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Face Liveness Detection Competition (LivDet-Face) - 2024 18

Face Liveness Detection Competition (LivDet-Face) - 2024

引用

18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Igene, Lambert Hossain, Afzal Chowdhury, Mohammad Zahir Uddin Rezaie, Humaira Rollins, Ayden Dykes, Jesse Vijaykumar, Rahul Komaty, Alain Marcel, Sébastien Schuckers, Stephanie Tapia, Juan E. Aravena, Carlos Schulz, Daniel Adami, Banafsheh Karimian, Nima Nunes, Diogo Marcos, João Gonçalves, Nuno Sikošek, Lovro Batagelj, Borut Alenin, Aleksandr Alkhaddour, Alhasan Pimenov, Anton Tregubov, Artem Avdonin, Igor Kazantsev, Maxim Pozigun, Mikhail Pryadchenko, Vasiliy Schei, Nima Pabon, David Tiedemann, Manuela Clarkson University United States Idiap Research Institute Switzerland Darmstadt Germany West Virginia University Morgantown United States University of Coimbra Institute of Systems and Robotics Portugal University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia Id R&d Inc Barcelona Spain Hummingbirds Ai FL United States Dermalog Identification Systems GmbH Hamburg Germany

ISBN: (纸本)9798350364132

Imagine a world where a copy of your face could trick the most advanced security systems. This isn't science fiction;it's a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year's competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%. © 2024 IEEE.

关键词： Biometrics

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Towards Content Oriented Integration of Product and Robot System Models

Towards Content Oriented Integration of Product and Robot Sy...

引用

International Symposium on Logistics and Industrial Informatics, LINDI

作者： Laszlo Horvath Imre J. Rudas Karel Jezernik John von Neumann Faculty of Informatics Budapest Technology Institute of Intelligent Engineering Systems Budapest Hungary Faculty of Electrical Engineering and Computer Science Institute of Robotics University of Maribor Maribor Slovenia

An approach and method to integrate virtual engineering and robot systems is introduced. The main idea is extended application of the principle of shape aspect that is well-proven in part modeling. Shape intensive robot system is engineered by using of shape modeling. Shapes are connected by special aspects in a shape aspect oriented model system. The main purpose of the proposed approach and method is better communication between product modeling and robot related engineering. Associativity definitions integrate part level functions into product level functions while behaviors and adaptivities are also handled as aspects. Paper starts with introduction and comparison of classical industrial product modeling and the proposed integrated product and robot system modeling. Following this, extended application of shape aspects and definition of connective aspects are detailed. Finally, implementation in open architecture industrial modeling systems is explained and future work is outlined.

关键词： Service robots systems engineering and theory robotics and automation Intelligent robots Solid modeling Informatics Shape control computer aided manufacturing Logistics Electrical products industry

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth David Taniar Robertas Damaševičius Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Faculty of Information Technology Monash University Melbourne VIC Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database.

关键词： Training Deep learning Accuracy Infectious diseases Databases Medical treatment Feature extraction Skin Distance measurement Public healthcare

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Brownian-Distribution Algorithm Optimized Efficientnet Features for Normal/Glaucoma Retinal Fundus Image Classification

Brownian-Distribution Algorithm Optimized Efficientnet Featu...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Venkatesan Rajinikanth Robertas Damaševičius David Taniar Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India Centre of Real Time Computer Systems Kaunas University of Technology Kaunas Lithuania Faculty of Information Technolog Monash University Melbourne VIC Australia

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The eye is a critical sensory organ in human physiology, and any abnormality in the eye will result in vision problems ranging from mild to severe. Image-guided methods are frequently employed to conduct clinical-level eye health assessments, and retinal fundus imaging (RFI) is among the most frequently employed modalities for eye analysis. This research aims to implement a Deep-Learning (DL)-based technique for classifying the Normal/Glaucoma RFI database. This scheme consist following phases, image collection and labelling, feature extraction with EfficientNet, feature optimization using a novel Brownian-Distribution Algorithm (BDA), and binary classification with 3-fold cross validation. Initially, the proposed approach performs the classification with EfficientNet-model features and documents the results. Subsequently, the BDA is employed to optimize the features in order to identify the most significant ones. Serial features-fusion is then implemented to generate a new features vector. The results of this study confirm that this approach is capable of achieving a 100 % accuracy rate when the BDA optimized feature vector is utilized for the classification task. In the future, this application may be utilized to analyze the RFI collected from hospital.

关键词： Glaucoma Accuracy Hospitals Databases Retina Feature extraction Vectors Physiology Classification algorithms Optimization

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Femoropopliteal Artery Stent Detection in Fluoroscopy Using Otsu's Preprocessing and CNN Segmentation

Femoropopliteal Artery Stent Detection in Fluoroscopy Using ...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

computer algorithm supported medical image analysis is a prevalent method in clinics, which effectively alleviates the diagnostic burden associated with traditional image assessment methods. This study aims to create a method that utilizes a Convolutional-Neural-Network (CNN) to accurately segment the Femoropopliteal Artery Stent (FAS) in Digital Fluoroscopy Images (DFI). In order to improve the accuracy of segmentation, this study used the combination of Otsu's tri-level thresholding and the Lévy-Flight Distribution Algorithm (LFDA). The suggested technique consists of many stages: collecting and resizing images and masks, boosting the DFI using Otsu and LFDA, using CNN-segmentation to extract the stent region, comparing segmented stent with mask, and calculating the required performance metrics. The efficacy of the suggested system is validated by analyzing the raw and thresholded DFI. The investigational results of this work affirm that this method achieves a higher level of accuracy (>96%) in segmenting images when the Otsu's preprocessed image is utilized. The experimental study employs the UNet-variants, and in the future, alternative CNN-segmentation methodologies can be utilized to validate the effectiveness of the presented scheme.

