Reviewerly/exHarmony huggingface dataset:
Exploring Students' Motivation in Online English Learning during the COVID-19 Pandemic: A Case Study of Eighth-Grade Students at MTsN 2 Palu

Education has become a new challenge in Indonesia during the covid-19 pandemic because of many changes that occurred in the education system, either in the learning methods or the learning strategies which finally affects the learning motivation of the students. This research aims to explain in detail how the objective conditions of motivation experienced by the students in online learning English and what factors affect the students’ learning motivation in online learning English during the covid-19 pandemic. The research question of this research was, “How is the eighth grade students’ motivation in learning English at MTsN 2 Palu during the covid-19 pandemic?” This research was conducted with a qualitative descriptive approach. The participants involved in this research were 1 teacher and 82 students in MTsN 2 Palu. Data collection techniques used by the researcher were interview and documentation techniques. The validity of the data was done by the triangulation method. The analysis technique applied refers to Miles and Huberman's interactive data inductive model, which includes data reduction, data presentation, and drawing conclusions or verification. The results of this research shows that some students were adequately motivated and some students had low motivation during the online learning. This can be seen directly from the results of this research through interviews and documentation to respondents. Moreover, the factors that influence the students’ learning motivation during the pandemic are students’ learning enthusiasm, family economic problems, and the lack of connection with the students’ parents.
Interval-valued picture fuzzy sets in UP-algebras

We used the notion of interval-valued picture fuzzy sets (IvPFSs) in UP-algebras to introduce the eight new notions of IvPFSs. We also discuss about how the eight new notions of IvPFSs in UP-algebras relate to each other. This idea is extended to level subsets of IvPFSs in UP-algebras. Furthermore, we define an IvPFS in the same manner that a characteristic function is defined, and we examine its characterizations using the associated subset.
Fiction vs. Reality: How Students Discover Real-Life Representations in Prose Using Engaged Reading

Background: The reading of fiction texts requires intense effort to integrate mind, emotion, and intrinsic reading motivation, in order to discover real-life representations. There is limited research in this area.&#x0D; Purpose: This study employs the engaged reading strategy combined with discovery learning, in order to investigate improvements in students’ competence in prose appreciation.&#x0D; Method: This study used a mixed-method design. Thirty-two Indonesian Language Education students participated in the study. Data was collected using student worksheets, observation, and semi-structured interviews. Data analysis was conducted by identifying and interpreting the results of prose appreciation for each reading activity.&#x0D; Results: This study found that while explaining the theme and through description students were able to activate prior knowledge. The students' reading motivation mainly focused on the intrinsic element of stories. They clearly understood the plot, characterizations, and messages, but supporting details varied because of the differences in students’ knowledge, experience, and social background related to short stories. In the post-reading stage, students revealed new knowledge and mental imagery. Based on our findings, engaged reading combined with discovery learning can enrich students' experience and ability to elaborate information, as well as to discover new knowledge about real-life representation in prose.&#x0D; Conclusion: Based on the research findings, teachers and lecturers are able to utilize the engaged reading strategy combined with discovery learning to promote students’ ability to read literature. Further research should involve more diverse participants. Experimental research could also examine the advantages and disadvantages of engaged reading and discovery learning.
Probabilistic Nearest Neighbors Classification

Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.
Exploring the Social and Cultural Implications of Hate Speech on Facebook in Malaysia

The study provides insights into the prevalence of hate speech on Facebook among Malaysian users and highlights the need for greater efforts to counteract hate speech in Malaysia. Effective policies and interventions could be developed to address this issue based on the findings. This study examines hate speech trends among Facebook users in Malaysia and their attitudes towards regulating such speech. The study employs a four-part survey questionnaire: Part A covers demographics (gender, age, education); Part B examines social media engagement (platform choice, Facebook usage); Part C explores Facebook hate speech (locations, types, frequency); and Part D investigates responses (experiences, emotions, actions, perspectives). It was conducted with 225 respondents. The study found that race-related content is not the most commonly observed form of hate speech on Facebook in Malaysia; instead, users commonly encounter other types of hate speech. Additionally, users' level of awareness regarding hate speech is influenced by their level of activity on the platform. Notably, the duration of time spent on Facebook is not correlated with the frequency of exposure to hate speech. Experiences of being victimized by hate speech do not necessarily lead to support for regulating hate speech on Facebook. These findings provide valuable insights into the prevalence of hate speech on Facebook among Malaysian users and underscore the need for greater efforts to counteract hate speech in Malaysia. The study suggests the importance of developing effective policies and interventions to address this issue based on the findings.
Developing Academic Writing Learning Module to Improve Writing Skills of Graduate Program Students

