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arXiv 2023年

作者： Lauditi, Clarissa Troiani, E. Mézard, M. Department of Applied Science and Technology Politecnico di Torino Italy Statistical Physics of Computation Laboratory EPFL Switzerland Department of Computing Sciences Bocconi University Milano Italy

In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning. Statistical physics provides analytical tools to study fundamental limitations in their solutions and proposes algorithms to solve individual instances. In these notes, based on the lectures by Marc Mézard in 2022 at the summer school in Les Houches, we will present a general framework that can be used in a large variety of problems with weak long-range interactions, including the compressed sensing problem, or the problem of learning in a perceptron. We shall see how these problems can be studied at the replica symmetric level, using developments of the cavity methods, both as a theoretical tool and as an algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Statistical Physics

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Analysis of Fraudulent Job Postings Using Machine Learning 5

Analysis of Fraudulent Job Postings Using Machine Learning

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina School of Science Engineering and Environment University of Salford Manchester United Kingdom United Arab Emirates University and Yarmouk University Media and Creative Industries Jordan United Arab Emirates University Media and Creative Industries Department United Arab Emirates Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798350354690

In the age of digital recruitment, the proliferation of fraudulent job postings poses significant challenges for job seekers and legitimate employers alike. These deceptive listings not only waste time and resources but also endanger personal data and propagate scams. Addressing this issue, we present a comprehensive machine learning methodology to accurately discern between genuine and counterfeit job opportunities. Leveraging a rich dataset procured from Kaggle, this paper details the deployment of a logistic regression classifier, judiciously trained on a fusion of textual and meta-features extracted from job advertisements. The classifier underwent rigorous evaluation, manifesting an impressive accuracy of 96.78% in segregating authentic posts from fraudulent ones. The implementation of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization on textual data, alongside meta-features such as job description length, enabled the model to learn and predict with high precision. The implications of this research are substantial, offering a scalable and efficient tool for job platforms to safeguard their users and ensure the integrity of their listings. © 2024 IEEE.

关键词： Logistic regression

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Chernobyl Disaster Optimizer Algorithm for Feature Selection Problems

Chernobyl Disaster Optimizer Algorithm for Feature Selection...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Hisham A. Shehadeh Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid growth of high-dimensional data in Machine Learning (ML) applications presents essential computational challenges. This phenomenon manifests primarily through redundant feature space dimensions, requiring robust dimensionality reduction approaches such as Feature Selection (FS). This research introduces an investigation using the Chernobyl Disaster Optimizer (CDO), a physics-inspired metaheuristic optimization algorithm for solving feature selection problems. We propose a CDO algorithm that utilizes dynamic radiation dispersion mechanisms for wrapper-based feature selection, where solution fitness is evaluated through accuracy metrics obtained from a K-nearest-neighbor (KNN) classifier. Comprehensive empirical analysis across eight UCI repository benchmark datasets shows a satisfactory performance of CDO in classification accuracy and feature selection efficiency with an average of 45.8% compared to competing metaheuristic approaches. Friedman tests, with p-value = 0.0302, confirm the algorithm's significant performance advantages. The results demonstrate that CDO is a reliable optimization algorithm for feature selection tasks, particularly effective in high-dimensional classification problems.

关键词： Dimensionality reduction Measurement Accuracy Heuristic algorithms Disasters Metaheuristics Nearest neighbor methods Feature extraction Classification algorithms Reliability

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Tri-Band Endfire Antenna Array with Wide Angles of Beam-Scanning Capability for 5G mmWave Mobile Communication

Tri-Band Endfire Antenna Array with Wide Angles of Beam-Scan...

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2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023

作者： Zidour, Ali Ayad, Mouloud Alibakhshikenari, Mohammad Basherlou, Haleh Jahanbakhsh Parchin, Naser Ojaroudi See, Chan Hwang University of m'Hamed Bougara Faculty of Technology Department of Electrical Systems Engineering Boumerdes Algeria University of Bouira Faculty of Sciences and Applied Sciences Department of Electrical Systems Bouira Algeria Universidad Carlos Iii de Madrid Leganés Department of Signal Theory and Communications Madrid Spain Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom

ISBN: (纸本)9798350319392

In this paper, a novel multiband phased array antenna with endfire radiation pattern is designed at millimeter-wave (mmWave) frequency bands for 5G mobile applications. The proposed antenna element is a compact quasi-Yagi with a modified dipole driver to generate multiband operation, arranged on low-loss RO4003C materiel. The antenna array achieves excellent performance and covers wide frequency ranges of (24.15-29.8 GHz), (36.2-50 GHz) to support 5G NR bands;26, 28, 39 and the new allocated 48 GHz. The array can exhibit a maximum realized gain from 8.4 dBi to 11.8 dBi. Meanwhile, wide beam-scanning angles from - 68° to 65° at 28 GHz, from 40° to 41° at 39 GHz, and from - 33° to 35° at 48 GHz are realized. © 2023 IEEE.

关键词： Millimeter waves

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Stiffness Measurement of Parallel Kinematic Machines Considering Gravity Effect 19

Stiffness Measurement of Parallel Kinematic Machines Conside...

