Reviewerly/exHarmony huggingface dataset:
Development of an Unsupervised LearningBased Automated Evaluation System of Descriptive Assessment

Current research on automatic scoring using traditional supervised learning methods cannot grade responses to questions that are newly generated or created spontaneously. Additionally, relying on pre-developed questions and their corresponding scoring models for lesson planning may limit the creativity and diversity of instruction. This study proposes a method that can quickly evaluate student responses and generate feedback without the need for pre-developed models. We introduces the SAAI system, which employs unsupervised learning techniques to instantly create scoring models based on student responses, thereby generating evaluation and feedback information. The SAAI system complements the automatic scoring of traditional supervised learning methods and supports scoring for a wide range of newly generated questions. This research elucidates the principles and significance of this system.
Securing Voice Biometrics: One-Shot Learning Approach for Audio Deepfake Detection

The Automatic Speaker Verification (ASV) system is vulnerable to fraudulent activities using audio deepfakes, also known as logical-access voice spoofing attacks. These deepfakes pose a concerning threat to voice biometrics due to recent advancements in generative AI and speech synthesis technologies. While several deep learning models for speech synthesis detection have been developed, most of them show poor generalizability, especially when the attacks have different statistical distributions from the ones seen. Therefore, this paper presents Quick-SpoofNet, an approach for detecting both seen and unseen synthetic attacks in the ASV system using one-shot learning and metric learning techniques. By using the effective spectral feature set, the proposed method extracts compact and representative temporal embeddings from the voice samples and utilizes metric learning and triplet loss to assess the similarity index and distinguish different embeddings. The system effectively clusters similar speech embeddings, classifying bona fide speeches as the target class and identifying other clusters as spoofing attacks. The proposed system is evaluated using the ASVspoof 2019 logical access (LA) dataset and tested against unseen deepfake attacks from the ASVspoof 2021 dataset. Additionally, its generalization ability towards unseen bona fide speech is assessed using speech data from the VSDC dataset.
PERBANDINGAN ORDINAL FOREST DAN REGRESI LOGISTIK ORDINAL

There are many methods used to identify the important predictors and predict the value of the ordinal response variable. However, currently, there are prediction methods for ordinal response variables that do not use the ordinal nature of the response variables. This study uses the ordinal forest method and as a comparison the ordinal logistic regression method. The accuracy and kappa value of the ordinal forest method in each replication (5 replications) is always greater than the ordinal logistic regression. Furthermore, the value of accuracy and kappa of each group based on GRDP in the ordinal forest method is always greater than the ordinal logistic regression. So that the ordinal forest method is better used in data on village development index in North Maluku Province in 2020.
Exploring Strategies of Teaching Speaking among Indonesian and Malaysian Secondary English Teachers

The current study aims at exploring strategies of teaching speaking among EFL/ESL teachers in Indonesian and Malaysian secondary schools. The data were obtained from 55 Indonesian teachers and 9 teachers from Malaysia, who were asked to voluntarily fill out an open-ended questionnaire consisting of 11 items. The questions ranged from teaching strategies to challenges in teaching speaking and the use of IT in the classroom setting. The findings of the descriptive analysis showed that teachers in both countries used relatively similar strategies, except for the aspect regarding the proportion of English use in the classroom. In this case, Malaysian teachers used English most of the time in their speaking class, compared to their Indonesia counterparts who admitted using L1 in various proportions and/or situations. It can be safely assumed that the status of English in Indonesia and Malaysia (EFL vs. ESL) is responsible for this difference, with Malaysian teachers and students being more comfortable with the use of English in their daily encounters.
Efficient Dynamic Clustering Mechanism

Generally, Data Mining or Knowledge Discovery is the procedure of analyzing information from various viewpoints and summary the data for further information Clustering is an unsupervised learning process where it generates an object group based on their similarity in such a way that the objects fit into another group are same and those belonging to other are dissimilar. Initialization of cluster size and iteration is the major problem of data clustering traditional clustering mechanisms like fuzzy C means algorithm and K-Means algorithm. To overcome the problems in data clustering a novel clustering mechanism is proposed in this research named Efficient Dynamic Clustering Mechanism. Our proposed mechanism computes the centroid value using Gaussian distribution and this system computes the new clusters depending on the distance between the data points. Finally, we conduct a set of experiments to prove our proposed Efficient Dynamic Clustering Mechanism is rather than other approaches.
IoT Revolutionizing Healthcare: A Survey of Smart Healthcare System Architectures

