Reviewerly/exHarmony huggingface dataset:
Selection of the Most Effective Augmented Reality Application for English Education

Efforts to take advantage of the opportunities offered by advancing information technologies to make the process of learning and teaching English more effective are increasing every day. One of the information technologies that has begun to be used in different fields of education and whose popularity is increasing day by day is Augmented Reality (AR) technology. This study aims to provide information about the general concept of AR technologies, to identify the application areas and examples of AR technology in education and English education, to identify and classify the difficulties encountered in teaching English, to make recommendations for AR technology applications that can solve the difficulties identified in English teaching, to present the priority rankings and importance levels of the identified difficulties, and to decide on the most effective AR technology application that can solve the identified difficulties. In the scope of the study, the difficulties encountered in teaching English are divided into four different classes: difficulties arising from students, education system, materials, and teachers. It has been determined that the difficulties arising from students have the most impact on teaching English. Among the nine solution proposals that could solve the problems in teaching English, the "virtual character wandering in the classroom" application has been identified as the most effective solution proposal. Since there are a limited number of studies on the use of AR in English education in the literature, it is expected that this study will fill the gap in the literature and will contribute to academicians and application developers, and be a source of inspiration for future research.
The Performance of K-Nearest Neighbor (KNN) Approach for Estimating Extreme Rain Events based on CCTV Images Camera Data

Abstract—Hydrometeorological disasters such as flooding in urban areas are a big problem that must be managed. Indonesia as part of the maritime continent, has high rainfall variability, both temporally and spatially. Unfortunately, the density of instruments for measuring rainfall is still low. To solve the problem, this research will try to utilize and modify Closed Circuit Television (CCTV) cameras which have a large number in terms of quantity as instruments for measuring rainfall. The purpose of this research is to obtain rainfall image information and data generated by CCTV cameras. The image data is converted to quantitative rainfall data. The method used is the K-NN algorithm and machine learning. The research location is located in a corner of the city of Bandung with a geographical position of 60 53”30.49'S and 107.035” 12.27' E. The results of this research show that the K-NN algorithm can be applied to estimate rainfall data from CCTV images with an accuracy of more than 98%. The level of accuracy generated between CCTV camera image data and AWS is 94%. The level of accuracy is high means that CCTV camera image data can represent or be converted into quantitative rainfall data.
Prophecy and Transformation of the Japanese Invasion of Korea in 1592

The seven-year Japanese invasion left a lot of human and material damage, and the aftereffects lasted for hundreds of years. The experiences and memories of the war at that time were left as oral tradition or record. In later generations, various evaluations and perceptions based on this were treated as retrospectives, lessons, or interests. Prophecy was one of them. Prophecy about the Japanese War is recorded around a private collection of literary work in the early 17th century and the mainstream content was about signs of occurrence and development and react. Prophecy was able to gain credibility because of people who were believed to be able to predict the future and The Japanese invasion was God's will and meant to be terminated. Meanwhile, as the 18th century entered, several new prophecies emerged. Under such circumstances, prophecies about the Japanese invasion are linked to predictions of new types and contents. As a result, the meaning was transformed or absorbed into a new type of prophecy. These prophecies appear in the book of prophecies. These prophecies appear mainly in prophecies, which differ in content and purpose made compared to before. This fact suggests that predictions about the Japanese War were being made in a different form from those of the 17th century. The future of prophecy, which had been made of at least two types, gradually diverged. Prophecies about the signs and processes of the Japanese War declined. On the other hand, prophecies that have been transformed or absorbed by other prophecies since the 18th century could have persistence and dynamism.
Aplikasi Peta Interaktif 3D Pada Gedung Fakultas Teknik Universitas Lampung

The advancement of technology allows for various developments, such as the creation of systems and applications that facilitate human tasks and transactions. However, the existence of applications also requires research to measure user satisfaction, one of which is the DANA application. DANA is a smart application that provides convenience in transactions. With DANA, people can easily make online and offline payments using electronic money. This research aims to determine the level of user satisfaction with the DANA application using the End User Computing Satisfaction (EUCS) method. This method focuses on user satisfaction by analyzing the application based on content, accuracy, format, user-friendliness, and timeliness. The research conducted is descriptive research using a questionnaire as the research instrument. The sampling technique used in this study is random sampling. This research is deemed necessary to determine the level of user satisfaction. Furthermore, the data is processed using SPSS Version 25. The results show that the level of user satisfaction for the five variables is as follows: the Content variable received a high satisfaction score with a percentage value of 78.8%, the Accuracy variable received a high satisfaction score with a percentage value of 78.7%, the Format variable received a high satisfaction score with a percentage value of 79.2%, the Ease of Use variable received a high satisfaction score with a percentage value of 79.2%, and the Timeliness variable received a high satisfaction score with a percentage value of 77.3%.
Boosting for Addressing Class Imbalance Problems in Financ