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19th International Conference on Manufacturing Research, ICMR 2022

作者： Bandara, Shakya Jin, Yan Van, Mien Sun, Dan Fu, Rao Curley, Patrick Higgins, Colm Queen's University Belfast University Road BelfastBT7 1NN United Kingdom Department of Applied Computing University of Kelaniya Sri Lanka Dalian University of Technology Liaoning Province China

ISBN: (纸本)9781643683300

Parallel Kinematic Machine (PKM) is a new type of machine tool, which has the potential to fill in the gap between traditional CNC machines and industrial robots, due to its flexibility and superior motion dynamics. Stiffness is an essential property of a machine tool, as it will affect the machining capability. Although much research has been conducted on stiffness modelling and analysis, most of them employ simplified models and gravity effects have not been well considered or characterized for PKMs. To fill in the knowledge gap, this paper introduces a new experimental stiffness measurement method considering the effect of gravity on the machine tool. An experimental procedure was developed in order to separate the gravity effects on stiffness from the machine structure. © 2022 The authors and IOS Press.

关键词： Stiffness

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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PHYLOGENETIC DIVERSITY INDICES FROM AN AFFINE AND PROJECTIVE VIEWPOINT

arXiv

引用

arXiv 2024年

作者： Moulton, Vincent Spillner, Andreas Wicke, Kristina School of Computing Sciences University of East Anglia UK. Merseburg University of Applied Sciences Germany. Department of Mathematical Sciences New Jersey Institute of Technology USA.

MSC Codes 05C05, 05C20, 05C90, 92D15Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other. Copyright © 2024, The Authors. All rights reserved.

关键词： Conservation

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The Exponentiated Hypoexponential Disribution

arXiv

引用

arXiv 2023年

作者： Nassabein, Anass Kadri, Therrar Kadry, Seifideen Smaili, Khaled Department of Mathematics Lebanese International University Lebanon Department of Science Northwestrern Polytecgnic Canada Department of Applied Computing and Technology Noroff University College Norway Department of Mathematics Lebanese University Lebanon

In this paper we study the Exponentiated Hypoexponential Distribution with different parameters. The distribution added a parameter to the n parameters of the Hypoexponenial distribution. We first derive a closed expression of the probability density function and the cumulative distribution function of Maximum Exponentiated Exponential distribution. These functions are used to determine an exact expression of the probability density function, the cumulative function, reliability function, and hazard function of our Exponentiated Hypoexponential Distribution. We discuss estimation of the parameters by maximum likelihood estimators. The distribution has been fitted to a real life data set and the fit has been found to be a serious competitor to the others. © 2023, CC0.

关键词： Maximum likelihood estimation

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Dataset Evaluation Framework for Secure Electric Vehicle Systems

Dataset Evaluation Framework for Secure Electric Vehicle Sys...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Ghadeer Albesani Ashraf Mashaleh Ammar Almomani Amjad Aldweesh Department of Information Security University of Petra Jordan King Abdullah II School of Information Technology Jordan University Jordan Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Science Higher Colleges of Technology Al-Balqa UAE Applied University Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Intrusion Detection Systems (IDS) play a critical role in safeguarding connected vehicular systems and Electric Vehicle Supply Equipment (EVSE) infrastructures against evolving cyber threats. The effectiveness of these IDS solutions largely depends on the quality and relevance of the datasets used for their training and evaluation. In this paper, we present a systematic framework for assessing datasets intended for IDS development in the automotive and EV charging domains. Our analysis covers essential criteria—such as data completeness, realism, labeling accuracy, and statistical distribution—and provides best practices for evaluating both traditional and novel datasets (e.g., CICIoV2024, CICEVSE2024). We further detail methodologies for intrinsic and extrinsic evaluations, including statistical analysis, time-based assessment, and benchmark testing using machine learning algorithms. Our approach is illustrated through a case study that demonstrates how these criteria and methods can be applied to real-world datasets, highlighting potential pitfalls and improvements. By offering concrete guidelines and metrics for dataset quality, this work aims to advance the development of robust IDS solutions in connected automotive and EV charging environments.

关键词： Training Systematics Statistical analysis Intrusion detection Electric vehicle charging Labeling Computer security Standards Automotive engineering Internet of Vehicles

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REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability 39

REPEAT: Improving Uncertainty Estimation in Representation L...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wickstrøm, Kristoffer K. Brüsch, Thea Kampffmeyer, Michael C. Jenssen, Robert Department of Physics and Technology UiT The Arctic University of Norway Norway Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Norwegian Computing Center Oslo Norway Pioneer Centre for AI University of Copenhagen Denmark

ISBN: (纸本)157735897X

Incorporating uncertainty is crucial to provide trustworthy explanations of deep learning models. Recent works have demonstrated how uncertainty modeling can be particularly important in the unsupervised field of representation learning explainable artificial intelligence (R-XAI). Current R-XAI methods provide uncertainty by measuring variability in the importance score. However, they fail to provide meaningful estimates of whether a pixel is certainly important or not. In this work, we propose a new R-XAI method called REPEAT that addresses the key question of whether or not a pixel is certainly important. REPEAT leverages the stochasticity of current R-XAI methods to produce multiple estimates of importance, thus considering each pixel in an image as a Bernoulli random variable that is either important or unimportant. From these Bernoulli random variables we can directly estimate the importance of a pixel and its associated certainty, thus enabling users to determine certainty in pixel importance. Our extensive evaluation shows that REPEAT gives certainty estimates that are more intuitive, better at detecting out-of-distribution data, and more concise. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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