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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Qiang Zhang Ling Guo Xianglian Xue Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dahan University of Technology Dahan China

A new image encryption algorithm based on DNA sequence addition operation is presented. First, a DNA sequence matrix is obtained by encoding the original image. Second, divide the DNA sequence matrix into some equal blocks and use DNA sequence addition operation to add these blocks. Thirdly, carry out DNA sequence complement operation for the result of added matrix by using two Logistic maps. At last, decoding the DNA sequence matrix from the third step, we will get the encrypted image. The simulation experimental results and security analysis show that our algorithm not only can get good encryption effect, but also can resist exhaustive attack, statistical attack and differential attack.

关键词： Cryptography DNA Sequences Image coding Logistics Decoding Analytical models Security Algorithm design and analysis Resists

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Aspect-Level Sentiment Classification of Chinese Patient Comments Based on Pre-trained Sentiment Embedding

Aspect-Level Sentiment Classification of Chinese Patient Com...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Shan, Yongxue Zhong, Zhaoqian Che, Chao Jin, Bo Wei, Xiaopeng Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China Dalian University of Technology School of Innovation and Entrepreneurship Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781665401265

With the development of information technology, online health care service platforms have collected a large amount of patient comment information. Through fine-grained sentiment classification of this information, we can provide references for patients to seek medical treatment and help doctors understand their work. Therefore, we proposed a model that integrated pre-trained emotional information and semantic information at the word and character level for aspect-level sentiment classification. Specifically, we employed adversarial learning for training sentiment word embeddings and the two-layer bidirectional long short-term memory network to extract the sentiment embedding of the entire sentence in a specific aspect. We also combined the pre-trained sentiment feature vector with the structured semantic information by linear weighting and the multi-head self-attention mechanism, enabling the model to pay more attention to the information most relevant to a given aspect category. We performed experiments on the Chinese patient comments data set constructed by our research team and the proposed model outperformed the state-of-the-art methods, which proved the effectiveness of the proposed model for aspect-level sentiment classification of Chinese patient comments. © 2021 IEEE.

关键词： Embeddings

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Facial animation based on feature points

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Telkomnika - Indonesian Journal of Electrical Engineering 2013年第3期11卷 1697-1706页

作者： Li, Beibei Zhang, Qiang Zhou, Dongsheng Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Liaoning Dalian China

This paper presents a hybrid method for synthesizing natural animation of facial expression with data from motion capture. The captured expression was transferred from the space of source performance to that of a 3D target face using an accurate mapping process in order to realize the reuse of motion data. The transferred animation was then applied to synthesize the expression of the target model through a framework of two-stage deformation. A local deformation technique preliminarily considered a set of neighbor feature points for every vertex and their impact on the vertex. Furthermore, the global deformation was exploited to ensure the smoothness of the whole facial mesh. The experimental results show our hybrid mesh deformation strategy was effective, which could animate different target face without complicated manual efforts required by most of facial animation approaches. © 2013 Universitas Ahmad Dahlan.

关键词： Animation

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PFV2: Packet fragmentation with variable size and vigorous mapping in time-sensitive networking

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Journal of Systems Architecture 2025年 166卷

作者： Wenyan Yan Bin Fu Dongsheng Wei Renfa Li Yixue Lei Yuhang Jia Guoqi Xie Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha 410082 Hunan China Center for Convergence of Automobile and Cyberspace Research Institute of Hunan University in Chongqing Chongqing 401120 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems School of Computer and Artificial Intelligence Xiangnan University Chenzhou 423000 Hunan China Future Network Laboratory Tencent Beijing 100080 China

With the rapid advancement of intelligent automobiles, the ever-growing communication data put forward high bandwidth and low latency requirements. To meet these requirements, automotive Original Equipment Manufacturers (OEMs) widely adopt domain-centralized Electrical/Electronic (E/E) architecture. In this architecture, Time-Sensitive Networking (TSN) is expected to serve as the backbone network because of its high bandwidth and deterministic communication. TSN uses the Gate Control List (GCL) to divide its time length into multiple time slots, and the time intervals of these time slots are non-uniform (equal or unequal). Different flows have varying requirements for time slot sizes. To enhance the acceptance ratio of Time-Triggered (TT) flows through GCL time slot allocation, packet fragmentation (i.e., flow fragmentation) is introduced into TSN in the recent study. The state-of-the-art packet fragmentation solution divides one un-schedulable TT flow into multiple equal-sized packets (i.e., equal-sized time slots). In other words, these fixed-size packets are difficult to be mapped into the time slots with different sizes. This study develops a Packet Fragmentation with Variable-size and Vigorous-mapping (PFV2) technique based on the following three innovations: (1) we implement variable-size packet fragmentation, which iteratively divides the un-schedulable TT flow into smaller packets and then dynamically reschedules these packets; (2) we implement the vigorous mapping solution from packets to time slots by deeply searching for available time slots within the flow’s deadline; and (3) we verify PFV2 based on the LS1028A with Cortex-A72 (i.e., the NXP automotive-grade development board). PFV2 improves the acceptance ratio by up to 20.18% and bandwidth utilization by up to 7.024% compared with the state-of-the-art solution. The theoretical and practical co-verification experiments demonstrate that the PFV2 can effectively improve the flow acceptance ratio and outpe

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K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION ALGORITHM FOR DOA ESTIMATION

K-MEANS CLUSTERING BASED SPARSE CROSS-ENTROPY MINIMIZATION A...

