Reviewerly/exHarmony huggingface dataset:
Transforming the Future of Furniture Woodworking Instruction Through VR-Enhanced Distance Teaching During the COVID-19 Pandemic

During the COVID-19 pandemic, distance teaching became the main solution, including for the furniture woodworking course at National Taipei University of Technology in Taipei, Taiwan, which relied on video and online software. However, this posed challenges for maintaining teaching quality and achieving objectives in technical practice courses. To address this, this study introduced remote technical guidance using VR in technical practice courses. This method combined distance-teaching and live dual-teacher broadcasts, allowing students to participate in real-time online discussions. During these broadcasts, instructors used VR to demonstrate operations on a virtual platform, explaining as they went along. Students could observe from the operator's perspective, gaining insights into furniture production processes. They also engaged in group interactions, assuming roles like technical operators, thereby mastering key furniture production concepts. This innovative teaching approach offered a solution that combined remote technical guidance with VR. It provided immediate teaching enhancements and problem-solving solutions in the post-COVID-19 era.
Humanity as a Soteriological Trend of University Education under the Conditions of Necessity for the Survival of Humanity

The article substantiates the need for the transition of university education to the format of human dimension, substantiates humanity as a soteriological trend of university education in the conditions of the need to realize the imperative of the survival of humanity. The scientific origins of the deepening global apocalyptic crisis-labyrinth are revealed, namely: the dogmas of humanism as a spiritless secular religion, bio-social understanding of human nature, which kills all living things – dialectical materialism, sociality, as always, the positivity of human actions. he trialectics, which is designed to form a new holistic saving worldview, is characterized, the specifics of the trialectics are revealed.Unomics is shown as a new fateful science about the unfolding of Human Eternity, which should set a new soteriological format for the development of university education, when the understanding and practical implementation of the imperative of human/humanity survival determines its (un)future.
Magic GIFT for Digital Library Innovation with Grouping, Indexing, Folding, and Targeting

Digital library service is in need of innovation to meet the demands of the modern user and effectively sort through increasingly growing web and cloud content. One such innovation introduces the idea of the magic lamp that can be embodied through three comprehensive views: conceptual foundation, technical roadmap, and operational fulfillment to better serve the user. The Magic Lamp represents a pioneering invention that is both a multidimensional and “smart” approach to discovering, grouping, assimilating, organizing, and delivering archival content while eliminating unnecessary and antiquated sequential search patterns that consume or overwhelm both the user and computer. Central to the concept of the Magic Lamp as a visible icon is the underlying and automated process of the Magic GIFT throughout Grouping, Indexing, Folding and Targeting. The magic of GIFT enacts domain-specific search (like “google-it”) as grouped, contextualizes multidimensional &amp; multilinguals (as indexed) for diverse and global readers, and dedicates the user to multi-folded content, and propels decent experience and deep exploration on target. As a result, this may be seen in overlying three views that helps streamline large amounts of information in user-friendly ways which are readily available at one’s fingertips.
Cheque Truncation Mechanism Using Blockchain

Cheque truncation system (CTS) is an image-based cheque-clearing framework. The semi-manual process has certain limitations and takes up to three working days to clear an inter-bank national cheque. Faced with the limitations of this system, cheque users and commercial banks need an efficient and secure system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. All banks intending towards participating in this framework must connect towards the proposed blockchain-based system. A comprehensive framework among four primary phases—(1) paper check clearing process, (2) digital check issuing and clearing process, (3) check fraud detection process, and (4) check transaction security procedure—was presented as a solution.
Littératie de la traduction automatique (TA) neuronale et traduction spécialisée : s’approprier les outils de la TA au travers de projets de recherche interdisciplinaires

We present a set of approaches to facilitate the integration of Neural Machine Translation (NMT) in teaching and research favouring an interdisciplinary perspective. The study suggests that a nuanced comparison of NMT generated translations requires translation experts to move from passive consumption to active participation in the construction of controlled models. This transition requires an understanding of the fundamentals of NMT, which calls for changes in pedagogical models to promote digital literacy while maintaining the standards of professional translation education in line with translation competence frameworks.
Introduction

