Reviewerly/exHarmony huggingface dataset:
Random Forest Distance Estimator Based on Monocular Egocentric RGB Camera in Human-Robot Collaboration

This paper presents a method for obtaining 3D gesture poses from monocular RGB camera images. A random forest based distance estimator is constructed and trained to generate depth information for hands. Furthermore, to improve efficiency, we employ a federated learning approach to reduce the model complexity of random forests. The RNN recognizes the topological structure of the hand, transmitting two-dimensional information of the hand keypoints to a random forest for the corresponding distance estimation. Experimental and application demonstration results show that this method can obtain stable 3D gestures from 2D images and reduce the complexity of the system implementation and registration. Because of input normalization, it can adapt to other cameras without the need for preconfiguration. Therefore, this method offers an effective and cost-efficient solution for obtaining 3D gesture poses, resulting in a significant cost advantage in the application of human-robot collaboration and making it highly promising for real-world applications.
Multi Criteria Decision Making for Ranking Digital Vocational Education

In this paper, the researchers were interested in multi criteria decision making (MCDM) for ranking of digital vocational education. The framework for such a ranking, when combined with academic processes, can be significantly improved. This paper aims to 1) Synthesise the components of digital vocational education. 2) Synthesise the ranking process using the MCDM method. 3) Propose a conceptual framework for digital vocational education ranking. The results revealed that the framework of digital vocational education consists of five components: (1) the learner, (2) the teacher, (3) the course of study, (4) participation and (5) fundamentals. The MCDM method has five method for ranking: (1) collect data, (2) data normalization, (3) incorporate weightings, (4) analyze, and (5) ranking. At the analysis stage, there are four ideal methods for sequencing data. These are AHP, Fuzzy-AHP, TOPSIS and Fuzzy-TOPSIS.
Design and Development of Hand Movement Detection Device for Sign Language using IMU and sEMG Sensors

Individuals with hearing impairments often communicate using sign language, which can create communication challenges with hearing individuals who are unfamiliar with sign language. Therefore, the objective of this work is to design, develop and construct a prototype of the hand movement device for translating sign language into text displayed on a screen for individuals with hearing impairments. The developed device is designed to wear on wrist of people with hearing impairments. It uses sEMG sensor to obtain EMG signals, whereas the acceleration data are collected using IMU sensor. The hand movements in 4 gestures (thank, sorry, sad, and lucky) are tested with a data acquisition frequency of 25 Hz for 10 times in each gesture. The results show that the sEMG sensor together with IMU sensor can be used to distinguish those 4 gestures since the signals collected from these sensors show different patterns clearly. Future study will be focused on finding the best position to install the sEMG sensor and testing the developed device with more gestures.
Smart Shelve System with Application

The growth of technology today is a fast leap, causing development in many areas including shops, products, businesses, and services. However, the development of shops is relatively expensive, especially modern shelves with intelligent systems that makes it less popular. For this reason, the researcher therefore foresees that there should be shelves that are not very expensive, easy to use, and have a system that can use applications and databases. By using the Arduino IDE, MIT App Inventor, and Firebase program to help create, it results in the development of stores that are applicable.
Improving Lower Limb Activity Recognition Based on Inertial Sensors Using CNN-LSTM Network

The utilization of wearable sensors has gained significant popularity across various domains. This widespread adoption has driven the need for advancements in sensor technology, focusing on attributes such as lightweight design, dependability, and integration capabilities. One specific area where wearable sensor networks have faced challenges is in human activity recognition, particularly in accurately inferring movement data. The effectiveness of this process depends on several factors, such as the quality of sensor-derived features, the discriminatory power of selected features, and the chosen classification methodology. To address these challenges, this research introduces a novel hybrid deep learning architecture called CNN-LSTM. The primary goal of this architecture is to enhance the accuracy of recognizing human lower limb movement. To evaluate the effectiveness of the proposed model, a series of investigations were conducted using the publicly available HuGaDB dataset. This dataset consists of data collected from a body sensor network comprising six wearable inertial sensors, including accelerometers and gyroscopes. These sensors were positioned on both the right and left thighs, shins, and feet. The outcomes of the investigation demonstrated that the suggested model achieved an impressive precision of 97.81 % and a maximum Fl-score of 95.50%. These results highlight the effectiveness of the CNN-LSTM model in accurately recognizing human lower limb movement.
Poisoning Attack-Based Security Evaluation Framework for Credit Risk Evaluation Models

