Reviewerly/exHarmony huggingface dataset:
ESH: A Non-Monotonic Activation Function For Image Classification

By providing non-linearity and enabling the network to understand complicated associations in the data, activation functions play a vital role in the performance of neural networks. Here, we introduce Esh, a brand-new activation function with the formula, $f(x) = xtanh(sigmoid(x))$. Using CNN architectures, we assess Esh’s performance on the MNIST, CIFAR10, and CIFAR-100 data sets. Our tests demonstrate that the Esh activation function outperforms a number of well-known activation functions, including ReLU, GELU, Mish, and Swish. In fact, compared to other activation functions, the Esh activation function has a more consistent loss landscape. Esh is a potential new activation function for deep neural networks, according to the findings of our study, and we anticipate that it will be widely used in the machine learning industry.
Detecting Deepfakes With ResNext and LSTM: An Enhanced Feature Extraction and Classification Framework

With the growing availability of advanced editing tools and machine learning algorithms, it has become easier to create realistic and compelling deepfake videos, which are altered media content that is deliberately intended to deceive viewers. Deepfakes can have severe societal consequences, ranging from political propaganda to financial fraud, and they offer significant challenges for content authentication and fact-checking. This paper proposes an AI based technique which uses ResNext CNN and in combination with LSTM classifier to authenticate a video based on various visual features. The accuracy of this model is calculated at various sequence lengths for different datasets, and it is observed that the accuracy of this model rose with sequence length when testing it on various sequence lengths, and it almost begins to stabilize from the sequence length of 60.
An improved image segmentation model of FCN based on residual network

In this paper, the fcn model with Resnet as backbone is used for training and testing on voc2007 dataset. Twenty categories were trained, tested, and trained for around of about 10,000 pictures. There cognition and segmentation ofimages are realized. The overall recognition rate reaches 92.6%. The overall IoU reaches 68.3%, which is higher than the 5.6% of FCN-8s in theoriginal paper. We have adjusted the network structure of Resnet and improved the algorithm to achieve the accuracy of identification and segmentation with less time cost. The weight of thisalgorithm after training can be applied to specificoccasions of image recognition or object detection.
Transformer-based computer vision technology empowers drones

With the rapid development of drones. The traditional “manual feature extraction + classifier-based” object detection algorithm can no longer meet the accuracy requirements. Aiming at the problem that the reasoning speed is slowed down due to the complex background of UAV aerial images, we propose a target detection model based on a multi-head attention mechanism: Swim-Transformer. This model introduces a multi-head attention mechanism based on the YOLOv5, and introduces a decoupling head method in the head to improve detection accuracy and speed up network convergence. Experiments show that the new target detection framework is superior to the traditional target detection algorithm on the UAV aerial photography data set VisDrone, and increasing mAP by about 0.0067%.
Design of zero knowledge proof algorithm for data transaction

This paper proposes a zero-knowledge proof data transaction verification algorithm, aiming to solve the problem that buyers cannot verify data in advance in blockchain-based data transactions. The algorithm combines zero-knowledge proof technology with blockchain to verify a portion of the data in advance. Buyers can specify the data segment to be verified without disclosing other information to ensure their interests. By verifying the data in advance, it is beneficial to promote trust between the two parties and achieve transactions. In practical tests, the verification algorithm of this paper is deployed in the blockchain through smart contracts. The seller sends the data segment and proof to the buyer, and the buyer verifies the data locally. Algorithm analysis and experimental results show that the algorithm performs well in security, efficiency, and has certain practicality and promotion value.
Towards Fraudulent URL Classification with Large Language Model based on Deep Learning

The issue of fraud has become increasingly prevalent, with personal information frequently obtained through web addresses resulting in a high incidence of fraud. Traditional machine learning methods or black and white lists are often employed to classify URLs involved in fraudulent activities, however, these methods are time-consuming and often yield poor results. To address this, the present study introduces a novel approach utilizing language models to detect fraudulent websites, with a focus on interpretability analysis, and ultimately the application of the model to a server.
Research on Trusted Sharing Technology of Measurement Test Data in Blockchain

With the development of the Internet of things, big data and artificial intelligence technology, the value of data has been paid more and more attention. The research on trusted sharing technology of measurement and test data in the blockchain aims to develop a new generation of digital assets based on the blockchain technology. It aims to provide a trust based mechanism for the exchange and transfer of information between the parties and can act as an intermediary to achieve mutual verification and consensus. The main function is that it can verify whether there is any problem with the data and ensure that all transactions are accurate. This paper uses the blockchain technology to build a distributed data sharing system, which not only helps data flow, but also protects users' privacy and rights.
Comprehensive study of image processing techniques for low-altitude target recognition

