Reviewerly/exHarmony huggingface dataset:
QUALITATIVE KNOWLEDGE GRAPH FOR THE EVALUATION OF METAVERSE(S) - IS THE METAVERSE HYPE OR A PROMISING NEW FIELD FOR ARCHITECTS?

With the advancement of augmented and virtual reality technologies both in scale as well as accessibility, the Metaverse (Stephenson, 1992, Hughes, 2022) has emerged as a new digital space with potential for the application of architectural creativity and design.With blockchain integration, the concept of the Metaverse shows promise in creating a "decentralised" space for design and creativity with rewards for its participants.As a platform that incorporates these technological components, does the Metaverse have utility for architectural design?Is there something truly novel in what the Metaverse brings to architectural computing, and architectural design?The paper constructs a qualitative knowledge graph that can be used for the evaluation of various kinds of Metaverses in and for architectural design.We use Design Science Research methods to develop the knowledge graph and its evaluative capacity, stemming from our experience with two Metaverses, Decentraland and Cryptovoxels.The paper concludes with a discussion of knowledge and practice gaps that are evident, framing the opportunities that architects might have in the future in terms of developing Metaverse(s).
Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.
Experimental work on the methodology of preparing future teachers for the inclusion of schoolchildren in the conditions of a university technopark

The relevance of the research is determined by the need for the dissimilation of the experience of the effective use of university technoparks in the process of organizing the educational and professional activities of future teachers. The research is based on synergetic, personality-oriented, competence-based, activity-based approaches to the development and implementation of the pedagogical process. The general scientific methodological basis of the process under study and its conceptual framework were a systematic approach that allows us to consider it as a single whole in all the diversity of internal and external relations with the elements of the educational environment of the University technopark. The aim of the study was to evaluate the effectiveness of experimental work on the methodological preparation of future teachers for the inclusion of schoolchildren on the basis of the university technopark. To analyze the readiness of future teachers for the inclusion of schoolchildren, the levels of its formation were determined: low, medium, potentially high, and actually high. The experimental work was carried out with students – future teachers – and had a step-by-step nature. At the stage of the ascertaining experiment, a quantitative and qualitative assessment of the initial level of formation of the readiness of future teachers for the inclusion of schoolchildren was carried out. At the stage of the formative experiment, educational work was carried out with students aimed at forming their readiness for the inclusion of schoolchildren within the framework of the academic discipline Education of Persons with Disabilities and industrial (technological in the system of inclusive education) practice. The key form of the educational process was the workshops of students’ professional tests, as specially organized educational tests that provide immersion in the simulated elements of educational and professional activities of future teachers in order to gain experience of actions and build an individual trajectory of their professional formation and development. At the stage of the control experiment, the dynamics of the level of formation of the future teachers’ readiness for the inclusion of schoolchildren was analyzed. A comparative analysis of the level groups of students at the ascertaining and control stages of the experimental work showed a decrease in the group of a low level of formation of the readiness of future teachers for the inclusion of schoolchildren from 26% to 5%. There was also an increase in the potentially high-level group from 26% to 53% of respondents. The performed experimental work revealed the positive dynamics of the formation of the readiness of future teachers for the inclusion of schoolchildren in the conditions of the university technopark and also confirmed the validity of the formulated theoretical provisions underlying the model of their methodological training.
Game as an educational practice: A phenomenological and hermeneutic aspect

