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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Xue, Haiwei Yan, Xueming Jiang, Shengyi Lai, Helang Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510006 China Guangdong University of Foreign Studies Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangzhou510006 China South China Normal University School of Computer Science Guangzhou510631 China

ISBN: (纸本)9780738124261

Multimodal sentiment analysis is a highly sought-after topic in natural language processing. In this paper, a multi-tensor fusion network with hybrid attention architecture for multimodal sentiment analysis is proposed. Firstly, Bi-LSTM is applied to encode contextual representation in different modalities. Following this, modalities features are extracted and interacted with by the hybrid attention mechanism. Finally, multi-tensor fusion approach is used to further enhance the effectiveness of fusing interaction features in different modalities. The proposed approach outperforms the existing advanced approaches on two benchmarks through a series of regression experiments for sentiment intensity prediction, as it improves F1-score by 3.4% and 2.1% points respectively. Our architecture would be open-sourced on Github1 for researchers to use. © 2020 IEEE.

关键词： Sentiment analysis

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Survey on Multi-Objective Evolutionary Algorithms 5

Survey on Multi-Objective Evolutionary Algorithms

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5th Annual International Conference on Network and information Systems for computers, ICNISC 2019

作者： Huang, Wenlan Zhang, Yu Li, Lan School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin150028 China

Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. This paper surveys the development of MOEA and its research status, classifies it into four categories, analyzes the advantages and disadvantages of these algorithms, and summarizes the main application fields of MOEA. Finally several viewpoints for the future research of MOEA are presented. © Published under licence by IOP Publishing Ltd.

关键词： Evolutionary algorithms

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An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

arXiv

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arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

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Research on Wave-Off Risk Estimation Based on WODCP 3

Research on Wave-Off Risk Estimation Based on WODCP

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3rd IEEE Advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2019

作者： Li, Hui Su, Xiaodong Zhang, Yuru Zhao, Ming Li, Yun Hao, Gang Harbin University of Commerce School of Computer and Information Engineering China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing China Heilongjiang University School of Electronic Engineering Harbin China

ISBN: (纸本)9781728105130

Consider the high risk of carrier-based aircraft wave-off operation, this paper has a research on wave-off decision making technology based on Wave-Off Decision Critical Position (WODCP). In this paper, after analyzing wave-off operation force, the wave-off decision making critical position should be defined. There are four steps of wave-off decision making technological process, including flight risk judge, wave-off risk judge, wave-off warning judge and wave-off decision making. Multiple examples simulations with and without LSO wave-off command based on flight path, wave-off surplus distance and wave-off risk have been compared to explain the important of wave-off decision making critical position. © 2019 IEEE.

关键词： Risk perception

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Abnormal network traffic detection based on leaf node density ratio 9

Abnormal network traffic detection based on leaf node densit...

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9th International Conference on Communication and Network Security, ICCNS 2019

作者： Liu, Huajun Liu, Zhanghui Liu, Yanhua Gao, Xiaoling College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

As the network evolves, cyber-attacks become more and more diverse. In the process of detecting network traffic, the most complicated but also the most important task is to find unknown abnormal network traffic data in time. In the existing abnormal network traffic detection method based on Extended Isolation Forest, there are limitations such as unbalanced detection accuracy and insufficient generalization ability. An improved abnormal network traffic detection method EIF-LNDR is proposed for the above problems. Based on the leaf node density ratio, the anomaly score of the instance can be calculated differently for each iTree. The experiments show that EIF-LNDR has significant improvement in precision, false negative rate, and detector efficiency compared with Extended Isolation Forest and LOF methods. © 2019 Association for Computing Machinery.

关键词： Data mining

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Alzheimer's disease distinction based on gait feature analysis 22

Alzheimer's disease distinction based on gait feature analys...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： You, Zhiyang You, Zeng Li, Yilong Zhao, Shipeng Ren, Huixia Hu, Xiping Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Wisconsin-Madison Computer Sciences Department Madison United States Department of Neurology Shenzhen People's Hospital The 2nd Clinical Medical College of Jinan University Shenzhen China Clinical Medicine Postdoctoral Research Station Jinan University Guangzhou China

ISBN: (纸本)9781728162676

Alzheimer's disease(AD) is a neurodegenerative disease that progresses slowly but worsens gradually, also, the most common kinds of dementia. Clinically, the diagnosis of AD is mainly based on rating scales and neuroimaging technology which is invasive, costly and time-consuming. Other than that, the clinical pathology has become irreversible when neuroimaging characteristics appear. It is imperative to develop new noninvasive methods for early diagnosis of AD. Several studies indicated the probable association of cognitive decline with gait changes might shed light on potential features for distinction of AD. This paper aims to exploit the feasibility of gait features for early diagnosis of mild cognitive impairment(MCI) and AD by using machine learning methods. A device-free AD detection system is built, with a natural undisturbed gait collecting system and a well-performed Long Short-Term Memory(LSTM) based model, in this article. Moreover, it can serve as a simplified, non-invasive, and highly accurate clinical auxiliary tool for early diagnosis and distinction of AD. Experimental results showed a 90.48%, 92.00%, and 88.24% in accuracy, sensitivity, and specificity respectively for distinguishing AD by using the method with LSTM based model. Furthermore, the gait cycle and stride length in MCI or AD were more variable than in healthy controls through redefining and calculating the gait features with skeleton data obtained by Kinect devices. © 2021 IEEE.

