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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Recycled Polycarbonate and Polycarbonate/Acrylonitrile Butadiene Styrene Feedstocks for Circular Economy Product Applications with Fused Granular Fabrication-Based Additive Manufacturing

SSRN

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SSRN 2023年

作者： Romani, Alessia Levi, Marinella Pearce, Joshua M. Dep. of Electrical & Computer Engineering Western University LondonON Canada Dep. of Chemistry Materials and Chemical Engineering "Giulio Natta" Politecnico di Milano Piazza Leonardo da Vinci 32 Milano20133 Italy Design Department Politecnico di Milano Via Durando Milano20158 Italy Ivey Business School Western University LondonON Canada

Distributed recycling and additive manufacturing (DRAM) holds enormous promise for enabling a circular economy. Most DRAM studies have focused on single thermoplastic waste stream. This study takes three paths forward from the previous literature: 1) expanding DRAM into high-performance polycarbonate/ acrylonitrile butadiene styrene (PC/ABS) blends, 2) extending PC/ABS blend research into both recycled materials and into direct fused granular fabrication (FGF) 3-D printing and 3) demonstrating the potential of using recycled PC/ABS feedstocks for new applications in circular economy contexts. A commercial open source large-format FGF 3-D printer was modified and used to assess the different printability and accuracy of recycled PC and PC/ABS. The mechanical properties (tensile and impact) following the ASTM D638 and D6110-18 standards were quantified. A weather simulation test (ASTM D5071-06) was performed to assess outdoor performance. Finally, two applications in sporting goods and furniture were demonstrated. In general, better printability was achieved with recycled PC/ABS compared to recycled PC, as well as good dimensional accuracy at printing speeds of 30 and 40 mm/s. Minimal qualitative differences and discoloration were visible on the samples after accelerated weather exposure, with results in accordance with the state-of-the-art. The rPC/ABS results from tensile tests show similar values to those of rPC for elastic modulus (2.1 ± 0.1 GPa), tensile strength (41.6 ± 6.3 MPA), and elongation at break (2.8 ± 0.9 %), which are also comparable with previous studied virgin 3-D printed filaments. Similarly, impact energy (115.78 ± 24.40 kJ/m2) and resistance values (810.36 ± 165.77 J/m) are comparable in the two tested formulations, reaching similar results compared to FFF 3-D printed filaments, as well as virgin materials for injection molding. Finally, the two demonstration products in the sporting goods and furniture sectors were successfully fabricated with rP

关键词： 3D printing

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Enhancing Industrial 4.0 Connectivity: A D2D-Based Algorithm for Blind Spot Mitigation in 5G Future Networks Enabled Smart Industry

Enhancing Industrial 4.0 Connectivity: A D2D-Based Algorithm...

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IEEE Globecom Workshops

作者： Muhammad Farhan Khan Adeel Iqbal Atif Shakeel Adnan Rashid Dirk Pesch Confirm Centre for Smart Manufacturing University College Cork Cork Ireland Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan Dep. of Electrical & Information Engineering Politecnico di Bari Bari Italy

The Industry 4.0 is characterized by the integration of advanced and emerging technologies such as Fifth Generation (5G) and Next-Generation Networks & beyond, Artificial Intelligence (AI), Internet-of- Things (IoT), Machine Learning (ML), Robotics, Automation, Blockchain, Cloud Computing, Edge Computing, Augmented Reality (AR), Virtual Reality (VR) and many others. Despite the widespread adoption of 5G, Wireless Fidelity (Wi-Fi) routers, and other wireless technologies in the industry, there are still areas with limited or no coverage, commonly known as blind spots or coverage holes. The Device-to-Device (D2D) communication enables direct communication without a central entity, known as a coordinator or base station. By leveraging D2D communication, IoT nodes can serve as relays to deal with blind spots. In this research, we propose a novel D2D communication-based algorithm to cope with the challenge of blind spots in IoT -based smart industries. The algorithm focuses on nodes situated in blind spots by establishing connections to the network through nearby IoT nodes that function as relays. We demonstrate through simulations that our approach can effectively remove blind spots, improve coverage, increase throughput, and enhance overall system efficiency.

