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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

引用

TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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Reversible Data Hiding Using Convolutional Neural Network and Digital Signal Processing Techniques

Reversible Data Hiding Using Convolutional Neural Network an...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Peng Qingmei Li Shaozi Lin Yaojin Yu Xiehua Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province MinNan Science and Technology University Quanzhou China School of Informatics Xiamen University Xiamen China School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development.

关键词： Training System testing Signal processing algorithms Digital signal processing Reliability Cryptography Time factors

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Diagrammatic Sentence Pattern Structure System and Its Treebank Construction 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Peng, Weiming Zhao, Min Song, Yuchen Hu, Jiajia Song, Tianbao Sui, Zhifang Song, Jihua Laboratory for Chinese Character and Language Research and Social Application Beijing Normal University Beijing100875 China Linguistic Data Consortium University of Pennsylvania PhiladelphiaPA19104-2653 United States Beijing Han-sky Education Technology Co. Ltd Beijing100084 China Department of Linguistics University of Michigan Ann ArborMI48109-1220 United States School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Key Laboratory of Computational Linguistics Ministry of Education Peking University Beijing100871 China

ISBN: (纸本)9780000000002

The Sentence Pattern Structure (SPS) is a formalized syntactic structure based on sentence-based grammar, which presents the structure of sentences in a customized diagram form. This paper proposes an SPS system covering three aspects: clause structure, lexical structure, and inter-sentence structure, elucidating its design philosophy and parsing principles of sentence-based grammar. Finally, an overview of the engineering progress of building a Chinese treebank based on this system is provided. © 2024 China National Conference on Computational Linguistics.

关键词： Trees (mathematics)

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Personalized Heart Disease Detection via ECG Digital Twin Generation

arXiv

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arXiv 2024年

作者： Hu, Yaojun Chen, Jintai Hu, Lianting Li, Dantong Yan, Jiahuan Ying, Haochao Liang, Huiying Wu, Jian College of Computer Science and Technology Zhejiang University China Computer Science Department University of Illinois at Urbana-Champaign United States Medical Big Data Center Guangdong Provincial People’s Hospital Southern Medical University China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences China School of Public Health Zhejiang University China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine China

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals’ anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. The code can be found at https://***/huyjj/LAVQ-Editor. Copyright © 2024, The Authors. All rights reserved.

关键词： Electrocardiograms

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STAN: Adversarial Network for Cross-domain Question Difficulty Prediction

STAN: Adversarial Network for Cross-domain Question Difficul...

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IEEE International Conference on Data Mining (ICDM)

作者： Ye Huang Wei Huang Shiwei Tong Zhenya Huang Qi Liu Enhong Chen Jianhui Ma Liang Wan Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China iFLYTEK Research iFLYTEK CO. LTD

ISBN: (纸本)9781665423991

In intelligent education systems, question difficulty prediction (QDP) is a fundamental task of many applications, such as personalized question recommendation and test paper analysis. Previous work mainly focus on data-driven QDP methods, which are heavily relied on the large-scale labeled dataset of courses. To alleviate the labor intensity, an intuitive method is to introduce domain adaptation into QDP and consider each course as a domain. In educational psychology, there are two factors influencing difficulty common to different courses: the obstacles of comprehending the question and generating a response, namely stimulus and task difficulty. To this end, we propose a novel Stimulus and Task difficulty-based Adversarial Network (STAN) that models question difficulty from the views of stimulus and task. Then, in order to align the difficulty distribution of the source domain and the target domain, we utilize the conditional adversarial learning with readability-enhanced pseudo-labels. Meanwhile, we proposed a sampling method based on density estimation to implicit alignment. Finally, we conduct experiments on the real questions datasets to evaluate the effectiveness of our QDP model and domain adaptation method. Our method significantly improves accuracy over state-of-the-art methods on real-world question data of multiple courses.

