Reviewerly/exHarmony huggingface dataset:
The use of machine translation in L2 education: Japanese university teachers’ views and practices

Machine Translation (MT) is receiving increasing attention within language education due to both its affordances and the potential it offers for academic misconduct. To understand more about teachers’ views and practices, a survey was conducted with 153 foreign language (L2) educators who teach at Japanese universities. The survey examined their; (1) use of MT to assist with L2 reading, writing, speaking, and listening, both personally and in their courses; (2) views on MT in language education; and (3) knowledge of how to help students use MT effectively and their willingness to learn about this. The results indicated that most teachers supported the use of MT as a learning tool, but many had concerns over its misuse. Few felt they had enough knowledge to guide students toward effective use and the vast majority wanted to learn more about how to do so. These findings highlight a need for teacher support and underscore the necessity of more research and workplace discussions on the integration and regulation of MT within L2 education.
Automatic exercise generation to support macro-adaptivity in intelligent language tutoring systems

Foreign language teaching achieves best learning outcomes when individual differences of learners are taken into account. While it is difficult for teachers to support internal differentiation in the classroom, digital tools can adaptively propose individual learning paths through activities so that students can practice with appropriately challenging exercises. But how can sufficiently varied, systematically parametrized exercises be provided to enable a system to match them to individual learner needs? We present an approach for high-variability exercise generation that transforms a single specification into a multitude of exercises varying in complexity. The approach is currently being evaluated in a randomized controlled study in regular German seventh grade English classes, facilitating a systematic empirical exploration of adaptive exercise complexity in relation to learning outcomes.
Creating a Security Culture

Every organization across every industry should take information security seriously. Security attacks can take place nearly every day and can result in exposing applications to the outside world. By establishing a strong culture of cybersecurity, enterprise organizations lay a foundation that leads to decreased number of threats in the long run.
THE DEVELOPMENT AND CHANGES OF TRIPLE JUMPERS’ BALANCE BOARD COMBINED WITH SPECIAL TRAINING BY DEEP LEARNING APPROACH

This study aims to improve the athletic performance of triple jumpers in special physical training. First, the triple jumper’s balance board is explained with the special training method. Second, three-dimensional tracking scan image analysis is used to videotape the training process of triple jumpers. Convolutional Neural Networks (CNNs) perform image analysis of triple jumpers performing specific training with a balance board. Finally, the training of triple jumpers is subjected to image analysis. According to the special training speed, angle, and time of triple jumpers combined with the balance board, the physical fitness changes are analyzed. The results show that triple jumpers combined with the balance board training improved the technique of step jump from the aspects of take-off speed, landing speed, all angles of step jump, and support time. The combination of the balance board and the special training significantly improves the speed of the triple jumpers, and the changes of various angles during the training process are also relatively improved, which improves the balance and coordination of the training limbs of the triple jumpers. The balance board combined with special training can significantly improve the physical fitness of triple jumpers. CNN is used to analyze and validate triple jumpers combined with balance board training images. The experimental training analysis has achieved the ideal effect. This study uses the image analysis method to analyze the training process of athletes based on deep learning and provides direction for athletes’ special physical training.
A WEIGHTED NEURAL MATRIX FACTORIZATION HEALTH MANAGEMENT RECOMMENDATION ALGORITHM INTEGSCORING DEEP LEARNING TECHNOLOGY

With the rapid development of Internet medical information technology, a large amount of medical data appeared on the Internet, however, how to extract effective information from the massive and complex medical data to provide professional medical services and suggestions to users has become a hot spot for this research. The recommendation system can effectively solve the problem of accurate matching of complex medical data resources; however, the cold start, data sparsity and user interest migration of the system in the complex data environment have a large impact on the recommendation effect; therefore, this paper proposes a weighted neural matrix decomposition improved health management recommendation scheme incorporating deep learning techniques. The scheme first uses an implicit feedback method to improve the prediction scores and improve the linear model performance of the matrix decomposition algorithm to form a weighted neural matrix decomposition health management recommendation algorithm. Second, the improved method and deep neural network are fused to improve the performance of the nonlinear model part of the algorithm by using the structural properties of the neural network. Finally, this paper’s method is compared with the mainstream six recommendation algorithms on four publicly available real datasets. The experimental results show that the root mean square error (RMSE) of the WENMF algorithm is smaller than that of the comparison algorithm on all four datasets, and the convergence speed is faster. The hit rate (HR) and normalized discounted cumulative gain (NDCG) of the WENMF algorithm are higher than those of the comparison algorithm on all four datasets, and the maximum difference is 0.04. Therefore, the recommendation accuracy and ranking quality of the WENMF algorithm in the recommendation system are verified, and the cold start and data sparsity problems of the recommendation system are effectively alleviated.
Enhancing Picture Book Reading for Young EFL Learners

