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Smart World Congress (SWC), IEEE

作者： Gang Xu Di Wu Christina Hellevik Yushan Pan Department of Applied Mathematics Xi’an Jiaotong-Liverpool University China Department of ICT and Natural Sciences Norwegian University of Science and Technology Norway Department of International Business Norwegian University of Science and Technology Norway Department of Computing Xian Jiaotong-Liverpool University China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

When datasets have missing parts, we typically use data characteristics to select a suitable model to generate these missing parts. However, the poor quality of real data often affects the fitting accuracy of the ideal model, leading to lower-thanexpected results. In this paper, we improve the LatticeKrig model by using local basis function spanning. This enhancement aims to improve the model’s fitting ability and robustness to poor quality data while avoiding potential overfitting. In our experiment, we demonstrated the model’s fitting ability with ideal data and used it to fit the missing parts of garbage collection data from the Norwegian coast. The results show that our improved model achieves better fitting performance with non-ideal data.

关键词： Accuracy Fitting Lattices Data models Spatial databases Robustness Overfitting

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AI-Driven Network Forensics Using Hybrid Anomaly Detection with Enhanced Transformer Based ISO-Encoder and LSTM-CNN Fusion

AI-Driven Network Forensics Using Hybrid Anomaly Detection w...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Abisha B. Sheeja Kumari V. Wilfred Blessing N. R Sutherlin Subitha G. Seldev Christopher C. Wasim Haidar SK Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Stella Mary's College of Engineering Affiliated to Anna University Tamil Nadu India Department of Computer Science and Engineering St. Xavier's Catholic College of Engineering Nagercoil Tamil Nadu India College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Traditional network forensic techniques frequently find it difficult to identify and look into sophisticated cybercrimes in today's quickly changing cyber scene. A Transformer-based Iso-Encoder and a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) fusion model are combined in this research to present an AI-driven method that makes use of a novel hybrid anomaly detection model. The LSTM-CNN fusion model performs exceptionally well in temporal and spatial feature extraction, while the Iso-Encoder is improved to capture complex patterns in network traffic. This hybrid approach is intended to accurately detect known and undiscovered anomalies. By offering practical insights into the nature of the dangers, the suggested framework not only helps with the comprehensive investigation of cyber incidents but also identifies anomalies in real-time. The model is a powerful tool for cybersecurity experts since it incorporates cutting-edge AI techniques to guarantee that it adjusts to changing cyberthreats. According to experimental data, the hybrid model maintains a low false positive rate and processing time of 0.5 ms/packet while outperforming current techniques in terms of detection accuracy (98.7%), precision (98.4%), recall (97.8%), and F1-score (98.1%). Traditional models with somewhat poorer performance include Random Forest (94.5% accuracy), Support Vector Machine (SVM) (93.7% accuracy), and LSTM (96.2 % accuracy). This research paves the way for more resilient and intelligent network forensic solutions, enhancing the ability to counteract and investigate complex cyber threats effectively.

关键词： Support vector machines Accuracy Forensics Telecommunication traffic Transformers Real-time systems Computer crime Long short term memory Anomaly detection Random forests

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REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability 39

REPEAT: Improving Uncertainty Estimation in Representation L...

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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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Social Media Threat Intelligence: A Framework for Collecting and Categorizing Threat-Related Data

Social Media Threat Intelligence: A Framework for Collectin...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Obojo, Victor Galadima, Haula Ikuesan, Richard Cyber Threat Intelligence Division CyberDome Nigeria Maitama No. 3 Bobo Close Abuja900271 Nigeria Department of Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031893599

The exponential growth of social media, now encompassing approximately five billion users globally, has transformed these platforms into critical sources of information, mirroring diverse societal interactions. However, this vast data repository also introduces significant threats, including terrorism, online fraud, and the spread of disinformation, underscoring the need for robust monitoring and categorization mechanisms. This study presents an innovative framework designed to systematically collect and categorize social media data using weighted keywords tailored to various threat categories. By leveraging semi-automated data collection and keyword weighting techniques, this framework enhances threat detection accuracy and integrates diverse data collection methods such as APIs, bots, and scraping tools. Preliminary results demonstrate the framework’s efficacy in identifying and categorizing threat-related content, highlighting its potential to significantly advance threat intelligence capabilities. This groundbreaking approach promises to revolutionize social media threat intelligence, equipping organizations with the tools to anticipate emerging threats and bolster national security. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Tweets

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements

Exploring the Synergy of Artificial and Computational Intell...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Prajoona Valsalan Wilfred Blessing N. R S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Electrical and Computer Engineering Department College of Engineering Dhofar Univesity Salalah Sultanate of Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things.

