Reviewerly/exHarmony huggingface dataset:
The Students’ Perceptions in Using Youtube as Media to Enrich English Speaking Skill

In order to create effective teaching-learning process teacher must find appropriate method and media. Lately, the use of technology as a media in teaching-learning process has become a very common thing to do. Nowadays, YouTube is one of the social media platform that most used in teaching-learning process. This study aims to find out how does the teacher use YouTube in teaching English speaking skill at SMK Miftahussalam and also how is the students’ perception in using YouTube as media to Enrich English speaking skill at SMK Miftahussalam. The design of this research was qualitative. The subjects that has examined in this research are teacher and students of SMK Miftahussalam. The findings show that not all of the material/chapters are suitable to use YouTube as the media, so the teacher must determines whether the material is suitable or not. Then, the teacher searches for the most attractive video. Then, the teacher bought a link to the students. After the students watch the video, the teacher starts the discussion also question and answer section. Then, the students are asked to practice the material by themselves. The findings also show that most students think that the use of YouTube in learning speaking is very important, especially to learn how to pronounce a word. Furthermore, YouTube can be used as a learning media that can support student learning success because there are various kinds of videos about education (on it). It shows that the use of YouTube as media to enrich the students speaking skills is worth it. The use of YouTube helps the students to enrich their students speaking skills, especially in terms of pronunciation and vocabulary.
Hybrid Partitioning for Embedded and Distributed CNNs Inference on Edge Devices

Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) are ubiquitously utilized in many Internet of Things applications, especially for real-time image-based analysis. In order to cope with concerns such as resiliency, privacy and near real time analysis, these models must be deployed on edge devices. Particularly for large models, the large number of parameters becomes a bottleneck for the inference process because edge devices are resource constrained, subjects to failures and/or hardware faults. New solutions to cope with these issues are required. This paper proposes a hybrid partitioning strategy, architecture and implementation (called HyPS), which identifies the best positions in the model structure to split the network structure into small partitions that fit resources constraints of edge devices noticeably by decreasing instantaneous memory needs. The generated partitions consume less memory than the original network and each partition can be processed almost separately, resulting in new ways to process CNN’s execution at the edge. Thanks to this partitioning strategy, large CNNs inference can be run without modifying the main model architecture. The proposed approach is assessed on the well-known neural network structure of VGG16 for image classification. The results of the experimental campaign show that the partitioning method allows for the successful inference of large models on devices with limited overhead and high accuracy.
An Hybrid Part of Speech Tagger for Setswana Language using a Voting Method

Part-of Speech (PoS) tagging is a corpus linguistics that deals with assigning appropriate lexical categories to each word in a sentence. To effectively address challenges associated with PoS tagging, several Natural Language Processing (NLP) tasks modelling techniques have been employed, including Conditional Random Fields (CRF), Support Vector Machines (SVM), and Decision Trees in diverse languages. These PoS taggers implement the process of associating the correct PoS (nouns, verbs, adjectives, adverbs, etc.) with each word in a sentence. However, creating language resources is an expensive process for many languages, including the indigenous languages of South Africa that are classified as resource-scarce. Therefore, using Setswana as a language with limited resources, this study explores and applies methods to increase the utilization of existing resources and tagger accuracy. This is done using Setswana's two PoS taggers: a Maximum Entropy (MaxEnt) and an SVM, which achieved an accurateness of 94.4 per cent and 95.59 per cent respectively. To find errors in the taggers, an error analysis is carried out. The Setswana PoS Tagger was then built using a voting algorithm to improve results and attain 97.06 per cent accuracy. The combination of taggers reduces the error rate by up to 2.01 per cent.
Digital Literacy and Academic Presentation Strategies of Foreign Graduate Students

This study aimed to examine the effect of foreign graduate students’ digital literacy competency on their performance of academic presentation strategies in a digital literacy environment. For digital literacy, Shin So-young and Lee Seung-hee’s (2019) “basic work utilization ability” measurement tool was used, and academic presentation strategy was measured by the internal consistency between the questions by modifying or supplementing the types and questions of Lee Da-heom and Kim Jihyun (2022). First, the digital literacy capacity of foreign graduate students is less sufficient than that of Korean graduate students. Second, foreign graduate students lack the ability to perform presentations using academic presentation strategies than Korean graduate students. Specifically, it was confirmed that foreign graduate student lacked the ability to perform presentations using strategies in the stage of presentation progress compared to Korean graduate students. Third, as the digital literacy of foreign graduate students improves, the academic presentation strategy can also improve. This study is meaningful in that the results confirmed that digital literacy is a necessary competency for successful academic presentations by foreign graduate students.
Blog App Part 3: Showing Posts

