Reviewerly/exHarmony huggingface dataset:
A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction

This paper proposes a collaborative human-computer interaction recommendation model for English major translation courses to address the problems of poor course recommendation and lack of robustness to noisy data in traditional recommendation models for English major translation courses. First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. A recommendation supervision task and an auxiliary task for contrast learning are constructed for joint optimization. The human-computer interaction model of the knowledge graph is designed, and the dialogue entities are embedded in the knowledge graph ripple network to obtain potentially interesting content for students. Finally, the student interaction content and node representations are combined to obtain the optimal translation course recommendation. The experimental results indicate that the proposed model in this work is capable of producing higher-quality English major course recommendations and beats other current models. This model is suitable for English primary translation course content recommendation and helps to improve students’ translation ability.
Coverless Image Steganography: Review

Many of the existing image steganographic techniques embed secret information into cover images by slightly altering their contents. These modifications have several effects, on the other hand, Stego-images distorted by these problems become vulnerable to steganalysis tools. Coverless information hiding represents a solution to this problem. Unlike traditional techniques, which alter the carrier, a coverless information hiding procedure does not change it. This paper reviews some of the recent works that have been conducted on the topic of coverless image steganography and provides an important insight into how these techniques are performed.
Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

Camera sensor identification can have numerous forensics and authentication applications. In this work, we follow an identification methodology for smartphone camera sensors using properties of the Dark Signal Nonuniformity (DSNU) in the collected images. This requires taking dark pictures, which the users can easily do by keeping the phone against their palm, and has already been proposed by various works. From such pictures, we extract low and mid frequency AC coefficients from the DCT (Discrete Cosine Transform) and classify the data with the help of machine learning techniques. Traditional algorithms such as KNN (K-Nearest Neighbor) give reasonable results in the classification, but we obtain the best results with a wide neural network, which, despite its simplicity, surpassed even a more complex network architecture that we tried. Our analysis showed that the blue channel provided the best separation, which is in contrast to previous works that have recommended the green channel for its higher encoding power.
Design and Implementation of Web Application Based on MVC Laravel Architecture

Developing web applications with traditional methods causes large limitations such as a lot of time consuming, number of unexpected errors. A novel approach technology of MVC framework is built by some companies to deal with such issues. This paper presents a design and implementation for web application with Laravel framework. This research makes us find out that the development was standardized and much scalability, so it gives us more efficiency through the implementations.
Literary Reading Lesson in the First Grade: What Is Important to Consider in Accordance with the Updated Standard

The author discusses ways to implement the requirements of the Federal State Educational Standard for Primary General Education (FSES PGE) when teaching literary reading to first-graders. The changes that, in accordance with the updated version of the standard, have been introduced into the exemplary program for literary reading are analyzed: the goals, planned results and features of the teaching methodology are characterized, taking into account the level of development of reading literacy and interest in reading among first-graders. Particular attention is paid to the problem of students' acceptance of spiritual and moral values reflected in fiction. Specific examples reveal the methodology for the formation of meta-subject learning outcomes in the first grade.
Single Image Raindrop Removal Using a Non-Local Operator and Feature Maps in the Frequency Domain

Taking a photo on a rainy day may result in a photo with raindrops. Images containing raindrops have a significant impact on the visual impression and accuracy when applied to image recognition systems. Thus, an automatic high-quality raindrop removal method is desired for outdoor image processing systems as well as for acquiring good-looking images. Several existing methods have been proposed to tackle this problem, but they often fail to keep global consistency and generate unnatural patterns. In this paper, we tackle this problem by introducing a non-local operator. The non-local operator combines features in distant locations with matrix multiplication and enables consistency in distant locations. In addition, high-frequency components such as edges are more affected in images with raindrops. Inspired by the nature that high-frequency components can be separated from other components in the frequency domain, we also propose to process feature maps in the frequency domain, which are obtained by the fast Fourier transform operation and processed by several convolution layers. Experimental results show that our method effectively removes raindrops and achieves state-of-the-art performance.
Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing

