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Application of Artificial Intelligence in Rock Tunnel engineering: A Survey on Where and How

Expert systems 2025年第7期42卷

作者： Yu, Xiaojie He, Ben-Guo Xu, Xu Zhou, Yicong Diaz, Miguel A. Chen, Junxin Camacho, David School of Software Dalian University of Technology Dalian China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Department of Computer and Information Science University of Macau China Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in-depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in-depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours. © 2025 John Wiley & Sons Ltd.

关键词： Disaster prevention

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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IEEE International Conference on Big Data

作者： Deepti Gupta Lavanya Elluri Avi Jain Shafika Showkat Moni Omer Aslan Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA College of Business University of Tampa Florida USA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida USA Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enabl

关键词： Multi-factor authentication Smart contracts Organizations Real-time systems Regulation Blockchains Security Time factors Risk management Resilience

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Surface Recognition from Wheelchair-induced Noisy Vibration Data: A Tale of Many Cities

Surface Recognition from Wheelchair-induced Noisy Vibration ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Rochishnu Banerjee Md Fourkanul Islam Shaswati Saha Vaskar Raychoudhury Md Osman Gani Department of Computer Science & Software Engineering Miami University Oxford OH USA Department of Information Systems University of Maryland Baltimore County Baltimore MD USA

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.

关键词： Vibrations Deep learning Navigation Wheelchairs Urban areas Neural networks Sensors

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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Editorial: Advanced deep learning approaches for medical neuroimaging data with limitation

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Frontiers in Computational Neuroscience 2023年 17卷 1272448页

作者： Tang, Zhiri Li, Ming Hu, Ruihan Dev, Kapal Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong School of Software Harbin Institute of Technology Harbin China Department of Computer Science Munster Technological University Cork Ireland

This research topic collects the latest works focusing on approaches and analysis for medical neuroimaging data with limitations. With the increasing development of deep learning and artificial intelligence, it also attracts attention from researchers in medical and clinical fields [1]. Adopting deep learning technology in processing medical images and signals and benefiting from the powerful feature extraction ability of convolutional neural networks, deep learning-based artificial medicine has achieved great success in the past few years [2]. However, owing to the difference between natural and medical images, in other words, some specific characteristics of medical images, adopting deep learning to process medical images directly may face numerous problems. Particularly, medical imaging, including neuroimaging data, may acquire data with few annotations, low signal-to-noise ratio, or small experimental samples, resulting in serious performance degradation during data processing [3]. Hence, how to solve these issues and improve the data analysis performance is still a challenge. In recent years, some works have emerged towards these issues in medical neuroimaging data, such as semi-supervised learning [4], weakly supervised learning [5], unsupervised learning [6], transfer learning [7], and so on. In this research topic, there are also some feasible ways for medical neuroimaging data with limitations. In the first article of this topic [8], a Soft-DTW-based single-subject short-distance event-related potential averaging method is introduced for the low signal-to-noise ratio and feature extraction issues of N400 data. Specifically, N400 data is a time-domain EEG feature, which is thought to reflect the information processing ways of human brains. Leveraging the benefits of the differentiable and efficient Soft-DTW loss function, this work also involves performing partial Soft-DTW averaging within a single-subject range, effectively exploiting the advantages of the

关键词： neuroimaging analysis of insufficient data brain diseases data processing data augmentation neuroimaging application

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The Minimum Covariance Determinant Technique for Insider Threat Detection in Communications and Network Security

SSRN

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SSRN 2024年

作者： Al-Shehari, Taher Kadri, Mohammed Al-Mhiqani, Mohammed Nasser Al-Razgan, Muna Alfakih, Taha Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University Riyadh11362 Saudi Arabia Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Insider threats present a critical concern for network security of organizations, as malicious activities (data theft, hacking, security breaching, etc.) perpetrated by insiders with privileged access systems can result in substantial damage. Many approaches are proposed to detect insider threats, but still there are many challenges on the field such as the imbalanced nature of insider threat datasets, where instances of malicious behavior are often limited in comparison to benign activities. This makes insider threat detection an understudied research area. Existing approaches tried to address the imbalance issue of the insider threat dataset by manipulating the data level utilizing the duplication (over-sampling) and removal (under-sampling) of the dataset’s observations which is not reflecting the real-world data stream within organizations’ networks. To tackle this challenge, Artificial Intelligent (AI) algorithms like the Minimum Covariance Determinant (MCD) algorithm is adopted due to its robustness against outliers and its potential to handle imbalanced data effectively. The proposed approach is evaluated using the CERT insider threat dataset, which encompasses diverse real-world scenarios. Through comprehensive experimentation, the MCD algorithm demonstrates promising results, achieving a remarkable detection rate of 98%. This study underscores the significance of leveraging advanced anomaly detection techniques to address imbalanced insider threat datasets and highlights the practical applicability of the MCD algorithm in enhancing detection accuracy of threats within organizations’ networks. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Scalable analysis of stop-and-go waves

arXiv

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arXiv 2024年

作者： Ji, Junyi Gloudemans, Derek Wang, Yanbing Zachár, Gergely Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States The Transportation and Power Systems Division Argonne National Laboratory United States School of Sustainable Engineering and the Built Environment Arizona State University United States Department of Mathematical Sciences Rutgers University Camden United States

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in this data. This paper makes a first step towards addressing this shortcoming by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before;(b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane;(c) wave fronts and tails propagate at different speeds;(d) wave boundaries capture rich and non-trivial wave topologies (with

关键词： Traffic congestion

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Physical Layer Security of Wireless Communications: When Moving Target Defense Meets Unmanned Aerial Vehicles

SSRN

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SSRN 2024年

作者： Ghourab, Esraa M. Jaafar, Wael Naser, Shimaa Muhaidat, Sami Al-Qutayri, Mahmoud Damiani, Ernesto Center for Cyber-Physical Systems Department of Computer and Communication Engineering Khalifa University United Arab Emirates Department of Software and Information Technology Engineering École de Technologie Supérieure MontrealQCH3C 1K3 Canada 6G Research Center Khalifa University United Arab Emirates Center Department of Computer and Communication Engineering Khalifa University United Arab Emirates

In the realm of unmanned aerial vehicle (UAV) communication, the utilization of UAVs as aerial relays for ground nodes (GNs) introduces strategic flexibility, especially in scenarios where ground base stations may experience unforeseen impairments. However, this form of communication is vulnerable to eavesdropping by malicious entities due to the broadcast nature of wireless channels. In this paper, we tackle this problem by introducing a spatiotemporal diversification-based artificial noise (AN) injection strategy, known as moving target defense (MTD), aiming to confuse potential attackers without compromising legitimate communications. The proposed approach targets maximizing the average secrecy rate (ASR) by jointly optimizing the UAV's trajectory and transmit power, aligned with optimizing the MTD transmit power splitting factor between legitimate and AN signals at the GN source. The formulated problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the non-convexity of the secrecy rate. To solve it, we formulate our system as a Markov decision process, and then we propose a novel deep reinforcement learning (DRL)-based approach to enhance the ASR under various system *** results demonstrate the superiority of the proposed algorithm over benchmarks in terms of ASR and intercept probability, showcasing its effectiveness in enhancing the security of UAV-assisted communications. © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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