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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Inie, Nanna Falk, Jeanette Tanimoto, Steve It University of Copenhagen Copenhagen Denmark Dep. of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

ISBN: (纸本)9781450394222

Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. Such technologies potentially introduce a massive disruption to creative fields. This paper presents the results of a qualitative survey (N = 23) investigating how creative professionals think about generative AI. The results show that the advancement of these AI models prompts important reflections on what defines creativity and how creatives imagine using AI to support their workflows. Based on these reflections, we discuss how we might design participatory AI in the domain of creative expertise with the goal of empowering creative professionals in their present and future coexistence with AI. © 2023 Owner/Author.

关键词： computer programming

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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A Bayesian Take on Gaussian Process Networks

arXiv

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arXiv 2023年

作者： Giudice, Enrico Kuipers, Jack Moffa, Giusi Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Division of Psychiatry University College London London United Kingdom

Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network. The model allows the description of continuous joint distributions in a compact but flexible manner with minimal parametric assumptions on the dep.ndencies between variables. Bayesian structure learning of GPNs requires computing the posterior over graphs of the network and is computationally infeasible even in low dimensions. This work implements Monte Carlo and Markov Chain Monte Carlo methods to sample from the posterior distribution of network structures. As such, the approach follows the Bayesian paradigm, comparing models via their marginal likelihood and computing the posterior probability of the GPN features. Simulation studies show that our method outperforms state-of-the-art algorithms in recovering the graphical structure of the network and provides an accurate approximation of its posterior distribution. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning

Deep Feature Extraction Framework Based on DNN for Enhancing...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Hasan Gharaibeh Mohammad Aljaidi Ahmad Nasayreh Qais Al-Na'amneh Ameera S. Jaradat Ghassan Samara Rabia Emhamed Al Mamlook Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Business Administration Trine University Indiana USA Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%.

关键词： Support vector machines Accuracy Machine learning algorithms Botnet Artificial neural networks Feature extraction Boosting

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dep.ndencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Machine Learning-Enhanced Standard Deviation to Detect and Handle of Outlier in the 3D Point-Cloud Data

Machine Learning-Enhanced Standard Deviation to Detect and H...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Mohammad Rasoul Tanhatalab Manuel Forrer Michael Nelz Dep. of Information Engineering and Computer Science Trento University Italy ferrisol Co Graz Austria Nelo Intelligence Eichenau Germany

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Outlier removal is an important step in 3D point cloud data processing. Various methodologies for outlier removal will be analyzed shortly in this paper by describing a range of statistical and machine-learning techniques. Thus, the intent is to analyze different techniques and evaluate their performance in order to justify unique advantages and limitations of each technique. These limitations and advantages advance the discussion by introducing a new approach combining statistical and machine learning techniques on a real-world dataset. It not only identifies the spatial position of outliers but also enhances the measurement precision at those specific points. This work has a lot of significance from many aspects for researchers, engineers, and practitioners. Especially in robotics, industrial processes, augmented reality, and autonomous driving, the nature of 3D point cloud data requires sophisticated outlier handling. We believe our work will serve as a reference to optimize data quality in these dynamic applications under development.

关键词： Point cloud compression Three-dimensional displays Filtering Statistical analysis Service robots Machine learning Signal processing Position measurement Mathematical models Standards

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An Efficient Quantum cryptanalysis of lightweight hash function - PHOTON-Beetle 2

An Efficient Quantum cryptanalysis of lightweight hash funct...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Allu, Swamy Naidu Tentu, Appala Naidu Kancharla, Gangadhara Rao Dep. of Computer Science & Engineering Acharya Nagarjuna University Andhra Pradesh Guntur522510 India CR Rao AIMSCS UoH Campus Telangana Hyderabad500046 India CR Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad500046 India

ISBN: (纸本)9798350340235

Quantum cryptanalysis methods are used to crack the majority of symmetric key ciphers. With this knowledge, cryptologists should be able to create post-quantum symmetric ciphers by incorporating these new quantum cryptanalysis techniques into their toolkit. Secure communication is crucial for applications related to the Internet of Things (IoT) to avoid cyberattacks. Cryptographic hash functions can be used to protect data integrity. The development of large-scale, fault-tolerant quantum computers is the most significant challenge. Recent techniques to attack symmetric key schemes can be achieved by using the Pre-image attack method, Simon’s algorithm, or by combining Simon’s and Grover’s algorithms. In this study, we explored some of the most recent methods for quantum cryptanalysis of symmetric key ciphers. Firstly, we discussed the design of quantum cryptanalysis methods, along with the advantages over classical procedures. In this paper, we illustrate the implementation of the NIST-standard lightweight hash function PHOTON-Beetle on a Quantum computer (using IBM Qiskit), which is mostly used in IoT devices. Efficient quantum implementation of PHOTON-Beetle is proposed in this paper. This paper calculates the quantum cost of PHOTON-Beetle for every layer and compares it with [1]. © 2023 IEEE.