Deep learning techniques have shown their capabilities to discover knowledge from massive unstructured data, providing data-driven solutions for representation and decision making [...].
Instance-Level Semantic Alignment for Zero-Shot Cross-Modal Retrieval

Zero-shot Cross-Modal Retrieval (ZS-CMR) is challenging due to the heterogeneous distributions across different modalities and the inconsistent semantics across seen and unseen classes. Previous methods usually perform class-level semantic alignment of data from different modalities by introducing auxiliary word embeddings of class labels, which have a fatal limitation as the learning of class-level information will lead to the ignorance of intra-modal variance. To solve this problem, we propose our Instance-Level Semantic Alignment (ILSA) method to make full use of the instance-level information. We use two disentanglement variational auto-encoders to decompose the data from two modalities into modal specific and modal invariant features. With an instance-level semantic features extractor and a distribution generator, ILSA could generate more appropriate distributions by the learned instance-level semantic features, without any auxiliary knowledge. We perform the experiment on six widely used datasets on two scenarios of ZS-CMR, the results show that our method establishes the new state-of-the-art performance on all datasets.
Photonic Multiplexing Techniques for Optical Neuromorphic Computing

The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physical dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallelism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advances of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.
Understanding Multi-View Collaboration between Augmented Reality and Remote Desktop Users

Establishing an effective collaboration between augmented-reality (AR) and remote desktop users is a challenge because collaborators do not share a common physical space and equipment. Yet, such asymmetrical collaboration configurations are common today for many design tasks, due to the geographical distance of people or unusual circumstances such as a lockdown. We conducted a first study to investigate trade-offs of three remote representations of an AR workspace: a fully virtual representation, a first-person view, and an external view. Building on our findings, we designed ARgus, a multi-view video-mediated communication system that combines these representations through interactive tools for navigation, previewing, pointing, and annotation. We report on a second user study that observed how 12 participants used ARgus to provide remote instructions for an AR furniture arrangement task. Participants extensively used its view transition tools, while the system reduced their reliance on verbal instructions.
Linking Artificial Intelligence Use to Improved Decision-Making, Individual and Organizational Outcomes

Artificial Intelligence (AI) plays a significant role at the organizational and operational levels. Managers of companies started to employ AI in decision-making to achieve their operational goals. However, not all managers have the same adaptability to this new technology. Therefore, the present study investigates the impact of utilizing AI on the decision-making process. It also aimed to examine the impact of improved decision-making on three dependent variables: Organizational Performance (OP), Individual Productivity (IP), and Organizational Culture (OC). Statistical Package for Social Sciences (SPSS) software was used to analyze the obtained data and test the hypotheses. The sample of this study included 133 participants working in Saudi organizations, selected from different levels of management (i.e., top managers, middle managers, first line managers, and non-managerial employees). The results of the study showed that AI plays a significant role in the process of decision-making. The results also revealed a positive direct relationship between improved decision-making and organizational performance, individual productivity, and organizational culture. Based on the results of this study, some implications and recommendations were provided concerning the relationship under investigation.
Research on Anti-Frequency Sweeping Jamming Method for Frequency Modulation Continuous Wave Radio Fuze Based on Wavelet Packet Transform Features

Frequency modulation continuous wave (FMCW) radio fuze is widely used in military equipment, due to its excellent range and anti-jamming ability. However, the widespread use of radio fuze jammers on modern battlefields poses a serious threat to fuzes. In this study, a classification method of targeting and sweeping frequency jamming signals of FMCW radio fuze based on wavelet packet transform features is proposed, which improves the anti-jamming ability of fuze. The wavelet packet transform of the output signal of the radio fuze detector is used to form a feature vector, which is fed into a support vector machine for targeting and jamming signal classification. The experimental results of the measured data show that the proposed method can achieve a high accuracy rate of classification and identification of FMCW radio fuze targets and frequency sweeping jamming signals. The highest recognition accuracy reached is 98.81% ± 0.0037. The lowest false alarm probability is 0.57% ± 0.0043, which indicates its potential application values in the near future.
Flexible non-parametric regression models for compositional response data with zeros

