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Cyber Resilience (ICCR), International Conference on

作者： Shamma Alshebli Chan Yeob Yeun Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Computer Science C2PS Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The widespread use of mobile payment systems such as Apple Pay, Google Pay, and Samsung Pay has led to them becoming a prime target for malicious attacks. Consequently, concern regarding their safety has risen, and demands for securing and protecting them against such attacks have increased. Thus, this paper surveys recent literature and research published on this topic in the last five years. A thorough analysis and comparison of the selected papers is carried out, with the secure electronic transaction (SET) protocol acting as a benchmark for the proposed attacks and solutions. Then, we offer recommendations and suggestions for future work based on the observations and findings of our analysis and conclude the survey. The purpose of this survey is to illustrate the current state of this research field, serve as a starting point for future research, and steer efforts toward any noticed gaps.

关键词： Surveys Protocols Online banking Reviews Benchmark testing Non-repudiation Safety

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EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

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Neural Computing and Applications 2024年第26期36卷 16141-16161页

作者： Al-Gaashani, Mehdhar S. A. M. Muthanna, Ammar Chelloug, Samia Allaoua Kumar, Neeraj School of Resources and Environment University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Sichuan Chengdu610056 China 6 Miklukho-Maklaya St Moscow117198 Russia Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of Telecommunications 6 Miklukho-Maklaya St Saint Petersburg Saint Petersburg193232 Russia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh610101 Saudi Arabia Department of Computer Science Thapar Institute of Engineering and Technology Patiala147004 India

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. © The Author(s), under exclusive licence to Springer-

关键词： Image classification

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Computation of Mechanical Properties of Earthen Matrix Reinforced by Straw Fibers Using 3D Modeling 4

Computation of Mechanical Properties of Earthen Matrix Reinf...

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4th International Conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Regragui, Soukaina Bousfia, Mohammed El Mabchour, Fatima-Ezzahra Taha Janan, Mourad Aboussaleh, Mohamed Zemmouri, El Moukhtar Rabat Morocco Ensam Moulay Ismail University Team M2I Laboratory of Sciences and Engineering Meknes Morocco Ensam Moulay Ismail University Department of Mathematics and Computer Science Meknes Morocco

ISBN: (纸本)9798350311112

Over the past decades, there has been an increasing interest to raw earth as an eco-construction material for its local character and the low energy consumption that characterizes its global life cycle. However, its relatively low mechanical resistance and its sensitivity to water block the development of this material. The aim of the present work is to raise the mechanical properties of earthen bricks by adding straw fibers. Thus, a 3D model is developed under Catia V5, and then imported to Ansys software to carry out the tensile test taking into account the boundary conditions and the computation hypotheses in order to calculate the Young's modulus and the Poisson's ratio of the earth-straw composite material. The obtained results were in line with those deducted from the 2D investigation and the existing theoretical models. They show an improvement in the mechanical behavior of the homogenous material. © 2023 IEEE.

关键词： Elastic moduli

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Unseating Risk of a Continuous PC Box-Girder Bridge: Experimental Monitoring and Numerical Analysis

Unseating Risk of a Continuous PC Box-Girder Bridge: Experim...

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International Symposium of the International Federation for Structural Concrete, fib Symposium 2023

作者： De Domenico, Dario Messina, Davide Cavallaro, Angelo Recupero, Antonino Department of Engineering University of Messina Messina98166 Italy AC2 s.r.l Zafferana Etnea Catania95019 Italy Department of Mathematics and Computer Science Univ. of Catania Catania95131 Italy

