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SSRN 2023年

作者： Zhuang, Tianming Qin, Zhiguang You, Li Deng, Erqiang Ding, Yi Cao, Mingsheng Guo, Yingkun Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China Department of Molecular Genetics Erasmus University Medical Center Rotterdam Netherlands Department of Radiology Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects Ministry of Education West China Second University Hospital Sichuan University Sichuan Chengdu610072 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

The electrocardiogram diagnosis played an important role in early arrhythmia prevention and cardiovascular disease detection. How to analysis and detect the electrocardiogram automatically becomes a challenging task in clinical practice. Convolutional neural network based approaches have been widely applied in the field of automatic arrhythmia detection and analysis. However, the existing methods fails to preserve the abundant and essential latent representatives from multiple dimensions. To address these problems, a dual spatial-channel aggregation module is proposed, which expects to concentrate on the prominent target information along spatial and channel dimension of electrocardiogram signals. Moreover, a multi-scale enhancement module with channel-wise gating mechanism is also proposed to enlarge the receptive resolution and further to improve the learning ability of long-range dependencies from the global information. Furthermore, these two proposed modules are integrated as a "plug-and-play" block that can be easily adopted as a drop-in replacement for any 3×3 convolution layers in convolutional neural networ based architectures. In order to evaluate the effectiveness and generalization of the proposed block, it is plugged in a series of convolutional neural network based frameworks and are evaluated on two benchmark datasets, MIT-BIH and PTB. The extensive experimental results show that the proposed "plug-and-play" block can significantly improve the arrhythmia classification performance. Moreover, compared to other state-of-the-art methods, the framework plugged with the proposed block can achieve a competitive classification performance, proving the feasibility and advancement of the proposed dual attention-based multiscale feature learning block. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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A N-gram based approach to auto-extracting topics from research articles

arXiv

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arXiv 2021年

作者： Zhu, Linkai Huang, Maoyi Chen, Maomao Wang, Wennan Institute of Software Chinese Academy of Sciences Beijing China Institute of Data Science City University of Macau China Product Development Ericsson Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden

A lot of manual work goes into identifying a topic for an article. With a large volume of articles, the manual process can be exhausting. Our approach aims to address this issue by automatically extracting topics from the text of large numbers of articles. This approach takes into account the efficiency of the process. Based on existing N-gram analysis, our research examines how often certain words appear in documents in order to support automatic topic extraction. In order to improve efficiency, we apply custom filtering standards to our research. Additionally, delete as many noncritical or irrelevant phrases as possible. In this way, we can ensure we are selecting unique keyphrases for each article, which capture its core idea. For our research, we chose to center on the autonomous vehicle domain, since the research is relevant to our daily lives. We have to convert the PDF versions of most of the research papers into editable types of files such as TXT. This is because most of the research papers are only in PDF format. To test our proposed idea of automating, numerous articles on robotics have been selected. Next, we evaluate our approach by comparing the result with that obtained manually. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency

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Study of facial generation methods after orthodontic treatment

Study of facial generation methods after orthodontic treatme...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Jia– Liang Tian Qin– Yan Zhang Hai– Zhen Li Qing Wang Yi Lei Lin Zang Xue– Mei Gao Ji– Jiang Yang College of artificial intelligence Beijing University of Posts and Telecommunications Beijing Department of Orthodontics Peking University School and Hospital of Stomatology Beijing Department of Automation Tsinghua University Beijing Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing Pharmacovigilance Research Center for information technology and Data Science Xiamen Fujian

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on Encoder-Decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.

关键词： Measurement Technological innovation Medical services Transforms Predictive models Prediction algorithms Generative adversarial networks

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A novel nest-based scheduling method for mobile wireless body area networks

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Digital Communications and Networks 2020年第4期6卷 514-523页

作者： Zhijun Xie Guangyan Huang Roozbeh Zarei Zhenyan Ji Hongwu Ye Jing He Department of Information Science and Engineering Ningbo UniversityNingboChina School of Information Technology Deakin UniversityMelbourneAustralia Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboChina School of Software Engineering Beijing Jiaotong UniversityBeijingChina Zhejiang Fashion Institute of Technology NingboChina Institute of Information Technology Nanjing University of Finance and EconomicsJiangsuNanjingChina Swinburne Data Science Research Institute Swinburne University of TechnologyMelbourneAustralia