Abstract Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k--NN regression, termed α--k--NN regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the α-transformation. Our model is further extended to the α--kernel regression by adopting the Nadaraya-Watson estimator. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they can be incorporated into the proposed models without modification. Extensive simulation studies and real-life data analyses highlight the advantage of using these non-parametric regressions for complex relationships between the compositional response data and Euclidean predictor variables. Both suggest that α--k--NN and α-kernel regressions can lead to more accurate predictions compared to current regression models which assume a, sometimes restrictive, parametric relationship with the predictor variables. In addition, the α--k--NN regression, in contrast to current regression techniques, enjoys a high computational efficiency rendering it highly attractive for use with large scale, massive, or big data.
Adding Visual Information to Improve Multimodal Machine Translation for Low-Resource Language

Machine translation makes it easy for people to communicate across languages. Multimodal machine translation is also one of the important directions of research in machine translation, which uses feature information such as images and audio to assist translation models in obtaining higher quality target languages. However, in the vast majority of current research work has been conducted on the basis of commonly used corpora such as English, French, German, less research has been done on low-resource languages, and this has left the translation of low-resource languages relatively behind. This paper selects the English-Hindi and English-Hausa corpus, researched on low-resource language translation. The different models we use for image feature information extraction are fusion of image features with text information in the text encoding process of translation, using image features to provide additional information, and assisting the translation model for translation. Compared with text-only machine translation, the experimental results show that our method improves 3 BLEU in the English-Hindi dataset and improves 0.47 BLEU in the English-Hausa dataset. In addition, we also analyze the effect of image feature information extracted by different feature extraction models on the translation results. Different models pay different attention to each region of the image, and ResNet model is able to extract more feature information compared to VGG model, which is more effective for translation.
Hyperparameter Optimization on Ensemble Regression Tree for Lip Coloring Simulation

Technology helps us in many activities and keeps growing, so it makes activities more efficient, time-saving, using fewer resources and also information and entertainment are accessible. Machine Learning technology is the fastest-growing field in computer science that is used in many areas such as marketing, healthcare, manufacturing, information security, and transportation. One of the machine learning methods is the Ensemble of Regression Tree (ERT) which has succeeded in detecting facial features on the eyebrows, eyes, nose, and lips. However, utilization ERT method has not been found to detect specific areas such as lips only for gaining optimization. Then this research will be conducted to extract the facial feature annotation dataset from the iBUG 300W dataset with 68 facial features to 20 lip area points. The results of the extraction are reduced error rate, resources saving, lip features still detected and lip coloring simulation was successfully carried out using the configuration of hyperparameter values, tree = 4, regularization = 0.25, cascade = 8, feature pool = 500, oversampling = 40 and translation jitter = 0. From observations also discovered optimization that hard disk resource savings are 69.36%, RAM 30.8%, and CPU 3.8%; reduce the error rate by 0.058%; and increase inference speed by 39%.
Membrane Fouling Diagnosis of Membrane Components Based on MOJS-ADBN

Given the strong nonlinearity and large time-varying characteristics of membrane component fouling in the membrane water treatment process, a membrane component-membrane fouling diagnosis method based on the multi-objective jellyfish search adaptive deep belief network (MOJS-ADBN) is proposed. Firstly, the adaptive learning rate is introduced into the unsupervised pre-training phase of DBN to improve the convergence speed of the network. Secondly, the MOJS method is used to replace the gradient-based layer-by-layer weight fine-tuning method in traditional DBN to improve the ability of network feature extraction. At the same time, the convergence of the MOJS-ADBN learning process is proven by constructing the Lyapunov function. Finally, MOJS-ADBN is used in the membrane packaging diagnosis to verify the performance of the model diagnosis. The experimental results show that MOJS-ADBN has a fast convergence speed and a high diagnostic accuracy, and can provide a theoretical basis for membrane fouling diagnosis in the actual operation of membrane water treatment.
Exploring Headteachers, Teachers and Learners’ Perceptions of Instructional Effectiveness of Distance Trained Teachers

