Reviewerly/exHarmony huggingface dataset:
The importance of using information and communication technologies in the educational process of natural science disciplines

The article considers the features of teaching experimental natural science disciplines based on the use of modern information and communication technologies, relevance of classical practical classes with the staging of experiments on living organisms. Using a survey of the university’s teaching staff, the most frequently used electronic and digital resources were identified, which can help to conduct laboratory or practical classes in natural sciences in an online format. Several problems related to the computerization of laboratory work are considered. The necessity of virtual laboratory work in distance learning is justified. The main types of virtual laboratory classes are listed, and the advantage of using such classes as a good source of knowledge and material for analysis is shown. The analysis of the state of the available virtual laboratories for conducting classes is given. For students of biological educational programs, problem topics in the discipline “vertebrate zoology” are defined. The authors draw attention to the fact that, in creating virtual laboratories in biological disciplines, it is possible to implement a system of trilingual education. The article also provides recommendations for the creation of virtual laboratories in the discipline “vertebrate zoology”. The authors conclude that introducing information and communication technologies in the process of training specialists in natural sciences contributes to the effective formation of their readiness for professional activity.
Students Viewed on Digital Platforms for Online Learning During Covid-19 Pandemic

The study aims to obtain the digital platforms students use for online learning, perceptions, and problems using digital platforms at Madrasah Tsanawiyah during the Covid-19 pandemic. This research used qualitative research through a case study. The participants in this study were 100 students at MTs Miftahul Huda who were taken randomly from grade one to grade three. Documents, observations, and a questionnaire were used to collect data. Data analysis entailed gathering information, reducing it, presenting it, and drawing conclusions. The findings showed that students preferred offline learning to online learning. At the same time, the preferred online applications are zoom and WA. The obstacles faced were the internet network, the ability to provide internet quota, and psychological challenges related to being active in class. So, various digital platforms only sometimes provide comfort and suit the needs of students in online classes. These findings provide an understanding from the student's perspective that digital platforms must be adapted to the learning environment at home, family conditions, and the readiness of the teacher or students themselves.
The Use of Podcast as a Creative Learning Resource in Social Studies

This study aims to describe the use of podcast media as a learning resource in social studies learning and identify obstacles encountered. The method used in this study was the phenomenological method. The study subjects were students of SMP Kesatrian 1 Semarang City, which totaled 26 students, and teachers who taught social studies subjects. Interviews, observation, and documentation were done to collect the data. Interviews were used to obtain data using question and answer with informants. Data analysis was carried out in a study to draw conclusions from all the data that had been obtained. Researchers used data analysis techniques consisting of (1) Data collection, (2) Data Reduction, (3) Data presentation, and (4) Conclusion retrieval or verification. The research results were podcast media as an innovative and creative learning resource to be used in social studies learning in and outside the classroom. The use of podcast media as a learning resource is divided into three stages: the three stages' planning, implementation, and evaluation stages are carried out systematically. Based on this research, 76.92% of students understood learning materials with podcast media as a learning resource. In addition, there were 23.08% of students had difficulty using podcast media. It was due to several obstacles, both internally and externally. The internal obstacle was the need for memory capacity to access the Spotify application on the student's mobile phone. External obstacles existed due to learning distractions from the family environment if learning was carried out at home.
The Prototype of ACCDR Model: M-Learning Multimedia Development Design Model Stem-Based for the Online Science Learning

Online learning is learning carried out without face-to-face but supports the development of virtual academic mobility. It can be integrated with multimedia either in e-learning or m-learning. M-learning is a learning paradigm that takes advantage of developments in wireless and mobile technology. It is flexible, independent, and accessible anywhere and anytime. Integration of science and technology in multimedia m-learning becomes more comprehensive when combined with the principles of Science, Technology, Engineering, and Mathematics. STEM-based multimedia m-learning is developed using a specific development design model. The multimedia m-learning development design model is a structured framework or reference for developing multimedia m-learning. Some of the existing m-learning multimedia development design models do not fully cover the criteria for a suitable model for developing STEM-based multimedia m-learning. One solution that can be given is to develop a new model concept using the F2-O1-S1-A2 type model development procedure through literature studies. The instrument consisted of the model development matrices. Afterward, a STEM-based multimedia m-learning development design model for science learning consisted of five syntaxes, namely: analysis, concepting, design, development, and review. The ACDDR model is a linear diagrammatic-narrative procedural model which is then shortened to the ACDDR model and presented in a prototype form. The ACDDR prototype model is expected to be able to be used for the development of STEM-based multimedia m-learning in the 21st century learning.
English Speaking Class in a Mathematics Program: Students’ Difficulties

