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Meeting the Information Assurance Crisis in the U.S. Now

EDPACS 2006年第6期28卷 1-14页

作者： Corey Schou Deborah Frincke Charles Reynolds James Frost The Associate Dean of Information Systems Programs at Idaho State University (ISU) in Pocatello and the director of the Center for Decision Support. Currently he serves as the Chair of the National Colloquium on Information System Security Education. Schou also directs the ISU National Center of Excellence in Information Assurance Education and the ISU Technology Innovation Center. Contact Corey Schou one of the authors of this article and the Chair of the NCISSE by email at Schou@*** for help in finding additional ways to address the issues raised in this article. A member of the University of Idaho College of Engineering faculty. She graduated from the University of California at Davis. Frincke is the co-director and co-founder of the university's Center for Secure and Dependable Software (CSDS). She was the program chair for the 2nd International Workshop on Recent Advances in Intrusion Detection (RAID). A professor of Computer Science at the College of Integrated Science and Technology at James Madison University in Harrisonburg Virginia. He holds a Ph.D. in computer science from the University of Texas at Austin and a BA in philosophy from the University of Houston. Reynolds is the Director of Information System Security Education at James Madison University. Since 1997 Reynolds has led the development of the first remote Internet-based curriculum that leads to an M.S. degree in computer science with a concentration in information security. Thus far 160 students have completed this program. A professor of computer information systems at Idaho State University and the manager of its Simplot Decision Support Center.

T he only difference etween evolution and revolution is the speed at which each occurs.

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K, T and D-like fuzzy Kripke models

K, T and D-like fuzzy Kripke models

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Benjamin Bedregal Regivan H. N. Santiago Mario Benevides Petrúcio Viana Group of Logic Language Information Theory and Applications-LoLITA Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Program of Engineering of Systems and Computing-PESC Department of Computer Science Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil Department of Analysis Fluminense Federal University (UFF) Niteroi Rio de Janeiro Brazil

The idea of a Kripke semantics endowed with possibility/plausibility information is not new; in fact there are different approaches for that; see: [6], [13], [16], [19]. This paper follows the approach found in [6], but whereas [6] provides a fixed interpretation for connectives into [0,1] here we provide a characterization of a fuzzy semantics for connectives in such a way that the resulting fuzzy frames: K, T and D, are described precisely by the set of statements which also describes, respectively, the usual modal systems K, T and D.

关键词： Semantics Fuzzy sets Fuzzy logic Bismuth Electronic mail Uncertainty Reliability

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Structured Downsampling for Fast, Memory-efficient Curation of Online Data Streams

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Zaman, Luis Dolson, Emily Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems Ann Arbor United States Michigan Institute for Data and AI in Society Ann Arbor United States University of Michigan Ann Arbor United States Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior East Lansing United States Michigan State University East Lansing United States

Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memory efficient - particularly in resource-constrained or real-time contexts. Here, we address the problem of extracting a fixed-capacity, rolling subsample from a data stream. Specifically, we explore "data stream curation" strategies to fulfill requirements on the composition of sample time points retained. Our "DStream" suite of algorithms targets three temporal coverage criteria: (1) steady coverage, where retained samples should spread evenly across elapsed data stream history;(2) stretched coverage, where early data items should be proportionally favored;and (3) tilted coverage, where recent data items should be proportionally favored. For each algorithm, we prove worst-case bounds on rolling coverage quality. In contrast to previous work by Moreno, Rodriguez Papa, and Dolson (2024), which dynamically scales memory use to guarantee a specified level of coverage quality, here we focus on the more practical, application-driven case of maximizing coverage quality given a fixed memory capacity. As a core simplifying assumption, we restrict algorithm design to a single update operation: writing from the data stream to a calculated buffer site - with data never being read back, no metadata stored (e.g., sample timestamps), and data eviction occurring only implicitly via overwrite. Drawing only on primitive, low-level operations and ensuring full, overhead-free use of available memory, this "DStream" framework ideally suits domains that are resource-constrained (e.g., embedded systems), performance-critical (e.g., real-time), and fine-grained (e.g., individual data items as small as single bits or bytes). In particular, proposed power-of-two-based buffer layout schemes support O(1) data ingestion via concise bit-level operations. To further practical applica

