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arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA. © 2023, CC BY.

关键词： Deep learning

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

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Influence of silicon nanocone on cell membrane self-sealing capabilities for targeted drug delivery - computer simulation study

arXiv

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arXiv 2022年

作者： Raczyński, Przemyslaw Górny, Krzysztof Marciniak, Beata Beldowski, Piotr Pöschel, Thorsten Dendzik, Zbigniew Faculty of Science and Technology University of Silesia in Katowice 75 Pulku Piechoty 1A Chorzow41-500 Poland Faculty of Telecommunications Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Kaliskiego 7 Bydgoszcz85-796 Poland Faculty of Chemical Technology and Engineering Institute of Mathematics and Physics Bydgoszcz University of Science and Technology Kaliskiego 7 Bydgoszcz85-796 Poland Institute for Multiscale Simulation Friedrich-Alexander-Universität Erlangen-Nürnberg IZNF Cauerstraße 3 Erlangen91058 Germany Helmholtz-Zentrum Hereon Institute for Metallic Biomaterials Max-Planck-Straße 1 Geesthacht21502 Germany

Efficient and non-invasive techniques of cargo delivery to biological cells are the focus of biomedical research because of their great potential importance for targeted drug therapy. Therefore, much effort is being made to study the characteristics of using nano-based biocompatible materials as systems that can facilitate this task while ensuring appropriate self-sealing of the cell membrane. Here, we study the effects of indentation and withdrawal of nanospear on phospholipid membrane by applying steered molecular dynamics (SMD) technique. Our results show that the withdrawal process directly depends on the initial position of the nanocone. The average force and work are considerably more significant in case of the withdrawal starting from a larger depth. This result is attributed to stronger hydrophobic interactions between the nanocone and lipid tails of the membrane molecules. Furthermore, when the indenter was started from the lower initial depth, the number of lipids removed from the membrane was several times smaller than the deeper indentation. The choice of the least invasive method for nanostructure-assisted drug delivery is crucial for possible applications in medicine. Therefore, the results presented in this work might be helpful in efficient and safe drug delivery with nanomaterials. © 2022, CC BY.

关键词： Cytology

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Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

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Journal of Earth science 2023年第5期34卷 1433-1446页

作者： Qinjun Qiu Zhen Huang Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China Wuhan Geomatics Institute Wuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China Geological Environmental Center of Hubei Province Wuhan 430034China Faculty of Earth Resources China University of GeosciencesWuhan 430074China State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhan 430078China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

关键词： geological hazard report spatiotemporal information geological hazard ontology natural language processing gazetteers onlology machine learning

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A Survey on computer Vision based Human Analysis in the COVID-19 Era

arXiv

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arXiv 2022年

作者： Eyiokur, Fevziye Irem Kantarcı, Alperen Erakın, Mustafa Ekrem Damer, Naser Ofli, Ferda Imran, Muhammad Križaj, Janez Salah, Albert Ali Waibel, Alexander Štruc, Vitomir Ekenel, Hazım Kemal Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Engineering Istanbul Technical University Istanbul Turkey Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Qatar Computing Research Institute HBKU Doha Qatar Faculty of Electrical Engineering University of Ljubljana Tržaška cesta 25 Ljubljana1000 Slovenia Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey Carnegie Mellon University Pittsburgh United States

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for comp

关键词： COVID-19

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Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging 5

Performance Analysis of a Cloud Computing System using Queui...

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5th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2021

作者： Kumar, Rakesh Soodan, Bhavneet Singh Kuaban, Godlove Suila Czekalski, Piotr Sharma, Sapana School of Mathematics Shri Mata Vaishno Devi University Jammu and Kashmir Katra182320 India Department of Mathematics and Statistics Namibia University of Science and Technology Private Bag 13388 Windhoek Namibia 13 Jackson Kaujeua Street Windhoek Namibia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44 - 100 Poland Department of Computer Graphics. Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Mathematics Cluster University of Jammu Jammu and Kashmir India

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like a vera ge queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Cloud computing

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Revisiting constraints on proton PDFs from HERA DIS, Drell-Yan, W and Z boson production, and projected EIC measurements