Wireless Body Area Networks(WBANs)comprise various sensors to monitor and collect various vital signals,such as blood pressure,pulse,heartbeat,body temperature,and blood sugar.A dense and mobile WBAN often suffers from interference,which causes serious problems,such as wasting energy and degrading *** reality,not all of the sensors in WBAN need to be active at the same ***,they can be divided into different groups so that each group works in turn to avoid *** this paper,a Nest-Based WBAN Scheduling(NBWS)algorithm is proposed to cluster sensors of the same types in a single or multiple WBANs into different groups to avoid ***,we borrow the graph coloring theory to schedule all groups to work using a Time Division for Multimodal Sensor(TDMS)group scheduling *** theoretical analysis and experimental results demonstrate that the proposed NBWS algorithm performs better in terms of frequency of collisions,transmission delay,system throughput,and energy consumption compared to the counterpart methods.

关键词： Interference elimination Nest Sensors Scheduling Wireless body area networks Graph coloring theory

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A call to action to address escalating global threats to academic research

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The Innovation 2025年第4期6卷 17-19页

作者： Gaelle Piret Fun Man Fung Josie Fullerton Giuseppe Fico Dmitriy Ponkratov Wenxin Chen Daniela Latorre Kirsty Y.Wan Nima Aghaeepour Jules Welgryn Adeel Razi Patricia Silveyra Ahmet Altun Renata Z.Jurkowska Alice C.Hughes Joy Wolfram INSERM U1205 UGA38400 Saint Martin d’HèresFrance School of Chemistry University College DublinD04 C1P1 BelfieldIreland UCD Geary Institute for Public Policy University College DublinD04 N9Y1 DublinIreland School of Cardiovascular&Metabolic Health University of GlasgowG128TA GlasgowUK European Alliance for Medical and Biological Engineering and Science 3001 LeuvenBelgium Life Supporting Technologies Universidad Politécnica de Madrid28040 MadridSpain Siemens Digital Industries Software B908BG SolihullUK Department of Philosophy of Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Microbiology ETH Zurich8093 ZurichSwitzerland Living Systems Institute&Department of Mathematics and Statistics University of ExeterEX44QD ExeterUK Department of Anesthesiology Painand Perioperative MedicineDepartment of PediatricsDepartment of Biomedical Data ScienceStanford University School of MedicineStanfordCA 94305USA UniversitéParis Nanterre 92000 NanterreFrance Turner Institute for Brain and Mental Health School of Psychological Sciencesand Monash Biomedical ImagingMonash UniversityClaytonVIC 3800Australia Wellcome Centre for Human Neuroimaging University College LondonWC1N 3AR LondonUK CIFAR Azrieli Global Scholars Program TorontoON M5G 1M1Canada Department of Environmental and Occupational Health Indiana University School of Public Health BloomingtonBloomingtonIN 47408USA Department of Pharmacology Sivas Cumhuriyet UniversitySivas 58140Turkey Division of Biomedicine School of BiosciencesCardiff UniversityCF103AX CardiffUK School of Biological Sciences University of Hong KongHong Kong SAR 999077China Australian Institute for Bioengineering and Nanotechnology The University of QueenslandBrisbaneQLD 4072Australia School of Chemical Engineering The University of QueenslandBrisbaneQLD 4072Australia

This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.

关键词： academic research calls action global threats public mistrust sciencechallenges translating academic research disconnect academics policymakers call action translation research

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Mushroom classification using machine-learning techniques

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AIP Conference Proceedings 2023年第1期2979卷

作者： Omar Tarawneh Monther Tarawneh Yousef Sharrab Moath Husni Software Engineering department. Amman Arab University Amman Jordan. Computer Science department. Isra University Amman Jordan Artificial intelligence and Data Science department. Isra University Amman Jordan Software Engineering department The world Islamic Sciences and education university Amman Jordan

Mushroom is one of the important ingredient in our food that has good nutrients. Most types of mushroom are poisonous (inedible), and because of its importance, we need to identify poisonous from eatable mushrooms. Machine learning (ML) techniques such as naïve Bayes, decision tree, SVM, and more applied on mushroom features to classify it into edible or not. There is a limited research on mushroom classification, existed research focus on applying ML techniques individually, where some algorithm perform better in term of accuracy. This research proposed an integrated model that combine most accurate technique’s decisions into one decision instead off treating them individually. Mushroom dataset downloaded from UCI repository. Results shows that the performance of the integrated model outperform other techniques by 94% accuracy.

