Reviewerly/exHarmony huggingface dataset:
MLBR-YOLOX: An Efficient SAR Ship Detection Network With Multilevel Background Removing Modules

On the remote sensing images of marine synthetic aperture radar (SAR), ship targets often occupy only a small part of an image, and the rest are all sea and coastal backgrounds.Existing neural networks based on SAR ship detection often directly detect an entire SAR image, which consumes a large amount of computing resources.In this article, a new marine SAR ship detection network, called multilevel background removing-you only look once X (MLBR-YOLOX), is proposed.First, a new plug-and-play module, called standalone spatial patch detector, is proposed to predetect the position of ships and filter out most of the sea backgrounds at the source image level.Second, a deep spatial feature detector is presented for detecting deep semantic features of the output of the backbone module to further reduce the computational cost of the neck and head modules.Finally, the original YOLOX network is adopted to locate and classify the pre-detected results.Experimental results on the SAR ship detection dataset and the high-resolution SAR images dataset indicate that the detection performance of MLBR-YOLOX is close to that of YOLOX, but the computational complexity is merely 23.97% and 12.50% of YOLOX's, respectively.Moreover, the experiment conducted on a large-scene Sentinel-1 SAR image illustrates that the proposed network has good migration application capability.
Robust Registration of Multimodal Remote Sensing Images With Spectrum Congruency

Among the existing registration methods, most feature descriptors are designed with image intensity, gradient information and phase congruency (PC). However, both intensity and gradient are sensitive to image illumination changes, complex intensity differences, noise, etc. Despite the fact that PC is invariant to image illumination and contrast, it does not perform well when images are corrupted with noise and nonlinear radiation distortions. In this article, we propose a novel feature called spectrum congruency (SC), which is robust to noise and variations of image illumination and intensity. SC focuses on exploiting the correlation of the multiscale patches based on their local energy and measures the congruency of the energy distribution in a data-driven transform domain. To demonstrate the superiority of SC, we apply it to multimodal image registration. We construct a histogram-based feature descriptor based on SC, termed as HOSC. Then the HOSC descriptor is integrated with two similarity metrics for multimodal remote sensing image registration. Extensive experimental results on both real and noisy image pairs show that the proposed method presents superior registration accuracy and excellent performance in resisting the nonlinear distortion and noise.
Transformer-Based Semantic Segmentation for Extraction of Building Footprints from Very-High-Resolution Images

Semantic segmentation with deep learning networks has become an important approach to the extraction of objects from very high-resolution remote sensing images. Vision Transformer networks have shown significant improvements in performance compared to traditional convolutional neural networks (CNNs) in semantic segmentation. Vision Transformer networks have different architectures to CNNs. Image patches, linear embedding, and multi-head self-attention (MHSA) are several of the main hyperparameters. How we should configure them for the extraction of objects in VHR images and how they affect the accuracy of networks are topics that have not been sufficiently investigated. This article explores the role of vision Transformer networks in the extraction of building footprints from very-high-resolution (VHR) images. Transformer-based models with different hyperparameter values were designed and compared, and their impact on accuracy was analyzed. The results show that smaller image patches and higher-dimension embeddings result in better accuracy. In addition, the Transformer-based network is shown to be scalable and can be trained with general-scale graphics processing units (GPUs) with comparable model sizes and training times to convolutional neural networks while achieving higher accuracy. The study provides valuable insights into the potential of vision Transformer networks in object extraction using VHR images.
Teachers’ sense of competence in terms of ICT use: the case of secondary school teachers

In the current digital age, the integration of information and communication technologies (ICT) into teaching practices has become a determining factor in learning quality. The teachers’ digital competence issue has come back to the forefront because of the schools’ closure due to the COVID-19 pandemic. Our study aims to assess the sense of competence in terms of ICT use of a sample of 260 secondary school teachers in the Directorate of Education in Rhamna, Morocco, by adopting a quantitative methodology. Our findings suggested that only 26.1% of the individuals in our sample reported they feel effectively competent. The pandemic made a significant change in teachers’ perceptions of the importance of ICT integration and training in the field. Moreover, a cross-analysis highlighted significant relationships between the sense of competence in terms of ICT use and six independent variables: continuous training, learning readiness, gender, age, teaching experience, and school subject. Our findings would be helpful for policymakers to guide educational policies by focusing on ICT continuous training to enhance teachers’ digital competence.
Graph Neural Network Assisted Quantum Compilation for Qubit Allocation

Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - size and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable at the current size of computers, requiring thousands of qubits, while NISQ systems have nearly one hundred at most. One common approach to improve reliability is to adjust the compilation process to create a more reliable final circuit, where the two most critical compilation decisions are the qubit allocation and qubit routing problems. We focus on solving the qubit allocation problem and identifying initial layouts that result in a reduction of error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network to process the mesh topology of the quantum computer, known as the backend, and make mapping decisions, creating a Graph Neural Network Assisted Quantum Compilation (GNAQC) strategy. We train the architecture using a set of four backends and six circuits and find that GNAQC improves output fidelity by roughly 12.7% over pre-existing allocation methods.
A Fast Method for Robust Video Watermarking Based on Zernike Moments

