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Harmonizing open banking in the European Union: an analysis of PSD2 compliance and interrelation with cybersecurity frameworks and standards

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International Cybersecurity Law Review 2024年第1期5卷 79-120页

作者： Gounari, Marianna Stergiopoulos, George Pipyros, Kosmas Gritzalis, Dimitris Dept. of Information & Communication Systems Engineering University of the Aegean Samos Greece Dept. of Computer Science Philips University Nicosia Cyprus Dept. of Informatics Athens University of Economics & Business Athens Greece

This paper focuses on the security protocols enacted in banking transactions across the European Economic Area (EEA), as stipulated by the Second or Revised Payment Service Directive (commonly referred to as ‘PSD2’ or simply ‘the Directive’). The study aims to comprehensively analyse the implementation and efficacy of these security measures within the specified jurisdiction. The Directive incorporates fundamental rights and obligations that all stakeholders are compelled to adhere to and delineates specific security measures and standards that both traditional banking institutions and third-party providers (TPP) are mandated to implement. In particular, one of the cardinal mandates for banking and financial institutions under PSD2 is the obligation to facilitate third-party access to customer data via open application programming interfaces (API). While this open banking paradigm and the consequent proliferation of data sharing unquestionably bring about various advantages, such as enhanced consumer choice and market competition, they concurrently expose the financial ecosystem to a slew of potential security vulnerabilities and privacy risks. Upon conducting a comprehensive review of the security requirements and measures stipulated under PSD2 and a comparative analysis with essential cybersecurity frameworks and standards (NIS2, Cybersecurity Act, GDPR, ISO 27001:22 and PCI DSS), we have ascertained a discernible lack of harmonisation and clarity concerning the technical security specifications for its effective implementation. This lacuna substantiates the challenges banks face in fully grasping the extensive spectrum of compliance obligations mandated by PSD2. The aim of this research is to offer a valuable contribution to both the comprehension and the pragmatic deployment of security standards in the context of banking transactions, as regulated by the PSD2. The paper serves as a valuable resource for traditional banking institut

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An Efficient Detection Model for Brain Stroke Based on Transfer Learning

An Efficient Detection Model for Brain Stroke Based on Trans...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Faroq H. M. Mohammed Abdulfattah Ba Alawi Ferhat Bozkurt M. Dilruba Geyikoğlu Electrical and Electronic Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Computer Engineering Dept. Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Dept. Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Brain stroke is one of the most common causes of death, ranking as the second leading cause worldwide. With the advancements of deep learning, the detection of brain strokes from CT images becomes possible. In this study, we propose a method for classifying brain stroke images and predicting the presence of a stroke using convolutional neural networks (CNNs), which are particularly effective in image classification tasks. These techniques involve training different CNN models (EfficientNetB0-B7, InceptionResNetV2, ResNet50, ResNet152, and VGG19) on a dataset of labeled brain strokes and normal brain images. Through this process, the model learns to identify patterns and features that are indicative of a stroke. The results show that the different CNN models were able to classify stroke images with high accuracy and predict the presence of stroke with an overall accuracy of 99.95%, which is a significant improvement compared to similar studies. This demonstrates the potential of deep learning-based approaches using CNNs in assisting the diagnosis of strokes and aiding in early-stage treatment. Finally, we provide an analysis of model performance, highlighting each CNN model used, and providing insights for future research.

关键词： Training Analytical models Accuracy Transfer learning Stroke (medical condition) Brain modeling Convolutional neural networks

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Design and Control of a High-Bandwidth Dual Active Bridge DC-DC Converter 14

Design and Control of a High-Bandwidth Dual Active Bridge DC...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Uzum, Alper Hasan, Syed Imam Sozer, Yilmaz Loparo, Kenneth A. The University of Akron Dept. of Electrical and Computer Engineering AkronOH United States Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH United States

ISBN: (纸本)9798331516116

For the stable operation of sensitive systems, achieving high-bandwidth control in power converters is critical, as the current and voltage ripple directly reflects the quality of the synthesized waveform. The selection of an appropriate DC-link capacitor, which significantly influences the system's bandwidth, plays a pivotal role in ensuring performance. This paper explores the intricate relationship between input and output DC-link capacitance and its effect on the system bandwidth in Dual Active Bridge (DAB) converters, a topic that remains relatively unexplored despite numerous research on high-bandwidth control for other power converters. To address the challenges posed by the bulky DC-link capacitor typically required to manage ripple current, the proposed approach integrates a harmonic trap filter to effectively mitigate these limitations. Using state-space modeling and state-variable feedback control, the design and control strategy is validated on a 5 kW DAB converter, demonstrating its ability to enhance system bandwidth without compromising ripple performance. © 2025 IEEE.