This article presents a comprehensive overview of image processing techniques for low-altitude target recognition. The analysis includes various algorithms that incorporate shape, texture, and motion features, highlighting their advantages, disadvantages, and applicability. Additionally, this article explores multiple methods for low-altitude target recognition based on multi-feature fusion, comparing their advantages and disadvantages, and discussing future technological development directions. The article serves as a valuable reference for researchers interested in low-altitude target recognition, providing insights on algorithm selection and the application of multi-feature fusion. The presented research is expected to contribute to the advancement of low-altitude target recognition technology.
Multi-criteria Fusion Method for Line Selection of Single-phase-to-ground Fault with Small Current based on GRU Neural Network

Based on the GRU neural network model, a multi-criteria fusion method of fault line selection for small current grounding system is proposed. In the experiment, using MATLAB/Simulink to build the small current grounding fault simulation model, using Fourier transform and Wavelet packet decomposition to obtain the data of the fundamental component, the fifth harmonic component and the wavelet energy component of various fault types, then input the data into the trained fault line selection model of GRU neural network to verify the feasibility and validity of this selection method.
Research on Image Recognition based on Reinforcement Learning

Nowadays, the images recognition has become a fundamental component in computer vision and assist to identify the objectives in the complex images. With the development of images processing methods, the image-level objectives recognition accuracy has been greatly enhanced by utilizing the machine learning models or neural networks. However, existing methods are mainly concentrated on the primary features of input images and concentrate on some certain areas, which ignore the environment features and the deep investigation of the image data-set. In this paper, we propose a novel image recognition method to identify the objectives and obtain the policy gradients for decreasing orders. Furthermore, we compare our proposed models with existing traditional machine learning methods to evaluate the performance of recognition accuracy. From our extensive experimental results, we can conclude that our proposed methods can achieve the subjective detection from numerous images data-set with reasonable computation costs.
Flame Recognition Algorithm for aerial cruising firefighting Robot based on RGB and improved YOLOv5

With the development of automation, intelligence, and unmanned technology in the warehousing industry, aerial cruise fire robots will become safety protection equipment for large logistics centers, large ports and docks, and other public places. The visual recognition system of aerial cruise firefighting robots is crucial for the recognition performance of flames. During the process of robot cruising, lighting, sunlight, and reflective objects are the main obstacles that confuse flame recognition. This article uses the image preprocessing algorithm RGB to filter out the areas suspected of being in flames through RGB threshold. Then, the filtered image is expanded and corroded through a filter, connecting the adjacent areas suspected of being in flames. Finally, the YOLOv5 algorithm model incorporating SE attention mechanism is used to perform flame recognition on the preprocessed image. This article uses the final algorithm model for experiments, and performs flame recognition on 5000 random photos, with a recognition accuracy of 96.291%.
Detecting Sarcasm in Reddit Comments: A Comparative Analysis

Sarcasm is the use of sarcastic words to mock or mockingly show disdain for something. Several people frequently use it on social media sites like Reddit and Twitter. This study investigates the effectiveness of deep learning and machine learning algorithms in detecting sarcasm using SARC dataset consisting of 1.3 million Reddit comments with almost equal amounts of sarcastic and neutral comments. We compare several well-known machine learning classification methods, including Logistic Regression, Naïve Bayes, Decision Tree Classifier, and Convolutional Neural Networks (CNN). Our results, with an accuracy of 73.2%, demonstrate that the model designed using a fusion of CNN and Long Short-Term Memory Networks (LSTM) techniques performed better than alternative classification algorithms. Our findings show how machine learning techniques will be used in the future to identify sarcasm on social networking websites.
RCCNet: Reducing Channel Convolution Network for Monocular Depth Estimation

Monocular depth estimation is an important research field in computer vision, with many applications in autonomous driving, 3D reconstruction, and other fields. However, previous research on monocular depth estimation often resulted in significant computational overhead. Therefore, we design a lightweight model for monocular depth estimation, which is Reducing Channel Convolution Network (RCCNet). RCCNet introduces Reducing Channel Convolution (RCConv) to extract local features with a lower computation cost. Extensive experiments show that this method can achieve comparable results while a tiny computation cost.
Automatic Web Services Recommendations using the Robust Deep Learning Approach

