Reviewerly/exHarmony huggingface dataset:
Research on Oversampling Method of Imbalanced Dataset Based on Improved Generative Adversarial Network

Binary classification frequently entails the challenge of imbalanced datasets, characterized by substantial disparities in sample sizes between the two different classes. The data imbalance usually makes it difficult for classifiers to learn positive samples, leading to unsatisfactory classification effects. Generative adversarial network (GAN) can be used as an oversampling method to rectify data imbalance by generating positive samples. However, when confronted with a multi-cluster distribution of positive samples, GAN often experiences a mode collapse, that is, only a partial distribution of a few samples can be learned, resulting in the aggregation of generated data into a cluster or a segment. To solve this problem, this paper proposes a novel method MIX-NOISE GAN. It employs Gaussian Mixture Model (GMM) to generate GMM noise that aligns with the distribution of positive samples. The GMM noise is then combined with traditional random noise as the generator input, referred to as the mixture of noise. Empirical results and visualizations from experiments conducted on simulated and benchmark datasets demonstrate that MIX-NOISE GAN significantly enhances classification performance on 17 imbalanced datasets compared to six classical sampling methods in terms of G-mean, F-measure, and AVC.
Zeroth-Order Proximal Stochastic Recursive Momentum Algorithm for Nonconvex Nonsmooth Optimization

In this paper, we propose a zeroth-order proximal stochastic recursive momentum algorithm(ZO-ProxSTORM) with mini-batch for solving problems where the explicit gradients of the objective function are difficult to calculate or only the objective function values are available. Then, we analyze the convergence of the proposed algorithm under suitable assumptions. Finally, some numerical experiments are given to illustrate the efficiency of the proposed algorithm.
Control of Sewage Treatment Based on Optimization and Deep Learning Algorithm

In order to enhance the effluent water quality while reducing energy consumption, this paper proposes a multi-objective optimization control algorithm based on IDBO-SENet-BiLSTM-SelfAttention and MOALO. Firstly, an improved DBO algorithm is employed to optimize the hybrid prediction model for predicting energy consumption and effluent water quality. Secondly, the MOALO algorithm is utilized to obtain dynamic optimal setpoints <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{S}_{\mathbf{O},\mathbf{5}}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{S}_{\mathbf{NO},\mathbf{2}}$</tex> . Subsequently, a PI controller is employed for setpoint tracking control. Finally, the proposed method is applied to the BSM1 simulation model. The results demonstrate that this approach can dynamically optimize setpoints and achieve tracking control, resulting in significant improvements in both energy consumption and water quality. Moreover, the average values of various effluent water quality indicators meet the emission standards, confirming the effectiveness of this method in the field of wastewater treatment.
A Hybrid Algorithm of Quantum-Behaved Particle Swarm Optimization and Adam for ANN Training and Application for Classification Prediction

The study of neural networks has become increasingly popular as artificial intelligence has advanced. In addition to several new types of networks and their applications in various fields, neural network optimization has also drawn a lot of attention. Traditional techniques for optimizing the parameters of neural networks are gradient descent algorithms. The drawback of gradient descent algorithms is that they may easily fall into local optima because of the characteristics of local optimization. Meanwhile, several meta-heuristic algorithms have been attempted to optimize neural networks, but the performance is also limited due to insufficient fine search capability. This work proposes the hybrid algorithm HAQPSO to improve the draw-back of gradient descent algorithms, which not only combines Adam with the Quantum-behaved Particle Swarm Optimization (QPSO) but also introduces the hybrid strategy to facilitate information transfer between the algorithms and enhances the efficiency. The proposed algorithm was tested on the Iris dataset and compared with classical gradient descent algorithms and Particle Swarm Optimization (PSO). The experimental results prove that HAQPSO can improve the searching capability while enhancing stability and achieving fast convergence, which is a competitive algorithm.
Deep Learning in Paraphrase Generation: A Systematic Literature Review

Paraphrase generation, the task of generating diverse and semantically equivalent rephrasings of a given sentence or text, has garnered considerable attention in the field of natural language processing. This systematic literature review provides a comprehensive analysis of the current state-of-the-art techniques, their improvements, datasets and evaluation metrics in paraphrase generation. The findings from the gathered research papers reveal that transfer learning, particularly utilizing models such as T5, GPT, and BERT, has demonstrated significant improvements in paraphrase generation, focusing on the model-focused approach. Attribute-focused approaches, such as diversity and controllability, have also gained attention, though their impact on evaluation scores may not be immediately apparent, as they prioritize achieving specific paraphrase qualities. The review highlights that there is still ample room for improvement in enhancing paraphrase generation techniques. This systematic literature review serves as a valuable resource for researchers and practitioners, providing an in-depth understanding of the current landscape of paraphrase generation in natural language processing. It offers insights into the state-of-the-art techniques, their improvements, datasets, and the evaluation metrics employed, enabling readers to navigate the field and contribute to further advancements in paraphrase generation.
Target Guided Knowledge Distillation for Cross-Domain Few-Shot Learning

Existing few-shot classification methods will suffer severe performance degradation when there is a domain gap between the base classes data used for training and the new classes data used for the downstream task. For this reason, cross-domain few-shot learning (CD-FSL) is trying to solve this problem that exists in few-shot learning (FSL), which breaks the restrictive hypothesis that the base (source) classes and the novel (target) classes are sampled from the same domain in FSL. Due to the domain gap between the source and target domain data, the model trained on the source domain often has a deviation when transferred to the target domain, which leads to the model not being well generalized to the target task and produces the problem of inadaptation. To overcome this problem, a new target guided knowledge distillation (TGKD) framework is proposed in this work. We design two distinct knowledge distillation methods to enhance the generalization of the model and its pertinence to the target task in the teacher training and knowledge distillation stages, respectively. Specifically, in the teacher training phase, we propose a self-cross distillation to reduce intra-class semantic variations, thereby improving the robustness of the model's feature representation. In the knowledge distillation phase, a dual-student network is proposed to alleviate the bias of the model when migrating to the target data by utilizing a small portion of unlabeled target samples. Extensive experimental results on several benchmark datasets demonstrate the effectiveness of our method.
Breast Cancer Prediction Based Clinical Symptoms Using K-Means

One of the highest contributors to death in 2022 in Indonesia is breast cancer. This condition is a disease of breast cells that develop abnormally and out of control. If it is not treated immediately, it is feared that it will spread to other parts of the body, including the bones, liver and lungs. The aim of this paper is to predict based on clinical symptoms of breast cancer using K-Means. It is hoped that this approach will be a simple approach but has a good level of accuracy. The data considered in this study were clinical data consisting of BMI (kg/m2), age (years), insulin ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu\mathrm{U}$</tex> /mL), glucose (mg/dL), Homeostatis (HOMA), adiponectin ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu\mathrm{g}$</tex> /mL), leptin (ng/mL), MCP-1 (pg/dL), and resistin (ng/mL). The stages of this research consisted of data analysis, data normalization, modeling based on clinical data using K-Means, and further testing using both data testing and data learning. Tests using training data obtained accuracy value 78%, a precision value of 0.84 and sensitivity value 0.84. The test results using data testing obtained accuracy value 75.76%, a precision value of 0.76, and a specificity value of 0.710.
A Peephole Bidirectional the Long Short-Term Memory Network for Multi-Scale Imbalanced Time Series Classification

For unbalanced multivariate time series with missing values, a multi-scale prediction model based on long short-term memory network variant (multi-PB-LSTM) is constructed. The multi-PB-LSTM utilizes multi-task Gaussian processes to transform asynchronous time series into unified and complete input data by learning the correlation between timestamps and features. At the same time, several classifiers based on LSTM variants are constructed aiming to capture dependencies over a long span in sequence data. Finally, through the decision fusion component of the multi-PB-LSTM model, the prediction results of each classifier are combined to obtain the final classification result. The proposed model can achieve satisfactory performance on some public datasets (MIMIC-III, PhysioNet).
Implementation of the K-Means Cluster Analysis Algorithm to Determine Student Self-Regulated Learning (SRL) Groupings in Carrying Out e-Learning in Yogyakarta

This research implements the K-means cluster analysis algorithm to determine student SRL groupings when carrying out e-Learning. The research results reveal variations in students' SRL profiles during e-Learning, using two approaches in selecting the best model by optimizing the Silhouette value and the Elbow method. The first approach, using the Silhouette method, selected two clusters that had the highest Silhouette scores, namely 0.111 for cluster 1 and 0.232 for cluster 2. The number of members in Cluster 1 was 1645 students, while Cluster 2 had 2229 students. The results of this analysis show that there are differences in SRL characteristics between the two groups. The second approach, using the Elbow method with the best model is a model with five clusters. Although the Silhouette score value in this model is lower (0.090) than the model with two clusters, this approach can help to gain deeper insight into the characteristics that may exist in the data. These clusters have different SRL characteristics. The results of student cluster analysis using the K-means algorithm provide a deeper understanding of student characteristics in organizing their learning. This research provides a deeper look at how e-Learning influences students' SRL and how the use of cluster analysis can support this understanding.
A Durian Yield Prediction Method Based on an Improved Multiple Regression Model

