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IEEE International Conference on Bioinformatics and Biomedicine

作者： Dreycey Albin Dan Nasko R. A. Leo Elworth Jacob Lu Advait Balaji Christian Diaz Nidhi Shah Jeremy Selengut Chris Hulme-Lowe Pravin Muthu Gene Godbold Mikael Lindvall Madeline Diep Adam Porter Mihai Pop Krista Ternus Todd J. Treangen Program in Systems Synthetic and Physical Biology Rice University Center for Bioinformatics and Computational Biology University of Maryland Department of Computer Science Rice University Signature Science LLC Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland

ISBN: (纸本)9781728118680

Rapid advancements in synthetic biology and nucleic acid synthesis, in particular concerns about its intentional or accidental misuse, call for more sophisticated screening tools to identify genes of interest within short sequence fragments. One major gap in predicting genes of concern is the inadequacy of current tools and ontologies to describe the specific biological processes of pathogenic proteins. The objective of this work is to design software that sensitively assigns taxonomic classifications, functional annotations, and biological processes of interest to short nucleotide sequences of unknown origin (50bp-1,000bp). The overarching goal is to perform sensitive characterization of short sequences and highlight specific pathogenic biological processes of interest (BPoIs). The SeqScreen software executes these tasks in analytical workflows with Nextflow and outputs results in a tab-delimited report. Local and global alignments differentiate hits to taxonomically-related sequences from similar but unrelated sequences, and an ensemble approach leverages multiple tools and databases to assign a variety of functional terms to each query sequence. Final biological process assessments are made from the predicted functional annotations, which leverage information in pre-existing databases, as well as new custom biocurations. Machine learning models predict each biological process of interest on large protein databases before incorporation into the SeqScreen framework to streamline computational efficiency, ensure reproducible results, allow for version control, and facilitate the review of the automated predictions by expert biocurators. The SeqScreen source code is available at .

关键词： Biological Process interest Base Sequence Nucleic acids Version control protein database Database synthetic biology ANNOTATIONS Leverage

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Empirical testing: The prototyper's evaluation mechanism

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NAVAL ENGINEERS JOURNAL 1997年第1期109卷 41-46页

作者： Hafner, AN Arnold N. Hafner Ph.D.:is founder and president of Information Systems Research (ISR). He has twenty-five years of experience in systems development and is published in the field of systems development management. He served as corporate research scientist at Systems Exploration Inc. from 1988 to 1991 program director at Computer Science Corporation from 1983 to 1988 director of operations at Republic Management Systems Corporation from 1981 to 1983 and program manager at Computer Science Corporation from 1972 to 1981. A 1962 graduate of the US. Naval Academy he holds a doctoral degree in human behavior and engineering degrees in electronics and communications. He has taught courses on information systems and systems management at most of the colleges in the San Diego area. Dr. Hafner has presented fourteen refereed research papers while publishing sixteen articles and a book A Manager's Guide to Software System Development.

Evaluating complex systems is the subject of this paper, the third in a series investigating prototyping. It provides an interesting and helpful overview of how to evaluate systems prototypes and outlines the iterative stream of developer-user interactions that is replacing older approaches to testing and evaluating new military systems, which promise to reduce the time required to develop and field future military capabilities. Changes to the acquisition process, such as those the paper sketches, will facilitate the nation's rapid transit through its current revolution in military affairs.

关键词： systems analysis

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Bridging the Gap: Towards an Expanded Toolkit for AI-Driven Decision-Making in the Public Sector

arXiv

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arXiv 2023年

作者： Fischer-Abaigar, Unai Kern, Christoph Barda, Noam Kreuter, Frauke Dept. of Statistics LMU Munich Germany Munich Center for Machine Learning Germany Joint Program in Survey Methodology University of Maryland United States Dept. of Software and Information Systems Engineering Dept. of Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Israel

AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker’s utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives. © 2023, CC BY-NC-ND.

关键词： Crime

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Inflatable habitat health monitoring: Implementation, lessons learned, and application to lunar or Martian habitat health monitoring

Inflatable habitat health monitoring: Implementation, lesson...

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作者： Rojdev, Kristina Hong, Todd Hafermalz, Scott Hunkins, Robert Valle, Gerard Toups, Larry NASA Johnson Space Center Houston TX 77058 United States NASA-JSC Systems Architecture and Integration Office University of Southern California - Astronautical Engineering MC: EA-341 2101 NASA Parkway Houston TX 77058 United States Project Management Office MC: EV17 2101 NASA Parkway Houston TX 77058 United States NASA-JSC Avionics Systems Division Electromagnetic Systems Branch MC: EV4 2101 NASA Parkway Houston TX 77058 United States ESCG Electronics and Software Systems NASA-JSC MC: JE22 2101 NASA Parkway Houston TX 77058 United States Systems Architecture and Integration Office NASA-JSC MC: EA-3 2101 NASA Parkway Houston TX 77058 United States Constellation Program Lunar Surface Systems Project Office NASA-JSC MC: ZS1 2101 NASA Parkway Houston TX 77058 United States

ISBN: (纸本)9781563479793

NASA's exploration mission is to send humans to the Moon and Mars, in which the purpose is to learn how to live and work safely in those harsh environments. A critical aspect of living in an extreme environment is habitation, and within that habitation element there are key systems which monitor the habitation environment to provide a safe and comfortable living and working space for humans. Expandable habitats are one of the options currently being considered due to their potential mass and volume efficiencies. This paper discusses a joint project between the National Science Foundation (NSF), ILC Dover, and NASA in which an expandable habitat was deployed in the extreme environment of Antarctica to better understand the performance and operations over a one-year period. This project was conducted through the Innovative Partnership program (IPP) where the NSF provided the location at McMurdo Station in Antarctica and support at the location, ILC Dover provided the inflatable habitat, and NASA provided the instrumentation and data system for monitoring the habitat. The outcome of this project provided lessons learned in the implementation of an inflatable habitat and the systems that support that habitat. These lessons learned will be used to improve current habitation capabilities and systems to meet the objectives of exploration missions to the moon and Mars.

