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Historical Arabic Images Classification and Retrieval Using Siamese Deep Learning Model

computers, Materials & Continua 2022年第7期72卷 2109-2125页

作者： Manal M.Khayyat Lamiaa A.Elrefaei Mashael M.Khayyat Computer Science Department Umm Al-Qura UniversityMakkahSaudi Arabia Computer Science Department King Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored ***,there were lots of efforts trying to automate the classification operation and retrieve similar images *** reach this goal,we developed a VGG19 deep convolutional neural network to extract the visual features from the images ***,the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural *** Siamese model built and trained at first from scratch but,it didn’t generated high evaluation ***,we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation ***,three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method formeasuring the similarities among the retrieved *** that the highest evaluation parameters generated using the Cosine distance ***,the Graphics Processing Unit(GPU)utilized to run the code instead of running it on the Central Processing Unit(CPU).This step optimized the execution further since it expedited both the training and the retrieval time *** extensive experimentation,we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval,respectively.

关键词： Visual features vectors deep learning models distance methods similar image retrieval

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 1-10页

作者： Li, Yong Liu, Menglin Lao, Lingjie Wang, Yuanzhi Cui, Zhen Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Micro-expressions are spontaneous, rapid and subtle facial movements that can hardly be suppressed or fabricated. Micro-expression recognition (MER) is one of the most challenging topics in affective computing. It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period. Recently, many deep learning-based MER methods have been developed. However, how to effectively capture subtle temporal variations for robust MER still perplexes us. We propose a counterfactual discriminative micro-expression recognition (CoDER) method to effectively learn the slight temporal variations for video-based MER. To explicitly capture the causality from temporal dynamics hidden in the micro-expression (ME) sequence, we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t. original ME sequences and the counterfactuals w.r.t. counterfactually-revised ME sequences, and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues. Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER, which results in comparable and superior MER performance compared with that of the state-of-the-art methods. The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces. © The Author(s) 2024.

关键词： Affective computing Causal graph Counterfactual reasoning Micro-expression recognition Temporal variation

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A Compositional Simulation Framework for Testing Adversarial Robustness of Deep Neural Networks

A Compositional Simulation Framework for Testing Adversarial...

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2023 Design and Verification Conference and Exhibition Europe, DVCon Europe 2023

作者： Nader, Youssef Maher Hatab, Mostafa Lotfy Ghaleb, Mazen Mostafa Bakr, Safia Medhat Awaad, Tasneem A. AlGanzouri, Ahmed Abdelsalam, Mohamed El-Kharashi, M. Watheq The Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Department of Electrical and Computer Engineering Faculty of Engineering & Computer Science University of Victoria Victoria Canada Siemens EDA Cairo Egypt

ISBN: (纸本)9783800762064

Deep neural networks (DNN) have reached impressive performance in computer vision, making them a natural choice for object detection problems in automated driving. However, DNNs used for object detection are known to be highly vulnerable to adversarial attacks. Even small changes to the input such as adding a customized noise pattern that remains invisible to the human eye, can stimulate silent prediction errors. In this study, we present a compositional simulation framework for testing the adversarial robustness of DNNs used in object detection. We demonstrate our framework with a comprehensive case study of a speed-sign detector model with two different adversarial attacks. © 2023 Design and Verification Conference and Exhibition Europe, DVCon Europe

关键词： Object detection

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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"Risky or Trustworthy?": User Behavior towards Biometric Authentication Method 7

"Risky or Trustworthy?": User Behavior towards Biometric Aut...

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7th Information systems International Conference, ISICO 2023

作者： Kusyanti, Ari Catherina, Harin Puspa Ayu Effendrik, Popong Santoso, Nurudin Ekowati, Nurul Syamsi Department of Computer Science Universitas Brawijaya Indonesia Department of Information Systems Universitas Brawijaya Indonesia Department of Electrical Engineering Politeknik Negeri Malang Indonesia

Smartphones as a means of communication are not only used to send and receive messages and make phone calls. Today's smartphones are also intended to store personal data and sensitive information hence it is vulnerable to be illegally accessed by others. Fingerprint as a biometric lock screen for smartphones can be trusted to ensure the security and privacy of smartphone users. A fingerprint is closely related to a user's identity. When using fingerprints, users provide their personal biometric pattern, unknowingly giving their privacy and making them vulnerable to unauthorized use of their biometric pattern. This study is designed to reveal the factors that influence users when using fingerprints as a smartphone lock, using eight variables adapted from previous research. The data analysis used is Partial Least Squares (PLS) analysis. The results of this study suggest that trust, attitudes, and perceived risk are the factors that influence users when using their fingerprint as a smartphone lock. © 2023 The Authors. Published by Elsevier B.V.

