Reviewerly/exHarmony huggingface dataset:
Intelligent Inspection Route Planning Based On the Improved Ant-Lion Optimization Algorithm

The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.
A Comprehensive Approach to Mitigate Return-Oriented Programming Attacks: Combining Operating System Protection Mechanisms and Hardware-Assisted Techniques

This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.
Emotional Analysis Based On LSTM-CNN Hybrid Neural Network Model

In the realm of natural language processing (NLP), text classification is an important task. Current techniques for text classification tasks still need development, however, because of the complex abstraction of text semantic information and the considerable impact of context. This research synthesizes the strengths of the Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN) models of neural networks. The LSTM-CNN hybrid model is a combination of the two models: the LSTM-generated text feature vector is then extracted using the CNN structure. The Bi-LSTM can retain long text sequences while taking into account the importance of the whole text, and it can subsequently use the organizational principles of the CNN to extract local text features. The research's experiment compares the effectiveness of the LSTM-CNN, the Bi-LSTM, and the Random Forest (RF) in a sentiment analysis project based on the ternary classification (Neutral, Positive, and Negative) of the metaverse, which can be described as a virtual reality space where individuals can interact with a computer-generated environment and other users in real-time, related topics. From what can be gathered from this research experiments, the LSTM-CNN outperforms other models when it comes to boosting text classification accuracy and achieving lower loss.
Recognition Textual Entailment on Bahasa Using Biplet Individual Comparison and BiLSTM

Recognizing Textual Entailment (RTE) is one of the important tasks in Natural Language Processing (NLP). Various approaches have been taken, starting from a simple statistical framework to the neural network (NN) that is currently the mainstay, including RTE in Bahasa Indonesia. Currently, RTE in Bahasa Indonesia has started using the neural network approach, but the value of the resulting accuracy is still less than 77%. This is because the new NN architecture has just accommodated the lexical elements and not yet the syntactical elements of sentences. Syntactical elements in sentences are important components in obtaining the local information contained therein. In RTE, local information is useful for determining how closely related text fragments are. This study proposes a new approach to NN-based Bahasa Indonesia RTE using the Biplet (head-dependency) individual comparison technique. Biplet is generated from the process of word pair dependency. The concept of word pair dependency is used to improve alignment and inference assessment that is optimized by adjusting the weight of the phrase using an attention mechanism. From experiments conducted using the SNLI dataset that has been translated into Bahasa Indonesia (SNLI Indo), it was obtained that the highest training accuracy value is 83.56% with the validation accuracy value is 64.61% for the number of pairs of sentences of 100k.
A New Approach of Midrange Exploration Exploitation Searching Particle Swarm Optimization for Optimal Solution

The conventional Particle Swarm Optimization (PSO) was introduced as an optimization technique for real applications such as image processing, tracking, localization, and scheduling. However, conventional PSO still has its limitation in finding optimal solutions and is always trapped in the local optima. Therefore, the concept of conventional PSO was unsuitable to be used in dynamic problems. In order to address these issues, we have introduced a novel enhancement approach known as Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) to categorize the particle into resident particles and migrant particles according to midrange value. A migrant particle will execute the process of exploration to other search spaces, meanwhile resident particles went through the process of exploitation accordingly to the best solution. The comparison result shows that MEESPSO has the talent to increase the accuracy in a real application.
An Overview of Part-of-Speech Tagging Methods and Datasets for Malay Language

The purpose of this review is to summarise the knowledge about Malay Part-of-Speech (POS) training methods and datasets, and to identify its future research directions. A total of ten research papers related to Malay POS model training methods has been reviewed and five datasets were found from online resources. Two major issues were identified − first, of all the ten papers reviewed, it was found that dataset used to train Malay POS were not standardized. Second, limited dataset was found from online resources − only five datasets were available, and only three were annotated. This review highlights two directions worth future investigation: how to train a high-performance POS model using only a small amount of annotated data, and how to utilize existing high-performance POS models to reduce the burden of annotating data.
A Comparative Analysis on Three Duplication Elements in Copy-Move Forgery using PatchMatch-Based Detection Method

Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets.
A Meta-Requirement Approach to Validate User Requirement Specification: Threshold Definition

The software requirement specification document is critical for ensuring that software development projects are completed on time, within budget, and meet the needs of all stakeholders. User requirement completeness refers to the extent to which user requirements accurately and fully capture the needs and expectations of stakeholders for a given system or application. The completeness of user requirements is critical for the successful design and implementation of information systems, as incomplete requirements can lead to a range of issues, including system failure, delays, and cost overruns. The author has developed a meta-requirement validation approach to validate the completeness of a set of requirements. Based on a numerical reading, the user of the approach will be able to determine the completeness of the requirement. This publication's main objective is to identify the threshold value through a method of literature search. The identified threshold value will be used to determine the minimum reading for the result to be deemed complete. The result of a 70% value has been identified to be suitable for the use of the validation approach.
Light-Weight Object Detector for Power Intelligent Inspection via Re-parameterization

Electric power edge intelligent inspection, which conducts power intelligent detection on edge devices, can alleviate the issues of high computational load and lengthy data transmission time in traditional cloud computing mode. However, the low computational power of edge devices results in slow inference speeds for detection algorithms at the edge, and their limited memory constrains the spatial occupancy of the object detection models. Addressing these challenges, this paper proposes a power transmission line defect edge intelligent detection method via improved YOLOv5. First, R-D block is introduced in this paper, which combines RepVGG and diverse branch block (DBB) to enhance the backbone and neck networks of YOLOv5 and expedite the model's inference speed through re-parameterization. Subsequently, re-parameterized spatial pyramid pooling (RepSPP) is proposed to fuse multi-scale features and improve the accuracy of YOLOv5 through the integration of large kernel convolution and depth-wise (DW) convolution. Finally, extensive experiments are carried out on a power transmission line defect dataset, which includes pin missing, insulator defect, and nest. The experimental results demonstrate that, compared to the original YOLOv5, the proposed method enhances the inference speed by a factor of 1.8 and improves the accuracy by 3.6%, significantly enhancing the efficiency of power transmission line edge intelligent inspection.
Comparison of Solar Power Meter and Intelligence Solar Power Meter and Its Impact on Solar Photovoltaic Design

The terrestrial measurement of solar irradiation power in the selected location is crucial for the solar photovoltaic power plant design. However, it should be carried out manually onsite using the current Solar Power Meter (SPM). A device that can record this value remotely online is needed to ensure more efficient work. Hence, an Intelligent Solar Power Meter (ISPM) using a BH1750 sensor equipped with a cover in the form of an LED light cover was designed. The terrestrial measurement using both devices along with satellite measurement has been carried out in the Merangin site for performance comparison purposes. The result showed that the difference between each device in measuring the probability distribution, the financial model and the intermittent profile is less than 5%, with data that can be monitored remotely using internet connected IoT features.
Metaverse in Education: Insights from South Korea and Potentials for Malaysia

Metaverse, a cutting-edge technology offering immersive and engaging educational experiences, is revolutionizing learning by enabling real-world users to interact and collaborate with others in a virtual space. South Korea is one of leading countries in this field, with extensive research and application at various levels of education and across disciplines. Beyond enhancing teaching and learning, the metaverse also supports non-academic educational experiences. In Malaysia, various educational technology been employed in response to limitation of conducting face-to-face class during Covid-19 era. However, metaverse remains underutilized and under-discussed within the Malaysian school educational context. Consequently, this study aims to inform potential metaverse usage for Malaysia school education by specifically investigates the application of metaverse technology in South Korea education and reviewing existing research of metaverse education in Malaysia. As a result, we propose potential utilization and challenges of metaverse in Malaysia's school educational environment. The findings of this article are expected to pave the way for more promising metaverse research within the Malaysian education system.
Machine Learning Regression Models for Real-Time Touchless Interaction Applications