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A Novel Tetrahedral Spectral Element Method for Kohn-Sham Model: For General Tetrahedral Meshes

SSRN

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SSRN 2023年

作者： Zhan, Hongfei Jia, Lueling Li, Huiyuan Hu, Guanghui School of Mathematical Sciences Peking University Beijing100871 China School of Mathematics and Statistics Shandong Normal University Shandong250358 China Institute of Software Chinese Academy of Sciences Beijing100190 China Department of Mathematics Faculty of Science and Technology University of Macau 999078 China Zhuhai UM Science & Technology Research Institute Guangdong519099 China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of Macau 999078 China

This paper is the second in a series of works. Building upon the previous paper, which introduced the generalization from a spectral-Galerkin method to a tetrahedral spectral element method, our focus in this work is on enhancing versatility. This is achieved by developing a C0 tetrahedral spectral element method using generalized Koornwinder polynomials. Instead of ensuring continuity along common geometries by preserving the orientation of mesh elements, a transformation between the degree of freedoms on local elements and the ones on global geometries is proposed to accommodate general tetrahedral meshes. Moreover, to ensure computational efficiency, the locally optimal block preconditioned conjugate gradient method is employed as the eigensolver during self-consistent field iterations. The numerical implementation is based on AFEPack, and example codes for both solving the model problem and applying to ground state calculations are provided. Several numerical experiments are conducted to validate the effectiveness of our method, demonstrating expected spectral accuracy and producing reliable ground states of various atoms and molecules such as azobenzene. © 2023, The Authors. All rights reserved.

关键词： Ground state

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Quadratic sparse Gaussian graphical model estimation method for massive variables 29

Quadratic sparse Gaussian graphical model estimation method ...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Jiaqi Wang, Meng Li, Qinchi Wang, Sen Chang, Xiaojun Wang, Beilun College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China School of Artificial Intelligence Southeast University China Electrical Engineering and Automation YOUPEI College Yancheng Institute of Technology China School of Information Technology and Electrical Engineering University of Queensland Australia Department of Data Science and AI Monash University Australia

ISBN: (纸本)9780999241165

We consider the problem of estimating a sparse Gaussian Graphical Model with a special graph topological structure and more than a million variables. Most previous scalable estimators still contain expensive calculation steps (e.g., matrix inversion or Hessian matrix calculation) and become infeasible in high-dimensional scenarios, where p (number of variables) is larger than n (number of samples). To overcome this challenge, we propose a novel method, called Fast and Scalable Inverse Covariance Estimator by Thresholding (FST). FST first obtains a graph structure by applying a generalized threshold to the sample covariance matrix. Then, it solves multiple block-wise subproblems via element-wise thresholding. By using matrix thresholding instead of matrix inversion as the computational bottleneck, FST reduces its computational complexity to a much lower order of magnitude (O(p2)). We show that FST obtains the same sharp convergence rate O(p(log max{p, n}/n) as other state-of-the-art methods. We validate the method empirically, on multiple simulated datasets and one real-world dataset, and show that FST is two times faster than the four baselines while achieving a lower error rate under both Frobenius-norm and max-norm. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Inverse problems

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Attention mechanism meets with hybrid dense network for Hyperspectral Image Classification

arXiv

引用

arXiv 2022年

作者： Ahmad, Muhammad Mehmood Khan, Adil Mazzara, Manuel Distefano, Salvatore Roy, Swalpa Kumar Wu, Xin Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Institute of Data Science and Artificial Intelligence Innopolis University Innopolis420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Dipartimento di Matematica e Informatica MIFT University of Messina Messina98121 Italy Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixel-wise classification challenging for conventional methods. To deal with nonlinearity issues in Hyperspectral Image Classification (HISC), Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting attention-fused hybrid network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using highlevel features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models. The proposed pipeline achieved, indeed, an overall accuracy of 97% for the Indian Pines, 100% for Botswana, 99% for Pavia University, Pavia Center, and Salinas datasets. © 2022, CC BY.

关键词： Convolution

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