Reviewerly/exHarmony huggingface dataset:
Imagine &amp; immerse yourself: Does visuospatial imagery moderate learning in virtual reality?

Findings on the effectiveness of learning in immersive virtual reality (IVR) are mixed. While studies suggest that the sense of spatial presence in IVR enhances enjoyment and thus learning, it is unclear whether IVR also presents information in a way that is more conducive to learning than less immersive presentations. Because IVR presents spatial details more accurately than less immersive media, in addition to motivational effects, IVR presentations should promote the acquisition of spatial and (aurally presented) semantic knowledge. Research also suggests that the effects of IVR presentation on spatial presence, enjoyment, and learning may be offset by visuospatial imagery skills. We tested these assumptions by conducting a randomized laboratory experiment with two conditions in which a virtual learning environment was presented using either immersive IVR technology or less immersive desktop VR. Results show that IVR did not better support the acquisition of spatial and semantic knowledge, and this was not moderated by visuospatial imagery. However, spatial presence mediated the acquisition of semantic knowledge through enjoyment in both conditions, and this effect was moderated by learners' level of imagery: IVR's advantages over desktop VR in promoting spatial presence, enjoyment, and semantic knowledge diminished as learners' imagery increased.
Systematic reviews in mobile app software engineering: A tertiary study

A number of secondary studies in the form of systematic reviews and systematic mapping studies exist in the area of mobile application software engineering. The focus of this paper is to provide an overview and analysis of these secondary studies of mobile app software engineering for researchers and practitioners. We conducted a systematic tertiary study following the guidelines by Kitchenham et al. to classify and analyze secondary studies in this area. After going through several filtration steps, we identified 24 secondary studies addressing major software engineering phases, such as initiation, requirements engineering, design, development and testing. The majority of the secondary studies focused on testing and design phases. Specific research topics addressed by the included studies were: usability evaluation, test automation, context-aware testing, cloud-based development, architectural models, effort and size estimation models, defect prediction, and GUI testing. We found that the trend in secondary studies is towards more specific areas of mobile application software engineering such as architectural design models, context-aware testing, testing of non-functional requirements, mobile cloud computing, and intelligent mobile applications. Research directions and some identified practices for practitioners were also identified. Mobile application software engineering is an active research area. The area can benefit from additional research in terms of secondary studies targeting evolution, maintenance, requirements engineering, and cross-platform mobile application development. Additionally, some of the secondary studies identify some useful practices for practitioners.
SOFNet: Optical-flow based large-scale slice augmentation of brain MRI

Brain magnetic resonance imaging (MRI) data from multiple centers often exhibit variations in imaging conditions, such as the types of nuclear magnetic resonance instruments used and the presence of random noise. Additionally, discrepancies in the gap between MRI slices further complicate the usability of the data for advanced artificial intelligence (AI) analysis. Deep learning-based methods have emerged as practical solutions to address the challenge. However, existing research has largely overlooked the augmentation of brain MRI data, particularly when confronted with significant slice gaps, such as around 6 mm observed in our clinical brain MRI slices. In response to this research gap, we aim to develop novel approaches for augmenting brain MRI data, focusing on more significant slice gaps. To achieve this, we propose SOFNet, which utilizes the optical flow-based and encoder–decoder backbone. The primary objective of our model is to interpolate MRI slices while preserving feature consistency. Leveraging the optical flow method, which has exhibited exceptional performance compared to other super-resolution algorithms, our proposed approach has been evaluated on three distinct brain MRI datasets, explicitly addressing the gap between 4.2 mm and 6.0 mm. The experimental results highlight the significant enhancement in super-resolution quality achieved by SOFNet in generating adapted brain MRI data, surpassing other single-image super-resolution (SISR) methods in feature completion. To ensure the credibility of the interpolated brain MRI slices, we conducted experiments on three axes of MRI based on metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These experiments demonstrate the effectiveness of our approach in transforming low-resolution MRI data into clear and reliable brain MRIs, thereby enabling improved analysis using AI techniques.
Information centric wireless communication for variation detection and Mitigation Model in industrial internet of Things

