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3rd International Conference on Advances in Cyber Security, ACeS 2021

作者： Al-Hashedi, Khaled Gubran Magalingam, Pritheega Maarop, Nurazean Samy, Ganthan Narayana Manaf, Azizah Abdul Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789811680588

Soon after its inception in 2009, Bitcoin was used as a tool by malicious attackers who exploit its pseudo-anonymity to establish untraceable frauds. Recently, several Bitcoin users and institutions have confirmed that thousands of Bitcoins were lost due to the failure to implement a fraud detection system, causing significant damage to individuals or institutions and resulting in bankruptcy. The anonymous nature of the Bitcoin system makes it a desirable option for malicious people to carry out illegal activities, making it difficult for law enforcement to detect suspicious behavior and making the current fraud detection techniques impractical. Thus, identifying illegal activities becomes an important factor to protect the reputation of the Bitcoin system. In this paper, we propose a model to identify illegal transactions in the Bitcoin system. Firstly, we collect illegal addresses for data labeling purposes from different sources such as online public bitcoin forums and related datasets from previous papers and then verify them with a raw Bitcoin dataset. Secondly, we introduce new types of features by using a time-based approach to segment transactions into time slices over a period in addition to the most meaningful features of the prior studies. Thirdly, we evaluate the proposed model on five popular supervised classifiers (KNN, SVM, RF, XGB, and KNN). Finally, this paper considers the problem of class imbalance and attained better optimization when using an adaptive oversampling technique (ADASYN). Results obtained from this study demonstrate that RF and XGB outperform KNN, SVM, and NN in terms of detection rate. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Bitcoin

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Analysis of Electromagnetic Properties of EBG Structures for Directional Planar Antennas 3

Analysis of Electromagnetic Properties of EBG Structures for...

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3rd International Conference on Electrical, Communication and computer engineering, ICECCE 2021

作者： Djoudi, Mounia Tounsi, Mohamed Lamine Ababou, Noureddine Yagoub, Mustapha C.E. Usthb University Laboratory of Instrumentation Faculty of Electronics and Informatics Algiers Algeria Usthb University Pedcl Laboratory Faculty of Electronics and Informatics Algiers Algeria University of Ottawa Element Laboratory School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (纸本)9781665438971

The aim of this work is to design Photonic Band Gap (PBG) structures for the purpose of directional antennas by illuminating these materials with a plane wave and deduced the reflection and transmission coefficients. We have also studied the electromagnetic (EM) properties of periodic structures as well as the distribution of the electric field in order to place the antenna in the right place within these structures in order to optimize their gain. The obtained results show a significant enhancement in directivity and reflection coefficient. © 2021 IEEE.

关键词： Energy gap

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Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

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computers, Materials & Continua 2021年第12期69卷 2883-2901页

作者： Mohamed Abdel-Basset Reda Mohamed Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Mohamed Abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Capability Systems Centre School of Engineering and ITUNSWCanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature *** technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary ***,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating *** strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI *** is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected *** the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.

关键词： Generalized normal distribution optimization feature selection transfer function novel restarting strategy UCI repository

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USEFUSE: UTILE STRIDE FOR ENHANCED PERFORMANCE IN FUSED LAYER ARCHITECTURE OF DEEP NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Ibrahim, Muhammad Sohail Usman, Muhammad Lee, Jeong-A Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-Si Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Computer Engineering Chosun University Gwangju Korea Republic of

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. © 2024, CC BY.

关键词： Convolutional neural networks

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Addressing imperfect symmetry: A novel symmetry-learning actor-critic extension

arXiv

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arXiv 2023年

作者： Abreu, Miguel Reis, Luís Paulo Lau, Nuno LIACC/LASI/FEUP Artificial Intelligence and Computer Science Laboratory Faculty of Engineering University of Porto Porto Portugal IEETA/LASI/DETI Institute of Electronics and Informatics Engineering of Aveiro Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal

Symmetry, a fundamental concept to understand our environment, often oversimplifies reality from a mathematical perspective. Humans are a prime example, deviating from perfect symmetry in terms of appearance and cognitive biases (e.g. having a dominant hand). Nevertheless, our brain can easily overcome these imperfections and efficiently adapt to symmetrical tasks. The driving motivation behind this work lies in capturing this ability through reinforcement learning. To this end, we introduce Adaptive Symmetry Learning (ASL) — a model-minimization actor-critic extension that addresses incomplete or inexact symmetry descriptions by adapting itself during the learning process. ASL consists of a symmetry fitting component and a modular loss function that enforces a common symmetric relation across all states while adapting to the learned policy. The performance of ASL is compared to existing symmetry-enhanced methods in a case study involving a four-legged ant model for multidirectional locomotion tasks. The results show that ASL can recover from large perturbations and generalize knowledge to hidden symmetric states. It achieves comparable or better performance than alternative methods in most scenarios, making it a valuable approach for leveraging model symmetry while compensating for inherent perturbations. © 2023, CC BY.