This is a qualitative research with a case study research design which aimed to find out the students’ difficulties in speaking at Non-English Speaking Class in Mathematics program study at UIN Raden Fatah Palembang. The researchers selected three students who met the criteria as a research participant by using criterion sampling. In collecting the data, the researchers used an interview as instrument. Thematic analysis was used to analyze the data. The result showed that the students faced some difficulties in speaking, such as lack of vocabulary, inhibition, mother-tongue use, and lack of confidence. The findings of the study encouraged students to practice speaking English more until they developed a speaking habit, which made them able to improve their speaking skills and also expanded their vocabulary better.&#x0D; &#x0D;
The Feasibility of E-Modules Based on Flipbook Pdf Professional to Improve Student Learning Outcomes on Work and Energy Material

Digital-based learning has become a trend in 21st-century education. However, most teaching materials are currently in the form of printed books which tend to be monotonous and less attractive, thus affecting low student learning outcomes. This research aims to produce a professional PDF flipbook-based e-module to improve student learning outcomes on work and energy material. The development model used is ADDIE. The implementation test used a one-group pre-test and post-test design on 34 students of class X MIPA of one of the high schools in Banjarmasin, South Kalimantan, Indonesia. Data were obtained through e-module validation instruments and student response questionnaires. The results obtained content and construct validity values of e-modules were 3.36 and 3.31, respectively, with valid categories. In addition, student responses amounted to 3.03 in the practical category. Thus, the developed e-module is feasible for improving student learning outcomes.
IMPROVING CHILDMINDERS’ QUALITY OF CARE; A LEGAL PERSPECTIVE

Quality childcare services are essential in ensuring that children are well cared for not only in terms of their health and safety, but also their mental and intellectual development. Childcare services should not be thought of as should not be considered as mere care services but rather it is the key foundation for young children's education. According to practice in Malaysia, home childcare centres that care for less than four children do not need to register with the authorities and are not monitored by any laws or regulations. The issue of home childminders who are not regulated by specific laws is crucial to be highlighted in an effort to improve the quality of childcare services in Malaysia through legislative efforts. Hence, the purpose of this research is to examine the legal requirements for regulating home child care centres. Highlights of incidents and cases involving home day care are analysed to see the extent to which children's safety is exposed to the risk of danger as a result of the absence of specific laws to supervise home childminders. This study was conducted using the method of doctrinal research in the form of data collection based on document analysis and case reviews. The findings of this study indicate that there is a need to draft a specific law to regulate home childminders to protect the welfare and safety of children.
A Case Study on the Change of Duties and Required Competencies of HRD Practitioners in Digital Transformation environment

The purpose of this study was to find out the changed duties and core competencies of HRD practitioners in a business environment where the introduction of digital transformation has been accelerated and to explore difficulties encountered in performing tasks. For the research, in-depth one-on-one interviews were conducted with ten HRD practitioners. The results are as follows; First, duties for HRD practitioners in digital transformation era are categorized into reinforced, reduced and newly added based on the existing NCS-based competency units. Second, core competencies of HRD practitioners in digital transformation era are presented by categorizing into knowledge, skill, attitude and ability. It was found the attitude and ability to actively and positively respond to use of technology and change are important. Last, the overall amount of work increased due to continuous learning of digital trends and skills. In this study, practical implications, limitations of research and suggestions for future research directions were presented to HRD practitioners who should lead changes in organizational members based on the above results.
Service Classification through Machine Learning: Aiding in the Efficient Identification of Reusable Assets in Cloud Application Development

Developing software based on services is one of the most emerging programming paradigms in software development. Service-based software development relies on the composition of services (i.e., pieces of code already built and deployed in the cloud) through orchestrated API calls. Black-box reuse can play a prominent role when using this programming paradigm, in the sense that identifying and reusing already existing/deployed services can save substantial development effort. According to the literature, identifying reusable assets (i.e., components, classes, or services) is more successful and efficient when the discovery process is domain-specific. To facilitate domain-specific service discovery, we propose a service classification approach that can categorize services to an application domain, given only the service description. To validate the accuracy of our classification approach, we have trained a machine-learning model on thousands of open-source services and tested it on 67 services developed within two companies employing service-based software development. The study results suggest that the classification algorithm can perform adequately in a test set that does not overlap with the training set; thus, being (with some confidence) transferable to other industrial cases. Additionally, we expand the body of knowledge on software categorization by highlighting sets of domains that consist ‘grey-zones’ in service classification.
Acceleration of DNN-based Hand Pose Estimation with Monocular Camera Using TVM

We propose a method to accelerate hand detection and 3D hand landmark estimation using the MediaPipe Hands. MediaPipe Hands performs in real time by switching neural networks. However, we focused on speeding up the neural network for smoother detection. We used Tensor Virtual Machine (TVM) and AutoScheduler to optimize the neural network model and maximize CPU and GPU execution performance. In our experiment, we compared the output of three metrics: total inference time, total joint coordinate error, and total program time, in which we constructed a hand pose estimation system. Experimental results showed that the palm detection model and the hand landmark estimation were 3.9-7.5 times faster on the Ryzen9 3950X and RTX3070. Based on the number of frames per second from the total program time, the hand pose estimation system ran 1.3-1.4 times faster on the CPU but 0.8 times slower on the GPU. Our proposed method effectively speeds up neural networks based on the experimental results. On the other hand, we conclude that further investigation is needed to determine how our proposed method can contribute to the original MediaPipe Hands system.
Image Compression Using AMBTC with Artificial Neural Networks

