning algorithm over networks, termed as DLAGD, which combines the consensus mechanism with an auto-switchable local optimizer. Specifically, each node updates its local model parameter based on either gradient descent algorithm or Adam-like algorithm, which depends on a local switching function. Such switchable local optimizer can better capture the structure information of local loss function than existing algorithms relying on a fixed local optimizer, then has the potential to accelerate the convergence rate and improve the generalization performance. We theoretically prove that DLAGD can converge to the neighborhood of a stationary point at a sublinear rate for non-convex problems. Finally, numerical experiments are presented to show the superior performance of DLAGD in terms of the training rate and test accuracy.

关键词： Training Accuracy Federated learning Switches information processing Consensus protocol Convergence intelligent control

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Research on Mango Ripeness based on Yolo

Research on Mango Ripeness Based on Yolo

引用

international conference on intelligent Control and information Processing (ICICIP)

作者： Tiezheng Qiao Xiaonan Luo Xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Mangoes are a crucial agricultural crop in southern China, especially in the Guangxi region, where their production significantly contributes to the local economy. However, challenges in ripeness identification lead to quality control issues in storage, transportation, and sales. this research proposes a detection method for mango ripeness based on an improved YOLOv5 model. Traditional fruit ripeness detection methods have certain limitations in terms of accuracy and efficiency. To improve detection accuracy, this research replaces the backbone network of the YOLOv5 model with ResNet50 and introduces the Coordinate Attention (CA) mechanism to enhance the models feature extraction ability and the accuracy of ripeness judgment Experimental results show that the improved model demonstrates higher precision and robustness in detecting mangoes at different stages of ripeness. the research results indicate that the improved YOLOv5 model can effectively identify and distinguish mango ripeness, providing technical support for fruit quality management and harvesting in the agricultural field.

关键词： YOLO Accuracy Transportation Production Quality control Feature extraction Robustness Real-time systems Timing Residual neural networks

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DSGCN: Dual-Stream Graph Convolutional Network for Skeleton-based Action Recognition Under Noise Interference 17th

DSGCN: Dual-Stream Graph Convolutional Network for Skeleton-...

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17th international conference on intelligent Robotics and Applications, ICIRA 2024

作者： Cui, Yanxin Li, Yufeng Fan, Weiming Wang, Xuna Yu, Jiahui Ju, Zhaojie School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China Department of Biomedical Engineering Zhejiang University Hangzhou China Innovation Center for Smart Medical Technologies and Devices Binjiang Institute of Zhejiang University Hangzhou China University of Portsmouth PortsmouthPO13HE United Kingdom

ISBN: (纸本)9789819607761

the advancement in action recognition is crucial for enhancing interactive systems, improving surveillance accuracy, and optimizing autonomous driving technologies. Traditional RGB-based recognition methods rely on obtaining complete human body information but often face challenges such as sensitivity to environment conditions, loss of spatial information, and interference from ambient noise. these factors pose significant challenges to the accuracy of dynamic action recognition, especially in uncontrolled environments. To address these issues, our proposed Dual-Stream Graph Convolutional Network (DSGCN) leverages the inherent structures of skeleton data. the data flow is divided into two streams: a joint stream and a bone stream, each of which processed through a graph convolutional network. the joint stream is equipped with Class Activation Maps (CAM) to enhance feature recognition. We assessed the DSGCN model using the NTU RGB + D 60 and NTU RGB + D 120 datasets，comparing it with the Richly Activated Graph Convolutional Network (RAGCN) model. By integrating five types of simulated noise into our test, DSGCN demonstrated a 3% higher overall accuracy than RAGCN. the results indicate that despite noise interference, DSGCN maintains an excellent accuracy, demonstrating its Practicality in real-world scenarios with incomplete data and occlusions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

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Mining Intelligence Hierarchical Feature for Malware Detection 15th

Mining Intelligence Hierarchical Feature for Malware Detecti...