The Internet of Things (IoT) has had a profound impact on the healthcare sector by paving the way for the creation of smart healthcare systems that improve patient monitoring, diagnosis, and individualized care. The purpose of this study is to provide a review of the state of the art in IoT-based healthcare system architectures. In this study, we take a look at the current state of the art of Internet of Things (IoT) technologies, protocols, and communication models used in healthcare settings. The survey digs into the roles and interconnections of key components and layers of IoT architecture, such as sensor nodes, gateways, cloud platforms, and data analytics, in a smart healthcare ecosystem. Furthermore, it investigates the potential for incorporating wearable devices, medical sensors, and other smart healthcare gadgets that enable real - time data gathering and transmission. In addition, the paper explores the dangers and vulnerabilities inherent in IoT - based healthcare systems, as well as the current solutions available for addressing these issues and safeguarding sensitive patient data. The challenges of interoperability and standardization are also explored in order to guarantee the smooth integration and communication of various healthcare equipment and systems. Using examples like remote patient monitoring, chronic illness management, fall detection, and elderly care, the survey emphasizes the beneficial effects of IoT on patient outcomes and healthcare efficiency.
An Efficient and Fast Method for Image Super Resolution by Block Based Image up Sampling

In today’s environment image super resolution is the challenging task for researchers. This research work proposes an innovative upsampling technique for images in the domain of Discrete Cosine Transform, employing Orthogonal Rotation Invariant Moments (ORIMs). The method capitalizes on the inherent sparsity of DCT coefficients, effectively reconstructing high-resolution images from their low-resolution counterparts. By harnessing the unique properties of ORIMs, precise estimation of high-frequency DCT coefficients is achieved, ensuring superior image quality with minimal artifacts. Additionally, a strategic quantization scheme is implemented to optimize computational efficiency and memory usage. Extensive experimental evaluations demonstrate the method's superiority over conventional spatial domain interpolation and up-to-the-minute DCT domain procedures in terms of visual fidelity and Peak Signal-to-Noise Ratio. This DCT-based approach using ORIMs holds great promise for high-quality image upsampling in diverse applications.
A Rule-based Semi-automated OCR Postprocessing Method for Aligning Multi-language Transcripts with Multi-column Text

Optical character recognition (OCR) is essential for converting physical documents into digital format, but it faces challenges with complex documents containing multi-language transcripts and multi-column text. This paper proposes a rule-based, semi-automated OCR postprocessing method to address text alignment issues and enhance the quality and readability of recognized text in such scenarios. The following approach combines techniques such as max gap detection for page separation; bounding box sorting and merging for text alignment; header and subheader extraction; and column-wise text alignment. Results demonstrate a mean alignment accuracy of 91.5%, with a standard deviation of 1.2%. The qualitative analysis further confirms the effectiveness of the proposed approach. Limitations and future research directions are discussed, emphasizing the potential for broader applicability, integration of advanced techniques, and evaluation on diverse datasets. Overall, the postprocessing method significantly improves text alignment accuracy and has implications for document digitization and information retrieval.
Transfer-Learning and YOLO V7 Hybridised for Human Cropping for Deepfake Detection Algorithms

The advent of deep learning technology has created images and videos convincingly fake to appeal viewers enough in appearing close to real. Such image and video modalities, known as Deepfakes, can cause harm to individuals, communities, and countries. Detecting Deepfakes is essential to prevent their negative impacts, and several detection techniques that utilize deep learning algorithms have been developed over the recent past years. The algorithms reduce the target modality to dimensional latent space to include features important for subsequent reconstruction of the same modality from the developed latent distribution data. However, manual data preprocessing such as splitting frames and cropping regions from Deepfake videos for the facial features can be tedious and time-consuming. This article proposes YOLO V7 hybridised with transfer-learning to automate the cropping process in Deepfake videos and frames splitting, making it easier to extract facial features from videos. Various popular datasets have been used for Deepfake detection, and this article explores the benefits of the proposed approach. The results present a comparison of the proposed approach with state-of-the-art models from the levels of Precision, Recall, and mAP@.5 to demonstrate the robustness of the proposed technique. Notably, it is evident that our approach outperforms other models, boasting a precision value of 0.9009, a recall value of 0.8979, and an mAP@.5 value of 0.8745.
Robust Semantic Communication Systems based on Image Transmission