The Industrial Internet of Things (IIoT) harmonizes smart devices, machines, and intelligent technologies to improve production efficiency through the information-centric network. Information-Centric Networking (ICN) is a potentially useful method in control sequences and intelligent operations due to adversary impacts. Here, the sharing of content chunks between different machines' varying control spans and operation cycles has been partially connected to the internet. This article introduces a Variant Loop Detection and Mitigation Model (VLDMM) for preventing illegitimate entries in ICN. The proposed model is backboned by recurrent learning for training the loop closures and time achievements in ICN. The harmonization is utilized under different production outputs observed from the previous cycles. The interruptions and process halts are identified using recurrent training by correlating the previous operation logs in ICN. Therefore, ICN may solve the challenges based on the available control slots and variations. This learning retains the production efficiency and prevents halts under controlled time and loop closures.
A granular sigmoid extreme learning machine and its application in a weather forecast

Extreme Learning Machine (ELM) is a class of machine learning systems or methods based on feedforward neural networks, which are suitable for supervised learning problems. The ELM is regarded as a special kind of FNN in the research, which is characterized by the fact that the weights of hidden layer nodes are randomly or artificially given. As a result, the ELM feature mapping is random. The generic approximation theorem states that any nonlinear piecewise continuous function may be used as the feature map. By introducing the theory of granular computing, this paper proposes a new granulation method of random sigmoid function. The Granular Sigmoid Extreme Learning Machine (GSELM) algorithm was proposed by combining the random sigmoid function with the extreme learning machine algorithm. After granulated data sets are processed by GSELM, the granulated multi-feature data will form granular vectors. The granular vector is processed in parallel, which makes the granular sigmoid extreme learning machine parallel computing. The GSELM is utilized to address the fundamental real-time issue of weather forecasting. The GSELM algorithm can predict whether the next day will be sunny or rainy more accurately and quickly, which is crucial to the field of meteorology. Several UCI data sets are used to test the feasibility of the GSELM algorithm. The verified GSELM algorithm is applied to the Australian weather forecast data set. According to the experimental findings, the GSELM algorithm can predict whether the next day will be sunny or rainy more accurately and quickly.
Artificial intelligence literacy for the language industry – with particular emphasis on recent large language models such as GPT-4

Abstract This article explores the concept of artificial intelligence (AI) literacy in the context of the language industry, placing particular emphasis on recent large language models such as GPT-4. After a brief introduction in which the relevance of AI literacy in the language industry is highlighted, the article provides a concise overview of artificial neural networks and a brief history of neural network-based artificial intelligence. This is intended to lay the conceptual groundwork for the subsequent discussion of the basic principles and capabilities of large language models. Then, the article investigates in detail the concept of AI literacy, discussing the AI Literacy Framework proposed by Long/Magerko (2020) and illustrating the interface between AI literacy and the two adjacent digital literacies of professional machine translation literacy and data literacy. The article then zooms in on the practical applicability of AI technologies by discussing areas where workflows in the language industry (with a focus on the computer-assisted translation process) could be automated or optimised through large language models. The article concludes with some general reflections on the relevance of field-specific and societal AI literacy in the presence of powerful AI technologies.
Maschinelle, posteditierte und menschliche Übersetzung publizistischer und populärwissenschaftlicher Texte aus dem Slowakischen ins Deutsche

Abstract Machine translation from small into big languages, in our case a transfer from Slovak into German, has not yet undergone thorough research. Our goal is an analysis and comparison of machine translation (MT) and post-edited machine translation (PEMT) errors with human translation (HT) as well as detecting the efficiency rate of post-editing within that mentioned comparison. Hereby we apply a method of automatic and manual metrics by percentages and evaluation numbers. Results indicate that post-editing is more efficient which however does not mean that human translation has a lower quality. A higher degree of freedom of HT allows for more creative solutions contrasting with post-edited MT in the results.
Unpacking the equity of accessing the digital library and educational television channels for primary and secondary school students in Ethiopia

This study investigates the equity of access to government e-learning platforms for Ethiopian primary and secondary school pupils. In order to achieve this, secondary data were accessed from the Mini-Demographic and Health Survey (Mini-DHS-2019) and the Planning and Development Commission’s document on Poverty and Economic Growth in Ethiopia. According to the findings, only a small proportion of primary and secondary school students from urban areas and economically privileged families who could afford computer, television, smartphone, and internet access can benefit from the digital library and educational televised programs. Thus, the digital learning program that was intended to increase educational accessibility exacerbated the existing inequities and intensified the pre-existing disparities between economically privileged and economically disadvantaged students and urban and rural resident students. The findings provide grounds for several recommendations on the implementation of e-learning through digital libraries and televised education programs in low-income countries like Ethiopia.
Digital resources' role in university teaching and learning exploration