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15th international conference on intelligent Computing and Networking, IC-ICN 2024

作者： Davuluri, Sandeep Kumar Soni, Mukesh ALMahadin, Ghayth Rivera, Richard Upadhyay, Jinal Patel, Pavan Department of Information Technology University of the Cumberlands Williamsburg United States Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Faculty of Information Technology Al Ahliyya Amman University Amman Jordan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador HasmukhGoswami College of Engineering Gujarat Vahelal India Ahmedabad Institute of Technology Gujarat Ahmedabad India

ISBN: (纸本)9789819786305

In recent years, a new strain of malware, known as mining malware, has emerged, resulting in significant losses for its victims due to its encryption techniques. this research proposes an innovative approach to detect mining malware by integrating hierarchical threat intelligence features, focusing on the static attributes of mining malware. From the perspective of mining malware threat intelligence, we train classifiers using distinct layers of features, including byte-level features, Portable Executable (PE) structure features, and mining operation execution features. By leveraging diverse malicious software attributes for detection preferences, we develop a comprehensive mining malware detector based on a hierarchical feature integration methodology. To evaluate our approach, we conducted experiments in both controlled lab settings and real-world datasets. the results indicate that our hierarchical feature-integrated mining malware detection method achieves an impressive accuracy rate of 97.01% on the simulated real-world dataset, demonstrating a substantial 6.13% accuracy improvement compared to the baseline mining malware detection method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Non-Intrusive Load Monitoring Method based on Relative Position Matrix and Residual Network

A Non-Intrusive Load Monitoring Method Based on Relative Pos...

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international conference on intelligent Control and information Processing (ICICIP)

作者： Jin-Yang Huang Chun-Hua Chen Yi Jiang Junwei Yan Jun Zhang Zhi-Hui Zhan School of Software Engineering South China University of Technology Guangzhou China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Non-Intrusive Load Monitoring (NILM) technology identifies various appliances’ types and operational status by analyzing electrical data collected from sensors in residential or commercial buildings, offering significant practical value. However, due to the limitations of existing methods for constructing appliance features, the identification performance of NILM models needs improvement. Addressing this, we propose a method for constructing appliance features based on a relative position matrix (RPM), which more accurately captures the temporal characteristics of electrical data compared to other methods. Additionally, a deep learning model based on the improved ResNet18 with multi-scale fusion is proposed for the load identification task. In this paper, comparative experiments were conducted on the PLAID dataset. We compared the RPM with three appliance feature construction methods: voltage-current trajectory (VI), markov transition field (MTF), and gramian angular field (GAF). We also compared our results with advanced methods reported in other literature. the results show that this method outperforms existing methods in terms of precision, recall, and F1 score across various evaluation metrics.

关键词： Measurement Load monitoring Buildings information processing Feature extraction Sensors Trajectory Load modeling Residual neural networks intelligent control

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Anomaly Detection Under Normality-Shifted IoT Scenario: Filter, Detection, and Adaption 18th

Anomaly Detection Under Normality-Shifted IoT Scenario: Filt...

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18th international conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Pan, Mengying Tang, Wenyi He, Zaobo Chen, Bingyu School of Cyber Science and Engineering Sichuan University Chengdu Sichuan610207 China College of Information Science and Technology Jinan University Guangdong Guangzhou510632 China

ISBN: (纸本)9783031714665

Learning-based anomaly detection methods train models using normal data samples to capture normal behavioral patterns for identifying anomalies. Unfortunately, the distribution of normal data in Internet of things (IoT) environments always shifts because of device upgrades and security patch implementations. Although few efforts have been made, current approaches fail to effectively exclude anomalies during the phase of normality shifts detection, and suffer from a high labeling costs during the adaptation phase. To overcome these drawbacks, this study proposes a comprehensive solution encompassing three modules. the first Normality Shift Filter is preposed to filter anomalies, meanwhile, leverages the latent space representation of an Autoencoder (AE) to capture representative samples, thereby significantly reducing the cost of labeling. the second Normality Shift Detector employs a tripartite ensemble method to accurately detect normality shifts. the third Normality Shift Adapter is designed as a customized progressive neural network to further adapt incoming shifts while retaining the valuable knowledge learned from historical data. Empirical tests conducted on open datasets demonstrate that our proposed method outperforms the state-of-the-art (SOTA) methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Normal distribution

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