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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Kabdjou, Joelle Tagne, Elie Fute Rawat, Danda B. Acosta, Jaime Kamhoua, Charles University of Dschang Department of Mathematics and Computer Science Dschang Cameroon University of Buea Department of Computer Engineering Dschang Buea Cameroon Howard University Department of Electrical Engineering and Computer Science Washington United States Network Security Branch Devcom Army Research Laboratory Adelphi United States

ISBN: (纸本)9798350336092

The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, which serve as the primary communication media for MEC servers and IoT applications, is due to their open nature of communication. This work proposes a MEC architecture that utilizes Network Function Virtualization (NFV) and Service Function Chain (SFC) to combat cyber adversaries. Additionally, we evaluate the performance of a cyber deception system, which leverages a software-defined networking (SDN) controller and decision- maker to continuously monitor incoming MEC traffic and direct cyber adversaries to a deception MEC server. The deception MEC server collects information about cyber adversaries' intent, capabilities, and potential damage to the system, thus securing legitimate users from cyber attacks. The proposed approach was evaluated using Monte Carlo simulations, and numerous results were obtained which demonstrate the effectiveness of cyber deception in MEC. © 2023 IEEE.

关键词： Network function virtualization

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Multiobjective Resource Allocation for Cloud-Edge–Terminal Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 17024-17033页

作者： Liu, Xin Wang, Zhaokun Zhang, Chunqing Cao, Bin Fridenfalk, Mikael Kumar Singh, Amit Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin300401 China Uppsala University Department of Game Design Uppsala751 05 Sweden National Institute of Technology Patna Department of Computer Science Patna800005 India

This article proposes a cloud-edge–terminal collaborative resource allocation architecture that efficiently allocates resources. Traditional resource allocation often focuses solely on optimizing delay and service cost, making it less suitable for intensive real-world scenarios. In response, a comprehensive multiobjective resource allocation model is developed, encompassing delay, service cost, load balancing, and resource utilization. This article proposes a diversity-filling large-scale multiobjective evolutionary algorithm based on generative adversarial networks (DFGAN-LSMOEA). The Otsu-based grouping method in DFGAN-LSMOEA is employed to group decision variables and improve optimization performance. Compared with state-of-the-art algorithms, the proposed method validates its effectiveness and advantages in the applications, particularly when handling high-dimensional decision variables and dynamic demands. © 2014 IEEE.

关键词： Generative adversarial networks

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Functional plastic films:nano-engineered composite based flexible microwave antennas with near-unity relative visible transmittance

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Light(Advanced Manufacturing) 2024年第2期5卷 95-105页

作者： Cheng Zhang Liang Zhu Chengang Ji Zhilu Ye Nabeel Alsaab Minye Yang Yuhui Hu Pai-Yen Chen L.Jay Guo School of Optical and Electronic Information&Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan430074China Department of Electrical and Computer Engineering University of Illinois at ChicagoChicagoIL 60607USA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA

Microwave antennas are essential elements for various applications,such as telecommunication,radar,sensing,and wireless power *** antennas are conventionally manufactured on rigid substrates using opaque materials,such as metal strips,metallic tapes,or epoxy pastes;thus,prohibiting their use in flexible and wearable devices,and simultaneously limiting their integration into existing optoelectronic ***,we demonstrate that mechanically flexible and optically transparent microwave antennas with high operational efficiencies can be readily fabricated using composite nanolayers deposited on common plastic *** composite nanolayer structure consists of an ultra-thin copper-doped silver film sandwiched between two dielectric films of tantalum pentoxide and aluminum *** material and thickness of each constituent layer are judiciously selected such that the whole structure exhibits an experimentally measured averaged visible transmittance as high as 98.94%compared to a bare plastic substrate,and simultaneously,a sheet resistance as low as 12.5Ω/*** representative types of microwave antennas are implemented:an omnidirectional dipole antenna,unidirectional Yagi-Uda antenna,low-profile patch antenna,and Fabry-Pérot cavity *** devices exhibit great mechanical flexibility with bending angle over 70°,high gain of up to 13.6 dBi,and large radiation efficiency of up to 84.5%.The proposed nano-engineered composites can be easily prepared over large areas on various types of substrates and simultaneously overcome the limitations of poor mechanical flexibility,low electrical conductivity,and reduced optical transparency usually faced by other constituent materials for flexible transparent microwave *** demonstrated flexible microwave antennas have various applications ranging from fifth-generation and vehicular communication systems to bio-signal monitors and wearable electronics.

关键词： Optically transparent antenna Flexible microwave antenna Transparent conductor Ultra-thin silver Doped silver Wearable optoelectronics

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Attentional Conditional Generative Adversarial Network for Ambient Occlusion Approximation 2nd

Attentional Conditional Generative Adversarial Network for ...