关键词： Contrastive Learning

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Application of Blockchain Technologies to Improve the Quality of Microgrid Network Management

Application of Blockchain Technologies to Improve the Qualit...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Volodymyr Khilenko Halina Bielokha Sergii Denysiuk Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

The use of blockchain technologies and Smart contracts to improve the quality of Microgrid network management is considered. The necessity of using a special mathematical apparatus for system analysis is noted, taking into account the nonlinear nature of dynamic processes in the network and, in particular, the presence of sharply changes in the load. The advantages and disadvantages of the approach known in the literature are analyzed, when the search for the optimal power distribution among generators to ensure the minimum costs of the Microgrid is carried out on the basis of nonlinear programming methods. Graphs of transient processes in the network are given and their corresponding analysis is carried out.

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Smart room for patient monitoring based on IoT technologies 23

Smart room for patient monitoring based on IoT technologies

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Nataly Zhukova Anna Motienko Dmitriy Levonevskiy Alexey Subbotin Pham Tuan Anh Laboratory of big data technologies in socio-cyberphysical systems St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Russia Faculty of Computer Technologies and Informatics St. Petersburg State Electrotechnical University Russia Faculty of Software Engineering and Computer Systems ITMO University Russia

ISBN: (纸本)9798400716225

This article presents a method for building a hospital room with extended intelligent features based on Internet of Things (IoT) technologies. We begin by reviewing existing vision-based health care systems and identifying important and useful functions that they should have. We then present a method for building a smart room that allows increase the level of the intelligence of the existing hospital rooms due to extensive use of the up-to-date machine learning methods. The proposed solution assumes using readily available hardware devices to reduce the cost of smart room building. Compared to commercially available assistive technologies, the proposed smart room turn out to have high level of intelligence and to be very cost effective. The system's non-intrusive nature makes it easy to use it both in hospitals and at home for patient care.

关键词： Internet of Things IoT automation in medicine intellectual room machine learning smart room smart ward

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Application of Parallel Signal Processing Using Reverse Blockchain Technology in Conditions of Network Instability

Application of Parallel Signal Processing Using Reverse Bloc...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Volodymyr Khilenko Andrii Zinchenko Oleksii Stepanov Ivan Kotuliak Michal Ries Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing.

关键词： Analytical models Heuristic algorithms Instruction sets Signal processing algorithms Process control Parallel processing Control systems Smart systems Data models Blockchains

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Autonomous Take-off Control System with Stall Prevention for Fixed-Wing UAVs Utilizing LQR Method

Autonomous Take-off Control System with Stall Prevention for...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ariesta Martiningtyas Handayani Andi Dharmawan Jazi Eko Istiyanto M. Ridho Fuadin Triyogatama Wahyu Widodo Danang Lelono Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Informatics Engineering Vocational School Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

During the launch sequence of a Fixed-wing Unmanned Aerial Vehicle (UAV), the process encompasses several phases: taxiing on the ground, ascending, and transitioning to steady flight. The initiation of take-off is contingent upon a control mechanism’s ability to regulate the UAV’s operations. The control system must keep the Angle of Attack (AoA) within specific limits during ascent to avert a reduction in lift, known as a stall. This study’s autonomous take-off methodology incorporates the Linear Quadratic Regulator (LQR) approach and fuzzy logic principles. The LQR’s calculated gain serves as the foundational input for the UAV’s system, subsequently translated into Pulse Width Modulation (PWM) signals. These signals are responsible for adjusting the brushless motor’s rotation speed and the servo’s angle of deflection. The UAV is programmed to sustain the AoA at a pivotal threshold when operating autonomously. The desired pitch angle is determined through the interplay between the lift generated and the UAV’s critical AoA. Findings from this study demonstrate the UAV’s capability to preserve the AoA at this essential threshold.

关键词： Industrial electronics Fuzzy logic Regulators Prevention and mitigation Pulse width modulation Autonomous aerial vehicles Control systems

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