Reviewerly/exHarmony huggingface dataset:
A Pruning Method with Adaptive Adjustment of Channel Pruning Rate

Convolutional neural networks have different channel pruning sensitivities for different layers, and the existing network pruning methods cannot perform adaptive pruning of channels within each layer according to the sensitivity, resulting in irreversible damage to the network during the pruning process. To solve this problem, a network pruning method with adaptive adjustment of channel pruning rate is proposed in this paper, which disassembles the dynamic adjustment process of channel pruning rate in each layer into an iterative optimization problem and finally obtains a network structure with high compression rate and high accuracy. This method performs iterative pruning of the network under the supervision of loss constraint according to the preset pruning initial step for the benchmark model, and then chooses to reduce the pruning step or skip the pruning of this layer according to the accuracy loss of the network. Finally achieves the pruning of each layer in the network with Appropriate pruning rates after multiple iterations. Experimental results show that this method can maintain 93.64% recognition accuracy in CIFAR10-VGG16 pruning task with 87.45% reduction of parameter amount, which has higher compression rate and less accuracy loss compared with PEEC, FPGM, RFP and other pruning algorithms.
Back Translation-EDA and Transformer for Hate Speech Classification in Indonesian

Hate speech is speech or writing carried out by individuals or groups with the aim of pitting parties against each other for various purposes. The impact of hate speech is very dangerous. Machine learning can help with early detection of hate speech, especially on social media. Unfortunately, data providing hate speech in Indonesian is still limited. This study combines augmentation and classification techniques. The augmentation techniques used are EDA and back translation. By combining these two techniques, hate speech data in Indonesia can be produced with many variations. In classification, this research applies machine learning using the transformer method with the Leaky ReLU function in the hidden layer. The purpose of using the Leaky ReLU function is to avoid neuron death because it has a negative value. The performance results in this research using the proposed method obtained an accuracy of 85.21%, precision of 83.09%, recall of 88.78%, and f1-score of 85.84%. These results show the effectiveness of the proposed method approach with good performance for classifying hate speech in Indonesian on social media such as Twitter.
Face Image Inpainting with Parallel Multi-scale Feature Fusion Network

The image inpainting method based on deep learning shows good performance and has a wide range of applications in many fields. In this paper, we propose a face image inpainting method based on Parallel Multi-scale Feature Fusion Network (PMFFN) for the problems of structural distortion and unreasonable semantic information in face image inpainting. Firstly, encoder downsampling extracts image feature to capture the semantic part of the missing area of the image. Then the context information is obtained through the Parallel Multi-scale Aggregation (PMA) module to improve the consistency of the generated area and the known area. Finally, image reconstruction is completed by decoder upsampling, and the Feature Attention Fusion (FAF) module is added to the network to fuse the encoded and decoded feature information. We evaluate the model on public datasets using regular masks and irregular masks. The experimental results show that our model is able to generate vivid textures and achieve satisfactory results.
Enhancing Basic Data Visualization Design through Experimental Approaches: Assessing Preference and Cognition

With the growing demand for effective data visualization techniques, the significance of presenting data clearly and concisely has become a vital process. However, the current focus on visually striking effects and complex interactive elements may divert attention from this primary objective during information learning. To address this issue, it is imperative to thoroughly investigate the impact of visual attributes in data visualization on readers' cognition and preferences. This study aims to simplify the complexity of visualization by examining it through the lens of its fundamental form: the infographic. This paper conducted an online experiment using a custom-built chart library, wherein participants' preference, cognition, and memory for infographics with various visual attributes were measured. The findings suggest that infographics employing contrasting colors and relevant symbols have a positive influence on cognition and memory retention, while the inclusion of dynamic effects did not significantly enhance these cognitive processes. Our study unveils a direct and indirect link between readers' preferences and their cognitive processes.
Visibility Detection Methods in Road Traffic Scene-A Survey

Visibility is an important index to measure the clarity of visible objects in the atmosphere, which is of great significance to traffic safety, aviation navigation, environmental monitoring and other fields. Visibility detection is a process that uses sensors and algorithms to estimate and predict visibility levels. Different visibility detection methods were summarized, shortcomings and limitations of existing methods were summarized, principles and application of image based detection methods and neural network based algorithms were introduced. At the end, a conclusion concerning current methods and multimodal data fusion methods as future research interest were given to readers.
BnSepCNN: Convolutional Neural Network for P300 Detection

