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International Symposium on Distributed Objects and Applications

作者： K.S. Nagendra O. Bukhres S. Sikkupparbathyam M. Areal Z.B. Miled L. Olsen C. Gokey D. Kendig T. Northcutt R. Kordova G. Major Indiana University Purdue University Computer Science Department Purdue University School of Science Indianapolis IN USA Engineering and Technology Department of Electrical and Computer Engineering Indiana University Purdue University Purdue University Indianapolis IN USA Goddard Space Flight Center Global Change Data Center NASA Greenbelt MD USA

The Global Change Master Directory (GCMD) is an earth science repository that specifically tracks research data on global climatic change. The GCMD is migrating from a centralized architecture to a globally distributed replicated heterogeneous federated system. One of the greatest challenges facing database research is the integration of heterogeneous systems without compromising the local autonomy, reliability and transparency of the various databases that are participating in the integration. This paper discusses these challenges in the context of the design and implementation of the next version of the GCMD software (Version 8.0). The proposed system has been designed and developed using an object-oriented system architecture based on Java, RMI (Remote Method Invocation) and JDBC. This system enables other sources to be integrated into the GCMD system, with limited changes to the local system itself. This paper describes the components of the GCMD system and addresses the issues of heterogeneity, distribution and autonomy.

关键词： NASA Protocols Java databases Space technology computer architecture Environmental management computer science Aerospace engineering Geoscience

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Laboratory investigation: Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions

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computer aided Surgery 1995年第6期1.0卷 326-338页

作者： Warfield, Simon Dengler, Joachim Zaers, Joachim Guttmann, Charles R.G. Wells, William M. Ettinger, Gil J. Hiller, John Kikinis, Ron School of Computer Science and Engineering University of New South Wales Sydney Australia Vision Systems Neckargemuend Germany German Cancer Research Center Heidelberg Germany Department of Radiology Harvard Medical School and Brigham and Women's Hospital Boston MA United States AI Lab NE43-771 Massachusetts Institute of Technology Cambridge MA United States Department of Radiological Sciences UCLA School of Medicine Los Angeles CA United States

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier. Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion. © 1995 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted.

关键词： Gray matter MRI MS lesion Segmentation

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Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions

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Journal of Image Guided Surgery 1995年第6期1卷 326-338页

作者： Warfield, Simon Dengler, Joachim Zaers, Joachim Guttmann, Charles R.G. Wells, William M. Ettinger, Gil J. Hiller, John Kikinis, Ron School of Computer Science and Engineering University of New South Wales Sydney Australia Neckargemuend Germany German Cancer Research Center (DKFZ) Heidelberg Germany Department of Radiology Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts United States Massachusetts Institute of Technology AI Lab NE43-771 Cambridge Massachusetts NE43-771 United States Department of Radiological Sciences UCLA School of Medicine Los Angeles California United States

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity‐based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three‐dimensional, region‐growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two‐class minimum‐distance classifier. Analysis of double‐echo spin‐echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion. J Image Guid Surg 1:326–338 (1995). © 1996 Wiley‐Liss, Inc. Copyright © 1996 Wiley‐Liss, Inc.

关键词： gray matter MRI MS lesion segmentation

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Abstracts

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Environmental Management 1977年第1期1卷 67-96页

