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arXiv 2021年

作者： Blom, Michelle Budurushi, Jurlind Rivest, Ronald L. Stark, Philip B. Stuckey, Peter J. Teague, Vanessa Vukcevic, Damjan School of Computing and Information Systems University of Melbourne Parkville Australia Cloudical Deutschland GmbH Berlin Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia Thinking Cybersecurity Pty. Ltd. Melbourne Australia School of Mathematics and Statistics University of Melbourne Parkville Australia Melbourne Integrative Genomics University of Melbourne Parkville Australia

Risk-limiting audits (RLAs), an ingredient in evidence-based elections, are increasingly common. They are a rigorous statistical means of ensuring that electoral results are correct, usually without having to perform an expensive full recount - at the cost of some controlled probability of error. A recently developed approach for conducting RLAs, SHANGRLA, provides a flexible framework that can encompass a wide variety of social choice functions and audit strategies. Its flexibility comes from reducing sufficient conditions for outcomes to be correct to canonical 'assertions' that have a simple mathematical form. Assertions have been developed for auditing various social choice functions including plurality, multi-winner plurality, super-majority, Hamiltonian methods, and instant runoff voting. However, there is no systematic approach to building assertions. Here, we show that assertions with linear dependence on transformations of the votes can easily be transformed to canonical form for SHANGRLA. We illustrate the approach by constructing assertions for party-list elections such as Hamiltonian free list elections and elections using the D'Hondt method, expanding the set of social choice functions to which SHANGRLA applies directly. Copyright © 2021, The Authors. All rights reserved.

关键词： Hamiltonians

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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

arXiv

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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Enhancing Wireless Networks with Attention Mechanisms: Insights from Mobile Crowdsensing

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Xiong, Zehui Niyato, Dusit Jamalipour, Abbas Han, Zhu College of Communications Engineering Army Engineering University of PLA Nanjing210000 China College of Computing and Data Science NTU Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Electrical and Computer Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

The increasing demand for sensing, collecting, transmitting, and processing vast amounts of data poses significant challenges for resource-constrained mobile users, thereby impacting the performance of wireless networks. In this regard, from a case of mobile crowdsensing (MCS), we aim at leveraging attention mechanisms in machine learning approaches to provide solutions for building an effective, timely, and secure MCS. Specifically, we first evaluate potential combinations of attention mechanisms and MCS by introducing their preliminaries. Then, we present several emerging scenarios about how to integrate attention into MCS, including task allocation, incentive design, terminal recruitment, privacy preservation, data collection, and data transmission. Subsequently, we propose an attention-based framework to solve network optimization problems with multiple performance indicators in large-scale MCS. The designed case study have evaluated the effectiveness of the proposed framework. Finally, we outline important research directions for advancing attention-enabled MCS. © 2024, CC BY.

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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MULTILEVEL DELAYED ACCEPTANCE MCMC

arXiv

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arXiv 2022年

作者： Lykkegaard, Mikkel B. Dodwell, Tim J. Fox, Colin Mingas, Grigorios Scheichl, Robert Centre for Water Systems Institute for Data Science AI University of Exeter EX4 4QF United Kingdom The Alan Turing Institute Institute for Data Science AI University of Exeter EX4 4QF United Kingdom Department of Physics University of Otago Dunedin9016 New Zealand The Alan Turing Institute British Library 96 Euston Road NW1 2DB United Kingdom Institute for Applied Mathematics Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg69120 Germany

We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target distribution. Broadly, the method rewrites the Multilevel MCMC approach of Dodwell et al. (2015) in terms of the Delayed Acceptance (DA) MCMC of Christen & Fox (2005). In particular, DA is extended to use a hierarchy of models of arbitrary depth, and allow subchains of arbitrary length. We show that the algorithm satisfies detailed balance, hence is ergodic for the target distribution. Furthermore, multilevel variance reduction is derived that exploits the multiple levels and subchains, and an adaptive multilevel correction to coarse-level biases is developed. Three numerical examples of Bayesian inverse problems are presented that demonstrate the advantages of these novel methods. The software and examples are available in *** Codes 62F15, 62M05, 65C05, 65C40 © 2022, CC BY.

关键词： Inverse problems

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IoT-Based Wearable Devices for Patients Suffering from Alzheimer Disease

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Contrast Media and Molecular Imaging 2022年第0期2022卷 3224939页

作者： Salehi, Waleed Gupta, Gaurav Bhatia, Surbhi Koundal, Deepika Mashat, Arwa Belay, Assaye Yogananda School of Artificial Intelligence Computer and Data Sciences Shoolini University HP Solan 173229 India Department of Information Systems College of Computer Science and Information Technology King Faisal University Al Hasa Saudi Arabia Department of Systemics School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India Faculty of Computing and Information Technology King Abdulaziz University Rabigh Saudi Arabia Department of Statistics Mizan-Tepi University Ethiopia

The disorder of Alzheimer's (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life's daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices. © 2022 Waleed Salehi et al.

