Reviewerly/exHarmony huggingface dataset:
Mobile-assisted vocabulary learning through the Shanbay App outside the classroom: Effects of self-regulation and peer scaffolding

Recent decades have witnessed an increasing academic interest in mobile-assisted vocabulary learning. To explore the possible influencing factors on learning outcomes, this study aimed at examining the effects of self-regulation and peer scaffolding on mobile-assisted vocabulary learning among undergraduate students using Shanbay App beyond the classroom. To this end, altogether 71 intermediate-level English learners aged 17-19 years were chosen as participants, with 37 in the experimental group (with peer scaffolding) and 34 in the control group (without peer scaffolding). Data were collected through the Shanbay App regarding participants' vocabulary learning performance, a self-regulation questionnaire and semi-structured interviews. The results of factorial ANOVA revealed that peer scaffolding significantly affected mobile vocabulary learning in terms of the days spent in vocabulary learning and the sum of words participants have learned; a main effect of self-regulation and an interaction effect of self-regulation and peer scaffolding were also observed on the sum of learned words. The research is innovative in providing a motivational peer scaffolding framework in mobile vocabulary learning settings, and may provide pedagogical implications for vocabulary teaching in EFL context in higher education.
Guiding principle of reservoir computing based on “small-world” network

Reservoir computing is a computational framework of recurrent neural networks and is gaining attentions because of its drastically simplified training process. For a given task to solve, however, the methodology has not yet been established how to construct an optimal reservoir. While, "small-world" network has been known to represent networks in real-world such as biological systems and social community. This network is categorized amongst those that are completely regular and totally disordered, and it is characterized by highly-clustered nodes with a short path length. This study aims at providing a guiding principle of systematic synthesis of desired reservoirs by taking advantage of controllable parameters of the small-world network. We will validate the methodology using two different types of benchmark tests-classification task and prediction task.
THE PROBLEM OF CHOICE OF THE TRADITIONAL AND INNOVATIVE TEACHING TECHNOLOGIES IN THE PROCESS OF FORMATION OF FUTURE ENGLISH TEACHERS

The article presents the results of the scientific research on the choice of effective technologies for teaching future English teachers. Under the concept of «teaching technology» the author understands the sequence of actions of a teacher and a student to achieve the educational goals in the study of the subject, which involves the use of a set of forms, methods and tools for implementing the content of education. The need to find the new teaching technologies is due, on the one hand, to the growing role of digital technologies in searching for information, the emergence of a large number of the open educational resources, and, on the other hand, to the limitations of face-to-face communication because of the Covid-19 pandemic. According to the author, the active use of digital teaching technologies significantly expands the possibilities of integrating traditional and innovative teaching tools in the process of teaching English. The main task of today’s Philology teacher is the necessity to vary the traditional methods of teaching the language with the modern effective teaching methods. Its realization can be possible mainly through the use of information and communication technologies. Based on the generalization of the positive experience in the use of traditional and innovative technologies in the process of forming English competence of the applicants for higher education, the author argues that teachers have an extensive experience in traditional teaching technologies, namely: explanatory-illustrative, problem-based, programmed and differentiated teaching technologies. The author proposes a rational integration of classical and modern methods of teaching English in higher education institutions. The author identifies the didactic and systemtechnical factors that influence on the choice of a particular technology in order to intensify the process of teaching English in the context of blended learning and analyzes the methods and techniques of presenting the new material using the traditional and innovative teaching technologies.
GraphSKT: Graph-Guided Structured Knowledge Transfer for Domain Adaptive Lesion Detection

