Reviewerly/exHarmony huggingface dataset:
An Audience Response System for Monitoring Classroom Emotional Climate in Elementary School

Even though the emotional state of a student or the student body in a classroom greatly influences teaching, learning, and motivation, there is a small number of measurements for classroom climate in early education. Significantly, research of human-robot interaction (HRI) would benefit from the information gained from classroom climate analysis. Classroom emotional climate (CEC) comprises the socio-emotional factors of the classroom; student-student interactions and the student's emotional intelligence and mood. In this paper, we propose a novel, cheap and easy-to-implement audience response system device that records students' emotional states in a classroom in an elementary school. The device is developed based on psychological, mechanical, and electronic requirements. The first aim of this study was to investigate the usability of the device in a K-8 classroom for CEC measurement. On the other hand, the second objective was to test students' openness to new educational technologies, and the effect of introducing said technologies on the CEC. The device was tested during two consecutive lectures of foreign language in two fifth-grade English classrooms in primary school. During the lecture, pupils also used educational robots STEMI as an educational tool for one activity. Data collection was done on the devices and using post-questionnaires after both lectures. As a result of the fact that the device was introduced during two different school days, and due to the variability of the participating students and the curriculum for the two days, the analysis was done separately. The analysis was done on data from post-questionnaires and from the device. The questionnaire results and the results from the device show that pupils conform to new educational technologies easily. Activities where students had to write or read, registered the lowest number of presses, since they were concentrating on the assignment. As observed in the questionnaires the activities that were positively evaluated in the questionnaires had the highest number of presses for positive emotions on the device. Similarly the activities that had negative evaluations in the questionnaires had higher number of negative emotion presses than the activities with the positive connotations. Current students in primary schools expect a classroom where interaction is similar to the virtual world they have grown up in, requiring faster information rates and visual learning. Nevertheless, not every type of technology, or the pedagogical aspect of the introduced technology may be suitable to their needs. Teachers that receive feedback about the influence of their lesson plan on the students' emotional states can adjust their instructional and teaching strategies to correspond to the students' states. In other words, the results observed in this pilot study are relevant to educators and researchers as well.
Intelligent Tutoring for Large-Scale Personalized Programming Learning Based on Knowledge Graph

The development of learning analytics technology has created the necessary conditions for the implementation of large-scale personalized education. By taking advantage of the current advancements available, we can adopt a deep integration of “artificial intelligence + education”, take educational big data as the research object, aim at achieving precise teaching, and use cognitive diagnosis as a means to construct an intelligent guidance model. The model will conform to a learner's cognitive patterns and curriculum characteristics to meet his personalized learning needs under different knowledge states. Firstly, use knowledge map and abstract syntax trees to extract the characteristics of teaching objectives from questions and standard programing codes. Secondly, Use “target characteristics - learning characteristics - learning sequence - learning effect” cognitive diagnosis model to assess student achievement in learning. Finally, according to the correlation between knowledge points, the initial learning path consisting of all relevant knowledge points is identified from a knowledge map. And the most optimal learning path is selected based on the importance of knowledge points and student's learning situation. There are three innovation points in this paper. First, the traditional method which only focuses on discovering the one-dimensional knowledge features implicated in the questions, is extended to construct a knowledge-ability multidimensional feature matrix. Secondly, it optimized static cognitive diagnosis into dynamic cognitive arbitrariness, which has a modeling capability of dynamic temporal sequence. Thirdly, it regarded the correlation between knowledge points, and the importance of knowledge points as the heuristic conditions for constructing a learning path.
Autograder Impact on Software Design Outcomes

