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Knowledge Graph for China's Genealogy1

IEEE Transactions on Knowledge and data engineering 2023年第1期35卷 634-646页

作者： Wu, Xindong Jiang, Tingting Zhu, Yi Bu, Chenyang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei230009 China Hefei University of Technology Research Institute of Big Knowledge Anhui Hefei230009 China Mininglamp Technology Mininglamp Academy of Sciences Beijing100102 China Hefei University of Technology School of Computer Science and Information Engineering Anhui Hefei230009 China Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230009 China

Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descendant paths, and explore the origins of a family. However, the heterogenous, autonomous, complex, and evolving natures of genealogical data bring challenges to the development of contemporary genealogical knowledge graph models. Applying existing methods to genealogical data may be improper because general knowledge graph models lack in-depth domain knowledge. In this paper, we propose a genealogical knowledge graph model named Huapu-KG that combines HAO intelligence (human intelligence + artificial intelligence + organizational intelligence) to implement the construction and applications of genealogical knowledge graphs. Furthermore, challenges in constructing genealogical knowledge graphs are demonstrated, and experiments conducted on real-world genealogical datasets verify the feasibility and effectiveness of our proposed model. © 1989-2012 IEEE.

关键词： History

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An accurate cell segmentation method for high-resolution complex brain image 9

An accurate cell segmentation method for high-resolution com...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Xu, Xiaofeng Li, Shuai Zheng, Wei Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (纸本)9798350385991

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks. © 2024 IEEE.

关键词： Image segmentation

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Transparent Access to Heterogeneous IoT Based on Virtual Resources

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Computers, Materials & Continua 2023年第3期74卷 4983-4999页

作者： Wenquan Jin Sunhwan Lim Young-Ho Suh Chanwon Park Dohyeun Kim Big Data Research Center Jeju National UniversityJeju63243Korea Autonomous IoT Research Section Intelligent Convergence Research LaboratoryElectronics and Telecommunications Research InstituteDaejeon34129Korea Department of Computer Engineering Jeju National UniversityJeju63243Korea

The Internet of Things(IoT)inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily *** computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected *** to the purpose of the environment including privacy level,domain functionality,network scale and service quality,various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge ***,for providing user-friendly service scenarios based on the transparent access to heterogeneous devices in edge computing,a consistent interface shall be provided to deliver services from edge computing to *** this paper,we propose transparent computing based on virtual resources to access heterogeneous IoT devices without considering the underlying network configuration at the edge of the *** supporting transparent access to different edge computing environments through a consistent interface,the virtual resource of edge gateway is proposed to bridge the Internet and devices which are deployed on the edge of the *** proposed edge gateway exposes the services of the Internet of Things devices to the Internet using virtual resources that represent the resources of physical *** virtual resources provide a consistent interface to enable clients to access devices in edge computing without considering underlying *** virtual resource is generated by the resource directory in the edge gateway through the registration of a *** on the device registration,the device information is stored in the gateway to link virtual resources and devices for translating messages according to the destination protocols and identifying physical devices that are represented by virtual ***,through collaboration with the serv

关键词： Internet of things edge computing edge gateway transparent computing virtualization resource directory virtual resource

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Tropical cyclone detection by combining wavelet transform with deep learning using infrared satellite images (vol 44, pg 4617, 2023)

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第18期44卷 I-I页

作者： Zhang, Changjiang Zhang, Liu Ma, Leiming Lu, Xiaoqin School of Electronics and Information Engineering (School of Big Data Science) Taizhou University Taizhou China College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China Shanghai Central Meteorological Observatory Shanghai China Shanghai Typhoon Research Institute China Meteorological Administration Shanghai China

A tropical cyclone (TC) is a meteorological disaster that occurs over tropical or subtropical oceans. Automatic TC identification from a satellite image is essential to the subsequent automatic intelligent determination of TC positioning and intensity. Few recent studies have examined the automatic detection of TCs from satellite images. In this paper, wavelet (W) transformation, efficient (E) channel attention mechanism and YOLOX (Y) are combined to construct a deep learning model for TC detection, referred to as WEY-TCNet. WEY-TCNet automatically detects TCs from infrared satellite images. To improve the detection accuracy, a discrete wavelet transform is first used to decompose the infrared satellite image, after which the horizontal high-frequency components, including the TC rainband structure and inner core area, are extracted and fused with the original image. This step enhances the TC structure and brightness temperature gradient characterization. The fused image is then used as the input to WEY-TCNet. The ECANet channel attention mechanism was added to the original YOLOX-S network structure to focus on the key features of TC, such as structure and brightness temperature while suppressing invalid features and noise. This study constructed a satellite image dataset based on the infrared images of the Chinese Fengyun2D geostationary satellite. The dataset contains infrared satellite images of TCs in various life cycle stages, from generation to extinction. Our experiments showed that the WEY-TCNet model proposed in this paper achieves 91.49% detection mAP on the TC dataset, compared with the 86.69% detection mAP of the original YOLOX-S model, significantly improving it.

