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IEEE International Conference on Networking, Sensing and Control

作者： Xiaoyan Li Christoforos N. Hadjicostis Zhiwu Li School of Information and Communication Engineering North University of China Taiyuan China Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Institute of Systems Engineering Macau University of Science and Technology Macau SAR China

Opacity is a security and privacy property that guarantees predefined secret behavior of a cyber-physical system will not be inferred with certainty by a malicious entity. If a system is opacity-violating, extended insertion functions that insert fake symbols can be implemented to enforce opacity. By constricting the ways in which the fake symbols are inserted, this paper addresses opacity enforcement under the framework of finite state automata using extended insertion functions under event insertion constraints and limited insertion events constraints, i.e., only a limited number of symbols in a specified set can be inserted before and after an actual system output. The notion of (k b ,k a )-EIC-enforceability is proposed to describe how to enforce opacity via extended insertion functions in the presence of both event insertion constraints and limited insertion events constraints. A necessary and sufficient condition to check (k b ,k a )-EIC-enforceability is derived by constructing an appropriate verifier.

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data 11

PheME: A deep ensemble framework for improving phenotype pre...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Zhang, Shenghan Li, Haoxuan Tang, Ruixiang Ding, Sirui Rasmy, Laila Zhi, Degui Zou, Na Hu, Xia Xi'an Jiaotong-Liverpool University Department of Computer Science Xi'An China Wuhan University Department of Computer Science Wuhan China Rice University Department of Computer Science Houston United States Texas A&m University Department of Computer Science and Engineering College Station United States UTHealth Houston School of Biomedical Informatics Houston United States Texas A&m Univerisity Department of Engineering Technology & Industrial Distribution College Station United States

ISBN: (纸本)9798350302639

Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. Firstly, we employ multiple deep neural networks to learn reliable representations from the sparse structured EHR data and redundant clinical notes. A multi-modal model then aligns multi-modal features onto the same latent space to predict phenotypes. Secondly, we leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions. We chose seven diseases to evaluate the phenotyping performance of the proposed framework. Experimental results show that using multi-modal data significantly improves phenotype prediction in all diseases, the proposed ensemble learning framework can further boost the performance. © 2023 IEEE.

关键词： Forecasting

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Controlling the transmission bandwidth of anti-resonant hollow-core fibers

Controlling the transmission bandwidth of anti-resonant holl...

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International Conference on Transparent Optical Networks

作者： S. Yerolatsitis J. Keohane S. Vinta R. Mears K. Harrington R. J. A. Francis-Jones K. Kalli K. Rusimova Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus CREOL The College of Optics and Photonics University of Central Florida 4304 Scorpius St Orlando Florida USA Centre for Photonics and Photonic Materials University of Bath Claverton Down Bath U.K. ORCA Computing London 30 Eastbourne Terrace U.K.

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

We have developed an innovative anti-resonant hollow-core fiber design with capillaries of alternating thicknesses. This unique arrangement, featuring six silica capillaries of two different thicknesses, effectively narrows the transmission bandwidth while maintaining low attenuation. Validated through simulations, our design marks a significant step in developing the first in-line hollow-core bandwidth filter fiber. We envision that our novel fiber design has a variety of applications, such as suppressing non-linear effects and developing wavelength-selective components for future conventional and quantum communication networks.

关键词： Band-pass filters Optical fibers Optical design Optical attenuators Bandwidth Optical fiber networks Attenuation

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Performing Aerobatic Maneuver with Imitation Learning 23rd

Performing Aerobatic Maneuver with Imitation Learning

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23rd International Conference on Computational Science, ICCS 2023

作者： Freitas, Henrique Camacho, Rui Silva, Daniel Castro Department of Informatics Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n. Porto4200-465 Portugal LIAAD/INESC TEC - Artificial Intelligence and Decision Support Laboratory/Institute for Systems and Computer Engineering Technology and Science Porto Portugal LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal

ISBN: (纸本)9783031359941

The work reported in this article addresses the challenge of building models for non-trivial aerobatic aircraft maneuvers in an automated fashion. It is built using a Behavioural Cloning approach where human pilots provide a set of example maneuvers used by a Machine Learning algorithm to induce a control model for each maneuver. The best examples for each maneuver were selected using a set of objective evaluation metrics. Using those example sets, robust models were induced that could replicate (and in some cases outperform) the human pilots that provided the examples (the clean-up effect). Complete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloning

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Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

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computer Systems Science & engineering 2022年第9期42卷 1181-1198页

作者： Mohammad Khalid Imam Rahmani Fahmina Taranum Reshma Nikhat Md.Rashid Farooqi Mohammed Arshad Khan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Muffakham Jah College of Engineering and TechnologyOsmania UniversityHyderabadTelanganaIndia Department of Commerce and Business Management Maulana Azad National Urdu UniversityHyderabadTelanganaIndia Department of Accountancy College of Administrative and Financial SciencesSaudi Electronic UniversityRiyadSaudi Arabia

The COVID-19 pandemic is a virus that has disastrous effects onhuman lives globally;still spreading like wildfire causing huge losses to humanityand economies. There is a need to follow few constraints like social distancingnorms, personal hygiene, and masking up to effectively control the virus *** proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtainedfor mask detection are found to be effective. The system is trained using4500 images to accurately judge and justify its accuracy. The aim is to developan algorithm to automatically detect a mask, but the approach does not facilitatethe percentage of improper usage. Accuracy levels are as low as 50% if the maskis improperly covered and an alert is raised for improper placement. It can be usedat traffic places and social gatherings for the prevention of virus transmission. Itworks by first locating the region of interest by creating a frame boundary, thenfacial points are picked up to detect and concentrate on specific features. Thetraining on the input images is performed using different epochs until the artificialface mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-sourcedatasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier isused to load and pre-process the image dataset for building a fully connectedhead. The objective is to assess the accuracy of the identification, measuringthe efficiency and effectiveness of algorithms for precision, recall, and F1 score.

关键词： COVID-19 pandemic convolution neural network deep learning face detection facemask detection global health machine learning

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Fluid Antenna Systems with Outdated Channel Estimates

Fluid Antenna Systems with Outdated Channel Estimates

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IEEE International Conference on Communications (ICC)

作者： Constantinos Psomas Ghassan M. Kraidy Kai-Kit Wong Ioannis Krikidis Department of Electrical and Computer Engineering University of Cyprus Cyprus Department of Electronic Systems Norwegian University of Science and Technology Norway Department of Electronic and Electrical Engineering University College London London United Kingdom

A desired characteristic of future communication networks is the notion of reconfigurability. For a wireless device, this can be realized through the employment of the so-called fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the device's antennas. Therefore, due to the nature of liquids, an FA can essentially take any size and shape, making them both flexible and reconfigurable. In this paper, we study the outage probability of FAs where the scheduled port, based on selection combining, is subject to scheduling delays. An analytical framework is provided for the performance with and without estimation errors, as a result of post-scheduling delays. We show that even though FAs achieve maximum channel (spatial) diversity, this cannot be attained in the presence of delays.

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

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arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

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