Touchless technologies have gained significant popularity, particularly amidst the COVID-19 pandemic, as they addressed concerns related to germ transmission and hygiene during human-device interactions. This study aimed to develop an intuitive and user-friendly touchless system by combining eye gaze and hand gesture methods. Four regression machine learning models, namely Ridge regression, Lasso regression, Linear regression, and Gradient-boosting regressor, were trained and tested using standard metrics such as coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The results indicated that Ridge regression outperformed the other models, demonstrating a high R2 value of 0.98. Leveraging this model, a simulation was conducted to evaluate the effectiveness of integrating eye gaze and hand gestures in realtime touchless interactions. The simulation demonstrated the successful integration of these modalities in the item selection process, providing users with a seamless and intuitive interaction method. This touchless interaction technology enabled effortless and accurate navigation of screens and facilitated item selections. The promising results highlighted the potential of this technology, presenting exciting opportunities for its integration with actuators. By incorporating actuators, touchless interaction systems could revolutionize various industries, including retail, healthcare, hospitality, and smart home automation.
A Data Clustering and Machine Learning Based Approach to Measuring Urban Carbon Emissions

Measuring urban carbon emissions accurately and practically is of great significance for the construction of low-carbon cities. However, the current methods for carbon emissions calculation have difficulty in balancing accuracy and practicality. In this paper, a method for measuring urban carbon emissions is proposed. This method determines the pilot user set for urban carbon emissions calculation through clustering algorithm, and explores the mapping relationship between pilot users' operation information data and carbon emissions using a multilayer perceptron model based on artificial neural networks (ANN), and then extends the mapping mechanism to other users. Simulation results show that this method can achieve the calculation of urban carbon emissions with good accuracy and practicality.
Automatic Generation Method for High Quality Joint Inspection Points of Intelligent Equipment in Substations

The challenges of autonomous and joint inspection of intelligent equipment, such as Unmanned Aerial Vehicles (UAVs) and robots, in substations are constantly gaining attention. To efficiently capture high-quality images of power equipment, we propose a method for automatically generating joint inspection points for multiple intelligent equipment. It introduces a shooting potential model and a spatial relationship model to assess the suitability of shooting power equipment at different spatial points using various intelligent equipment. A point selection method is designed based on shooting quality constraints and spatial constraints to optimize inspection efficiency. The focus is on generating non-redundant and complete inspection points that are suitable for multiple intelligent equipment to perform with. Additionally, we study cooperative path planning of the heterogeneous robot systems to reduce the time required for the inspection task. An improved Ant-Lion Optimization Algorithm is proposed to decouple the routes of mobile robots and UAVs and plan their routes simultaneously. The effectiveness of the proposed method is demonstrated on a virtual substation experimental platform, revealing improvements in shooting quality, inspection efficiency, and reduced inspection time.
Priority Based Fair Scheduling: Enhancing Efficiency in Cloud Job Distribution

In recent years, there has been a growing interest in cloud computing as a means to enhance user access to shared computing resources, including software and hardware, through the internet. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to inadequate scheduling. To overcome this challenge, numerous researchers have focused on improving existing Priority Rule (PR) cloud schedulers by developing dynamic scheduling algorithms, but they have fallen short of meeting user satisfaction. In this study, we introduce a new PR scheduler called Priority Based Fair Scheduling (PBFS), which takes into account key parameters such as CPU Time, Job Arrival Time, and Job Length. We evaluate the performance of PBFS by comparing it with five existing algorithms, and the results demonstrate that PBFS surpasses the performance of the other algorithms. The experiment was conducted using the CloudSim simulator, utilizing a dataset of 300 and 400 jobs. In order to assess the performance, three key metrics were employed: flow time, makespan time, and total tardiness. These metrics were chosen to evaluate and analyze the effectiveness of the proposed scheduling algorithm.
Feature Selection Using Law of Total Variance with Fast Correlation-Based Filter