关键词： Neuroimaging

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An End-to-End Network for Single Image Dedusting

An End-to-End Network for Single Image Dedusting

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International Conference on information Science and Control Engineering (ICISCE)

作者： Jiayan Huang Zhaochai Yu Zuoyong Li Xiangpan Zheng Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

Dust storm not only reduces visibility of natural scene, but also affects capture capability of outdoor monitoring equipment. Image dedusting aims to remove dust storm in images and make image scenes clearer. However, most of existing image enhancement-based methods have focused on defogging and deraining, and have paid little attentions to image dedusting. Therefore, research of image dedusting is necessary. In this paper, we presented an end-to-end network for single image dedusting. First, the proposed method uses an encoder module to encode a dusty image into image feature maps. Second, it uses the latest smoothed dilated residual convolution module to alleviate gridding artifacts caused by a widely-used dilated convolution. Third, it adopts an attention-based module to fuse image features at different levels and extract more contextual image information. Finally, it utilizes a decoder module, which decodes the feature maps, to obtain a residue, and adds it to the input dusty image to obtain the final image dedusting result. Experiments demonstrate the effectiveness of the proposed method in image dedusting.

关键词： information science Control engineering Convolution Storms Fuses Feature extraction Decoding

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Self-Supervised Speech Denoising Using Only Noisy Audio Signals

arXiv

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arXiv 2021年

作者： Wu, Jiasong Li, Qingchun Yang, Guanyu Li, Lei Senhadji, Lotfi Shu, Huazhong LIST Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing210096 China RennesF-35042 France The 28th Research Institute of China Electronics Technology Group Corporation Nanjing210007 China Univ Rennes INSERM LTSI UMR 1099 RennesF-35042 France

In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising module. The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs. The sub-sampled pairs are then fed into a novel complex-valued speech denoising module. Experimental results show that the proposed method not only eliminates the high dependence on clean targets of traditional audio denoising tasks, but also achieves on-par or better performance than other training strategies. Availability-ONT is available at https://***/liqingchunnnn/Only-Noisy-Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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MR-APG: An improved model for swarm intelligence movement coordination 2nd

MR-APG: An improved model for swarm intelligence movement co...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Yang, Hui Ci, Linlin Zhang, Fuquan Yang, Minghua Mao, Yu Niu, Ke School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China RocketMilitary Equipment Research Institute Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783030045845

Swarm intelligence based on the bionics have become an emerging research field, and researchers have proposed some kinds of intelligent models, such as ant colony, free group wolves, fish flock, insects group, bacteria and bacteria immune etc. The behavior of common features is: the individual behavior and function is simple, but a rally for the swarm after a complex cluster behavior ability. In mobile swarm group individuals within a certain space can keep a certain distance to avoid collision in order to group coordinated movement. In this paper, an attempt has been made to optimize the continuous and collaborative of the swarm, when the swarm nodes in the process of moving and turning. This paper puts forward the Multi-Restrained Artificial Pigeon Group (MR-APG) model based on multiple constraint factors, and introduce the concepts of quantitative coordination of nodes to optimize the nodes "over-dispersion" or "over-assembly" phenomenon. The experimental results show that, the higer α1 value will lead to average movement synchronization ratio μsync of MR-APG model will be lower. The results also show that, comparing with the traditional A/R model (μsync = 73%) and 3D-VFA model (μsync = 84%), the proposed MR-APG method can make μsync equal to 95%, moreover, its only takes half time of A/R model to make μsync equal to 80%. © Springer Nature Switzerland AG 2019.

关键词： Swarm intelligence

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Alkaline lysine additive enables highly stable Zn anode for aqueous zinc-ion batteries

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Chinese Chemical Letters 2025年

作者： Guangbin Wang Binrui Xu Bo Zhao Yifei Pei Haoming Li Wanhong Zhang Yong Liu School of Material Science and Engineering Provincial and Ministerial Co-Construction of Collaborative Innovation Center for Non-Ferrous Metal New Materials and Advanced Processing Technology Henan University of Science and Technology Luoyang 471023 China School of Information Engineering Henan University of Science and Technology Luoyang 471023 China Key Laboratory of Cluster Science of Ministry of Education Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials School of Chemistry and Chemical Engineering Beijing Institute of Technology Beijing 100081 China

Rechargeable aqueous zinc-ion batteries (RAZIBs) have been considered as viable alternatives to lithium-ion batteries in electrochemical energy storage due to their intrinsic safety, low cost, and environmental friendliness. However, the further practical application of RAZIBs is restricted by the growth of zinc dendrites and severe side reactions during cycling. To address these issues, we proposed a new lysine (Lys) additive to the ZnSO 4 electrolyte, the hydrolyzed Lys + cations can be adsorbed on the Zn anode's surface to modify the interface between the zinc electrode and the ZnSO 4 electrolyte. This modification helps weaken the “tip effect” and guides the uniform zinc deposition, effectively alleviating the formation of zinc dendrites. Additionally, introducing alkaline Lys can regulate the pH value of the ZnSO 4 electrolyte and suppress side reactions, thereby decreasing the production of by-products. Consequently, the Zn||Zn symmetric cell with Lys additive stably cycled for 4500 h at 1 mA/cm 2 , and the Zn||NH 4 V 4 O 10 full cell with Lys additive exhibited improved performance (with a capacity retention of 72% after 1000 cycles) at 5 A/g. This strategy provides valuable insights for developing stable Zn anode toward high-performance RAZIBs.

关键词： Zn anode Electrolyte additive Zinc dendrite Side reactions Electrochemical performance

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