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AGG: attention-based gated convolutional GAN with prior guidance for image inpainting

Neural Computing and Applications 2024年第20期36卷 12589-12604页

作者： Yu, Xiankang Dai, Lei Chen, Zhihua Sheng, Bin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Image inpainting has made great achievements recently, but it is often tough to generate a semantically consistent image when faced with large missing areas in complex scenes. To address semantic and structural alignment in existing methods for image inpainting, this paper proposes an end-to-end attention-based gated convolution GAN with prior guidance named AGG, which designs the spatial and channel attention mechanisms for full extraction of semantic and structural features. Moreover, AGG constructs the attention-based upsampling module based on the channel attention to refine the feature map and capture more details from features of up-level low sizes. AGG uses the image contour as prior, allowing the gated convolution and attention mechanism may fill the image efficiently by focusing on the contour information. The attention-based gated convolution can effectively capture the global features and compensate for the limitations of the restricted receptive field of the naive convolution. Compared to other models, AGG generates images with finer outline features and no common problems such as the watermark and blur, which shows the best overall performance on the Paris StreetView, CelebA-HQ, and Places2 datasets. The best FID, LPIPS, PSNR, and SSIM values achieved by AGG are 2.18, 0.046, 30.82, and 0.951 on the CelebA-HQ dataset, with at least 3.21% and 6.52% performance improvement on FID and LPIPS compared to state-of-the-art methods, respectively. The source code will be available at https://***/Shawn-Yu-1/AGGNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Convolution

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 201-211页

作者： Krishna Sandeep Ayyagari Reynaldo Gonzalez Yufang Jin Miltiadis Alamaniotis Sara Ahmed Nikolaos Gatsis Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioUSA

Scalable coordination of photovoltaic(PV)inverters,considering the uncertainty in PV and load in distribution networks(DNs),is challenging due to the lack of real-time *** PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks(ANNs).To this end,we first use an offline,centralized data-driven conservative convex approximation of chance-constrained optimal power flow(CVaR-OPF)in which conditional value-at-risk(CVaR)is used to compute reactive power setpoints of PV inverter,taking into account PV and load uncertainties in *** that,an artificial neural network(ANN)controller is trained for each PV inverter to emulate the optimal behavior of the centralized control setpoints of PV inverter in a decentralized ***,the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs(local controllers)developed using model-based learning(regressionbased controller),optimization(affine feedback controller),and case-based learning(mapping)*** tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

关键词： Chance constraint decentralized control distributed energy resource(DER) data-driven control neural network voltage regulation

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Learning 101 reloaded: Revisiting the basics for the GenAI era

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IEEE Potentials 2024年第6期43卷 6-12页

作者： Qadir, Junaid College of Engineering Qatar University Department of Computer Science and Engineering Doha Qatar

In this paper, we delve into the transformative landscape of education amidst the disruptive advances of generative AI (GenAI), characterized by an unprecedented capacity to generate new information with tools such as ChatGPT. Building upon my previous publication, "Learning 101: the Untaught Basics,"in this magazine, I interrogate what has changed since then and how learners can harness the power of GenAI while avoiding its pitfalls, and learn how to learn effectively using GenAI tools. The dual role of GenAI in education is highlighted with immense potential for personalized learning side by side with the potent challenges it poses such as ethical concerns, intellectual property issues, biases, misinformation, and halluncations. Central to our argument is the development of metacognitive skills and discernment, enabling learners to navigate abundant resources effectively while avoiding overdependence and outsourcing of thinking to technology. This paper proposes strategies for integrating these tools into learning processes, aiming to equip educators and students with the skills to critically assess information and adapt to the evolving educational landscape. Our goal is to provide actionable insights for thriving in this new era, balancing the enhancement of learning and wisdom with technological advancements. © 1988-2012 IEEE.

关键词： Job analysis

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A New Profiling-Based Side-Channel Attack on Graphics Processing Units

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Journal of Computing science and engineering 2023年第1期17卷 30-40页

作者： Wang, Xin Zhang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Computer Science and Engineering University of Louisville LouisvilleKY United States

