Reviewerly/exHarmony huggingface dataset:
Research on Helmet Wearing Detection Based on Improved YOLOv4 Algorithm

In view of the difficulty and low precision of the traditional method in the detection of helmet wearing, this paper proposes an improved YOLOv4 algorithm model. First, use the k-means clustering algorithm to readjust the parameters of the prior frame to improve the matching degree between the prior frame and the target, and then add a layer of feature layer to process the features, improve the network structure of YOLOv4, and improve the accuracy of the target through feature fusion. On this basis, the Pyramid Split Attention (PSA) model is introduced to further process multi-scale feature information and improve detection accuracy. The experimental results show that the improved YOLOv4 algorithm has an average accuracy of 95.20% on the helmet target detection data set, which is 1.64% higher than the original YOLOv4 algorithm, and meets the accuracy of the helmet wearing detection task.
Graph Contrastive Learning with Intrinsic Augmentations

Graph contrastive learning has become an important approach for learning unsupervised representations of graphs, with the key idea of maximizing the consistency of representations in both augmented views through data augmentation. Existing graph contrastive learning models concentrate on topology enhancement of the graph structure by simply removing/adding edges between nodes randomly, which may not only destroy the important structure of the graph but also generate meaningless graphs, leading to a significant degradation of the contrastive learning performance. In addition, current research is too focused on minimizing losses, while ignoring the intrinsic factors that can affect the quality of node representations, which is detrimental to the training of models and the generation of high-quality node representations. To address these issues, we propose intrinsic augmented graph contrastive learning, named IAG, which consists of two components: 1) In the topology augmentation part, we propose a novel topology augmentation strategy based on potential connections in the feature space, which complements the traditional topology augmentation by allowing different graphs to obtain augmentation strategies more suitable for their own characteristics to advance the traditional graph contrastive learning. 2) We explored the effect of temperature coefficient in the loss function on the quality of the final representation and proposed dynamic temperature with penalty terms, which helps to generate high-quality node representations. Finally, we conducted extensive node classification experiments on 8 real-world datasets. The experimental results show that our proposed method is highly competitive with the existing state-of-the-art baselines and even surpasses some supervised methods.
Reinforcement Learning Based Vertical Scaling for Hybrid Deployment in Cloud Computing

To maximize the CPU utilization of the server, offline tasks are usually deployed to the same server where the online service is running. Considering the necessity to ensure the service quality of online services, it is common practice to isolate the resources of online services. How to set the resource quota for online services not only affects the service quality of online services, but also affects the number and the stability of offline tasks that can be run on the server. Traditional rule-based methods or prediction-based methods will cause over-provision and fail to consider the stability of offline tasks, which often cannot achieve stability and efficiency. In this paper, reinforcement learning is proposed for the first time to solve the hybrid deployment of online services and offline tasks and dynamically adjust the resource quota of online services more effectively. Compared with the original state of the server, our proposed method reduces CPU idleness rate by 35.32% and increases CPU resource utilization rate by 3.84%.
An Improved Harris Hawk Optimization Algorithm Based on Spiral Search and Neighborhood Perturbation

Aiming at the problems of the basic Harris Hawk optimization (HHO), such as insufficient global search ability, slow convergence speed, low convergence accuracy and easy to fall into local optimization, this paper proposes an improved HHO (IHHO) algorithm based on spiral search and neighborhood perturbation. First, Tent chaotic mapping is used to initialize the population, enhance the diversity of the population and improve the initial convergence speed of the proposed algorithm. Secondly, the collaborative updating strategy based on spiral search and neighborhood perturbation is used in the exploration stage to expand the search space and improve the global search ability of the proposed algorithm. In the exploitation stage, the adaptive inertia weight is introduced to improve the convergence speed. Then, the dimensional cross-variation is used to improve the ability of the algorithm to jump out of the local optimum. CEC2021 test function and 8 classic test functions are used to demonstrate the effectiveness of the proposed method. Compared with the basic HHO and the other improved swarm intelligence algorithms, the results show that the proposed algorithm has better global search ability, faster convergence speed and higher accuracy. Finally, the proposed IHHO algorithm is used to solve tension spring design problems.
Research on Multi-modal Multi-objective Path Planning by Improved Ant Colony Algorithm