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IET International Radar Conference 2023, IRC 2023

作者： Guo, Qiang Jiang, Hanyu Xiang, Jianhong Zhong, Yu College of Information and Communication Harbin Engineering University Harbin China Key Laboratory of Advanced Ship Communication and Information Technology Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

Aiming at the problem that traditional greedy class compressed sensing algorithms are influenced by noise in the estimation of direction of arrival (DOA), this paper proposes the K-Means Clustering based Sparse Cross-Entropy Minimization (KMCSCEM) algorithm. First the algorithm selects atoms by minimizing the sparse cross-entropy to improve the accuracy of atom selection at the low signal-to-noise ratio (SNR). Subsequently, K-mean clustering is added in each iteration to reduce the range of atom selection, which further improves the estimation accuracy. Finally, the speed of convergence is improved by introducing an orthogonal least squares (OLS) criterion. Simulation experiments show that the proposed algorithm has an exact estimation ratio close to 90% when the SNR is less than -5 dB. © The Institution of Engineering & Technology 2023.

关键词： Signal to noise ratio

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Wireless power transmission system with CC and CV output based on hybrid LCC-S/P topology 5

Wireless power transmission system with CC and CV output bas...

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5th International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2023

作者： Li, Kun Gong, Zheng Meng, Jiaheng Liu, Qing Tianjin University of Technology Tianjin Key Laboratory of Film Electronic and Communication Device Tianjin China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China

ISBN: (纸本)9798350328660

Wireless Power Transfer (WPT) technology provides a convenient and efficient power supply solution for mobile devices. The utilization of constant current (CC) and constant voltage (CV) control in WPT systems proves valuable for charging battery applications. This paper presents a novel WPT system with CC and CV output characteristics, employing a switching topology. Specifically, the LCC-S and LCC-P topologies are applied and rigorously simulated. The proposed MCR-WPT system's output characteristics in both CC and CV modes are meticulously validated using Saber software. The simulation results distinctly demonstrate the system's capability to achieve CC and CV outputs within a varying load resistance range of 5Ω to 30Ω, thus conclusively establishing the efficacy of the topology switching technology. © 2023 IEEE.

关键词： Topology

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Sentiment Analysis of Chinese Product Reviews Based on BERT Word Vector and Hierarchical Bidirectional LSTM

Sentiment Analysis of Chinese Product Reviews Based on BERT ...

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2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering, CSAIEE 2021

作者： Zhang, Kui Hu, Min Ren, Fuji Hu, Pengyuan Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei230009 China University of Tokushima Graduate School of Advanced Technology Science Tokushima7708502 Japan

ISBN: (纸本)9781665422048

Sentiment analysis data on Chinese shopping comments has gained much attention in recent years. Many previous studies focus on the relationship between words in a single sentence but ignore the context relationship between sentences. To better serve this problem, we propose a method based on Bidirectional Encoder Representations from Transformers (BERT) pre-training language model, Hierarchical Bi-directional Long Short-Term Memory (Hierarchical Bi-LSTM) and attention mechanism for Chinese sentiment analysis. We first use BERT pretraining language model to obtained word vector, then applies Hierarchical Bi-LSTM model to extract contextual feature from sentences and words. Finally, we inj ect attention mechanism to highlight key information. Base on the experimental results, our method achieves more idealistic performance. © 2021 IEEE.

关键词： Sentiment analysis

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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Constructing DNA Barcode Sets Based on Particle Swarm Optimization

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IEEE/ACM transactions on computational biology and bioinformatics 2018年第3期15卷 999-1002页

作者： Bin Wang Xuedong Zheng Shihua Zhou Changjun Zhou Xiaopeng Wei Qiang Zhang Ziqi Wei the Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Department of Computing Science University of Alberta Edmonton AB Canada

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

关键词： Bioinformatics DNA Particle Swarm Optimisation Particle Swarm Optimization Next Generation Sequencing DNA Barcode Sets DNA Sequential Analysis Algorithm design And Analysis Next Generation Networking Particle Swarm Optimization Hamming Distance Filtering Algorithms DNA Barcodes Edit Distance Particle Swarm Optimization PSO Lower Bound

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Gene-disease association extraction by text mining and network analysis 5

Gene-disease association extraction by text mining and netwo...

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5th International Workshop on Health Text Mining and Information Analysis, Louhi 2014 at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014

作者： Quan, Changqin Ren, Fuji AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information HeFei University of Technology China Faculty of Engineering University of Tokushima Japan

ISBN: (纸本)9781937284909

Biomedical relations play an important role in biological processes. In this work, we combine information filtering, grammar parsing and network analysis for gene-disease association extraction. The proposed method first extracts sentences potentially containing information about gene-diseases interactions based on maximum entropy classifier with topic features. And then Probabilistic Context-Free Grammars is applied for gene-disease association extraction. The network of genes and the disease is constituted by the extracted interactions, network centrality metrics are used for calculating the importance of each gene. We used breast cancer as testing disease for system evaluation. The 31 top ranked genes and diseases by the weighted degree, betweenness, and closeness centralities have been checked relevance with breast cancer through NCBI database. The evaluation showed 83.9% accuracy for the testing genes and diseases, 74.2% accuracy for the testing genes. © 2014 Association for Computational Linguistics

关键词： Genes

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