Reviewerly/exHarmony huggingface dataset:
Research on Resistance Current Forecast Model of Lightning Arrester Based on VMD-ELM-AEFA Combination

Metal Oxide Varistor (MOV) is an overvoltage protection device used in power systems, as well as in electronic devices. They can quickly change the resistance when the voltage exceeds its rated value, dispersing overvoltage energy into thermal energy, thereby protecting other components in the circuit from damage. In power systems, MOV lightning arresters are typically used to protect equipment from lightning strikes, grid disturbances, or other voltage surges. In this context, a lightning arrester resistive current prediction model based on the combination of VMD-ELM-AEFA is proposed to determine the cracking trend of MOA. Variational Mode Decomposition (VMD) can effectively extract the intrinsic trend of resistive current, reduce sequence nonlinearity, and combine it with the Extreme Learning Machine (ELM) model optimized by the Artificial Potential Field Algorithm (AEFA) to greatly improve the accuracy compared to the model before decomposition, The prediction of resistive current has great guiding significance.
Design of Financial Management Teaching System Based on Genetic Algorithm

This article studies the design and implementation of a financial management teaching system based on genetic algorithms. By introducing genetic algorithm as an optimization tool, the system can provide more scientific, effective, and personalized solutions for financial management teaching, helping students better understand and master the knowledge and skills of financial management. This article provides a detailed description of the design and implementation process of the system, including requirement analysis, system design, algorithm implementation, and experimental verification. The experimental results indicate that the system can improve teaching effectiveness and learning experience, providing new ideas and methods for financial management teaching.
Deep Learning Based Channel Estimation for 5G

In this paper, we propose a channel estimation method based on deep learning and image Super-Resolution for an orthogonal frequency division multiplexing (OFDM) system in 5G scenario. To be specific, Cascading Residual Channel Estimation Network (CRCEN) and Resource Block Network (RBnet) are introduced in order to meets different needs. Because of well designed network architecture, the computational cost and the number of parameters of CRCEN can be greatly reduced. Moreover, CRCEN shows outstanding performance compared with other models. RBnet is a denoising network which can run on any resource block to support multi-user requirement. Both models have great potential in 5G applications.
Web-Based Online Exhibition by Implementing Virtual and Augmented Reality to Visualize Architecture Building Design

Web-based online exhibition using immersive technology, namely Virtual Reality &amp; Augmented Reality, has a major effect on the efficiency of time, place, and cost in providing visitors with an understanding of building architecture. Interaction in understanding architectural modeling is also assisted by a technology called Avatar Mediated Communication (AMC) which is the scope of interaction with other visitors in a Virtual Environment created by Virtual Reality technology. Technology that is able to support all website-based reality activities is made with the Laravel &amp; Express Js framework (Website), Spoke Mozilla Hubs (Virtual Reality Environment), and Library AR Js (Augmented Reality Marker Based). The technology is selected, built, and combined in the stages of the Research &amp; Development (R&amp;D) method, the Multimedia Development Life Cycle (MDLC) model in examining the development stages and user responses to web-based Virtual &amp; Augmented Reality development products. The results of the responses obtained are based on 6 categories in the UEQ (User Experience Questionnaire) instrument. The result showed that the three largest categories that give a positive response are stimulation, attractiveness, and novelty. Meanwhile, the three categories below are efficiency, perspicuity, and dependability. This indicates that the online exhibition in the support of immersive visualization is able to increase user curiosity in trying to enter the virtual reality room presented in web-based Virtual &amp; Augmented Reality.
https://combinatorialpress.com/jcmcc-articles/volume-117/gregarious-y_5-tree-decompositions-of-tensor-product-of-complete-graphs/

Yk-tree is defined as (v1,v2,…,vk−1;vk−2vk) by taking their vertices as (v1,v2,…,vk) and edges as {(v1v2,v2v3,…,vk−2vk−1)∪(vk−2vk)}. It is also represented as (Pk−1+e). One can obtain the necessary condition as mn(m−1)(n−1)≡0(mod2(k−1)), for k≥5 to establish a Yk-tree decomposition in Km×Kn. Here the tensor product is denoted by ×. In this anuscript, it is shown that a Y5-tree (gregarious Y5-tree) decomposition exists in Km×Kn, if and only if, mn(m−1)(n−1)≡0(mod8).
Video Streaming QoS Prediction based on Downlink Control Information of LTE Cell

Although 5G networks are becoming more active, there is still a high reliance on LTE networks at least 5G networks are operated in Non-standalone (NSA) mode. Accordingly, it is necessary to continuously monitor and evaluate the quality of service (QoS) of LTE. In general, quality measurement of an LTE network is conducted with a small number of devices during the short time. In this study, to overcome the existing quality measurement limitations, we propose a method of predicting the quality evaluation of video streaming services using downlink control information of targeted LTE base station, enhancing assessment scope and accuracy.
Digital Phenotype Collection System Utilizing Smart Devices