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Reducing Overconfidence Predictions for Autonomous Driving Perception

arXiv

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arXiv 2022年

作者： Melotti, Gledson Premebida, Cristiano Bird, Jordan J. Faria, Diego R. Gonçalves, Nuno Federal Institute of Espirito Santo Brazil The Dep. of Electrical and Computer Engineering University of Coimbra-Portugal Portugal Department of Computer Science Nottingham Trent University United Kingdom School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Dep. of Electrical and Computer Engineering University of Coimbra-Portugal Portuguese Mint and Official Printing Office Lisbon Portugal

In state-of-the-art deep learning for object recognition, Softmax and Sigmoid layers are most commonly employed as the predictor outputs. Such layers often produce overconfidence predictions rather than proper probabilistic scores, which can thus harm the decision-making of 'critical' perception systems applied in autonomous driving and robotics. Given this, we propose a probabilistic approach based on distributions calculated out of the Logit layer scores of pre-trained networks which are then used to constitute new decision layers based on Maximum Likelihood (ML) and Maximum a-Posteriori (MAP) inference. We demonstrate that the hereafter called ML and MAP layers are more suitable for probabilistic interpretations than Softmax and Sigmoid-based predictions for object recognition. We explore distinct sensor modalities via RGB images and LiDARs (RV: range-view) data from the KITTI and Lyft Level-5 datasets, where our approach shows promising performance compared to the usual Softmax and Sigmoid layers, with the benefit of enabling interpretable probabilistic predictions. Another advantage of the approach introduced in this paper is that the so-called ML and MAP layers can be implemented in existing trained networks, that is, the approach benefits from the output of the Logit layer of pre-trained networks. Thus, there is no need to carry out a new training phase since the ML and MAP layers are used in the test/prediction phase. The Classification results are presented using reliability diagrams, while detection results are illustrated using precision-recall *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Polarimetric compressed sensing with hollow, self-assembled diffractive films

arXiv

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arXiv 2024年

作者： Feng, Ji Perry, Altai J. Weng, Xiaojing González-Alcalde, Alma K. Arteaga, Oriol Mencagli, Mario J. Vuong, Luat T. Department of Mechanical Engineering University of California at Riverside RiversideCA92521 United States Dep. Física Aplicada Plat Group IN2UB Universitat de Barcelona Barcelona08028 Spain Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Sensing light’s polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder. These nanostructures scatter light into lattice modes, which encode the wavefront direction and the polarization ellipticity in the linearly-polarized components of the diffracted, interference patterns. Combining optical encoders with a neural network, the system predicts pointing and polarization when the interference patterns are adequately sampled. A comparison of "ordered" and "random" optical encoders shows that the latter both blurs the interference patterns and achieves higher resolution. Our work centers on the unexpected modulation and spatial multiplexing of incident light polarization by self-assembled hollow nanocavity arrays as a class of materials distinct from traditional metasurfaces that will not only enable encoding for polarization and optical computing but also for compressed sensing and imaging. © 2024, CC BY-NC-ND.

关键词： Wavefronts

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An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents

arXiv

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arXiv 2021年

作者： Monaci, Marta Agasucci, Valerio Grani, Giorgio Sapienza University of Rome Dep. of Computer Science Control and Management Engineering Rome Italy Sapienza University of Rome Dep. of Statistics Rome Italy

In this work, we present a method that applies Deep Reinforcement Learning, an approximate dynamic programming procedure using deep neural networks, to the job shop scheduling problem (JSSP). The aim is to show that a greedy-like heuristic trained on a subset of problems, can effectively generalize to some extent to unseen instances, and be competitive compared to other methods. We model the JSSP as a Markov Decision Process and we exploit the efficacy of reinforcement learning to solve the problem. We adopt an actor-critic scheme based on policy gradients, specifically the Proximal Policy Gradient method, where the action taken by the agent is influenced by policy considerations on the state-value function. The procedures take into account the challenging nature of JSSP, where the state and the action space change for every instance and after each decision. To tackle this variability, we introduced a novel model based on two incident Long-Short Term Memory networks, followed by an encoding model, different in structure for both the actor and the critic. Experiments show the algorithm reaches good solutions in a short time, proving that is possible to generate new greedy heuristics just from learning-based methodologies. We compared our algorithms against several established heuristics, an adaptive method, a commercial solver based on branch and cut, and another approach based on Deep Reinforcement Learning, proving the validity of the proposed method in terms of time and makespan. The model can generalize, to some extent, to larger problems originating from a different *** Codes 90-08 © 2021, CC BY.