ISBN: (纸本)9783031325106

A peculiar problem occurred in the Furiano viaduct, a continuous prestressed concrete (PC) box-girder bridge (890 m long) of the A20 highway in Sicily, Italy. This viaduct is exhibiting large sliding displacements on the bearings pertinent to the "movable abutment", almost approaching the maximum allowable stroke (in relationship to the installed plate) and causing the rupture of the overlying expansion joint. This accident was plausibly due to an erroneous placement of the bearings in the construction phase. The viaduct was then closed to the traffic as a precautionary measure, and a monitoring system, consisting of temperature and displacement sensors providing synchronous measurements, was implemented in situ. In this paper, we shed light on the technical causes of the expansion joint collapse, and we investigate the unseating risk (i.e., the bearings fall beyond the support plate) via experimental data accompanied by analytical evaluations of long-deform deformation phenomena (shrinkage and creep) and comparisons with numerical thermal-induced movements obtained from a finite element model incorporating the mild curvilinear configuration and the statically indeterminate nature of the bridge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Box girder bridges

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SMDF: Spatial Mass Distribution Features and Deep Learning-Based Technique for Human Activity Recognition

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SN computer science 2024年第1期5卷 129页

作者： Raychaudhuri, Amlan Maity, Satyabrata Chakrabarti, Amlan Bhattacharjee, Debotosh Department of Computer Science and Engineering B. P. Poddar Institute of Management and Technology 137 VIP Road Kolkata-52 India Department of Information Technology Techno International New Town 1/1 Service Rd. DG Block New Town Kolkata-156 India A. K. Choudhury School of Information Technology University of Calcutta JD-2 JD Block Sector-III Kolkata-106 India Department of Computer Science and Engineering Jadavpur University 188 Raja S. C. Mallick Road Kolkata-32 India

Automatic detection of human activity is one of the growing research areas due to the wide range of applications like elderly and patient monitoring for ambient assistive living, visual surveillance, etc. This paper presents a novel bi-channel deep learning model to recognize several human daily living activities. One channel includes activity classification using key poses, based on the spatial mass distribution feature (SMDF). The key poses of all the training classes are estimated using the K-means algorithm on the SMDF of the corresponding silhouette images. Similarly, the key poses of a testing video are extracted, and the k-nearest neighbor (KNN) technique is used thereafter to determine the activity classes. The other channel utilizes VGG-16 to classify human activity on preprocessed frame sequence of a video. Ultimately, the results of the two channels are combined to determine the final activity classification. The bi-channel procedure provides better accuracy over any of the individual channels. The proposed technique is verified using the single-view Weizmann and multi-view MuHAVi datasets. For the Weizmann dataset, the accuracy is 98.8%, and for MuHAVi-14 and MuHAVi-8 both, the accuracy is 98.5%. The accuracy rate for the proposed technique is better than the other state-of-the-art methods concerning different parameters that establish the effectiveness of our proposed method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Human activity recognition Human silhouette Key pose Spatial mass distribution features (SMDF) VGG-16

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Dividable Configuration Performance Learning

arXiv

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arXiv 2024年

作者： Gong, Jingzhi Chen, Tao Bahsoon, Rami The Department of Computer Science and Engineering University of Electronic Science and Technology of China 610056 China The Department of Computer Science Loughborough University LE11 3TU United Kingdom The School of Computer Science University of Birmingham B15 2TT United Kingdom

Machine/deep learning models have been widely adopted to predict the configuration performance of software systems. However, a crucial yet unaddressed challenge is how to cater for the sparsity inherited from the configuration landscape: the influence of configuration options (features) and the distribution of data samples are highly sparse. In this paper, we propose a model-agnostic and sparsity-robust framework for predicting configuration performance, dubbed DaL, based on the new paradigm of dividable learning that builds a model via "divide-and-learn". To handle sample sparsity, the samples from the configuration landscape are divided into distant divisions, for each of which we build a sparse local model, e.g., regularized Hierarchical Interaction Neural Network, to deal with the feature sparsity. A newly given configuration would then be assigned to the right model of division for the final prediction. Further, DaL adaptively determines the optimal number of divisions required for a system and sample size without any extra training or profiling. Experiment results from 12 real-world systems and five sets of training data reveal that, compared with the state-of-the-art approaches, DaL performs no worse than the best counterpart on 44 out of 60 cases (within which 31 cases are significantly better) with up to 1.61× improvement on accuracy;requires fewer samples to reach the same/better accuracy;and producing acceptable training overhead. In particular, the mechanism that adapted the parameter d can reach the optimal value for 76.43% of the individual runs. The result also confirms that the paradigm of dividable learning is more suitable than other similar paradigms such as ensemble learning for predicting configuration performance. Practically, DaL considerably improves different global models when using them as the underlying local models, which further strengthens its flexibility. To promote open science, all the data, code, and supplementary materials of this