关键词： Feedback control

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Gourav Datta Zeyu Liu Anni Li Peter A. Beerel Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland USA Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and ImageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10.

关键词： Training computer vision Sensitivity Neurons Signal processing algorithms Spiking neural networks Transformers Energy efficiency Convolutional neural networks Speech processing

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks 4

Improving the Efficiency of a Multistage Evaporator Station ...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Zaiets, Nataliia Lutska, Nataliia Vlasenko, Lidiia National University of Life and Environmental Sciences of Ukraine Det. of Automation and Robotic Systems Kyiv Ukraine National University of Food Technologies Dept. of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Dept. of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9798350346831

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department. © 2022 IEEE.

关键词： Evaporation

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Generalized Likelyhood Ratio based Detection on Cyber-Attacks 37

Generalized Likelyhood Ratio based Detection on Cyber-Attack...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Wu, Hao Zhang, Deyu Dept. of Software Henan University of Engineering Zhengzhou China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781665465366

In this paper, a generalized likelihood ratio based detection algorithm with a statistical method for threshold setting is proposed to detect cyber-attacks on a unified remote control architecture. Based on an encoder and a decoder embedded on plant side and remote controller side, respectively, not only additive interrupted signal (only effecting output or input signal), but also multiplicative attack signal could be detected easily. Finally, three numerical examples are provided to validate our proposed scheme. © 2022 IEEE.

关键词： Remote control

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture

Augmented Karuta: Interactive Playing Cards on the Floor for...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Nobuyuki Umezu Hideki Bando Dept. of Computer and Information Sciences Ibaraki University Hitachi Japan Dept. of Mechanical Systems Engineering Ibaraki University Hitachi Japan

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as ‘Karuta’. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 “grabbing” cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding “reading” card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected ‘grabbing’ card that matches the ‘reading’ card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness.

关键词： Laser radar Tracking Virtual reality Learning (artificial intelligence) Museums History Floors

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Network-based Intrusion Detection Datasets: A Survey 23

Network-based Intrusion Detection Datasets: A Survey

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Ahmed, Lubna Ali Hassan Hamad, Yahia Abdalla Mohamed Abdalla, Ahmed Abdallah Mohamed Ali Sudan University of Science and Technology Dept. of Electronics Engineering Khartoum Sudan Sudan University of Science and Technology Dept. of Computer Networks and Systems Khartoum Sudan

ISBN: (纸本)9798350320244

The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fields that could highly contribute to cyber-Attacks detection and mitigation. Datasets are necessary for NIDS training and evaluation. This paper provides a comprehensive survey of publicly available NIDS datasets. The study covers the most used NIDS datasets, each dataset is briefly described along with its main properties and shortcomings. Moreover, the paper highlights the most important dataset characteristics and provides a clear datasets comparison accordingly. Finally, the paper discusses observations and provides some recommendations to help researchers identify future work. © 2022 IEEE.

关键词： Network security

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Muhammad Ikram Salman Ahmed Safdar Nawaz Khan Marwat Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets.

关键词： Renewable energy sources Costs Estimation Consensus control Market research Electricity supply industry Peer-to-peer computing

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Improving ASTRO's Perception Capability for a more Natural Follow-Me Task 6

Improving ASTRO's Perception Capability for a more Natural F...

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6th Experiment at International Conference, exp.at 2023

作者： Ferreira, Bruno Sorrentino, Alessandra Menezes, Paulo Batista, Jorge Dias, Jorge Fiorini, Laura Cavallo, Filipo University of Coimbra Institute of Systems and Robotics Dept. of Electrical and Computer Engineering Coimbra Portugal Khalifa University of Science and Technology Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates University of Florence Dept. of Industrial Engineering Florence Italy BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy

ISBN: (纸本)9798350314403

Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consumption while maintaining a higher order of responsiveness to the surroundings. Therefore, we present in this paper, an improvement of the follow-me system on ASTRO. In detail, we propose the use of Mediapipe SDK for human detection and tracking, when ASTRO is meant to accompany someone that is walking. A study on the new velocity and distance profiles that the robot keeps from the individuals is presented and we have also evaluated how it affects their perception of being safe. The presented results show that our new approach allows the system to achieve real-time performance by becoming ≈ 9.1× faster, smoothing, and keeping a more natural distance from the user. © 2023 IEEE.

关键词： Human robot interaction

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