This study examined the perceptions of headteachers, teachers, and learners regarding instructional effectiveness of University of Cape Coast distance trained teachers. The convergent parallel mixed-method design was employed to conduct the study. Research participants comprising 25 headteachers, 667 distance trained teachers, and 138 learners were selected through purposive sampling, stratified random sampling, and simple random sampling techniques respectively. Mean and standard deviations, independent sample t-test, and thematic analysis were employed to analyse the data. The study revealed that headteachers, teachers and learners perceived distance trained teachers as instructionally effective because they demonstrated professional knowledge, professional practice and professional values and attitudes in their instructions. Results also indicated that there was statistically significant difference between male and female teachers with regard to perceived instructional effectiveness. It is recommended that stakeholders such as parents and prospective applicants should consider UCC distance education programme as an alternative to the conventional education.
Digital Competence of Digital Native Students as Prerequisite for Digital Transformation of Education

Current students of higher education institutions are referred to as digital natives and so they are expected to have a good level of digital competence. The aim of the paper is to examine whether digital native students of non-technical study programs consider themselves ready for the digital transformation of education and whether, from their point of view, they meet the preconditions for such digital education. A survey (questionnaire) was conducted for this purpose. The results showed that most students have appropriate technical support for online education (technical equipment, internet connection and access to the necessary software). Most students have a positive attitude towards the use of digital technologies in general and specifically in education too. This result is consistent with the characteristics of digital natives. Consistency was also found in their self-assessment of their own level of technical skill (77.4% of students do not need help working with digital technologies or only to a small extent and 84.4% of students can actively participate in online education). The results also indicate students’ propensity for multitasking as a feature of digital natives (during online education, 77% of students use multiple devices at once and a total of 85.2% of students also engage in other activities during online learning). Most students are also satisfied with the support of the subjects they study in the form of e-courses. However, the preference for a particular form of study materials (printed or digital) is not such ambiguous.
Explaining Semantics and Extension Membership in Abstract Argumentation

This paper explores the computation of explanations in the specific context of abstract argumentation. The explanations that we define are designed to be visual, in the sense that they take the form of subgraphs of the argumentation graph. Moreover, these explanations rely on the modular aspects of abstract argumentation semantics and can consequently be either aggregated or decomposed. We investigate graph properties of these explanations, and their adequacy to desirable explanatory criteria.
A Case Study on Elementary Convergence Education Using Metaverse Platform

Objectives The study attemped to explore the educational value of metaverse by elementary school students using the metaverse platform and applying it to mathematics and convergence education Methods For 120 sixth graders in elementary school in Seoul, teaching and learning were conducted in connection with math learning games using ‘Seoul Edu Math Metaverse’ platform. Teaching and learning based on metaverse focused on different characteristics from existing math classes, and discussed what convergence education using metaverse platform would consider for future education. Interviews were conducted with students on their thoughts on math classes using metaverse, and the results of qualitative observation by instructors were analyzed. Results As a result of teacher observation and student interview analysis, metaverse convergence education was positive in terms of interest and immersion, but inappropriate communication in a virtual space, device use and functional limitations, and learning system problems were negative. In order to properly utilize the metaverse, classes should be designed tailored to students, but there was a limit to reflecting this diversity in the current curriculum. In the process of designing metaverse convergence classes and conducting teaching and learning, AI ethics and digital citizenship education were required for proper interaction on metaverse. In addition, it was confirmed that support and teaching and learning strategies such as infrastructure construction and teacher training for education using metaverse were needed. Conclusions In conclusion, the goal of education skills, creativity, critical thinking, communication skills and collaboration of converged to make a man-made disasters. To that end, metaverse platform is how it can be used at the scene of school education in this study math learning fusion, empirical analysis as an illustration. The study is metaverse platform to a variety of fusion of education, and explore the possibility of a metaverse literacy recharge will contribute to the research.&#x0D;