Our goal in this chapter is to build a component that represents an individual post, which will be displayed on the home page. This also implies adding a collection in our database that allows hosting these posts.
Blog App Part 6: Editing Posts

In this chapter, we will focus on editing posts, getting closer to finalizing the basic functionality of the application, although of course there will be details that we will have to deal with later.
RestApp Part 1

In this chapter, we’ll learn how to generate a new project using angular-cli. We’ll create new components and a service to communicate with a rest API. As for the purely aesthetic aspect, we’ll include bootstrap in our application through CDNs.
RestApp Part 2

In the previous chapter, we included the fantastic Bootstrap framework in our application, in the fastest way possible, that is, using CDNs. However, we have an alternative, and it is to use the node package manager to install bootstrap. In this chapter, we’ll learn how to use npm to install bootstrap. We’ll also build a simple contact form, the logic to perform CRUD operations on our users, a loading component to improve the user experience, and a pipe to handle the case of missing user profile images. Then you can decide which of these two alternatives is the most convenient.
The Programmer’s Assistant User Experience

The Programmer’s Assistant is an experimental prototype development environment that integrates a conversational chat component with a code editor. The conversational aspect is powered by a code-fluent large language model. The prototype user experience provides for discussion of code in the code editor as well as for easy transfer of generated code to the editor under human supervision.
Instrument played by nodding

We believe that many people are interested in the field of music. Due in part to Corona's influence, there has been an increase in the number of people who are home-based. Therefore, the number of people who are interested in music has probably increased even more due to the idea of increasing the number of hobbies that can be done at home. However, we believe that it is difficult for people to actually play musical instruments, produce music, or otherwise engage in music on their own initiative. In addition, playing a musical instrument requires fine manipulation for people with disabilities, and some people are not able to play a musical instrument to begin with. Therefore, we began researching simple instruments that can be played with a nod by people who have given up playing musical instruments or who are unable to play due to hand disabilities. The purpose of this research was to lower the barrier to playing a musical instrument by making the operation of playing a musical instrument simpler. In the process of developing the instrument, we thought that it could also be used for stretching the neck, so we added a variety of stretching exercises, from nodding (forward bending) to back bending and side bending, to correct straight necks, which plague many people in modern society, while performing on the instrument. Exercises used for stretching were added to the playing method. As the first stage of the research, we used OpenCV's cascade classifier, an object detection feature, to detect faces and nod to enable simple playing of the instrument. In the second stage, we used FaceOSC to improve the accuracy of face recognition, making the research more interesting and usable for stretching. Finally, measurements were made on the range of motion for forward bending, back bending, and side bending, and experiments were conducted on the actual use of what was produced in the research. The range-of-motion data from the measurement side could not be used well, and problems arose, such as the fact that the recognition of faces was off, and if the face was covered with hair, the recognition would be off. In order to improve these issues, various improvement measures were taken, including reviewing the programming in the first place to take into account the range of motion of the face and neck, and reviewing the position of the camera.
A transformer-based semantic segmentation model for street fashion images

Semantic segmentation is a pixel-level classification problem in computer vision, in which pixels of the same class are grouped into a single category in order to interpret pictures at the pixel level. In this field, semantic segmentation of street fashion images is a challenging task since the clothing items would appear with wide variations in fabrics, layering, occlusion and viewpoint. To help better understanding the street fashion images, we propose a lightweight Semantic Context Aware Transformer (SCAT) to be applied to the semantic segmentation task for street fashion images, which integrates semantic context into the encoding, and models the relationship between multi-level outputs from transformer layers. Extensive experiments and comparisons show that the proposal achieves the state-of-the-art results on ModaNet dataset with relatively small model size, with over 1.1 point improvement compared to Shunted Transformer, and even surpasses other CNNs and Transformers with a large margin of over 2 point in mIoU.
A study of next-step prediction method using smartphone IMU

Advanced, safe, and secure walking support could be possible if the next step of a visually impaired can be estimated before the foot actually touches the ground. We define the next step as one step to touch the ground now. We aim to predict the behavior of this next steps. In this study, we investigate a method to realize next-step landing position prediction using an IMU on a smartphone and discuss the possibility of next-step prediction. Since it is difficult for a person to walk while holding a smartphone steadily against the body, next-step prediction using only an IMU is considered an ill-posed problem. In this study, we assume that there is a pattern in the sway of a person's gait. To learn the pattern, we created a training dataset consisting of pairs of IMU outputs, positions of next steps, and the smartphone position to realize the next-step landing position prediction. We used the angle and distance errors to validate the next-step landing position prediction as the evaluation indices.
Evaluation of a video generation method linking dance and scenes