The last decade has seen major technological changes related to artificial intelligence, particularly in the fields of machine translation (MT) and natural language processing (NLP). The remarkable progress made in research into the automation of text and speech translation (machine translation, speech synthesis and recognition, image/text linking, evaluation metrics, etc.), combined with what is known as artificial intelligence (AI), is broadening the field of application of translation tools. These developments and the democratisation of neural translation are leading us to rethink our working methods and the organisation of the professions themselves.
“I Looked It Up in DeepL”: Machine Translation and Digital Tools in the Language Classroom

This article looks at a widespread yet erroneous use of machine translation (MT): looking up single words, thus treating MT systems as online bilingual dictionaries (ODs). After a literature review of this trend in research about MT, we consider data from a large survey that we carried out in 2021 at all Swiss universities on MT use and users’ attitudes. When analysing users’ metalinguistic awareness, we discovered that non-translators perceive the text to translate, mostly at word level, leading to the misuse of MT systems as ODs. Moreover, the survey results revealed confusion between the different digital tools for language learning, namely MT, online parallel corpora like Linguee and ODs. We therefore suggest broadening the scope of MT literacy to include training learners in general digital literacy to enable them to use such tools appropriately.
Investigating the Usability of Automatic Metrics for Characterizing Translated vs Post-edited Texts in the Post-editing Classroom to Further Students’ MT Literacy

In order to foster professional attitudes towards machine translation (MT) among translation trainees, learners need to understand the limits of the technology and the scope for human intervention in MT-enhanced workflows, as well as the impact MT use has on the end product. Using a corpus of post-edited and translated texts produced by Master’s students in translation, this paper investigates the usability and value of automatic metrics as a pedagogical tool in the post-editing classroom for characterizing post-edited texts in comparison with human translation, and suggests a practical exercise for enhancing MT literacy in translation training.
A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&amp;W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods.
Toward a Model to Evaluate Machine-Processing Quality in Scientific Documentation and Its Impact on Information Retrieval

The lack of quality in scientific documents affects how documents can be retrieved depending on a user query. Existing search tools for scientific documentation usually retrieve a vast number of documents, of which only a small fraction proves relevant to the user’s query. However, these documents do not always appear at the top of the retrieval process output. This is mainly due to the substantial volume of continuously generated information, which complicates the search and access not properly considering all metadata and content. Regarding document content, the way in which the author structures it and the way the user formulates the query can lead to linguistic differences, potentially resulting in issues of ambiguity between the vocabulary employed by authors and users. In this context, our research aims to address the challenge of evaluating the machine-processing quality of scientific documentation and measure its influence on the processes of indexing and information retrieval. To achieve this objective, we propose a set of indicators and metrics for the construction of the evaluation model. This set of quality indicators have been grouped into three main areas based on the principles of Open Science: accessibility, content, and reproducibility. In this sense, quality is defined as the value that determines whether a document meets the requirements to be retrieved successfully. To prioritize the different indicators, a hierarchical analysis process (AHP) has been carried out with the participation of three referees, obtaining as a result a set of nine weighted indicators. Furthermore, a method to implement the quality model has been designed to support the automatic evaluation of quality and perform the indexing and retrieval process. The impact of quality in the retrieval process has been validated through a case study comprising 120 scientific documents from the field of the computer science discipline and 25 queries, obtaining as a result 21% high, 39% low, and 40% moderate quality.
VQ-InfraTrans: A Unified Framework for RGB-IR Translation with Hybrid Transformer

Infrared (IR) images containing rich spectral information are essential in many fields. Most RGB-IR transfer work currently relies on conditional generative models to learn and train IR images for specific devices and scenes. However, these models only establish an empirical mapping relationship between RGB and IR images in a single dataset, which cannot achieve the multi-scene and multi-band (0.7–3 μm and 8–15 μm) transfer task. To address this challenge, we propose VQ-InfraTrans, a comprehensive framework for transferring images from the visible spectrum to the infrared spectrum. Our framework incorporates a multi-mode approach to RGB-IR image transferring, encompassing both unconditional and conditional transfers, achieving diverse and flexible image transformations. Instead of training individual models for each specific condition or dataset, we propose a two-stage transfer framework that integrates diverse requirements into a unified model that utilizes a composite encoder–decoder based on VQ-GAN, and a multi-path transformer to translate multi-modal images from RGB to infrared. To address the issue of significant errors in transferring specific targets due to their radiance, we have developed a hybrid editing module to precisely map spectral transfer information for specific local targets. The qualitative and quantitative comparisons conducted in this work reveal substantial enhancements compared to prior algorithms, as the objective evaluation metric SSIM (structural similarity index) was improved by 2.24% and the PSNR (peak signal-to-noise ratio) was improved by 2.71%.
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
Eritrean teachers’ challenges of their enthusiasm for digital competency and its impact on teaching performance