关键词： Measurement Image segmentation Accuracy Image analysis Boosting Arteries Medical diagnostic imaging

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GES tightly coupled attitude estimation based on a LBL/USBL positioning system

GES tightly coupled attitude estimation based on a LBL/USBL ...

引用

European Control Conference (ECC)

作者： Pedro Batista Carlos Silvestre Paulo Oliveira Institute for Systems and Robotics Universidade Técnica de Lisboa Lisboa Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology of the University of Macau.

ISBN: (纸本)9781479901890

Typical attitude estimation solutions for underwater vehicles rely on magnetometers, which are prone to magnetic field distortions. This can preclude its use in intervention scenarios, in the vicinity of objects with strong magnetic signatures, severely endangering not only the intervention mission but also the operation of the underwater vehicle. This paper presents a novel attitude estimation solution, based on a combined Long Baseline / Ultra Short Baseline (LBL/USBL) acoustic positioning system, with application to underwater vehicles. The range and range differences of arrival obtained with the LBL/USBL are directly embedded in the estimator dynamics, without any linearization whatsoever, and globally exponentially stable (GES) error dynamics are achieved. Simulation results evidence good performance of the proposed solution.

关键词： Observers Vehicles Acoustics Vehicle dynamics Vectors Magnetometers

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A Multi-User Multi-Robot Collaboration through Augmented reality

A Multi-User Multi-Robot Collaboration through Augmented Rea...

引用

IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Gabriel M. Costa Marcelo R. Petry Paulo Costa António Paulo Moreira CRIIS – Centre for Robotics in Industry and Intelligent Systems INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Current industrial environments have multiple robots working alongside humans, thus providing an operator the ability to perceive the robot’s workspace correctly and to anticipate its intentions and movements through the visualization of the robot’s digital twin is of utmost importance for safe and productive human-robot collaboration scenarios. Much has been studied regarding single human-single robot collaborative scenarios, but few address multi-user multi-robot scenarios. To this end, this paper presents a multi-robot multi-operator architecture, where the users’ awareness is enhanced through an augmented reality head-mounted display. A multi-robot, multi-user collaborative scenario is presented in a laboratory environment with two industrial robots. Besides being able to interact with both robots in the system, each user becomes more aware of the robot’s workspace and its pre-defined trajectories. Furthermore, it presents how fiducial markers can help to establish the relation between the different coordinate frames.

关键词： Visualization Service robots Robot kinematics Scalability Collaboration User interfaces Trajectory

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Long-time Resistance and Impurities Influence on Hydrogen Sensors Based on Palladium Expansion and Bragg Gratings 28

Long-time Resistance and Impurities Influence on Hydrogen Se...

引用

28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Almeida, Miguel A.S. de Almeida, J.M.M.M. Coelho, Luís C.C. INESC TEC Institute of Systems and Computer Engineering Technology and Science Department of Physics Faculty of Sciences University of Porto Rua do Campo Alegre Porto4169-007 Portugal Department of Physics School of Sciences and Technology University of Trás-os-Montes e Alto Douro Vila Real5001-801 Portugal

An optical fiber sensor for hydrogen detection is presented. It is based on processed fiber Bragg gratings coated with palladium thin films where its expansion due to the hydrogen adsorption is monitored as strain mea... 详细信息

ISBN: (纸本)9781957171302

关键词： Palladium

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Continuous media filesystem services on a real-time JAVA server

Continuous media filesystem services on a real-time JAVA ser...

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IEEE International Conference on Embedded and real-time Computing systems and Applications (RTCSA)

作者： A. Molano A. Miyoshi H. Tokuda R. Rajkumar Real Time and Multimedia Laboratory Department of Computer Science Carnegie Mellon University Pittsburgh PA USA Faculty of Environmental Information Keio Research Institute at SFC Keio University Fujisawa Kanagawa Japan

We investigate filesystem support for continuous media on the Java language. We describe a prototype implementation on a real-time Java Server, developed as an application level server on the real-time Mach microkernel environment. The Java virtual machine has been extended to support filesystem bandwidth reservation facilities existing on real-time Mach. Such facilities allow continuous media applications to specify their disk bandwidth usage requirements by creating a disk bandwidth reservation. The operating system, upon acceptance of the requests, internally enforces and guarantees such a share of the disk bandwidth for every active reservation. We present a performance evaluation, including both a synthetic application and real multimedia application based on a Quicktime video player which make use of real time Java threads and filesystem bandwidth reservation facilities. Our experiments conclude that the proposed filesystem extensions to the Java language are suitable for continuous media application requirements.

关键词： Java File servers Bandwidth Application software Timing Contracts Prototypes Virtual machining Yarn Electrical capacitance tomography

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