In this paper, we introduced a system that collects daily sleep data and daily routine data to explore the relationship between sleep and daily life. The daily data is quantified into various social and behavioral fingerprint information by analyzing sensor data obtained from carrying and using smartphones and smartwatches. By analyzing the quantified daily data in combination with the quality of sleep on the same day, we can discover factors that greatly affect sleep during daily life. To analyze the association between daily life and sleep quality, we described the sensor data collected daily and the system that generates descriptive features processed from the sensor data.
Design of Cloud-based remote collaborative system for broadcasting production workflow

This paper presents the design of a cloud-based remote collaborative production system with a service platform architecture involving virtualization of the production environment and remote control of equipment. The system mainly includes an IP-based media gateway supporting various media data specifications in a local production domain and a service platform with a video switcher module, a remote control& management module, and an automated production module in the cloud. And for enabling small-scale production and individual content creators as well as professional AV experts to participate easily in remote sites the system will be provided with various interfaces and equipment compatibility to enable to participate in production easily.
A Study on Performance Analysis of Question Generation based on Korean Pretrained Language Model

This paper presents the results of a performance analysis of the question generation task based on a Korean pretrained language model for assessing elementary school students’ reading comprehension using literary texts. The Korean pretrained language model is based on SKT-KoBart and employs transfer learning using literary texts of narrative and expository structures, along with the KorQuAd dataset for question-answering tasks. Through the extraction of factual and inferential assessment items, BLEU scores are measured based on the type of literary text, and the performance analysis results of the downstream task of question generation are presented.
Secure Image Authentication and Copyright Protection Using Integer Wavelet Transform

Widespread usage of the internet and digital communication technologies, multimedia data may be vulnerable to piracy as unauthorized users can easily alter multimedia data such as images. In this study, IWT and SVD based on human visual characteristics are proposed, to embed the watermark in the image, making it more resistant to attacks while low distortion on image quality. The watermarked image is then embedded with authentication bits obtained from the image content of 3x3 block pixels. The authentication bits are embedded into the LSB of pixel to ensure that RGB images can be authenticated if there is any tampering attacks. Various geometrical, compression, image processing attacks and tampering attacks are used to test the effectiveness of the proposed approach. Hence, the test results are compared to other dual watermarking systems. The results of these experiments show that our approached can accurately detect tampering in most cases, even when the changes made to the image affect a small number of blocks. Additionally, we found that our approach provides high robustness, even when image processing attacks are extensive. The results demonstrate the PSNR value of about 48 dB, effectiveness of our authentication approach obtained accuracy of 0.98 for tampering attacks and show that it is a promising technique for robustness of embedded copyright.
Handling Imbalanced Data in Convolutional Neural Network on Apache Spark

With the growing pervasiveness of Big Data, many approaches have been made in several fields. The concept of big data has gained support as a result of this rising data advancement. Big data is required to show how things are related, predict future trends, and provide decision-makers with additional knowledge. Apache Spark is an effective, scalable, real-time data analytics engine that is rapidly taking over as the de facto center for big data and data science. Nowadays, how to completely collect and evaluate a huge amount of different and complicated data is a major problem. Due to these complicated data, one of the major problems is imbalanced data, which are distributed unevenly throughout the target class in datasets with; for example, one class label may contain many observations while the other may have a small number. As a result, the proposed algorithm, which is based on an oversampling method that evenly distributes the data, is used to address the problem of data imbalance. This study also emphasizes the Convolutional Neural Network (CNN), a deep learning model used with Apache Spark. The proposed system implementation is performed for balanced and imbalanced nature of NSL-KDD dataset, the benchmark dataset of intrusion detection. The experiment shows that the proposed sampling algorithm outperforms CNN models on Apache Spark compared with CNN on traditional platform.
Akuntansi Air, Manajemen Impresi, dan Biaya Ekuitas: Studi Bibliometrik

This study aims to visualize research trends regarding water accounting, impression management, and the cost of capital with various variables in the network. The research method uses a bibliometric study with VOSviewer analysis. Publication of article data was obtained from Scopus for the period 2013-2023. This research found that publications on water accounting, impression management, and cost of capital grew rapidly from 2019 to mid-2023. The United States dominated in the most publications category and China dominated in the most productive author and funding sponsor categories. The results also show that this research topic has a publication area that covers the scope of business, accounting, finance, economics, management, business psychology, and the environment. Furthermore, this study contributes to providing an overview of the next research agenda.
Geochemistry of core sediments from Cauvery delta South-East India: Inferences on weathering and paleo-redox conditions.

Using the geochemical characteristics, this paper describes the degree of chemical weathering and redox conditions of the Late Pleistocene to the Holocene sediments cored at Uttrangudi (UG) and Porayar (PR) locations from the Cauvery delta, Southeast India. In both cores, the variability of Si with depth shows an opposite pattern to that of Al, Fe, Mg, Mn, and Ti primarily due to the dilution effect by quartz. The trends in Ni, Cr, and Y were similar to Al, Fe, Mg, Mn, and Ti, as these elements are held by mafic and clay minerals. Ba and Sr show variations similar to Na, Ca, and K, and thus connoting the control of feldspar. Different weathering indices, including Index of Compositional Variability (ICV), Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW) and Plagioclase Index of Alteration (PIA) indicated that the source area underwent a low to moderate intensity of chemical weathering under warm to humid climatic conditions during the denudation of the sediments. The overall CIA for the Pleistocene and the Holocene sediments of Uttrangudi (UG), range from 52-97 and 67-84 respectively, while for the Porayar (PR) core of the Holocene age, it ranged between 50-80. Higher CIA and low absolute Sr, Ca, and Na in the Pleistocene sediments reflect terrestrial conditions and intense weathering. The relatively lower CIA values with higher Sr, Ca, and Na in the Holocene sediments indicate a lower rate of weathering & marine contributions compared to the Pleistocene. The redox proxy sensitive elements such as Mn, Ni, Cr, Cd, and Ce indicated an oxic depositional environment for both sediment cores. The Ce/Ce* ratios are lower and Mn* values (Mn* = log [(Mnsample/Mnshales) / (Fesample/Feshales)]) are positive in the studied sediments, suggesting their deposition in an oxidizing environment. The study is important in reconstructing the past climate and environmental conditions of the Cauvery delta and its surroundings.
Low-frequency electrical prospecting with measurements of spatio-temporal invariants of the magnetic field

Introduction. In the article discussed the theory, methodology and interpretation of inductive electrical prospecting with the measurement of the characteristics of the polarization ellipse of the magnetic feld for low frequency (induction zone), which is of primary interest in the study of the conductive properties of the geological environment. Te purpose of this work is to substantiate the advantage of using measurements of the semiaxes of the full polarization ellipse of the magnetic feld in ground and air methods of electrical exploration, the values of which do not depend on the spatial orientation of the magnetic feld sensors. Methodology. Mathematical modeling of the scattering of the harmonic magnetic feld of an inductive source on a conductive 3D object and experimental feld work at an ore deposit using the ungrounded loop method to test the operability of a model of electrical exploration equipment and determine the information content of using the parameters of the ratio of the semiaxes of the magnetic feld polarization ellipse when identifying a conductive ore body. Results. Te conductive ore body is distinguished at a number of discrete frequencies by local anomalies in the ratio of the semiaxes of the full polarization ellipse of the magnetic feld obtained as a result of processing the measurement materials by the inductive method of an ungrounded loop. Conclusions. Measurements of the semiaxes of the full ellipse of magnetic feld polarization in surface and air methods of inductive electrical prospecting have an advantage over measurements of the spatial components of the magnetic feld, since they do not depend on the spatial orientation of the sensors of the feld components, which completely eliminates the methodological measurement error. Te use of measurements of the relative phases of the magnetic feld components makes it possible to signifcantly simplify the measuring device for determining the semiaxes of the complete polarization ellipse, since it does not require a channel for transmitting the current phase from the feld source to the meter.
Development and Performance Analysis of an AI based Agent to Play Computer Games using Reinforcement Learning Techniques

Exploring the potential of Artificial Intelligence (AI) in understanding and improving the tactics to play computer games is one of the most trending subjects being explored in recent times. Whenever a new game hits the market, users might struggle to play the game, but creating an AI agent would help understand how the game must be played. The purpose of developing AI agents to play video games are Self- improvement, Learning and Flexibility. Reinforcement learning (RL) has emerged as a promising approach for tackling intricate tasks across different fields, including the realm of gaming. Among the multitude of games, Flappy Bird has gained popularity due to its challenging nature, proving to be a formidable environment for traditional artificial intelligence techniques. This project aimed to develop an AI agent capable of playing Flappy Bird by employing three distinct RL algorithms: Deep Q-learning Network (DQN), State-Action-Reward-State-Action (SARSA), and Double Deep Q-learning Network (DDQN).Performance of each algorithm is evaluated by measuring the rewards and scores achieved in the game. Results show that DDQN outperformed DQN and SARSA in terms of achieving higher scores and rewards. An epsilon-greedy strategy is incorporated into the training process to effectively balance the exploration and exploitation aspects. Additionally, the utilization of experience replay has significantly enhanced the learning efficiency of the reinforcement learning algorithms by storing and reusing previous experiences. Analysis of the proposed system demonstrates the potential of RL algorithms in solving challenging tasks in the gaming domain. Future work could focus in investigating how to transfer learning from one environment to another and how to optimize hyperparameters for each algorithm.
T-Shirt Vending Machine Using ESP32

