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IEEE Symposium (IUS) Ultrasonics

作者： Denys Iablonskyi Carlos-Omar Rasgado-Moreno Madis Ratassepp Arto Klami Edward Hæggström Ari Salmi Department of Computer Science Department of Physics University of Helsinki Helsinki Finland Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn Estonia Department of Computer Science University of Helsinki Helsinki Finland Department of Physics University of Helsinki Helsinki Finland

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals.

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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Exploring the Use of WebAssembly in HPC

arXiv

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arXiv 2023年

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Chair of Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian Institute of Information Technology Kerala Kottayam India

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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Enhancing OSN Security: Detecting Email Hijacking and DNS Spoofing Using Energy Consumption and Opcode Sequence Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 333-354页

作者： Rawat, Romil Borana, Kamal Gupta, Shweta Ingle, Mandakini Dibouliya, Ashish Bhardwaj, Purvee Rawat, Anjali LabGeoInf Research LABoratory in GEOmatics and INFormation systems Rome Italy Department of CSE SVIIT SVVV Indore India Indore India Data Architecture Webster Bank United States Rabindranath Tagore University MP Bhopal India Department of Computer and Communication Technology University of Extremadura Spain

The rapid increase in automation within Online Social Networks (OSNs) has led to a surge in cyber threats, notably Email Hijacking and DNS (Domain Name System) Spoofing, which leverage malicious scripts to manipulate traffic, steal credentials, and evade detection. Traditional security mechanisms fail to effectively identify such automation-based attacks, necessitating an advanced detection framework. Objective & Purpose-This study introduces the Automated Social Network Attack Detection Model (ASNADM), which combines Energy Consumption Footprint (EComp-FP) Analysis and Automated Software Opcode Sequence Analysis (ASOSA-OSM- opcode sequence mining) for high-precision OSN security. EComp-FP detects deviations in power consumption linked to malicious automation tools, while ASOSA-OSM analyzes opcode sequences to differentiate between benign and attack behaviors. The Self-Adaptive Fuzzy Pattern Matching Clustering (SAFPMC) Algorithm enhances classification accuracy, reducing false alarms and improving real-time threat detection. Methodology and Dataset-The model was rigorously evaluated using the SPEMC-15K-E (Spam Email Classification dataset in English) dataset (15,000 samples: 7,500 benign, 7,500 malicious). EComp-FP achieved 99.87% accuracy with a 1.4W power deviation, while ASOSA-OSM attained 99.81% accuracy, detecting automation tools with an Opcode Frequency Variance (OFV) of 8.7 in malicious samples versus 3.5 in benign ones. The hybrid EComp (Energy Consumption) + OSA (Opcode Sequence Analysis) model outperformed both standalone methods, achieving 99.93% accuracy, 99.91% F1-score, a false positive rate of just 0.07%, and a false negative rate of 0.05%. Among classifiers, the Self-Adaptive Soft Fuzzy C-Means (SSFCM) Hybrid model achieved the highest performance, with 99.93% accuracy, 99.85% precision, 99.9% recall, and the lowest misclassification rate of 0.05%, surpassing Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Mach

关键词： Tweets

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Countering DoS Threats in WSN: Inventive Machine Learning Strategies with a Minimalistic Twist

Countering DoS Threats in WSN: Inventive Machine Learning St...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Senthilnathan Chidambaranathan Vanitha C.S. Sasireka Naduvathezhath Nessariose Jose K. Madhusudhana Rao T. Lakshmi Devi Architecture and Design Virtusa Department of Computer Science and Engineering Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering SRM Institute of Science and Technology Regent Strategic Solutions Rochester USA KKR&KSR Institute Of Technology & Science Guntur A.P Department of Data Science Siddhartha Institute of Engineering and Technology (SIET) (A UGC Autonomous Institute) Hyderabad Telangana India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes.

关键词： Wireless sensor networks Renewable energy sources Scalability Machine learning Telecommunication traffic Network security Denial-of-service attack

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IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

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Discover Cities 2024年第1期1卷 1-34页

作者： Omrany, Hossein Al-Obaidi, Karam M. Hossain, Mohataz Alduais, Nayef A. M. Al-Duais, Husam S. Ghaffarianhoseini, Amirhosein School of Architecture and Built Environment The University of Adelaide Adelaide Australia Department of the Natural and Built Environment College of Social Sciences and Arts Sheffield Hallam University Sheffield UK Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Architecture Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia School of Future Environments Auckland University of Technology Auckland New Zealand

Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

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Application of Digital Twin in Smart Battery Electric Vehicle: Industry 4.0

Application of Digital Twin in Smart Battery Electric Vehicl...