Academic writing is one of the skills needed by students, especially postgraduate students. However, there were still many writing problems experienced by Islamic Education Management Postgraduate students of IAIN Curup batch 2020. Therefore, it was necessary to develop a learning model for Academic Writing course. This study aims to investigate the development of a learning model in the Academic Writing course. This study used 4D development research model (Define, Design, Develop, and Disseminate). The result of the study shows that the learning model in the Academic Writing course was carried out into these stages: prewriting stage, drafting stage, responding stage, revising stage, and evaluating stage. By carrying out the academic writing course, there were improvements in the writing skills of the postgraduate students. In the prewriting stage, it was observed that the students had already the skills needed to be possessed in the pre-writing stage. Then in the writing stage, the students had possesed skills in processing or analyzing data, and the students also had been able to revise their writings. This research produced a learning module for the Academic Writing course that has been refined from the modules used so far. The contribution of learning academic writing is in the academic writing course which teaches writing systematics, rules for writing, reference management, how to submit articles to nationally reputable journals, paraphrasing. The results of this course can be seen from student articles which have been published in many accredited journals Sinta 3 and Sinta 4.
GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System

Speaker adaptation systems face privacy concerns, for such systems are trained on private datasets and often overfitting. This paper demonstrates that an attacker can extract speaker information by querying speaker-adapted speech recognition (ASR) systems. We focus on the speaker information of a transformer-based ASR and propose GhostVec, a simple and efficient attack method to extract the speaker information from an encoder-decoder-based ASR system without any external speaker verification system or natural human voice as a reference. To make our results quantitative, we pre-process GhostVec using singular value decomposition (SVD) and synthesize it into waveform. Experiment results show that the synthesized audio of GhostVec reaches 10.83% EER and 0.47 minDCF with target speakers, which suggests the effectiveness of the proposed method. We hope the preliminary discovery in this study to catalyze future speech recognition research on privacy-preserving topics.
An Evolving Population Approach to Data-Stream Classification with Extreme Verification Latency

Recognising and reacting to change in non-stationary data-streams is a challenging task. The majority of research in this area assumes that the true class label of incoming points are available, either at each time step or intermittently with some latency. In the worse case this latency approaches infinity and we can assume that no labels are available beyond the initial training set. When change is expected and no further training labels are provided the challenge of maintaining a high classification accuracy is very great. The challenge is to propagate the original training information through several timesteps, possibly indefinitely, while adapting to underlying change in the data-stream. In this paper we conduct an initial study into the effectiveness of using an evolving, population-based approach as the mechanism for adapting to change. An ensemble of one-class-classifiers is maintained for each class. Each classifier is considered as an agent in the sub-population and is subject to selection pressure to find interesting areas of the feature space. This selection pressure forces the ensemble to adapt to the underlying change in the data-stream.
IoT-Based Behavioral Analysis Model for Smart Learning

The role of education is of paramount importance in influencing the cognitive development of future generations. This paper presents a novel IoT-based Behavioural Analysis (IoT -BA) Model for Smart Learning integrated with neural network technology. The Internet of Things (IoT) enables educational institutions to perform management tasks more quickly and risk- freely by analyzing organized data and acquiring insights from it to inform decisions. The significance of teaching college students in the self-directed application of effective learning strategies has evolved with various trending digital twin frameworks. The proposed IoT -BA model can collect the motion features and facial expressions of the students from the classroom video while attending classes through self-learning applications. The collected images can then be preprocessed using augmentation and a local binary pattern approach. This deep-preprocessed data has been passed to the deep learning algorithm convolutional neural network (CNN) that is trained with the preprocessed data for classification. Spatiotemporal feature extraction and selection from the preprocessed data can be performed using the preprocessing algorithm principal component analysis (PCA). The student's attention level classification for the IoT -BA uses the CNN algorithm. This experimental research uses the real-time data collected by setting the classroom environment for model training and validation. The CNN model has been trained with the data collected from the environment using the simulation platform TensorFlow and performs five-cross-validation with the realistic data. The IoT -BA model can predict the student's attention level in the classroom based on the collected features from the classroom video. The simulation results of the CNN -based IoT -BA model showed better performance in terms of 96.42% classification accuracy and a minimal error rate of 1.82%.
An immersive platform for three-dimensional virtual events