Abstract The interaction of education and digital technology today is one of the key characteristics of development. Many industrialized nations place a high value on digital learning practices and have created several national strategic plans to encourage and support the use of digital practices in education. In its reform and development of education, Eritrea has also made the strategic decision to assist educational modernization using a clear digital policy. Eritrea has made significant advancements in the creation of technology infrastructure, the use of digital resources, innovative teaching methods, and technology-supported educational management to date. However, there are still issues with execution brought on by teachers' preparation, public spending, and human resources. The study collected useful information from n = 141 active student-teachers participating in post-graduate diploma (PGD) practice at Asmara College of Education utilizing both qualitative and quantitative research methodologies. Results have been reported using special and original quotes together with both descriptive and inferential statistics. The study's conclusions show that teachers' enthusiasm for using ICT in the classroom will have a favorable effect on their ability to teach, assisting them in becoming aware of the modernized world and adjusting to its expectations. The knowledge of the teachers has a big impact on digital activities. Additionally, the research has revealed that Eritrean school instructors have little knowledge of using ICT in teaching and learning to improve student performance. The perception of teachers' preparation for digital competency is positive, even though a noteworthy problem is being highlighted that prevents teaching and learning from succeeding. They have a very high and favorable opinion of how the use of technology has affected their ability to teach.
Reservoir computing for macroeconomic forecasting with mixed-frequency data

Macroeconomic forecasting has recently started embracing techniques that can deal with large-scale datasets and series with unequal release periods. Mixed-data sampling (MIDAS) and dynamic factor models (DFMs) are the two main state-of-the-art approaches to modeling series with non-homogeneous frequencies. We introduce a new framework, called the multi-frequency echo state network (MFESN), based on a relatively novel machine learning paradigm called reservoir computing. Echo state networks (ESNs) are recurrent neural networks formulated as nonlinear state-space systems with random state coefficients where only the observation map is subject to estimation. MFESNs are considerably more efficient than DFMs and can incorporate many series, as opposed to MIDAS models, which are prone to the curse of dimensionality. All methods are compared in extensive multistep forecasting exercises targeting U.S. GDP growth. We find that our MFESN models achieve superior or comparable performance over MIDAS and DFMs at a much lower computational cost.
Smartphones as One of Smart University Factors and its Effectiveness in Learning English Case study: Applied College, King Khalid University

This study aimed to exfoliate light on the effectiveness of smartphones and their part in developing English language literacy for womanish scholars. The study used the descriptive approach. The study was conducted on a arbitrary sample of (120) womanish scholars from king Khalid university, applied council, Khamis Mushait branch. This exploration tried to give answers to some important questions related to the effective use of smartphones in learning English language at king Khalid university, Applied council. It handed answers to the following questions What's the scholars ’ perception over the respectable use of smartphones in learning English language? How do different Smartphone apps ameliorate scholars ’ vocabulary and pronunciation? What are the factors that affect the use of smartphones in learning English language regarding scholars ’ opinions? The findings of the questionnaire show that the scholars ’ answers were positive with moderate operation of their smartphones in their attempt to learn English. It also illustrates that the maturity of scholars approved it's applicable to learn English vocabulary through mobile apps since it's available at any place and at any time. also, the results showed that scholars agreed that smartphones are veritably useful tools to be used in their literacy process.
The cross-sectional stock return predictions via quantum neural network and tensor network

Abstract In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains the lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests capability of model’s capturing non-linearity between input features.
Secure Control of Linear Controllers Using Fully Homomorphic Encryption

In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency.
RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design a RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-art approaches in smartphone identification.