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An XML-based 3-dimensional graphic database system 1st

1st International Conference on Human.Society@Internet, 2001

作者： Hwang, Jong Ha Ro, Jae Ho Lee, Keung Hae Park, Jong Sou Hwang, Soochan Department of Computer Engineering Hankuk Aviation University Koyang Korea Republic of Samsung Data System Information Technology Seoul Korea Republic of

ISBN: (纸本)3540423133

Supporting the semantics of 3-D objects and their spatial relations in database systems has been little addressed in the literature. Despite its importance, most 3-D graphic systems lack this capability, mainly focusing on the visualization aspects of 3-D images. We have developed a 3-D graphic data model based on XML that accommodates the semantics of 3-D scenes. This model offers content-based retrievals of scenes containing a particular object or those satisfying certain spatial constraints on them. The model represents scenes as compositions of 3-D graphic objects with associated spatial relations. Complex 3-D objects are modeled using a set of primitive 3-D objects rather than the lines and polygons that are found in traditional graphic systems. This paper presents the data model and its implementation called 3DGML, an XML vocabulary that we developed for modeling 3-D graphic data. This paper also describes a Web-based prototype database system that we developed to support the data model. © Springer-Verlag Berlin Heidelberg 2001.

关键词： XML

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on data Science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. Computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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An Advanced Approach for Understory Terrain Extraction Utilizing TomoSAR and MCSF Algorithm

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Bin, Xi Yu, Zhang Wenmei, Li Lei, Zhao Kunpeng, Xu Yunmei, Ma Yuhong, He Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210003 China Nanjing University of Posts and Telecommunications School of Internet of Things and the Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210003 China Research Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing100091 China NFGA Key Laboratory of Forestry Remote Sensing and Information System Beijing100091 China University of Toronto Department of Geography Geomatics and Environment MississaugaONL5L 1C6 Canada

The understory terrain is an essential component of forest vertical structure and ecosystem health, providing crucial insights for resource assessment and forestry surveys. This letter proposes a novel method for extracting understory terrain through forest backscattering power profiles and the modified cloth simulation filtering (MCSF) algorithm. It innovatively reconstructs synthetic aperture radar (SAR) signals into a 3-D point cloud, eliminating sidelobe signals to reduce noise while only retaining the mainlobe signals. The MCSF algorithm is subsequently utilized to extract ground and nonground points based on the vertical distribution of the mainlobe signals. The extracted ground points offer a more precise representation of actual terrain conditions. The feasibility of the method was validated utilizing airborne P-band multi baseline SAR data obtained from the Saihanba test site in Hebei Province. The outcomes clearly indicate that our approach exhibits superior correlation (0.999) and a smaller root mean square error (RMSE) (3.07 m) in comparison to conventional methods when compared with the reference digital elevation model (DEM). © 2004-2012 IEEE.

关键词： Landforms

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of Computer Science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Gaining water bodies by climate change benefits water crisis mitigation in central Asia

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Science Bulletin 2025年

作者： Su, Yanan Chen, Shengqian Sui, Yijie Li, Xin Xu, Jinhao Che, Xianghong Xie, Tingting Chen, Jianhui Sheng, Yongwei Feng, Min Chen, Fahu State Key Laboratory of Tibetan Plateau Earth System Environment and Resources Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Research Center of Geospatial Big Data Application Chinese Academy of Surveying and Mapping Beijing100830 China College of Water Resources and Architectural Engineering Tarim University Alar843300 China Key Laboratory of Western China's Environmental Systems Ministry of Education College of Earth and Environmental Sciences Lanzhou University Lanzhou730000 China Department of Geography University of California Los AngelesCA90095 United States Academy of Plateau Science and Sustainability Qinghai Normal University Xining810016 China

Central Asia (CA) faces a severe water crisis exemplified by the shrinking Aral Sea. However, little is known about the entire region, particularly the numerous small water bodies that are vulnerable to climate change yet vital for regional sustainability. We examined water bodies as small as 0.0045 km2 across CA from 1992 to 2020, identifying 66,215 water bodies in 2020, 82.2% of which were previously unstudied. In contrast to the well-documented decline of the Aral Sea, other water bodies have expanded by 10.7% (8714.3 km2), with a net gain of 15,831 lakes since 1992. These findings challenge the perception of a drying CA and provide evidence of a warm-wet climate trend, which is redistributing water resources and creating opportunities for transforming water management to address the long-standing water crisis in CA. © 2025 Science China Press

关键词： Central Asia Climate Change Small Water Bodies Water Crisis Water Resource Management

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Sensorless commutation of micro PMSMs for high-performance high-speed applications

Sensorless commutation of micro PMSMs for high-performance h...

