Reviewerly/exHarmony huggingface dataset:
SExpSMA-based T5: Serial exponential-slime mould algorithm based T5 model for question answer and distractor generation

Authors:
raw_author_name: Nikhila T. Bhuvan
raw_affiliation_string: Department of IT, Rajagiri School of Engineering and Technology, Kakkanad, Kochi, India
raw_author_name: Jisha G
raw_affiliation_string: Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kakkanad, Kochi, India

Abstract: Generally, multiple choice questions are an effective and extensive form used in standard tests in order to evaluate the learner’s skills and knowledge. Nonetheless, the multiple-choice question composition particularly the distractor construction is quite difficult. The distracters are needed to be both plausible and inappropriate and adequate to mystify the learners who did not master the information. Thus, the distractor generation emergence is important that can help several standard tests in an extensive range of domain. In this research, question-answer generation system is developed with a distractor model by developing an optimized T5 model. At first, BERT tokenization is used to pre-process the passage/context and question, which are given as the input to train the approach. Then, the question and answer generation is performed by utilizing the T5 approach that is trained by proposed Serial Exponential-Slime Mould approach (SExpSMA). Exponential weighted moving average is extended to Serial Exponential weighted moving average and incorporated in Slime Mould Algorithm (SMA) to propose SExpSMA. In addition, the proposed SExpSMA-based T5 model is employed to generate distractors for the questions. Eventually, experimentation analysis exhibits that proposed SExpSMA-based T5 model achieves better outcomes regarding the metrics, like ROUGE, BLEU, and METEOR with the values of 0.919, 0.918, 0.488, respectively.
Grammar and Technology: A Bibliometric Exploration of Research Trends and Influential Works

Authors:
raw_author_name: Hafiza Abas
raw_affiliation_string: Advanced Informatics School, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Abstract: The study delves into the pivotal role of Grammar in scholarly communication, emphasising the need for a nuanced understanding of evolving grammatical research. Conducting a bibliometric analysis of 381 papers from the Scopus database (2015-2023), it explores grammar teaching and learning via technology. Employing advanced methods, the research identifies influential authors, institutions, and countries, tracks evolving themes, and assesses funding sources. The study utilises tools like Microsoft Excel, VOS Viewer, and Harzing to unveil partnerships, current research areas, and impact assessment. By categorising five clusters, it sheds light on underexplored research areas, offering insights for future investigations into grammar research.&#x0D;
Assessing Gender Differences in Technological Pedagogical Content Knowledge (TPACK) among EFL High School Teachers in Inner Mongolia

Authors:
raw_author_name: Jinyao Li
raw_affiliation_string: Faculty of Education, Languages, Psychology & Music, SEGi University, Malaysia
raw_author_name: Aruna Zhao
raw_affiliation_string: Baotou  Teachers' College, Inner Mongolia University of Science and Technology, China
raw_author_name: Yu Zhang
raw_affiliation_string: Faculty of Fashion Design, Jiangxi Institute of Fashion Technology, Nanchang, China

Abstract: This study examines gender differences in Technological Pedagogical Content Knowledge (TPACK) among 437 English as a Foreign Language (EFL) high school teachers in Inner Mongolia, China. Quantitative data revealed significant TPACK disparities between male and female teachers, with males exhibiting higher levels. The findings suggest that male teachers demonstrate greater confidence in integrating technology into their English classrooms. To address the TPACK gender gap, it is recommended that female EFL teachers receive specialized training through mentorship programs, online communities, and workshops. Policy changes are urged to create a collaborative, supportive, and inclusive digital environment for effective technology integration.
Research on error detection in English translation texts using machine learning algorithms

Authors:
raw_author_name: Xiaoyan Huang

Abstract: This paper provides a concise overview of a grammatical error correction algorithm based on the encoder-decoder structure. The traditional unidirectional long short-term memory (LSTM) in the encoder was transformed into a bidirectional LSTM. Subsequently, the grammatical error correction algorithm was simulated and experimented with. In the experiments, it was compared with two other error correction algorithms: one based on the LSTM classification model and the other on the traditional unidirectional LSTM translation model. The results indicated that the three error correction algorithms exhibited little difference in detection performance when faced with distinct corpus databases. Furthermore, when dealing with the same corpus database, the bidirectional LSTM algorithm demonstrated the most robust detection performance, followed by the one based on the traditional unidirectional LSTM translation model, and lastly, the one based on the LSTM classification model performed the least effectively.
A study on human perception of document paragraph layout using surveys1