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2nd International Conference on Intelligent Systems and Pattern Recognition, ISPR 2022

作者： Abbas, Fayçal Malah, Mehdi Babahenini, Mohamed Chaouki LESIA Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria ICOSI Laboratory Computer Science Department University of Abbes Laghrour Khenchela Khenchela Algeria LESIA Laboratory Computer Science Department University of Mohamed Khider Biskra Biskra Algeria

ISBN: (纸本)9783031082764

Ambient occlusion is a method that increases the realism of computer generated images by estimating the reflected illumination in areas where light fails to illuminate them due to occlusions imposed by surrounding objects. In this paper, we introduce a new approach able to generate an approximation of the ambient occlusion, thus estimating the location of soft shadows. Our approach is based on using a conditional generative adversarial network with an attention mechanism to perform a space translation from the input data to the output data space by enforcing the conditioning with an attention mechanism. Our attention module is integrated into the generator architecture and the discriminator to allow the neural network to pay more attention to relevant areas to estimate the location of soft shadows and generate a good approximation of the ambient occlusion. Our method generates images with a satisfactory similarity with the reference method and does not require any pre-processing of the generated image, so it does not perform any computation in the three-dimensional space of the 3D scene. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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HyperplaneGAN: a unified consistent translation framework for facial attribute editing

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Multimedia Tools and Applications 2024年 1-25页

作者： Li, Defang Deng, Huiqi Qin, Peng Chen, Weifu Feng, Guocan College of General Education Guangzhou Vocational College of Technology and Business Guangzhou China Department of Computer Science Guangzhou Maritime University Guangzhou China School of Mathematics Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory Sun Yat-sen University Guangzhou China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China

Facial attribute editing has been obtaining remarkable progress as the rapid development in deep generative models. Existing algorithms can be roughly grouped into two distinct categories: attribute-guided models and exemplar-guided models. These models achieve impressive facial attribute editing results, however, there are some limitations. For example, images generated by current attribute-guided models are lack of diversity and attribute styles are not controllable. For exemplar-guided models, low transfer precision and fidelity of generated images are commonly complained issues. In order to generate high-quality attribute-controllable facial images, we propose a novel unified translation framework called HyperplaneGAN which has following advantages: (1) the proposed model can do both attribute-guided facial editing and exemplar-guided facial editing;(2) by employing latent unit swapping and linear separation constraint for learning pair-wise linearly separable disentangled representations, the model can do flexible and controllable translation;(3) cycle-consistency loss and residual attribute vectors are used to guide the model to manipulate specific attributes precisely while other attributes are kept intact. Substantial experimental results demonstrate that HyperplaneGAN outperforms state-of-the-art models on both attribute-guided facial editing and exemplar-guided facial editing, in terms of quantitative evaluation and qualitative evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Dates Detection System for Automatic Harvesting Using Deep Learning 1

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Zarouit, Yousra Aksasse, Brahim Ouhda, Mohamed Department of Computer Science Faculty of Sciences and Technics Moulay Ismail University Errachidia Morocco MAIS Laboratory Department of Computer Science Faculty of Sciences Moulay Ismail University Meknes Meknes Morocco Department of Computer and Mathematics Higher School of Technology Sultan Moulay Slimane University Khenifra Morocco

ISBN: (数字)9783031352485

ISBN: (纸本)9783031352478

An accurate vision system for detecting and analyzing fruit in real-time requires fast and accurate algorithms for object detection. This will allows robotic systems to analyze and process in real-time identically like to the human visual system making them more efficient and cost-effective. However, success in this domain is still limited. No research has addressed the problem of date fruit detection and localization in an orchard environment. The system of our interest is a date fruit-harvesting robot. The robot will detect, analyze and cut the date bunch from the palm tree. In this work, a state of art object detection algorithm, the You Only Look Once version 3 (YOLOv3), is used to solve the detection and localization of date bunches problem. Unstructured agricultural environments with constantly changing light, and shadow, make date fruit detection challenging. Experimental results show the effectiveness of the proposed algorithm to deal with these problems, giving high accuracy. Based on the model output, the robot can detect and locate dates in images to make the harvesting decision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Harvesting

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A Machine Learning Based Framework for a Stage-Wise Classification of Date Palm White Scale Disease

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Big Data Mining and Analytics 2023年第3期6卷 263-272页

作者： Abdelaaziz Hessane Ahmed El Youssefi Yousef Farhaoui Badraddine Aghoutane Fatima Amounas STI Laboratory IDMSFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia 52000Morocco. IA Laboratory Department of Computer ScienceFaculty of SciencesMoulay Ismail University of MeknesMeknes 50070Morocco. RO.AL&I Group Computer Sciences DepartmentFaculty of Sciences and TechniquesMoulay Ismail University of MeknesErrachidia 52000Morocco.

Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional *** diseases endanger this precious tree,putting a strain on the economy and *** scale Parlatoria blanchardi is a damaging bug that degrades the quality of *** an infestation reaches a specific degree,it might result in the tree’s *** counter this threat,precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is *** decision is crucial for farmers who wish to minimize yield losses while preserving production *** this purpose,we propose a feature extraction and machine learning(ML)technique based framework for classifying the stages of infestation by white scale disease(WSD)in date palm trees by investigating their leaflets images.80 gray level co-occurrence matrix(GLCM)texture features and 9 hue,saturation,and value(HSV)color moments features are extracted from both grayscale and color images of the used *** classify the WSD into its four classes(healthy,low infestation degree,medium infestation degree,and high infestation degree),two types of ML algorithms were tested;classical machine learning methods,namely,support vector machine(SVM)and k-nearest neighbors(KNN),and ensemble learning methods such as random forest(RF)and light gradient boosting machine(LightGBM).The ML models were trained and evaluated using two datasets:the first is composed of the extracted GLCM features only,and the second combines GLCM and HSV *** results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%.The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease(DPWSD)and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.

关键词： machine learning feature extraction ensemble learning diseases precision agriculture date palm

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CRP2-VCS: Contrast-Oriented Region-based Progressive Probabilistic Visual Cryptography Schemes

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5501-5517页

作者： Wu, Xiaotian Song, Bofan Fang, Jia Yan, WeiQi Peng, Qing-Yu Jinan University Guangdong Key Laboratory of Data Security and Privacy Preserving College of Cyber Security College of Information Science and Technology Guangzhou China Auckland University of Technology Department of Computer and Information Systems Auckland New Zealand Jinan University Sino-French Joint Laboratory for Astrometry Dynamics and Space Science Guangzhou China

Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique. © 2025 IEEE.

关键词： Visualization Cryptography Probabilistic logic Interference Encoding Circuits and systems Polynomials Optimization Minimax techniques Computational modeling

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FUNKNN: NEURAL INTERPOLATION FOR FUNCTIONAL GENERATION 11

FUNKNN: NEURAL INTERPOLATION FOR FUNCTIONAL GENERATION

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11th International Conference on Learning Representations, ICLR 2023

作者： Khorashadizadeh, AmirEhsan Chaman, Anadi Debarnot, Valentin Dokmanić, Ivan Department of Mathematics and Computer Science University of Basel Switzerland Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States

Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models that focus on generating images at different scales do better, but employ complex architectures not designed for continuous evaluation of images and derivatives. We take a signal-processing perspective and treat continuous image generation as interpolation from samples. Indeed, correctly sampled discrete images contain all information about the low spatial frequencies. The question is then how to extrapolate the spectrum in a data-driven way while meeting the above design criteria. Our answer is FunkNN-a new convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset. Combined with a discrete generative model it becomes a functional generator which can act as a prior in continuous ill-posed inverse problems. We show that FunkNN generates high-quality continuous images and exhibits strong out-of-distribution performance thanks to its patch-based design. We further showcase its performance in several stylized inverse problems with exact spatial derivatives. Our implementation is available at https://***/swing-research/FunkNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Inverse problems

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CIL4EMTD: A Novel Class Incremental Learning Method for Encrypted Malware Traffic Detection 9

CIL4EMTD: A Novel Class Incremental Learning Method for Encr...

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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Li, Jiarui Du, Lei Chen, Yilu Zeng, Liyi Li, Hao Gu, Zhaoquan School of Computer Science and Technology Shenzhen China Pengcheng Laboratory Department of New Networks Shenzhen China National Key Laboratory of Advanced Communication Networks Shijiazhuang China

ISBN: (纸本)9798350391367

With the widespread adoption of traffic encryption technologies, detecting attack behaviors in encrypted malware traffic has become a critical and challenging problem. The encrypted malware traffic contains a lot of redundant information and noise, which necessitates additional computational resources. In addition, new attack categories continue to emerge over time, resulting in traditional detection methods losing effectiveness in identifying these new attacks. To handle the above challenge, in this paper, we propose CIL4EMTD, a novel Class Incremental Learning method targeted for Encrypted Malware Traffic Detection. Our method leverages clustering techniques to enrich the extraction of dataset features, while specifically customizing LwF for detecting encrypted malware to optimize the stability of model training via efficient knowledge distillation. To verify the effectiveness of CIL4EMTD, we conduct experiments comparing it with other class incremental learning methods. The experiment results show that CIL4EMTD demonstrates state-of-the-art performance on a real-world dataset, achieving a recognition accuracy of 94.34% for new categories while maintaining the average accuracy for known categories within a margin of less than a 10% decline. © 2024 IEEE.

关键词： Adversarial machine learning

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