关键词： Tropical cyclone detection infrared satellite imagery YOLOX-S efficient channel attention discrete wavelet transform

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services Computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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A Low-Complexity Three-Dimensional Parameter Estimation Algorithm for FMCW Radar 6

A Low-Complexity Three-Dimensional Parameter Estimation Algo...

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

作者： Yuhan, Yuan Yingchun, Li Qi, Long Peng, Wu Tao, Jin Zhiquan, Zhou School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798350399059

Frequency Modulated Continuous Wave (FMCW) radar has been widely used in the field of vehicle mounted millimeter wave radar to detect the parameters of targets. A low complexity three-dimensional (3D) parameter estimation algorithm for time-division multiplexing multiple-input multiple-output (TDM-MIMO) FMCW radar is proposed to detect the range, velocity, and azimuth angle of targets. The proposed algorithm utilized the data block selection and rank-reduced technique to avoid the high computational burden of the traditional subspace-based algorithm. Moreover, the proposed algorithm solves the problem of velocity ambiguity in conventional TDM-MIMO FMCW signal processing. The simulation results show that the accuracy and resolution of the algorithm are much higher than the conventional algorithms, such as 3D-FFT, and 3D-MUSIC. © 2023 IEEE.

关键词： Parameter estimation

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Monitoring discharge from deep-sea mining ships via optical satellite observations

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Journal of Oceanology and Limnology 2024年第6期42卷 1853-1864页

作者： Ziyao YIN Yingcheng LU Yuru LIU Wenfeng ZHAN Haoran ZHANG Changyong DOU Chenchen WU Dong SUN Zihan LIU Chunsheng WANG Yuntao WANG International Institute for Earth System Science Collaborative Innovation Center for the South Sea StudiesNanjing UniversityNanjing 210046China Second Institute of Oceanography Ministry of Natural ResourcesHangzhou 310012China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China

Deep-sea mining may disturb the water column environment,including the surface water and deep-sea,and these disturbances should be carefully *** sensing provides high-resolution and accurate long-term observations in the area around deep-sea *** from mining ships can be identified within few days from satellite observations based on changes in reflectance.A pioneer twomonth experimental deep-sea mining cruise was conducted by The Metal Company in the eastern Pacific Ocean from September 15 to November 17,2022.A report from Greenpeace indicated incidents of surface discharge and leakage during this mining *** this study,satellite observations captured a clear signal over the surface water from September 24 to October 28,indicating the location with discharged water from the *** number of pixels where the potential discharged water was identified in the satellite imagery ranged from 4 to *** discharged water was transported by the combined effects of wind and currents,locating continuously to the downwind side of the mothership's mooring *** sensing provides a timely and accurate monitoring system for tracking water discharge during deep-sea mining.

关键词： remote sensing deep-sea mining chlorophyll-a concentration water discharge environmental monitoring

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Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

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Self-Transriber: Few-Shot Lyrics Transcription With Self-Training 48

Self-Transriber: Few-Shot Lyrics Transcription With Self-Tra...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Xiaoxue Yue, Xianghu Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to benefit from unlabeled data and alleviate limited data problem have not been explored for lyrics transcription. We propose the first semi-supervised lyrics transcription paradigm, Self-Transcriber, by leveraging on unlabeled data using selftraining with noisy student augmentation. We attempt to demonstrate the possibility of lyrics transcription with a few amount of labeled data. Self-Transcriber generates pseudo labels of the unlabeled singing using teacher model, and augments pseudo-labels to the labeled data for student model update with both self-training and supervised training losses. This work closes the gap between supervised and semi- supervised learning as well as opens doors for few-shot learning of lyrics transcription. Our experiments show that our approach using only 12.7 hours of labeled data achieves competitive performance compared with the supervised approaches trained on 149.1 hours of labeled data for lyrics transcription. © 2023 IEEE.

关键词： Lyrics transcription music information retrieval singing processing and self-training

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