关键词： IoT PHOTON-Beetle Post-quantum cryptography Quantum algorithms Quantum cryptanalysis Symmetric cryptanalysis

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Machine learning algorithms in Bigdata Analysis and its applications: A review 4

Machine learning algorithms in Bigdata Analysis and its appl...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khoshaba, Farah Kareem, Shahab Awla, Hoshang Mohammed, Chnar Erbil Polytechnic University Technical Engineering College Information System Engineering Department Erbil Iraq Lebanese French University College of Engineering and Computer Science Dep. of Information Technology Kurdistan Erbil Iraq Soran University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9781665468350

A wide range of disparate variety of heterogeneous and even disparate data sources has been integrated into the computer science research principles through the assistance of Artificial Intelligence (AI) and Machine Learning (ML) delivering excellent results in accuracy and data quality. Even so, taking machine learning approaches to very broad and dynamic datasets is both logistically and computationally costly. Some people are generating so much data regularly, which means that advanced analytics platforms have greater importance than ever before. Spark Machine Learning has extensive capabilities for a variety of ML activities such as regression, grouping, dimension reduction, and extraction of rules. While it has numerous datasets of high quality and consistency of structure. The specification of the MapReduce programming model was intended for the massive density of large-volume computation. It accomplishes this by splitting the job into several well-defined sub-sized units. There are various sets of data mining frameworks that link to a distributed device. Hadoop serves as the infrastructure for the distributed system. Several resources claim that Mahout will scale to handle massive datasets. This article discusses and demonstrates the use of algorithms and their significant impact on Bigdata, as well as a comparison between their implementations among Apache Spark and Apache Mahout. © 2022 IEEE.

关键词： Big data

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Biological age estimation from ECG using Artificial Intelligence approaches

Biological age estimation from ECG using Artificial Intellig...

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Biomedical engineering Conference (JIBEC), Jordanian International

作者： Jana Sleiman Mohamad Daher Mohamad Khalil Ahmad Diab Abdallah Kassem Computer and Communications Engineering Lebanese University Tripoli Lebanon Dep. of Computer Science/Dep. of Computer Science & Information Technology Faculty of Science/Faculty of Applied Sciences & Technology Tripoli Lebanon Electrical Engineering Lebanese University Tripoli Lebanon Biomedical Engineering Lebanese International University Beirut Lebanon ECCE Dept. Faculty Of Engineering Notre Dame University Zouk Mosbeh Lebanon

ISBN: (数字)9798331539542

ISBN: (纸本)9798331539559

This paper explores the application of Artificial Intelligence (AI) techniques in healthcare, specifically focusing on electrocardiogram (ECG) data analysis and biological age estimation. The study begins with an overview of ECG as a diagnostic tool and discusses the integration of Deep Learning (DL) models, particularly Convolutional Neural Networks (CNNs), in healthcare for various applications. Previous studies reviewed in this context consistently identify CNNs as the superior model for ECG analysis and prediction tasks. The primary research investigates the use of Machine Learning (ML) algorithms to estimate biological age from ECG data, employing metrics such as Mean Absolute Error (MAE) and variance to evaluate model performance. Subsequently, DL models are implemented and compared, demonstrating superior accuracy in estimating the biological age of new patients. While both ML and DL approaches were initially explored, this paper primarily focuses on deep learning methodologies due to their demonstrated superiority in healthcare applications. The findings highlight the efficacy of deep learning over traditional ML methods, underscoring its potential for enhancing diagnostic accuracy and personalized patient care. For instance, the most accurate deep learning model, the Convolutional Neural Network (CNN), achieved a Mean Absolute Error (MAE) of 1.49 and an R 2 score of 0.53, whereas the best-performing traditional ML model, Random Forest, achieved an MAE of 1.54 and an R 2 score of 0.55. These results clearly indicate the advantage of deep learning models in capturing complex patterns in ECG data, leading to more precise and reliable estimations of biological age.

关键词： Deep learning Maximum likelihood estimation Accuracy Data analysis Biological system modeling Medical services Electrocardiography Data models Convolutional neural networks Random forests

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Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-computer Interface Technology

Secure Password Using EEG-based BrainPrint System: Unlock Sm...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Alkhyeli, Zuwaina Alshehhi, Ayesha Alhemeiri, Mazna Aldhanhani, Salma Albalushi, Khalil Alnuaimi, Fatima Ali Belkacem, Abdelkader Nasreddine UAE University CIT Dep. Computer Science and Software Engineering Al Ain United Arab Emirates UAE University CIT Dep. Information Systems and Security Al Ain United Arab Emirates United Arab Emirates University CIT Dep. Computer & Network Engineering Al Ain United Arab Emirates Autonomous Robotics Research Center TII Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665468190

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for individuals with mobility impairments. We used the P300-based BCI controlling paradigm which dep.nds on reading the electrical brain activity of the user when observing a particular object. The other feature of the system is that it can extract unique features of each individual brain to produce a network that uniquely identifies them, which is used as a security layer. Users need to enter their unique network to access their device with failed attempts requiring an EEG test to identify the user. The system plays an active role in facilitating user processes for authentication while accessing devices. The system establishes an urgent call whenever the user's brain currents command it to. The project outcomes were assessed by simulating the BCI before real-time implementation to determine errors and resolve the validity of the project scope. © 2022 IEEE.

关键词： Brain computer interface

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