Owing to the impact of COVID-19, the venues for dancers to perform have shifted from the stage to the media. In this study, we focus on the creation of dance videos that allow audiences to feel a sense of excitement without disturbing their awareness of the dance subject and propose a video generation method that links the dance and the scene by utilizing a sound detection method and an object detection algorithm. The generated video was evaluated using the Semantic Differential method, and it was confirmed that the proposed method could transform the original video into an uplifting video without any sense of discomfort.
Video super-resolution by generative adversarial network with 3D convolutional neural networks

For effective super-resolution processing of video images, video images should not be processed as frame-by-frame two-dimensional information, but as spatio-temporal information, including information in the time axis direction. Most of the proposed video super-resolution processing based on deep learning uses 2D convolutional neural networks (CNNs). Therefore, the system is based on the 2D CNNs with the additional processing related to change from frame to frame. Instead of processing video images separately in space and time, comprehensive processing as spatio-temporal information can be expected to be more flexible and effective. In this research, we propose a video super-resolution process that processes spatio-temporal information by 3D-CNNs and GAN (Generative adversarial networks). By using 3D-CNNs, the configuration does not require motion alignment as preprocessing. Since the essential purpose of the super-resolution process is to predict missing high-frequency components, we added a process that directly predicts the difference between the high-resolution image and the corresponding bicubic interpolated low-resolution image.
Parallel and distributed asynchronous adaptive stochastic gradient methods

Stochastic gradient methods (SGMs) are the predominant approaches to train deep learning models. The adaptive versions (e.g., Adam and AMSGrad) have been extensively used in practice, partly because they achieve faster convergence than the non-adaptive versions while incurring little overhead. On the other hand, asynchronous (async) parallel computing has exhibited significantly higher speed-up over its synchronous (sync) counterpart. Async-parallel non-adaptive SGMs have been well studied in the literature from the perspectives of both theory and practical performance. Adaptive SGMs can also be implemented without much difficulty in an async-parallel way. However, to the best of our knowledge, no theoretical result of async-parallel adaptive SGMs has been established. The difficulty for analyzing adaptive SGMs with async updates originates from the second moment term. In this paper, we propose an async-parallel adaptive SGM based on AMSGrad. We show that the proposed method inherits the convergence guarantee of AMSGrad for both convex and non-convex problems, if the staleness (also called delay) caused by asynchrony is bounded. Our convergence rate results indicate a nearly linear parallelization speed-up if $$\tau =o(K^{\frac{1}{4}})$$ , where $$\tau $$ is the staleness and K is the number of iterations. The proposed method is tested on both convex and non-convex machine learning problems, and the numerical results demonstrate its clear advantages over the sync counterpart and the async-parallel nonadaptive SGM. Our code has been released at https://github.com/RPI-OPT/APAM .
Multi-scale Generative Adversarial Deblurring Network with Gradient Guidance

&lt;&gt;With regards to the lack of crisp edges and a poor recovery of high frequency information such as details in deblurred motion pictures, this research proposes a multi-scale adversarial deblurring network with gradient guidance (MADN). The algorithm uses the classical generative adversarial network (GAN) framework, consisting of a generator and a discriminator. The generator includes a multi-scale convolutional network and a gradient feature extraction network. The multi-scale convolutional network extracts image features at different scales with a nested connection residual codec structure to improve the image edge structure recovery and to increase the perceptual field. This gradient network incorporates with intermediate scale features to extract the gradient features of blurred images to obtain their high frequency information. The generator combines the gradient and multiscale features to recover the remaining high-frequency information in a deblurred image. The loss function of MADN is formed in this research combining adversarial loss, pixel L2-norm loss and mean absolute error. Compared to those experimental results obtained from current deblurring algorithms, our experimental results indicate visually clearer images retaining more information such as edges and details. This MADN algorithm enhances the peak signal-to-noise ratio by an average of 3.32dB and the structural similarity by an average of 0.053.&lt;&gt;
Strategi Guru Untuk Meningkatkan Kemampuan Membaca Siswa Kelas IV Sekolah Dasar Negeri 15 Pagaruyung