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2022 International Conference on IT and Industrial Technologies, ICIT 2022

作者： Shoukat, Muhammad Usman Yan, Lirong Du, Changqing Mair Raza, Muhammad U Adeel, Muhammad Khan, Tauqeer Wuhan University of Technology School of Automotive Engineering Wuhan430070 China Shenzhen University College of CS and SE Shenzhen518060 China Punjab Tianjin Univ. of Technology Department of Architecture Lahore53720 Pakistan Islamia College University Peshawar Department of Computer Science Peshawar25120 Pakistan

ISBN: (数字)9781665489454

ISBN: (纸本)9781665489454

It is believed that using electric vehicles (EVs) for transportation is essential for addressing environmental and sustainable development challenges. Current ecofriendly concerns, such as the fast depletion of fossil fuels, rising air pollution, rising energy demand, global warming, and climate change, have made it possible to electrify the transportation industry. EVs can address all of the aforementioned issues. For electric vehicles (EVs), and particularly those powered by lithium-ion (Li-ion) batteries, portable power supplies have become indispensable. Although Li- ion batteries have been the focus of EV research for a long time, issues like battery aging and safety have yet to be fully understood. According to our current understanding of smart technology, we have the ability to use the digital twin (DT) to overcome a problem that has been holding back battery development, as well as the preliminary DT applications in complicated systems such as industry 4.0. This research focuses on the characteristics of batteries and how they relate to their modeling, state estimation, remaining usable life prediction, safety, and control. We put together an analysis of some of the most current achievements in battery prognostics and health monitoring. Finally, we provide prospects for the development of DTs in the field of EV batteries. © 2022 IEEE.

关键词： Lithium-ion batteries

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

引用

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

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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Optimization of antique assembly building frame construction based on BIM technology and Matlab genetic algorithm

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Zhang, Han Ping, Hongming School of Civil Engineering and Architecture Henan University of Science and Technology Henan Luoyang471000 China Department of Electrical and Computer Engineering University of Washington SeattleWA98101 United States

With the rapid development of science and technology, the traditional construction industry is facing the urgent need for transformation and upgrading, and how to achieve the performance optimization of the assembly building frame has become an urgent problem to be solved. The research constructs the antique assembly building frame using BIM technology and uses a Matlab genetic algorithm to optimize the model of the building frame. The efficacy of the optimized assembly building is analyzed, and the safety performance of the BIM-Matlab frame building proposed in this paper is compared with that of the traditional frame building and the anti-buckling frame building to explore the effectiveness of the performance optimization of the BIM-Matlab frame building. The values of the structural non-lateral stiffness ratio of the BIM-Matlab assembly building are all greater than or equal to 1, and the seismic shear weight ratio meets the requirements. The displacement ratio and inter-story displacement ratio meet the code requirements under all working conditions. The building foundation's zero stress zone is 0.00%, and the minimum stiffness-to-weight ratio value of 38.69 is enough to pass the overturning resistance and stability test. The BIM-Matlab framed building can endure earthquakes with greater intensity and safety reserves than conventional framed buildings and anti-flexural framed buildings, which are more secure in earthquakes. © 2024 Han Zhang et al., published by Sciendo.

关键词： Genetic algorithms

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Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

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Intelligent and Converged Networks 2021年第1期2卷 16-29页

作者： Yu Zhou Hatim Alhazmi Mohsen H.Alhazmi Alhussain Almarhabi Mofadal Alymani Mingju He Shengliang Peng Abdullah Samarkandi Zikang Sheng Huaxia Wang Yu-Dong Yao Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA the College of Information Science and Engineering Huaqiao UniversityXiamen 361021China the College of Engineering Architecture and TechnologyOklahoma State UniversityStillwaterOK 74078-1010USA

Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive *** tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol *** this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular ***,we utilize deep learning methods in those identification and classification *** evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

关键词： cellular system deep learning signal classification spectrum awareness Convolutional Neural Network(CNN)

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