Reviewerly/exHarmony huggingface dataset:
Development and validation of the teachers’ augmented reality competences (TARC) scale

Abstract While augmented reality (AR) can offer many advantages in education, one reason for the difficulty of integrating it in instructional practices is the lack of teachers’ AR competences. Therefore, there is an increasing need to address the required competences needed by teachers to effectively integrate augmented reality (AR) in their teaching. This study develops and validates a comprehensive augmented reality competences scale for teachers. The suggested instrument encompasses skills related to the creation, use and management of augment reality resources for teaching. The scale was validated on a sample of 150 educators from 45 countries teaching in primary, secondary or tertiary levels. Confirmatory factor analysis demonstrated valid results in terms of model fit criteria, factor loadings, validity, and reliability. The final scale is composed of 11 items and 4 competence components. Teaching subject, general digital skills and previous AR class experience revealed significant differences across the scale components, while gender and age did not reveal any significant associations. Educators in higher education institutions self-reported higher competence level for designing, developing, and modifying AR resources compared to secondary and primary levels. The scale can be used by educators to self-assess their AR competences, teacher professional development institutions and policy makers to develop training programs in AR and software companies to develop AR experiences that can empower educators.
Bidirectional mutual guidance transformer for salient object detection in optical remote sensing images

ABSTRACTSalient object detection in optical remote sensing images presents great challenges due to the characteristics of remote sensing images such as cluttered background, varying object scales, and unstable imaging conditions, etc. In this paper, we present a Bidirectional Mutual Guidance Transformer (BMGT), which mitigates the locality issue of CNN-based models, and exploits the mutual guidance between global context-aware object representations and fine-grained boundary structures. It contains a hierarchically structured Transformer encoder that extracts multi-level multi-scale token representations, and a dual-stream cross-task MLP decoder that performs joint salient object detection and salient boundary detection in an end-to-end manner. In particular, the dual-stream decoder consists of two sub-branch networks with symmetric architectures, that are connected by a newly proposed Mutual Guidance MLP layer (MG-MLP). Through MG-MLP, salient object features and salient boundary features interact with each other, facilitating complementary learning at multiple network levels. Extensive evaluations demonstrate that our proposed method outperforms other existing methods in two public remote sensing image benchmarks. It proves that our BMGT is advantageous in exploiting long-range context dependencies as well as preserving fine-grained boundary structures.KEYWORDS: Salient object detection, optical remote sensing imagesTransformer Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the National Science Foundation of China under Grant 62101316, in part by the China Postdoctoral Science Foundation Grant 2022TQ0259, in part by the Jiangsu Provincial Double–Innovation Doctor Program Grant JSSCBS20220942.
FullStop: punctuation and segmentation prediction for Dutch with transformers

When applying automated speech recognition (ASR) for Belgian Dutch, the output consists of an unsegmented stream of words, without any punctuation. A next step is to perform segmentation and insert punctuation, making the ASR output more readable and easy to manually correct. We present the first (as far as we know) publicly available punctuation insertion system for Dutch that functions at a usable level and that is publicly available. The model we present here is an extension of the approach of Guhr et al. (In: Swiss Text Analytics Conference. Shared task on Sentence End and Punctuation Prediction in NLG Text, 2021) for Dutch: we finetuned the Dutch language model RobBERT on a punctuation prediction sequence classification task. The model was finetuned on two datasets: the Dutch side of Europarl and the SoNaR corpus. For every word in the input sequence, the model predicts a punctuation marker that follows the word. In cases where the language is unknown or where code switching applies, we have extended an existing multilingual model with Dutch. Previous work showed that such a multilingual model, based on “xlm-roberta-base” performs on par or sometimes even better than the monolingual cases. The system was evaluated on in-domain data as a classifier and on out-of-domain data as a sentence segmentation system through full stop prediction. The evaluations on sentence segmentation on out of domain data show that models finetuned on SoNaR show the best results, which can be attributed to SoNaR being a reference corpus containing different language registers. The multilingual models show an even better precision (at the cost of a lower recall) compared to the monolingual models.
AESTHETIC VALUE: ATTACHMENT TO DIGITAL EXPERIENCE AND REAL-LIFE EXPERIENCE