This Innovative Practice Full Paper presents an analysis of autograder usage on software engineering design outcomes. Autograders have found increasing use in software engineering and computer science courses due to a variety of reasons. They are often introduced to support large class sizes, increase the reproducibility of grades, and provide formative feedback to students. Many of the classes that apply autograders are introductory in nature, such as CS1 and CS2 courses, or an introduction to algorithms course. These courses typically focus on assessing student programming ability on specific aspects of a programming language (e.g., Java inheritance) or algorithms (e.g., sorting, searching). One common drawback to automated assessment with programming is the need to impose additional structure on assignments. For example, students asked to implement a game might be given a detailed list of functions to implement, skipping analysis and design to determine what functions and algorithms are relevant. In this work, we discuss the experience of introducing an autograder into a second-year course that teaches software engineering topics such as personal software process, object-oriented design, and UML. Although we have the traditional motivations to use an autograder, it is challenging in a software engineering course since the additional structure required by an autograder is potentially detrimental to the engineering design outcomes. To address this, we developed an autograder in such a way as to minimize the design choices that it imposed on the students. The partial structure that is imposed together with the formative feedback produced by the autograder helps to scaffold student learning as they transition to more complex programs. The autograder was used during a spring 2023 offering of our software engineering course. We analyzed the data gathered in this course to evaluate the course change by asking: how does introducing an autograder impact course design outcomes? And, how does introducing an autograder impact personal process data that is collected? Answering these questions helps to inform the software engineering community about the suitability of introducing automated assessment tools into advanced computing and software engineering courses.
Mapping Learning Objectives of Project-Based Undergraduate Software Engineering Courses to CC2020 Competency Model

This qualitative research performs a thematic analysis of the learning objectives in existing project-based undergraduate software engineering courses to align them with the competency model defined in the Computing Curricula 2020 reports (CC2020). This study identifies the trends, strengths, and gaps in how the reviewed course learning objectives cover the knowledge, skill, and disposition components of the CC2020 competency model. The learning objectives were categorized according to knowledge elements, skills, and dispositions as defined in the CC2020 competency model. Our analysis shows that 54% of knowledge elements from the reviewed learning objectives do not have any skill level specified and overall, only two out of the eleven dispositions in CC2020 are specified (“Collaboration” and “Professional”). We also find that technical knowledge elements from the software development category (e.g., software process, software design, and software quality, verification & validation) and systems modeling category (e.g., systems analysis & design, and requirements analysis and specification), probably unsurprisingly, are covered the most often. Similarly, collaboration & teamwork, and oral & written communication are unsurprisingly the most common professional & foundational knowledge elements in the reviewed course's learning objectives as they are essential to project-based learning. Although they are essential for the completion of a successful software project, knowledge elements such as time management, security technology & implementation, and user experience design are rarely mentioned. We discuss the implications of our findings on course design.
GOVERNMENTAL INFLUENCE ON CRYPTO ASSETS IN FINANCE: A CASE STUDY OF GERMAN REGULATORY INITIATIVES

We review the case study of Germany with its regulatory initiatives regarding the possible governmental influence on crypto assets in finance. In doing so, we conclude that regulation shows adequate tendencies to foster innovation and adaption of digital assets such as crypto securities or currencies. This can mainly be referred to as creating a legal framework in favor of consumer protection, thereby reducing operational and reputational risks for financial companies which seek to engage in the digital assets business.
Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data

Abstract The geological data of the mineral resource potential evaluation results (MRPERs) are diverse and extremely large; efficiently retrieving data remains a challenging problem. In this work, a new way of using the Hadoop platform is proposed. The Hadoop distributed file system is used to store the massive data and construct the data storage model of geological and mineral resources. Using a distributed Hadoop database (HBase) that supports the fast query of a single record, it manages its metadata and retrieves the data of MRPERs quickly. At the same time, a multi-level index directory is designed to support the non-main key query on the HBase. This overcomes the shortcoming that the HBase only supports the simple index based on the main key and realizes the intelligent, efficient retrieval of MRPERs. The validity and feasibility of the proposed method are further verified by experiments using the MRPER data in the Institute of Mineral Resources, Chinese Academy of Geological Sciences.
Machine Learning Approaches for Handling Imbalances in Health Data Classification

Imbalanced data classification is an important area of statistical learning that has gained research attention over the years. Despite extensive research, imbalanced data classification remains one of the most challenging problems in data science and machine learning, especially for large data sets. Health data imbalance poses challenges for machine learning models to classify data points accurately and can lead to biased and inaccurate predictions with severe consequences in medical settings. The majority of real world datasets are skewed. Reports from previous researchers suggest that sampling approaches are effective for data imbalances. To tackle this problem, in this study, we discuss sampling approaches, including oversampling and undersampling methods, such as Random Oversampling, SMOTE, ADASYN, Random Undersampling, Tomek links, NearMiss and so on, and conduct experiments on four different skewed health datasets to achieve promising performances. The four imbalanced secondary health data sets used are on Diabetics, Anaemia, Lung Cancer, and Obesity classification respectively. Results show that Repeated Edited Nearest Neighbours (RENN) undersampling technique with logistic regression is more effective in handling data skewness. RENN technique should be adopted in cases of imbalance in health research.
Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers: Application to Anaemia Diagnosis