During the COVID-19 pandemic, various successful adaptation methods have brought about new trends and contributed to increasing customer expectations to have a similar experience when accessing a business or product from home, as if they were on-site. Maintaining large interactive events, such as fairs and conferences, was impossible during the pandemic. In many cases, organizers either postponed the events or resorted to the development or use of already existing solutions that allowed users to access the online presentation of the event through personal devices, such as mobile phones or computers. Therefore, this paper shows a novel platform for interactive events in a three-dimensional virtual space which is modeled after actual physical space, which can be accessed through a web browser and without any additional equipment. The virtual environment is rendered in real-time and dynamically changing, with a first-person perspective and interaction to enhance immersion. Results show that the platform shows solid performance, even on weaker computers, and positive feedback from the users.
An Efficient System for Audio-Based Sign Language Translator Through MFCC Feature Extraction

This research study discusses about converting speech to text and text to visual Indian Sign Language (ISL) with MFCC feature extraction that uses Mel Frequency Cepstral Coefficients (MFCC), Gaussian Hidden Markov Models (GHMM) to convert speech to text and Natural Language Toolkit (NLTK). The proposed system follows a traditional speech recognition pipeline, including preprocessing, feature extraction, model training, and recognition. The recognized speech in the text format is processed through the NLTK framework to preprocess the text. The preprocessed text is considered as keywords, which are further used to display the output as a video containing the sign language for the spoken keywords. The system achieves high accuracy in recognizing spoken words and translating them into their corresponding words using the GHMM model. The system's real-time implementation and high accuracy make it a promising tool for facilitating communication and enhancing accessibility. The proposed system's advantages over existing translator systems include its reliance on widely-used MFCC and GHMM algorithms, real-time performance, NLTK speech processing, and high recognition accuracy.
Human Detection in Crowded Scenes Using Hybrid ResNet

Human identification in crowded places is challenging because of the overlapping and obscuring of things. Research on crowd management and surveillance techniques is critical for the protection of the general public. The development of an efficient CMS is complicated by issues including density fluctuations, unequal distribution of objects, occlusions, posture estimation, etc. The usefulness of human posture much exceeds that of widespread vehicle detection. To address the challenge of human object recognition in congested and overlapping environments, we present a hybrid ResNet, a realtime human identification network. By adding a deep cascade fusion module, the anchor-free model is able to better extract features from sparse settings including small objects. The centre region's prediction score is improved by the activation module's use of low occlusion features. In crowded areas, the network has an easier time differentiating between persons despite their changing postures. When it comes to realtime inference, our hybrid ResNet is virtually as quick as previous approaches, and it achieves competitive performance on the Crowd Human dataset.
Experimenting with Supervised Drift Detectors in Semi-supervised Learning

Machine learning algorithms to aid decision-making processes are increasingly common in several areas. When fully-trained, algorithms tend to perform better, but the availability of data labels shortly after testing without human intervention is a challenging task in many areas, especially in data stream learning with concept drifts, where data is generated very fast, in real-time, with the possibility of changes in the data distribution. Concept drifts have been addressed in different ways, but using drift detectors with base classifiers in semi-supervised learning is not so common. This article shows how to use state-of-the-art supervised detectors in semi-supervised learning problems, and it also includes an extension to the MOA framework. The Experiments designed to test our proposal used Hoeffding Tree (HT) as base classifier, combined with eight drift detectors and a total of 62 artificial and five real-world datasets, configured with 15 % and 30 % labeled instances. The results indicate that drift detectors designed for supervised learning can also be effectively used in semi-supervised environments. This finding could lead to a change of paradigm for future research, since supervised drift detectors have never been considered as a viable alternative due to the absence of labels shortly after testing in many real-world data streams.
Rat Swarm Optimizer with Graph Convolutional Neural Network Based Waste Object Detector and Classification