At tourist locations, souvenirs are usually found at specific spots, but when utilizing a vending machine, souvenir sales can be made anywhere. The purpose of this research is to create a t-shirt sales system using a vending machine. This vending machine can be placed at tourist attractions, airports, or terminals. The method used in this research is experimental. From the client-side, this system is controlled using ESP32. Meanwhile, from the server-side, the system is supervised by the admin using a web application. The admin is tasked with verifying incoming orders via the web application and sending the barcode to the buyer's email. ESP32 will wait for the buyer to scan the barcode on the vending machine. If it’s is valid, the ESP32 will inform the buyer via OLED. Furthermore, the ESP32 will activate the DC motor to run the conveyor. The completion of the t-shirt release process is detected using an infrared sensor. ESP32 then informs the server that the process has been completed. Modular testing is carried out to test each tool component's function. Meanwhile, overall testing is carried out to test system functionality. Based on the results of these two tests, the Vending Machine Using ESP32 works 100% as expected. This tool can read the QR-Code input, validate data to the server, issue t-shirts according to validated orders, and request order deletion to the server when the transaction has been completed. With this tool, it is hoped that it can help sellers and make it easy for tourists.
Fraud Detection in Credit Card Transactions Using HDBSCAN, UMAP and SMOTE Methods

Credit card abuse and fraud in credit card transactions pose a serious threat to financial companies and consumers. To overcome this problem, accurate and effective fraud detection is essential. In this study, we propose an approach that combines HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise), UMAP (Uniform Manifold Approximation and Projection), and SMOTE (Synthetic Minority Over-sampling Technique) methods to detect fraud in credit card transactions. The HDBSCAN method is used to group transactions based on their spatial density, allowing identification of suspicious groups of transactions. UMAP is used to reduce the dimension of transaction data, thus enabling better visualization and more efficient data analysis. In addition, we use SMOTE to overcome class imbalances, namely differences in the number of fraudulent and non-fraudulent transactions. In our experiments, we used. In this experiment, we used a dataset of credit card transactions that included both fraudulent and non-fraudulent transactions. The experimental results show that the proposed approach is able to detect fraud with high accuracy. The HDBSCAN method is able to effectively identify suspicious groups of transactions, while UMAP helps in better understanding and visualization of data. The use of SMOTE has successfully overcome class imbalances, resulting in more balanced fraud detection results between fraud and non-fraud. The results of this study show that the combination of HDBSCAN, UMAP, and SMOTE methods is effective in detecting fraud in credit card transactions. This approach can help financial companies identify suspicious transactions with high accuracy, reduce fraud losses, and improve the security of credit card transactions.
Development of an eyeball integrated with a wide-angle lens camera and vision-sharing system for android avatars

This paper presents the development of an eyeball integrated with a wide-angle lens camera, and a vision-sharing system for android avatars, aiming to provide both a comfortable sense of operation and a natural, human-like appearance for the avatars. The developed eyeball closely resembles a human eyeball and is capable of vertical and horizontal rotation. The vision-sharing system enables the operator to shift their perspective in a manner similar to how a human would in a real environment via the avatar, as the avatar’s gaze direction aligns with the operator’s gaze. The integrated camera eyeball and its operating system developed in this research hold promise for facilitating natural remote communication by enabling eye contact through avatars.
Unveiling the Invisible an In-Depth Analysis of Text Steganography Techniques, Challenges, and Advancement

Text steganography is the art and science of concealing hidden messages inside ordinary text without raising red flags. It's an intriguing field. Because of the increasing dependence on digital communication and the urgent need for secure and confidential information transfer, text steganography has attracted a lot of attention as a dependable means of establishing covert communication and data verification. This work provides a comprehensive review of the most recent results in text steganography, including a variety of approaches, challenges, and possible directions for further research. At the beginning of the article, the idea of text steganography is presented along with its significance for preserving secure communication. It looks at several text steganography methods such as alignment modification, synonym substitution, word order change, and linguistic steganography. Every method is thoroughly discussed, with a focus on the key ideas, benefits, and drawbacks of each. The challenges of text steganography are examined, emphasising resistance and detection. The study looks at comparison and statistical analysis methods for finding concealed messages and research into developing robust steganographic systems that may thwart detection attempts. It also examines the most recent advancements in text steganography, including the use of natural language processing and deep learning to enhance concealment and get around detection challenges.