Convolutional neural network (CNN) has shown impressive advantages in various applications like computer vision and speech recognition. However, CNN’s applications in P300 detection are still in primary stage. Different from the image and voice data, P300 data have very low SNR and high subject variability so that traditional CNN architectures do not perform well at P300 detection. In this paper, we proposed BnSepCNN based on batch normalization and separable convolution, aiming at reaching high rate for P300 detection with effective CNN architecture. We compared its performance with state-of-the-art architectures: SepConv1D and BN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Our experimental results show that, BnSepCNN performs better than SepConv1D which is based on separable convolution, and it has far fewer parameters than BN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> which is based on batch normalization.
Khmer Calligraphy Style Transfer Using Pix2Pix

The Khmer language has a complex script since it has 137 alphabets and symbols. In this article, we focus on using pix2pix, or image-to-image translation, to transfer font styles from the existing English characters set to the Khmer characters set. We collected and investigated 15 calligraphy fonts that comprise Khmer and English characters. We used the Structure Similarity Index Measure (SSIM) to assess the performance of generated images compared to ground truth images. An experiment shows that pix2pix can perform image generation with an SSIM of 0.93 on average for training using an English calligraphy set and transferring the style to Khmer calligraphy.
Method of the Kernel-based Maximum Entropy Fuzzy C-means Clustering

Most studies incorporating entropy into the fuzzy c-means clustering (FCM) often overlook the fuzzy coefficient, while considering this coefficient typically involves complex iterative derivation and computation. In this paper, a method of the kernel-based maximum entropy fuzzy c-means clustering (K-MEFCM) is proposed. By preserving the fuzzy coefficient, information entropy is added as a regularization term to the objective function, leading to more balanced results. Moreover, to enhance the algorithm’s stability in noisy environments and its ability to handle non-linear and complex data, the Euclidean distance is replaced with a distance induced by the Gaussian kernel function. The derivation process innovatively utilizes the Lambert function to obtain explicit solutions and derive concise iterative formulas, avoiding the complexity of approximate solutions. Additionally, the k-means++ initialization method is adopted to reduce the influence of randomization. Experimental results validate the excellent performance of the proposed method.
Optimization Based Random Forest Algorithm Modification for Detecting Monkeypox Disease

Monkeypox is a zoonotic infectious disease caused by orthopoxvirus. Common symptoms that could indicate Monkeypox are fever, headache, muscle aches, back pain, tiredness or unwell, and swollen lymph nodes. In this research, we conducted a predictive study on Monkey Pox using the Random Forest algorithm optimized with Particle Swam Optimization, which we call PRFO. This algorithm can improve the performance of ordinary Random Forests by making a relatively fast running time and increasing the accuracy value of the algorithm. The results of tests that have been carried out on three datasets, namely: 1) The MonkeyPox dataset, which has 25 thousand data, shows an increase in the accuracy value of 2.08% from 67.80% to 69.88%, 2) The Health dataset with 20 thousand data increased by 0.89% from 92.79% to 93.67%, and 3) The PulsarStar dataset with 12 thousand data increased by 0.27%, From 97.89% to 98.16%. The increase in value is based on the PRFO parameter, which only uses 30 particles with a maximum of 50 iterations. From the tests that have been carried out, the application of the PRFO algorithm can find the best solution on a dataset with more than 10 thousand data in a relatively short time.
PoLaNet: Head Pose Estimation and Face Landmarks Detection Network

In this paper, We proposed an improved efficient structure named PoLaNet for head pose estimation and landmarks detection from a single image while maintaining continuous representation. which transforms the Head Pose Estimation (HPE) problem into a problem of directly predict continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group (SO(3)), then use 3D Morphable Models (3DMM) to predict the landmarks and combined the two loss to enhance the accuracy. The model uses the light MobileNet-V2 as the backbone, and the improved combining loss function. Experiments show that compared with the original SOTA methods, our model parameters were reduced by about 83.3%, and the accuracy improved about 8%.
Digital watermarking encryption algorithm based on synthetic key and wavelet decomposition

This paper presents a novel image encryption method that combines symmetric and asymmetric encryption with a watermark embedding extraction algorithm based on wavelet transform. The algorithm ensures the invisibility and robustness of the watermark, providing the first layer of encryption. The second layer of encryption is implemented by leveraging the efficiency of symmetric encryption and the security of asymmetric encryption. The integrity of the watermarking is evaluated using MEB(%), PSNR(dB), and SSIM(%), while the algorithm's efficiency is assessed through the encryption time T(ms). Experimental results show that the watermark achieves a PSNR of 59.671dB and an SSIM of 99.9, confirming its integrity and robustness. Furthermore, the synthetic encryption process takes only 50 seconds, ensuring both security and efficiency without increasing the time complexity. In conclusion, the proposed synthetic encryption algorithm demonstrates excellent performance in terms of efficiency and security.
Improving Biomedical Claim Detection using Prompt Learning Approaches

