Reviewerly/exHarmony huggingface dataset:
RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality

Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP.
Elliptic Curve Cryptography; Applications, challenges, recent advances, and future trends: A comprehensive survey

Elliptic Curve (EC) is the most recent and advanced technique of Elliptic Curve Cryptography (ECC). EC is often used to improve the security of open communication networks and to let specific persons with confirmed identities into the Modern Digital Era (MDE). Users of MDE make use of many technologies, such as social media, the cloud, and the IoT industry, among others. No matter what tool the users are using, the whole environment has to be able to keep their security and privacy preserved. The study of cryptography is required because unsecure networks make data transmission and the transfer of information susceptible to data theft and attack via an open channel. This makes it necessary to learn cryptography. The art of encrypting documents and communications using keys in such a way that only the individuals who are intended to receive them are able to decode and process them is referred to as cryptography. A digital signature, cryptographic data integrity, and authentication method all rely on the address of the receiver and the sender in addition to mathematical operations to find the signature. During the process of signature and verification, the solution that was presented is compared with the technique that is currently being used by ECDSA in order to illustrate the differences that exist between the two processes. This comprehensive survey of EC seeks to thoroughly investigate many scientific concepts, state-of-the-art, and innovative methodologies and implementations. This work will be useful for academics, who are interested in further analysis. Use and development of EC based schemes for cloud computing, e-health, and e-voting, is more secure as compared to RSA, and Diffie–Hellman schemes. In this comprehensive study, we claim that the adoption of EC methods in distributed computing and asynchronous networking provides significant benefits in distributed computing and interdependent networking.
RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.
Design and Implementation of IoT based Framework for Air Quality Sensing and Monitoring

This article describes a system that uses Internet of Things (IOT) architecture to deliver real-time air quality data. Real-time air quality monitoring enables us to limit the degradation of air quality. The degree of pollution in the air is measured using the Air Quality Index (AQI). In general, a higher AQI indicates that the air quality is more dangerous to breathing. With this setup, it is possible to measure gas concentrations such as NO2, CO, and PM2.5 with the help of an Arduino UNO running on both software and hardware. An IoT platform called Thing Speak serves as an IoT analytics platform that is connected to the hardware through the ESP8266 Wi-Fi module in this research. Additionally, it's capable of integrating real-time data with our Android Studio-built mobile phone app. Finally, an Android app that pulls data from Thing Speak displays the PPM and Air Quality levels of gases in the circuit. Successful development of this model has made it suitable for usage in real-world systems.
Speed-up coherent Ising machine with a spiking neural network

Coherent Ising machine (CIM) is a hardware solver that simulates the Ising model and finds optimal solutions to combinatorial optimization problems. However, for practical tasks, the computational process may be trapped in local minima, which is a key challenge for CIM. In this work, we design a CIM structure with a spiking neural network by adding dissipative pulses, which are anti-symmetrically coupled to the degenerate optical parametric oscillator pulses in CIM with a measurement feedback system. We find that the unstable oscillatory region of the spiking neural network could assist the CIM to escape from the trapped local minima. Moreover, we show that the machine has a different search mechanism than CIM, which can achieve a higher solution success probability and speed-up effect.
CoditT5: Pretraining for Source Code and Natural Language Editing

Pretrained language models have been shown to be effective in many software-related generation tasks; however, they are not well-suited for editing tasks as they are not designed to reason about edits. To address this, we propose a novel pretraining objective which explicitly models edits and use it to build CoditT5, a large language model for software-related editing tasks that is pretrained on large amounts of source code and natural language comments. We fine-tune it on various downstream editing tasks, including comment updating, bug fixing, and automated code review. By outperforming standard generation-based models, we demonstrate the generalizability of our approach and its suitability for editing tasks. We also show how a standard generation model and our edit-based model can complement one another through simple reranking strategies, with which we achieve state-of-the-art performance for the three downstream editing tasks.
UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics

Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. The dataset is available at https://github.com/Hou-Guojia/UID2021 .
Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.
Metaverse Security: Issues, Challenges and a Viable ZTA Model

The metaverse is touted as an exciting new technology amalgamation facilitating next-level immersive experiences for users. However, initial experiences indicate that a host of privacy, security and control issues will need to be effectively resolved for its vision to be realized. This paper highlights the security issues that will need to be resolved in the metaverse and the underlying enabling technologies/platforms. It also discussed the broader challenges confronting the developers, the service providers and other stakeholders in the metaverse ecosystem which if left unaddressed may hamper its broad adoption and appeal. Finally, some ideas on building a viable Zero-Trust Architecture (ZTA) model for the metaverse are presented.
Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7

The safety of construction site personnel is highly dependent on the adherence of personal protective equipment (PPE) wearing. Safety helmet monitoring has become a popular topic in recent years as a result of the success in the field of image processing. Deep learning (DL) is widely used in object detection tasks due to its ability to create features based on raw data. Constant improvements in the DL models have led to numerous successful outcomes in the implementation of safety helmet detection tasks. The performance of different DL algorithms from previous studies will be assessed and studied in this review paper. The YOLOv5s (small) model, YOLOv6s (small) model, and the YOLOv7 model will be trained and evaluated in this paper.
Blockchain Traceability Adoption in Agricultural Supply Chain Coordination: An Evolutionary Game Analysis

