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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Mamun, Quazi Pan, Zhenni Shimamoto, Shigeru Charles Sturt University School of Computing Mathematics and Engineering Australia Waseda University Global Center for Science and Engineering Faculty of Science and Engineering Japan Waseda University School of Fundamental Science and Engineering Faculty of Science and Engineering Japan

ISBN: (纸本)9798331517786

Integrating autonomous vehicles (AVs) within our transportation ecosystem presents exciting possibilities and introduces intricate challenges. Systemic failures due to cyberattacks on AVs could have far-reaching consequences, impacting safety and financial stability. This paper emphasizes the interdependence of stakeholders, particularly within the financial and insurance sectors. We propose a proactive approach, focusing on three objectives: (1) Systemic Risk Mitigation, (2) Premortem Analysis with a focus on cascading failures, and (3) Integration of Cybersecurity Standards tailored for autonomous electric vehicles (EVs). By addressing EV-specific vulnerabilities in charging infrastructure and telematics systems, we aim to significantly enhance the security of safety-critical cyber-physical systems, strengthen the resilience of interconnected industries, and contribute to broader economic stability. © 2024 IEEE.

关键词： Autonomous vehicles

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Biochemical and Physiological Responses of Arabidopsis thaliana Leaves to Moderate Mechanical Stimulation

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Phyton-International Journal of Experimental Botany 2023年第3期92卷 901-920页

作者： Iva Šutevski Klara Krmpotić Sandra Vitko Nataša Bauer Eva Fancev Mario Cifrek Željka Vidaković-Cifrek Department of Biology Faculty of ScienceUniversity of ZagrebZagreb10000Croatia Faculty of Electrical Engineering and Computing University of ZagrebZagreb10000Croatia

Mechanical stimulation of plants can be caused by various abiotic and biotic environmental *** from the negative consequences,it can also cause positive changes,such as acclimatization of plants to stress ***,it is necessary to study the physiological and biochemical mechanisms underlying the response of plants to mechanical *** aim was to evaluate the response of model plant Arabidopsis thaliana to a moderate force of 5 N(newton)for 20 s,which could be compared with the pressure caused by animal movement and weather conditions such as heavy *** stimulated leaves were sampled 1 h after exposure and after a recovery period of 20 *** study a possible systemic response,unstimulated leaves of treated plants were collected 20 h after exposure alongside the stimulated leaves from the same *** effect of stimulation was assessed by measuring oxidative stress parameters,antioxidant enzymes activity,total phenolics,and photosynthetic *** leaves showed increased lipid peroxidation 1 h after treatment and increased superoxide dismutase activity and phenolic oxidation rate after a 20-h recovery *** photosynthetic performance after the 20-h recovery period,the effective quantum yield of the photosystem II was lower in the stimulated leaves,whereas photochemical quenching was lower in the unstimulated leaves of the treated *** quenching was lower in the stimulated leaves 1 h after *** study suggested that plants sensed moderate force,but it did not induce pronounced change in metabolism or photosynthetic *** component analysis distinguished three groups–leaves of untreated plants,leaves analysed 1 h after stimulation,while stimulated and unstimulated leaves of treated plants analysed 20 h after treatment formed together the third *** grouping of stimulated and unstimulated leaves of treated plants could indicate signal transducti

关键词： Arabidopsis thaliana mechanical stimulation oxidative stress antioxidant enzymes photosynthetic performance systemic response

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The impact of backbone selection in YOLOv8 models on brain tumor localization

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Iran Journal of Computer science 2025年 1-23页

作者： Ranjbarzadeh, Ramin Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

This study investigates the utilization of the You Only Look Once (YOLOv8) deep learning framework for accurately identifying the location of brain tumors in medical imaging. We investigate the effects of model size and pretraining on the accuracy and computational efficiency of tumor detection by utilizing different setups of the YOLOv8 model. These setups include various configurations, ranging from very small to large, and can be pretrained on the COCO dataset or not. The experimental results, carried out on Google Colaboratory using NVIDIA Tesla T4 GPUs, show that pretrained models often achieve better performance by utilizing the extensive feature representations learned from the COCO dataset, resulting in increased precision in tumor location. For instance, the YOLOv8-XS model pretrained on COCO achieves an IoU of 0.278 and a Dice coefficient of 0.435, whereas its non-pretrained counterpart attains only 0.241 IoU and 0.388 Dice, indicating a 15% improvement in tumor localization accuracy. Similarly, pretrained YOLOv8-L achieves 0.269 IoU, outperforming standard object detection models such as Mask R-CNN (IoU: 0.212) and Faster R-CNN (IoU: 0.228). These results highlight the impact of pretraining on model performance, particularly for lightweight architectures, while also revealing diminishing returns for larger models. The research uncovers a subtle connection between the complexity of the model, pretraining, and the time required for training. It emphasizes the possible advantages and constraints of pretraining for various sizes of models. © The Author(s) 2025.

关键词： Brain tumor localization COCO dataset Deep learning Object detection Transfer learning

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Emotion identification in human faces through ensemble of deep learning models

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Journal of Intelligent and Fuzzy Systems 2023年第6期45卷 9729-9752页

作者： Naveen Kmuar, M. Godfrey Winster, S. Department of Computer Science and Engineering Saveetha Engineering College Thandalam India Department of Computing Technologies Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset. © 2023 - IOS Press. All rights reserved.