Adversarial-based adaptation has dominated the area of domain adaptive detection over the past few years. Despite their general efficacy for various tasks, the learned representations may not capture the intrinsic topological structures of the whole images and thus are vulnerable to distributional shifts especially in real-world applications, such as geometric distortions across imaging devices in medical images. In this case, forcefully matching data distributions across domains cannot ensure precise knowledge transfer and are prone to result in the negative transfer. In this paper, we explore the problem of domain adaptive lesion detection from the perspective of relational reasoning, and propose a Graph-Structured Knowledge Transfer (GraphSKT) framework to perform hierarchical reasoning by modeling both the intra- and inter-domain topological structures. To be specific, we utilize cross-domain correspondence to mine meaningful foreground regions for representing graph nodes and explicitly endow each node with contextual information. Then, the intra- and inter-domain graphs are built on the top of instance-level features to achieve a high-level understanding of the lesion and whole medical image, and transfer the structured knowledge from source to target domains. The contextual and semantic information is propagated through graph nodes methodically, enhancing the expressive power of learned features for the lesion detection tasks. Extensive experiments on two types of challenging datasets demonstrate that the proposed GraphSKT significantly outperforms the state-of-the-art approaches for detection of polyps in colonoscopy images and of mass in mammographic images.
Semantic-aware deidentification generative adversarial networks for identity anonymization

Abstract Privacy protection in the computer vision field has attracted increasing attention. Generative adversarial network-based methods have been explored for identity anonymization, but they do not take into consideration semantic information of images, which may result in unrealistic or flawed facial results. In this paper, we propose a Semantic-aware De-identification Generative Adversarial Network (SDGAN) model for identity anonymization. To retain the facial expression effectively, we extract the facial semantic image using the edge-aware graph representation network to constraint the position, shape and relationship of generated facial key features. Then the semantic image is injected into the generator together with the randomly selected identity information for de-Identification. To ensure the generation quality and realistic-looking results, we adopt the SPADE architecture to improve the generation ability of conditional GAN. Meanwhile, we design a hybrid identity discriminator composed of an image quality analysis module, a VGG-based perceptual loss function, and a contrastive identity loss to enhance both the generation quality and ID anonymization. A comparison with the state-of-the-art baselines demonstrates that our model achieves significantly improved de-identification (De-ID) performance and provides more reliable and realistic-looking generated faces. Our code and data are available on https://github.com/kimhyeongbok/SDGAN
Prediction Model and Data Simulation of Sports Performance Based on the Artificial Intelligence Algorithm

There is still a certain deviation between the current artificial intelligence technology and the traditional learning mode, which makes it unable to be effectively applied in teaching and learning. Therefore, an effective method needs to be proposed to use functions to predict data. Function calculation can not only solve the complex problems of data calculation process but also make the data evenly distributed to take full advantage of the capabilities of each system. In this experiment, we mainly use the control function. After substituting the data into the control function, the function will automatically classify the data. In this paper, according to the actual situation of physical education in colleges and universities, from the two aspects of artificial intelligence and comprehensive learning algorithm, to build a system which can collect and analyze the past achievements of college students' physical education performance simulation can effectively help the design of physical education curriculum. According to the distribution of experimental data, a specific conclusion can be drawn; that is, the test model we choose can calculate and measure the physical fitness level of students, but there are big differences. In contrast, our experimental method using the ensemble computing model can not only predict and analyze the physical fitness level of college students but also reduce errors and shorten the time required for the experiment.
IDEST: International Database of Emotional Short Texts

We introduce a database (IDEST) of 250 short stories rated for valence, arousal, and comprehensibility in two languages. The texts, with a narrative structure telling a story in the first person and controlled for length, were originally written in six different languages (Finnish, French, German, Portuguese, Spanish, and Turkish), and rated for arousal, valence, and comprehensibility in the original language. The stories were translated into English, and the same ratings for the English translations were collected via an internet survey tool (N = 573). In addition to the rating data, we also report readability indexes for the original and English texts. The texts have been categorized into different story types based on their emotional arc. The texts score high on comprehensibility and represent a wide range of emotional valence and arousal levels. The comparative analysis of the ratings of the original texts and English translations showed that valence ratings were very similar across languages, whereas correlations between the two pairs of language versions for arousal and comprehensibility were modest. Comprehensibility ratings correlated with only some of the readability indexes. The database is published in osf.io/9tga3, and it is freely available for academic research.
Threat-Oriented Collaborative Path Planning of Unmanned Reconnaissance Mission for the Target Group

Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern in UAV cluster combat. This paper discusses the collaborative path planning problem of unmanned aerial vehicle formations (UAVFs) and refueling tankers in a URMFTG with threat areas and fuel constraints. The purpose of collaborative path planning is to ensure that the UAVFs (with fuel constraints) can complete the reconnaissance mission for the target group with the assistance of refueling tankers, which is one of the most important constraints in the collaborative path planning. In this paper, a collaborative path-planning model is designed to analyze the relationship between the planning path of the UAVFs and the tankers, and a threat avoidance strategy is designed considering the threat area. This paper proposes a two-stage solution algorithm. It creates a UAVFs path-planning algorithm based on the fast search genetic algorithm (FSGA) and a refueling tanker path-planning algorithm based on the improved non-dominated sorting genetic algorithm II (NSGA-II). Based on simulation experiments, the solution method proposed in this paper can provide a better path-planning scheme for a URMFTG. That is, it decreases the rate of the UAVF’s distance growth from 3.1% to 2.2% for the path planning of UAVFs and provides a better Pareto solution set for the path planning of refueling tankers.
Changes in Public Sentiment under the Background of Major Emergencies—Taking the Shanghai Epidemic as an Example

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.
Pengaruh Bahan Ajar E-Modul Berpendekatan Contextual Teaching Learning Terhadap Peningkatan Kemampuan Pemahaman Konsep Peserta Didik

The results of the observations show that there are no teaching materials in the form of e-modules used by mathematics teachers in junior high schools. Based on the problem and research objectives, this type of research is quasi-experimental. This research was conducted in two groups, namely the learning experiment class using e-modules with a CTL approach and the control class using conventional learning. The population in this study were all seventh-grade students at Blue Junior High School for the 2020/2021 academic year which consisted of 2 classes. To obtain the sample class, namely the experimental and control classes, a random sampling technique was used. VII B was the experimental class, and class VII A was chosen as the control class. The instrument used is a test consisting of 5 questions about conceptual understanding test. Data analysis using the t-test. From the students' responses to the e-module, the criteria were "very interesting" with a percentage of 90.6%. The calculation results obtained is 2.704 with a significance level of 0.009. Based on the results of the hypothesis test, Ho is rejected, this means that there is a significant effect on the use of e-module teaching materials with a CTL approach to improving students' conceptual understanding abilities.
A Comparative Study of Frequent Pattern Mining with Trajectory Data

Sequential pattern mining (SPM) is a major class of data mining topics with a wide range of applications. The continuity and uncertain nature of trajectory data make it distinctively different from typical transactional data, which requires additional data transformation to prepare for SPM. However, little research focuses on comparing the performance of SPM algorithms and their applications in the context of trajectory data. This study selected some representative sequential pattern mining algorithms and evaluated them with various parameters to understand the effect of the involved parameters on their performances. We studied the resultant sequential patterns, runtime, and RAM consumption in the context of the taxi trajectory dataset, the T-drive dataset. It was demonstrated in this work that a method to discretize trajectory data and different SPM algorithms were performed on trajectory databases. The results were visualized on actual Beijing road maps, reflecting traffic congestion conditions. Results demonstrated contiguous constraint-based algorithms could provide a concise representation of output sequences and functions at low min_sup with balanced RAM consumption and execution time. This study can be used as a guide for academics and professionals when determining the most suitable SPM algorithm for applications that involve trajectory data.
A proposal model using deep learning model integrated with knowledge graph for monitoring human behavior in forest protection

In conventional monitoring of human behavior in forest protection, deep learning approaches can be detected human behavior significantly since thousands of visitors’ forest protection is abnormal and normal behaviors coming to national or rural forests. This paper has presented a new approach using a deep learning model integrated with a knowledge graph for the surveillance monitoring system to be activated to confirm human behavior in a real-time video together with its tracking human profile. To confirm the proposed model, the proposed model has been tested with data sets through case studies with real-time video of a forest. The proposed model provides a novel approach using face recognition with its behavioral surveillance of the human profile integrated with the knowledge graph. Experimental results show that the proposed model has demonstrated the model’s effectiveness.
Analysis of the current state of deepfake techniques-creation and detection methods

Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algorithms and datasets used to build deepfakes, as well as the approaches presented to detect deepfakes to date. By reviewing the background of deepfakes methods, this paper provides a complete overview of deepfake approaches and promotes the creation of new and more robust strategies to deal with the increasingly complex deepfakes.
Real-time Streaming Video Denoising with Bidirectional Buffers

Video streams are delivered continuously to save the cost of storage and device memory. Real-time denoising algorithms are typically adopted on the user device to remove the noise involved during the shooting and transmission of video streams. However, sliding-window-based methods feed multiple input frames for a single output and lack computation efficiency. Recent multi-output inference works propagate the bidirectional temporal feature with a parallel or recurrent framework, which either suffers from performance drops on the temporal edges of clips or can not achieve online inference. In this paper, we propose a Bidirectional Streaming Video Denoising (BSVD) framework, to achieve high-fidelity real-time denoising for streaming videos with both past and future temporal receptive fields. The bidirectional temporal fusion for online inference is considered not applicable in the MoViNet. However, we introduce a novel Bidirectional Buffer Block as the core module of our BSVD, which makes it possible during our pipeline-style inference. In addition, our method is concise and flexible to be utilized in both non-blind and blind video denoising. We compare our model with various state-of-the-art video denoising models qualitatively and quantitatively on synthetic and real noise. Our method outperforms previous methods in terms of restoration fidelity and runtime.
LFBCNet: Light Field Boundary-aware and Cascaded Interaction Network for Salient Object Detection

In light field imaging techniques, the abundance of stereo spatial information aids in improving the performance of salient object detection. In some complex scenes, however, applying the 4D light field boundary structure to discriminate salient objects from background regions is still under-explored. In this paper, we propose a light field boundary-aware and cascaded interaction network based on light field macro-EPI, named LFBCNet. Firstly, we propose a well-designed light field multi-epipolar-aware learning (LFML) module to learn rich salient boundary cues by perceiving the continuous angle changes from light field macro-EPI. Secondly, to fully excavate the correlation between salient objects and boundaries at different scales, we design multiple light field boundary interactive (LFBI) modules and cascade them to form a light field multi-scale cascade interaction decoder network. Each LFBI is assigned to predict exquisite salient objects and boundaries by interactively transmitting the salient object and boundary features. Meanwhile, the salient boundary features are forced to gradually refine the salient object features during the multi-scale cascade encoding. Furthermore, a light field multi-scale-fusion prediction (LFMP) module is developed to automatically select and integrate multi-scale salient object features for final saliency prediction. The proposed LFBCNet can accurately distinguish tiny differences between salient objects and background regions. Comprehensive experiments on large benchmark datasets prove that the proposed method achieves competitive performance over 2-D, 3-D, and 4-D salient object detection methods.
Research on the Structure design of interactive Multimedia Distance Education System

In recent years, with the rapid development of Internet technology and multimedia information technology, multimedia remote interactive teaching system is gradually improved. Multimedia distance education can terminal via the Internet, so that the students, white-collar workers, the workers all kinds of people in any time, any place free teaching model, multimedia distance education system by uploading a high quality, high level of teaching materials, to meet the needs of all kinds of people, but also can realize the online communication with teachers to discuss, to mobilize the interest of learning staff. The multimedia distance education system not only changes the traditional offline teaching mode, but also makes the background of the multimedia education system more rigorous through continuous improvement and updating. This paper analyzes and studies the concrete design structure of interactive teaching mode adopted by multimedia distance education system and the teaching objectives achieved by the teaching mode.
Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools

Drowning is a major health issue worldwide. The World Health Organization's global report on drowning states that the highest rates of drowning deaths occur among children aged 1-4 years, followed by children aged 5-9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes.