Reviewerly/exHarmony huggingface dataset:
‘Reality’ influencing ‘Actuality’: the role of augmented and virtual reality on ecotourism travel intention

This study aims to understand the role of AR/VR technology in facilitating responsible travel at natural heritage sites. A theory-based approach was used in this study. A qualitative study was done through semi-structured, in-depth interviews with 12 participants to develop the conceptual model. Participants who visited a natural heritage site were selected for this study through snowball sampling. This study identifies the AR/VR content, medium quality, feelings towards the AR/VR, and planned behaviour as significant factors influencing the behavioural intention towards ecotourism. Several core themes and sub-themes were found that were found aligned with the existing theories. Three main theoretical backgrounds emerged from the present study: telepresence theory, feelings-as-information, and theory of planned behaviour. This study contributes to ecotourism literature as the evidence demonstrates that telepresence and feelings substantially boost the foundation of ecotourism. This study highlights several possible implications that will help in the inclination towards responsible behaviour of tourists and also gives direction for future research in this field.
Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter?

Abstract Background One of the top four malignancies affecting women worldwide is breast cancer. Breast density is a risk factor for breast cancer on its own and also a limiting factor for the sensitivity of screening mammography. Tools of artificial intelligence (AI) can help radiologists to make decisions, potentially reducing perceptual and interpretation errors, or as a way to prioritize exams based on the likelihood of malignancy. Objectives The purpose of this study was to assess the impact of breast density on the performance of AI in mammography (MG) for the diagnosis of breast malignancy. Methods In total, 110 patients with pathologically proven breast cancer participated in this retrospective study. These patients had full field digital mammography, and the mammogram pictures were exported to the AI software system. Heat maps displaying the location of discovered lesions then highlighted the affected area or areas and also provided abnormality scores indicating the probability of malignancy (POM). The results of the histopathological analysis were correlated with the breast density and AI category. Results The artificial intelligence software gave a breast density score to each patient as well as POM scoring. Both the software and the radiologist agreed on the breast density in 80.00% ( N = 88) of the patients. Upon correlation of AI results to the BI-RADS given by radiologist, demonstrated statistically very significant correlation ( P value 0.001), indicating that the likelihood of error is less than one in a thousand. Upon correlating the pathology results with the AI abnormality score, the AI showed sensitivity of 93.64% as it detected 103 true positive lesions. AI showed 100% sensitivity in both ACR A and ACR B, and 94.74%, 76.47% in ACR C, ACR D, respectively. False negative results represented 5.26% in ACR C group and the highest with 23.53% in ACR D group of patients. The P value was found less than 0.001. Pearson correlation coefficient was calculated ( R = 0.27) which was interpreted as a weak correlation between the decrease in sensitivity of AI and the breast density. Conclusions Our study showed that there is a slight link between increasing breast density and a relative decline in AI's ability to detect malignant lesions, suggesting that AI can detect breast cancer effectively in breasts of different parenchymal densities, with its effectiveness being highest in breasts with lower parenchymal density.
Implementasi Model Pembelajaran TGT dan Media Quizizz untuk Meningkatkan Hasil Belajar Matematika pada Siswa Kelas 3 di SDN 23 Ampenan

This research is entitled: "Implementation of the TGT Learning Model and Quizizz Media to Improve Mathematics Learning Outcomes for Grade 3 Students at SDN 23 Ampenan". The aim of this research is to improve learning outcomes in mathematics lessons regarding angles through the Teams Games Tournaments type learning model for students in Class III SDN 23 Ampenan. The method used in this research is Action Research which consists of 2 (two) cycles, and each cycle consists of: Planning, Implementation, Observation and reflection. Based on the results of action research, the Teams Games Tournaments Type Learning Model can improve the Mathematics Learning Outcomes of Class III students at SDN 23 Ampenan. Furthermore, the researcher recommends: (1) Teachers who experience the same difficulties can apply the Teams Games Tournaments Type Learning Model to improve the Mathematics Learning Outcomes of Class III students. (2) In order to get maximum results, teachers are expected to better understand the Teams Games Tournaments Learning Model.
Application Entropy Weight and TOPSIS Method in English Teaching Quality Evaluation of "Smart Classroom"

