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arXiv 2025年

作者： Zontou, Ellie Kyprioti, Antonia Dept. of Computer Engineering and Informatics University of Patras Patras26504 Greece Dept. of Medicine University of Thessaly Larisa41334 Greece

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol’s objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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Analysing and Modelling the Accuracy and Latency Trade-offs in Rate Limiting on API-Gateway

Analysing and Modelling the Accuracy and Latency Trade-offs ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： Krishnamoorthy Caucidheesan Guhanathan Poravi Dept. of Computer Science and Engineering University of Westminster London UK Dept. of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

The rate limiting service in API gateways controls request entry by throttling requests with a boundary. The rate limiting accuracy determines how efficiently it works and whether it allows requests within the throttle count or exceeds the throttle count. Latency, on the other hand, is the round-trip time of a particular API call. The accuracy of rate limiting service is defined using spillover error percentage. It is the error calculation for the requests that rate limiting service allows more than the throttle count. Requests must be sent to the rate limiting service in order to rate limit requests in the API gateway. The rate limiting service decides whether the incoming requests must be throttled. The time taken for the requests to be decided by the rate limiting service adds additional latency to the round-trip time of requests. This additional latency can be controlled by introducing a timeout. However, this could result in a degradation in the accuracy of the rate limiting. This paper investigates the particular problem and models the relationship between accuracy and round-trip latency. The findings of this research address the analysis of the accuracy and latency trade-off with respect to the parameters influencing them, and also address the prediction outcome using random forest regressor and present key findings.

关键词： Hands Degradation Analytical models Limiting Accuracy Logic gates Predictive models Low latency communication

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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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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Machine Learning Prediction Based UI for Aircraft Cockpit

Machine Learning Prediction Based UI for Aircraft Cockpit

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International Conference on Electrical and Electronics engineering, ELECO

作者： Bilge Topal Levent Çarkacıoğlu Behçet Uğur Töreyin Dept. of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey Informatics Institute Istanbul Technical University Istanbul Turkey

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial.

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification

A Comparative Analysis of Transformer Models in Zero-Shot Te...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantinos Kyritsis Charalampos M. Liapis Nikolaos Spatiotis Isidoros Perikos Michael Paraskevas Electrical & Computer Engineering Dept. University of Peloponnese Patras Greece Mathematics Dept. University of Patras Patras Greece Computer Technology Institute & Press “Diophantus” Patras Greece Computer Engineering & Informatics Dept. University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification.

关键词： Training Analytical models Text categorization Transformers

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance 63

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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63rd IEEE Annual Symposium on Foundations of computer Science, FOCS 2022

作者： Das, Debarati Gilbert, Jacob Hajiaghayi, Mohammad Taghi Kociumaka, Tomasz Saha, Barna Saleh, Hamed Pennsylvania State University Dept. of Computer Science and Engineering State CollegePA United States University of Maryland Dept. of Computer Science College ParkMD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany University of California San Diego Halicioglu Data Science Institute Dept. of Computer Science and Engineering San DiegoCA United States

ISBN: (纸本)9781665455190

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann;TALG'10);however, Mao (FOCS'21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an O(n+k2)-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica'86) and Landau and Vishkin (JCSS'88). The existence of an O(n+poly(k))-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP'21), who give a stateof-the-art O(nk2)-time algorithm. In this paper, we answer this question positively. © 2022 IEEE.

关键词： Trees (mathematics)

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Transformer-Based Embeddings for Greek Language Categorization

Transformer-Based Embeddings for Greek Language Categorizati...

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International Conference on computer and Information Science (ACIS)

作者： Charalampos M. Liapis Konstantinos Kyritsis Isidoros Perikos Michael Paraskevas Mathematics Dept. University of Patras Computer Technology Institute & Press “Diophantus” Patras Greece Electrical and Computer Engineering Dept. University of Peloponnese Patras Greece Computer Engineering and Informatics Department Electrical and Computer Engineering Dept. University of Patras University of Peloponnese Computer Technology Institute and Press “Diophantus” Patras Greece Electrical and Computer Engineering Dept. University of Peloponnese Computer Technology Institute and Press “Diophantus” Patras Greece

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems.

关键词： Information science Accuracy Computational modeling Text categorization Education Machine learning Transformers Vectors

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