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Enhancing eyeglasses removal in facial images: a novel approach using translation models for eyeglasses mask completion

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Multimedia Tools and Applications 2024年 1-20页

作者： Esmaily, Zahra Ebrahimpour-Komleh, Hossein Department of Electrical and Computer Engineering University of Kashan Kashan Iran

Accurately removing eyeglasses from facial images is crucial for improving the performance of various face-related tasks such as verification, identification, and reconstruction. This paper presents a novel approach to enhancing eyeglasses removal by integrating a mask completion technique into the existing framework. Our method focuses on improving the accuracy of eyeglasses masks, which is essential for subsequent eyeglasses and shadow removal steps. We introduce a unique dataset specifically designed for eyeglasses mask image completion. This dataset is generated by applying Top-Hat morphological operations to existing eyeglasses mask datasets, creating a collection of images containing eyeglasses masks in two states: damaged (incomplete) and complete (ground truth). A Pix2Pix image-to-image translation model is trained on this newly created dataset for the purpose of restoring incomplete eyeglass mask predictions. This restoration step significantly improves the accuracy of eyeglass frame extraction and leads to more realistic results in subsequent eyeglasses and shadow removal. Our method incorporates a post-processing step to refine the completed mask, preventing the formation of artifacts in the background or outside of the eyeglasses frame box, further enhancing the overall quality of the processed image. Experimental results on CelebA, FFHQ, and MeGlass datasets showcase the effectiveness of our method, outperforming state-of-the-art approaches in quantitative metrics (FID, KID, MOS) and qualitative evaluations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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An incremental tree seed algorithm for balancing local and global search behaviors in continuous optimization problems

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Neural Computing and Applications 2024年第31期36卷 19879-19914页

作者： Beşkirli, Mehmet Department of Computer Engineering Şırnak University Şırnak73000 Turkey

Population-based tree seed algorithm (TSA), a popular optimization algorithm, was used in this study. The purpose of this study is to develop a TSA via improving its exploitation and exploration capability which is the most important element of the algorithm. Accordingly, the study was aimed at increasing the convergence rate and performance level of the algorithm. The population diversity of the algorithm was studied, and the incremental social learning method in the literature was integrated into a TSA. The new algorithm obtained was called "Incremental tree seed algorithm" (ITSA). Four different ITSA methods were obtained by using four different methods. With the new methods obtained, the TSA method was applied to twelve low-dimensional benchmark functions. The success of the proposed methods on functions was extraordinary, and the results were shown in the tables. At the same time, Wilcoxon p-test analysis, sign test and ROC curve analysis of the obtained results were also performed. According to the results of p-test, sign test and ROC curve analysis, the proposed method was found to be successful. It can be concluded that the proposed method is more robust than its original version. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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Continuously non-malleable codes from block ciphers in split-state model

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Cybersecurity 2024年第2期7卷 68-80页

作者： Anit Kumar Ghosal Dipanwita Roychowdhury Department of Computer Science and Engineering I KharagpurKharagpurWest BengalIndia

Non-malleable code is an encoding scheme that is useful in situations where traditional error correction or detection is impossible to *** ensures with high probability that decoded message is either completely unrelated or the original one,when tampering has no ***,standard version of non-malleable codes provide security against one time tampering *** ciphers are successfully employed in the construction of non-malleable *** construction fails to provide security when an adversary tampers the codeword more than *** non-malleable codes further allow an attacker to tamper the message for polynomial number of *** this work,we propose continuous version of non-malleable codes from block ciphers in split-state *** construction provides security against polynomial number of tampering attacks and it preserves *** the tam-pering experiment triggers self-destruct,the security of continuously non-malleable code reduces to security of the underlying leakage resilient storage.

关键词： Block cipher Non-malleable code Split-state model Tamper-resilient cryptography

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Knowledge distillation on spatial-temporal graph convolutional network for traffic prediction

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International Journal of computers and Applications 2025年第1期47卷 45-56页

作者： Izadi, Mohammad Safayani, Mehran Mirzaei, Abdolreza Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only (Formula presented.) of network parameters. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cost functions

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Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

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computer Systems Science & engineering 2023年第3期44卷 2349-2360页

作者： I.Vallirathi S.Ebenezer Juliet Department of Computer Science and Engineering PET Engineering CollegeVallioor627117India Department of Computer Science and Engineering VV College of EngineeringThisayanvilai627657India

Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.