Biomedical claim detection is an effective method to uncover negative effects arising from the treatment of disease and detect misinformation about medications from online platforms. Due to the power of pre-trained language models (PLMs), such as BERT, RoBERTa and T5, fine-tuned PLMs perform exceptionally well in biomedical claim detection. However, a gap exists in the text classification task between objective forms used in pre-training and fine-tuning for PLMs methods, preventing these models from taking full advantage of the information for biomedical claim detection. Motivated by the prompt learning approach, we propose a method, in which the classification task is transformed into a masked language modeling task that fully utilizes the mask learning capability of PLMs for better prediction of biomedical claim detection. In our method, a template with a mask representing the label is first constructed, and the mask is then filled and mapped to the corresponding label. We use three PLMs as backbone models, i.e., BERT, RoBERTa, and T5, with both hard and mixed templates which are fully and partially predefined templates. Experimental results using the BioClaim dataset demonstrate the superiority of the prompt learning methods over the BERT and RoBERTa classification baselines. Furthermore, the T5 model with mixed template consistently outperforms the rest of experimented models and achieves state-of-the-art performance with an increase of 5.3% on F1-score compared to previous research on this dataset.
L-HRNet: A Lightweight High-Resolution Network for Human Pose Estimation

Aiming at the problem that the human pose estimation network model is trying to improve the accuracy and thus leads to a large number of parameters and computations, this paper proposes a lightweight human pose estimation network model. We present a solution called L-HRNet that incorporates Depthwise Separable Convolution, Sandglass module, and Attention Mechanism to reduce the network’s parameters and computational complexity while maintaining a high level of accuracy. Compared to the high-resolution network (HRNet [10]), L-HRNet’s model size (#Params) is only 5.6%, and computational complexity (FLOPs) is only 11.9%. Our L-HRNet demonstrates both effectiveness and efficiency on a benchmark dataset: COCO keypoint detection dataset, achieving 65.2 AP on the COCO test-dev set with only 1.59 M parameters and 0.89 GFLOPs. The code and models are publicly available at https://github.com/ApingJJ/L-HRNet.git.
A Study of Audio-to-Text Conversion Software Using Whispers Model

This paper explores the potential of utilizing the Whispers model to create unified interfaces for audio-to-text in the context of Natural Language Processing (NLP). It offers possibilities for accurately converting spoken language into written texts. Whispers model by OpenAI is a state-of-the-art model in the field of NLP and is employe. In this study, various metrics and criteria are considered to evaluate the performance of the developed audio-to-text conversion software, including loading time, stress test, and transcription accuracy and speed. The proposal is capable of handling up to 180 concurrent users with an average response time of 309 ms and 471.5 requests per second. The results and findings of this study provide valuable insights into the effectiveness and limitations of the Whispers model in the context of audio-to-text conversion.
Remote sensing image registration based on multi-head self-attention mechanism

Remote sensing image registration is the process of matching and superimposing multiple sets of images. This technology plays an important role in ground object detection, aerial image classification and satellite image fusion, mainly including traditional methods and methods based on deep learning. Among them, the traditional remote sensing image registration algorithm consumes a lot of manpower and runs for too long during registration. Although the remote sensing image registration algorithm based on deep learning reduces labor costs and improves the adaptive learning ability of the model, the registration accuracy and running time of the algorithm still need to be improved. Aiming at the problems existing in the registration algorithm based on deep learning, a registration algorithm combining the multi-head self-attention mechanism and the convolutional triple attention module is proposed. It replaces residual network residual blocks and combines with other pre-trained convolutional layers for feature extraction. The cross-correlation network was designed, and the Pearson cross-correlation algorithm was used to establish the correspondence between remote sensing images. The registration method proposed in this paper is suitable for three types of remote sensing image registration applications: time difference (multi-time phase), different angle of view (multi-viewing angle), and different shooting sensor (multi-modality). Under these three types of registration applications, the proposed algorithm has high registration accuracy and registration efficiency.
Feasibility Analysis of Machine Learning-Based Autoscoring Feature for Korean Language Spoken by Foreigners

This paper introduces a research concept and feasibility analysis for a Machine Learning (ML)-based autoscoring feature in a mobile application to evaluate foreigners' Korean language proficiency. The proposed concept involves a mobile application that records users' voices, transcribes them using a Speech-To-Text (STT) engine, and generates score predictions using an ML model. In the feasibility analysis, a dataset that consists of a sentence and score pair is collected using the STT engine and Sentence Transformer similarity check. An ML model with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) is developed to train the gathered dataset to generate score prediction that resulted in 0.08054, 0.28380, and 0.20939 for MSE, RMSE, and MAE scores respectively, as performance evaluation. This paper also highlights various challenges and issues that may arise during the implementation of the proposed system.
STM32 miner : A lightweight blockchain mining system

