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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Monolithic Integration of Silicon Quantum Photonics and Electronics in a 45nm SOI CMOS Foundry Platform

Monolithic Integration of Silicon Quantum Photonics and Elec...

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2024 Optical Fiber Communication Conference, OFC 2024

作者： Kramnik, Danielius Wang, Imbert Fargas Cabanillas, Josep M. Ramesh, Anirudh Gluhović, Ðord Buchbinder, Sidney Zarkos, Panagiotis Adamopoulos, Christos Kumar, Prem Popović, Miloš A. Stojanović, Vladimir M. Department of Electrical Engineering and Computer Sciences UC Berkeley BerkeleyCA94720 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Center for Photonic Comm. and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

Silicon photonics can enable useful quantum information processing, but scaling the interface to calibration and control circuits remains challenging. We monolithically integrate quantum photonics with electronics in ... 详细信息

ISBN: (纸本)9781957171326

关键词： Silicon photonics

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Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

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computer Systems Science & engineering 2022年第3期40卷 1023-1042页

作者： Ahad Alloqmani Omimah Alsaedi Nadia Bahatheg Reem Alnanih Lamiaa Elrefaei Computer Sciences Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt

Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer ***,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and *** also compares these methods by the number of iterations and time required to display the *** study helps students learn these methods using interactive learning tools instead of relying on traditional teaching *** tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with *** with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed *** positive results of the user evaluation showed that the tool can be recommended to other users.

关键词： Graphical user interface(GUI) interactive learning tool design principles nonlinear equations experimental design

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Random hyperbolic graphs in d+1 dimensions

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Physical Review E 2024年第5期109卷 054131-054131页

作者： Gabriel Budel Maksim Kitsak Rodrigo Aldecoa Konstantin Zuev Dmitri Krioukov Faculty of Electrical Engineering Mathematics and Computer Science Department of Physics Network Science Institute Department of Computing and Mathematical Sciences Department of Physics Department of Mathematics Department of Electrical & Computer Engineering

We consider random hyperbolic graphs in hyperbolic spaces of any dimension d+1≥2. We present a rescaling of model parameters that casts the random hyperbolic graph model of any dimension to a unified mathematical framework, leaving the degree distribution invariant with respect to the dimension. Unlike the degree distribution, clustering does depend on the dimension, decreasing to 0 at d→∞. We analyze all of the other limiting regimes of the model, and we release a software package that generates random hyperbolic graphs and their limits in hyperbolic spaces of any dimension.

关键词： Network formation & growth Network structure

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Optimisation and Evaluation of Breadth First Search with oneAPI/SYCL on Intel FPGAs: From Describing Algorithms to Describing Architectures 24

Optimisation and Evaluation of Breadth First Search with one...

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12th International Workshop on OpenCL and SYCL, IWOCL 2024

作者： Olgu, Kaan Kenter, Tobias Nunez-Yanez, Jose McIntosh-Smith, Simon School of Electrical & Electronics Engineering University of Bristol United Kingdom Paderborn Center for Parallel Computing Department of Computer Science Paderborn University Germany Dept. Electrical Engineering Linkoping University Sweden

ISBN: (纸本)9798400717901

FPGAs are often used in scientific fields to process graph algorithms due to their energy efficiency, reconfigurability, and fine-grained parallelism. However, these algorithms face challenges in memory access patterns, scalability, and programmability. The SYCL2020 implementation in the Intel oneAPI toolchain supports FPGA targets alongside SYCL2020 features like modern C++ with a single-source offloading to improve programmability. This study analysed the Breadth-First Search algorithm on Stratix 10 FPGA with the Intel oneAPI toolchain. The implementation was done in two phases. At first, we applied the typical optimisations proposed in the official guidelines alongside an automatic cache to achieve proper pipelining and improve random memory accesses performance. However, limitations occurred with fine-grained parallelism, and it was competitive only to some related work that utilised hardware-description languages or established high-level synthesis tools. For the second phase, we added bit-level representations of data in memory, banking in on-chip memory, and fine-grained control over parallel data streams. The second implementation was generally superior or on par with all compared designs, outperforming other works in 10 out of 15 tested datasets, including various synthetic RMAT and real-world datasets, with a peak performance of 1021 MTEPS. © 2024 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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Dynamic Model Reduction for Decentralized Learning in Heterogeneous Mobile Networks 20

Dynamic Model Reduction for Decentralized Learning in Hetero...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Lee, Sangsu Zheng, Xi Hua, Jie Yu, Haoxiang Julien, Christine University of Texas Department of Electrical and Computer Engineering Austin United States Macquarie University School of Computing Australia

ISBN: (纸本)9798350324334

Collaborating with nearby devices to train and personalize deep learning models opens the potential to support new mobile application scenarios. In emerging decentralized learning algorithms, devices communicate over a peer-to-peer network to share knowledge obtained from local data. However, communication bandwidth, computing power, and the duration for which these connections are available are limited and heterogeneous. In this paper, we explore the feasibility and efficacy of adaptive model reduction strategies for decentralized learning algorithms (Dynamic Reduction (DR)). For this study, we use as an exemplar an existing opportunistic learning algorithm (OppCL) that relies on device-to-device model exchanges to iteratively train a local model based on encounters. In layering model reduction on OppCL, when a device encounters a potential learning partner, it dynamically constructs a model reduction suitable for given computation and communication budget by quantizing weights and building a dropout version of a neural network. We term our new approach DR-OppCL and show that DR-OppCL leads to faster convergence with minimal effort in tuning hyperparameters related to model reduction, using both simulated and real-world mobility traces. While we demonstrate our DR approaches in the context of OppCL, they are generic and can be easily applied to other decentralized learning algorithms. © 2023 IEEE.

