Reviewerly/exHarmony huggingface dataset:
Language Attitudes and Motivation of ESL Secondary School Students

English is recognized as one of the most important languages in the world as it plays a significant role in business, education, and information technology. Therefore, developing a strong command of the English language is essential. However, motivating students and changing their attitude towards learning English remains a challenge. This study aims to investigate the attitude and motivation of secondary school students in learning English at a secondary school located in Kuala Lumpur. Data were obtained from 37 Form 4 secondary school students using modified version of AMTB (Attitude and Motivation Test Battery) questionnaire. The overall findings revealed that the majority of students showed high levels of motivation in their language learning endeavors and were predominantly driven by both instrumental and integrative factors. Understanding these motivational factors enables educators to design language learning programs that foster authentic language use, cultural exchange, and students' overall language proficiency.
The Effect of Control on The Prevention of Online Examination Fraud.

The purpose of this study is to identify the effect of controls and heavy punishment on theprevention of online examination fraud for professional students at one of the universities in Malaysia. The study has listed five potential key factors as follows: (1) identity verification, (2) monitoring, (3) toilet visit procedures, (4) punishment, and (5) embarrassment. The students were given the questionnaire. Correlation and multiple regression analyses were performed on the data collected from the respondent. The results of the analyses have shown that certain controls, such as monitoring and punishment, have a positive impact on the prevention of online examination fraud in the university. Whereas the other controls, which are identity verification, toilet visit procedures, and embarrassment, do not significantly have a positive impact on the prevention of online examination fraud at the university. This study contributes to higher education by providing additional beneficial information that could strengthen their control and also enhance their punishment in preventing the occurrence of online examination fraud. This, in turn, will improve the quality of their graduates and eventually the image of the university.
DEVELOPMENT OF DIGITAL COMPETENCE OF FUTURE PHILOLOGISTS: CASE OF TURKISH AND UKRAINIAN UNIVERSITIES

The paper calls into question the development of the digital competence of future philologists, taking into account the Turkish and Ukrainian experiences. The study of digital competence development is attracting increasing interest due to a growing need for experts capable of processing the enormous streams of continuously transmitted information and experts willing to improve their digital skills for professional growth. The purpose of the study is to broaden the current knowledge about the ways of developing digital competence. In this respect, the paper investigates two systems of future philologists' training in Ukraine and Turkey in the Digital Tools for Translation course. The authors analyze the experience of the Turkish and Ukrainian higher education institutions regarding the use of appropriate ICT tools using theoretical and empirical methods. Fifty-two future philologists from Turkey and Ukraine took part in the research. The study presents the results from observing the training and interviews with Ukrainian and Turkish students. The Shapiro-Wilcoxon method (non-parametric criterion) was used to verify the results. An analysis has highlighted the positive experience of Turkish universities, which can be implemented in future philologists training in Ukrainian universities. The findings of this study support the idea that the insufficient level of digital competence development lengthens and complicates the period of adaptation of graduates to modern working conditions. The authors found that the integration of Ukrainian higher education into the European higher education area occurs in times of globalization and the digitalization of society, which radically changes the nature of many activities. This research has clearly shown that digital competence development is more effective with the implementation of a holistic approach when students enhance digital competence while acquiring all other competencies. Our research suggests that the digitalization of education reduces the gap between graduates' digital competence level and modern society's expectations.
Machine Learning Based Flight State Prediction for Improving UAV Resistance to Uncertainty

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model . Subsequently, a trajectory planning method leveraging the DWA(Dynamic Window approach) algorithm was designed to mitigate the safety risks associated with uncertain control command tracking. By employing the obtained model of nominal command execution results of UAVs subjected to uncertainty, and by adjusting the original assessment criteria to a probability-weighted comprehensive optimal metric, optimal control commands that factor in uncertainty are derived. The simulation results affirm the effectiveness of the designed method.&lt;/div&gt;&lt;/div&gt;
Metaverse-Based Learning in the Digital Era

