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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Gennaro De Luca Yinong Chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 U.S.A.

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

As IoT and robotics applications continue to become increasingly complex and decentralized, there is an increase in the difficulty of verifying requirements such as guarantees of reliability, efficiency, and correctness of the entire system. Modern IoT and robotics research and applications employ a variety of technologies to accommodate the distribution of real world IoT devices. Many of these technologies support the orchestration of these devices with concurrent computation and workflow-based processing, e.g., Intel Service Orchestration Layer. We developed a new workflow-based programming language VIPLE (Visual IoT/Robotics Programming Language Environment), which supports the orchestration of decentralized IoT and robotics devices through concurrent computation. VIPLE has been used by many universities for teaching robotics programming, event-driven computing, service-oriented computing, and parallel computing concepts. Historically, simple single threaded applications could be verified by employing formal systems such as Hoare logic. It is difficult to verify parallel and distributed computing. In our recent research, we developed a novel system to verify the semantics and other requirements of decentralized applications written in VIPLE. This system is developed using a combination of the techniques offered in Hoare logic and Rely-Guarantee logic, and it is applied using the automatically generated Pi-Calculus representation of VIPLE applications. This paper focuses on the foundations of this system, its ability to handle concurrent computing and decentralized applications, as well as its ability to support automated verification and semantic analysis.

关键词： Computer languages Robot kinematics Internet of Things Semantics Visualization Instruction sets

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Recovering Decentralized Critical Archival Data From Tampering in Smart City Environment Using Blockchain

Recovering Decentralized Critical Archival Data From Tamperi...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Vineet Mishra Stephen S. Yau Chandralekha Yenugunti Center for Cybersecurity and Digital Forensics School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

A large modern organization usually has a large number of computers and smart devices continuously deployed to store and share critical data among the users inside and outside the organization. Critical data storage needs to be secure and scalable for such an organization. While current cloud-based services offer a centralized view and control over the IT operations of these smart devices, it is prone to attacks, data tampering, and rogue administrator problems. In this paper, an approach using blockchain to recovering critical data from tampering is presented. Although our approach incurs a large amount of overhead due to the use of blockchain, our approach is shown to recover the critical data from all the data tampering. Simulation of our approach used in a smart city environment is conducted to show that the overhead of our approach is compared favorably with that of using a centralized cloud-based approach. Our approach is suitable for recovering critical archival data, instead of real-time data, because the speed is not a very important factor for recovering critical archival data from tampering.

关键词： Frequency modulation IP networks Iron Cloud computing Big Data Smart cities

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Breast Cancer Detection and Classification on Mammogram Images Using Morphological Approach

Breast Cancer Detection and Classification on Mammogram Imag...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Wan Azani Mustafa Arrif Amirul Azmi Mohd Aminudin Jamlos Hiam Alquran Wan Khairunizam Shahrina Ismail Ahmed Alkhayyat Juhara Haron Faculty of Electrical Engineering Technology Advanced Computing (COE) Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Electronic Engineering Technology Engineering Centre of Excellence (ACE) University Malaysia Perlis Malaysia Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Bandar Baru Nilai Negeri Sembilan Malaysia Faculty of Engineering The Islamic University Najaf Iraq School of Medical Sciences Universiti Sains Malaysia Kota Bharu Kelantan Malaysia

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

Breast cancer is one of the most common cancers affecting women worldwide. Mammography is the most well-known and effective method to detect early signs of breast cancer. The purpose of this paper is to detect breast cancer on the mammogram image to classify the disease through morphological techniques. Using conventional methods makes radiology difficult to detect cancer found in the patient’s breast. This proposal can be divided into several elements, which are input database, image preprocessing, image segmentation, morphological analysis, and object recognition. First, image preprocessing will be done using the Weiner and Median filters. Second, the thresholding method for image segmentation will be performed, and lastly, morphology will remove imperfections introduced during the image segmentation process. Finally, the image is classified into two classes: normal and cancerous images. A median filter and 0.95 thresholding achieve an accuracy of 93.71%, a sensitivity of 94.36%, and a specificity of 82.53% for the cancerous images.

关键词： Image segmentation Sensitivity Morphology Radiology Breast cancer Mammography Proposals

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Rapid Detection of Hot-spot by Tensor Decomposition with Application to Weekly Gonorrhea Data

arXiv

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arXiv 2020年

作者： Zhao, Yujie Yan, Hao Holte, Sarah E. Kerani, Roxanne P. Mei, Yajun School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center SeattleWA United States Public Health-Seattle & King County SeattleWA United States

In many bio-surveillance and healthcare applications, data sources are measured from many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications, we are typically interested in detecting hot-spots, which are defined as some structured outliers that are sparse over the spatial domain but persistent over time. In this paper, we propose a tensor decomposition method to detect when and where the hot-spots occur. Our proposed methods represent the observed raw data as a three-dimensional tensor including a circular time dimension for daily/weekly/monthly patterns, and then decompose the tensor into three components: smooth global trend, local hot-spots, and residuals. A combination of LASSO and fused LASSO is used to estimate the model parameters, and a CUSUM procedure is applied to detect when and where the hot-spots might occur. The usefulness of our proposed methodology is validated through numerical simulation and a real-world dataset in the weekly number of gonorrhea cases from 2006 to 2018 for 50 states in the United States. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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Hardware acceleration of sparse and irregular tensor computations of ML models: A survey and insights

arXiv

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arXiv 2020年

作者： Dave, Shail Baghdadi, Riyadh Nowatzki, Tony Avancha, Sasikanth Shrivastava, Aviral Li, Baoxin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States School of Computer Science University of California Los AngelesCA90095 United States Parallel Computing Lab at Intel Labs Bangalore India