关键词： Computers Planets Internet of Things Artificial intelligence Information technology Computational intelligence Creativity

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Code Refactoring for Software Reusability: An Experimental Study

Code Refactoring for Software Reusability: An Experimental S...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah Almogahed Hairulnizam Mahdin Mazidah Mat Rejab Abdulwadood Alawadhi Samera Obaid Barraood Manal Othman Omar Al-Jamili Abdulwahab Ali Almazroi Shazlyn Milleana Shaharudin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok Malaysia College of Computers and Information Technology Hadhramout University Hadhramout Yemen Computer Science Department College of Applied Science Taiz University Taiz Yemen Faculty of Engineering and Computing University of Science and Technology Yemen Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Refactoring plays a critical role in enhancing the quality of software systems, enabling them to accommodate growing user bases, feature sets, and performance demands. Software reusability is an essential aspect of software quality. It is crucial in modern software development as it offers several benefits, including increased productivity, improved code quality, and cost-effectiveness. However, there is a lack of studies investigating how specific refactoring techniques affect reusability. Therefore, this study aims to explore the effects of code refactoring on software reusability. This study was conducted in four stages: selecting refactoring techniques, selecting a large dataset, selecting software metrics to measure the reusability, and applying the refactoring techniques. The five most common refactoring techniques (Extract Interface, Encapsulate Field, Extract Class, Inline Class, and Inline Method) used in practices were selected and applied in the jEdit dataset. The refactoring techniques were carried out 338 times through five experiments in the jEdit dataset. The finding reveals that Extract Interface, Encapsulate Field, and Extract Class significantly improve the code reusability while Inline Class and Inline Method impair it. Developers and software architects can use this knowledge to make well-informed judgments about refactoring techniques and develop more robust and reusable software systems.

关键词： Productivity Industries Codes Software quality Software systems Maintenance Software measurement

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Threat Assessment Model in Electrical Power Grid Environment 1

Threat Assessment Model in Electrical Power Grid Environment

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1st International Conference on Engineering and technology, ICoEngTech 2021

作者： Qassim, Qais Saif Jamil, Norziana Mahdi, Mohammed Najah Rahim, Fiza Abd Cob, Zaihisma Che Sidek, Lariyah Mohd University of Technology and Applied Sciences Ibri Oman College of Computing and Informatics Universiti Tenaga Nasional Malaysia Institute of Energy Infrastructure Universiti Tenaga Nasional Malaysia

At the heart of every critical national infrastructure, there is a SCADA system. The wide range of supported applications has made this life-saving management system the key element of every industrial control infrastructure in various sectors. The complexity and sophistication of SCADA systems have been increased dramatically over the last few years. Various cyberattacks have targeted these systems by exploiting zero-day vulnerabilities or targeting a nearby Internet-enabled appliance or computer system. To this end, many efforts have been proposed previously to model the threats on SCADA systems to eliminate potential danger and diminish the catastrophic consequences in the physical domain. The main contribution of this work is to present a multi-facets threat assessment model to aid security teams to systematically perform various cybersecurity analysis practices such as penetration testing, vulnerability assessment and risk analysis. © Published under licence by IOP Publishing Ltd.

关键词： SCADA systems

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WOMBAT v2.S: A Bayesian inversion framework for attributing global CO2 flux components from multiprocess data

arXiv

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arXiv 2025年

作者： Jacobson, Josh Bertolacci, Michael Zammit-Mangion, Andrew Schuh, Andrew Cressie, Noel School of Mathematics and Applied Statistics University of Wollongong Australia School of Physics Mathematics and Computing University of Western Australia Australia Colorado State University United States Jet Propulsion Laboratory California Institute of Technology United States

Contributions from photosynthesis and other natural components of the carbon cycle present the largest uncertainties in our understanding of carbon dioxide (CO2) sources and sinks. While the global spatiotemporal distribution of the net flux (the sum of all contributions) can be inferred from atmospheric CO2 concentrations through flux inversion, attributing the net flux to its individual components remains challenging. The advent of solar-induced fluorescence (SIF) satellite observations provides an opportunity to isolate natural components by anchoring gross primary productivity (GPP), the photosynthetic component of the net flux. Here, we introduce a novel statistical flux-inversion framework that simultaneously assimilates observations of SIF and CO2 concentration, extending WOMBAT v2.0 (WOllongong Methodology for Bayesian Assimilation of Trace-gases, version 2.0) with a hierarchical model of spatiotemporal dependence between GPP and SIF processes. We call the new framework WOMBAT v2.S, and we apply it to SIF and CO2 data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) satellite and other instruments to estimate natural fluxes over the globe during a recent six-year period. In a simulation experiment that matches OCO-2’s retrieval characteristics, the inclusion of SIF improves accuracy and uncertainty quantification of component flux estimates. Comparing estimates from WOMBAT v2.S, v2.0, and the independent FLUXCOM initiative, we observe that linking GPP to SIF has little effect on net flux, as expected, but leads to spatial redistribution and more realistic seasonal structure in natural flux components. © 2025, CC BY.

关键词： NASA

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Developing a Competency Framework for Intergenerational Startup Innovation in a Digital Collaboration Setting 13

Developing a Competency Framework for Intergenerational Star...

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13th International Conference on Knowledge Management and Information Systems, KMIS 2021 as part of 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021

作者： Nurhas, Irawan Geisler, Stefan Pawlowski, Jan Institute of Positive Computing Hochschule Ruhr West-University of Applied Sciences Bottrop Germany Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (纸本)9789897585333

This study proposes a framework for the collaborative development of global start-up innovators in a multigenerational digital environment. Intergenerational collaboration has been identified as a strategy to support entrepreneurs during their formative years. However, integrating and fostering intergenerational collaboration remains elusive. Therefore, this study aims to identify competencies for successful global startups through intergenerational knowledge transfer. We used a systematic literature review to identify a competency set consisting of growth virtues, effectual creativity, technical domain, responsive teamwork, values-based organization, sustainable networking, cultural awareness, and facilitating intergenerational safety. The competency framework serves as a foundation for knowledge management research on the global innovation readiness of people to collaborate across generations in the digital age. Copyright © 2021 by SCITEPRESS – Science and technology Publications, *** rights reserved.

关键词： Knowledge management

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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