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ICEMS 2005: 8th International Conference on Electrical Machines nd systems

作者： Zhang, J. Jiang, Q. Department of System Engineering University of Arkansas Little Rock AR 72204 United States A Star Data Storage Institute Singapore 117608 Singapore

ISBN: (纸本)7506274078

This paper presents a preliminary analysis of a sensorless sinusoidal commutation of micro slotless PMSMs which can be used in high-performance, high-speed applications. Since the inductance of a micro slotless PMSM is extremely small, three-phase back electromotive forces (BEMFs) can be estimated with a first-order delay filter. The terminal phase voltages of the micro PMSM are generated based on the estimated BEMFs and a required current amplitude. The resulted terminal voltages are sinusoidal and almost synchronous to BEMFs in a high operating-speed range.

关键词： Synchronous motors

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Experiments with MHEG Player/Studio: An interactive hypermedia visualization and authoring system 24

Experiments with MHEG Player/Studio: An interactive hypermed...

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24th EUROMICRO Conference, EURMIC 1998

作者： Oh, Seungtaek Yi, Yung Jeong, Seunghoon Choi, Yanghee Ko, Younghwa Department of Computer Engineering Seoul National University Shinlim-dong Kwanak-goo Seoul Korea Republic of Samsung Data System Korea Republic of

ISBN: (纸本)0818686464

With the growing needs of information sharing and exchange, MHEG-6 (Multimedia Hypermedia information coding Expert Group-part 6) standard is defined so as to provide the international standard for representing common coded interactive multimedia information, presenting and exchanging information using the same syntax and semantics in a heterogeneous environment. We developed a prototype system for visualizing and authoring hypermedia information based on MHEG-6 coded representation. The MHEGPlayer is a visualizing system designed to satisfy the various system requirements of the MHEG-6 and DAVIC (Digital Audio Video Council) standards, including support for various time-critical complicated synchronizations and interactions between MHEG objects on a MHEG-5 Engine and execution elements on a virtual machine. The MHEGStudio is a user friendly interactive integrated authoring system designed to support convenient and simple operations without requiring overcomplicated standard-specific details. We comment on the standard and the overall architecture of the MHEG Player/Studio. A scheme to use MHEG-6 in the WWW (World Wide Web) environment is suggested. © 1998 IEEE.

关键词： Semantics

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Optimizing Hate Speech Detection in the Indonesian Language using FastText and LSTM Algorithms 2

Optimizing Hate Speech Detection in the Indonesian Language ...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Wenando, Febby Apri Yusoff, Nooraini Izrin, Nurul Universitas Andalas Department of Information System Padang Indonesia Universiti Malaysia Kelantan Department of Data Science Kelantan Malaysia Universiti Teknikal Malaysia Department of Software Engineering Malaysia

ISBN: (纸本)9798350389241

The term hate speech encompasses communication that conveys hostility or prejudice toward individuals or groups based on specific attributes, including race, ethnicity, gender, or religion. Given the prevalence of such harmful content on major social media platforms like Twitter, it is imperative to prioritize ongoing research into automated detection methods. This study employs advanced artificial intelligence and machine learning techniques, involving pivotal stages such as data collection, preprocessing, oversampling, fastText word embedding, and classification using Long Short-Term Memory (LSTM) networks to achieve the objective. The amalgamation of fastText embeddings with LSTM classification yielded noteworthy results, with the model achieving 94% accuracy, 92% precision, 94% recall, and a 93% F1 score. These outcomes underscore the effectiveness of sophisticated machine learning methodologies in accurately discerning and addressing the proliferation of hate speech online, particularly in the context of Indonesian-language content. © 2024 IEEE.

关键词： Speech recognition

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An efficient retrieval of relational database information based on knowledge graph

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International Journal of Information and Communication Technology 2023年第3期23卷 201-214页

作者： Zhu, Hongqin Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Jiangxi Nanchang330013 China Department of Information Engineering East China University of Technology 418 Guanglan Avenue Jiangxi Province Nanchang330013 China

In order to optimise the effect of database information retrieval and overcome the problem of high index cost of traditional retrieval methods, an efficient retrieval design method of relational database based on knowledge graph is designed in this paper. Firstly, the distribution model of database information retrieval nodes is constructed to analyse the data structure, and the semantic characteristics of stored data of retrieval nodes are fused. Then the knowledge graph model analysis method is used to retrieve the database node attribute clustering and adaptive scheduling to realise the design of efficient information retrieval model for the database. The experimental results show that the index distance cost of this method is always below 5 × 1,010 byte, and the index distance cost is always below 40 s, which has certain application value. © 2023 Inderscience Enterprises Ltd.

关键词： Information retrieval

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