The class imbalance problem is essential to be solved in order to improve the performance of classification and prediction models. This study proposes an AUCBooost technique that introduces a direct performance optimization technique to improve the performance of boosting algorithms applied to class imbalance problems in the financial sector. In this study, the performance of AUCBoost is verified on class imbalance problems such as corporate bankruptcy, card insolvency, and card fraud. For comparative analysis of performance, logistic, AdaBoost, GBM, and XGBoost are adopted as benchmark models. The results of repeated 10-fold cross-validation are as follows. First, unlike conventional algorithms only focusing on majority class in class imbalance problems, AUCBoost shows a balanced learning behavior that simultaneously considers the specificity of multiple class and the sensitivity of minority class. Second, compared to the benchmarking model, RMA shows that AUCBoost shows significantly improved performance in terms of AUC. Third, in class balanced data generated from data sampling, AUCBooost also shows the significantly improved performance compared to benchmarking models.
Using LLMs to Customize the UI of Webpages

LLMs have capabilities to understand natural language and code, which makes them a great candidate for user-driven customization of webpages. A process that focuses on natural language can be useful for those who are less technologically literate. In this paper, we explore the potential of using LLMs to modify webpages, and what kinds of opportunities and challenges that come with it. We observe that specific prompts referring to color or targeted components can succeed, vague requests and any complex website tend to perform poorly.
Content-Adaptive Block Clustering for Improving VVC Adaptive Loop Filtering

Adaptive loop filtering (ALF) is an important tool in the H.266/VVC standard, and offers significant compression efficiency gain. ALF adopts a set of linear filters to process the reconstructed samples of one picture, where the filter coefficients are adaptive to the picture, and the filter selection is based on classifying the samples at the granularity of 4 $$\times $$ 4 block. We investigate how to make ALF more adaptive by focusing on the sample classification. We argue that the classification may be replaced with kinds of clustering, since we perform the filter selection based on the clusters without requiring the definition of classes. Thus, we propose to cluster the samples, also at 4 $$\times $$ 4 block level, according to the content-based features extracted from the blocks. By an extensive set of experiments with different kinds of features, we observed that the proposed method achieves on average 0.69% BD-rate reduction than the H.266/VVC anchor on a number of 2K-resolution images.
Research on Optimization of Boundary Detection and Dangerous Area Warning Algorithms Based on Deep Learning in Campus Security System

This study designs and implements a boundary detection and dangerous area warning algorithm based on deep learning from the perspective of typified campus security situation resources such as data, information, and knowledge. Based on integrating multiple campus security factors, real-time perception and further prediction of campus security situation can be achieved. Through coordinated operation among various algorithm modules, object intrusion in specific areas can be accurately identified and early warning can be given. The research results show that when an object invades a specific area, the difference coefficient will increase, and the larger the change value in the intrusion area, the larger the corresponding difference coefficient. By using this feature, the threshold of the difference coefficient can be determined. When a region is invaded, the contour length of the foreground will sharply increase. Based on the statistical information of the contour length of the foreground, the threshold can be set to determine whether someone has invaded the region. The deep learning algorithm in this study accurately extracts the contour of moving targets and can identify foreground targets. The real-time performance of the algorithm is also guaranteed, and it has high practical value in intelligent video monitoring. This algorithm greatly improves the efficiency of intrusion detection by utilizing the joint constraints of two types of time-domain and scene-space transformations in monitoring images. This method is not affected by the brightness of the regional environment, nor will it cause misjudgment due to significant differences in brightness of the regional environment. The detection and inference time of deep learning-based detection methods is controlled within 2-3ms, and the FPS value of the detection method is always at a high level, which can quickly increase to over 350frames/s after transmission begins. The detection method based on deep learning has higher detection efficiency.
Eye Controlled and Demand Conveying Automated Wheel Chair