With increasing globalization, global English and its association with literacy worldwide have gained widespread attention. Following the law of language learning, more and more countries are making efforts to help their citizens become literate in English from their early years. Picture books have played a crucial part in developing children’s emergent literacy skills. They provide children with fascinating illustrations, advancement in vocabularies, and meaningful sentence structures that go beyond the present contexts. However, picture book reading can be difficult for young English as a Foreign Language (EFL) learners due to the lack of English-speaking environment and language accumulation. With regard to this situation, this article analyzes and discusses the common debates on the print concept, phonological awareness, and vocabularies within an EFL early picture book reading setting to shed light on the practical direction in this field. Following this, this article coins relevant supporting strategies to ensure effective English picture book reading among children by developing supplementary activities, encouraging parent-child interaction, and applying multimedia materials. This article ends with a brief conclusion and questions that may need further exploration and research.
A Feasibility Review of Novel Avian-Based Optimization Algorithms for Damage Detection in a Truss Bridge

During their lifecycle, bridge structures have to withstand various uncertainties loads such as wind, typhoon, and accident loads which may pose serious threats to the integrity as well as the safety of the structure, especially when they induced significant damages to the structure. For many years, researchers have been trying to develop heath monitoring tools, which can identify accurately not only the location, but also the level of structural damage. In this paper, two novel avian-based optimization algorithms-Artificial Hummingbird Algorithm (AHA) and African Vulture Optimization Algorithm (AVOA) are reviewed for their feasibility in detecting structural damages in truss bridge. The accuracy of the proposed algorithms is compared against two other famous algorithms: particle swarm optimization (PSO) and cuckoo search (CS). The results of the feasibility review for damage detection capability are discussed.
Analysis and Design of Regional Tourism Information System “SIPARDA” of Morotai Island Regency

The analysis and design of the regional tourism information system known as SIPARDA is a follow-up to the results of the strategic planning of the information system to optimize the performance of the bureaucracy, especially the Tourism Office of Morotai Island Regency, related to the main tasks and functions of the destination and tourism industry, tourism marketing, and creative economy. SIPARDA's design was carried out using the concept of Software Development Life Cycle (SDLC) through a waterfall approach. The application design method begins with identifying user needs and designing a tourism information system relevant to the main tasks and functions of the Tourism Office in the Morotai Island Regency. Unified Modeling Language (UML) is used to describe the business processes of SIPARDA. Five primary actors can operationalize the system: the head of the tourism officer, tourism industry and destination department, tourism marketing department, creative economy department, and administrator. Thus, each department can document attractions, amenities, accessibility, and accommodation data in the Morotai Island Regency as a regional tourism development decision-making database.
Sentiment Analysis of Customer Feedback Reviews Towards Hotel’s Products and Services in Labuan Bajo

The feedback review column on the TripAdvisor website can process the sentiment analysis by classifying the positive and negative impressions of the consumer using machine learning, namely text mining. This study analyzes consumer sentiment for hotel products and services in Labuan Bajo based on feedback review data on the TripAdvisor website. Meanwhile, the algorithm used is Nave Bayes Classifier (NBC), Support Vector Machine (SVM), also SMOTE Upsampling for the k-Nearest Neighbor (k-NN) algorithm. The classification result shows 702 negative feedback reviews and 2531 positive feedback reviews. The evaluation of algorithm performance shows the accuracy of SVM is 78,30% and NBC is 78,29% compared with k-NN with 85,24% accuracy, using SMOTE Upsampling with a class precision description of 100% prediction negative (1784 true Neg &amp; 0 true Pos) and 77,21% prediction positive (747 true Neg &amp; 2531 true Pos). The class recall description also shows 70,49% true Negative (1784 pred Neg &amp; 747 pred Pos) and 100% true Positive (0 pred Neg &amp; 2531 pred Pos). These findings indicate that the k-NN algorithm shows the best result instead of the SVM and NBC algorithm, according to the sentiment analysis result of customer feedback reviews on hotel products and services in Labuan Bajo through TripAdvisor website.
The Principles, Algorithms and State-of-Art Applications of Quantum Computing