关键词： Adversarial machine learning

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Detection of Malicious Node in VANETs Using Digital Twin

Detection of Malicious Node in VANETs Using Digital Twin

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International Conference on Big Data Intelligence and Computing, DataCom 2022

作者： Arya, Varsha Gaurav, Akshat Gupta, Brij B. Hsu, Ching-Hsien Baghban, Hojjat Insights2Techinfo Jaipur India Ronin Institute MontclairNJ07043 United States International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

ISBN: (纸本)9789819922321

Vehicular ad-hoc network (VANET) plays an essential role in helping the development of smart cars and intelligent transportation systems. By easing congestion and speeding up data transfer, VANET makes it possible for connected cars to function more smoothly. However, VANETs are affected by different types of cyber attacks, such as distributed denial of services (DDoS) attacks. A digital twin (DT) is a replica of a physical system that operates in tandem with the actual thing, allowing for continuous monitoring and management. The DT prepares the way for the monitoring of a physical entity on a regular basis and for its automated management. The improved efficiency in keeping tabs on the physical world is largely attributable to DT. For this reason, academics are advocating for its use in a variety of settings. In this research, we use DT to solve the problem of identifying and stopping malignant nodes on a VANET infrastructure. In this paper, we proposed a framework that uses the concepts of DT for the identification of malignant nodes. Our suggested approach employs machine learning to distinguish between regular traffic and attack traffic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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Structural RC computer aided intelligent analysis and computational performance via experimental investigations

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Structural engineering and Mechanics 2024年第3期90卷 253-261页

作者： Huang, Y.C. Lulios, TuMuli Chang, Chu-Ho Noor, M. Nasir Shao, Jen-Chung Chiu, Chien-Liang Lee, Tsair-Fwu Wang, Renata Information Technology Faculty Duy Tân University 254 Nguyen Van Linh Danang Viet Nam Princess Margaret Hospital Funafuti Island Private Mail Bag Tuvalu College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan College of Maritime National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Medical Physics and Informatics Laboratory of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam’s directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures. © 2024 Techno-Press, Ltd.

关键词： Boundary conditions

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Integrating Artificial Intelligence into Electric Vehicle Energy Systems: A Survey 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Hua, Weiqi Mullen, Daniel Wahid, Abdul Sitabkhan, Khadija Mason, Karl Department of Electronic Electrical and Systems Engineering University of Birmingham BirminghamB15 2TT United Kingdom School of Engineering University of Edinburgh EdinburghEH9 3DW United Kingdom School of Computer Science University of Galway GalwayH91 FYH2 Ireland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

Electric vehicles play an important role in the global transition to "Net Zero" and the decarbonisation of point source emissions from road transport, as their market share continues to grow each year. However, integrating these electric vehicles into existing electricity grids, refuelling stations and supply chains presents significant challenges. But most challenges can be effectively addressed through the implementation of artificial intelligence solutions. This paper starts with an introduction to artificial intelligence and an investigation of the challenges associated with the widespread adoption of electric vehicles in society. Then, the paper analyses in detail how artificial intelligence can facilitate the smooth integration of electric vehicles. In addition, the paper provides a detailed investigation of the life-cycle emissions of electric vehicles and examines the limitations of existing research in this area and suggests potential avenues for future research. © IFIP International Federation for Information Processing 2024.

关键词： Renewable energy

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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