Authors:
raw_author_name: Benjamin Aziz
raw_affiliation_string: School of Creative and Digital Industries, Buckinghamshire New University, High Wycombe HP11 2JZ, UK
raw_author_name: Aysha Bukhelli
raw_affiliation_string: Office of the Prime Minister, Bahrain

Abstract: We identify a list of characteristics of a text document that we use as the basis for a study on human perception of what the structure of a document should look like. Our study reveals that the native language dimension has a significant impact on the human perception of some such characteristics, including the average number of paragraphs starting with a verb, as well as other characteristics describing average paragraph proportions. The study results also show what the mean values are, for the different document characteristics, and their distribution across the native language dimension, therefore providing some idea of what a normalised structure of a document should look like. The results of the study have a direct application in a new method for embedding secret messages in text documents that has been recently proposed by the authors and which uses manipulations in the paragraph layout of a document.
B-HPD: Bagging-based hybrid approach for the early diagnosis of Parkinson’s disease1

Authors:
raw_author_name: Ritika Kumari
raw_affiliation_string: Department of Artificial Intelligence and Data Sciences, IGDTUW, Delhi, India; USICT, GGSIPU, New Delhi, India
raw_author_name: Jaspreeti Singh
raw_affiliation_string: USICT, GGSIPU, New Delhi, India
raw_author_name: Anjana Gosain
raw_affiliation_string: USICT, GGSIPU, New Delhi, India

Abstract: Parkinson’s disease (PD) is a neurodegenerative condition that affects the neurological, behavioral, and physiological systems of the brain. According to the most recent WHO data, 0.51 percent of all fatalities in India are caused by PD. It is a widely recognized fact that about one million people in the United States suffer from PD, relative to nearly five million people worldwide. Approximately 90% of Parkinson’s patients have speech difficulties. As a result, it is crucial to identify PD early on so that appropriate treatment may be determined. For the early diagnosis of PD, we propose a Bagging-based hybrid (B-HPD) approach in this study. Seven classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Naïve Bayes (NB), K nearest neighbor (KNN), Random Under-sampling Boost (RUSBoost) and Support Vector Machine (SVM) are considered as base estimators for Bagging ensemble method and three oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and SVMSmote are implemented under this research work. Feature Selection (FS) is also used for data preprocessing and further performance enhancement. We obtain the Parkinson’s Disease classification dataset (imbalanced) from the Kaggle repository. Finally, using two performance measures: Accuracy and Area under the curve (AUC), we compare the performance of the model with ALL features and with selected features. Our study suggests bagging with a base classifier: RF is showing the best performance in all the cases (with ALL features: 754, with FS: 500, with three Oversampling techniques) and may be used for PD diagnosis in the healthcare industry.
Forensic Analysis of Drones Attacker Detection Using Deep Learning

Authors:
raw_author_name: Arda Surya Editya
raw_affiliation_string: Faculty of Computer Science, Universitas Nahdlatul Ulama Sidoarjo, Indonesia
raw_author_name: Neny Kurniati
raw_affiliation_string: Faculty of Computer Science, Universitas Nahdlatul Ulama Sidoarjo, Indonesia
raw_author_name: Mochammad Machlul Alamin
raw_affiliation_string: Faculty of Computer Science, Universitas Nahdlatul Ulama Sidoarjo, Indonesia
raw_author_name: Anggay Luri Pramana
raw_affiliation_string: Faculty of Computer Science, Universitas Nahdlatul Ulama Sidoarjo, Indonesia
raw_author_name: Angga Lisdiyanto
raw_affiliation_string: Faculty of Computer Science, Universitas Nahdlatul Ulama Sidoarjo, Indonesia

Abstract: Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.
Probability Matching

