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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Wang, Lihua Cao, Baofeng Li, Peng Li, Zongxiang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Nuclear and Biochemical Calamity Protection for Civilian Beijing102205 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei203601 China

ISBN: (纸本)9798350399059

A novel wideband miniaturized composite monopole antenna is proposed, analysed and investigated in detail, where the antenna consists of a conical monopole structure with a loaded capacitive disc. The antenna is composed of multiple nested section, aiming to reduce the profile of the antenna. The loading disc at its top is equivalent to a capacitive structure to improve the antenna matching and the stability of the pattern. According to the simulation results, the impedance bandwidth (S11 © 2023 IEEE.

关键词： Monopole antennas

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Research on Machine Reading Comprehension Model Based on Bidirectional Attention

Research on Machine Reading Comprehension Model Based on Bid...

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2023 IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2023

作者： Jiang, Chenglin Ma, Ning Wan, Fucheng Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Northwest Minzu University Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Gansu Lanzhou730000 China

ISBN: (纸本)9798350313734

Machine Reading Comprehension (MRC) is a significant subfield of Natural Language processing (NLP) and has become a hot research topic in recent years. It is an algorithm that allows a machine to understand natural language text and answer based on questions. With the rapid development of deep learning, MRC, especially attention mechanisms, has emerged as a primary technique in *** paper focuses on the development and principles of attention mechanisms, along with introducing three MRC models based on bidirectional attention. Bidirectional attention helps the models extract crucial information, enhancing accuracy and enabling broader applications in various NLP tasks. © 2023 IEEE.

关键词： Deep learning

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Geological Disaster Named Entity recognition with Small Samples Based on Data Augmentation and Prompt Engineering 24

Geological Disaster Named Entity Recognition with Small Samp...

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2024 7th International Conference on Computer information Science and Artificial Intelligence, CISAI 2024

作者： Ma, Xiangfei Li, Lin School of Computer Science Qinghai Normal University Qinghai Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining China Academy of Plateau Science and Sustainability Qinghai Normal University Qinghai Xining China

ISBN: (纸本)9798400707254

This paper uses a large language model to perform generative data enhancement on the original small sample data by performing random synonym replacement and random mask filling operations. In accordance with the reasoning logic of the large language model, three prompt templates are designed and the reasons are explored. Experiments show that when the parameters remain unchanged, the data enhanced by this method has been greatly improved under the three prompt templates, alleviating the difficulty of low resources of geological disaster data. And by comparing the performance of different instructions under different learning rates, the fine-tuning learning rate range suitable for the field of geological disasters is summarized. The limitation is that it is constrained by local computing resources, which reduces the parameter scale of LLM, and the recognition performance is low for extremely long or complex texts. © 2024 Copyright held by the owner/author(s).

关键词： Engineering geology

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CTNet:A Convolutional Transformer Network for Color Image Steganalysis

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Journal of Computer Science & Technology 2025年第2期40卷 413-427页

作者： Kang-Kang Wei Wei-Qi Luo Shun-Quan Tan Ji-Wu Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Compared with convolutional neural network(CNN),Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer *** to the particularity of steganography,however,almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding *** this paper,we propose a novel convolutional Transformer network for color image ***,we firstly obtain various image residuals for each color channel of an input image in the pre-processing *** capture more comprehensive steganalytic features,the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer *** CNN group aims to extract local receptive fields features,while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic ***,we employ a global covariance pooling(GCP)and two fully-connected(FC)layers with dropout for *** comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art *** the proposed method is originally designed for color images,it can also obtain competitive results for grayscale images compared with the current best *** addition,we provide numerous ablation studies to verify the rationality of the proposed network architecture.

关键词： steganalysis steganography convolutional neural network Transformer color image

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Multi-feature Chinese Semantic Role Labeling combined with global information

Multi-feature Chinese Semantic Role Labeling combined with g...

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2023 International Conference on Electronic information Engineering and Data processing, EIEDP 2023

作者： Ma, Ning Wang, Jiahao Zhu, Ao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9781510666160

Semantic role labeling serves as a crucial approach to obtaining semantic information and enabling shallow semantic analysis. Currently, the BiLSTM-CRF model is the primary method used for semantic role labeling. However, this model has many network parameters and is unable to effectively capture semantic information in long sentences. To address these issues, this paper proposes a CNN-BiLSTM-MaxPool-CRF fusion model for Chinese semantic role labeling. The model utilizes MaxPool to sample and extract the output of the BiLSTM network to optimize the network structure. Convolution kernels of differing sizes are utilized to capture local features of sentences, and these features are then combined through average pooling to form new feature vectors. These new features incorporate semantic information of the sentence context and are inputted into the model alongside multi-level linguistic feature groups such as part of speech and sentence phrase structure. Through multiple sets of experimental demonstrations, the method proposed in this paper has demonstrated significant improvements in the performance of the semantic role labeling model. © 2023 SPIE.

关键词： Semantics

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Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in intelligent Digital Healthcare 18th

Resource Binding and Module Placement Algorithms for Conti...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

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Extending generalized unsupervised manifold alignment

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Science China(information Sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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keypoints and Action Units Jointly Drive Talking Head Generation for Video Conferencing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Wuzhen Xue, Zibang Wen, Yang Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China

This paper introduces a high-quality talking head generation method that is jointly driven by keypoints and action units, aiming to strike a balance between low-bandwidth transmission and high-quality generation in video conference scenarios. Existing methods for talking head generation often face limitations: they either require an excessive amount of driving information or struggle with accuracy and quality when adapted to low-bandwidth conditions. To address this, we decompose the talking head generation task into two components: a driving task, focused on information-limited control, and an enhancement task, aimed at achieving high-quality, high-definition output. Our proposed method innovatively incorporates the joint driving of keypoints and action units, improving the accuracy of pose and expression generation while remaining suitable for low-bandwidth environments. Furthermore, we implement a multistep video quality enhancement process, targeting both the entire frame and key regions, while incorporating temporal consistency constraints. By leveraging attention mechanisms, we enhance the realism of the challenging-to-generate mouth regions and mitigate background jitter through background fusion. Finally, a prior-driven super-resolution network is employed to achieve high-quality display. Extensive experiments demonstrate that our method effectively supports low-resolution recording, low-bandwidth transmission, and high-definition display. © 1991-2012 IEEE.

关键词： Bandwidth

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Auto-Enhanced Population Diversity with Two Options 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Ou, Yangcong Yang, Ming Guan, Jing School of Computer Science China University of Geosciences Wuhan430078 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430078 China China Ship Development and Design Center Wuhan430064 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

This paper proposes an auto-enhanced population diversity with two options (TO-AEPD) for addressing the premature convergence of CMA-ES. TO-AEPD is founded on a modification of AEPD. By quantifying the population distribution in each dimension, TO-AEPD allows the identification of population convergence or stagnation instances. When convergence or stagnation is identified, the population must be diversified to acceptable levels. Two population diversification enhancement methods are used to balance exploration and exploitation. TO-AEPD selects the population diversification strategy with exploration when the current round of optimization does not produce a new optimal solution or when convergence accuracy is achieved. Otherwise, population diversification with an exploitation strategy is implemented. TO-AEPD-CMAES describes the CMAES that incorporates TO-AEPD. Experimentation demonstrated that TO-AEPD-CMAES outperforms several competing algorithms on benchmarks, significantly enhancing the algorithm’s performance and validating the model’s viability and efficacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optimization

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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