The historical past of this research is one of the trainer's strategies to improve simple college students' studying capabilities. This takes a look at ambitions to explain the trainer's approach to enhancing students' reading talents. This form of research is a qualitative description. The informants in this examination were school principals and teachers at faculty. the important thing instrument itself is the researcher himself when the implementation is assisted with the aid of interview publications, commentary sheets, and tape recorders. facts series is finished by way of interviews, observation, and documentation. records evaluation changed into carried out using the Miles and Huberman model along with facts discount, records show, and additionally conclusions. The validity of the facts with records triangulation techniques. The consequences of the research display that the strategies used to enhance studying abilities are by using studying literacy, spelling methods, and good-sized analyzing. therefore it could be concluded that this use can enhance analyzing abilities.
The Introduction of Soft Skills in the Study of Disciplines of the Second Stage of Higher Education at the Sukhoi State Technical University of Gomel

Professional skills, as opposed to interpersonal skills, are types of skills that students learn in university. These skills are easy to quantify. The proficiency in these skills is easy to verify. Soft skills cannot be measured and tested as simply as professional skills. The authors highlight the most in-demand soft skills among employers. The article presents the results of several surveys of employers about the important requirements for jobseekers required for employment. Similar surveys were conducted among employers and employees of IT companies in the Republic of Belarus. It is concluded that soft skills are very important both for getting a first job and building a successful career. However, in the universities of the Republic of Belarus, teaching soft skills is poorly represented. The article shows how the process of teaching students of the second stage of higher education was modernized. Only the IT specialties of five Belarusian universities have undergone modernization. This work was carried out with the support of the international project MaCICT. At the same time, the experience of teaching similar disciplines in the universities of the European Union was used. To assess the effectiveness of students' acquisition of soft skills at the university, a system for assessing the results of the modernization of the learning process was developed. Questionnaires for teachers, students and employees of IT companies have been developed. A survey of all stakeholders in the results of the international project MaCICT was carried out. The paper provides information on the dissemination of the main approaches for organizing the teaching of IT specialties for teachers of another specialties of the Sukhoi State Technical University of Gomel, as well as for teaching IT disciplines of another universities in the Gomel region.
Instrumental Digital Didactics in Modern Pedagogy

nstrumental digital didactics (IDD) is a practical component of modern pedagogy that reflects the process of acquiring knowledge using a variety of digital means of obtaining, processing and interpreting empirical data. In the process of e-learning digitized content acts only as an activator of sensory perception of information, simplifying the understanding of learning objects. Instead, IDD is based on active usage of technological means in training (e-learning, classical, distance, blended and hybrid learning) which complement and accompany nature experiment. Both concepts are parts of a wider category of digital didactics and logically protract the traditional theory of education in current conditions. DID complements the scientific method of conducting research and engineering design with such opportunities as creation of interactive computer models, visualization of experimental data and their comparison with theory, predicting the course of the process due to changes of influence factors. The development of digital didactics determines changes in the structure of teacher activity, which can be interpreted as didactic engineering. The IDD-based teaching techniques developed at the STEM-Laboratory MANLab are an example of an innovative knowledge management system.
Collaborative Working

Abstract Research collaborations offer numerous professional opportunities as well as challenges, especially for early career researchers. This is why it is important to know the rights and responsibilities of researchers in collaborations. The aim of this chapter is to help early career researchers apply principles of good research practices of European Code of Conduct for Research Integrity in collaborative working, including interdisciplinary and international collaborations. We will indicate potential problems that can arise in different stages of collaborations. We will also provide recommendations with regard to determining your roles and responsibilities in collaborations, procedures on handling research misconduct, and possible publication disputes.
WAIR: Watermark Attack on Image Retrieval Systems

Recent studies show that image retrieval systems are vulnerable to adversarial attacks that adds imperceptible noise to the images. But the imperceptibility of noise limits the attack performance. As a commonly acceptable interference, watermark often appears in images, which can be treated as ‘‘imperceptible’’ but is unexploited. In this paper, we propose a Watermark Attack method on Image Retrieval systems (WAIR). Attacking retrieval systems is challenging due to black-box model, the absence of confidence guide, and attack failures and low efficiency caused by the randomness of traditional evolutionary algorithms. To solve these challenges, we propose a new evolutionary algorithm called Gene Joint Selecting Algorithm (GJSA) that jointly optimize the watermark parameters. We also design the Fitness Record Table (FRT), a new data structure that records the historical attack effect to guide the following evolution and avoid local optimal solutions. Thanks to FRT, WAIR can also reduce the duplicate searching caused by algorithm randomness. The extensive experiments show that WAIR can attack the black-box image retrieval system with a successful rate of 0.806, which is 12.4% higher than the traditional evolutionary algorithm. Moreover, for attacking commercial image retrieval system, we achieve 2$\times$ higher attack success rate on Baidu Image Retrieval API than the existing methods.