关键词： Learning algorithms

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Evaluating Large Language Models for Enhanced Intrusion Detection in Internet of Things Networks

Evaluating Large Language Models for Enhanced Intrusion Dete...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nwafor, Ebelechukwu Baskota, Ujjwal Parwez, Md Salik Blackstone, Jeremy Olufowobi, Habeeb Villanova University Department of Computing Sciences PA United States University of Texas at Arlington Department of Computer Science & Engineering TX United States Howard University Department of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350351255

The Internet of Things (IoT) landscape has grown exponentially in recent years, making robust and efficient intrusion detection systems (IDS) even more critical. While Large Language Models (LLMs) have gained significant traction, their effectiveness in network intrusion detection remains largely unexplored. This paper proposes an LLM-based framework for enhanced threat detection and analysis in IoT networks. We explore using advanced LLMs like OpenAI's Generative Pre-trained Transformer (GPT) model, focusing on techniques such as fine-tuning and embedding similarity. Using real-world intrusion datasets, we evaluate the proposed LLM's performance in detecting common network attacks and compare it with ensemble-based IDS solutions. We assess the efficiency of the LLM in binary class and multiclass classification task using standard metrics, such as accuracy, recall, precision, and F1 scores. While the fine-tuning approach does not produce comparable results to the current baseline ensemble-based IDS models, the embedding approach, however, yields comparable results. This analysis represents a starting point for exploring the utilization of advanced large language models for intrusion detection within an IoT ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Smooth Motion Planning of an Omnidirectional Mobile Robot in Dynamic Environments

Smooth Motion Planning of an Omnidirectional Mobile Robot in...

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2022 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Šelek, Ana Seder, Marija Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, a real-time smooth motion planning method for a four mecanum wheeled omnidirectional mobile robot in dynamic environments that generates a smooth collision-free trajectory is proposed. The method employs a graph-search-based path planning algorithm and a smoothing technique based on clothoids. It is suitable for real-time operation due to its computational simplicity and allows fast path replanning in case the robot encounters unknown and dynamic obstacles. Such a path is feasible for holonomic mobile robots since it does not contain sharp turns so the energy consumption is reduced. Our method is compared to the reactive model predictive control scheme in simulations. The comparison showed advantages of the proposed method in the reduced task execution time, path length, cumulative replanning time, and smooth velocity profile. © 2022 IEEE.

关键词： Motion planning

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SVM Machine Learning Model for Detection of Etherlock Vulnerability in Solidity Smart Contracts 6

SVM Machine Learning Model for Detection of Etherlock Vulner...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Almobaideen, Wesam Alnuman, Rashed Sajid, Tayyab Hasan, Qusai Department of Electrical Engineering and Computing Sciences Rochester Institute of Technology Dubai Dubai United Arab Emirates Department of Computer Science The University of Jordan Amman Jordan

ISBN: (纸本)9798350351538

Blockchain is a disruptive technology that changed how financial operations and ownership exist over the internet. With the implementation of smart contracts in blockchains, facilitation of transactions without intermediaries is possible and fully automated. Smart contracts allow relatively safe transactions and transfer of ownership, but as with any piece of software, it proved to have vulnerabilities and challenges. Unfortunately, considering that smart contracts hold and facilitate valuable asset and funds transfer, the damage caused by exploitation of these contracts is devastating with billions of dollars’ worth of cryptocurrency and other assets lost or stolen up until now. One attack on Ethereum smart contracts is the Etherlock attack, which exploits bad logic in the code to stall the transaction indefinitely, effectively sealing user funds. In this paper, we propose a high-accuracy Support Vector Machine model for detection of Etherlock vulnerability in Solidity smart contracts. The model is developed into a standalone tool and achieves 98.44% accuracy accompanied with 87% recall using a generated dataset of Smart Contracts from the Ethereum blockchain. Our promising results still show the necessity of more sophisticated accurate detection models for protection of crypto assets on the blockchain. © 2024 IEEE.

关键词： Smart contract

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A Representation Learning Induced Property Inference Attack on Machine Learning Models for E-Health

A Representation Learning Induced Property Inference Attack ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Bukhari, Moomal Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab University of Nebraska-Lincoln School of Computing LincolnNE68588 United States National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350351255

Privacy concerns have become increasingly prominent as machine learning (ML) models are adopted in an increasing number of sectors. The potential of unintended or malicious exposure of sensitive data, especially in E-Health solutions, has increased as these models are shared and deployed more broadly. In order to highlight the important problem of property inference attacks, which can result in privacy and data confidentiality breaches, this study focuses on inferring global characteristics of the underlying datasets used to train the ML models. Building upon the intriguing work by Ateniese et al. on property inference attacks on ML models, we present a novel property inference attack using Variational Auto-Encoders (VAEs). VAEs offer a strong answer to the difficult problem of inferring dataset attributes because of their reputation for being successful in modeling complex data distributions and producing synthetic data samples. Experiments on three healthcare and the US census datasets show that the proposed attack can effectively reveal underlying patterns in the training dataset with up to 94.29% accuracy. A comparison with the popular meta-classifier based property inference attacks shows that the proposed attack not only has better success rate, but can do so with half training data and a smaller number of shadow models. © 2024 IEEE.

关键词： Electronic health record

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