Machine learning has been widely used in the field of credit risk evaluation (CRE). However, existing studies have mainly focused on the evaluation performance of the models, and little attention has been paid to the security of machine learning models. It has been shown that machine learning models are susceptible to the influence of adversarial samples and thus make incorrect judgments. In this paper, we propose a security evaluation framework based on poisoning attack for CRE models (SEF-PAC), using Generative Adversarial Nets (GAN) to achieve automatic large batch generation of adversarial samples, and evaluate the security of 8 CRE machine learning models in the face of poisoning attack. It is found that the adversarial samples generated using GAN are still transferable to the 8 CRE machine learning models under poisoning attack, and are able to make target models significantly less accurate in identifying samples with bad credit without a significant decrease in overall classification accuracy. This indicates that the current machine learning-based CRE models have huge security risks and proves the feasibility and effectiveness of the security evaluation framework proposed in this paper, which provides an important reference for enterprises to evaluate and improve the security of CRE models.
Weakly-Supervised Temporal Action Localization Based on Attention Regularization

The primary objective of temporal action localization is to identify the beginning and end moments of actions in unedited videos. We propose a weakly supervised temporal action localization model based on attentional regularity to address the difficulty of accurately identifying these temporal boundaries. Weakly supervised action localization frequently encounters the issue of conflating actions and backgrounds as a result of the absence of frame-level labels. To address the problem of difficulty in distinguishing actions from backgrounds, we introduce an attention mechanism to improve the loss function, which after attentional regularization allows the model to more accurately distinguish between actions and backgrounds. We construct a pseudo-label mutual supervision mechanism to achieve more complete localization supervision in order to reduce potential omissions during action localization. Extensive tests on benchmark datasets like THUMOS14 show that our method outperforms other weakly supervised methods in terms of average accuracy.
Generalized Enhanced Convolution Block for Super-Resolution Networks

Balancing the computational complexity and performance of CNN-based super-resolution (SR) networks is a challenging task in practical scenarios. Leveraging linear reparameterization techniques to improve model performance with minimal negative impact is a promising approach. In this work, we find that previously proposed re-parameterization techniques do not adapt well to different architectures of SR networks or yield significant performance gains. Hence, we propose a highly versatile and high-performance re-parameterization block called Generalized Enhanced Convolution Block (GECB) for efficient training of SR networks. During the training phase, GECB can replace grouped k × k convolutions with N groups for efficient training. GECB has six different paths with carefully designed structures, aimed at improving the representation capability of various features and generating more adaptive gradient updates. During the inference phase, GECB can be transformed to a normal convolution, without affecting the original model structure. Additionally, we further investigate the gradient update rules of GECB to demonstrate its outstanding adaptability. Extensive experiments demonstrate that our GECB achieves similar or better performance than previous SR re-parameterization methods on multiple benchmark datasets. Moreover, our method can adapt to a broader range of network architectures, making it applicable to more SR networks and enhance their performance.
Research on the Regulation Strategy of False Propaganda of Live E-commerce Based on Three-party Evolutionary Game

The problem of false propaganda in live e-commerce is becoming increasingly prominent and serious. Based on this background, the article constructs a game model between the main stakeholders of false propaganda in live e-commerce, i.e., live-streaming platforms, live e-commerce sellers, and government regulators, from the perspective of evolutionary games, and uses MATLAB to simulate the behavior of stakeholders. It is found that: at the early stage of market regulation, government regulators do not need to strengthen regulation, and at the middle and late stage of regulation, increasing the intensity of regulation will prompt live e-commerce sellers to develop regulated behavior fully. The fine system is bounded. When the number of fines set by government regulators meets the condition of making the live e-commerce sellers' choice of false advertising strategy less profitable, it is meaningless to increase the amount of fine regulation strategy. Improving the detection rate of false propaganda violations by live broadcast platforms is a key strategy in the regulation of false propaganda for live e-commerce.
Prevention and Detection Attack of Social Media Networks Using Machine Learning Methods