Semantic communication, commonly relying on deep neural networks (DNNs), transmits the semantics of data instead of transmitting bits of data accurately or delivering signal waveforms precisely, which can remove redundant data and improve communication efficiency. Due to their sensitivity, deep neural networks (DNNs) are highly susceptible to semantic noise. The mismatch of semantic information between transmitters and receivers in semantic communications is referred to as semantic noise, which is interference that has an impact on semantic interpretation. It can be introduced at the source and in the wireless channel. In this paper, we propose a modular approach to remove semantic noise present at various phases. To eliminate the impact of the semantic noise introduced at the source, we first use a GAN-based denoising approach before the semantic encoder. In order to eliminate the impact of the semantic noise introduced at the wireless channel, we secondly use a denoising autoencoder before the channel decoder. Simulation results show that the classification accuracy of our proposed modular denoising approach is higher than that of the joint transmitter-receiver adversarial training approach at low SNR. Therefore, our proposed modular denoising approach significantly improves the robustness of the system under different channel conditions.
Analyzing Low Birthweight in Java Based on Logistic Regression Model for Matched Pair Data

Low birthweight is one of the leading causes of neonatal death. Generally, the study of low birth weight is done by modeling logistic regression without considering the influence of confounding variables that can deviate the actual relationship between the explanatory variables and the response. This paper aims to identify low birth weight determinants in Java based on the logistic regression model for conditional study design, in which the analysis is based on matching the education level of the mother with one control. The results of the analysis showed that matched logistic regression can be used to correct bias due to the influence of a confounding variable. It reveals that based on the results of modeling, the frequency of pregnancy examinations and the parity of children are significantly affect the risk of low birth weight in Java Island.
Repackaging Character Literacy and Numeracy-Oriented RADEC Learning Model Through Teacher Professional Development Program

A number of literature state that numeracy and literacy skills are the initial capital for every individual to be able to learn. However, it is known that teachers still experience difficulties in teaching character, literacy and numeracy to students in the learning process. This research was conducted to transfer abilities to teachers to design learning with student worksheets which integrate the Read, Answer, Discuss, Explain, and Create (RADEC) learning model. The research model used in this research is Educational Design Research (EDR) since in the research an intervention will be carried out in the form of a workshop on preparing RADEC-based learning designs at Private Elementary Labschool UPI Purwakarta while in several previous studies EDR was also conducted through interventions such as professional development activities in the form of workshop. At the workshop stage itself, numeracy indicators were explained in detail and thoroughly with guidance from the Guide to Strengthening Literacy and Numeracy in Schools. In relation to numeracy, teachers' understanding of numeracy competency is still not comprehensive but on the other hand, surprisingly the teachers were able to integrate numeracy indicators in the lesson plan of non-mathematic subject. Overall, through RADEC learning model, all teachers be able to select appropriate spoken, written, or visual texts or resources based on their individual students' needs and interests. As a result, they can make the necessary changes to the syllabus's contents; choose the best methods, approaches, and techniques to use; plan the most appropriate activities to carry out; and select, design, or adapt the teaching materials.
Phishing Detection: A Hybrid Model with Feature Selection and Machine Learning Techniques

Various phishing problems increase in cyber space with the progress of information technology. One of the prominent cyber-attacks rooted in social engineering is known as phishing. This malicious activity aims to deceive individuals into divulging sensitive information, including credit card details, login credentials, and passwords. The main importance of this research is finding the best outcome by various machine learning (ML) techniques. This paper uses a Tree Classifier (ETC), Forward Selection, Pearson correlation, Logit-LR model and Principal_Component_Analysis for feature selection. The Logistic_regression (LR), Naïve_Bayes (NB), Decision_Tree (DT), K-Nearest Neighbor (K-NN), Support_Vector_Machine (SVM), Random_Forest (RF), AdaBoost and Bagging classifiers are used for developing the phishing detection model. We have studied the model in four cases. Case 1 has 6 commonly selected features by ET, forward selection and Pearson's correlation, case 2 has 25 features by logit model, case 3 has all features, and case 4 has principal component analysis (3 and 5 components). We find the highest accuracy of 97.3% in case 2 with the random forest model.
Educational Game Tools to Improve the Language Development of Children Aged 5-6 Years