INTRODUCTION: Based on TOPSIS (Technological Ordering of Superiority and Inferiority) and entropy weight method, it aims to evaluate the quality of intelligent classroom English teaching. The brilliant classroom teaching model has attracted much attention for its highly interactive, personalized, and real-time feedback features; however, how to accurately evaluate the quality of intelligent classroom teaching remains a challenge.&#x0D; OBJECTIVES: To combine the TOPSIS and entropy weight methods in practical application and consider the index ordering and weight calculation comprehensively to arrive at the quality evaluation results of each brilliant classroom teaching.&#x0D; METHODS: The TOPSIS method is first used to rank multiple indicators of teaching quality to determine the optimal teaching quality. The TOPSIS method can consider the interrelationships between the hands and find the solution closest to the positive ideal solution and farthest away from the negative perfect solution by calculating each indicator's positive and negative perfect solutions. Then, the weight of each hand is calculated by combining the entropy weight method. The entropy weight method can consider the indicators' information and differences and measure the degree of their contribution to the evaluation results by calculating the entropy value of the hands.&#x0D; RESULTS: The results show that the method can comprehensively consider the correlation and weight of multiple indicators, provide teachers and educational administrators with accurate teaching quality evaluation and improvement suggestions, and thus promote the optimization and enhancement of innovative classroom teaching.&#x0D; CONCLUSION: By analyzing the actual smart classroom teaching data, the Author found that the method can effectively evaluate the quality of intelligent classroom teaching and provide valuable guidance for English teaching improvement.
Improving the grammatical competence of future English teachers based on authentic texts

The article discusses the grammatical competence in the English language of future teachers in the undergraduate program as a component of effective foreign language teaching in the form of ability to correctly apply the morphological, syntactic and lexical norms of the English language in solving practical problems. Taking into account the teaching of English as a pedagogical specialty, it is necessary to take into account the communicative and pedagogical components of professional competence. Based on theoretical provisions in the form of an analysis of pedagogical conditions of the organization of education, the current study views the specifics of improving morphological grammatical skills in the form of teaching tense forms of English verbs on the material of authentic texts from classical literature. As part of the study, as part of a pedagogical experiment, according to the results in the experimental group, difficulties in mastering the tense forms of verbs were identified. Increasing the level of foreign language communicative competence will contribute to the improvement of theoretical and linguistic knowledge, to the formation of practical skills in the form of teaching the norms of the literary language.
Considering Vocabulary Mappings in Query Plans for Federations of RDF Data Sources

Federations of RDF data sources offer great potential for queries that cannot be answered by a single data source. However, querying such federations poses several challenges, one of which is that different but semantically-overlapping vocabularies may be used for the respective RDF data. Since the federation members usually retain their autonomy, this heterogeneity cannot simply be homogenized by modifying the data in the data sources. Therefore, handling this heterogeneity becomes a critical aspect of query planning and execution. We introduce an approach to address this challenge by leveraging vocabulary mappings for the processing of queries over federations with heterogeneous vocabularies. This approach not only translates SPARQL queries but also preserves the correctness of results during query execution. We demonstrate the effectiveness of the approach and measure how the application of vocabulary mappings affects on the performance of federated query processing.
Arapça – Türkçe Çeviri Türlerinde Nöral Makine Çeviri Modellerinin Verimliliği: ChatGPT Örneği

Artificial intelligence applications, which are gaining currency day by day due to their new features, in parallel with the development of technology, have attracted attention with the development of neural machine translation models, such as ChatGPT in the field of translation, especially in recent times, and have been regarded as a valuable phase and promising development in the translation world.With the development of ChatGPT, which is a neural machine translation model, "translation proofreading" has gained popularity in the
Carrying the cross for justice: Digital traces of victimhood in Sri Lanka

Victimhood claims in the digital space are often affectively and morally negotiated based on the extent of sharedness of those sentiments and values. However, media-centric and populist political interpretations based on ‘mobilization’ often curtail the exploration of ‘sharedness’ and ‘belonging’ in digital discourses of victimhood. Therefore, this article explores the nature of digital victimhood discourses, their constitution, and how those victimhoods are negotiated in digital collectives. The article combines affective victimhood with chronotope to analyze digital discourses on ‘public acts’ carried out by members of the public seeking justice for the Easter attack victims of 2019. Accordingly, the article focuses on Facebook posts and comment sequences on “Jehan Appuhamy carrying the cross” to mark the third anniversary of the attacks in April 2022. Data is gathered from four Facebook posts containing a collection of videos and images combined with a selected 3142 post-comment sequences. The article finds that affective and moral understandings of victimhood are negotiated in interaction with right-wing populist undertones and resistance discourses of the people’s protest. The narratives of justice, threat, empathy, and solidarity constitute a multifaceted expression of discontent, representing both a struggle against those in power and a quest to connect with like-minded individuals, irrespective of the specific cause for such sentiments. Additionally, it involves rallying support from friends, sharing feelings of discontent, and motivating them to take action. Thus, digital belonging is fractally nested in specific timespace configurations.
Clustering Raw Sensor Data in Process Logs to Detect Data Streams