Blockchain technology has brought about profound revolutions in supply chain management. Notably, in the agricultural sector, blockchain-based traceability has become an essential tool to maintain the safety and quality of farm commodities. However, the implementation of blockchain technology in agricultural traceability is not prevalent. In this paper, mathematical modeling and simulation methods were used to investigate the decision making regarding the adoption of blockchain traceability in agriculture, which comprises producers, processors, and governments. This paper provides further analysis of the optimal blockchain-based traceability strategies of the members of the agricultural product supply chain in different scenarios. The results reveal the following: (1) Producers and processors should manage the traceability costs for adopting blockchains to improve their brand image and gain more benefits. (2) The government should encourage supply chain agents to participate in traceability by establishing an effective reward-and-punishment mechanism. In addition, the research will help agricultural supply chain agents to design strategies to implement traceability in agriculture and create a transparent and efficient data-driven agricultural products supply chain. Furthermore, these findings provide guidance to policymakers to develop policies to accelerate the implementation of blockchain-based traceability systems to guarantee fraud-free and sustainable agricultural supply chains.
Multi-scale ship target detection using SAR images based on improved Yolov5

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.
Robust Semantic Communications Against Semantic Noise

Although the semantic communications have exhibited satisfactory performance in a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise is a particular kind of noise in semantic communication systems, which refers to the misleading between the intended semantic symbols and received ones. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. Particularly, we analyze the causes of semantic noise and propose a practical method to generate it. To remove the effect of semantic noise, adversarial training is proposed to incorporate the samples with semantic noise in the training dataset. Then, the masked autoencoder (MAE) is designed as the architecture of a robust semantic communication system, where a portion of the input is masked. To further improve the robustness of semantic communication systems, we firstly employ the vector quantization-variational autoencoder (VQ-VAE) to design a discrete codebook shared by the transmitter and the receiver for encoded feature representation. Thus, the transmitter simply needs to transmit the indices of these features in the codebook. Simulation results show that our proposed method significantly improves the robustness of semantic communication systems against semantic noise with significant reduction on the transmission overhead.
RSSFormer: Foreground Saliency Enhancement for Remote Sensing Land-Cover Segmentation

High spatial resolution (HSR) remote sensing images contain complex foreground-background relationships, which makes the remote sensing land cover segmentation a special semantic segmentation task. The main challenges come from the large-scale variation, complex background samples and imbalanced foreground-background distribution. These issues make recent context modeling methods sub-optimal due to the lack of foreground saliency modeling. To handle these problems, we propose a Remote Sensing Segmentation framework (RSSFormer), including Adaptive TransFormer Fusion Module, Detail-aware Attention Layer and Foreground Saliency Guided Loss. Specifically, from the perspective of relation-based foreground saliency modeling, our Adaptive Transformer Fusion Module can adaptively suppress background noise and enhance object saliency when fusing multi-scale features. Then our Detail-aware Attention Layer extracts the detail and foreground-related information via the interplay of spatial attention and channel attention, which further enhances the foreground saliency. From the perspective of optimization-based foreground saliency modeling, our Foreground Saliency Guided Loss can guide the network to focus on hard samples with low foreground saliency responses to achieve balanced optimization. Experimental results on LoveDA datasets, Vaihingen datasets, Potsdam datasets and iSAID datasets validate that our method outperforms existing general semantic segmentation methods and remote sensing segmentation methods, and achieves a good compromise between computational overhead and accuracy. Our code is available at https://github.com/Rongtao-Xu/RepresentationLearning/tree/main/RSSFormer-TIP2023.
Research on Image Denoising in Edge Detection Based on Wavelet Transform

Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.
DTCSMO: An efficient hybrid starling murmuration optimizer for engineering applications

Starling murmuration optimizer is a newly well-developed swarm intelligence algorithm inspired by the behavior of starlings during stunning murmuration and has performed competitively with other well-known methods. However, starling murmuration optimizer faces the issues of poor search capability, iterative stagnation, and low convergence accuracy when dealing with complex engineering optimization and high-dimensional problems. To ameliorate these deficiencies, an efficient hybrid starling murmuration optimizer that combines dynamic opposition, Taylor-based optimal neighborhood strategy, and crossover operator is developed in this paper. Firstly, introducing the Taylor-based optimal neighborhood strategy ensures a more careful and reasonable reliance on optimal individuals, thus reducing the possibility of falling into local optima. Meanwhile, dynamic opposition successfully balances the exploration and development phases by incorporating the ideas of quasi- and reflective opposition. In addition, the exploration-development capability is further enhanced by introducing the crossover operator. To ensure the adaptivity of the crossover factor, 12 different inertia weights are explored for crossover factors, and the sigmoidal decreasing inertia weights achieve optimality. Finally, the excellent performance of the proposed algorithm is verified by comparing it with the improved algorithm and the state-of-the-art search algorithms on 26 benchmark functions and the CEC2020 test suite, respectively. In addition, the proposed algorithm is used to optimize ten practical engineering optimization problems. Experimental results demonstrate that the proposed algorithm has strong competitiveness in this algorithm regarding computational power and convergence accuracy. Finally, the proposed algorithm is applied to two truss topology optimization designs. The experimental results demonstrate the applicability and potential of the proposed algorithm in practical applications. The proposed algorithm is a competitive optimization algorithm for solving optimization problems.