The eye-controlled and demand conveying automated wheelchair is a novel assistive technology that allows individuals with physical difficulties to control their wheelchair using eye movements and communicate their destination to the device through eye recognition. This technology is designed to improve mobility and independence of individuals with limited mobility, enabling them to navigate their environment more easily and communicate their needs more effectively. This project is mainly made to assist the patients affected by paralysis, paraplegia and stroke. The patient can move the wheelchair left or right just by looking in the appropriate direction. It does not require the guidance of third person. The affected person can indicate their needs by pressing the buttons attached with the wheelchair. The combination of these features provides a user- friendly and efficient solution for individuals with physical disabilities to enhance their mobility and independence.
Develop a Hybrid Classification using an Ensemble Model for Phishing Website Detection

Solutions to threats posed by technical and social vulnerabilities must be found to secure the web interface. Social engineering attacks frequently use phishing as one of their vectors. The importance is promptly detecting phishing attacks has increased. The classifier model was constructed using publicly accessible data from trustworthy and phishing websites. A variety of methods were used to extract relevant features to build the model. Before a user experiences any harm, Machine Learning algorithms can reliably identify phishing attacks. To identify phishing attacks on the website, this study presents a novel ensemble model. In this paper, the Artificial Neural Network (ANN) and the Random Forest Classifier (RFC) are used in an ensemble method along with the Support Vector Machine (SVM). Compared to previous studies, this ensemble method more accurately and efficiently detects website phishing attacks. According to experimental findings, the proposed system detects phishing attacks 97.3% of the time.
An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)

Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The proposed methodology is referred to as Chaos-GWO (CGWO). The CGWO improves the search space's exploration and exploitation abilities while avoiding local optima trapping. Using different benchmark functions, five distinct chaotic map functions are examined, and the best chaotic map is considered to have great mobility and ergodicity characteristics. The results demonstrated that the best performance comes from using the suitable chaotic map function, and that CGWO can clearly outperform standard GWO.
An Improvements of Deep Learner Based Human Activity Recognition with the Aid of Graph Convolution Features

Many researchers are now focusing on Human Action Recognition (HAR), which is based on various deep-learning features related to body joints and their trajectories from videos. Among many schemes, Joints and Trajectory-pooled 3D-Deep Geometric Positional Attention-based Hierarchical Bidirectional Recurrent convolutional Descriptors (JTDGPAHBRD) can provide a video descriptor by learning geometric features and trajectories of the body joints. But the spatial-temporal dynamics of the different geometric features of the skeleton structure were not explored deeper. To solve this problem, this article develops the Graph Convolutional Network (GCN) in addition to the JTDGPAHBRD to create a video descriptor for HAR. The GCN can obtain complementary information, such as higher-level spatial-temporal features, between consecutive frames for enhancing end-to-end learning. In addition, to improve feature representation ability, a search space with several adaptive graph components is created. Then, a sampling and computation-effective evolution scheme are applied to explore this space. Moreover, the resultant GCN provides the temporal dynamics of the skeleton pattern, which are fused with the geometric features of the skeleton body joints and trajectory coordinates from the JTDGPAHBRD to create a more effective video descriptor for HAR. Finally, extensive experiments show that the JTDGPAHBRD-GCN model outperforms the existing HAR models on the Penn Action Dataset (PAD).
Income Inequality, Finance, and Economic Growth Relationships

Since the early 1980s, income inequality has risen between and within countries in the neo-liberal era. In literature, there are different views on the relationship of financial sector and the real economy with income inequality. Two main hypotheses in the literature regarding this relationship are as follows: The first hypothesis claims that developments in financial sector can only benefit people with higher incomes. Those with higher incomes can offer collateral and are more likely to repay loans, while those with low-income levels may have difficulty in getting loans, and this may increase inequality. The second hypothesis argues that the growth of the financial sector can provide previously excluded low-income individuals with access to credit. This hypothesis suggests that income inequality decreases when financial markets are developed. This study presents the income inequality, finance, and growth relationships via panel data methodology. Our dataset consists of emerging markets, and our data source is the World Bank database. Our study contributes to the existing literature with its results, which give evidence of a negative relationship between income inequality and economic growth with policy implications. Specific policies toward the financial sector and the real sector would be implemented for poverty alleviation.&#x0D; &#x0D;
Blockchain-Based Energy Trading Scheme for EVs Coordination: A Smart Contract Approach