Digital disruption in the world of education refers to the impact of technology on traditional education systems and models, where the current education system is felt to need to be adjusted to the progress of the times and the needs of students. The business model of education in this digital era requires the innovation and creativity of teachers. After the Covid 19 pandemic, in addition to providing challenges in the world of education, it also provides an opportunity where students and educators are accustomed to online learning applications, teleconference applications, or collaboration applications that were previously not or rarely used by the world of Education. Many apps have been created to make online learning easier. Metaverse has been around since 2010, but due to the pandemic, the use of metaverse in learning is again being used with the development of Virtual reality, extended reality, and augmented reality technology. These three technologies can display a virtual environment just like the original environment. The method used to design metaverse-based learning is to use the ADDIE research and development model while learning development follows the stages of the Dick and Carey model. The results of field trials using survey methods and experiments on 20 junior high school students for one semester using the metaverse, resulted in a score of 4.82 with the conclusion of excellent learning material, students felt very happy, motivated to complete tasks, and always followed the learning stages designed in the form of games. Implementing the metaverse in learning can increase student engagement, creativity, technology skills, collaboration, and interaction as well as improve learning experiences that are more fun and engaging.
Understanding the Concept of Heavy and Light Through Weighing Activities for Children Aged 5-6 Years

This research is that children's understanding of the concept of heavy and light is not optimal, it can be seen from the number of children 14 people 5 children can sort correctly, 9 other children have not been able to sort correctly at RA Hasanuddin Pontianak who has introduced the concept of heavy and light. The purpose of this study was to find out whether there is an understanding of the concept of heavy and light through weighing activities in children aged 5-6 years at RA Hasanuddin Pontianak Tenggara. This study uses a quantitative approach to the experimental method. Data collection techniques namely observation, documentation. The sample in this study was group B children, which consisted of 14 children. Testing the paired sample t test on the aspects assessed by the child between the pre-test and post-test, namely with an alpha significance level of 5% obtained a t-count value of -11,678 with a significance value of 0.01 which is less than an alpha of 0.05, so The decisions that can be made are rejecting Ho and accepting Ha, meaning that there is an influence on understanding the concept of heavy and light after the implementation of weighing activities in children aged 5-6 years.
Comparative Study of Block Ciphers Implementation for Resource-Constrained Devices (Review)

Lightweight cryptography (LWC) is critical for securing data between devices with limited resources. In this paper, we look at hardware-based block ciphers (BCs). The block ciphers are thoroughly described using the Substitution permutation network (SPN) and Feistel network (FN) structures. A detailed summary of each SPN and FN-based BC is provided. Furthermore, the paper compares and thoroughly analyses the performance analysis of modern SPN and FN-based BCs. For each block cipher, performance measures such as chip area (slices), frequency, latency, throughput, and hardware efficiency are reviewed. The paper also recommends appropriate ciphers for the greatest and lowest performance indicators. Among BCs, the PRINCE cipher has minimal latency and great throughput. The research also reveals that the LED and PRESENT ciphers are more compact than other ciphers. The open research challenges are also presented with a forward-looking perspective.
Digital Tools and Electronic Data Interchange Process in Public Administration and Vocational Education and Training

The organization and utilization of Electronic Data Interchange is part of the methodology of submitting creative proposals for the simplification of administrative practices that can contribute to the effective operation not only of school units but also of public enterprises and organizations in general. This article presents tools for electronic data exchange processes that are essential for digital transition in education. Tools for standardization and digital transition in education along with unified technological standards are proposed to improve the level of information security and introduce new digitization tools that combine ease of use with high functionality. The procedures of an effective and efficient electronic data exchange in public school administration and especially in Vocational Education and Training are highlighted.
AniraBlock: A leap towards dynamic smart contracts in agriculture using blockchain based key-value format framework

Blockchain technology offers data transparency and traceability, which is particularly useful in the agricultural sector, especially within the supply chains of commodities like coffee and fish. This sector often encounters issues such as quality degradation, unclear information, and socioeconomic injustice affecting stakeholders. The implementation of Static Smart Contracts (SSCs) on blockchains provides a structured method for executing agreements. However, this approach also has limitations, including a lack of flexibility and responsiveness to dynamic changes in the supply chain. Despite these challenges, blockchain remains a valuable tool for ensuring transaction transparency, traceability, and integrity, which are vital in agriculture. These limitations involve unchangeable parameters, rigid rules, and constraints on adaptability and scalability. This study aims to tackle these issues by designing a more dynamic and responsive smart contract system. We introduce AniraBlock, a revolutionary concept for the agricultural supply chain, particularly in the coffee and fish sectors, by implementing Dynamic Smart Contracts (DSCs) based on a key-value format framework. Unlike SSCs, DSCs offer enhanced adaptability and scalability, addressing the former's limitations. Our study adopts a mixed-method approach, utilizing both qualitative and quantitative data to validate AniraBlock's effectiveness. Preliminary results show significant improvements in data management and supply chain transparency. The proposed framework has the potential to influence the agricultural sector by boosting data integrity and operational efficiency.
Kemandirian Belajar Mahasiswa melalui Blended Learning Berbasis Literasi Digital pada Mata Kuliah Aljabar Linier dan Matriks