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Physical Review D 2025年第3期111卷 034023-034023页

作者： Majid Azizi Maryam Soleymaninia Hadi Hashamipour Maral Salajegheh Hamzeh Khanpour Ulf-G. Meißner School of Particles and Accelerators Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531 Tehran Iran Istituto Nazionale di Fisica Nucleare Gruppo collegato di Cosenza I-87036 Arcavacata di Rende Cosenza Italy Helmholtz-Institut für Strahlen-und Kernphysik and Bethe Center for Theoretical Physics Universität Bonn D-53115 Bonn Germany AGH University Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 30-055 Krakow Poland Department of Physics University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran Institute for Advanced Simulation (IAS-4) Forschungszentrum Jülich D-52425 Jülich Germany Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China

We present new parton distribution functions (PDFs) at next-to-leading order and next-to-next-to-leading order in perturbative QCD, derived from a comprehensive QCD analysis of high-precision datasets from combined HERA deep-inelastic scattering, the Tevatron, and the LHC. To improve constraints on quark flavor separation, we incorporate Drell-Yan pair production data, which provides critical sensitivity to the quark distributions. In addition, we include the latest W and Z boson production data from the CDF, D0, ATLAS, and CMS collaborations, further refining both quark and gluon distributions. Our nominal QCD fit integrates these datasets and examines the resulting impact on the PDFs and their associated uncertainties. Uncertainties in the PDFs are quantified using the Hessian method, ensuring robust error estimates. Furthermore, we explore the sensitivity of the strong coupling constant, αs(MZ2), and proton PDFs in light of the projected measurements from the Electron-Ion Collider, where improvements in precision are expected. The analysis also investigates the effects of inclusive jet and dijet production data, which provide enhanced constraints on the gluon PDF and αs(MZ2).

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Learning privacy-enhancing face representations through feature disentanglement

Learning privacy-enhancing face representations through feat...

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International Conference on Automatic Face and Gesture Recognition

作者： Blaž Bortolato Marija Ivanovska Peter Rot Janez Križaj Philipp Terhörst Naser Damer Peter Peer Vitomir Štruc Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.

关键词： Face recognition Privacy Training Neural networks Decoding Training data Task analysis

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Minimizing the Annotation Effort for Detecting Wildlife in Camera Trap Images with Active Learning 51

Minimizing the Annotation Effort for Detecting Wildlife in C...

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Die 51. Jahrestagung der Gesellschaft fur Informatikin, INFORMATIK 2021 - 51st Annual Conference of the German Informatics Society, INFORMATIK 2021

作者： Auer, Daphne Bodesheim, Paul Fiderer, Christian Heurich, Marco Denzler, Joachim Computer Vision Group Friedrich Schiller University Jena Jena07737 Germany Bavarian Forest National Park Germany Visitor Management and National Park Monitoring Freyunger Str. 2 Grafenau94481 Germany Wildlife Ecology and Wildlife Management Faculty of Environment and Natural Resources University of Freiburg Freiburg79106 Germany Inland Norway University of Applied Science Institute for forest and wildlife management Campus Evenstad KoppangNO-2480 Norway Institute for Data Science Mälzerstraße 3 Jena Germany Michael Stifel Center Jena for Data-Driven and Simulation Science Ernst-Abbe-Platz 2 Jena Germany

ISBN: (纸本)9783885797081

Analyzing camera trap images is a challenging task due to complex scene structures at different locations, heavy occlusions, and varying sizes of animals. One particular problem is the large fraction of images only showing background scenes, which are recorded when a motion detector gets triggered by signals other than animal movements. To identify these background images automatically, an active learning approach is used to train binary classifiers with small amounts of labeled data, keeping the annotation effort of humans minimal. By training classifiers for single sites or small sets of camera traps, we follow a region-based approach and particularly focus on distinct models for daytime and nighttime images. Our approach is evaluated on camera trap images from the Bavarian Forest National Park. Comparable or even superior performances to publicly available detectors trained with millions of labeled images are achieved while requiring significantly smaller amounts of annotated training images. © 2021 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Cameras

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