关键词： Emotion Recognition

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Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1757-1776页

作者： Minfeng Qi Ziyuan Wang Qing-Long Han Jun Zhang Shiping Chen Yang Xiang School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn3122VictoriaAustralia Faculty of Data Science City University of MacaoMacaoChina CSIRO Data61 SydneyNew South WalesAustralia

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

关键词： Blockchain internet of healthcare things(IoHT) privacy-preserving techniques(PPTs).

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Evaluation of Shallow Convolutional Neural Network in Open-World Chart Image Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第6期48卷 185-198页

作者： Bajić, Filip Habijan, Marija Nenadić, Krešimir University Computing Centre University of Zagreb Zagreb10000 Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia

Data’s role is pivotal in the era of internet technologies, but unstructured data poses comprehension challenges. Data visualizations like charts have emerged as crucial tools for condensing complex information. Classifying charts and applying various processing techniques are vital to interpreting visual data. Traditional chart image classification methods rely on predefined rules and have limited accuracy. The advent of support vector machines (SVMs) and convolutional neural networks (CNNs) significantly improved the accuracy of these methods. This research evaluates our previously introduced Shallow convolutional neural network (SCNN) architecture for chart image classification, comprising four convolutional layers, two max-pooling layers, and one fully-connected layer. The network achieves state-of-the-art results, requiring smaller datasets and reduced computational resources. When two networks are combined into Siamese SCNN (SSCNN), emphasizing generalization, it achieves high accuracy with small datasets and excels in open-set classification. The evaluation process encompasses the utilization of six publicly available datasets. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Data visualization

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Life and death of a data set: a forensic investigation

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Spectroscopy Europe 2023年第2期35卷 32-35页

作者： Davies, Antony N. SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom

Davies talks about spectroscopic data set. As originally delivered the spectrometer was capable of generating some of the best data sets people had ever been able to measure. Excellent signal-to-noise ratios and extremely stable calibrations. The associated computer hardware was somewhat behind the state-of-the-art, but this was quite normal due to the long development times for the instrument hardware. And so, in the hands of many expert, and some less expert, scientists, this wonderful spectrometer gave birth to many spectroscopic data sets--reinforcing theories and dispelling some myths. Over its lifespan serving as an excellent measurement platform allowing many modifications to the original basic equipment. So, while our data was young there were no problems. All the measurement parameters were stored with the data set so we could check the instrument had been set up correctly.

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Advanced Pedestrian Detection in Low Visibility Scenarios Using UltraLytics YOLOv8 and Kalman Filtering 5

Advanced Pedestrian Detection in Low Visibility Scenarios Us...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pattem, Sanjay Kumar Reddy Rajagopal, Manjula Nirujogi, Nandan SRM Institute of Science and Technology Computing Technologies Chennai India SRM Institute of Science and Technology Computing Technologies Faculty of Engineering and Technology Chennai India

ISBN: (纸本)9798331530013

Nighttime detection of pedestrians brings important problems such as low light conditions, limitations of sensors, and environmental noise. To tackle this problem, in this study, we propose a novel visual-radar data fusion to enhance a pedestrian detection system using the UltraLytics YOLOv8 deep learning model together with Kalman Filtering technique. The system is based on a Low-Light Vision Infrared Paired (LLVIP) dataset that includes infrared vision data to attenuate the performance decrement of a pedestrian detector in the dark. The combination of infrared cameras and millimeter-wave radar achieves a detection rate of 97.6%, in which UltraLytics YOLOv8 exhibits excellent object recognition performance by generating accurate spatial & category information from the infrared image;complementary distance and velocity data are obtained by millimeter-wave radar. It brings Kalman Filtering to track the radar-detected pedestrian targets, making the system more robust by predicting target and noise reduction. It was capable of fusing infrared and radar data at decision-level to make the system provide very precise position accurately in even nighttime situations. The experimental results show that the proposed model has improved capability of detection accuracy, robustness, and response time in comparison with the single-sensor system, which is suitable for practical autonomous driving scenarios. © 2024 IEEE.

关键词： Kalman filters

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Crimes associated with geological materials: mining, minerals, metals and fossils

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Geology Today 2024年第4期40卷 159-164页

作者： Donnelly, Laurance John Pirrie, Duncan AHK International Limited and IUGS Initiative on Forensic Geology Faculty of Computing Engineering and Science University of South Wales

Civilization has always relied on geological materials and it would not exist as we know it without the use of minerals. For the foreseeable future, minerals will remain fundamentally important commodities. As technology changes, so does the demand for different mineral commodities. For example, as we enter the era of the battery revolution, expedited by vehicle electrification and a reduction in the burning of traditional fossil fuels (coal, oil and gas), demand for minerals used in battery technologies is predicted to increase significantly. In 2022 alone, the global mining industry had a value of over 2000 billion US dollars. Where such huge amounts of money are involved, it is unsurprising that criminal activity follows. Criminal activity in the mining, minerals and metals industries includes illegal mining and smuggling, theft (including adulteration and substitution), illegal trade in conflict minerals, fakes, fraud and environmental crime (e.g. pollution, contamination and degradation of the land, air and water). In this article, we provide an overview of the types of criminal activities associated with geological materials and the potential role of forensic geologists in mitigating the associated risks. © 2024 The Author(s). Geology Today published by The Geological Society of London and The Geologists’ Association and John Wiley & Sons Ltd.

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