preventing malicious and cyberbullying activity, which is primarily carried out by fake profiles, spam messages, and other such actions. The privacy practices of the social network communities are questioned by these actions. These phony profiles are to blame for the dissemination of incorrect information on social networks. Much study has been done in this field to identify fraudulent profiles, duplicate posts, spam accounts, and bot accounts. The majority of bogus accounts were successfully identified by employing a machine-learning system. This paper is a variety of feature selection techniques as well as a variety of machine learning-based methodologies are employed to identify fake accounts that can deceive users. Eight feature selection strategies were combined with five machine learning methods to preprocess the dataset and identify bogus accounts, The feature selection process (which uses multiple methods to rank features) includes eight feature selection processes (Chi-Square Test Feature Selection, ANOV A Feature Selection, Mutual Information Feature Selection, Logistic Regression Feature Selection, Additional Tree Feature Selection, Embossed Feature Selection, mrMR features Selection and, Light GBM Trait Selection), all outputs are evaluated by finding similar features in the process of choosing between the outputs of the eight algorithms to find the best features to be used by the classifier for best performance. A classification method consisting of five machine learning algorithms: Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Stochastic Gradient Descent (SGD), Random Forest (RF), and Decision Tree (DT) was used and tested, the best of which proved to be the best in the identification process we mentioned.
Group Sparsity Based Higher Order Sparse Spectrum Fitting Approach for USV Localization

The problem of unmanned surface vehicle (USV) localization is investigated in this paper. We develop a group sparsity based higher order sparse spectrum fitting (HOSpSF) approach to improve the accuracy of localization. In the proposed approach, the off-grid issue, and advantages of group sparse are combined. Firstly, a higher-order SPSF method is proposed to solve the off-grid DOA estimation problem by using the second-order Taylor approximation. Next, we present a USV localization approach that optimizes all sensor array nodes by incorporating the group sparsity theory. Simulation results demonstrate the effectiveness and superiority of the proposed method against the state-of-the-art arid-based approaches.
Review: Adaptive Mobile Learning Algorithms Current State-of-the-Art

Adaptive mobile learning algorithms offer a promising approach to addressing the challenges of mobile learning, such as learner engagement and personalized learning. Recent advances and innovations in these algorithms have shown promising results in improving learning outcomes and user satisfaction. However, there are still several limitations and challenges to overcome, including the need for standard evaluation metrics, large-scale data and computational resources, and addressing ethical and privacy concerns. Future research should focus on developing effective and efficient adaptive mobile learning algorithms that can optimize the learning experience for all learners. To review and compare the current state-of-the-art adaptive mobile learning algorithms, we conducted a systematic literature review following the PRISMA guidelines. We searched multiple academic databases, including IEEE Xplore, ACM Digital Library, and ScienceDirect, using specific keywords related to adaptive mobile learning algorithms. After applying inclusion and exclusion criteria, we selected a total of 10 studies published between 2017 and 2021 for our analysis as in table 2, and studies that were published before the year 2010 as in table 1. We extracted data from each study, including the research design, sample size, algorithm used, evaluation metrics, and results. We then synthesized the data and identified common themes and patterns across the studies.
Lossless Compression for Hyperspectral Images Using Cascaded Prediction

In the paper, a three-level cascaded prediction based lossless compression method for hyperspectral imagery (HSI) is proposed. This method contains three main steps: preprocessing, prediction and coding. Firstly, k-means algorithm is employed to classify spectral vectors in preprocessing stage. In the prediction stage, a three-level cascaded predictor consisting of a similar neighborhood mean (SNM) predictor, a low-order recursive least squares (RLS) predictor and a high-order least mean square (LMS) predictor is adopted. SNM predictor is used to eliminate spatial redundancy, RLS predictor is used to eliminate spectral redundancy, and LMS predictor is used to obtain final predicted value. Finally, Huffman encoder is employed to encode the prediction residuals. The experimental results demonstrate that this method combines the advantages of the three predictors, significantly reduces the computational complexity and improves the compression effect.
Graph Contrastive Learning and Auxiliary Intent Across Sessions for Session-Based Recommendation

The target of session-based recommendation is to predict the future behaviour of the user based on limited amount of data over a short period of time in the past, which faces two key challenges: severe data sparsity, and dynamic user intent. A new kind of Contrastive Learning approach is presented in this research that is effective for the session-based recommendation task. Our method involves an effective data augmentation technique, which adds noise to the embedding space in a uniform manner to generate two contrastive views. We then apply Contrastive Learning to supervise these views, preserving complete session information and maintaining efficient graph augmentation to solve the data sparsity. Moreover, in order to better infer the dynamic intention of the current user, we exploit the intent of similar users in a subtle way. It is experimentally demonstrated that our approach outperforms the SOTA session-based recommendation methods in three real-world datasets.
Blockchain Based System for Secure Production Information Management