Despite being a promising area of exploration, the application of embedded systems in blockchain technology, particularly with FFGA or Raspberry technology, remains relatively limited. Despite the advantages offered by FPGAs, such as high-speed memory, parallel computing blocks, and a flexible architecture, their use in blockchain has primarily been focused on Proof of Work (PoW) consensus mechanisms.In this paper, our goal is to address this gap by presenting the implementation of a blockchain application specifically tailored for the STM32 platform. By leveraging the capabilities of the STM32 microcontroller, we aim to demonstrate the practicality and effectiveness of embedded systems in blockchain technology, overcoming the limitations observed with previous platforms.
A Just-In-Time Software Prediction Model Based On A Double-Weighted Adaboost

Just-in-time software defect prediction can not only ensure software quality in the development process but also enable developers to check and repair defects on time. Instant defect prediction has the advantages of fast speed and easy tracking, but it is susceptible to class imbalance. In addition, the dataset contains many redundant and irrelevant features, which increases the complexity of the model and reduces prediction accuracy. To improve the performance of software defect prediction, we attempt to overlay ensemble learning and propose a Double-Weighted AdaBoost Just-In-Time software defect prediction framework DWA_JIT, which utilizes various classification algorithms to improve the performance of just-in-time software defect prediction. In the feature selection stage, DWA_JIT adopts the method of randomly selecting feature vectors based on a percentage for each base classifier to reduce data dimensions and improve model accuracy; In the classification stage, multiple AdaBoost frameworks are iterated through the internal base classifiers, and the external multiple AdaBoost are iterated again to form the final dual weighted AdaBoost classification model. The results show that the framework proposed in this article has good classification performance on software defect datasets, and has significantly improved the performance compared to the baseline model.
Blockchain privacy protection scheme based on ring signature

In recent years, blockchain has received extensive attention due to its characteristics of decentralization and non-tampering. However, in the traditional blockchain transaction process, transaction information is recorded in the ledger. Because the ledger is open and transparent, attackers can obtain privacy information such as user account address and transaction amount through the ledger information. Therefore, in order to make the blockchain widely used, it is necessary to solve the security problems brought by the disclosure of its ledger. This paper mainly improves the ring signature technology to ensure the privacy and anonymity of transactions, and realizes the privacy protection of blockchain data. At the same time, an audit node is introduced to determine whether the user is the user who signed the illegal transaction according to the commitment calculated by the user, so as to realize the traceability of illegal users. The simulation results show that the scheme is feasible.
Cloud storage Audit Scheme Based on ORAM and SGX

With the increasing demand for Cloud storage, user access mode protection has also begun to come into people's sight. This scheme combines software protection expansion and Ring ORAM to verify the integrity of cloud data. While hiding the user's access mode and behavior to the ECS, Ring ORAM's Binary tree data structure combined with MHT (Merkel Merkle tree) is used to calculate the data root value to complete the audit, avoiding the generation of data labels The verification of Data integrity, the inspection of untrusted TPA audit results and other cumbersome audit processes, while hiding the user access mode, reduce the audit computing overhead.
Super-Resolution reconstruction of remote sensing image based on generative countermeasure network

The article proposes a method that combines generative countermeasure networks (GANs) and transfer learning on the DRN model to reconstruct remote sensing images. The aim of this method is to address the challenge of obtaining high-resolution remote sensing images by generating reconstructed images with four times higher resolution, richer details, and improved texture. To evaluate the effectiveness of the proposed method, the PP-YOLO model is used to perform object detection on both the original and reconstructed airplane images. The results demonstrate that the average confidence of object detection is significantly improved from 0.5190 to 0.7987, indicating a 0.2797 increase. These findings suggest that the reconstruction method presented in the article effectively enhances image resolution and improves the performance of object detection in remote sensing images.
Optimization of Management Information System for Enterprise Accounting in the Big Data Era - Taking UFIDA System as an Example

Big data technology affects all walks of life and is a new challenge for the accounting industry. Therefore, this paper explores the optimization of enterprise accounting management information systems in the context of big data. Taking the representative UFIDA(Where 'U' stands for 'User', that is, user; 'FID' means loyalty, trust, and comes from the root of English words such as Fidelity; Putting the aid 'A' at the end makes' UFIDA 'easier to read and remember.) system as an example, this paper puts forward the shortcomings of the accounting management information system: expensive maintenance of the information system, poor articulation between the system and the enterprise, lack of flexibility, and poor collaboration and linkage between the system and the enterprise, etc., and further gives the optimization suggestions for the accounting management information system of the big data enterprise.