Abstract Contemporarily, quantum computing has become a popular topic, which is a new approach that is different from classic computing. To be specific, it can deal with information by using mathematical modeling that used to represent quantum mechanics, including superposition, interference, and entanglement. In quantum Computing, information is storage in ‘qubit’, whereas classic computer use bit to store information. This article aims to introduce the principles and some algorithms of quantum computing, and then discuss some up-to-date important development. There are two important algorithms are mentioned in this article, Grover’s algorithms and Shor’s algorithms. According to the analysis, Grover’s algorithms is potentially faster than classic algorithms. However, it requires millions of qubits to make it. Besides, Shor’s algorithms have challenged classic encryption (RSA encryption). Two quantum computer Google Sycamore and Jiuzhang are also mentioned here. The interesting thing is, for some specific question, Jiuzhang performed better than classic computers. Finally, it is found out that the quantum computing is generally slower that classic computer now, due to the restriction of qubit numbers. However, quantum computing still has an advantage in some specific question. These results shed light on guiding further exploration of quantum computer designing.
The Design and Simulation of a Fifth-order Chebyshev Low-Pass Filter

Abstract Chebyshev filter is a design classification of filters. It uses the Chebyshev transfer function, and has many filter types, such as high-pass, low-pass, band-pass, high-resistance, band-stop and so on. Compared with the Butterworth filter, the transition band of the Chebyshev filter is very narrow, but the internal amplitude-frequency characteristics are unstable [1][2]. Chebyshev filter achieves complex transmission zeros to improve the group delay characteristics in the pass band and reduce the signal distortion. In the process of designing a five-order Chebyshev filter, an active filter network needs to be established, and each stage of the low-pass filter composed of an amplifier is connected in the same cascade to form a multi-order Chebyshev low-pass filter. To design a Chebyshev filter, a Sallen-key structure is needed, and the transfer function is determined according to the position and size of the different resistors and capacitors placed, and a Chebyshev filter with different functions is constructed. The Multisim Simulation software is used to simulate the design results, and the frequency response amplitude curve in the image is found to be fluctuating with equal ripple in the pass or stop band.
Robust air-light estimation for a single hazy image using haze-lines in Plüker coordinates

Image dehazing is a valid way to reduce the negative impacts of bad weather, such as fog and haze, on outdoor optical imaging. The effect of the image dehazing heavily relies on the air-light estimation. Most existing methods exploit the prior information, which is generally obtained by simply assessing the brightest pixels' intensity in a hazy image, to estimate the air-light. However, bright objects, such as lamps, lead to the failure of these methods. So, a novel method for robust and optimal air-light estimation using haze-lines described in Plüker coordinates is proposed. First, the exact analytic solution of the air-light, i.e., the intersection of haze-lines, is derived via analyzing the geometric relationship between haze-lines in three-dimensional RGB space using their Plüker coordinates. Then, a novel air-light estimation algorithm using haze-lines in Plüker coordinates is elaborated. Finally, extensive experiments are performed to validate the proposed algorithm. Experimental results reveal that the proposed algorithm can more robustly and accurately estimate the air-light.
Pulse-Level Scheduling of Quantum Circuits for Neutral-Atom Devices

We show how a pulse-level implementation of certain multiqubit gates allows for the simultaneous execution of single- and multiqubit gates acting on overlapping sets of qubits, in a mechanism we name absorption. With absorption as a foundation, we present an algorithm to schedule the execution of a quantum circuit as a pulse sequence on a neutral-atom quantum processor with a single channel for single- and multiqubit gate execution. For any quantum circuit of practical relevance, we observe that the algorithm results in an optimal utilization of the available resources that cannot be surpassed by a different scheduling strategy. Our benchmarks against a custom scheduler attempting to maximize parallelization at the gate level show the time gained by the pulse-level scheduler is proportional to the depth and is most pronounced for quantum circuits with fewer qubits. We finally illustrate how the concept of absorption can also be used in other architectures.
Applying Digital Image Correlation (DIC) Techniques to Characterise Plywood According to UNE-EN 314 Standards: Bonding Quality Tests

The determination of plywood bonding quality by shear testing is one of the most commonly used and informative tests, alongside bending strength, to characterise this compound material. Two types of birch plywood were studied: indoor use (class 1), thickness 18 mm, urea-formaldehyde glue; and exposed outdoor use (class 3), thickness 15 mm, phenolic formaldehyde glue. The corresponding pre-treatments indicated in standards UNE-EN 314-1 and -2 were applied to each type. To test the suitability of the method, the technique of digital image correlation (DIC) was used to obtain information about the complex modes of fracture present in compound materials such as plywood. Assessing the results based on the statistical extreme value theory provided more reliable and robust information about the failure strength values for low failure probabilities. The results confirm that DIC can be used to determine the true distribution of the deformations during the fracture process and, therefore, it provides greater knowledge of the details of the failure process. Statistical evaluation of the experimental data obtained with DIC indicates that test results exceeding the time limit of 30 ± 10 s should be evaluated, contrary to the operating procedure in standard UNE-EN 314-1, which states that they are invalid.
Steganographic secret sharing via AI-generated photorealistic images

