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arXiv 2021年

作者： Osorio-Roig, Dailé Rathgeb, Christian Drozdowski, Pawel Terhörst, Philipp Štruc, Vitomir Busch, Christoph Department of Computer Science Hochschule Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research Darmstadt Germany Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

In the recent past, different researchers have proposed privacy-enhancing face recognition systems designed to conceal soft-biometric attributes at feature level. These works have reported impressive results, but generally did not consider specific attacks in their analysis of privacy protection. We introduce an attack on said schemes based on two observations: (1) highly similar facial representations usually originate from face images with similar soft-biometric attributes;(2) to achieve high recognition accuracy, robustness against intra-class variations within facial representations has to be retained in their privacy-enhanced versions. The presented attack only requires the privacy-enhancing algorithm as a black-box and a relatively small database of face images with annotated soft-biometric attributes. Firstly, an intercepted privacy-enhanced face representation is compared against the attacker’s database. Subsequently, the unknown attribute is inferred from the attributes associated with the highest obtained similarity scores. In the experiments, the attack is applied against two state-of-the-art approaches. The attack is shown to circumvent the privacy enhancement to a considerable degree and is able to correctly classify gender with an accuracy of up to approximately 90%. Future works on privacy-enhancing face recognition are encouraged to include the proposed attack in evaluations on the privacy protection. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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3D Indoor Mapping with the Microsoft Hololens: Qualitative and Quantitative Evaluation by Means of Geometric Features 24

3D Indoor Mapping with the Microsoft Hololens: Qualitative a...

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2020 24th ISPRS Congress on Technical Commission I

作者： Weinmann, M. Jäger, M.A. Wursthorn, S. Jutzi, B. Weinmann, M. Hübner, P. Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany Institute of Computer Science II University of Bonn Bonn Germany

3D indoor mapping and scene understanding have seen tremendous progress in recent years due to the rapid development of sensor systems, reconstruction techniques and semantic segmentation approaches. However, the quality of the acquired data strongly influences the accuracy of both reconstruction and segmentation. In this paper, we direct our attention to the evaluation of the mapping capabilities of the Microsoft HoloLens in comparison to high-quality TLS systems with respect to 3D indoor mapping, feature extraction and semantic segmentation. We demonstrate how a set of rather interpretable low-level geometric features and the resulting semantic segmentation achieved with a Random Forest classifier applied on these features are affected by the quality of the acquired data. The achieved results indicate that, while allowing for a fast acquisition of room geometries, the HoloLens provides data with sufficient accuracy for a wide range of applications. © 2020 Copernicus GmbH. All rights reserved.

关键词： Mapping

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Exploring 3D Reconstruction with Drone Images: Advances and Challenges in Urban Environments

Exploring 3D Reconstruction with Drone Images: Advances and ...

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Annual Conference of Industrial Electronics Society

作者： Thiago João Miranda Baldivieso Taise Grazielle Da Silva Batista Fabio Suim Chagas Luiz Carlos Pacheco Rodrigues Velho Paulo Fernando Ferreira Rosa Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil State Institute of the Environment Inea/RJ Rio de Janeiro Brazil Department of Science and Industry Systems University of South-Eastern Norway Kongsberg Norway Department of Computer Graphics Institute for Pure and Applied Mathematics Rio de Janeiro Brazil Electrical Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software—both commercial and open-source—and concluding with a qualitative evaluation of the results.

关键词： Industrial electronics Three-dimensional displays Urban areas Aircraft Image reconstruction Photogrammetry Drones

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Assessing quality of decision reducts 24

Assessing quality of decision reducts

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Stanczyk, Urszula Zielosko, Beata Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bedzinska 39 Sosnowiec41-200 Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution. © 2020 The Authors. Published by Elsevier B.V.

关键词： Rough set theory

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

arXiv

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norway ID R&D Inc. New York United States School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

关键词： Competition

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Functional-voxel method in problems of geometric modeling of thermal characteristics of objects 30

Functional-voxel method in problems of geometric modeling of...