Watermarking by Zernike moments has been proven to be effective in providing high rotational resistance. However, due to the high computational complexity, the conventional video watermarking methods using Zernike moments are developed for videos with low resolution. Moreover, according to the properties of Zernike moments, only the matrices of equal height and width can be calculated since the inscribed circle of the original image matrix is selected as the area to be processed, but most of the available videos on the Internet do not meet such requirement. To solve the above problem, this paper proposes a fast watermarking method based on Zernike moments for high resolution videos to resist various attacks. In the proposed method, the frames of a video sequence are firstly grouped, from which a certain number of frame pairs are then selected for watermark embedding. For each frame pair to be embedded, we partition one frame into a set of disjoint blocks and apply singular value decomposition to each block to obtain a square feature matrix. Thereafter, by calculating all the Zernike moments, secret information is embedded into the selected Zernike moments to achieve superior robustness while keeping imperceptibility. Finally, according to the video encoding framework, we overwrite the frame difference of the frame pair by the watermark to resist compression and transcoding attacks. Furthermore, we propose two optional methods for compensating the special cases of rotation and scaling attacks during watermark detection. Experiments demonstrate the advantage of our method over the existing robust watermarking methods.
Area Efficient VLSI ASIC Implementation of Multilayer Perceptrons

We propose a simulated annealing (SA) based neural network (NN) optimization approach for designing an area and power efficient hardware model for multilayer perceptrons. Our approach targets optimizing the hidden layer neuron weights to integer values. For each SA move, the subset of the hidden weights is perturbed at random. The perturbation amount ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> ) is set proportional to the annealing temperature ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> ). All the newly generated weights that are proximate to integers are rounded to the nearest integer to produce an optimized model. To further minimize the hardware, register resizing and operator strength reduction are applied. The objective of preserving the model's accuracy is favored in our approach. Our proposed methodology is validated using five MLP benchmarks. We find the most optimal solution when the temperature reduction rate ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> ) is 0.99 and the number of iterations ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> ) at each temperature ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> ) is 10K. With an average model accuracy degradation of 2.74%, the average savings for the best case scenario for area, power, and FFs are 27.53%, 27.28%, and 27.71%, respectively.
Security and Privacy Preservation for Future Vehicular Transportation Systems: A Survey

Electric Vehicles (EVs) with numerous optimized features are becoming more popular in today’s world of vehicle technology. This is especially in these days when climate change is becoming more of a concern, and the usage of renewable energy is promoted. These EVs require frequent charging for daily use, and the usage of different charging stations would store sensitive data about EVs, e.g., location, driver’s license, etc. This leads to significant privacy and security concerns. In this paper, we will investigate the existing solutions proposed in the literature to address these concerns. Some of these solutions are implementing a framework that depends on cryptography and the Matching Market, where cryptography plays a vital role in securing user information privacy. Other solutions include implementing a secure and privacy-preserving physical layer-assisted scheme for charging stations to improve authentication and preserve user privacy. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance the security level for future electric vehicle transportation systems.
A survey on the use of blockchains to achieve supply chain security

Supply chain networks are becoming more complex and vulnerable to various attacks. We must tackle these attacks properly to ensure the required supply chain security. In this paper, I have classified major supply chain security issues with some real-life examples. I have also discussed the most recent countermeasures of these attacks. Furthermore, blockchain is a promising distributed information technology that creates opportunities for a new approach in the supply chain area. In my research work, I have described how to integrate blockchains into supply chain architectures to create more secure systems. I also discussed the benefits and challenges of introducing blockchain into the supply chain in this paper. To explain that better, I explored the performances of some in-practice blockchain platforms that we can apply to supply chains. I further mentioned the possible solutions for the challenges of integrating blockchain into supply chains.
Research on short-term load forecasting of power system based on IWOA-KELM

A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods.
An Improved Traffic Sign Detection and Recognition Deep Model Based on YOLOv5

In this paper, we aim at the traffic sign detection and recognition in complex road conditions.We proposed a deep model for traffic sign detection and recognition.There are a few difficulties in traffic sign detection task, such as, less recognizable, small target size, easily leading to detected failure and so on.First, for the failure detection, we introduce Coordinate Attention (CA); second, to accelerate the regression of prediction box, we introduce the angle loss into our objective function; third, for the overlapping and occlusion phenomenon of ground truth, a dynamic label assignment strategy-simple Optimal Transport Assignment (SimOTA) is utilized during label assignment; the last and the most important, for the target size problem, we propose a feature fusion network, named hierarchical-path feature fusion network (H-PFANet).Experiments were conducted on two public data sets, the results show that our improved model performed better than YOLOv5s which is the base model and other popular algorithms on precision, recall and mAP.For the difficult samples in data set CCTSDB-2021, the results show that compared to YOLOv5s, the mAP@0.5 is improved by 6.3%, the mAP@0.5 : 0.95 is improved by 5.3%, and our method achieved a detection speed of 91 FPS, with better robustness to changes in various traffic scenes, while maintaining the volume of the original YOLOv5s model.On the whole CCTSDB-2021 data set, the precision of our model reached 98.1%, the recall of our model reached 97.6% and the mAP@0.5 reached 98.8% with a speed of 91 FPS.We also compared our method with other current detection algorithms on TT100K data set, the results show that our proposed method performed better, and show the effectiveness of our method.
Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data

Abstract Objective When correcting for the “class imbalance” problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. Materials and Methods Multiple classification algorithms were trained on 7 resampled datasets: no correction, random undersampling, 4 ratios of Synthetic Minority Oversampling Technique (SMOTE), and random oversampling with the Adaptive Synthetic algorithm (ADASYN). Performance was evaluated in Area Under the Curve (AUC), precision, recall, Brier score, and calibration metrics. A case study on prediction modeling for 30-day unplanned readmissions in previously admitted Urology patients was presented. Results For most algorithms, using resampled data showed a significant increase in AUC and precision, ranging from 0.74 (CI: 0.69–0.79) to 0.93 (CI: 0.92–0.94), and 0.35 (CI: 0.12–0.58) to 0.86 (CI: 0.81–0.92) respectively. All classification algorithms showed significant increases in recall, and significant decreases in Brier score with distorted calibration overestimating positives. Discussion Imbalance correction resulted in an overall improved performance, yet poorly calibrated models. There can still be clinical utility due to a strong discriminating performance, specifically when predicting only low and high risk cases is clinically more relevant. Conclusion Resampling data resulted in increased performances in classification algorithms, yet produced an overestimation of positive predictions. Based on the findings from our case study, a thoughtful predefinition of the clinical prediction task may guide the use of resampling techniques in future studies aiming to improve clinical decision support tools.
Rewarding Developers by Storing Applications on Non-Fungible Tokens

Non-fungible tokens (NFTs) are individual tokens with valuable information stored inside them over blockchain technology. They can be purchased and sold like other physical and virtual art pieces because their worth is mostly determined by the market and demand. The unique data of NFTs render it simple to verify and authenticate their ownership and transfer of tokens between owners. However, in Pakistan, developers cannot acquire different licences to accomplish their projects not because they cannot afford it, but because they cannot invest in every piece of software to accomplish each new sensitive task. Rather, they can render the product platform independent. Considering this technology, this paper provides IT professionals with a new NFT approach and business policies that solely belong to the information technology domain. In addition, this paper also introduces how NFT tokens can hold software applications. Since we can store files, we can let NFTs also store complete applications to help developers in further utilising virtuality and having the metaverse at their fingertips. Whenever they succeed in a project, they never receive rewards, and their skills only pay the bills. In a nutshell, this paper presents a prototype of NFTs that would be further polished to save and utilise applications in a decentralised manner while rewarding the developers.
Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning

The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.
Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions

Fast-growing smart city applications, such as smart delivery, smart community, and smart health, are generating big data that are widely distributed on the internet. IoT (Internet of Things) systems are at the centre of smart city applications, as traditional cloud computing is insufficient for satisfying the critical requirements of smart IoT systems. Due to the nature of smart city applications, massive IoT data may contain sensitive information; hence, various privacy-preserving methods, such as anonymity, federated learning, and homomorphic encryption, have been utilised over the years. Furthermore, limited concern has been given to the resource consumption for data privacy-preserving in edge computing environments, which are resource-constrained when compared with cloud data centres. In particular, differential privacy (DP) has been an effective privacy-preserving method in the edge computing environment. However, there is no dedicated study on DP technology with a focus on smart city applications in the edge computing environment. To fill this gap, this paper provides a comprehensive study on DP in edge computing-based smart city applications, covering various aspects, such as privacy models, research methods, mechanisms, and applications. Our study focuses on five areas of data privacy, including data transmitting privacy, data processing privacy, data model training privacy, data publishing privacy, and location privacy. In addition, we investigate many potential applications of DP in smart city application scenarios. Finally, future directions of DP in edge computing are envisaged. We hope this study can be a useful roadmap for researchers and practitioners in edge computing enable smart city applications.
Fault Detection of Wastewater Treatment Plants Based on an Improved Kernel Extreme Learning Machine Method

In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.
More Accurate Cost Estimation for Internet of Things Projects by Adaptation of Use Case Points Methodology

This paper adapts the Use Case Points method to estimate the size and development effort required for the Internet of Things systems. Despite the extensive use of UCP in software engineering, it has yet to be adapted for IoT systems, which is essential for project management and resource planning. Our proposed adaptation, UCP for IoT, is based on a four-layer IoT architecture and tailors the standard software UCP to the specifications of IoT systems. It was validated using a case study of three IoT systems, demonstrating its applicability and effectiveness in estimating the development effort required for IoT projects. However, the results also highlight the need for further improvements, particularly given the absence of historical data sets for IoT projects. Our future work will focus on gathering such datasets and further refining the proposed model.