Authors:
raw_author_name: Andrew W. Lo
raw_author_name: Ruixun Zhang

Abstract: Abstract Chapter 5 describes an experiment in which human subjects are asked to play a computer game with real monetary payoffs for correctly guessing the outcome of a binary lottery. Most subjects’ guesses conform to an irrational behaviour known as probability matching, in which the frequency of their guesses matches the underlying probability of the binary lottery. In addition to probability matching, we find different tendencies towards randomization under different payoff schemes, as predicted by Chapter 2’s evolutionary model of behaviour, and even after controlling for a wide range of demographic and socio-economic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socio-economic factors. Surprisingly, subjects who have taken probability and statistics classes, and those who self-reported finding a pattern in the game, are more likely to act irrationally, underscoring the importance of environment as a driver of behavioural anomalies.
Bigdata clustering and classification with improved fuzzy based deep architecture under MapReduce framework

Authors:
raw_author_name: Valarmathi V
raw_affiliation_string: Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu, India
raw_author_name: Karthikeyan A
raw_affiliation_string: Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
raw_author_name: Malathi E
raw_affiliation_string: Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, India

Abstract: The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data’s explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics.
THE ROLE OF ARTIFICIAL INTELLIGENCE IN DEVELOPING THE SKILLS OF THE CENTURY THE TWENTY-FIRST AMONG EDUCATIONAL LEADERS IN LIGHT OF SMART DIGITAL TRANSFORMATIONS

Authors:
raw_author_name: Dr. Tahani Ibrahim El ALI

Abstract: The study aimed to identify proposals to activate the role of artificial intelligence in developing the skills of the century The twenty-first among educational leaders in light of smart digital transformations, and the study adopted the curriculum Qualitative, the researcher conducted interviews with (20) faculty members from Jordanian universities Governmental and private. The results of these interviews showed that faculty members presented a range Proposals to activate the role of artificial intelligence in developing twenty-first century skills among leaders Educational education in light of smart digital transformations included: activating modern technology in all its forms The educational process in Jordanian public and private universities continues on an ongoing basis within their centers and structures Administrative and organizational, universities innovate modern mechanisms that contribute to enhancing the use of intelligence applications Artificial among educational leaders, educational leaders in Jordanian universities seek to develop Their skills by accepting change and not resisting it, and the universities’ keenness to activate electronic platforms Based on modern technologies that are compatible with current and future requirements and goals, we direct Jordanian universities, in their policies and future plans, seek to raise awareness among educational leaders of the importance of activating Applications of artificial intelligence and spreading the culture of digital transformations in educational circles. The study recommended Conducting further studies related to artificial intelligence and modern technologies and linking them to other variables
Do the People Matter in Policymaking in Ghana? A Reflection on the E-Levy and Debt Exchange Programs

Authors:
raw_author_name: None Samuel Adu-Gyamfi
raw_affiliation_string: Kwame Nkrumah University of Science and Technology

Abstract: The extent to which the masses have a say in matters concerning their lives is crucial in governance. It makes a significant amount of knowledge to say that people vote for elected policymakers to make policies that will make their lives better off and not the opposite. However, in the making of policies, the views of the people who either benefit or suffer the ramifications of policies are not taken into consideration. Therefore, the content analysis methodology has been employed in this study to systematically analyze secondary sources about the recent adoption of the E-Levy policy and Debt Exchange Program to ascertain whether the people mattered in adopting and implementing these policies. The adoption of these policies has raised a lot of controversies, with the public agitating and calling for its termination. The government of Ghana, being keen on continuing with the implementation of these policies as the only way out of the country's economic hardship, raises a lot of questions. After a systematic analysis of the literature, the paper argues that both policies were passed without the involvement of the people. The implication of the government’s failure to adopt a participatory policymaking approach accounts for the citizenry's loss of trust in the government.
Study on the Visual Tactility of 3D Virtual Material Images According to Lighting Environments

Authors:
raw_author_name: Yoonmi Choi

Abstract: When shopping online, customers rely on three-dimensional images of digital products to make their purchase decisions. These decisions are often influenced by the emotional response the digital images elicit. Lighting can play a crucial role in enhancing the three-dimensional effect of virtual reality visualization technology, thereby creating a spatial atmosphere that can stimulate consumer emotions while providing valuable product information. The significance of this study lies in its ability to analyze how lighting conditions affect a customer
Optimizing Collaboration Skills in the 21st Century: Recent Innovations in Project Model Differentiated Learning

