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2020 Open Identity Summit

作者： Bisztray, Tamas Gruschka, Nils Mavroeidis, Vasileios Fritsch, Lothar University of Oslo Department of Informatics Oslo Norway Karlstad University Dept. of Mathematics and Computer Science Karlstad Sweden

ISBN: (纸本)9783885796992

Privacy issues concerning biometric identification are becoming increasingly relevant due to their proliferation in various fields, including identity and access control management (IAM). The General Data Protection Regulation (GDPR) requires the implementation of a data protection impact assessment for privacy critical systems. In this paper, we analyse the usefulness of two different privacy impact assessment frameworks in the context of biometric data protection. We use experiences from the SWAN project that processes four different biometric characteristics for authentication purposes. The results of this comparison elucidate how useful these frameworks are in identifying sector-specific privacy risks related to IAM and biometric identification. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Biometrics

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Tracking cellular and subcellular dynamics with quantitative oblique back-illumination microscopy 10

Tracking cellular and subcellular dynamics with quantitative...

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Quantitative Phase Imaging X 2024

作者： Filan, Caroline E. Bharadwaj, Srinidhi Casteleiro Costa, Paloma Davarzani, Amin Cappabianca, Dan Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology 801 Ferst Dr. NW AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology and Emory University 313 Ferst Dr. NW AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States Division of Neuroscience Biomedical and Health Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510669635

Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Tissue

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Approximate kNN classification for biomedical data

arXiv

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arXiv 2020年

作者： Anagnostou, Panagiotis Vrahatis, Aristidis G. Barbas, Petros Tasoulis, Sotiris K. Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece

We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNAseq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the"Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability. Copyright © 2020, The Authors. All rights reserved.

关键词： DNA sequences

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Design of Digital Image Encryption Scheme with Cryptographic Analysis 3

Design of Digital Image Encryption Scheme with Cryptographic...

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3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Kordov, Krasimir Zhelezov, Stanimir Nachev, Atanas Gueorguiev, Nikolay Pavlova, Daniela Konstantin Preslavsky University of Shumen Dept. of Computer Informatics Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria Institute of Metal Science Equipment and Technology with Hydroaerodynamic Center Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9781665439817

In this paper a new cryptographic algorithm, de-signed for digital image encryption is proposed. The encryption scheme is based on two chaotic maps, combined for constructing a pseudorandom generator. Using the chaotic behavior of the pseudorandom generator, permutation and substitution operations are performed for completing the encryption process. For proving the security of the cryptographic scheme, an extensive cryptographic analysis is performed including statistical tests for randomness, key-space analysis, key sensitivity, visual analysis, histogram analysis, correlation analysis, entropy analysis, number of pixels change rate and encryption/decryption key sensitivity analysis. The results from empirical experiments are presented in this paper. © 2021 IEEE.

关键词： Cryptography

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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Transfer Learning-based Real-time Handgun Detection

arXiv

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arXiv 2023年

作者： Elmir, Youssef Laouar, Sid Ahmed Hamdaoui, Larbi LITAN Laboratory Higher School of Sciences and Technologies of Informatics and Digital NR 75 Bejaia Amizour06300 Algeria SGRE Laboratory Tahri Mohammed University of Bechar Algeria Mathematics and Computer Science Dept. Tahri Mohammed University of Bechar Algeria

Traditional surveillance systems rely on human attention, limiting their effectiveness. This study employs convolutional neural networks and transfer learning to develop a real-time computer vision system for automatic handgun detection. Comprehensive analysis of online handgun detection methods is conducted, emphasizing reducing false positives and learning time. Transfer learning is demonstrated as an effective approach. Despite technical challenges, the proposed system achieves a precision rate of 84.74%, demonstrating promising performance comparable to related works, enabling faster learning and accurate automatic handgun detection for enhanced security. This research advances security measures by reducing human monitoring dependence, showcasing the potential of transfer learning-based approaches for efficient and reliable handgun detection. © 2023, CC BY.

关键词： computer vision

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Graph Neural Networks for Microseismic Event Detection: Focusing on Distributed Acoustic Sensing Data

Graph Neural Networks for Microseismic Event Detection: Focu...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Mohd Safuwan Bin Shahabudin Jafreezal Jaafar Nina Bencheva Irving Vitra Paputungan Nor Farisha Binti Muhamad Krishnan Computer and Information Sciences Department Universiti Teknologi PETRONAS Seri Iskandar Malaysia Centre for Research in Data Science (CeRDaS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Dept. of Telecommunications University of Ruse "Angel Kanchev" Ruse Bulgaria Informatics Department Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Microseismic event detection is paramount for surveilling subsurface activities including hydraulic fracturing, geothermal energy extraction, and evaluating seismic hazards. Distributed Acoustic Sensing (DAS) technology provides high-resolution spatial and temporal datasets, rendering it an indispensable instrument for identifying microseismic events. Nonetheless, the substantial volume of data and the intricate spatiotemporal interrelations inherent in DAS data present considerable obstacles to conventional signal processing techniques. This research presents a novel approach utilizing Graph Neural Networks (GNNs) for microseismic event detection in DAS data. By displaying the spatial and temporal relationships between DAS sensing points as graph structures, the GNN identifies the underlying spatiotemporal patterns linked to microseismic events. According to empirical results, the suggested approach outperforms conventional machine learning frameworks, achieving higher detection accuracy and more computational efficiency.

关键词： Accuracy Event detection Surveillance Soft sensors Distributed databases Machine learning Graph neural networks Acoustics Spatial databases Spatiotemporal phenomena

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Goal-Aware RSS for Complex Scenarios via Program Logic

Goal-Aware RSS for Complex Scenarios via Program Logic

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IEEE Symposium on Intelligent Vehicle

作者： Ichiro Hasuo Clovis Eberhart James Haydon Jérémy Dubut Rose Bohrer Tsutomu Kobayashi Sasinee Pruekprasert Xiao-Yi Zhang Erik André Pallas Akihisa Yamada Kohei Suenaga Fuyuki Ishikawa Kenji Kamijo Yoshiyuki Shinya Takamasa Suetomi National Institute of Informatics (NII) Tokyo Japan SOKENDAI (The Graduate University for Advanced Studies) Japan Japanese-French Laboratory for Informatics (IRL 3527) Tokyo Japan Dept. Computer Science Worcester Polytechnic Institute Worcester MA USA Inst. Software & Systems Engineering University of Augsburg Augsburg Germany Cyber Physical Security Research Center AIST Tokyo Japan Graduate School of Informatics Kyoto University Kyoto Japan Mazda Motor Corporation Fuchu Japan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

关键词： Intelligent vehicles computer architecture Software Cognition Safety Planning Complexity theory

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on Engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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On the Way to Realize the 5th Industrial Revolution: Achievements, Challenges and Research Areas

On the Way to Realize the 5th Industrial Revolution: Achieve...

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IEEE Symposium on Network Operations and Management

作者： George Dimirakopoulos Thomas Gutt Hans Ehm Alfred Hoess Konstantina Karathanasopoulou Anna Lackner Germar Schneider dept. of Informatics and Telematics Harokopio Univesity of Athens Athens Greece Infineon Technologies A.G. Munich Germany dept. of Electrical Engineering Media and Computer Science University of Applied Sciences Amberg-Weiden Amberg Germany eesy-innovation GmbH Unterhaching Germany Infineon Technologies Dresden GmbH&Co.KG Dresden Germany

The industrial world is rapidly moving towards its $5^{\text {th}}$ industrial revolution (a.k.a. “Industry5.0”). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing $A I$-enabled hardware, as well as $A I$ tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty.

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