The article aims to analyze the concept of an educational game in two philosophical concepts – phenomenology and philosophical hermeneutics. This approach is promising. It allows addressing the original meanings of the game, conducting a categorical analysis, and formulating a theoretical foundation and a methodological platform for the process of introducing game practices in education. This is relevant because, despite the numerous practices of using the game in education, the pedagogical and psychological justification of the game as a special method, a common semantic field regarding the concept of the game has not yet been formed. The ambiguity and instability of the term “game” today creates a situation of uncertainty in its use, and each time we need to redefine it for implementation into practice. To reach this aim, the authors use the works of Eugen Fink and deduce the main contradiction between the ontology of the game as the fundamental basis of human existence and the game as an educational practice. The game in its ontology takes place in “free time”, it has no purpose and is carefree, while educational practice always has a purpose. As an example of solving this contradiction, the authors refer to the works of Piero Bertolini. He proclaims the need to return to the real game. The teacher should leave the student alone with the game, give him/her freedom, realizing that the game itself is already part of learning. The game is a phenomenological method that allows the player to meet with the authentic and discover the realm of the “possible”. From this understanding of the game follows the problem of interpreting its meaning. As a source for this analysis, the authors use the phenomenological hermeneutics of Hans-Georg Gadamer. Based on the interpretation of hermeneutics, we cannot find objective meaning when interpreting games, but we are aware of our own subjectivity, the irreducibility of our own subjectivity in any act of interpretation. Moreover, this reasoning applies both to players inside the game who interpret game phenomena and actions of other players, and to a possible external observer of the game. The task of the interpreter in the case of a game as an educational event is to determine the factors that determine one’s own horizon of interpretation. The result of the review was the designation of another problem in understanding the game as an educational practice – the impossibility of its unambiguous interpretation, which also contradicts the external goal-setting of pedagogical methods. The solution of these two problems, according to the authors, is a promising task for subsequent research.
Ab initio quantum simulation of strongly correlated materials with quantum embedding

Quantum computing has shown great potential in various quantum chemical applications such as drug discovery, material design, and catalyst optimization. Although significant progress has been made in quantum simulation of simple molecules, ab initio simulation of solid-state materials on quantum computers is still in its early stage, mostly owing to the fact that the system size quickly becomes prohibitively large when approaching the thermodynamic limit. In this work, we introduce an orbital-based multi-fragment approach on top of the periodic density matrix embedding theory, resulting in a significantly smaller problem size for the current near-term quantum computer. We demonstrate the accuracy and efficiency of our method compared with the conventional methodologies and experiments on solid-state systems with complex electronic structures. These include spin polarized states of a hydrogen chain (1D-H), the equation of states of a boron nitride layer (h-BN) as well as the magnetic ordering in nickel oxide (NiO), a prototypical strongly correlated solid. Our results suggest that quantum embedding combined with a chemically intuitive fragmentation can greatly advance quantum simulation of realistic materials, thereby paving the way for solving important yet classically hard industrial problems on near-term quantum devices.
Novel Framework for Improving the Correctness of Reference Answers to Enhance Results of ASAG Systems

Usage of online learning platforms increases day by day and henceforth, there emerges the need for automated grading systems to assess the learner’s performance. Evaluating these answers demands for a well-grounded reference answer which aids a strong foundation for better grading. Since reference answers impacts the exactness of grading answers of learners, its correctness remains a great concern. A framework that addresses the reference answer exactness in Automated Short Answer Grading (ASAG) systems was developed. This framework includes material content acquisition, clustering collective content, expert answer as key components which was later fed to a zero-shot classifier for a strong reference answer generation. Then, the computed reference answers along with student answers and questions from Mohler dataset were fed to an ensemble of transformers to produce relevant grades. The aforementioned models’ RMSE and correlation values were compared against the past values of the dataset. Based on the observations made, this model outperforms the previous approaches.
On the Pursuit of Developer Happiness: Webcam-Based Eye Tracking and Affect Recognition in the IDE

Recent research highlights the viability of webcam-based eye tracking as a low-cost alternative to dedicated remote eye trackers. Simultaneously, research shows the importance of understanding emotions of software developers, where it was found that emotions have significant effects on productivity, code quality, and team dynamics. In this paper, we present our work towards an integrated eye-tracking and affect recognition tool for use during software development. This combined approach could enhance our understanding of software development by combining information about the code developers are looking at, along with the emotions they experience. The presented tool utilizes an unmodified webcam to capture video of software developers while interacting with code. The tool passes each frame to two modules, an eye tracking module that estimates where the developer is looking on the screen, and an affect recognition module that infers developer emotion from their facial expressions. The proposed work has implications to researchers, educators, and practitioners, and we discuss some potential use cases in this paper.
Neural heuristics for scaling constructional language processing

