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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian Department of Information Technology Uppsala University Lägerhyddsvägen 1 Uppsala75105 Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei 313 Bucharest060042 Romania Fraunhofer Institute for Open Communication Systems Berlin10589 Germany

In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task);and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19. © 2022 Copyright for this paper by its authors.

关键词： Statistical tests

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Contrast enhancement of optical videoscope images

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Iran Journal of computer science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Passivity of nonlinear time-delay systems

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IFAC-PapersOnLine 2023年第2期56卷 8568-8573页

作者： Yu Kawano Saeed Ahmed Michele Cucuzzella Jacquelien M.A. Scherpen Graduate School of Advanced Science and Engineering Hiroshima University Higashi-hiroshima 739-8527 Japan Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen The Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia Italy

This paper presents passivity-based control of nonlinear systems with retarded delays in the state. To this end, we first show that the standard passivity concept can naturally be generalized to time-delay systems, which readily implies that a feedback interconnection (with or without communication delays) of passive time-delay systems is also passive. Then, we propose a storage functional for passivity analysis and further use it for stability analysis of controlled-passive time-delay systems. In particular, invoking an invariance principle for retarded functional differential equations, we show that a passive time-delay system can always be stabilized by a static output feedback controller under a delayed version of the zero-state detectability assumption.

关键词： Passivity nonlinear systems time-delay systems retarded functional differential equations stabilizing control

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An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the SUPERIOT Framework

arXiv

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arXiv 2025年

作者： Bocus, Mohammud J. Häkkinen, Juha Fontes, Hélder Drzewiecki, Marcin Qiu, Senhui Eder, Kerstin Piechocki, Robert School of Computer Science University of Bristol Bristol United Kingdom Circuits and Systems Research Unit University of Oulu Oulu Finland MpicoSys Embedded Pico Systems Sp. z o. o. Gdynia Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk Poland INESC TEC Porto Portugal Faculty of Engineering University of Porto Porto Portugal

his paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display. Extensive measurements were conducted to establish a detailed energy profile, which serves as a benchmark for evaluating the effectiveness of subsequent optimizations and future node iterations. To minimize the energy consumption, multiple optimizations were implemented at both the software and hardware levels, achieving a reduction of over 60% in total energy usage through software modifications alone. Further improvements were realized by optimizing the E-ink display driving waveform and implementing a very low-power mode for non-communication activities. Based on the measured data, three measurement-based energy consumption models were developed to characterize the energy behavior of the node under: (i) normal, unoptimized operation, (ii) low-power, software-optimized operation, and (iii) very low-power, hardware-optimized operation. These models, validated with new measurement data, achieved an accuracy exceeding 97%, confirming their reliability for predicting energy consumption in diverse configurations. © 2025, CC BY.

关键词： Digital storage

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Optimization of Expert Solutions for Events Forecasting

Optimization of Expert Solutions for Events Forecasting

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International Conference on control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Merab Pkhovelishvili Natela Archvadze Lia Shetsiruli Faculty of Informatics and Control Systems Georgian Technical University Tbilisi Georgia Department of Computer Science Ivane Javakhishvili Tbilisi State University Tbilisi Georgia School of Business and Management Grigor Robakidze University Batumi Georgia

Expertise is a universal method for solving managerial decision problems and is used in cases where there are no (or not found) criteria that allow solving the problem optimally or rationally. At its core, the examination is based on the use of human experience and is carried out with the involvement of experts. Recently, expert evaluations have been intensively used in various fields, especially in the prediction of tectonic natural disasters, because statistical data in this field are very few or difficult to obtain and analyze. Our article discusses the initial and subsequent criteria for determining the competence of experts, what influence the competence of an expert has in calculating his rank, and how these ranks are used for the conclusion of a group of experts, if this or that event will occur or what is the percentage of the event's occurrence.

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Mathematical Model of Damping of UAV Oscillations in the Cargo Delivery Problem

Mathematical Model of Damping of UAV Oscillations in the Car...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Dmytro Kucherov Tetiana Shmelova Olexii Poshyvailo Valerii Tkachenko Ignat Miroshnichenko Ihor Ogirko dept. Computerized Control Systems Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine dept. Air Navigation Systems Faculty of Air Navigation Electronics and Telecommunications National Aviation University Kyiv Ukraine dept. Mathematic Ukrainian Academy of Printing City Lviv Ukraine

Currently, the use of unmanned vehicles for the delivery of goods is becoming a trend in many areas of human activity. At the same time, it is necessary to solve the problem of the integrity of the cargo because of its possible fluctuations. The paper deals with the problem of delivering cargo attached to a 4-motor UAV on a rigid cable. A control system is needed to achieve the UAV equilibrium position that can compensate for significant mismatches in the XY coordinate plane. A combination of two control algorithms is proposed for solving this problem, one of which provides a given position in the horizontal plane at small mismatch angles and the second at large mismatch angles. The first controller uses the PID controller algorithm with the corresponding optimal setting of its parameters, and the second one uses the bang-bang control scheme. An additional controller is used to coordinate these controllers, which ensures their timely switching. Some simulation is also proposed.

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An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

An Automatic CNN-based Face Mask Detection Algorithm Tested ...

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2022 International Conference of Yearly Reports on Informatics, Mathematics, and Engineering, ICYRIME 2022

作者： De Magistris, Giorgio Iacobelli, Emanuele Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

关键词： COVID-19

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, Engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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