As a tropical fruit, many consumers like to eat durian, especially in Southeast Asia. However, durian production is affected by many factors, such as climate change, soil conditions, and irrigation volume. In order to help durian farmers to predict durian yield, this study proposes a durian yield prediction method based on an improved multiple regression model. This method introduces the residual principal component method to improve the traditional multiple regression algorithm and calculates the residual after a single regression. It also extract principal components from the original correlation factors and use the residual as a principal component for regression modeling. The error rates of the training and test set of the improved multiple regression model are reduced by 9.662% and 7.134% compared with before unimproved model, which show that the improved multiple regression model has greatly improved the fitting degree and prediction accuracy of yield compared with the traditional model, which is beneficial to durian growers in formulating effective planting plans based on yield fluctuations in different years. It can achieve better price competitiveness in the market.
A Computerized Intelligent Diagnostic Model for Hemorrhagic Stroke Based on LSTM Neural Network Prediction

In this paper, we conducted an in-depth study on the diagnosis and prognosis prediction of hematoma dilatation and peripheral edema after hemorrhagic stroke. First, using the data extracted from the table such as the flow number of the imaging examination, a discriminative model of hematoma expansion was established, which discriminated against hematoma expansion based on explicit conditions and successfully realized the prediction of the probability of hematoma expansion with an accuracy rate of nearly 74%. Secondly, this paper analyzed the relationship between edema volume and imaging time, used K-means clustering to classify the edema volume, and explored the effect of different treatment methods on the progression of edema volume. Finally, this paper used LSTM neural network to successfully construct a prediction model for MRS score, which further optimized the accuracy of diagnosis and treatment.
Enhanced IoT Devices with Intelligent System for Object Detection

Object detection has gained significant interest in computer vision because of its many types of applications, ranging from surveillance to autonomous vehicles. In this context, this work explores the development of an object detection system utilizing the ESP32 camera module. The ESP32 camera module is compact in size, consumption of power is low, and wireless capabilities make it suitable for real-time object detection tasks. By leveraging the camera module's imaging capabilities and the computational power of ESP32, this work aims to create an effective and economical solution for object detection in various settings. The proposed system integrates the ESP32 camera module with image processing techniques to perform object detection. This involves capturing live video streams, processing frames in real-time, and identifying objects inside the camera's field of view. The ESP32's ability to execute complex machine learning models efficiently is expected to yield accurate and responsive object detection results. Moreover, its connectivity options enable remote monitoring and control, enhancing the system's versatility and applicability. The accuracy is determined by calculating the real and measured dimensions of the object detected, and it is evident that the suggested method recognizes objects with high accuracy.
Supplier Recommendation Method Based on Semantic Matching and Bipartite Graph Network Collaborative Filtering Algorithm

In recent years, e-commerce has achieved rapid growth through the use of big data technology. With the increasing number of participants in emerging markets, e-commerce intermediaries play a key role in finding the best match between demand and supply. This article relies on data mining techniques to analyze 14,330 inquiry orders and 40,131 quotation records, revealing hidden relationships, patterns, and trends in the order data, and constructed a personalized and target supplier recommendation system. Firstly, a bipartite graph collaborative filtering recommendation algorithm is adopted for supplier recommendation. Then, TF-IDF and Word2Vec pre-trained models are used to calculate cosine similarity of commodity text data to identify buyer's product demands. Finally, a bipartite graph collaborative filtering algorithm and semantic matching algorithm are used to provide a list of recommended suppliers for the target users. This article explores the opportunities brought by big data to the supplier recommendation system in the commodity market.
MATNet: MRI Super-Resolution with Multiple Attention Mechanisms

High-resolution magnetic resonance imaging (MRI) provides a clear anatomical structure for diagnosis, however, its high cost makes it unsuitable in practice. On the contrary, low-resolution MRI cannot provide fine structural information but is resource and time efficient. Super-resolution methods are high-resolution images obtained from low-resolution MRI. However, existing MRI super-resolution methods suffer from the following defects: (1) CNN-based super-resolution methods lack the ability to build relationships for remote features. (2) While models using transformer based on self-attentive mechanism have achieved a breakthrough in super-resolution, the single self-attentive model fails to fully utilize the useful features of the input image. Thus, in this paper, an MRI super-resolution network (MATNet) based on a multi-attention mechanism is proposed that can utilize multiple useful image features from different dimensions for image super-resolution, improved super-resolution performance. To enable the network to obtain noise-free image features before sampling the image, we also added a denoising module to it. Finally, we prove the effectiveness of the model using the IXI public dataset.
Image Inpainting Based on Dual Dynamic Predictive Filtering and Fast Fourier Convolution