A research about the path planning problem has been popular topic nowadays and some effective algorithms have been developed to solve this kind of problem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi-modal Multi-Objective Path Planning (MMOPP), the objective is to find all sets of Pareto optimal path solutions from the start point to the end point in a grid map. This paper proposes a multi-modal multi-objective ant colony path planning optimization algorithm based on matrix preprocessing technology and Dijkstra algorithm (MD-ACO). Firstly, a new method of storing maps that reduces the size of the map and reduces the size of the decision space has been proposed in this paper. Secondly, using the characteristics of the Dijkstra algorithm that can quickly find the optimal path, generate an initial feasible solution about the problem, and improve the problem that the initial pheromone of ant colony algorithm is insufficient and searching for solutions is slow. Thirdly, a reasonable threshold is set for the pheromone to avoid algorithm getting stuck in local optimal solution. Finally, the algorithm is tested on the MMOPP test sets to evaluate the performance of the algorithm, and the results show that MD-ACO algorithm can solve MMOPP and get the optimal solution set.
Multi-stage Objective Function Optimized Hand-Eye Self-calibration of Robot in Autonomous Environment

In the application scenario of robot autonomous tasks, the robot needs to be able to complete calibration online and automatically to achieve self-maintenance, which differs from traditional robot hand-eye calibration in that the traditional robot hand-eye calibration requires a dedicated calibration board to assist offline completion. Aiming at the problem that the existing self-calibration methods cannot be optimized as a whole, which leads to low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed, which describes the solution of hand-eye self-calibration as a minimization objective function problem involving multiple stages. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Two different types of experiments were designed to validate our method. One is a single camera dataset experiment, which shows that our method is more accurate and robust than the existing self-calibration method; the other is an application platform experiment, which verifies the feasibility and availability of our method.
Diffusion Optimization Algorithm Based on Membrane Calculation

In order to improve the efficiency and quality of solving complex optimization problems, a global optimization algorithm, called the Membrane Computation-based Diffusion Optimization Algorithm (MC-DOA), is proposed. Inspired by the dual membrane cell structure of chloroplasts and mito-chondria as well as the particle diffusion phenomenon, the algorithm uses a hybrid membrane structure system and four different particle search strategies to find the global optimum. Four types of benchmark test functions are selected to test the performance of MC-DOA, namely: single-peak function test, multi-peak function test and different dimensional optimization function test. The test results of MC-DOA are compared with various optimization algorithms (including some well-known intelligent optimization algorithms and some novel intelligent optimization algorithms), and the results show that MC-DOA has good solving ability and solving efficiency.
An Improved MPCA Algorithm with Weight Matrix Based on Many-Objective Optimization

Data compression and dimensionality reduction play an essential role in machine learning. In recent years, due to the relatively high dimensionality of data, dimensionality reduction based on vectors cannot be well processed and will destroy the structure of high-dimensional data. Therefore, research on dimensionality reduction based on tensors is increasing daily. This paper mainly studies the extended multi-linear principal component analysis (MPCA) of principal component analysis (PCA) in the tensor space. This paper proposes a MPCA dimension reduction algorithm based on the weight matrix, followed by the construction of a multi-objective model. An IP-NSGA-III is proposed to solve the weight matrix. Experimental results of hyperspectral image classification show that, compared with different algorithms, the MPCA algorithm based on the weight matrix proposed in this paper has a better dimension-reduction effect.
Incorporating Character Education And Bilingual Literacy Into Early Childhood Classroom: Practical Ideas

Character education and literacy becomes prominent issue in today’s education and it is suggested to be taught earlier. Since children have unique characteristics in learning, specific methods and media are frequently required to make them engage during the lesson. This article attempts to elaborate practical ideas for classroom practice for the inclusion of character education and bilingual literacy by using bilingual big book. Bilingual big book here refers to a big book with pictures created by the teachers. It uses teacher-made stories delivered in two languages: Bahasa Indonesia and English. Teacher-made stories are suggested for the character education so the stories can be adjusted with the students’ language abilities and preferences. Teacher made-stories also enable them to set the targeted moral values. In this paper, it will provide step-by-step activities to implement the bilingual big book in the classroom. It will also give some pedagogical recommendations for teachers to engage young learners in learning English. It is expected that this article can benefit teachers of early childhood for promoting bilingual literacy and inserting character education.
Global overview of research data repositories: an analysis of re3data registry