关键词： Reinforcement learning

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DPFTT: Distributed Particle Filter for Target Tracking in the Internet of Things

DPFTT: Distributed Particle Filter for Target Tracking in th...

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International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN)

作者： Sahar Boulkaboul Djamel Djenouri Miloud Bagaa CERIST Research Center Algiers Algeria Dep. of Computer Science and Creative Technologies CSRC University of the West of England (UWE) Bristol UK Department of Electrical and Computer Engineering Universit du Qubec Trois-Rivires Trois-Rivires QC Canada

A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.

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Deep Learning Technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： M. Bheemalingaiah R. Srinivasan V. Vijey Nathan Raju Kolluri Amit Gupta Department of CSE J.B Institute of Engineering and Technology Hyderabad Telangana Department of CSE School of Computer Science and Engineering Galgotia University U.P India Department of CSE SRM TRP Engineering College Tiruchirappalli Tamilnadu India Department of ECE Narasaraopet Engineering College Narasaraopet Andhra Pradesh India Dep. of AI & ML J.B Institute of Engineering and Technology Hyderabad Telangana

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology.

关键词： Training Uncertainty Filters Image color analysis Supervised learning Digital cameras High dynamic range Convolutional neural networks Image reconstruction Painting

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Machine Learning Approach to Detection of Offensive Language in Online Communication in Arabic 1

Machine Learning Approach to Detection of Offensive Language...

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1st IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic control and computer engineering, MI-STA 2021

作者： Alakrot, Azalden Fraifer, Muftah Nikolov, Nikola S. The College of Medical Technology Department of Information Technology Yefren Libya The College of Industrial Technologies-CIT Department of Electronic Engineering Misurata Libya University of Limerick Dep. of Computer Science and Information System Limerick Ireland

ISBN: (纸本)9781665418560

This paper presents the results of several machine learning experiments, conducted with a dataset of YouTube comments in Arabic. The experiments aim at studying the impact of various text preprocessing, feature-extraction and feature-selection techniques on the accuracy of a document classifier for detection of offensive language in online communication in Arabic. Regarding data pre-processing, our experiments focus on filtering out noisy characters and normalising inconsistencies present in casual online writing in Arabic. The combined effect of these data preprocessing techniques and a few feature-extraction and feature-selection methods is then evaluated by training document classifiers. Our results give evidence that it is possible to train a classifier for the detection of offensive language on Arabic social media with reasonable overall accuracy of 0.84, and precision, recall and F1-score of 0.89, 0.76 and 0.81, respectively. © 2021 IEEE.

关键词： Support vector machines

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Internet of Things Protocols (IoTp) Literature Review 23

Internet of Things Protocols (IoTp) Literature Review

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Ali Alamery Mohanad Abd Shehab Omar Abdulkareem Mahmood Yousif I Hammadi Ahmed Aziz Mohammed Saleh Ali Muthanna Balad Al-hadeth group company Balad Al-hadeth group company Iraq Electrical Engineering Department Mustansiriyah University Iraq Department of Communications Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Institute of Computer Technologies and Information Security Southern Federal University Russia

ISBN: (纸本)9798400709036

Considering today's variety of technologies and devices connected to the network, a huge number of manufacturers, there are many problems of their interaction and the need to create and adopt specialized communication standards and protocols. This article is devoted to the description of various protocols proposed by the IEEE, IETF and ITU for use in the framework of the concept of the Internet of Things. The article includes a description of the protocols of the application, transport, network and data link layers of the TCP / IP model. It also discusses protocols designed specifically to meet IoT requirements. A brief description of the protocols and the principle of operation of each of them are given. This review article contributes to a more correct choice of protocols when building a network, detecting and managing devices, and establishing interaction between objects of the Internet of Things.

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