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30th International Conference on computer graphics and Machine Vision, GraphiCon 2020

作者： Plaksin, Aleksandr Tolok, Alexey Laboratory of Computer Graphics V.A. Trapeznikov Institute of Control Science Russian Academy of Sciences 65 Profsoyuznaya street Moscow117997 Russia

The paper presents an approach developed on the basis of the functional voxel method to the geometric representation of the thermal expansion of objects and temperature stresses in a material when exposed to a surface of a heat source. A discrete geometric law of a single temperature stress in an isotropic heat-conducting body is derived, applicable in the concept of functional voxel modeling. Based on this law, functional-voxel models of thermal stress are developed for a single and distributed application of a heat source. Algorithms of functional-voxel modeling of temperature stress and expansion in the case of distributed thermal loading are presented, which make it possible to construct a loading region of a complex configuration, uniformly form a contour (surface) after material expansion and obtain information about changes in the length (volume) of products. The advantages of the proposed functional-voxel approach to modeling thermal expansion and stress over approaches based on the FEM are substantiated. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Thermal expansion

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Algorithmic Intelligence Instantaneously for Complex Real-Time Supply Chain Optimization

Algorithmic Intelligence Instantaneously for Complex Real-Ti...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： G Lalitha Himanshu Kumar Rajora Asha V Ginni Nijhawan Praveen Karrar.R.Al- Shami Institute of Aeronautical Engineering Dundigal Hyderabad Docse GyanGanga Institute of Technology and Sciences Jabalpur M.P India Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The modern world of supply chain management is complex and ever-evolving, thus the ability to make informed decisions and react quickly are of the utmost importance. In response to these issues, we have created a ground-breaking method that we call “Dynamic Real-Time Supply Chain Optimization with Algorithmic Intelligence (DRT-SCAI).” Better real-time visibility, autonomous decision-making, predictive accuracy, efficiency, customer happiness, risk mitigation, and sustainability are all outcomes of this suggested methodology’s use of state-of-the-art algorithmic intelligence, which simultaneously optimizes the supply chain in an instant. By using this approach, several enhancements are realized. To achieve these improvements, it is necessary to use this strategy.

关键词： Technological innovation Supply chain management Heuristic algorithms Supply chains Decision making Prediction algorithms Real-time systems

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Advanced Graph Convolutional Networks for Semantic Relationship Mining in Large-Scale Ontologies

Advanced Graph Convolutional Networks for Semantic Relations...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R Anuradha Swathi B Rakesh Kumar Amandeep Nagpal Ravi Kalra Hayder Saadoon Abdulaali Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering Applications GLA University Mathura Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research introduces novel methodologies in graph convolutional networks (GCNs) to enhance semantic relationship mining within large-scale ontologies. Traditional approaches to understanding and extracting meaningful patterns from ontological structures often struggle with the complexity and scale of data. The proposed framework leverages advanced GCNs, incorporating a series of innovative mechanisms designed to optimize performance, accuracy, and scalability. Firstly, an enhanced graph convolutional layer is introduced, specifically tailored to capture the nuanced hierarchical and relational data typical of extensive ontological datasets. This layer employs a multidimensional feature extraction technique, significantly improving the depth and quality of semantic relationship identification. Secondly, the paper presents a unique embedding algorithm that efficiently maps semantic and relational data into a lower-dimensional space, facilitating faster computation while retaining critical information. To address the challenges of scalability, a distributed processing architecture is also proposed, enabling the handling of ontologies at an unprecedented scale. Empirical evaluations demonstrate the superiority of the approach in terms of precision, recall, and computational efficiency compared to existing methods. This research contributes to the field by providing a robust tool for experts and practitioners involved in knowledge discovery, data mining, and semantic web technologies, paving the way for more intelligent and efficient ontology management.

关键词： Semantic Web Distributed processing Scalability Semantics computer architecture Ontologies Feature extraction

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Spatial Modulation Techniques for Improved ISAC Throughputs

Spatial Modulation Techniques for Improved ISAC Throughputs

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sanjay Kumar Suman B Rajalakshmi Irfan Khan V Alekhya Sorabh Lakhanpal Abduljabbar A. Ali St. Martin’s Engineering College Secunderabad Telangana Bharat Department of Computer Science New Horizon College of Engineering Bangalore Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Hyderabad Dundigal Lovely Professional University Phagwara College of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces “Adaptive Spatial Modulation for Enhanced ISAC Throughputs (ASM-ISAC)” to speed up Intelligent Space-Air-Ground Communication (ISAC). An adaptive spatial modulation technique, CSD for cooperative spatial diversity, and EESM-ISAC for energy-efficient spatial modulation are used. To boost communication throughput and reduce bit error rates, ASM-ISAC adjusts modulation and spatial patterns in real time. Cooperation between close ISAC nodes improves performance in CSD. EESM-ISAC develops energy-efficient communication with restricted resources. This research evaluates ASM-ISAC against earlier approaches like “Conventional Modulation Scheme for ISAC (CMS-ISAC)”. The test evaluates frequency efficiency, error performance, latency, complexity, energy efficiency, and security. ASM-ISAC outperforms CMS-ISAC in several aspects, suggesting it might increase ISAC throughput.

关键词： Technological innovation Frequency modulation Spatial diversity Modulation Throughput Energy efficiency Real-time systems

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