The importance of self-regulated learning in students is able to make them responsible learners, easy to solve problems, have certain strategies in acquiring knowledge, and control themselves in thinking and acting. Blended Learning is a method that combines conventional learning with virtual learning. The various features presented via the internet can make students active and independent in looking for reference sources as literature to develop their knowledge. The aim of this research is to determine the learning independence of students who take lectures using Blended Learning based on digital literacy. The research method used is a qualitative method. Data analysis was carried out by processing student response questionnaire data and calculating the percentage of response scores to the ideal score. The results obtained were that on average respondents gave the option of agreeing with the highest percentage, namely 45%. Meanwhile, the percentage result of the respondent's response score was 83%, which shows that the respondent's response was in good criteria, because it had a range between 68.01–84.00. So it can be concluded that learning through digital literacy-based Blended Learning has a positive impact on student learning independence.
Learning a Contextualized Multimodal Embedding for Zero-shot Cooking Video Caption Generation

This paper proposes CookingCLIP, which introduces the latest CLIP (Contrastive Language-Image Pre-training) embedding from the general domain into the specific domain of cooking understanding, and makes two adaption upon the original CLIP embedding for better customization to the cooking understanding problems: 1) from the upstream perspective, we extend the static multi-modal CLIPembedding with a temporal dimension, to facilitate context-aware semantic understanding; 2) from the downstream perspective, we introduce the concept of zero-shot embedding to sequence-to-sequence dense prediction domains, facilitating CLIPbeing not only capable of telling “Which” (cross-modal recognition), but also capable of telling “When” (cross-context localization). Experiments conducted on two challenging cooking caption generation benchmarks, YouCook and CrossTask, demonstrate the effectiveness of the proposed embedding.
An Optical Binary Neural Network Processor Enabled by Homodyne Detection Technology

We present a compact approach to implementing an optical binary neural network processor utilizing homodyne detection technology. Convolutional operations are performed using our proposal and simulation results have validated its efficacy in neural network calculations.
Distribution-Agnostic Database De-Anonymization Under Synchronization Errors

There has recently been an increased scientific in-terest in the de-anonymization of users in anonymized databases containing user-level microdata via multifarious matching strate-gies utilizing publicly available correlated data. Existing literature has either emphasized practical aspects where underlying data distribution is not required, with limited or no theoret-ical guarantees, or theoretical aspects with the assumption of complete availability of underlying distributions. In this work, we take a step towards reconciling these two lines of work by providing theoretical guarantees for the de-anonymization of random correlated databases without prior knowledge of data distribution. Motivated by time-indexed microdata, we consider database de-anonymization under both synchronization errors (column repetitions) and obfuscation (noise). By modifying the previously used replica detection algorithm to accommodate for the unknown underlying distribution, proposing a new seeded deletion detection algorithm, and employing statistical and information-theoretic tools, we derive sufficient conditions on the database growth rate for successful matching. Our findings demonstrate that a double-logarithmic seed size relative to row size ensures successful deletion detection. More importantly, we show that the derived sufficient conditions are the same as in the distribution-aware setting, negating any asymptotic loss of performance due to unknown underlying distributions.
ARMOS 2.0: an ultra-secure commercial QKD product against PNS attacks

Commercial Quantum Key Distribution (QKD) products operating using weak coherent pulses are prone to Photon Number Splitting (PNS) attacks. Here, we present ARMOS 2.0, a commercial QKD product that is based on the Decoy-DPS QKD protocol. We can detect the PNS attack from the change in the difference of the photon-dependent yield of signal and decoy pulses. We determine the photon-dependent yield of signal and decoy pulses continuously at every cycle of 67 Milliseconds. In the scenario where the difference between the photon-dependent yield of signal and decoy pulses as well as the difference between the quantum bit error rate of the two pulses is within the tolerance value then no trace of eavesdropping is observed. The signal clicks corresponding to those scenarios are used for the generation of secure keys after undergoing the post-processing stage. We have observed that the difference between the mean photon number of the signal and decoy pulses has a direct relation to the ability to detect PNS attacks.
Detection of Malaria Parasites in Thick Blood Smear Images using Shallow Neural Networks and Digital Image Processing Techniques