The lack of teacher creativity in using educational game tools has limited costs and a lack of teacher knowledge in early childhood education causes a learning medium. This research aims to determine educational game tools that can improve the language development of children aged 5-6 years. The research methodology used was library research. The research results show that educational game tools are not only able to improve language development but other developments are also stimulated, such as children's cognitive and social development. Data obtained from relevant previous research results through game tools, such as toy telephones (can telephones), word cards (flashcards), hand puppets, and posters, can improve the development of children aged 5-6. Therefore, this research makes it easier for readers to learn about educational game tools that can enhance language development in early childhood and can be used as a reference for PAUD educational institutions that need them.
MULTISENSORY METHOD IN IMPROVING CHILDREN'S READING ABILITY IN TAHFIDZ YATIM DHUAFA VILLAGE

The problem in this study is about the low ability to read because there are limitations that are owned by children. This study aims to find out how the influence of the multisensory method in improving reading skills in children from Quran Village RTYD. The research method used in this research is quantitative research with experimental methods using Pre Experimental Design type two group pretest-posttest design. The population in this study were students of Quran Village RTYD and the samples in this study were children of class one Quran Village RTYD. The analysis technique uses independent sample t-test . The results showed that there was an effect of the multisensory method on children's reading ability, this could be seen in the control group, there was no significant difference in score (p &gt; 0.05), then an independent sample t-test was performed and the results showed that the difference in pretest scores between the experimental and control groups was not significant (P &lt; 0.05).
The Influencing Factors on the Growth of Economic and Management Talents Based on Structural Equation Model—A Case Study of Sichuan Province

<p id="C1">To analyze the influencing factors on the growth of economic and management talents in China's Sichuan Province, this paper constructs an analysis framework for the growth of economic and management talents, takes 340 economic management scholars in 53 higher education institutions in Sichuan Province as the research objects, uses CV analysis to organize their CVs and information, and constructs an evaluation index system combined with system science theory for the influencing factors from five dimensions: Educational experience, work experience, research experience, part-time experience, and award experience. Correlation analysis and structural equation model are used to systematically analyze the influencing factors on the growth of economic and management talents. The experimental results show that work experience, research experience, and award experience have a direct positive significant influence on talent growth; research experience and award experience play mediating roles in the influence of talent growth. This paper enriches the theoretical dimensions of this research field and explores the interactions among these factors. It also helps to improve the cultivation and development mode of economic and management talents in the western region. Furthermore, it provides guidance and reference for the role of talents in promoting economic growth, industrial upgrading, and sustainable development.
Design and Implementation of a Cloud Based Decentralized Cryptocurrency Transaction Platform

Trading in the crypto-currency market has seen rapid growth and adoption, as well as the interest in crypto related technologies like blockchain and smart contracts. Smart contracts have gained popularity in building so called Decentralized Applications (dApps) and Decentralized Finance (DeFi) apps, mainly because they are more secure, trustworthy, and largely distributed (removes centralized control). DeFi applications run on the blockchain technology and are secured by blocks (nodes) connected by cryptographical hash links. DeFi applications have a great potential in the crypto-currency trading domain, providing more secure and reliable means of trading, and performing transactions with crypto-currencies. Only verified transactions are added to the blockchain after being approved by miners through a consensus mechanism and then it is replicated (distributed) among the nodes on the blockchain network. This research paper proposes a DeFi Crypto Exchange by integrating a numerous-signature stamp with a crypto API. A numerous-signature stamp solves the issue of transaction verifiability and authenticity. A crypto API provides the data about each crypto currency with which trades and transactions will be performed. This paper also discusses the technical background of the technology and a few related works. Decentralization of transactions through smart contracts on the blockchain will improve trust, security and reliability of transactions and trades.
Two-Factor Authentication Protocol with Zero-Knowledge over an Extended Field of Elliptic Curves

The paper proposes a cryptographic protocol two-factor authentication with the zero-knowledge over the extended field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) on elliptic curves using biometric data and private key of the user. The implementation of a cryptographic protocol with zero-knowledge proof based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking). The cryptographic protocol was modeled in the High-Level Protocol Specification Language, the model validation and protocol verification was performed using the Security Protocol Animator tool for Automated Validation of Internet Security Protocols and Applications. The software verification of the cryptographic protocol was performed using the software modules On the Fly Model Checker and Constraint Logic based Attack Searcher.