关键词： WSNs MCs WRSN K-means algorithm shortest hamiltonian cycle regression analysis

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Drawing a parallel between the trend of confirmed COVID-19 deaths in the winters of 2022/2023 and 2023/2024 in Italy, with a prediction

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Mathematical Biosciences and engineering 2024年第3期21卷 3742-3754页

作者： Roccetti, Marco Department of Computer Science and Engineering University of Bologna Bologna Italy

We studied the weekly number and the growth/decline rates of COVID-19 deaths of the period from October 31, 2022, to February 9, 2023, in Italy. We found that the COVID-19 winter wave reached its peak during the three holiday weeks from December 16, 2022, to January 5, 2023, and it was definitely trending downward, returning to the same number of deaths as the end of October 2022, in the first week February 2023. During this period of 15 weeks, that wave caused a number of deaths as large as 8,526. Its average growth rate was +7.89% deaths per week (10 weeks), while the average weekly decline rate was -15.85% (5 weeks). At the time of writing of this paper, Italy has been experiencing a new COVID-19 wave, with the latest 7 weekly bulletins (October 26, 2023 – December 13, 2023) showing that deaths have climbed from 148 to 322. The weekly growth rate had risen by +14.08% deaths, on average. Hypothesizing that this 2023/2024 wave will have a total duration similar to that of 2022/2023, with comparable extensions of both the growth period and the decline period and similar growth/decline rates, we predict that the number of COVID-19 deaths of the period from the end of October 2023 to the beginning of February 2024 should be less than 4100. A preliminary assessment of this forecast, based on 11 of the 15 weeks of the period, has already confirmed the accuracy of this approach. ©2024 the Author(s), licensee AIMS Press.

关键词： COVID-19

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Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 288-290页

作者： Xin Liao Khoi Hoang Xin Luo the College of Computer and Information Science Southwest University the Department of Computer Science and Engineering Washington University in St.Louis IEEE

Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].

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Network traffic analysis using machine learning techniques in IoT network

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 113-131页

作者： Mishra, Shailendra Department of Computer Engineering Majmaah University Majmaah11952 Saudi Arabia

End-node internet-of-things devices are not very intelligent and resource-constrained;thus, they are vulnerable to cyber threats. They have their IP address, and once the hacker traces the IP, it becomes easy to get into the network and exploit the other devices. The support vector machine learning technique is used to classify normal and abnormal traffic. Mininet emulator is selected for network design, VMware fusion for creating a virtual environment, hosting OS is Ubuntu Linux, the network topology is a tree topology. Wireshark was used to open an existing packet capture file that contains network traffic. Signature-based and heuristic detection techniques were used to analyse the signature of the record found using a hex editor, and proposed rules are applied for searching and detecting that these files have this signature. The support vector machine classifier demonstrated the best performance with 99% accuracy. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Internet of things

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Privacy-preserving intrusion detection with federated learning: enhancing privacy and efficiency in IoT networks

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International Journal of Machine Learning and Cybernetics 2025年 1-19页

作者： Sharma, Hemant Yadav, Gyan Singh Department of Computer Science and Engineering IIIT Kota Rajasthan Kota India

Intrusion detection systems (IDS) are crucial in the identification of unauthorized activities on a digital network, enabling cybersecurity measures to initiate prevention protocols to protect the security of their networks and data. With Internet of Things (IoT) as a ubiquitous phenomenon, IDS has become increasingly important owing to the massive growth of connected devices. Machine learning algorithms can detect anomalies in large datasets, but centralized methods pose security risks by gathering all data in one place. Federated learning (FL) offers a safer alternative, allowing multiple clients to collaborate on a model without sharing data, reducing the risk of data leaks and enhancing privacy. In this paper, we propose a FL-based IDS. Herein, we have applied the FL approach to a number of clients that are grouped in sets where the number of clients in a set varies from 2 to 10. After that, we applied feature ranking via a random forest approach, wherein the features are ranked based on their importance to find the optimal and reduced dataset for reducing the inference time. Further, three models for each of the clients were trained using the refined dataset obtained. Next, the central server aggregates the data from the clients using three methods. We have updated the dataset with two types of scenarios, one with two classes (Benign and Attacks) and the second with a multi-class (Benign, Dodag, Flooding, Rank, Blackhole) for identifying the attack. The experimental results establish that the proposed methodology achieves superior performance as compared to similar existing methodologies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Federated learning

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