关键词： Smartphones

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Electric Bike Security and Protection System 2

Electric Bike Security and Protection System

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Maniraj, P. Murugan, A. Durai Gokul, R. Shalini, A.S. Kesavan, K. Kavin, P. M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350349221

With the developing prevalence of electric bicycles (e-bicycles) as a feasible and proficient method of transportation, guaranteeing their security has turned into a foremost concern. Electric bicycles are important resources helpless against robbery and harm, to conquer this issue the venture proposes the improvement of an Electric Vehicle Burglary Location and Warning Framework utilizing IOT innovation. The framework integrates sensors to screen electric vehicles for unapproved access and utilizes Radio Recurrence ID innovation to improve security. After recognizing an unfair RFID section or any dubious movement, the framework sets off a caution to tell the proprietor and naturally locks the back tire, forestalling robbery. This framework could be an instrument for deflecting the robbery of electric vehicles and keeping proprietors from monetary misfortunes. In a period of developing IOT-based security arrangements, this framework offers an important expansion to safeguard electric vehicle and their venture. © 2024 IEEE.

关键词： Electric vehicles

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Coupling Mobile Brain Imaging and Virtual Reality Omnidirectional Treadmill to Explore Attenuated Situational Awareness during Distracted Walking

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期16卷 1063-1076页

作者： Chuang, Chun-Hsiang Chiu, Tsai-Feng Hsu, Hao-Che Lin, Shih-Syun National Tsing Hua University Research Center for Education and Mind Sciences College of Education The Institute of Information Systems and Applications College of Electrical Engineering and Computer Science Hsinchu300 Taiwan National Tsing Hua University Research Center for Education and Mind Sciences Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300193 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung20224 Taiwan

Walking while engaging in distracting activities affects our perception of changes happening on the road, leading to exposure to hazardous conditions. This study aimed to evidence that pedestrians who become distracted when absorbed in their smartphones fail to maintain situational awareness. Thirty-six recruited participants performed a psychomotor vigilance task and a wayfinding task designed to simulate a multitasking scenario;meanwhile, an auditory oddball paradigm presented to participants tested their situational awareness of an external stimulus. The experiment was conducted in virtual reality (VR) environment with an omnidirectional treadmill. Participant's behavioral and electroencephalographic (EEG) responses were measured and analyzed using a newly proposed brain network measure, phase locking distance (PLD), to capture multitasking-associated couplings between brain regions. The current study supports the notion that multitasking can lead to diminished behavioral performance and changes in walking patterns. Despite the increased interregional brain connectivity, which may be an adaptive response to heightened cognitive demands, the associated reduction in event-related EEG activity suggests competition for neural resources during multitasking. Moreover, the observed EEG activities could indicate additional information processing capacity limitations and provide evidence of decreased situational awareness during multitasking, thereby increasing susceptibility to potentially hazardous situations. © 2016 IEEE.

关键词： Electroencephalography

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Developments and trends in emergency lighting systems: from energy-efficiency to zero electrical power consumption

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Life Cycle Reliability and Safety engineering 2024年第2期13卷 129-145页

作者： Abdelmoumene, Abdelkader Bentarzi, Hamid Iqbal, Atif Krama, Abdelbasset Qatar Airways Group -HIA Doha Qatar Signals and Systems Research Laboratory IGEE Boumerdes University Boumerdes Algeria Department of Electrical Engineering Qatar University Doha Qatar Department of Electrical and Computer Engineering Texas A & amp

Emergency lighting systems are an essential component of building safety infrastructure and play a critical role in ensuring the safety of people during power outages or other emergencies. As energy efficiency and environmental sustainability continue to be a focus in building design and construction, there is a growing interest in developing emergency lighting systems that not only reduce energy consumption but also operate on zero electrical power. This paper presents a comprehensive review of emergency lighting systems, with a focus on energy efficiency and zero electrical power consumption. The study first examines traditional lighting systems such as incandescent bulbs and their limitations in terms of energy consumption and efficiency. Then discusses energy-efficient options (CFLs and LED lighting), which have significantly improved energy efficiency and reduced power consumption. It also explores the latest generation of emergency lighting systems (Tritium and Photoluminescent), which have zero electrical power consumption throughout their lifetime and provide long-lasting illumination without the need for external electrical sources. Additionally, the paper presents a detailed comparative study of the energetic, environmental, economic, and life cycle assessment of the different emergency lighting technologies. The results of the study revealed that the use of Power Free emergency lighting signs not only reduces the installation and maintenance cost but also eliminates utter energy consumption and greenhouse gas (GHG) emissions. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Emergency lighting Energy efficiency Green building Life cycle assessment Smart design Zero electrical power

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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

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IEEE Transactions on Circuits and systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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Digital Twin Empowered PV Power Prediction

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Journal of Modern Power systems and Clean Energy 2024年第5期12卷 1472-1483页

作者： Xiaoyu Zhang Yushuai Li Tianyi Li Yonghao Gui Qiuye Sun David Wenzhong Gao the School of Information Science and Engi neering Northeastern UniversityShenyang 110004China the Department of Informatics Universi ty of OsloOslo 0316mNorway the Department of Computer Science Aalborg UniversityAal borg 9220Denmark the Electrification and Energy Infrastructures Division Oak Ridge National LaboratoryOak RidgeTN 37830USA the Department of Electrical and Computer Engineering University of DenverDenverCO 80208USA

The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power *** this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power *** this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital ***,a new DT-empowered PV power prediction method is ***,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal *** proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction ***,a real dataset is conducted to assess the effectiveness of the proposed method.

关键词： Photovoltaic power prediction digital twin hybrid prediction data recovery

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