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A Secure and Trustworthy Biometric Data Ecosystem for Cross-border Suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kyriakou, Katerina Apostolaras, Apostolos Velentzas, Polychronis Benos, Georgios Koutsoukos, Konstantinos Symvoulidis, Chrysostomos Liang, Kaitai Shi, Zeshun Leonidis, Asterios Miniadou, Kyriaki Veroni, Eleni Evangelatos, Spyridon Papadopoulos, Georgios Th. Korakis, Thanasis University of Thessaly Department of Electrical and Computer Engineering Volos Greece The Centre for Research & Technology Certh Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft Netherlands Crete Greece Netcompany-Intrasoft S.A. Research & Innovation Development Luxembourg Luxembourg Hellenic Mediterranean University Dept. of Electronic Engineering Crete Greece Harokopio University of Athens Department of Informatics and Telematics Athens Greece

ISBN: (纸本)9798350362480

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs. © 2024 IEEE.

关键词： Biometrics Data Sovereignty Biometrics Data Space Cross-Border Suspect Identification Homomorphic Encryption Privacy Enhancing Technologies

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model

The Integration of 6G and Blockchain into an Efficient AIoT-...

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IEEE World Symposium on Communication engineering (WSCE)

作者： Anna Lioupa Vasileios A. Memos Christos L. Stergiou Yutaka Ishibashi Konstantinos E. Psannis Dept. of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece Dept. of Computer Science Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication.

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Desman, Andy Muchtar, Kahlil Oktiana, Maulisa Fitria, Maya Away, Yuwaldi Fardian, Fardian Riza, Hammam Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Banda Aceh Indonesia Indonesia The Master Program in Artificial Intelligence The Department of Informatics Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798331521165

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE. © 2025 IEEE.

关键词： Traffic surveys

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The design space ofwearables for sports and fitness practices 21

The design space ofwearables for sports and fitness practice...

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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Vidal, Laia Turmo Zhu, Hui Waern, Annika Segura, Elena Márquez Dept of Informatics and Media Uppsala University Sweden Uppsala University Sweden Department of Computer Science and Engineering Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781450380966

The growing interest in wearables for sports and fitness calls for design knowledge and conceptualizations that can help shape future designs. Towards that end, we present and discuss a design space of wearables for these practices, based on a survey of previous work. Through a thematic analysis of 47 research publications in the domain, we surface core design decisions concerning wearability, technology design, and wearable use in practice. Building on these, we show how the design space takes into account the goals of introducing technology, that design decisions can be either directly designed, or left open for appropriation by end-users;and the social organization of the practice. We characterize prior work based on the design space elements, which yields trends and opportunities for design. Our contributions can help designers think about key design decisions, exploit trends and explore newareas in the domain of wearables for sports and fitness practices. © 2021 ACM.

关键词： Sports

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention

Big Data Analytics for Proactive Financial Fraud Detection a...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Yehezkeil Hasudungan Fitra Putri Oganda Ora Plane Maria Daeli Raihan Nesti Anggraini Santoso Dept. of Informatics Engineering University of Raharja Tangerang Indonesia Dept. of Information System University of Raharja Tangerang Indonesia Dept. of Digital Business University of Raharja Tangerang Indonesia Dept. of Computer System University of Raharja Tangerang Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics.

关键词： Economics Technological innovation Prevention and mitigation Finance Big Data Stability analysis Real-time systems Fraud Security Financial industry

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Acoustic measurement framework for audio systems based on structured periodic test signals 12

Acoustic measurement framework for audio systems based on st...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Kawahara, Hideki Yatabe, Kohei Sakakibara, Ken-Ichi Mizumachi, Mitsunori Kitamura, Tatsuya Wakayama University Cntr. Innovative Research and Liaison Wakayama Japan Tokyo University of Agriculture and Technology Dept. Electrical Engineering and Computer Science Tokyo Japan Health Sciences University of Hokkaido Dept. Speech-Language-Hearing Therapy Hokkaido Japan Kyushu Institute of Technology Dept. Electrical and Electronic Eng. Kitakyushu Japan Konan University Dept. Intelligence and Informatics Kitakyushu Japan

ISBN: (纸本)9798350340181

We introduce a framework for acoustic measurement that allows everyday sounds into acoustic measurement test signals and standard test signals such as swept-sine and MLS (Maximum Length Sequence). The framework extends our extended Time-Stretched-Pulse generation method and safeguarding method for any signals to make them appropriate for acoustic measurement. The extension uses the periodic nature of discrete Fourier transform combined with orthogonalization based on the Walsh-Hadamerd matrix. This framework enables simultaneous measurement of acoustic attributes such as linear time-invariant response, signal-dependent non-linear and time-invariant components, and random time-varying components. We also discuss and demonstrate practical scenarios in classes and laboratory lessons using tools developed based on the proposed framework. © 2023 IEEE.