In routine malaria microscopic diagnosis, the thick blood film exam is performed to detect malaria parasites. The microscopic diagnosis is based on counting the number of parasites (parasitemia) in stained blood slide. The most recent work published for malaria parasite detection and counting points in the direction of using complex neural network architectures, such as Efficient NET or ROENet convolutional networks, or specialized networks for object detection, such as YOLO and Faster- RCNN. Such models make the methods difficult to use on mobile devices. Simple models could run in mobile devices, mainly in regions of low-and-middle-income country, with poor internet access This work aims to present simple and fast models for malaria parasite counting and detection, based on shallow neural networks, as one layer perceptron, logistic regressor and multilayer perceptron, associated with digital image processing technique. The best results obtained, an F1-score of 97.57%, is better than other results previously published in the literature.
Exploring the Potential of Generative Adversarial Networks for Synthetic Medical Data Generation

Proper artificial intelligence (AI) and machine learning (ML) model implementation and training are highly reliant on quality input data. However, the high costs of clinical studies and increasing privacy concerns limit the availability of medical data to researchers. This work explores the potential of applying generative adversarial networks (GAN) for generating synthetic medical data based on publicly available real-world datasets. Synthetic data is generated based on real-world observations and used to train several contemporary ML and AI models. These models are subjected to comparison with models trained on real data. Both trained models are evaluated under identical conditions on real-world testing data. These well-known, high-quality datasets are explored in this study covering diabetes and heart disease. While models trained on synthetic data show a slight decrease in objective performance, they still demonstrate viably accurate outcomes. This suggests that the introduced approach presents a potential stepping stone towards improvising data availability for researchers while respecting patients’ best interests and patient privacy.
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training

JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we propose a training approach called curriculum pre-training (CPT) for crowd counting on compressed images, which alleviates the drop in accuracy resulting from lossy compression. We verify the effectiveness of our approach by extensive experiments on three crowd counting datasets, two crowd counting DNN models and various levels of compression. The proposed training method is not overly sensitive to hyperparameters, and reduces the error, particularly for heavily compressed images, by up to 19.70%.
A Knowledge Acquisition Framework for Autonomous Decision Making in Service Robots

Service robots are expected to autonomously perform a wide range of service tasks to satisfy users' needs, but are limited in practice by their weak decision-making capabilities. This work introduces a Knowledge Acquisition Framework (KAFS) to help robots make autonomous decisions through this knowledge. This framework is divided into two parts: service knowledge acquisition and scene knowledge construction and uses a variety of intelligent methods to easily and accurately acquire a large amount of service and scene knowledge. We demonstrate the knowledge acquired by KAFS and validate the effectiveness of KAFS on robot service tasks.
A Novel Robust Kernelized FCM Based Multi-Objective Simultaneous Learning Framework for Clustering and Classification

Clustering and classification are the two important tasks involved in pattern recognition. Both tasks are interrelated with each other. The generalization ability of classification learning can be enhanced with clustering results. On the contrary, the class information helps in improving the accuracy of clustering learning. Thus, both learning strategy complements each other. To amalgamate the benefits of both learning strategies, therefore in this paper, we proposed a novel robust kernelized Fuzzy c-Means based multi-objective simultaneous learning framework (RKFCM-MSCC) for both clustering and classification. RKFCM-MSCC employs multiple objective functions to compose the clustering and classification problem, respectively. Both the formulated objective functions are simultaneously optimized using the particle swarm optimization approach. Moreover RKFCM-MSCC uses Bayesian theory that make these multiple objective functions dependent on the single parameter i.e., cluster centers that connect both the clustering and classification learning. The Pareto-optimal solution attained with the RKFCM-MSCC approach complements the clustering and the classification learning process. The effectiveness of the proposed RKFCM-MSCC is empirically investigated on four benchmark datasets and the results are compared with the state-of-the-art approaches.