Abstract Steganographic secret sharing is an access control technique that transforms a secret message into multiple shares in a steganographic sense. Each share is in a human-readable format in order to dispel suspicion from a malicious party during transmission and storage. Such a human-readable format can also serve to facilitate data management. The secret can be reconstructed only when a sufficient number of authorized shareholders collaborate. In this study, we use neural networks to encode secret shares into photorealistic image shares. This approach is conceptually related to coverless image steganography in which the data are transformed directly into an image rather than concealed into a cover image. We further implement an authentication mechanism to verify the integrity of the image shares presented in the decoding phase. All coverless image steganography schemes can be used to achieve steganographic secret sharing, but our detection mechanism can further improve the robustness of these schemes. Experimental results confirm the robustness of the proposed scheme against various steganalysis and tampering attacks.
Visualization for AI Explainability

This special section features articles on human-centered design and the use of user interfaces and data visualizations in support of making systems, which employ artificial intelligence and machine learning, easier to understand and more accurately to interpret, thus supporting their transparency and increasing trust in their application, whether it is during the design and development phase of a model, during its training and execution, or in a post hoc phase focusing on the use of models in practical applications.
Building trust via blockchain in UAV‐assisted ultra‐dense 6G cellular networks

Abstract The ultra‐dense deployment of unmanned aerial vehicles (UAVs) as mobile small cell base stations (SBS) is expected to support ultra‐high‐speed, ultra‐reliable, and ultra‐low‐latency wireless connections in sixth generation (6G) wireless cellular networks. Inevitably, the explosively increasing number of SBSs each with ever‐shrinking cell size will result in the spectrum scarcity and pose critical challenges to the spectrum management. Towards the centralized spectrum management system, a third‐party authority is employed to coordinate the spectrum sensing, sharing, and allocation among participants. However, a centralized authority is vulnerable to numerous security threats, such as single point of failure, denial of service attacks, and privacy disclosure, and cannot guarantee fair and efficient spectrum management without mutual trusts among participants. To address these problems, a novel framework of blockchain‐aided spectrum management is designed to securely record the spectrum auction data and spectrum allocation data in the decentralized spectrum sharing. Moreover, a trust management scheme is proposed to evaluate the trusts of all UAVs participating in the decentralized spectrum sharing, where the trust increase depends on legal use of spectrum allocation and honest report of spectrum sensing. In particular, it is shown that the proposed trust management can not only incentivize the UAVs to comply with the legal rule of spectrum sharing in blockchain, but also punish the malicious behaviour that either violates the spectrum sharing rule or provides misleading spectrum sensing results.
Investigating the relationship between knowledge management practices and organizational learning practices in the universities’ environment

&lt;span lang="EN-US"&gt;The concept of knowledge management (KM) and organizational learning (OL) has been embraced by organizations to complement each other. Higher education institutions have embraced KM and OL as a means to improve organizational efficiency. This research explores the link between KM and OL. The target population included all the 432 academicians and administrators from 35 public universities in Iraq. The sampling was selected using a stratified random sampling technique. The correlation among the components of KM and OL was tested as well as the effect of KM components on OL. The findings were derived using smart partial least square. The findings showed that there is significant correlation between components of KM and components of OL. The regression analysis showed also that the effect of KM and its components; knowledge creation, knowledge sharing, knowledge storage, knowledge application and knowledge acquisition on OL are significant. These findings provide insights to universities management on strategies to implement KM practices that can align with OL practices to assure dynamic lifelong mechanisms for the basic daily activities such as teaching, learning, researching, and supervision.&lt;/span&gt;
Psychological readiness of creative industry workers to pursue a professional career: content, structure, research methods

Introduction. A professional career is becoming more and more multifaceted and industry-specific. Creative industry, which has a great economic potential and plays an important role in the development of Ukrainian and world economy, offers good possibilities for employees' career development. Aim. To determine the content and structure of the psychological readiness of creative industry workers to pursue a professional career and to substantiate the methods for its research. Results. The main components of the creative industry workers' readiness to pursue a professional career have been found to include: a) motivational and value component (career values, career motivation); b) cognitive component (understanding the goals and objectives of the industry, its prestige, innovativeness, social effect, employees' financial opportunities and autonomy); c) operational component (career self-efficacy, career adaptability, career involvement, propensity for cooperation/competition); d) personal component (creativity, professional viability, ability to introspect). The authors have also substantiated a diagnostic toolkit for studying different components of the creative industry workers' readiness to pursue a professional career. Conclusions. The knowledge of the content and structure of the components of the creative industry workers' readiness to pursue a professional career as well as the methods for studying this readiness can be helpful for career counsellors.