This study aims to fill the gap in the literature by proposing a deep learning framework for designing and implementing robust recommendations for web services. Previous studies have looked at the benefits of this approach in theory rather than in practise. There have been several attempts made regarding the practical situation of the recommendation system in high-quality online services using a deep learning model. We have also seen the failures and decline in performance of the deep learning framework in several recent publications. As a result, developing and assessing a deep learning module for a recommendation system has emerged as a significant area of inquiry. This study's main originality is the proposal of a deep learning-based architecture for the Recommendation system. The data obtained from the nodes is used in a recommendation approach that employs three distinct deep learning models: a multi-layer perceptual network, a convolutional neural network, and a recursive neural network. Research progress was applied to all three models. This study's original contribution is a methodology for visualising deep learning data's precision and performance. To apply the Robust web service recommendation strategy to the textual data of microblogs, the framework for the Recommendation system based on deep learning models on Robust web service recommendation makes use of a dummy variable approach for categorization. Within this study, we provide the planning and analysis of experiments for such a system.
Underwater Image Color Analysis and Indicators Based on Statistical Characteristics

When light propagates in water, different wavelengths of light have different degrees of attenuation, resulting in the color cast of underwater images. Color characteristics are important to solve the problem of color cast. Therefore qualitative and quantitative analysis of underwater image color characteristics and color indicators studies in RGB space are carried out in this paper. A qualitative comparative analysis between underwater images and land color cast-free images is carried out. It is concluded that there are differences in histogram and statistical characteristics between RGB channels of underwater images. Then a set of color characteristics indicators is established that can measure the degree of color cast and the differences between color channels of the underwater images. Finally, experiments on quantitative analysis of color characteristics are carried out. Different turbidity levels and colors of the water environment are set up to analyze the changes and differences of color characteristics. Underwater image color characteristics are obtained by comprehensive analysis.
Fine-Grained Image Recognition Method Based on Input Perception Joint Probability Prediction

A method of fine-grained image recognition in the simple background based on input perception joint probability prediction is proposed. The recognition network consists of an input perception module and a joint uncertainty estimation module. The input perception module takes the input image as the global scale image and automatically extracts the foreground scale image and key component scale image by introducing the attention mechanism and using the foreground perception mechanism and component perception mechanism. The joint uncertainty estimation module uses three dynamic probability estimation parameters to evaluate the prediction confidence of each branch and then combines three branch votes to make the final decision. In order to verify the effectiveness and rationality of the input-aware and Probabilistic Prediction Convolutional Neural Network (IAPP-CNN) algorithm, a comparative experiment was performed on the data set. Ablation experiments verified the feasibility and rationality of the algorithm. The results show that the value of each evaluation index of the algorithm is more than 99.0%, which has high recognition accuracy and robustness and meets the recognition requirements. IAPP-CNN algorithm can improve the recognition accuracy of bird images with excellent performance and can greatly improve the performance of fine-grained image recognition.
Comparison of Image Generation methods based on Diffusion Models

Diffusion models (DMs) is a new generative model based on deep learning with computational vision, which has been applied to image generation, image enhancement, image restoration, text-to-image and other fields. Compared with traditional autoregressive models, variational autoencoders (VAEs), energy-based models (EBMs), flow-based models (FBMs) and generative adversarial network (GAN) models, diffusion models can generate images with strong diversity and significant details, and have many advantages such as easier training and flexibility compared with generative adversarial networks. This paper mainly summarizes the existing image generation models based on diffusion model, compares them, and proposes possible improvement schemes.
Neural Network Algorithm for Short-term Photovoltaic Power Prediction Accuracy Improvement

One of the methods to improve the accuracy of short-term Photovoltaic(PV) power prediction is the improvement of prediction algorithms. To sort out the application of neural network algorithms in short-term PV power prediction, four algorithms, Back Propagation Neural Network(BPNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Bi-directional Long Short-Term Memory (Bi-LSTM), are selected for analysis and validated by practical arithmetic cases in this paper. The results show that the neural network algorithms are well adapted to short-term PV power prediction and have high prediction accuracy. The accuracy of short-term PV power prediction can be improved by improving the neural network prediction algorithm.
Passable Area Detection in Off-road Environments Based on Improved PSPNet

In order to improve the adaptability of autonomous vehicles in off-road environments, detecting passable areas is a necessary step that cannot be avoided. Compared to structured roads in urban environments, road extraction in off-road environments is more complex. This paper conducts research on various off-road scenarios and proposes an improved method for passable area extraction using a modified PSPNet. During feature extraction, a lightweight MobileNetv2 network is utilized with depthwise separable convolution and residual edge modules. In addition, dilation convolution modules are added and shallow image features are fused. Experimental results demonstrate that the proposed method achieves better performance in passable area detection in off-road environments, and has high application value and practicality.
Research on Road Marking Extraction Method Based on Machine Learning