Despite outstanding performances of the existing image inpainting methods in restoring corrupted images, they often generate images with artifacts or missing details. Predictive filtering is a widely used image restoration technique which can preserve local structures and avoid artifacts, but it cannot fill large missing areas. Convolutional neural networks are suitable for large missing areas, but it is difficult to restore fine details. Therefore, we treat image inpainting as a filtering task, and propose a novel image inpainting method consisting of two networks, namely the Attention-Enhanced Kernel Predictive Network (AE-KPNet) and the Deep Generative Network (DGNet). The two parallel networks are linked, and DGNet provides features to AE-KPNet, while AE-KPNet trains dual dynamic predictive filtering kernels for DGNet. To address the issue of large missing areas, we introduce fast Fourier convolution into DGNet, and the operation in the frequency domain expands the spatial receptive field. We evaluate our model on three public datasets, CelebA, Places2, and Paris StreetView. Through quantitative and qualitative experiments, the results show that our method achieves the performance of state-of-the-art methods.
ICGNN Model for Fall Detection Using Inertial Sensors

Falls are a major cause of increased morbidity and mortality in the elderly. To solve the problem that it is challenging to detect human activity due to the fixed perspective of the camera, easy to be impeded and affected by the environment, a human fall detection method based on a wearable inertial sensor and convolutional recurrent neural network is proposed. First, multiple inertial sensors are used to capture the acceleration, angular velocity, and angle signals of the subject's fall and non-fall action. Then, the signal data is pre-processed and a data representation method suitable for multi-sensors is designed to realize the mapping between labels and signals. In this paper, an Inception-based convolution module and gate recurrent unit neural network (ICGNN) is proposed to extract multi-scale and temporal features of human actions from inertial signals and automatically classify fall actions. Experimental results show that the recognition rate of the model is high, and the accuracy can reach 93.36%, which has a good effect on the recognition of fall actions.
Ocean Wave Information Extraction from Raw Ocean Video Based on Self-Attention ConvLSTM

Precise wave information is essential for assessing fatigue damage to offshore wind turbines. This study explores a visual method based on ConvLSTM combined with CNN and self-attention memory to extract ocean wave details. The network extracts both temporal and spatial information from wave impact videos to estimate significant wave height (Hs) and wave peak frequency (fp). Separate training for Hs and fp demonstrates the network's accuracy under varying imaging conditions. Additionally, the inclusion of a self-attention memory module based on ConvLSTM enhances model performance, improving prediction and regression capabilities.
A Comparative Study of Load Balancing Methods in Cloud-Based Village Information Systems

Internet usage has been steadly increasing, with many needs or information that must be sought. The problem that is often experienced when many visitors access a website is that the server acting as a web server is unable to handle requests so services cannot be processed. This can be handled using the load balancing method using HAProxy. A web-based application certainly requires an adequate server computer so that the application can run properly. Cloud computing has emerged as a new trend that promotes efficient resource utilization and optimal server infrastructure utilization. Presently, numerous cloud computing platforms are offered, one of which is Amazon Web Services (AWS). In this research, several stages were carried out, starting from literature study, software design, testing stages and analysis of the results of testing data, drawing conclusions, and finishing. In designing cloud infrastructure, researchers created three services on AWS including EC2 (Elastic Cloud Compute), RDS (Relational Database Service), and VPC (Virtual Private Cloud) services. The village information system's load balancing design successfully applied to cloud technology, Amazon Web Service, and successfully implemented HAProxy as the load balancer and OpenSID as the village information system's website. the HA proxy employs various algorithms for a website, including round robin, least connections, source IP Hash, and Uniform Resource Identifier (URI). By using the parameters of standard deviation, throughput, average bytes and error in performance tests consisting of load tests and soak tests in this research, it has been determined that the URI algorithm is the most suitable for OpenSID.
Solar Radio Spectrum Classification by Deep Conventional Neural Network Based on Transfer Learning

The damage to communication facilities and navigation equipments may be caused by the violent solar radio explosion. With the emergence of high-performance observation equipments, solar data shows a tremendous growth trend. Deep learning, especially convolutional neural network, is used to classify solar radio spectrum images because it performs well in the processing of massive data, especially image data. In response to the lack of solar image datasets, a transfer learning method based on four pre-trained models with ImageNet image dataset is improved to solve the problem of classifying and recognizing three categories of solar spectral images. The experimental results show that MobileNet based on this model can achieve 98.95% accuracy, while the training speed of the model is improved also.