The purpose of this academic article was to proof the phrase “Phygital Experiences” by present the merging of digital experiences and real-life experiences in the view of value and aesthetics through 5 roles of digital approach, namely, the role of digital age towards education, the role of digital age towards work, the role of digital age towards economy, the role of digital age towards art and culture, and the role of digital age towards tourism. The study results revealed that technological evolution brings about new forms of interaction that people across the world consume and treat each other. Though digital experience enables people to have comfort, access information and communicate with each other more easily, people do not have profundity and meticulousness to understand experience. On the contrary, real-life experiences offer more experiences of participation in terms of sensory perception or emotional perception. However, they are unable to offer borderless connection and data accessibility as comfortable as digital experiences. Therefore, it can be said that digital experiences and real-life experiences cannot be separated since both of them can fulfill human’s good experiences in a different way, making humans feel fulfilled and enjoyable while they can admire things surrounding themselves. Humans receive benefits from technologies while connecting to the world they can touch.
Merge in the Ethereum Blockchain

The food industry has been at the forefront of rapid implementation for several technological innovations. One of the main reasons for this is that food security has been of paramount importance in supplying a growing population, taking into account both quantitative and qualitative requirements. And the necessary development could only be ensured by incorporating the latest developments. &#x0D; Blockchain technology is also a tool to consider in terms of how it can help track food chains. Its widespread application is only a decade old, but in some areas, for example, the operation of cryptocurrencies has already accumulated enough experience to see if it really lives up to the hopes attached to it, and what problems still stand in the way of further spread.&#x0D; With this material, our primary goal is to present a significant technological change that aims to solve one of the main problems of blockchain-based data management. It will be presented how the technology works (with a specific focus on the proof of work mechanism) and the transition to a truly significant platform, the proof of stake mechanism at Ethereum. This gives us an idea of how much a relatively new technology can undergo changes, and at what rate a seemingly significant problem (in this case, e.g. environmental impact) can decrease. This potential for development provides the basis for counting blockchains as a technology that can be applied in other areas, such as the food industry.&#x0D; In addition to scientific treatises, we often rely on Internet sources in the material, since the change occurred so quickly that publications in scientific journals could not yet track it or only in a narrower circle.
Tracking a Maneuvering Object by Indirect Observations with Random Delays

A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described.
IRC-CLVul: Cross-Programming-Language Vulnerability Detection with Intermediate Representations and Combined Features

The most severe problem in cross-programming languages is feature extraction due to different tokens in different programming languages. To solve this problem, we propose a cross-programming-language vulnerability detection method in this paper, IRC-CLVul, based on intermediate representation and combined features. Specifically, we first converted programs in different programming languages into a unified LLVM intermediate representation (LLVM-IR) to provide a classification basis for different programming languages. Afterwards, we extracted the code sequences and control flow graphs of the samples, used the semantic model to extract the program semantic information and graph structure information, and concatenated them into semantic vectors. Finally, we used Random Forest to learn the concatenated semantic vectors and obtained the classification results. We conducted experiments on 85,811 samples from the Juliet test suite in C, C++, and Java. The results show that our method improved the accuracy by 7% compared with the two baseline algorithms, and the F1 score showed a 12% increase.
Gaze-Based Human–Computer Interaction for Museums and Exhibitions: Technologies, Applications and Future Perspectives

Eye tracking technology is now mature enough to be exploited in various areas of human–computer interaction. In this paper, we consider the use of gaze-based communication in museums and exhibitions, to make the visitor experience more engaging and attractive. While immersive and interactive technologies are now relatively widespread in museums, the use of gaze interaction is still in its infancy—despite the benefits it could provide, for example, to visitors with motor disabilities. Apart from some pioneering early works, only the last few years have seen an increase in gaze-based museum applications. This literature review aims to discuss the state of the art on this topic, highlighting advantages, limitations and current and future trends.
Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

