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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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MOPO-HBT: A movie poster dataset for title extraction and recognition

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Multimedia Tools and Applications 2024年第18期83卷 54545-54568页

作者： Ghosh, Mridul Roy, Sayan Saha Banik, Bivan Mukherjee, Himadri Obaidullah, Sk Md Roy, Kaushik Dept. of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Dept. of Computer Science & Engineering Aliah University Kolkata India Dept. of Electronics & Electrical Communication Engineering IIT Kharagpur Kharagpur India TISA Lab Dept. of Computer Science West Bengal State University Barasat India

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://***/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Character recognition

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AI in CS Education: Opportunities, Challenges, and Pitfalls to Avoid

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ACM Inroads 2024年第3期15卷 52-57页

作者： Vahid, Frank Dept. of Computer Science and Engineering University of California Riverside United States

Csinstructors realize education may be impacted by generative AI (artificial intelligence). This article describes (1) opportunities, like 24/7 help or auto-grading, (2) challenges, like increased cheating or student isolation, and (3) pitfalls to avoid, like inequitable access or false cheating accusations. The descriptions come from 15 CS educators and practitioners, from diverse institutions, who met at a 2023 NSF workshop on AI in CS education. The article aims to assist the CS community to take advantage of generative AI to improve courses, embark on new research, treat students equitably, and ultimately improve CS education for instructors and students. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Students

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Visual Analytics of Streaming Data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

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11th International Conference on Electrical, electronic and Computing engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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Efficiency Improvement Mechanism Analysis of Sidewall Passivation GaN based Micro-LEDs by Atomic Layer Deposition

Efficiency Improvement Mechanism Analysis of Sidewall Passiv...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Zhanghu, Mengyuan Liu, Yibo Liu, Zhaojun Dept. of Electric and Electronic Engineering Southern University of Science and Technology Shenzhen China Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Sidewall passivation using atomic layer deposition (ALD) can efficiently improve external quantum efficiency (EQE). Such treatment increases EQE, a combination of internal quantum efficiency (IQE) and light extraction efficiency (LEE), primarily by curtailing surface recombination according to the ABC model. Determined by the ratio of radiative recombination rate to total recombination rate, the IQE of an LED holds a significant correlation with the diode's ideality factor in the Shockley model. In this paper, we analysis the mechanism of ALD sidewall passivation enhancing device performance in detail from the aspects of electrical performance improvement and optical performance improvement. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Gallium nitride

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Enhancing the Kubernetes Platform with a Load-Aware Orchestration Strategy

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SN computer science 2025年第3期6卷 1-15页

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

Microservice architecture is increasingly favored for building large-scale applications designed for deployment in distributed and resource-constrained cloud-to-edge computing environments. As a cloud-native approach, microservices are particularly powerful due to their loosely coupled, independently deployable, and scalable characteristics. These features enable distributed deployment and flexible integration across robust cloud data centers and heterogeneous, often resource-limited, edge nodes. To fully leverage these advantages, there is a need for advanced placement algorithms that can optimize application performance by utilizing the strengths of microservices. In response to these challenges, we propose to extend Kubernetes with a load-aware orchestration strategy, which enhances its capability to deploy microservice applications within shared clusters characterized by dynamic resource usage patterns. Our approach orchestrates applications dynamically, based on real-time resource usage, allowing for continuous adjustment of their placement to match evolving conditions. Evaluation of a prototype system in a testbed environment demonstrates that this approach offers substantial benefits compared to the standard Kubernetes scheduler. © The Author(s) 2025.

关键词： Cloud continuum Container technology Kubernetes descheduler Kubernetes scheduler Microservice architecture

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Authentication and Authorization in Zero Trust IoT: A Survey 35

Authentication and Authorization in Zero Trust IoT: A Survey

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35th Irish Systems and Signals Conference, ISSC 2024

作者： James, Meha Newe, Thomas O'Shea, Donna O'Mahony, George D. Munster Technological University Dept. Computer Science Cork Ireland University of Limerick Dept. Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350352986

A critical challenge in implementing Zero trust (ZT) in Internet of Things (IoT) environments is ensuring secure authentication and authorization mechanisms. The work in this article delves into the intricate realm of ZT by critically examining the state-of-the-art authentication and authorization processes within the ZT framework for IoT. Despite the wealth of work on ZT and Zero Trust Architecture (ZTA), notable research gaps persist, specifically in identity authentication and fine-grained access control. Given the vast and diverse ecosystem of IoT devices, device identification complexities arise from a lack of unique hardware identifiers, rendering traditional methods susceptible to cyber-attacks, such as, spoofing. For dynamic IoT environments, achieving the optimal implementation in access policies poses a significant challenge. Numerous ZTA methodologies, integrating access controls, blockchain frameworks, and advancements in Artificial Intelligence (AI) and Machine Learning (ML), have been implemented to tackle these challenges. However, research gaps remain, particularly in understanding the behavioral aspects within these frameworks. As a result, this article focuses on identifying how device behavioral aspects can improve authentication and authorization in ZT for IoT. Finally, the article concludes by highlighting how these challenges can serve as future research directions. © 2024 IEEE.

关键词： Authorization

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Optimization of Cost-based Dynamic Economic Dispatch Problem using Mathematical Programming

Optimization of Cost-based Dynamic Economic Dispatch Problem...

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2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Mahmood, Mou Chowdhury, Nahid-Ur-Rahman Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This paper introduces a robust and effective methodology for addressing the dynamic economic dispatch (DED) optimization problem, which is pivotal in power system operations and planning. DED is basically an enhanced version of static economic dispatch (SED) problem that involves scheduling a set of generation units over a typical operational period, often spanning 24 hours, to cover the predicted load demand with the on/off status of each generation unit predetermined. The literature distinguishes between two main types of DED: cost-based and price-based. This paper focuses on the cost-based approach, utilizing quadratic constrained programming (QCP) to model the problem effectively. QCP is particularly suited for this task as it optimally manages the ramp rate constraints and capacities of the available generation units maintaining the minimum operating cost. To demonstrate the effectiveness of the proposed methodology, simulations were conducted using two distinct scenarios involving 5-unit and 10-unit power dispatch strategies. The results, obtained through the general algebraic modeling system (GAMS), clearly presents the superiority of GAMS in achieving optimal solutions when compared to other existing metaheuristic techniques. © 2024 IEEE.

关键词： Quadratic programming

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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