作者： Frankenfeld, John W. Schulz, Wolfgang McMurty, George J. Petersen, Gary W. May, G. A. Hering, F. S. Schwartz, J. I. Heywood, J. B. Chigier, N. A. Grohse, E. W. Walker, J. D. Colwell, R. R. Petrakis, L. Pergament, H. S. Thorpe, R. D. Schoepf, Richard W. Krzyczkowski, Roman Henneman, Suzanne S. Hudson, Charles L. Putnam, Evelyn S. Thiesen, Donna J. Parks, G. A. McCarty, Perry L. Leckie, J. O. Schrumpf, Barry J. Simonson, G. H. Paine, D. P. Lawrence, R. D. Pyott, W. T. Leh, M. Elders, W. Combs, J. Caplen, T. Harrison, F. L. Wong, K. M. Heft., R. E. Charnell, Robert L. Lehmann, Edward J. Mallon, Lawrence G. Hatfield, Cecile Adams, Gerald H. Johanning, James Talvitie, Antti Noll, Kenneth E. Miller, Terry Smiarowski, Joseph F. Willis, Cleve E. Foster, John H. Schlesinger, Benjamin Daetz, Douglas Lear, Donald U. Smith, Mona F. Hundemann, Audrey S. Crockett, Pernell W. Werner, Kirk G. Carroll, Thomas E. Maase, David L. Genco, Joseph E. Ifeadi, Christopher N. Lowman, F. G. Christensen, S. W. Van Winkle, W. Mattice, J. S. Harrison, Elizabeth A. Barker, James C. Chesness, Jerry L. Smith, Ralph E. Shaheeen, Donald G. Raney, R. Keith Borton, T. Wezernak, C. T. Raney, R. K. Sherwani, Jabbor K. Moreau, David H. Eisenberg, Norman A. Lynch, Cornelius J. Breeding, Roger J. Johnson, J. D. Foster, K. E. Mouat, D. A. Clark, R. Hyden, John William Owen, Wilfred Bayfield, Neil G. Barrow, Graham C. Stolz, Stephanie B. Wienckowski, Louis A. Brown, Betram S. Keyfitz, Nathan Wilson, W. L. Newman, Peter W. G. Bammi, Deepak Bammi, Dalip Goddard, James E. Chisholm, Tony Walsh, Cliff Brennan, Geoffrey Thompson, K. S. Richardson, R. Jensen, Clayton E. Brown, Dail W. Mirabito, John A. Cowing, Thomas G. Binghamton, Suny Siehl, George H. Albrecht, O. W. Alexander, Ariel Barde, Jean -Philippe Darby, William P. McMichael, Francis Clay Dunlap, Robert W. Muckleston, Keith W. Frankenhoff, Charles A. Giulini, Lorenzo T. Wyatt, T. Black, Peter E. Keating, William Thomas Leonard, M. E. Fisher, E. L. Brunelle, M. F. Dickinson, J. E. Pethig, Rudiger Clapham Exxon Research & Engineering Co. Linden Government Research Lab Coast Guard Washington D.C. Department of Transportation Washington D.C. Office For Remote Sensing of Earth Resources. NASA Earth Resources Survey Program Pennsylvania State University Washington D.C. Department of the Navy Washington D.C. Cambridge. Dept. of Mechanical Engineering Massachusetts Inst. of Technology USA Huntsville. School of Graduate Studies and Research Alabama Univ. USA Dept. of Microbiology. Office of Naval Research Maryland Univ. Arlington Research and Development Co. Pittsburgh AeroChem Research Labs. Inc. Princeton Dept. of the Interior Office of Library Services. Bibliographic Series (Final) Washington D.C. Interplan Corp. Santa Barbara Urban Mass Transportation Adm. Washington D.C. Naval Underwater Systems Center Newport Calif. Mercury Project. National Science Found Stanford Univ Washington D.C. Div. of Advanced Environmental Research & Tech. USA Corvallis. NASA Earth Resources Survey Program Oregon State Univ. Washington D.C. Riverside. Inst. of Geophysics and Planetary Physics National Science Foundation California Univ. Washington D.C. Livermore Lawrence Livermore Lab. California Univ. USA Atlantic Oceanographic and Meteorological Labs. National Oceanic and Atmospheric Administration Miami National Technical Information Service Springfield Miami Univ. Coral Gables School of Law National Oceanic and Atmospheric Administration Rockville National Academy of Sciences Office of Sea Grant Washington D. C. School of Law National Oceanic and Atmospheric Administration Office of Sea Grant. Rockville Norman. Dept. of Civil Engineering and Environmental Science. Urban Mass Transportation Administration Oklahoma Univ. Washington D. C. Knoxville.Dept. of Civil Engineering. Federal Highway Administration Tennessee Univ. Washington D. C. Amherst.Water Resources Research Center. Office of Water Research and Technology Massachusetts Univ. Washington D. C. Calif. Dept. of Industria

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Semi-Supervised Crowd Counting via Swin Transformer with Adaptive Soft Threshold and Contrastive Learning

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Mingwei Yao Kehua Guo Lingyan Zhang Xuyang Tan Xiaokang Zhou School of Computer Science and Engineering Central South University China School of Computer Science and Engineering Central South University China and Furong Laboratory Central South University China Faculty of Business Data Science Kansai University Japan and RIKEN Center for Advanced Intelligence Project Japan

Manual annotation for crowd counting remains labor-intensive and costly. Although existing semi-supervised methods partially alleviate this burden, they still face significant challenges regarding the quality of generated pseudo-labels and the utilization of unlabeled data. To address these issues, we propose a novel semi-supervised crowd counting framework, called Point-Adaptive Teacher (PAT). This framework integrates Adaptive Soft Threshold (AST) and contrastive learning to enhance pseudo-label quality and effectively leverage unlabeled data. Specifically, we employ the Swin Transformer as the backbone and develop Swin-P2PNet, which captures global contextual information through hierarchical window attention, improving the accuracy of pseudo-labels. Additionally, we design the AST that dynamically adjusts the sample loss weight by combining confidence and uncertainty predictions, thereby alleviating the effect of noise in pseudo-labels. Finally, we introduce a contrastive learning strategy requiring no extra parameters. This strategy enhances the model's ability to learn latent representations from unlabeled data. Extensive experiments have been conducted on three public datasets, namely ShanghaiTech, JHU-Crowd++, and UCF-QNRF. The results demonstrate that our method achieves performance comparable to state-of-the-art methods.