Anaemia is one of the deadliest incurable diseases globally, and its incidence continues to rise. Early identification of the disease helps to fight it; however, blood tests can be considered invasive, thereby discouraging its proper diagnosis. The manual method of detecting and classifying abnormal cells in blood films using a microscope is time-consuming, tedious, prone to error, and requires a trained haematologist. Hence, having an effective way of classifying the abnormalities present in anaemia will provide a better insight into managing the patient’s life. In this vein, this work aims to build machine learning models as an alternative to traditional examinations for identifying the disease by combining statistical learning models for anaemia classification. These models were used separately and together via hard and soft voting techniques. Oversampling technique (SMOTE) was used to balance the data set. The results showed that when a soft voting classifier and a hard voting classifier were employed, the Matthew’s Correlation Coefficient (MCC) provided a better result using the soft voting classifier than the individual models. However, using the hard and soft voting classifier on the second set of models weighted by the MCC did not result in improved performance. With the exception of sensitivity, where KNN excelled, the Light GBM Classifier (LGB) outperformed all other models. These methods would be useful in various biometric studies.
Renewable Energy Forecasting Using Deep Learning Models

Solar energy forecasting is challenging due to complex meteorological phenomena such as clouds. A major class of methods for time series forecasting is the Long Short-Term Memory (LSTM) model. Such a model is built based on specialised deep neural network architecture. However, the literature lacks specific recommendations for design decisions that have an impact on predicting accuracy. The necessity for pre-processing methods like deseasonalization, sorting the input data, network size, batch size, and choice of forecasting horizon are a few examples. We carry out an empirical analysis using information from three solar stations across two climatic regions of India over two seasons. We next extend our model to a bidirectional representation using a Bidirectional Gated Recurrent Unit (BGRU). This is more versatile as it can support context with different forward and backward lengths, Finally, we develop a regime-dependent forecasting model based on cluster-specific LSTMs. Clustering, based on cloud features within a neighbourhood of the location of interest, is used as a pre-processing step for this analysis.
Exploring Practical Applications and Python Code Snippets for Supervised Machine Learning Classification Algorithms

This paper presents a comprehensive exploration of practical applications and Python code implementations for supervised machine learning classification algorithms. Classification algorithms play a pivotal role in various domains, and this work offers insights into their real-world utility. The paper covers the application of algorithms such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, k-Nearest Neighbors, Naive Bayes, and Gradient Boosting in contexts ranging from customer churn prediction to email spam classification. Through code snippets and examples, readers gain practical knowledge of algorithm usage, emphasizing adaptability and fine-tuning to suit specific application requirements. This research contributes to the broader understanding of how classification algorithms can be harnessed effectively in Python programming for solving a wide range of contemporary challenges.
Analysis of Artificial Intelligence Machine Learning Technology for Mapping and Predicting Flood Locations in Pahlawan Batu Bara Village

This research proposes the application of artificial intelligence technology, especially machine learning, to improve flood predictions. Flooding is a serious threat that can cause major losses to society and the environment. In an effort to overcome this problem, machine learning methods are used to analyze historical data related to weather, rainfall, topography, drainage systems and other factors that influence the occurrence of floods. Machine learning algorithms such as neural networks, decision trees, and other models to predict the potential for flooding in an area. Data collected from weather sensors, satellite maps and other data sources is used to train the model so that it is able to identify patterns that lead to flooding conditions. The research results show that the machine learning approach is able to increase the accuracy of flood predictions with a better level of reliability compared to traditional methods. The implementation of artificial intelligence technology in flood prediction has great potential to provide early warning to the public and authorities, thereby reducing the negative impacts caused by flood disasters. It is hoped that this research can become the basis for developing a more effective early warning system in dealing with the threat of flooding in the future.
ChatGPT as an empowering catalyst: Unveiling the impact on political awareness and civic education