This article is devoted to solving the problem of converting sign language into a consistent text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation. The paper proposes an improved method of continuous recognition of sign language, the results of which are transmitted to a natural language processor based on analyzers of morphology, syntax, and semantics of the Kazakh language, including morphological inflection and the construction of an intonation model of simple sentences. This approach has significant practical and social significance, as it can lead to the development of technologies that will help people with disabilities to communicate and improve their quality of life. As a result of the cross-validation of the model, we obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation. We also identified 20 sentence structures of the Kazakh language with their intonational model.
The Implementation of School Literacy Movement in SMP Laboratorium Undiksha Singaraja

This research aimed at how was the school literacy program implemented in SMP Laboratorium Undiksha Singaraja. Mixed-method was used because the data of this research were analyzed quantitatively and qualitatively. There were six findings showed that the financial resources were getting from BOS fund and book donation. The time resources that students read books around 15 minutes before whilst-activity in the classroom while this school has special schedule in outside classroom that was called happy Friday. The facility resources were library, laboratory rooms, reading corner, and multimedia area in good condition. The processes of implementation were conducted in three stages; introduction stage, implementation stage, and establishment stage. The output measurements were through teachers’ evaluation, checking students’ project, checking students’ performance, students and teachers achievement, checking library visit, and students’ achievement in national exam. Furthermore, the sixth finding showed the impacts of reading motivation and reading habit were in moderate category.
Exponential Particle Swarm Optimization Algorithm for Complexly Structured Images Segmentation

Image segmentation is the process of dividing an image into homogeneous regions according to certain features and is widely used in image processing.Complexly structured images usually contain complex and essential objects.These images are non-linear structural images and they contain a large number of elements with required specifications.The main goal of the proposed EPSO (Exponential Particle Swarm Optimization) algorithm is to prevent local solutions and find the exact global optimal solutions for the task of segmenting medical images.The execution time is compared with well-known segmentation algorithms.The EPSO method is superior to the segmentation methods studied, including the graph algorithm.Comparisons were made with existing segmentation algorithms (Grow cut, Random Walker, DPSO, K-means PSO, and hybrid-K-means ant colony optimization algorithm) in tabular form.
Semi‐supervised classification to determine the provenance of archaeological obsidian samples

Abstract In archaeometry, the correct assignment of archaeological artefacts with their original geological sources continues to be an indispensable tool for determining exploitation patterns, trade routes, tool production and the movement of goods in ancient times. Statistically, Mahalanobis distance has been an integral part of the artefact provenance methodology. However, this method presents disadvantages when some of the theoretical assumptions are not fulfilled. In this way and with a focus on the veracity of the correct assignment of archaeological samples to their geological sources, we present a technique that employs a semi‐supervised classification based on finite mixture models. This procedure uses labelled and unlabelled data to assign samples to groups with the advantage that it does not depend on the sample size of the groups as required by Mahalanobis distance. This procedure was first applied in a controlled experiment where the exact origin of the samples is known in order to verify the accuracy of the method; afterwards, it was applied to artefacts recovered from the archaeological site of Xalasco, Tlaxcala (Mexico), from both surface and excavation collections and analysed with a portable X‐ray fluorescence spectrometer. The resulting groups defined that the ancient dwellers of Xalasco exploited six supply sources located in Central Mexico.
Integrating industry developed courseware in IT scripting courses

Abstract The alignment of higher education with the skill needs of employers and industry at large is a topic of active debate, particularly for engineering education. Formal and informal content developed by companies, technology‐specific associations, consulting firms, and individuals forms a body of knowledge that might fast‐track careers toward very specific opportunities. By contrast, academic courseware is known for its pedagogical rigor while being slow to adapt to the fast‐paced changes in technology, particularly information technologies, and reliance on expensive course materials that burden students. This paper explores the drivers for integrating industry‐developed courseware in an existing, higher‐ed course on Scripting for Information Technology in the Department of Technology Systems at East Carolina University and the implications of such integration. We hypothesize that industry courseware is a streamlined, cost‐effective way to provide students with the knowledge they need in the workforce. Students were exposed to the two types of content and their scholarly performance was analyzed. Student perception of the two types of content was assessed through a postclass survey. The student results and the feedback they provided suggest that a hybrid approach is more suitable to address the learning preferences and predispositions of students. This case study concludes that textbook and industry‐generated content can be integrated into existing courses and improving them in terms student outcomes and in terms of alignment with industry's hiring expectations.
Improved Retinex-Theory-Based Low-Light Image Enhancement Algorithm