The evolution of digital tools in education has created a disparity between the tools teachers use and those available to students, leading to an unequal relationship. This disparity has hindered the shift from traditional teaching to student-centered learning, a crucial aspect of novel approaches in cognitive psychology.&#x0D; The primary objective of this transition is to prioritize the learner in the education system, enabling them to acquire knowledge in a more active, autonomous, and participatory manner. However, achieving this transformation requires in-depth research to examine the nature of the digital tools accessible to teachers and compare them with those available to students.&#x0D; The analysis of research results reveals a prevalence of digital tools focused on teaching, creating an imbalance that favors instructional activities over the learning process. The tools available to teachers often prioritize one-way transmission of knowledge, emphasizing information presentation and dissemination.&#x0D; From a learning perspective, promoting active interactions, experimentation, practical exercises, and problem-solving activities is crucial. Unfortunately, digital tools intended for students often lack active engagement and participation opportunities, limiting their potential for autonomous and interactive learning.&#x0D; This article proposes to identify and evaluate the digital resources teachers and students use to determine the extent to which these tools lean toward teaching or learning. This evaluation aims to better understand the nature of current tools and identify the gaps that need to be addressed to foster a more balanced learning experience. The study focuses on teachers and students in teacher training programs as the target population.&#x0D; This research aims to develop more inclusive and effective educational practices that empower learners and enhance their overall educational experience by addressing the disparity between teacher-focused and student-centered digital tools.
Feature Extraction Face-Off: A Comparative Analysis of InceptionV3 and VGG19 for Face Recognition

Face Recognition is one of the popular interest of researchers due to its demand in security and authentication. Although traditional approaches such as LBPH and Eigenfaceshave still proven their performance, newly state-of-the-art algorithms are available for application. This study compared the performance of two CNN pre-trained architectures namely: InceptionV3 and VGG19 with an SVM classifier. Besides VGG19’s respectable results, it lags behind InceptionV3 by 3.86% in precision, 3.23% in recall, 3.54% in f1-score, and 1.85% in roc-auc score.
Application of Domain Adaptive Deep Learning Model for Face Recognition with Grid Search Optimization

This study examined the effectiveness of domain-adaptive deep learning models in the field of face recognition with ResNet50 pre-trained architecture. The model demonstrated excellent accuracy in the manual approach, achieving 93.57%, highlighting ResNet50's intrinsic skills in feature extraction and classification tasks. Furthermore, by using Grid Search Optimization, the accuracy increased to an astounding 100%, emphasizing the significant role of hyperparameter adjustment in optimizing the model's performance. These findings highlight the potential of domain adaptation methods to improve face recognition systems and highlight Grid Search Optimization as a key strategy for achieving the accuracy levels required for real-world implementations
Enhancing Image Classification Performance: A Comparative Analysis of Optimization Algorithms

In this paper, optimization algorithms are subjected to a comparative investigation. These include several optimization methods, including Adam, RMSprop, and SGDM, which might enhance the precision and discriminative ability of cutting-edge convolutional neural networks (CNNs).Adam excelled in performance in identifying different image classes with a 100% accuracy rate. In addition, Adam also achieved a mean ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) score of 100%, highlighting their unmatched ability to discern between positive and negative cases. Moreover, the results of this work highlight Adam's potential as a strong tool for image classification jobs where high accuracy and reliable discrimination are essential. Adam's dominance over RMSprop and SGDM highlights its potential to improve face image classification tasks, pushing the limits of what is possible in the field of computer vision and deep learning
Control (supervisory) activities in the field of higher education in the context of a risk-based approach: problems of law enforcement and prospects for development