Object Detection (OD) is a task of Computer Vision (CV) that includes locating and detecting intended object inside a video or an image. The objective of this OD is not just categorizing the objects but also to sketch bounding boxes surrounding the objects for precise identification of their location in the given visual. Deep Learning (DL) based OD is a CV model which utilizes Deep Neural Networks (DNNs) for automatically locating and detecting inside any video segment or image. It is one among the major crucial progression in the CV field that enables broad applications comprising robotics, self-driving cars, surveillance systems, etc. In this study, a novel Rat Swarm Optimizer with Graph Convolutional Neural Network based Waste Object Detector and Classification (RSOGCNN-WODC) technique is presented. The focus of the RSOGCNN-WODC technique lies in object detection and classification using optimal DL models. To obtain this, the RSOGCNN-WODC technique comprises residual network (ResNet50) model for feature extraction process. Afterward, the RSO system can be exploited for the optimum parameter chosen by the ResNet50 model. At last, the GCNN model is utilized for the recognition of the identified objects proficiently. The performance of the RSOGCNN-WODC system was simulated on the OD database. The results signify the improved results of the RSOGCNN-WODC technique compared to recent DL approaches.
Abnormal Automated Guided Vehicles Detection for Fleet Management Systems: A Reputation-based Distributed Velocity Estimation Approach

Fleet management systems usually require many automated guided vehicles (AGVs) work together to carry out one specific task. Due to the complexity of factory environments and the unreliability of onboard sensors/actuators, the AGVs may be compromised when executing tasks. To timely detect the abnormal AGVs, this paper proposes a reputation-based distributed velocity estimation approach. In this approach, each AGV can distinguish its neighbors’ abnormal behaviors by the unreasonable velocities estimated from the communication network. The convergence of this approach is analyzed based on the remaining communication topology after isolating the abnormal AGVs. Validations of the proposed approach are conducted under different categories of faults, and the results demonstrate its feasibility in abnormal AGV detection for fleet management systems.
Improving Metaheuristic Algorithm Design Through Inequality and Diversity Analysis: A Novel Multi-Population Differential Evolution

In evolutionary algorithms and metaheuristics, defining when applying a specific operator is important. Besides, in complex optimization problems, multiple populations can be used to explore the search space simultaneously. However, one of the main problems is extracting information from the populations and using it to evolve the solutions. This article presents the inequality-based multi-population differential evo-lution (IMDE). This algorithm uses the K-means to generate subpopulations (settlements). Two variables are extracted from the settlements, the diversity and the Gini index, which measure the solutions' distribution and the solutions' inequality regarding fitness. The Gini index and the diversity are used in the IMDE to dynamically modify the scalation factor and the crossover rate. Experiments over a set of benchmark functions with different degrees of complexity validate the performance of the IMDE. Besides comparisons, statistical and ranking average validate the search capabilities of the IMDE.
Go Fish English Game: Enhancing Special Needs Pupils with Hearing Disabilities Vocabulary

In 2013, The Ministry of Education (MOE) implemented the Common European Framework of Reference (CEFR) for English language learning in Malaysian primary and secondary schools. This framework involves special needs pupils with hearing disabilities - Murid Berkeperluan Pendidikan Khas (MBPK) Pendengaran as it has been standardized to be implemented to all pupils without discrimination. Innovation in language material is important for hearing-impaired pupils because they require more help in being a proficient language user compared to normal pupils. Therefore, this study seeks to help hearing-impaired pupils through an innovative card game known as ‘Go Fish English game’ by incorporating four language skills which are seeing, signing, reading and writing. The data for this research was collected through observation and interview sessions after the game was completed. The analysis for the data is meant to show the effectiveness of the vocabulary lesson as well as the comprehension level of the hearing-impaired pupils prior to the activity. The findings revealed that three out of four pupils were able to display the mastery of four language skills correctly based on the topic of the vocabulary lesson. Only one of the pupils did not manage to read the sentences correctly after the activity. Thus, this research has added value to enhance and strengthen vocabulary lessons with a gamification approach, namely Go Fish English Game towards special needs pupils with hearing disabilities. Further research can be conducted to explore and discover the possibilities of other types of gamifications in teaching and learning of different language skills focusing on special needs pupils with other disabilities according to specific age, gender, and proficient level of English language.