Road traffic markings can achieve traffic separation, channel traffic at level crossings, indicate and predict the road conditions ahead, and are the basis for traffic managers and citizens to abide by the law. Effective automatic extraction of road markings can help traffic achieve efficient operation. The article uses edge extraction operators, threshold segmentation, K-means clustering, and region growth methods to extract road markings, summarizes the advantages and disadvantages of the extraction methods, and provides technical guidance for the implementation of automatic road marking extraction.
Research on an embedded-based mask wearing detection system

Since the outbreak of the new crown epidemic began worldwide, the virus has been spreading very fast, and the most convenient and effective way to deal with the spread of the virus is to wear a mask properly. Up to now, the new crown is still in epidemic, not only the new crown virus, but also the current outbreak of influenza A virus. In order to reduce the speed of virus spread and protect our own health, in some public places, we have to wear masks according to the epidemic prevention and control requirements. In this paper, we propose a mask wearing detection system based on embedded Linux, using the optimized and improved YOLOv5 network model, and deploy it to the embedded system to achieve real-time detection of mask wearing.
Semantic segmentation of ultra-high resolution remote sensing images based on fully convolutional neural networks

Semantic segmentation has been widely used in various fields of remote sensing; however, ultra-high resolution remote sensing images, due to their extremely high resolution, exhibit more spatial details and cannot be directly segmented using manual segmentation or traditional semantic segmentation methods. We applied fully convolutional deep learning-based neural networks to the semantic segmentation of ultra-high-resolution remote sensing images and at the same time designed an efficient image segmentation strategy to reduce the difficulty of training while ensuring the integrity of the dataset. We conducted experiments on the ultra-high resolution remote sensing dataset Potsdam and demonstrated that the semantic segmentation method based on fully convolutional neural networks can achieve 93.78% MIoU and 97.39% PA on the training set.
Cell Image Segmentation Method Based on Edge Feature Residual Fusion

Automatic cell segmentation based on deep learning has become an important means of computer-aided pathological diagnosis. Aiming at the existing problems of rough segmentation boundaries and inaccurate segmentation in cell image segmentation, a cell image segmentation network model is proposed in this paper, which based on edge feature residual fusion from the perspective of mutual complementarity and constraint between edge features and object features. The model uses a hybrid architecture of CNN and Transformer to extract multi-scale features from cell images, uses independent edge feature extraction modules and residual fusion modules to enhance the extraction of edge features and their constraints when fusing with cell object features, thereby improving the accuracy of cell contour positioning. The experiments on two gland cell datasets, CRAG and GLAS, by comparing the segmentation effects with current popular deep learning models, show that the network model proposed in this paper has achieved good performance in both Dice coefficient and Hausdorff distance, which can effectively improve the segmentation effect of cell images.
A whole-slide pathology segmentation framework for the diagnosis of non-small cell lung cancer

Immunotherapy is one of the most important approaches to treat NSCLC. Patient treatment decisions are based on the analysis of NSCLC biopsy images by pathologists. Deep learning (DL) methods have the potential to enable automated segmentation and classification of tissues in biopsy images, allowing for quantitative analysis. However, performing segmentation requires high-precision algorithms and computational resources due to the large size of whole slide pathology images, which is a challenging task. In this paper, we propose a new approach that considers local and global information to effectively segment and classify tumor regions in pathology images. The core of our approach is a two-level hierarchical deep neural network that utilizes a classification network to quickly filter out background regions and form a global stage heatmap, to guide the segmentation task to improve segmentation speed and accuracy. Experimental results show that the proposed framework exhibits high segmentation performance compared to a single classification model, providing a new idea for developing an effective segmentation method.
Context-Aware Fusion for 3D Object Detection in LiDAR-Camera Systems

LIDAR and camera are essential sensors in autonomous driving systems, but each has shortcomings in handling various complex environments. Therefore, many studies use fusion technology to bridge the gap between sensors to achieve better perception performance. This paper proposes a novel context-aware decorator (CAD) to densify and decorate the point clouds for 3D object detection. The purpose of CAD is to provide the point cloud with prior knowledge of object position and context features. It would make the point cloud texture richer in the object region. The decorated point cloud can be fed to the LiDAR-based 3D object detectors for classification and 3D bounding boxes regression. The LiDAR-based detectors combined with the CAD experiment showed that our method could improve by 4.24% mAP for PointPillars, and at least provide +2% mAP widespread improvements for other methods in the KITTI benchmark.