In the field of production, the collection, transmission, and storage of production information have a significant impact on production management. This paper proposes a blockchain-based secure production information storage and management system to address the issues of incomplete information collection, unreliable data transmission, and data isolation in traditional production information management systems. The system leverages the immutability and decentralization properties of blockchain technology to enable secure and efficient storage and transmission of the data. Moreover, a “step-hyperledger” structural mode is established for various business links in the pre-production, during-production, and post-production processes of safety production to achieve hierarchical management of data storage and full-process traceability. Combined with big data analysis techniques, the system facilitates effective pre-warning and efficient accountability after an incident. Simulation experiments and stress tests are conducted to validate the traceability interface of the system. The results indicate that the system demonstrates good stability even under the condition of multiple concurrent users, meeting the expected requirements and exhibiting certain advantages over traditional safety production systems.
Image Enhancement Using HSV Color Space, DWT, and BiHE Techniques

Image enhancement is vital in computer vision and image processing applications. This study introduces an innovative approach combining HSV color space, Discrete Wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. The DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. The proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. The results show that the proposed method achieved high Peak Signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. The Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. These results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts.
Comparison Between Parameter Estimation for the Beta Distribution

The beta distribution is useful for modeling continuous random variables that lie Between 0 and 1, such as percentages and percentages. Beta distribution begins Many different shapes can be described by two shape parameters, and, and some statistical properties such as ordinal statistics of the beta distribution and a joint density function have also been shown. It can be challenging to estimate, hence the methods of calculating moments and maximum likelihood estimation have both been researched., although the moment method is more straightforward. We check Both methods here and their comparison is used in MATLAB through code and it was based on the initial parameters (a = 1 and b= 1.5) and for different sample sizes that started from (n = 25, 50, 100, and 200) and using simulation and iteration of 3000 and comparison between the two methods through known statistical measures as mean square error (MSE), correlation coefficient <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{R}^{\wedge}\mathbf{2})$</tex> and the total deviations (TD), so through simulation experiments, we concluded that the MLE method is the best.
Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

Computational intelligence (CI) is a group of nature-simulated computational models and processes for addressing difficult real-life problems. The CI is useful in the UAV domain as it produces efficient, precise, and rapid solutions. Besides, unmanned aerial vehicles (UA V) developed a hot research topic in the smart city environment. Despite the benefits of UAVs, security remains a major challenging issue. In addition, deep learning (DL) enabled image classification is useful for several applications such as land cover classification, smart buildings, etc. In this view, this study proposes novel meta-heuristics with a deep learning-driven secure UAV image classification (MDLS-UAVIC) model in a smart city environment. The major purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels. The proposed MDLS-UAVIC model follows a two-stage process: encryption and image classification. The encryption technique for image encryption effectively encrypts the UAV images. Next, the image classification process involves an Xception-based deep convolutional neural network for the feature extraction process. Finally, shuffled shepherd optimization (SSO) with a recurrent neural network (RNN) model is applied for UAV image classification, showing the novelty of the work. The experimental validation of the MDLS-UA VIC approach is tested utilizing a benchmark dataset, and the outcomes are examined in various measures.
Develop Type-2 Fuzzy Controller from Optimization of the Type-1 Fuzzy for the Engine Control Using the Bee Colony Optimization Technique

This article presents Type-2 fuzzy logic controller designs. A Type-2 fuzzy Logic Controller is developed from a Type-1 fuzzy logic controller. Type-1 fuzzy logic controller design for control speed of the engine. The main topic of this step is Type-1 fuzzy logic controller is adjusted by using the Bee Colony Optimization algorithm for the best speed response, by tuning fuzzy membership functions. The matter of the article is the development of the approach with a Type-1 fuzzy logic controller to find the best fuzzy parameter values by the Beet Colony Optimization algorithm and developed Type-1 fuzzy logic controller to Type-2 fuzzy logic controller. Bee Colony Optimization algorithm specifically for tuning the membership functions of the Type-1 fuzzy logic controller for trajectory tracking in an engine speed control. The Integral Time Absolute Error is a performance index to measure controller behavior. Addition disturbance in the model with the pulse speed command for each Fuzzy Logic System. Simulate result represents the Type-2 Fuzzy logic controller is better than the Type-1 Fuzzy logic controller with uncertainty and that the Type-2 Fuzzy logic controller shows better speed response than the Type-1 Fuzzy Logic System Controller.