Constructionist approaches to language make use of form-meaning pairings, called constructions, to capture all linguistic knowledge that is necessary for comprehending and producing natural language expressions. Language processing consists then in combining the constructions of a grammar in such a way that they solve a given language comprehension or production problem. Finding such an adequate sequence of constructions constitutes a search problem that is combinatorial in nature and becomes intractable as grammars increase in size. In this paper, we introduce a neural methodology for learning heuristics that substantially optimise the search processes involved in constructional language processing. We validate the methodology in a case study for the CLEVR benchmark dataset. We show that our novel methodology outperforms state-of-the-art techniques in terms of size of the search space and time of computation, most markedly in the production direction. The results reported on in this paper have the potential to overcome the major efficiency obstacle that hinders current efforts in learning large-scale construction grammars, thereby contributing to the development of scalable constructional language processing systems.
Retracted: Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

[This retracts the article DOI: 10.1155/2021/5529981.].
COVID-19 Pandemic and Online Distance Learning (ODL) Issues among Malaysian Secondary School Teachers

The implementation of the Movement Control Order (MCO), which has been in effect since 18 March 2020 to date, is a preventive action implemented by the federal government of Malaysia in response to the COVID-19 pandemic.The purpose of the MCO is to break the chain of transmission with the aim of levelling the rate of infection.In complying with this measure, Malaysian schools are also taking the effort to continue their learning activities by implementing the online distance learning (ODL) approach, which requires educators to conduct their teaching and learning activities via online methods from home.The variety of online platforms used in the ODL approach that include WhatsApp, Telegram, Google Meet, Hangout, and Google Classroom, can cater to the needs of students.E-learning has been introduced to facilitate the traditional teaching-learning processes, and it encompasses the use of electronic devices such as computers, mobile phones, or tablets to provide online learning materials.In addition, the ability of the learners, their computer competency and experience, and their attitudes toward technologies are among the significant factors found in previous studies related to the adoption of educational technology.The purpose of this study was to investigate the challenges and issues faced by secondary school teachers in Malaysia while using ODL.The study employed qualitative approaches.Primary data were collected using an interview protocol through an in-depth interview by using Zoom Meeting and phone calls.Informants' opinions and answers were analysed using thematic analysis, whereas themes were created and used based on the informants' answers to analyse and present the findings.The results showed that secondary school teachers faced several challenges in the implementation of ODL.
Student Struggle with Career-oriented Assignments: An Analysis of Résumés and Cover Letters

Résumé and cover letter formats have altered with the many digital platforms available (e.g., LinkedIn). However, constructing one’s identity as an ideal candidate is still the main goal for career-oriented documents and should be the focus of related lessons. The classroom activity system mediates the résumés and cover letters students create in business and technical communication courses. Students write these assignments for teachers, who are outside of professional activity systems. Because students are not immersed in the professional contexts for which they write these career-oriented assignments, they have difficulty constructing effective identities for being ideal candidates for assumed positions. Although résumés and cover letters are ubiquitous assignments for professional communication courses, little research has looked at how students struggle with composing a professional identity. Most résumé research analyzes materials submitted to employers after applicants leave school. The author reviews student texts in this genre and argues that career-oriented assignments are actually a classroom-specific genre with little connection to the assumed professional contexts to which students aspire. The author concludes that students should focus on more immediate attainable goals rather than future career goals many years away.
"Forecasting Breast Cancer: A Study of Classifying Patients’ Post-Surgical Survival Rates with Breast Cancer"

Breast cancer is the most lethal form of cancer that can strike women anywhere in the world. The most complex and tough undertaking in order to lower the death rate is the process of predicting a patient’s likelihood of survival following breast cancer surgery. Due to the fact that this survival prediction is linked to the life of a woman, effective algorithms are required for the purpose of making the prognosis.
Calibration of multi-sensor fusion for autonomous vehicle system