The increased dimensionality of data poses a formidable obstacle to completing data mining tasks. Due to the extraneous features associated with high-dimensional data, processing and analysis took longer and were less precise. As a pre-processing phase in the analysis of data mining tasks, feature selection is effective at reducing dimensionality, removing irrelevant characteristics, increasing accuracy, and enhancing the readability of the results. This research proposes the law of total variance with fast correlation-based filter (LTVFCBF) as a new feature selection method. LTVFCBF chose the significant features by identifying relevant features and remove redundant features among the relevant ones. The analysis was conducted with ten datasets of varied dimensionality to evaluate the performance of the proposed LTVFCBF and validated using four classifiers: K-nearest neighbours, Naïve Bayes, support vector machine, and bagging. The LTVFCBF and LTV methods have been compared in terms of the number of selected features, classification accuracy, and execution time. In overall, the suggested LTVFCBF has the potential to minimize the dimensionality of data by selecting a lower number of significant features with better accuracy. However, it requires a slightly higher execution time compared to LTV. Aside from that, LTVFCBF can achieve comparable accuracy with faster execution time when less than half of the original features are maintained. The proposed method can produce a promising outcome and may be regarded as an effective filter approach for feature selection.
Inertia Weight Strategies in GbLN-PSO for Optimum Solution

Particle Swarm Optimization (PSO) is the popular metaheuristic search algorithm that is inspired by the social learning of birds and fish. In the PSO algorithm, inertia weight is an important parameter to determine the searching ability of each particle. When the selected inertia weight is not suitable, the searching particles are more focused on one direction or area nearest to the local best. Therefore, the movement of the particles is limited and not spreading during the search process. Thus, this will cause the particles fast to converge. As the result, the particle is trapped in local optimal. To overcome this problem, we used three different inertia weight strategies such as Constant Inertia Weight (CIW), Random Inertia Weight (RIW), and Linear Decreasing Inertia Weight (LDIW) to analyze the impact of inertia weight on the performance of Conventional PSO and the enhancement of PSO called Global Best Local Neighborhood-PSO (GbLN-PSO) algorithm. In order to test the performance of the three different inertia weight strategies, we test these algorithms in different sizes of search space with random values. Based on the comparison result of 30 simulations, it shows that GbLN-PSO using RIW was producing a better search result compared to CIW and LDIW. Furthermore, the result shows an improvement in GbLN-PSO searching ability.
An Improvement of Interactive Priorization Technique for Requirements Interdependency in Prioritization Process

Requirement Prioritization (RP) was introduced as a solution to the project with huge number of requirements by eliminating some of the requirements based on fair judgement in terms of priority value or rank made by expertise or stakeholder or both parties depending on the technique's specific operation. Along with the RP concept, there is another concept that must be deeply understood, which is Requirement Interdependencies (RI), which describes the interdependencies of requirements with each other. However, not many RP techniques involve the RI element during the RP process, citing complexity as the key reason for this, even though the RI element plays a critical role in determining the most important requirement for improving the project success rate. This paper describes the propose technique, which improves the RP process while considering the RI element. Two conventional RP techniques, Three level-Scale and Rank Ordering. Each RP technique plays an important part in producing high accuracy results while improving the impact of RI element during the RP process.
Product Recommendation using Deep Learning in Computer Vision

Recently, recommendation models have gained popularity due to their effectiveness in improving customer satisfaction and deriving sales. However, current product recommendation models have a drawback: they lack personalized and targeted advertisements for individual users. Consequently, the recommendations provided are random and not tailored to users' preferences. This limitation negatively impacts the system's ability to deliver relevant and personalized advertisements, leading to reduced user engagement and potentially lower conversion rates. Moreover, the absence of personalized advertisements can result in user dissatisfaction as they may receive recommendations that are irrelevant or not aligned with their interests and needs. To address these challenges, this study proposed a targeted product recommendation model using Deep Learning (DL) techniques in computer vision. The study utilizes the dataset of human images obtained from the Kaggle website, which includes details such as gender, class, and age. Findings of the study demonstrated a high level of accuracy in product recommendations, indicating the potential for significant improvements in addressing the issues. In conclusion, the proposed method achieves good accuracy in predicting the gender and age, and provides appropriate product recommendations based on these features.