The subject of the study was the peculiarities of the legal regulation of the implementation of control (supervisory) activities in the field of higher education, due to the introduction of a risk-based approach. Some problems of law enforcement are analyzed and proposals are formulated to improve the legal framework of the system for assessing and managing the risks of harm (damage) in the implementation of federal state control (supervision) in the field of higher education. Special attention is paid to resolving the issue of the prospects of embedding monitoring in the system of control (supervisory) activities in the context of a risk-based approach in the field of education. The work uses a set of methods and methods of cognition inherent in modern science, system analysis is used as a general scientific method, and special legal analysis is used as a private scientific method. As a result, the following conclusions are made: 1) the introduction of a risk-based approach has become the core of reforming the system of state control (supervision), including in the field of education; 2) there are problems of law enforcement of the established system of criteria for assigning objects of control (supervision) to risk categories and a list of risk indicators; 3) implementation of public and professional control tools in the higher education system in activities of control (supervisory) bodies; 4) there is a potential for the use of monitoring in control (supervisory) activities. As a result, there is a need to improve the system of criteria for assigning objects of federal state control (supervision) in the field of education to risk categories, within which it is important to take into account foreign and regional experience in applying a risk-based approach. A special contribution of the author to the research of the topic is the development of his own proposals to supplement the list of criteria and update the established risk indicators. The novelty of the research lies in substantiating the importance of resolving the issue of the limits and prospects of embedding monitoring, as well as the results of public and professional control, in the system of control (supervisory) activities in the field of higher education in a risk-based approach.
Unlocking the future of swimming education: A breakthrough in pull buoy development as an innovative learning medium

The effectiveness of the learning process in swimming greatly depends on innovative learning media. This research aims to develop and produce a pull buoy as an effective swimming learning medium. This research uses the ADDIE model for Research and Development (R&amp;D) research. The study was conducted at Tadulako University with the collaboration of swimming material experts, instructional design experts, and learning media experts. The research involved 40 students enrolled in swimming learning courses, and data were collected through observation and questionnaires. The assessment by learning design and learning media experts revealed that the developed Pull Buoy achieved high-quality learning standards. Furthermore, both small group and large group evaluations demonstrated feasibility and outstanding performance. The research offers valuable insights and recommendations for future researchers to refine and expand the Pull Buoy's development, suggesting customization of its colour combinations and sizes to suit different age groups of users and advocating for a larger pool of subjects to yield more comprehensive results and insights into the learning medium's impact.
Fraud Transmission Mechanisms within Community: Peer Concealing and Hinting among Chinese Listed Corporations

Abstract We explored the transmission mechanisms of corporate fraud and its punishments within social network communities. Using fraud triangle theory and trust triangle theory, we hypothesize four transmitting channels of how fraud commission and detection are affected by peers’ fraud and punishment. Based on Chinese listed corporations from 2008 to 2018, we first construct and detect interlocked social network communities with a community-detecting algorithm, and then examine hypotheses using a bivariate probit model with partial observability. Our findings indicate that peer-concealing and -hinting effects exist within social network communities. The peer-concealing effect decreases the likelihood of being detected when committing fraud, for those with more and closer fraudulent peers. The peer-hinting effect increases the likelihood of being detected when committing fraud, for those with more and closer punished peers. There is no evidence to support peer-contagion and vicarious-punishment effects. Thus, an improved understanding of the transmission mechanism of corporate fraud commission and detection within communities is provided to prevent and detect corporate fraud. In addition, stakeholders and regulators should be aware of the deviant subculture and social distancing in social network communities.
Real-world prediction of preclinical Alzheimer’s disease with a deep generative model

Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.
Data-driven Learning Systems and the Commission of International Crimes

Abstract Current discussions on the military use of artificial intelligence (AI), in particular concerning autonomous weapons systems, have largely focused on the challenges for the attribution of individual criminal responsibility for war crimes whenever such systems do not perform as initially intended by human operators. Yet, recent observations evidence the pressing need to shift the discussion on the responsibility gap further to include challenges raised by the intentional use of AI systems for the commission of war crimes and other international crimes. Additionally, the increasing development and use of AI systems, based on data-driven learning (DDL) methods, demands particular attention due to the difficulty these systems’ lack of predictability and explainability poses in terms of anticipation of their effects. Against this background, this article complements the present discussion on the responsibility gap by discussing some concerns that the intentional use of DDL systems for the commission of international crimes raises regarding the required mental element and thus, the ascription of individual criminal responsibility. Ultimately, this article proposes preliminary avenues to address these concerns.