Purpose Data sharing is increasingly being recognized as an essential component of scholarly research and publishing. Sharing data improves results and propels research and discovery forward. Given the importance of data sharing, the purpose of the study is to unveil the present scenario of research data repositories (RDR) and sheds light on strategies and tactics followed by different countries for efficient organization and optimal use of scientific literature. Design/methodology/approach The data for the study is collected from registry of RDR (re3data registry) (re3data.org), which covers RDR from different academic disciplines and provides filtration options “Search” and “Browse” to access the repositories. Using these filtration options, the researchers collected metadata of repositories i.e. country wise contribution, content-type data, repository language interface, software usage, metadata standards and data access type. Furthermore, the data was exported to Google Sheets for analysis and visualization. Findings The re3data registry holds a rich and diverse collection of data repositories from the majority of countries all over the world. It is revealed that English is the dominant language, and the most widely used software for the creation of data repositories are “DataVerse”, followed by “Dspace” and “MySQL”. The most frequently used metadata standards are “Dublin Core” and “Datacite metadata schema”. The majority of repositories are open, with more than half of the repositories being “disciplinary” in nature, and the most significant data sources include “scientific and statistical data” followed by “standard office documents”. Research limitations/implications The main limitation of the study is that the findings are based on the data collected through a single registry of repositories, and only a few characteristic features were investigated. Originality/value The study will benefit all countries with a small number of data repositories or no repositories at all, with tools and techniques used by the top repositories to ensure long-term storage and accessibility to research data. In addition to this, the study provides a global overview of RDR and its characteristic features.
Suspicious Link Detection using AI

With the increase in internet usage, cybersecurity has become a major concern for computer systems, as malicious URLs can release different forms of malware and attempt to collect user data. The global lockdown in 2020 led to a significant rise in the use of internet services for business, which in turn resulted in a surge of cybercrimes committed by cybercriminals and significant data losses for businesses. To prevent such attacks, it is important to identify malicious URLs and understand the types of threats they pose. Signature-based approaches are often used to find such websites, and security tools are deployed to impose access restrictions on them. This chapter proposes using the linguistic aspects of related URLs to enhance the effectiveness of classifiers for identifying dangerous websites through Machine Learning algorithms such as Logistic Regression and Random Forest Technique. The study shows that being able to identify spam URLs solely based on URLs and categorizing them without relying on page content can lead to significant resource savings and a safer browsing experience for users.
The Implementation of Donation System using Blockchain

A donation system using blockchain technology can revolutionize the way donations are made and managed for various causes, including child adoption, fund distribution, stationary and food donation, and blood donation. This system can increase transparency, accountability, and trust between donors and recipients, as all transactions are recorded on an immutable blockchain ledger. The system can be implemented by creating a smart contract that specifies the terms and conditions of the donation system, including the types of donations accepted, the criteria for eligibility, and the process of distributing funds. Donors can use a digital wallet to store their cryptocurrency donations, which can be seamlessly integrated with the smart contract to ensure secure and transparent transactions. The system can also be used to verify the eligibility of recipients, preventing fraud and ensuring that donations go to the intended recipients. The smart contract can automatically distribute funds to eligible recipients based on the criteria specified in the contract.By implementing a donation system using blockchain technology, donors can trust that their contributions are making a real impact, and recipients can have greater access to resources for their needs. This system has the potential to bring greater transparency and fairness to the donation process, ultimately benefiting the causes and individuals in need.
COVID-19 contact tracking based on person reidentification and geospatial data