This study explores the role of education in a democracy in order to create informed people, advance democratic values, and enable active participation in the political process. It provides a comprehensive overview of the Indian political landscape, highlighting the political framework, important parties, hot button issues, and challenges to Indian democracy. The work looks into the potential of using ChatGPT, an interactive learning platform, to engage students in talks about Indian politics and foster a better understanding of political ideas, institutions, and procedures. Additionally, it examines potential biases, fact-checking techniques, and the responsibility of AI models to spread accurate political data while examining the accuracy and quality of the political information generated by ChatGPT. The study also looks at ChatGPT's potential to improve civic education by teaching students about their civic duties, democratic principles, political freedoms, and the significance of citizen participation in Indian politics. The research intends to showcase ChatGPT's revolutionary potential as a catalyst for building informed citizenship, critical thinking, and political dialogue in the Indian democracy by fusing these characteristics together.
Machine Learning Model for Prediction of the Chemicals Harmfulness on Staff and Guests in the Hospitality Industry: A Pilot Study

This article examines the trend around the adoption of machine learning in the hotel business in light of the significance of new technologies. According to previous research, the hospitality industry uses a variety of chemicals for cleaning. Cleaning supplies are the housekeeping department's primary tool in their daily routine to keep rooms and common areas clean and tidy. Guest and staff don't know the harmfulness of these chemicals. Providing hospitality that meets the needs of guests requires not only a positive attitude, but also high-quality and excellent services that keep guests warm, relaxed, and comfortable. But in some incidents, we find that the guest and staff health is affected by the chemicals. Also, no one worked on predicting the chemical's effects on staff and guest health in the hospitality sector with the use of Machine Learning models. For this purpose, data is collected from different hotels of Delhi NCR in India. There were two distinct fields utilized for assessment and instruction. For the investigation, machine learning methods were employed. The research project employed five machine learning methods. The newly developed MHC-CNN algorithm achieved the highest accuracy (93.75) in comparison to other cutting-edge machine learning techniques. The created technique can be expanded upon and applied in many hotels all around the world.
Exploring the Dynamics of Technological Innovation in Contemporary Environments and their Impact on Logistics Management Efficiency: An Empirical Study

The research aims to explore the extent of the impact of the interaction of (the contemporary environment and technological innovation) on the level of efficiency of supply management in the electric power production company in Basra Governorate. To achieve the purpose of this paper, The methodology was by the contents of the descriptive approach, where the theoretical presentation and discussion of the research topic were carried out. The population of the research was the Electric Power Production Company in Basra Governorate. The questionnaire was used as a surevy instrument to collect data from on service employees in the company. The SPSS used to assess the statistics of the data. It became clear from the analysis and discussion that technological innovation significantly enhances the efficiency of logistics management of the Iraqi Energy Production Company in the contemporary environment. Technological innovation was measured to fill the past studies gap to enhance the efficiency of logistics chain management, because we rarely find in Arab and foreign studies that have dealt with three variables, and this paper provides added scientific value to researchers in the field Electrical energy in Iraq, and the economic value of the company.
On the Concept of Translation Unit in a Machine Translation Framework

The article looks at the question whether the concept of translation unit might apply to the sphere of machine translation and whether the size of the unit influences the quality of translation. While modern machine translation systems offer an acceptable level of quality, a number of problems mainly related to the structural organization of the text remain unresolved, hence the question posed in the paper. The article offers a review of modern readings of the concept and pays special attention to the question whether the scope of the term changes depending on whether the object of research is the target text or the translation process. The paper also provides a quick look on the research methods for both text-oriented and process-oriented approaches, such as comparative analysis of language pairs and Think Aloud Protocol. Based on a review of existing machine translation models, each of them is analyzed to answer the question whether a unit of translation can be defined for a given system and what its size is. It is concluded that a unit of translation can be viewed as either a unit of analysis or a unit of processing with respect to text-oriented and process-oriented perspectives on to the study of translation. The unit of translation has a dynamic character and influences the quality of the target text. In machine translation, the unit of translation as a unit of analysis is not applicable for systems based on probabilistic non-linguistic methods. For rule-based machine translation systems, both readings of the unit of translation concept are applicable, but hardly go beyond a single sentence. Accordingly, at least one type of translation problem – intra-textual relations resolutions – remains largely unaddressed in the present state of affairs in machine translation.