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Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

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Optical and Quantum Electronics 2024年第2期56卷 239-239页

作者： Bhukya, Raghuram Arunsundar, B. Tatini, Narendra Babu Sadala, Triveni Mohan Elshafie, Hashim Qamar, Shamimul Deaprtment of Computer Science and Engineering Kakatiya Institute of Technologey and Science Telangana Warangal India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Kattankulathur India Department of Internet of Things Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department: CS & AI SR UNIVERSITY Telungana Warangal India Department of Computer Engineering College of Computer Science King Khalid University Main Campus Al Farah Abha 61421 Saudi Arabia Computer Science and Engineering College of Sciences and Arts King Khalid University Dhahran Al Janoub Campus Abha 64261 Saudi Arabia

The need for optoelectronic devices is growing right now, but the production of these devices is having a difficult time keeping up with the advancement of the machinery, instruments, and manufacturing techniques they support. Pictures from big, unlabeled image collections are commonly retrieved using a technique called content-based image retrieval (CBIR). The availability of photographs is also growing as internet growth and transmission networks increase. This study suggests brand-new image retrieval methods for multispectral images used in Optoelectronic device monitoring that are based on image segmentation and classification methods with deep learningtechniques for failure management. Optoelectronic device Monitoring field-based multispectral images were used as the input, which was then processed for noise removal, resizing, and *** c-means clustering-based image segmentation was used to divide up this processed image into its component parts. Following that, a hybrid multilayer transfer learning perception was used to classify the clustered segmented picture. The proposed technique has an accuracy of 95%, precision of 85%, recall of 75%, F-1 score of 63%, error rate of 51%, MAP of 55%;existing MRCN accuracy of 85%, precision of 75%, recall of 63%, F-1 score of 55%, error rate of 45%, MAP of 51%, OC-LBP accuracy of 89%, the precision of 79%, recall of 71%, F-1 score of 59%, error rate of 49%, MAP of 53%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Classification Deep learning Failure management Multispectral image retrieval Optoelectronic device Monitoring Segmentation

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The role of metadata in reproducible computational research

arXiv

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arXiv 2020年

作者： Leipzig, Jeremy Nüst, Daniel Hoyt, Charles Tapley Soiland-Reyes, Stian Ram, Karthik Greenberg, Jane Metadata Research Center Drexel University College of Computing and Informatics PhiladelphiaPA United States Institute for Geoinformatics University of Münster Münster Germany Laboratory of Systems Pharmacology Harvard Medical School Boston United States eScience Lab Department of Computer Science The University of Manchester Manchester United Kingdom INDE Lab Informatics Institute University of Amsterdam Amsterdam Netherlands Berkeley Institute for Data Science University of California Berkeley United States

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, RCR can significantly accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting RCR. Metadata provides context and provenance to raw data and methods and is essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described the relationship between metadata and RCR. This article employs a functional content analysis to identify metadata standards that support RCR functions across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our article provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work. © 2020, CC BY.

关键词： Pipelines

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A hardware-based multi-factor authentication - An initiative to secure web applications

引用

AIP Conference Proceedings 2024年第1期3075卷

作者： K. Sornalakshmi Revathi Venkataraman V. Kavitha P. Shamunesh Ashwin Raja Charu Khemka Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India

Hardware-based multi-factor authentication is a broad and extensive security solution approach to overcome the impact of cyber data compromises that are happening these days in applications using cutting-edge technologies. Our proposed work handles the high-end security modules by developing frameworks in parallel to the IoT-based security visualization to make the system’s security an unbreakable unit. This work depicts the idea of prototyping a hardware key, termed in this paper as “AppSec Key” that handles the MFA/2FA checkpoints into a hardware level making it impossible for hackers to bypass it online using the tools. The visualization of IoT setup to the software security is a new revolution to the complication of mishandling data online. This work proposes sequence-based authentication that adds an extra protection layer. The overall approaches to tackle the existing cyber threats are been discussed with many real-world instances.

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On the dynamics of a seventh-order generalized Hénon-Heiles potential

arXiv

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arXiv 2020年

作者： Dubeibe, Fredy L. Zotos, Euaggelos E. Chen, Wei Facultad de Ciencias Humanas y de la Educación Universidad de los Llanos Villavicencio Colombia Department of Physics School of Science Aristotle University of Thessaloniki ThessalonikiGR-541 24 Greece LMIB & School of Mathematics and Systems Science Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen Guangdong518055 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

This paper deals with the derivation and analysis of a seventh-order generalization of the Hénon-Heiles potential. The new potential has axial and reflection symmetries, and finite escape energy with three channels of escape. Based on SALI indicator and exits basins, the dynamic behavior of the seventh-order system is investigated qualitatively in cases of bounded and unbounded movement. Moreover, a quantitative analysis is carried out through the percentage of chaotic orbits and the basin entropy, respectively. After classifying large sets of initial conditions of orbits for several values of the energy constant in both regimes, we observe that when the energy moves away from the critical value, the chaoticity of the system decreases and the basin structure becomes simpler with sharper and well defined bounds. Our results suggest that when the seventh-order contributions of the potential are taken into account, the system becomes less ergodic in comparison with the classical version of the Hénon-Heiles system. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

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