关键词： Data streams

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Computational Methods for Single-Cell RNA Sequencing

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Biomedical Data science 1000年第1期3卷 339-364页

作者： Brian Hie Joshua Peters Sarah K. Nyquist Alex K. Shalek Bonnie Berger Bryan D. Bryson 1Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bab@mit.edu 3Ragon Institute of MGH MIT and Harvard Cambridge Massachusetts 02139 USA 2Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bryand@mit.edu 4Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 5Department of Chemistry Institute for Medical Engineering & Science (IMES) and Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

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Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes (vol 12, pg 631, 2020)

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GENETICS IN MEDICINE 2020年第8期22卷 1427-1427页

作者： Chang, Willie H. Mashouri, Pouria Lozano, Alexander X. Johnstone, Brittney Husic, Mia Olry, Annie Maiella, Sylvie Balci, Tugce B. Sawyer, Sarah L. Robinson, Peter N. Rath, Ana Brudno, Michael Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada Department of Computer Science Princeton University Princeton NJ USA Faculty of Medicine University of Toronto Toronto ON Canada Department of Materials Science & Engineering Stanford University Stanford CA USA Sunnybrook Health Sciences Centre Toronto ON Canada Orphanet Institut national de la santé et de la recherche médicale Paris France Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada Department of Genetics Children’s Hospital of Eastern Ontario and Children’s Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada The Jackson Laboratory for Genomic Medicine Farmington CT USA Institute for Systems Genomics University of Connecticut Farmington CT USA Department of Computer Science University of Toronto Toronto ON Canada Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada University Health Network Toronto ON Canada

PurposeComputational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student *** developed Phenotate, a web application for crowdsourcing disease phenotype annotations through assignments for undergraduate genetics students. Using student-collected data, we generated composite annotations for each disease through a machine learning approach. These annotations were compared with those from clinical practitioners and gold standard curated *** Phenotate in five undergraduate genetics courses, we collected annotations for 22 diseases. Student-sourced annotations showed strong similarity to gold standards, with F-measures ranging from 0.584 to 0.868. Furthermore, clinicians used Phenotate annotations to identify diseases with comparable accuracy to other annotation sources and gold standards. For six disorders, no gold standards were available, allowing us to create some of the first structured annotations for them, while students demonstrated ability to research *** enables crowdsourcing RD phenotypic annotations through educational assignments. Presented as an intuitive web-based tool, it offers pedagogical benefits and augments the computable RD knowledgebase.

关键词： rare diseases phenotype crowdsourcing medical education machine learning

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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THE ROLE OF PERFORMANCE ANALYSIS IN THE LIFE-CYCLE OF A WEAPONS SYSTEM

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NAVAL ENGINEERS JOURNAL 1987年第3期99卷 239-248页

作者： BAILEY, JL The authoris an operations research analyst in the Systems Engineering Branch Aegis Ship Combat Systems Division at the Naval Surface Weapons Center (NSWC). Dr. Bailey received a B. S. degree in mathematics from the Massachusetts Institute of Technology in 1964 a Ph.D. in mathematics from the University of Tennessee in 1968 and an M. S. in computer science from the Virginia Polytechnic Institute and State University in 1975. He was an assistant professor of mathematics at the University of Tennessee during the 1968-69 academic year then came to NSWC/Dahlgren in September 1969. He worked as an operations research analyst in mine warfare from 1969 to 1978 specializing in minefield planning. He headed the group which developed the minefield planning module of the Mine Warfare Trainer which was delivered to the Fleet and Mine Warfare Training Center in 1978-79. Dr. Bailey served as science advisor to the commander Mine Warfare Command in 1978-79 under the sponsorship of the Navy Science Assistance Program. In 1979-80 he led the development of military value assessment methodology for a ship survivability improvement program. In 1980 he moved to the Aegis Ship Combat Systems Division becoming leader of the Systems Performance Assessment Group in 1981. Dr. Bailey leads a group of analysts who do assessment of the predicted performance of the Aegis combat system and the effects of proposed changes to the system. Dr. Bailey has also served collateral duty as systems analysis manager for the Technical Division of the Aegis Project Office (PMS-400) in the Naval Sea Systems Command since 1981.