Researchers working on image processing have had a hard time handling low-light images due to their low contrast, noise, and brightness. This paper presents an improved method that uses the Retinex theory to enhance low-light images, with a network model mainly composed of a Decom-Net and an Enhance-Net. Residual connectivity is fully utilized in both the Decom-Net and Enhance-Net to reduce the possible loss of image details. Additionally, Enhance-Net introduces a positional pixel attention mechanism that directly incorporates the global information of the image. Specifically, Decom-Net serves to decompose the low-light image into illumination and reflection maps, and Enhance-Net serves to increase the brightness of the illumination map. Finally, via adaptive image fusion, the reflectance map and the enhanced illuminance map are fused to obtain the final enhanced image. Experiments show better results in terms of both subjective visual aspects and objective evaluation indicators. Compared to RetinexNet, the proposed method shows improvements in the full-reference evaluation metrics, including a 4.6% improvement in PSNR, a 1.8% improvement in SSIM, and a 10.8% improvement in LPIPS. Additionally, it achieved an average improvement of 17.3% in the no-reference evaluation metric NIQE.
Solving the Kemeny ranking aggregation problem with quantum optimization algorithms

The aim of a ranking aggregation problem is to combine several rankings into a single one that best represents them. A common method for solving this problem is due to Kemeny and selects as the aggregated ranking the one that minimizes the sum of the Kendall distances to the rankings to be aggregated. Unfortunately, the identification of the said ranking—called the Kemeny ranking—is known to be a computationally expensive task. In this paper, we study different ways of computing the Kemeny ranking with quantum optimization algorithms, and in particular, we provide some alternative formulations for the search for the Kemeny ranking as an optimization problem. To the best of our knowledge, this is the first time that this problem is addressed with quantum techniques. We propose four different ways of formulating the problem, one novel to this work. Two different quantum optimization algorithms—Quantum Approximate Optimization Algorithm and Quantum Adiabatic Computing—are used to evaluate each of the different formulations. The experimental results show that the choice of the formulation plays a big role on the performance of the quantum optimization algorithms.
Pengukuran Antarmuka Pengguna 3D Menggunakan Metode System Usability Scale pada Aplikasi Simulasi Videography Virtual Reality

The development of Virtual Reality (VR) technology has revolutionized user interaction with mobile applications. However, the success of a VR application depends on how well the user interface (UI) is implemented. Therefore, measuring the usability of the UI is necessary to ensure a satisfactory user experience. In measuring the quality of the user interface, we utilized the SUS (System Usability Scale) method, which is a standardized tool for assessing the user interface of a system. Our respondents consisted of 84 participants who had experience using VR applications. They were asked to use the application and complete a 10-statement SUS questionnaire with a Likert scale. Data analysis indicated that the average SUS score for the user interface of the VR videography simulation mobile application was 78.51. This score signifies that the VR Videography application's user interface received ratings of Passive, Acceptable, Good, and Grade B in their respective categories. These scores indicate that the user interface received positive evaluations from users. The questionnaire also revealed that the majority of respondents found the application easy to use and believed that others would also find it easy to comprehend
Visual Analytics Using Machine Learning for Transparency Requirements

Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often lengthy and complex, making it challenging for users to fully grasp their content. Additionally, existing transparency analytics tools typically rely on the manual extraction of information elements, resulting in a time-consuming process. To address these challenges, this paper proposes a novel approach that combines information visualization and machine learning analyses to automate the retrieval of information elements. The methodology involves the creation and labeling of a dataset derived from multiple software terms of use. Machine learning models, including naïve Bayes, BART, and LSTM, are utilized for the classification of information elements and text summarization. Furthermore, the proposed approach is integrated into our existing visualization tool TranspVis to enable the automatic detection and display of software information elements. The system is thoroughly evaluated using a database-connected tool, incorporating various metrics and expert opinions. The results of our study demonstrate the promising potential of our approach, serving as an initial step in this field. Our solution not only addresses the challenge of extracting information elements from complex terms of service but also provides a foundation for future research in this area.