Authors:
raw_author_name: Fery Muhamad Firdaus
raw_affiliation_string: Universitas Negeri Yogyakarta, Yogyakarta, Indonesia

Abstract: Collaboration skills are important. Therefore, group learning needs to be held for hands-on experience that results in understanding and effective learning methods. This study aims to analyze the effect of project model differentiated learning on students' collaboration skills in grade V elementary school in science subjects. This type of research is a quasi-experiment with a one-group pretest-posttest design. The sampling technique used was the saturated sampling technique with 26 students. The research instruments used in the form of learning style questionnaires, observation sheets of the learning process in the classroom, and observation sheets to measure student collaboration skills consist of initial and final observation sheets. Data analysis used in this research is descriptive analysis technique and inferential analysis technique with Paired Sample t Test. Before the hypothesis test, the prerequisite test includes the normality test and homogeneity test. The average collaboration skills of grade V students before being given treatment using the differentiated learning project model were none in the Very High category, 9.99% in the High category, 70.76% in the Medium category, and 19.76% in the Low category. After being given treatment using a differentiated learning project model, there were 9.23% in the Very High category, 59.23% in the High category, 31.54% in the Moderate category, and none in the Low category. Furthermore, data processing is carried out with t-value ≥ critical t-value (39.034 &gt; 2.05954), which indicates that there is a positive effect of the differentiated learning project model on student collaboration skills in grade V elementary school.
Analysis of Syntactic Material in the Textbook of SDN 03 Cemaka Putih Class 1-3

Authors:
raw_author_name: Muhammad Yaasiin Fadhilah
raw_affiliation_string: UIN Syarif Hidayatullah  Jakarta
raw_author_name: Nuryani Nuryani
raw_affiliation_string: UIN Syarif Hidayatullah Jakarta

Abstract: This research is entitled "Analysis of Syntactic Material in Textbook Material at SDN 03 Cempaka Putih Grades 1-3." Many people do not know that syntactic material has also been studied since elementary school, therefore the researcher decided to conduct this research. The purpose of this research is to find out what material is related to syntax in class 1-3 textbooks at SDN 03 Cempaka Putih. This type of research is descriptive qualitative. The data collection technique was carried out through observation by borrowing textbooks from grade 1-3 teachers at SDN 03 Cempaka Putih and carrying out reading, listening and note-taking techniques. The subject of this study is the syntactic material contained in the textbooks used at SDN 03 Cempaka Putih grades 1-3. The results showed that of the 17 textbooks used at SDN 03 Cempaka Putih grades 1-3, there were 9 materials related to syntax. The material found in the textbooks used at SDN 03 Cempaka Putih grades 1-3, namely words and sentences.
Databases in the investigation of household armed robberies: Challenges and ways of improvement

Authors:
raw_author_name: Oksana Bryskovska

Abstract: Amidst the full-scale war in Ukraine, an increase in the number of crimes against property involving weapons, characterised by suddenness, brutality, and aggressiveness, has occurred. Such crimes violate not only the inviolability of housing and property rights but also harm the health and lives of victims. In this regard, the study aims to examine the possibilities of using automated information retrieval systems of the Ministry of Internal Affairs and the National Police of Ukraine in the investigation of robberies involving the use of weapons. The following methods were used in the course of the study: comparative legal, analytical, systemic, and structural, generalisation, induction, deduction, and synthesis. These methods were used to analyse the world experience of databases of automated information retrieval systems to improve the investigation of robberies involving the use of weapons against citizensʼ homes. Based on this, the most progressive and effective methods of investigating this category of criminal offences are identified, proposals for improving national practice are formulated, and an indicative set of parameters for typical actions during such robberies is developed. The author proposes to improve information and analytical systems by filling in new databases of serious crimes committed with the use of weapons, which will facilitate ease of use and efficiency of their investigation. The development of such a database provides for the possibility of updating and collecting information based on specified requests. The content parameters are determined by the ability to collect information on criminals, the forensic traces that identify them, behavioural traces (use of a type or types of weapons, home invasion tools, nature of bodily harm, features of disguise, etc. The practical value of the study lies in the possibility of considering theoretical recommendations when creating databases that will ensure the completeness, efficiency and convenience of obtaining the necessary information for the effective investigation of robberies involving the use of weapons