The execution and analysis of processes is strongly influenced by sensor streams, e.g., temperature, that are measured in parallel to the process execution and stored in process event logs. This holds particularly true for application domains such as logistics and manufacturing. However, currently, these sensor streams are collected and stored in an arbitrary and unsystematic way. Hence, this work proposes an approach that prepares sensor streams into individual data streams that can be annotated to process tasks and used for process analysis and prediction.
17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining

Sentiment analysis is a subset of data mining that is used to locate, collect, and examine people’s opinions. It makes it possible for data mining tools to comprehend peoples’ feelings and ideas more fully. Natural language processing is one of the most used and established methods used for sentiment analysis tasks. This technique is mainly based on rules, principles, and algorithms which aid in understanding the text making it easier to segment and extract meaningful data. However, this technique is becoming obsolete due to the sheer volume and complexity of data present these days.
3 Classification and regression algorithms

Machine learning algorithms for predicting or categorizing data include classification and regression techniques. Regression algorithms are used to forecast a continuous numerical value, such as the anticipated value of a stock price. In contrast, classification algorithms predict a discrete label, such as whether something is a cat or a dog. The accuracy of the algorithm’s predictions and classifications determines how effective a prediction or classification is. Decision trees, support vector machines, k-nearest neighbors, and naive Bayes are examples of standard classification techniques. Regression methods often used include generalized, polynomial, and linear regression.
Managing the Variability of Component Implementations and Their Deployment Configurations Across Heterogeneous Deployment Technologies

Application systems often need to be deployed in different variants if requirements that influence their implementation, hosting, and configuration differ between customers. Therefore, deployment technologies, such as Ansible or Terraform, support a certain degree of variability modeling. Besides, modern application systems typically consist of various software components deployed using multiple deployment technologies that only support their proprietary, non-interoperable variability modeling concepts. The Variable Deployment Metamodel (VDMM) manages the deployment variability across heterogeneous deployment technologies based on a single variable deployment model. However, VDMM currently only supports modeling conditional components and their relations which is sometimes too coarse-grained since it requires modeling entire components, including their implementation and deployment configuration for each different component variant. Therefore, we extend VDMM by a more fine-grained approach for managing the variability of component implementations and their deployment configurations, e.g., if a cheap version of a SaaS deployment provides only a community edition of the software and not the enterprise edition, which has additional analytical reporting functionalities built-in. We show that our extended VDMM can be used to realize variable deployments across different individual deployment technologies using a case study and our prototype OpenTOSCA Vintner.
A wavelet selection scheme in underwater discharge signal analysis

Abstract The analysis of underwater discharge signals is of great significance for its application. Wavelet-based de-noising and analysis technology is an effective means to study underwater discharge signals. The selection of wavelets is the key to the accuracy of wavelet analysis. A scheme of wavelet selection is provided in this paper. Based on the signal characteristics and actual noise, the reference target signal and noisy signal are constructed to ensure the accuracy of wavelet performance evaluation. Cross-correlation coefficient, root mean square error, signal-to-noise ratio, and smoothness are chosen as evaluation indexes and fused by the coefficient of variation method. The selected optimal wavelet is used to process the underwater wire-guided discharge signals. The results show that the scheme is feasible and practical.
Bulletin WKU

The article is devoted to the problems of motivation in art education. Art teacher's motivation is viewed from the point of view of spiritual principles of fine arts, the peculiarities of stimulation of artistic creativity are shown. The aim of the study was to examine the influence of modern technologies on professional competence of students' motivation level in art education.
An empirical study on immersive technology in synchronous hybrid learning in design education