Performance analysis is the process of determining the predicted performance of a weapons system. It is generally used to examine predicted performance of systems in a variety of configurations and operational situations; it is accomplished through a variety of techniques, from the computation of straightforward algebraic functions to complex computer simulations. Performance analysis is necessary throughout the life-cycle of weapons systems. It is used to help determine original requirements, for conceptual and detailed design, and to support operational planning and determine upgrade requirements. The development, maintenance, and consistent application of a set of system specific performance analysis methods have enhanced the development of many weapons systems. This paper is in the form of a tutorial on the application of performance analysis techniques, using the development of the Aegis weapons system as a source of examples, and stressing the value of performance analysis methods which have been designed specifically for the Aegis system.

关键词： MILITARY EQUIPMENT

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Waters network's potential to transform environmental engineering education

Waters network's potential to transform environmental engine...

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114th Annual ASEE Conference and Exposition, 2007

作者： Eschenbach, Elizabeth Montgomery, Jami Johnson, James Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States WATERS Network - CLEANER Project Office Network Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States American Academy of Environmental Engineers United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute of Technology United States National Research Council U. S. Air Force United States NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Energy Environmental and Chemical Engineering Washington University St. Louis United States Environmental Studies Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States Education Planning Committee National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University or Illinois United States Knowledge and Learning Systems Group NCSA United States

The WATERS Network (WATer and Environmental Research systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practitioners to advance effective management of our nation's water resources by understanding human interactions with water and the natural and built environment. WATERS will provide easily accessible real time environmental data as well as analysis tools to engineers, scientists, educators, K- graduate students and policymakers so they can better understand how water quantity, quality and related components of the hydrologic cycle are impacted by natural and human influences. The WATERS strategic plan will be completed by July 2007 and requires constituent input. This paper describes the draft Conceptual Design and the Education Plan of WATERS Network. This paper will highlight the potential impact of WATERS on undergraduate and graduate environmental engineering education in order to elicit input from the environmental engineering education community on how WATERS Network could better meet the future needs of undergraduate and graduate students and educators. More information can be found at ***. © American Society for engineering Education, 2007.

关键词： Water resources

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Experimental Investigation of Lord Kelvin’s Isentropic Cooling and Heating Expression in Tensile Bars for Two engineering Alloys

arXiv

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arXiv 2023年

作者： Burns, S.J. Pratt, Christine E. Carrock, Joseph Gagnon, Jean-Philippe Sefkow, A.B. Materials Science Program College of Arts Science and Engineering University of Rochester RochesterNY14627 United States Department of Mechanical Engineering University of Rochester 236 Hopeman Hall RochesterNY14627 United States Field Application Engineering Telops GenevaNY14456 United States Systems Engineering Telops 100-2600 St-Jean-Baptiste Ave. QuébecQCG2E 6J5 Canada Laboratory for Laser Energetics University of Rochester 250 E. River Rd. RochesterNY14623 United States Department of Physics and Astronomy University of Rochester 206 Bausch and Lomb Hall RochesterNY14627 United States Department of Computer Science University of Rochester 2513 Wegmans Hall RochesterNY14627 United States

Solids when rapidly and elastically stressed change temperature, the effect proposed by Lord Kelvin is adiabatic thermo-elastic cooling or heating depending on the sign of the stress. A fast sensitive IR camera has measured temperature both decreasing and increasing. Temperature measurements made from the reversible, elastic part of the stress-strain curve during fast uniaxial tensile loading have been investigated. The isentropic temperature cooling from the loading curve is recovered by heating after the specimen fractures when the load is released. These measurements establish for the first time isentropic thermal recovery in two engineering alloys. The materials tested are an AISI 4340 steel and an aluminum 2024 alloy. Measurements of the isentropic thermo-elastic stress cooling are -0.61 K/GPa for steel and -1.7 K/GPa for aluminum alloy. The isentropic thermo-elastic stress heating is -1.16 K/GPa for steel and -1.6 K/GPa for aluminum alloy. These values are compared to Lord Kelvin’s theoretical thermodynamic prediction. Comparisons are also to equations of state tables. The experimental values are slightly below theoretical predictions. The isentropic, elastic part of the temperature is fully recoverable even after extensive plastic deformation upon fracture. Copyright © 2023, The Authors. All rights reserved.

关键词： Cooling

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