The extensive deployment of electric vehicles (EVs) globally has become an alternative to fossil-fuel or gasoline vehicles to tackle harmful greenhouse gas emissions. But, huge EV charging demand can bring challenges to the charging infrastructure by destabilizing the grid stability and affecting efficient EV charging. Thus, the aforementioned challenges can be addressed by facilitating the energy trading (ET) between EVs which can act as prosumers or consumers. Thus, we have proposed a blockchain-based secure ET scheme for EVs coordination using Interplanetary File System (IPFS) protocol to mitigate the data storage cost issues of the blockchain. Opportunistically, we have considered various ET scenarios to coordinate the communication between prosumer and consumer based on the waiting time and priority levels. Moreover, the data communication between EVs is considered via a 5G communication network for highly reliable and efficient ET. Finally, the proposed scheme is evaluated by executing the smart contract using Remix Integrated Development Environment (IDE) with various performance metrics such as gas consumption (SF5 acquires maximum value of 186571 gas) and cost (execution (SF5 acquires maximum value of 120371 gas) and transaction cost (SF4 acquires maximum value of 124865 gas) ) analysis. Additionally, security analysis of the proposed scheme is tested using Echidna tool which shows the working of the smart contract without any vulnerability and bug.
CrowdCaption++: Collective-guided Crowd Scenes Captioning

Crowd scenes analysis plays an important role in various fields, including public security, smart cities, and intelligent transportation systems. However, traditional crowd scenes captioning methods mainly focus on a single and prominent crowd collective, which limits their ability to describe the different crowd collectives in complex crowd scenes. To address this issue, we propose a collective-guided crowd scenes captioning model (CrowdCaption++) to explore a more comprehensive and detailed description. We design a crowd features encoder (CFE) including double-query features encoder and foreground crowd features encoder, which uses double-query attention module (DQ-ATT) to capture more representative visual features and extracts foreground crowd features to avoid interference from background for collectives prediction. Moreover, we build a collective-guided captioning decoder (CCD) to generate captions of different crowd collectives without requiring extra alignment between crowd collectives and captions. To achieve this, we first design a crowd collectives predictor to identify multiple potential crowd collectives and create crowd collectives guidance information. Finally, we use the crowd collectives guidance information to merge useful visual features and further generate corresponding caption. We evaluate our approach on the latest crowd scenes dataset CrowdCaption and demonstrate that our model can achieve a comprehensive understanding and describe the different crowd collectives in complex crowd scenes.
Energy Efficiency Bounds for Photonic Analog to Digital Converters

Many efforts have examined the prospect of photonic based analog to digital converters (ADCs) and shown that they can substantially outperform their electronic counterparts in terms of speed and resolution. In this paper we analyse the power consumption of photonic ADCs, which has not been meaningfully examined in previous literature yet is a critical figure of merit for analog to digital conversion. Firstly, we show that in a quantum noise limited regime photonic based converters cannot exceed the efficiency of conventional electronic designs in any reasonable operating environment. However, we further show that the exceptional performance of photonic ADCs at high frequencies may allow them to outperform high sampling rate electronic ADCs on a Schreier figure of merit basis, whose performance is limited by technological constraints such as clock jitter and the switching speed of the integrated circuit technology.
Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors 2022, 22, 5160

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Constructions of Boolean functions with five-valued Walsh spectra and their applications

Boolean functions with a few Walsh spectral values have important applications in sequence ciphers and coding theory. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra by using the secondary construction of Boolean functions, in particular, plateaued functions are included. Then, we construct three classes of Boolean functions with five-valued Walsh spectra using Kasami functions and investigate the Walsh spectrum distributions of the new functions. Finally, three classes of minimal linear codes with five-weights are obtained, which can be used to design secret sharing scheme with good access structures.
Product-Based Collaborative Filtering Recommendation System for E- Commerce

Recommendation System are an application of ML. RS in any E-commerce websites are an important aspect as they are needed to satisfy the customers and for a better user experience. They help to suggest the best possible products a user might want to buy. The Product- based Collaborative Filtering is used in RS to suggest the desired products in an efficient way. They will allow a customer using the website to buy a product of a particular brand or of a certain price limit. Further it will not need much struggle as it will use the previous history of the user to recommend the products. This method can further enhance the UI. Further RS also has its roots in DL where we can use Neural networks for the RS.