关键词： Adaptation models Conferences Education Psychology Estimation Sampling methods Adversarial machine learning

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Dilemma in All-optical Characterization of Single-layer NiI2 Multiferroics

arXiv

引用

arXiv 2023年

作者： Jiang, Yucheng Wu, Yangliu Zhang, Jinlei Wei, Jingxuan Peng, Bo Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China 8 Somapah Road Singapore487372 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The search for two-dimensional multiferroic materials is an exciting yet challenging endeavor. Recently, Song et al. reported the exciting discovery of type-II multiferroic order in an antiferromagnetic (AFM) NiI2 single layer, and concluded the ferroelectric (FE) polarization induced by a helical magnetic order1. Their finding was presented from all-optical experimental evidence, based on the methods of second harmonic generation (SHG) and linear dichroism (LD). However, the all-optical characterizations alone cannot serve as unanimous evidence of the "FE" existence, particularly in conjunction with magnetic orders in a single-layer multiferroic. A main argument is that their observed SHG and LD signals may just be manifested from the magnetic-order induced breaking of spatial-inversion or time-reversal symmetry instead of FE order, and thus cannot endorse the finding of a single-layer multiferroic. Accurate measurements of magnetic and electric orders are very essential to make a reliable judgement of a multiferroic. With the device’s thickness approaching atomic scale, its magnetic or electrical signals will become too weak to be collected by traditional measurements2. Therefore, advanced all-optical probing methods have been © 2023, CC BY-NC-SA.

关键词： Harmonic generation

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ComR:a combined OWL reasoner for ontology classification

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Frontiers of computer Science 2019年第1期13卷 139-156页

作者： Changlong WANG Zhiyong FENG Xiaowang ZHANG Xin WANG Guozheng RAO Daoxun FU School of Computer Science and Technology Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin 300072China School of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

Ontology classification,the problem of computing the subsumption hierarchies for classes (atomic concepts),is a core reasoning service provided by Web Ontology Language (OWL)*** general-purpose OWL 2 reasoners employ sophisticated optimizations for classification,they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient;however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL *** this paper,we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive *** optimize the workload,we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure *** the ontology classification,the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 *** proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology *** the wellknown ontology NCI,the classification time is reduced by 96.9%(resp.83.7%)compared against the standard reasoner Pellet (*** modular reasoner MORe).

关键词： OWL ontology classification reasoner

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TDP-SAR: Task-Driven Pruning Method for Synthetic Aperture Radar Target Recognition Convolutional Neural Network Model

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Sensors (Basel, Switzerland) 2025年第10期25卷 3117页

作者： Tong Zheng Qing Wu Chongchong Yu School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing 100048 China. Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin 150006 China.

Synthetic aperture radar (SAR) target recognition plays a crucial role in SAR image interpretation. While deep learning has become the predominant approach for SAR target recognition, existing methods face practical deployment challenges due to excessive model complexity. In addition, SAR images are less understandable compared to optical images, which leads to greater difficulties in analyzing the target features of SAR images in the spatial domain. To address the above limitation, we propose a novel task-driven pruning (TDP-SAR) strategy. Unlike conventional pruning techniques that rely on generic parameter importance metrics, our approach implements frequency domain analysis of convolutional kernels across different processing stages of SAR target recognition models. In the experimental section, we use the MSTAR benchmark dataset to prove that the TDP-SAR can not only effectively compress the model size but also adapt to different quality SAR images compared to baseline architectures. Particularly when processing the joint convolutional neural network (J-CNN) model proposed in the previous study, the number of parameters decreased by 17.7% before and after pruning. This advancement facilitates the practical deployment of deep learning solutions in resource-constrained SAR interpretation systems while preserving recognition reliability.

关键词： SAR target recognition pruning speckle noise spectrum analysis synthetic aperture radar (SAR)

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Supervised neighborhood based ensemble attribute reduction 19

Supervised neighborhood based ensemble attribute reduction

引用

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

作者： Song, Jingjing Jiang, Zehua Dou, Huili Tsang, Eric C. C. Jiangsu University of Science and Technology School of Computer Jiangsu Province Zhenjiang212003 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Macau University of Science and Technology Faculty of Information Technology China

ISBN: (纸本)9780738124261

In neighborhood based attribute reduction, neighborhood relation is a typical tool for distinguishing samples. Notably, the neighborhood relation may be powerless in providing satisfactory distinguishing ability. In view of this, the supervised neighborhood based attribute reduction has been explored. However, the supervised neighborhood based reduct may be lack of universality. To file such gap, an ensemble strategy for computing supervised neighborhood based reduct is proposed in our paper. Such ensemble strategy is realized through considering the requirement of each decision class. The experimental results on 8 UCI data sets show that the supervised neighborhood based ensemble strategy can generate reduct not only with higher generalization performance but also with higher stability. © 2020 IEEE.

关键词： Machine learning Tools Cybernetics

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