关键词： NASA

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Guest editorial: The 2016 capstone design conference

Guest editorial: The 2016 capstone design conference

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作者： Brackin, Patsy Howe, Susannah Rogers, Peter Stanfill, R. Keith Beyerlein, Steven Kanai, Junichi Vallino, Jim Palo, Scott Department of Mechanical Engineering Rose-Hulman Institute of Technology 5500 Wabash Avenue CM 165 Terre HauteIN47803 United States Picker Engineering Program Smith College 151 Ford Hall NorthamptonMA01063 United States Department of Engineering Education Ohio State University 3070 Smith Laboratory 174 W. 18th Avenue ColumbusOH43210 United States Engineering Innovation Institute University of Florida P.O. Box 116000 GainesvilleFL32611-6000 United States Department of Mechanical Engineering University of Idaho 875 Perimeter Drive MoscowID83844-0902 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Software Engineering Rochester Institute of Technology 134 Lomb Memorial Drive RochesterNY14623-5608 United States Department of Aerospace Engineering Sciences University of Colorado 429 UCB BoulderCO80309 United States

The goal of the 2016 Capstone Design Conference held in Columbus, OH was to build upon the success of previous conferences (2007 and 2010 in Boulder, CO, 2012 in Champaign, IL, and 2014 in Columbus, OH) and expand the community of educators, students, and industry members engaged in discussing, analyzing, and improving capstone design education. Sessions at the 2016 Capstone Design Conference were designed for vibrant sharing of ideas and experiences across the capstone community via interactive panel sessions, poster session socials, hands-on workshops, and other formal and informal networking activities. This editorial discusses conference planning, structure, and feedback. Technical papers that follow in this issue document scholarship surrounding noteworthy capstone course innovations. Most of these began as four page peer-reviewed papers included in the conference proceedings. © 2017 TEMPUS Publications.

关键词： Teaching

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

TechRxiv

引用

TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Lidar sensor performance in closed-loop flight testing of the morpheus rocket-propelled lander to a lunar-like hazard field

Lidar sensor performance in closed-loop flight testing of th...

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AIAA Guidance, Navigation, and Control Conference 2015, MGNC 2015 - Held at the AIAA SciTech Forum 2015

作者： Roback, Vincent E. Amzajerdian, Farzin Barnes, Bruce W. Hines, Glenn D. Petway, Larry B. Brewster, Paul F. Kempton, Kevin S. Pierrottet, Diego F. Bulyshev, Alexander E. Remote Sensing Flight Systems Branch NASA-Langley Research Center M/S 468 HamptonVA23681 United States Coherent Applications. Inc. 20 Research Drive. Suite 500 HamptonVA23666 United States Laser Remote Sensing Branch NASA-Langley Research Center M/S 468 HamptonVA23681 United States Analytical Mechanics Associates. Inc. 21 Enterprise Parkway HamptonVA23666 United States Systems Engineering & Engineering Methods Branch NASA-Langley Research Center M/S 131 HamptonVA23681 United States Laser Remote Sensing Branch NASA-Langley Research Center M/S 468 HamptonVA23681 United States Flight Software Systems Branch NASA-Langley Research Center M/S 472 HamptonVA23681 United States Game Changing Development Program Office NASA-Langley Research Center M/S 250 HamptonVA23681 United States

ISBN: (纸本)9781510801097

For the first time, a suite of three lidar sensors have been used in flight to scan a lunar- like hazard field, identify a safe landing site, and, in concert with an experimental Guidance, Navigation, and Control (GN&C) system, help to guide the Morpheus autonomous, rocket- propelled, free-flying lander to that safe site on the hazard field. The lidar sensors and GN&C system are part of the Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT) project which has been seeking to develop a system capable of enabling safe, precise crewed or robotic landings in challenging terrain on planetary bodies under any ambient lighting conditions. The 3-D imaging Flash Lidar is a second generation, compact, real-time, air-cooled instrument developed from a number of components from industry and NASA and is used as part of the ALHAT Hazard Detection System (HDS) to scan the hazard field and build a 3-D Digital Elevation Map (DEM) in near-real time for identifying safe sites. The Flash Lidar is capable of identifying a 30 cm hazard from a slant range of 1 km with its 8 cm range precision (1-a). The Flash Lidar is also used in Hazard Relative Navigation (HRN) to provide position updates down to a 250m slant range to the ALHAT navigation filter as it guides Morpheus to the safe site. The Navigation Doppler Lidar (NDL) system has been developed within NASA to provide velocity measurements with an accuracy of 0.2 cm/sec and range measurements with an accuracy of 17 cm both from a maximum range of 2,200 m to a minimum range of several meters above the ground. The NDL’s measurements are fed into the ALHAT navigation filter to provide lander guidance to the safe site. The Laser Altimeter (LA), also developed within NASA, provides range measurements with an accuracy of 5 cm from a maximum operational range of 30 km down to 1 m and, being a separate sensor from the Flash Lidar, can provide range along a separate vector. The LA measurements are also fed into the

关键词： Hazards

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia Computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) Computer-aided diagnosis (CAD)

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