Machine learning (ML) models are widely used in many important domains. For efficiently processing these computational- and memory-intensive applications, tensors of these over-parameterized models are compressed by leveraging sparsity, size reduction, and quantization of tensors. Unstructured sparsity and tensors with varying dimensions yield irregular computation, communication, and memory access patterns;processing them on hardware accelerators in a conventional manner does not inherently leverage acceleration opportunities. This paper provides a comprehensive survey on the efficient execution of sparse and irregular tensor computations of ML models on hardware accelerators. In particular, it discusses enhancement modules in the architecture design and the software support;categorizes different hardware designs and acceleration techniques and analyzes them in terms of hardware and execution costs;analyzes achievable accelerations for recent DNNs;highlights further opportunities in terms of hardware/software/model co-design optimizations (inter/intra-module). The takeaways from this paper include: understanding the key challenges in accelerating sparse, irregular-shaped, and quantized tensors;understanding enhancements in accelerator systems for supporting their efficient computations;analyzing trade-offs in opting for a specific design choice for encoding, storing, extracting, communicating, computing, and load-balancing the non-zeros;understanding how structured sparsity can improve storage efficiency and balance computations;understanding how to compile and map models with sparse tensors on the accelerators;understanding recent design trends for efficient accelerations and further opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Fractal Perfect Hash Families (Extended Abstract)

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Electronic Notes in Discrete Mathematics 2018年 65卷 37-42页

作者： Colbourn, Charles J. Dougherty, Ryan E. Computing Informatics and Decision Systems Engineering Arizona State University Tempe 85287 AZ United States

Constructions of perfect hash families (PHFs) are developed in which the number of rows is less than the strength, using a method due to Blackburn. The constructions use coverings of strength d along with a restricted class of perfect hash families, called fractal PHFs. Constructions for fractal PHFs are given, and applications to the construction of PHFs of large strengths are developed. © 2018 Elsevier B.V.

关键词： covering design fractal perfect hash family perfect hash family

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Modern computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Haptic vision: Augmenting non-visual travel and accessing environmental information at a distance 1st

Haptic vision: Augmenting non-visual travel and accessing en...

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1st International Conference on Smart Multimedia, ICSM 2018

作者： Duarte, Bryan McDaniel, Troy Tadayon, Ramin Devkota, Samjhana Strasser, Gracie Ramey, Ce Ce Panchanathan, Sethuraman School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (纸本)9783030043742

Independent travel is essential to an individual’s ability to lead a healthy and successful life. For individuals who are blind and low vision, however, access to critical safety and way-finding information is limited to the objects within their immediate vicinity. Due to the versatility and information obtained through its use, the long white cane remains the most widely used method of non-visual travel. Non-visual travelers are able to use a cane to interrogate objects to obtain perceptual information allowing them to identify physical characteristics such as position, distance, texture, and slope, but only in close proximity. This work aims to augment current methods of non-visual travel to extend the distance at which a non-visual traveler can obtain information about their surroundings. We propose a relative mapping between the angular direction and distance of an object with respect to the orientation of the user, displayed using a two-dimensional matrix of haptic (vibrotactile) motors. A preliminary experiment revealed that blind and low vision subjects could identify objects and their angular direction and distance through the sense of touch. © 2018, Springer Nature Switzerland AG.

关键词： Textures

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Evaluating a virtual human storyteller for improved decision support 23

Evaluating a virtual human storyteller for improved decision...

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23rd International Congress on Modelling and Simulation - Supporting Evidence-Based decision Making: The Role of Modelling and Simulation, MODSIM 2019

作者： Blackmore, K.L. Smith, S.P. Nesbitt, K.V. North, L. Wark, S. Nowina-Krowicki, M. School of Electrical Engineering and Computing University of Newcastle NSW Australia Joint and Operations Analysis Division Defence Science and Technology Group Decision Support Systems Joint Warfare & Operations SA Australia

ISBN: (纸本)9780975840092

Defence and security organisations face increasing challenges to maximise the real-time integration of data and information for improved decision making. This is particularly true in applications of human-machine teaming. These teams create complex interaction environments for human operators that integrate autonomous systems, multi-modal displays, and information streams at differing temporal scales. Examples of these complex environments can be seen in simulation pilots operating in large scale synthetic training scenarios, and operators of multi-unmanned aerial vehicle (multi-UAV) systems. In both these cases, an individual must perform a primary task (directing simulated or real entity behaviour), whilst simultaneously following a pre-defined scenario plan. On top of this, emergent information, with varying levels of uncertainty, must be acted on or ignored as appropriate. Additional contextual information may also be required such that these actions can occur with an understanding of broader contextual factors. The efficiency with which this primary, planned, emergent, and contextual information is presented underpins decision support and superiority in increasingly complex military contexts. In this research, we evaluate the use of storytelling via a virtual human to deliver complex geo-spatial conflict briefing information. Copyright © 2019 The Modelling and Simulation Society of Australia and New Zealand Inc. All rights reserved.

关键词： decision making

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