Graphics processing units (GPUs) have been increasingly used to solve a range of compute-intensive and data-parallel scientific computing problems that can be perfectly parallelized for performance speedups. Particularly, GPUs have recently become popular to host the encryption/decryption algorithms due to its high-throughput computing capability. However, the security issues of moving the cryptographic algorithms onto GPUs have not been studied adequately. Consequently, with absence of any protection strategy, the potential vulnerabilities of GPUs to side-channel attacks (SCAs) may expose the confidential information with high risk. In this paper, we proposed a new profiling-assisted correlationbased side-channel attack (pacSCA) to demonstrate that ignoring security issues and naively moving security services onto GPUs can offer adversaries fatal vulnerabilities to thieve critical information. The results showed that the proposed SCA can rebuild the secure key of the AES-128 algorithm in less than 6 seconds, revealing the urgency of protecting GPUs against side-channel threats. © Copyright 2023. The Korean Institute of Information Scientists and Engineers

关键词： Graphics processing unit

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Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

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IEEE Transactions on Control of Network Systems 2023年第3期10卷 1245-1254页

作者： Bakhshayesh, Babak Ghaffarzadeh Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran School of Computer Science Tajrish *** Iran

This article presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This article also takes into account the local and the coupling/infrastructure constraints of EVs, along with transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices. © 2014 IEEE.

关键词： Electric power distribution

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Free Space Optical Relaying via an Energy Harvesting Harvest-Store-Use Buffer-Aided Relay 5

Free Space Optical Relaying via an Energy Harvesting Harvest...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Abou-Rjeily, Chadi Minkara, Rania Department of Electrical and Computer Engineering Lebanon

ISBN: (纸本)9798331519957

This paper considers a free space optical (FSO) cooperative network with an energy harvesting (EH) relay with no permanent power supply. The relay implements the harvest-store-use strategy and, in addition to the energy buffer, it is equipped with a data buffer where the incoming packets are queued pending more favorable transmission conditions or the accumulation of more energy. Through an elaborate numerical analysis, we highlight on the limitations of the basic form of the relevant relaying strategy and suggest potential improvements. Results demonstrate reduced outage probabilities and improved management of energy and data buffers. © 2025 IEEE.

关键词： buffer buffer-Aided relaying cooperative networks energy harvesting FSO outage probability relaying

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 916-930页

作者： Ramij Raja Hossain Ratnesh Kumar Department of Electrical and Computer Engineering Iowa State UniversityAmesIA 50011 USA

This paper presents a machine-learning-based speedup strategy for real-time implementation of model-predictive-control(MPC)in emergency voltage stabilization of power *** success in various applications,real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems,and in power systems,the computation time exceeds the available decision time used in practice by a large *** long-standing problem is addressed here by developing a novel MPC-based framework that i)computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements,and ii)employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs,thereby accelerating the overall control computation by multiple ***,a realistic control coordination scheme among static var compensators(SVC),load-shedding(LS),and load tap-changers(LTC)is presented that incorporates the practical delayed actions of the *** performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems,with±20%variations in nominal loading conditions together with *** show that our proposed methodology speeds up the online computation by 20-fold,bringing it down to a practically feasible value(fraction of a second),making the MPC real-time and feasible for power system control for the first time.

关键词： Machine learning model predictive control(MPC) neural network perturbation control voltage stabilization

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Enhanced GRU-BiLSTM Technique for Crop Yield Prediction

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Multimedia Tools and Applications 2024年第41期83卷 89003-89028页

作者： Vashisht, Swati Kumar, Praveen Trivedi, Munesh Chandra Computer Science & Engineering Amity University Uttar Pradesh Noida201301 India Department of Computer Engineering Astana IT University Astana Kazakhstan Computer Science & Engineering NIT Agartala Tripura Agartala799046 India

Agriculture is the major source of food and significantly contributes to Indian employment, and the economy is intricately tied to the outcomes of crop management, where the final yield and market prices play crucial roles. The final yield and the market price completely determined the outcome of crop management or agriculture in India. Real-time observation emerges as a critical determinant of overall crop production success. Recognizing the significance of insightful analysis and precise crop yield predictions for effective farming practices, this study proposes an enhanced model to address the imperative of accurate yield forecasting. The pre-processing steps of the proposed model include Min-Max normalization, deletion of irrelevant data, and addition of missing values. The pre-processed data is then subjected to feature extraction using an Improved Shearlet transform (IST). After feature extraction, feature selection is done using an Enhanced multi-objective Grey Wolf optimization (EMGWO) technique. Finally, the prediction is made using an enhanced Gate Recurrent Unit with a Bidirectional LSTM (GRU-BiLSTM) model. This enhanced the accuracy (97%), precision (93%), recall (97.25%) and F-measure (95.14%) of agricultural yield predictions. Various measures related to errors, such as RMSE, MSE, MAE, MedAE, R2 and MSLE, are compared for the proposed model and other existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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