关键词： Swin Transformer Crowd counting Contrastive learning Semi-supervised learning

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Proceedings of the Second International Conference on Computing, Communication, Security and Intelligent Systems 1

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丛书名： Algorithms for Intelligent Systems

1000年

作者： Shahid Mumtaz Danda B. Rawat Varun G. Menon

ISBN: (数字)9789819983988

ISBN: (纸本)9789819985753;9789819983971

This book presents the best-selected research papers presented at the Second International Conference on Computing, Communication, Security & Intelligent Systems (IC3SIS 2023), organized by SCMS school of engineering and Technology, Kochi, on July 06–07, 2023. It discusses the latest technologies in communication and intelligent systems, covering various areas of computing, such as advanced computing, communication and networking, intelligent systems and analytics, 5G and IoT, soft computing, and cybersecurity in general. Featuring work by leading researchers and technocrats, the book serves as a valuable reference resource for young researchers, academics, and industry practitioners.

关键词： Computational Intelligence Communications engineering, Networks Artificial Intelligence

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ESALP2: Efficient Signature Aggregation with Location Privacy Preservation in Wireless Body Area Networks

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ACM Transactions on Sensor Networks 1000年

作者： Arun Sekar Rajasekaran Azees Maria Basker Palaniswamy Ashok Kumar Das Mohammed J.F. Alenazi Department of Electronics and Communication Engineering SR University Hanamakonda India School of Computer Science and Engineering VIT-AP University Amaravati India Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Cork Ireland Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad Hyderabad India Department of Computer Engineering College of Computer and Information Sciences CCIS) King Saud University Riyadh Saudi Arabia

For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.

关键词： Wireless Body Area Networks (WBAN) Authentication Aggregate signature Location privacy Security Scyther Smart healthcare.

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Comparing analytical, micromagnetic and statistical channel models at 4 Tcbpsi patterned media recording

Comparing analytical, micromagnetic and statistical channel ...

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作者： Chua, M. Elidrissi, M.R. Eason, K. Zhang, S.H. Qin, Z.L. Chai, K.S. Tan, K.P. Cai, K. Chan, K.S. Goh, W.L. Dong, Y. Victora, R.H. Data Storage Institute (DSI) Agency for Science Technology and Research (A STAR) 117608 Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University 637457 Singapore Singapore Center for Micromagnetics and Information Technologies Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN United States

Bit patterned media recording (BPMR) is a candidate technology proposed to extend the areal density growth capability of magnetic recording systems. In conventional granular magnetic recording (CGMR), bits of information are recorded onto a number of randomly distributed magnetic grains. The difficulty for the granular media approach is that due to the randomness of the grains, a certain minimum number of grains are required in order to maintain the media signal to noise ratio (SNR). This requires smaller grains as the bits are shrunk and we end up with either unstable, or unwriteable grains as per the well-known media-trilemma. In BPMR, instead of random positions, the ''grains'' are ordered into a well-defined lattice of magnetic islands. Because of this ordering, the media SNR is no longer determined by the number of grains per bit as in CGMR, but is defined by the geometrical and magnetic distributions of the fabrication process of the media. Analytical models exist for BPMR, that flip grains in the media based on the switching field distribution (SFD) and the knowledge of the applied head field. On the other side, a statistical model for CGMR has been proposed that measures probabilities of grains flipping based on micromagnetic simulations, named the grain flipping probability (GFP) model. In this work, we adapt the GFP model for BPMR and perform a comparison between densities predicted via the analytical and GFP models, by processing the signals from these models with the appropriate detectors and LDPC (low density parity check) decoders. © 2012 IEEE.

关键词： Signal to noise ratio

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GSFL: A Privacy-preserving Grouping-Split Federated Learning Approach in Resource-constrained Edge Computing Scenarios

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Qi Liu Zhilu Wang Xiaokang Zhou Yonghong Zhang Xiaodong Liu Haiyang Lin Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services China and Nanjing University of Information Science and Technology School of Software China Nanjing University of Information Science and Technology School of Computer Science China Kansai University Faculty of Business Data Science Japan and RIKEN Center for Advanced Intelligence Project Japan Nanjing University of Information Science and Technology School of Automation China Edinburgh Napier University School of Computing UK Nanjing University of Information Science and Technology School of Software China

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\), representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.

关键词： Federated learning Split learning Non-IID Clustering

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Camera-Based Document Analysis and Recognition 1

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丛书名： Lecture Notes in computer science

1000年

作者： Masakazu Iwamura Faisal Shafait

ISBN: (数字)9783642293641

ISBN: (纸本)9783642293634

This book constitutes the thoroughly refereed post-workshop-proceedings of the 4th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2011, held in Beijing, China, in September 2011. The 13 revised full papers presented were carefully selected during a second round of reviewing and improvement from numerous original submissions. Intended to give a snapshot of the state-of-the-art research in the field of camera based document analysis and recognition, the papers are organized in topical sections on text detection and recognition in scene images, camera-based systems, and datasets and evaluation.

关键词： Image Processing and computer Vision Pattern Recognition data Mining and Knowledge Discovery Natural Language Processing (NLP) Information Systems Applications (incl. Internet) User Interfaces and Human computer Interaction

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