Abstract Immersive technology plays an increasingly important role in design education, supporting digital literacy and experiential learning in higher education, particularly in the post-COVID-19 context. Many design disciplines, such as Architecture and Landscape Design, which used to rely heavily on physical field trips, and dialogic studios as signature pedagogies, had to adapt to the proliferation of innovative educational technologies due to the suspension of face-to-face teaching and learning. Augmented reality and virtual reality are now extensively used in technology and design education to support a more interactive, collaborative, and student-centred approach to learning. This paper expands on a pilot research study on the transition from traditional signature pedagogies of studio-based design education to technology-enhanced collaborative learning to support experiential learning. Based on Kolb’s experiential learning framework concerning four learning modes, namely, concrete experience (CE), reflective observation (RO), abstract conceptualisation (AC), and active experimentation (AE), this paper analyses data from 75 undergraduate students across four cohorts in three different higher education institutions in Hong Kong. They were surveyed on their experiences of synchronous hybrid virtual experiential learning with design and technology curricula. Furthermore, six students were interviewed about their experiences. The research findings confirm two research hypotheses showing that immersive experiential learning could constructively align RO and AE, thus bringing an overall positive impact on the experiential learning process, but shows no conclusive influence on promoting CE, especially AC. The quantitative findings and qualitative results gave new insights into the discussion of the theoretical and practical implications of the study.
Object tracking based on Siamese networks and attention mechanism

Most trackers based on the correlation operation mainly carry out a straightforward fusion of template features and search region features. The correlation operation, however, is a local linear matching process that might cause the loss of semantic information, leading to local optimums. We propose a tracker method that takes advantage of both the attention mechanism and feature fusion. This approach uses an attention-based feature fusion network to integrate the characteristics of the template area and search area, which effectively avoids the semantic information loss and local optimum induced by the correlation operation. Meanwhile, by adopting a modified ResNet50 network for extracting multilayer target features to fully utilize both shallow and deep features, our method takes full advantage of the template information to get better tracking results. Extensive experiments on four large-scale tracking benchmark datasets—VOT2019, OTB100, GOT-10k, and LaSOT—show that our tracker obtains more accurate tracking results and has a better performance.
Bulletin WKU

The article defines the basic concepts of life safety. The history of the emergence and development of the discipline”Safety of life " is described. Shown the importance of teaching the discipline “Life safety", including basic chemical knowledge. “The strategy of Kazakhstan-2030 states that” Kazakhstan in 2030 should become a country with clean, transparent water and green spaces”. As part of the implementation of this task, prerequisites for improving students&nbsp; knowledge of life safety are determined. The article defines the features and objectives of teaching chemistry in non-chemical specialties in higher educational institutions, analyzes the specifics of teaching the course “Chemistry in life safety” as an academic discipline. It is characterized by the need to apply new methods in teaching students and introduce an update to the content of training. In the writing of the article, the main object of research was the process of teaching chemistry at the Faculty of Geography in the specialty “Safety of life and Environmental Protection”. Keywords: life safety, chemical safety, ecology, pedagogical technology, V.F.Shatalov's framework-drawing method.
Battle of the Gr8’s: A Game-Based Strategy to improve academic writing skills of Grade 8 Learners

This classroom action with the goal of overcoming the students' academic writing challenges by enhancing the students' mechanics, organization, grammar, style, and composition determined the effectiveness of the researcher-made game-based strategy as an intervention on enhancing the academic writing skills of selected Grade 8 students in English at Goldenville School of Montessori Inc. for the school year 2022-2023. To determine the academic writing skills of the participants of the study before the intervention, pre-writing that requires students to compose a narrative essay on a specific topic has been administered. The students identified with difficulties were subjected for the intervention. The researchers held post-writing activities that determined the effectiveness of the game-based strategy in their academic writing performance in English. With a Purposive sampling method as the sampling technique to identify the ideal participants of the data gathering procedure, the researchers found several results after the implementation of the proposed intervention, specifically the significant difference between the scores of the respondents in the pre-writing and post-writing using the five-week intervention plan for Grade 8 students. Further analysis pondered that there was a significant difference in the academic performance between the pre-test and post-test scores of the Grade 8 students in terms of the game-based strategy in improving the academic writing skills of those students. Through the implications, the recommendations: conduct similar studies for Grade to Grade 10, implement this similar intervention to all quarters/grading periods and use differentiated instructional materials to make the game livelier and to catch the attention of the students.
6 Evaluation of AI model performance

Artificial intelligence (AI) models are becoming more complex as a result of recent advances in AI technology. As a result, there are numerous approaches to assess the effectiveness of an AI model. In order to evaluate a model’s performance, both its accuracy and how quickly it processes fresh data are measured. Examining the model’s robustness to data that is not in the training set and its capacity for sophisticated problem solving are also included in the evaluation process. Because it identifies opportunities for improvement and facilitates better decision-making, interpretability is a crucial component of evaluating an AI model’s performance. Finally, a range of measures and methods are used in performance evaluation of AI models to assess their efficacy.