Calibrating a platform with multiple sensors is a fundamental work for autonomous vehicle systems. This paper proposes an effective multi-sensor calibration method which consists of three aspects: single-sensor intrinsic calibration, multi-sensor extrinsic calibration and multi-sensor time synchronisation. Involved sensors in our proposed approach include LiDAR, Camera and Inertial Measurement Unit (IMU). Firstly, a motion model based method is proposed to calibrate the intrinsic parameters of the IMU. Secondly, besides using the distance factor of the laser beam to develop the intrinsic parameters calibration method of LiDAR, we also introduce the idea of frangibility to optimise the calibration parameters. The results of these two stages give the initial state information of the sensors for latter calibration. Thirdly, based on the idea of hand-eye calibration, we design an external parameter calibration method to estimate the actual external parameters for a multi-sensor system. Experiment results show the practical validity of the proposed algorithm.
Parameter-parallel distributed variational quantum algorithm

Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, the inefficient parameter training process due to the incompatibility with backpropagation and the cost of a large number of measurements, posing a great challenge to the large-scale development of VQAs. Here, we propose a parameter-parallel distributed variational quantum algorithm (PPD-VQA), to accelerate the training process by parameter-parallel training with multiple quantum processors. To maintain the high performance of PPD-VQA in the realistic noise scenarios, a alternate training strategy is proposed to alleviate the acceleration attenuation caused by noise differences among multiple quantum processors, which is an unavoidable common problem of distributed VQA. Besides, the gradient compression is also employed to overcome the potential communication bottlenecks. The achieved results suggest that the PPD-VQA could provide a practical solution for coordinating multiple quantum processors to handle large-scale real-word applications.
Optimization Techniques for Resource Allocation in Cloud Computing Systems

The distribution of available resources is an essential aspect of cloud computing systems. It entails allocating computer resources to various programmes and users in order to guarantee that the available resources are utilized in an efficient, effective, and equitable manner. The optimization of resource allocation in cloud computing systems presents a number of challenges, such as heterogeneity, multi-objective optimization, large-scale optimization, dynamic optimization, and user satisfaction. Other challenges include privacy and security, large-scale optimization, and dynamic optimization. Researchers have made tremendous progress in designing algorithms, models, and frameworks to address these concerns despite the difficulties they face in doing so. In this overview of the relevant literature, we focus on research that investigates methods for resource allocation and optimization in cloud computing systems. We provide a brief synopsis of the most important findings from these studies and provide them in table form, drawing attention to the study methodologies, algorithms, and optimization strategies that were applied. In addition, we address potential future research areas and outline the obstacles that researchers encounter when attempting to optimize the allocation of resources in cloud computing systems. The purpose of this literature review is to offer a complete overview of the current state of research in resource allocation and optimization in cloud computing systems, and it may also serve as a valuable reference for researchers and practitioners working in this subject.
PyCaret based URL Detection of Phishing Websites

The primary objective of the research project is to employ machine learning algorithms to conduct studies and identify instances of phished URLs that might direct people to fraudulent websites. The Kaggle repository, which contains more than 11,000 URLs, is where the authors received a phished dataset for this application. Examples of both genuine and phished URL links can be found in the collection. Also, the dataset contains 31 features that must be obtained using feature engineering stages and methodologies. Nevertheless, this dataset is also available as a csv file and has been further pre-processed to remove redundant and pointless data. This is followed by the feature extraction process, which extracts URL properties including domain-based, content-based, and address-based attributes. The implementation of PyCaret follows, with each line of code being in charge of the entire execution. Nonetheless, the testing at this level consists of three parts. In order to create accuracy, the initial stage of PyCaret's implementation includes running 14 built-in algorithms. The top three accuracy-generating algorithms are combined to build a stacking model in the last stage of the system model's implementation, which is divided into two stages. The second stage of the system model implementation entails taking random forest into account. In the conclusion, the accuracy of each algorithm is assessed together with its performance. After comparison, the technique with the highest generating accuracy is considered to be the optimised model.