关键词： Audio systems

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Analysis of the Effect of Capacitors on the Voltage Generated by a 3-Phase Induction Generator

Analysis of the Effect of Capacitors on the Voltage Generate...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Syukri, Mahdi Syuhada, Ahmad Tarmizi Suriadi Ramadhani, Satika Marwan Affan, Muzailin Yanis, Muhammad Universitas Syiah Kuala Doctoral Program School of Engineering Post Graduate Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Dept. of Mechanical and Industrial Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Geophisic Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Informatics Fmipa Banda Aceh Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. The advantage of induction generators can be used in small-scale plants such as micro-hydro power plants. However, the weakness of induction generators are weak voltage regulation and dependence on capacitors to generate voltage. This study conducted a test to see how the effect of capacitor capacity on the voltage generated by a induction generator when serving multiple incandescent lamp loads. The capacitor used is connected with a wye connection because the Wye connection can generate a higher voltage than the Delta connection circuit. This research has been successfully carried out with variations in capacitor values, namely 45 F, 60 F, and 75 F and variations in lamp load values, namely 5W, 15W, 25W, 40W and 60W. Based on the results of the study, the induction generators uses a 45 F capacitor with a load of 5 W, the terminal voltage for line to line is 374 V. However, when using a 75 F capacitor with a 60W load, the terminal voltage for line to line is 239 V. The terminals voltage of the induction generators can also be kept at a constant 380 V by providing varying capacitor values even though it serves varying loads. © 2022 IEEE.

关键词： Asynchronous generators

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A Dynamic Fuzzy Neural System for Time Series Classification 10

A Dynamic Fuzzy Neural System for Time Series Classification

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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Kandilogiannakis, George Mastorocostas, Paris Hilas, Costas University of West Attica Dept. of Informatics and Computer Engineering Athens Greece International Hellenic University Dept. of Computer Informatics and Telecommunications Engineering Serres Greece

ISBN: (纸本)9781728154565

A dynamic fuzzy neural system is proposed, for time series anomaly detection. The model is entitled BFuzzTiD (Block-diagonal Fuzzy Time-series Detector) and consists of fuzzy rules whose consequent parts are three-layer small recurrent neural networks. The hidden layer of each network has blocks of neurons that feed back to each other. BFuzzTid is trained by the Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis is conducted with a series of time series anomaly detection models, in order to investigate the capabilities of the proposed detector. © 2020 IEEE.

关键词： Time series

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Flood Evacuation Route Recommendation Using Particle Swarm Optimization Algorithm

Flood Evacuation Route Recommendation Using Particle Swarm O...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Ricky Mahendra Teuku Muh. Haedir M. Adnan Nur Nur Khaerat Nur Hazriani Dept. of Informatics Handayani University of Makassar Makassar Indonesia Dept. of Civil Engineering Fajar University Makassar Indonesia Dept. of Computer System Handayani University of Makassar Makassar Indonesia

Equipping the society with an evacuation route assistant is important as a preventive action in order to minimize anxiety as well as number of victims when disaster strike. Therefore, research on best route determination to the shelter is a noble task. Evacuation routes mean routes to pass in order to reach the gathering point or shelter safely. This study aims to implement the particle swarm optimization (PSO) algorithm in finding the optimal path to the designated shelter. PSO is a metaheuristic algorithm inspired by the behaviour of flocks of birds or fish to optimize the search for the best route. Taking into account the details of the PSO implementation, including initialization of the random particle position and velocity, selection of the best particle (Pbest) and Global Best (Gbest), and updating the speed and position of the particle using the specified formula. The objective function is recalculated based on the total distance of the particle route as well as the condition weight of the affected areas. The results showed that out of ten experiments with 100 iterations, six valid routes were found with the highest fitness value of 1,935, which indicates that PSO can provide optimal solutions in the context of finding flood evacuation routes.

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SegQNAS: Quantum-inspired Neural Architecture Search applied to Medical Image Semantic Segmentation

SegQNAS: Quantum-inspired Neural Architecture Search applied...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guilherme Carlos Karla Figueiredo Abir Hussain Marley Vellasco Dept. of Electrical Engineering PUC-Rio Rio de Janeiro Brazil Dept. of Informatics and Computer Science UERJ Rio de Janeiro Brazil Dept. of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physicians in diagnosing and analysing medical scans for the patient's benefit. Traditionally, several techniques have been applied in order to perform semantic segmentation. However, with the development of Deep Learning methods, there was a paradigm shift. Deep learning techniques are able to achieve great results, comparable to human-level performance. In order to achieve state-of-the-art results, researchers have to lean on the task of designing novel deep neural network architectures. That process is very time-consuming and heavily relies on experience and expert knowledge. Neural architecture search is the process of automatising the search for new deep neural network architectures. Quantum-Inspired Neural Architecture Search is a neural architecture search algorithm that leverages the benefits of quantum-inspired computing to search for neural network architectures efficiently. In this work, we adapt this to search for semantic segmentation neural networks and apply it to medical image analysis. The Spleen and Prostate datasets from the Medical Segmentation Decathlon challenge were used. Results show that our work was able to find better-performing semantic segmentation architectures for both datasets: $0.9583\pm 0.006$ in comparison to ResU-Net $0.9525\pm 0.008$ for the spleen dataset, and $0.6887\pm 0.067$ in comparison to $0.6529\pm 0.070$ for the prostate dataset.

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