Efficient contact tracing is a crucial step in preventing the spread of COVID-19. However, the current methods rely heavily on manual investigation and truthful reporting by high-risk individuals. Mobile applications and Bluetooth-based contact tracing methods have also been adopted, but privacy concerns and reliance on personal data have limited their effectiveness. To address these challenges, in this paper, a geospatial big data method that combines person reidentification and geospatial information for contact tracing is proposed. The proposed real-time person reidentification model can identify individuals across multiple surveillance cameras, and the surveillance data is fused with geographic information and mapped onto a 3D geospatial model to track movement trajectories. After real-world verification, the proposed method achieves a first accuracy rate of 91.56%, a first-five accuracy rate of 97.70%, and a mean average precision of 78.03% with an inference speed of 13 ms per image. Importantly, the proposed method does not rely on personal information, mobile phones, or wearable devices, avoiding the limitations of existing contact tracing schemes and providing significant implications for public health in the post-COVID-19 era.
A lightweight CP‐ABE scheme in the IEEEP1363 standard with key tracing and verification and its application on the Internet of Vehicles

Abstract Automated vehicle applications are becoming more and more widespread, and the data security of the vehicle network involved is particularly important. Similar to traditional data, the characteristics of autonomous driving data security are mainly confidentiality, integrity, and availability. If data related to autonomous driving is stolen or tampered with, it will directly cause loss of property or life, which is more harmful than traditional data destruction. To address the security issues on the Internet of Vehicles, we propose an encryption (CP‐ABE) scheme which is ciphertext policy attribute‐based and lightweight. And it can provide fine‐grained access control to data on the Internet of Vehicles with less computational overhead on the device itself, making it ideal for the Internet of Vehicles environment. The scheme also supports key traceability to address the user key leakage problem. Experiments show that the proposed system is effective.
Comparison and Bayesian Estimation of Feature Allocations

Feature allocation models postulate a sampling distribution whose parameters are derived from shared features. Bayesian models place a prior distribution on the feature allocation, and Markov chain Monte Carlo is typically used for model fitting, which results in thousands of feature allocations sampled from the posterior distribution. Based on these samples, we propose a method to provide a point estimate of a latent feature allocation. First, we introduce FARO loss, a function between feature allocations which satisfies quasi-metric properties and allows for comparing feature allocations with differing numbers of features. The loss involves finding the optimal feature ordering among all possible orderings, but computational feasibility is achieved by framing this task as a linear assignment problem. We also introduce the FANGS algorithm to obtain a Bayes estimate by minimizing the Monte Carlo estimate of the posterior expected FARO loss using the available samples. FANGS can produce an estimate other than those visited in the Markov chain. We provide an investigation of existing methods and our proposed methods. Our loss function and search algorithm are implemented in the fangs package in R.
Buckley–James boosting model based on extreme learning machine and random survival forests

Buckley-James (BJ) model is a typical semiparametric accelerated failure time model, which is closely related to the ordinary least squares method and easy to be constructed. However, traditional BJ model built on linearity assumption only captures simple linear relationships, while it has difficulty in processing nonlinear problems. To overcome this difficulty, in this paper, we develop a novel regression model for right-censored survival data within the learning framework of BJ model, basing on random survival forests (RSF), extreme learning machine (ELM), and L2 boosting algorithm. The proposed method, referred to as ELM-based BJ boosting model, employs RSF for covariates imputation first, then develops a new ensemble of ELMs-ELM-based boosting algorithm for regression by ensemble scheme of L2 boosting, and finally, uses the output function of the proposed ELM-based boosting model to replace the linear combination of covariates in BJ model. Due to fitting the logarithm of survival time with covariates by the nonparametric ELM-based boosting method instead of the least square method, the ELM-based BJ boosting model can capture both linear covariate effects and nonlinear covariate effects. In both simulation studies and real data applications, in terms of concordance index and integrated Brier sore, the proposed ELM-based BJ boosting model can outperform traditional BJ model, two kinds of BJ boosting models proposed by Wang et al., RSF, and Cox proportional hazards model.
Quality assessment of a satellite communication system using a weather-dependent parametric model

Link budgeting is a crucial part of the satellite communication systems design. The task becomes especially important, when the high-speed transmission is required. In this case, it is necessary to find a compromise between the system technical capabilities and the communication link quality accounting various conditions, including weather. This article proposes a model of a telecommunications system, which calculates the probability of data transmission error based on the results of the data transmission modelling, and allows estimating the final transmission speed. The use of a parametric signal attenuation model makes it possible to perform an assessment for a ground link station at any point on the Earth with various signal attenuation factors occurred probability: cloudiness and precipitation. Simulation results are presented in the form of two-dimensional graphs for the ease of communication system qualitative analysis.