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Foundations of Intelligent Systems 1

丛书名： Lecture Notes in computer science

1000年

作者： Ning Zhong Zbigniew W. Raś Shusaku Tsumoto Einoshin Suzuki

ISBN: (数字)9783540395928

ISBN: (纸本)9783540202561

This volume contains the papers selected for presentation at the 14th international Symposium on Methodologies for Intelligent Systems, ISMIS 2003, held in Maebashi City, Japan, 28–31 October, 2003. The symposium was organized by the Maebashi Institute of Technology in co-operation with the Japanese Society for Artificial Intelligence. It was sponsored by the Maebashi Institute of Technology, Maebashi Convention Bureau, Maebashi City Government, Gunma Prefecture Government, US AFOSR/AOARD, the Web Intelligence Consortium (Japan), Gunma Information Service Industry Association, and Ryomo Systems Co., Ltd. ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. Since then it has been held in Charlotte (North Carolina), Knoxville (Tennessee), Turin (Italy), Trondheim (Norway), Warsaw (Poland), Zakopane (Poland), and Lyon (France). The program committee selected the following major areas for ISMIS 2003: active media human-computer interaction, autonomic and evolutionary computation, intelligent agent technology, intelligent information retrieval, intelligent information systems, knowledge representation and integration, knowledge discovery and data mining, logic for artificial intelligence, soft computing, and Web intelligence.

关键词： Artificial Intelligence Computation by Abstract Devices Database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) User Interfaces and Human computer Interaction

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Photoluminescence of CdSe/ZnS quantum dots in GaN/quantum dots/GaN wafer bonded system

Photoluminescence of CdSe/ZnS quantum dots in GaN/quantum do...

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作者： Li, Y. Giles, A. Stokes, E. Optical Science and Engineering Graduate Program Department of Physics and Optical Science University of North Carolina at Charlotte 9201 University City Boulevard Charlotte NC 28223 United States Nanoscale Science Graduate Program Department of Chemistry University of North Carolina at Charlotte 9201 University City Boulevard Charlotte NC 28223 United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte 9201 University City Boulevard Charlotte NC 28223 United States

ISBN: (纸本)9781607681908

Wafer bonding technology is applied to the GaN/quantum dots/GaN system, where CdSe/ZnS Quantum Dots serve as both the active layer and the binding layer. Photoluminescence is observed from quantum dots following wafer bonding at various temperatures under ultra high vacuum condition. Temperature dependences of bond strength and photoluminescence properties are characterized. Annealing at elevated temperature to form wafer bonds degrades photoluminescence intensity and slightly blue shifts the wavelength. Transmission Electron Microscopy is applied to study the physical properties of vacuum annealed QDs, in which agglomeration is observed. ©The Electrochemical Society.

关键词： Gallium nitride

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th international Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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A Novel Deep Multi-Instance Convolutional Neural Network for Disaster Classification From High-Resolution Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 2098-2114页

作者： Li, Chengfan Zhang, Zixuan Liu, Lan Kim, Jung Yoon Sangaiah, Arun Kumar the School of Computer Engineering and Science Shanghai University Shanghai200444 China the Key Laboratory of National Geographic Census and Monitoring Ministry of Natural Resources Wuhan University Wuhan430079 China the Key Laboratory for Digital Land and Resources of Jiangxi Province East China University of Technology Nanchang330013 China the School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China the College of Future Industry Gachon University Seongnam-Si Gyeonggi-do13120 Korea Republic of the International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu64002 Taiwan the Department of Electrical and Computer Engineering Lebanese American University Byblos11020 Lebanon

—The fully supervised deep convolutional neural network (CNN) cannot detect the discriminant local information that is responsible for spatial transformations in high-resolution remote sensing images. To address the various types and missing labels of natural disasters, a new deep multi-instance convolutional neural network (DMCNN) model for disaster classification in high-resolution remote sensing image is presented in this article. Specifically, based on sample enhancement and atrous spatial pyramid pooling, we first extract and integrate the features via the CNN structure to obtain the instance feature of bags in the image. Besides, introducing a prototype learning layer with distance measure, the instance features extracted from pretrained CNN are mapped into a series of prototype instance features with bag-level. Subsequently, all instance features from prototype and bag take part in disaster detection and image classification. Finally, we conduct extensive experiments on xBD dataset and discussions from qualitative and quantitative aspects. Experimental results show that the proposed DMCNN model achieves better classification accuracy of natural disaster from high-resolution remote sensing images compared to traditional CNNs, and effectively improves the disaster classification performance with weakly supervised from high-resolution remote sensing images. © 2023 The Authors.

关键词： Remote sensing

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th international Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments 50

Exploring 3D Reconstruction with Drone Images: Advances and ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Miranda Baldivieso, Thiago João da Silva Batista, Taise Grazielle Chagas, Fabio Suim Rodrigues Velho, Luiz Carlos Pacheco Ferreira Rosa, Paulo Fernando Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea RJ Rio de Janeiro Brazil Department of Science and Industry systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (纸本)9781665464543

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software-both commercial and open-source-and concluding with a qualitative evaluation of the results. © 2024 IEEE.

关键词： Aerial photography

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Low-temperature spin Seebeck effect in nonmagnetic vanadium dioxide

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Physical Review B 2024年第2期110卷 024415-024415页

作者： Renjie Luo Tanner J. Legvold Liyang Chen Henry Navarro Ali C. Basaran Deshun Hong Changjiang Liu Anand Bhattacharya Ivan K. Schuller Douglas Natelson Department of Physics and Astronomy Applied Physics Graduate Program Rice Quantum Institute Department of Physics and Center for Advanced Nanoscience Materials Science Division Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination.

关键词： Spin Seebeck effect Paramagnets Strongly correlated systems

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th international Conference on computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Learning natural selection from the site frequency spectrum

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17th Annual international Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Ronen, Roy Udpa, Nitin Halperin, Eran Bafna, Vineet Bioinformatics Graduate Program University of California San Diego CA United States International Computer Science Institute Berkeley CA United States Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Department of Molecular Microbiology and Biotechnology Tel-Aviv University Tel-Aviv Israel Department of Computer Science and Engineering University of California San Diego CA United States

ISBN: (纸本)9783642371943

Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective pressures. Over the last two decades, many tests have been proposed to identify genomic signatures of natural selection. However, the power of these tests changes unpredictably from one dataset to another, with no single dominant method. We build upon recent work that connects many of these tests in a common framework, by describing how positive selection strongly impacts the observed site frequency spectrum (SFS). Many of the proposed tests quantify the skew in SFS to predict selection. Here, we show that the skew depends on many parameters, including the selection coefficient, and time since selection. Moreover, for each of the different regimes of positive selection, informative features of the scaled SFS can be learned from simulated data and applied to population-scale variation data. Using support vector machines, we develop a test that is effective over all selection regimes. On simulated datasets, our test outperforms existing ones over the entire parameter space. We apply our test to variation data from Drosophila melanogaster populations adapted to hypoxia, and identify new loci that were missed by previous approaches, but strengthen the role of the Notch pathway in hypoxia tolerance. © 2013 Springer-Verlag.

关键词： Genome

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