Image compression is one of the most crucial image processing techniques widely used in multimedia signal processing. Traditional lossy image compression techniques like block truncation coding (BTC) should still be improved to achieve higher compression rates. The developments of BTC mainly concentrate on improving the reconstructed image quality and reducing the bit rate. This study presents a new approach that uses absolute moment block truncation coding (AMBTC) with artificial neural networks (ANNs), which are used in solving several problems, like function approximation problems. The suggested approach aimed to minimize the bit rate further to increase compression's effectiveness. The simulation results demonstrated that the proposed method could achieve lower bit rates (BR) while maintaining acceptable PSNR values.
University Student Control Detection System Based on Machine Learning and Artificial Intelligence

The objective of this study was to program a school assistant based on artificial intelligence and integrated with surveillance cameras and loudspeakers in a university classroom. This was done to complement the supervision carried out by university teachers to identify student distractions, guarantee the proper use of masks, and evaluate student behaviors in class. The virtual assistant was developed using Python to generate audio warnings through a graphical interface built in the PyCharm environment. The results demonstrated the desired functionality of the virtual assistant, its ability to meet the requirements of a university classroom, and thereby the effectiveness of the YOLO v5 network andPyCharm used for the training, execution, construction, and implementation of the system.
FPX-NVC: An FPGA-Accelerated P-frame Based Neural Video Coding System

Hardware-oriented neural video coding (NVC) has been an emerging research topic recently. In this work, we present a P-frame based NVC system by leveraging the parallel neural inference capability of resource-constrained embed platforms, such as field programmable gate array (FPGA). The proposed system, namely FPX-NVC, consists of multiple resolution video capture, reconfigurable and integerized neural video codec and display. Our neural codec enjoys the learned unidirectional inter-predictive coding by jointly optimizing the implicit motion representation (IMR), feature domain motion compensation, and the corresponding prediction error compression, realizing end-to-end on-board encoding and decoding. Our system supports end-to-end video data operation flow from capturing to neural coding and display. To our knowledge, the FPX-NVC is the first inter-predictive coding (P-frame) based hardware NVC system.
On Data Annotation Efficiency for Image Based Crowd Counting

Crowd counting aims at automatically estimating the number of persons in still images. It has attracted much attention due to its potential usage in surveillance, intelligent transportation and many other scenarios. In the recent decade, most researchers have been focusing on the design of novel deep learning models for improved crowd counting performance. Such attempts include proposing advanced architectures of deep neural networks, using different training strategies and loss functions. Other than the capabilities of models, the crowd counting performance is also determined by the quantity and the quality of training data. Whilst the deep models are data-hungry and better performance can usually be expected with more training data, annotating images for training is time-consuming and expensive in real-world applications. In this work, we focus on the efficiency of data annotation for crowd counting. By varying the number of annotated images and the number of annotated points (one point is annotated per person head) for training, our experimental results demonstrate it is more efficient to annotate a small number of points per image across a large number of images for training. Based on this conclusion, we present a novel adaptive scaling mechanism for data augmentation to diversify the training images without extra annotation cost. The mechanism is proved effective via thorough experiments.
Recurrent Multi-connection Fusion Network for Single Image Deraining

Single image deraining is an important problem in many computer vision tasks because rain streaks can severely degrade the image quality. Recently, deep convolution neural network (CNN) based single image deraining methods have been developed with encouraging performance. However, most of these algorithms are designed by stacking convolutional layers, which encounter obstacles in learning abstract feature representation effectively and can only obtain limited features in the local region. In this paper, we propose a recurrent multi-connection fusion network (RMCFN) to remove rain streaks from single images. Specifically, the RMCFN employs two key components and multiple connections to fully utilize and transfer features. Firstly, we use a multi-scale fusion memory block (MFMB) to exploit multi-scale features and obtain long-range dependencies, which is beneficial to feed useful information to a later stage. Moreover, to efficiently capture the informative features on the transmission, we fuse the features of different levels and employ a multi-connection manner to use the information within and between stages. Finally, we develop a dual attention enhancement block (DAEB) to explore the valuable channel and spatial components and only pass further useful features. Extensive experiments verify the superiority of our method in visual effect and quantitative results compared to the state-of-the-arts.
The Metaverses of Yesteryear, Today, and Tomorrow

A discussion with friends about whether we are close to a true metaverse got me thinking, maybe we have had metaverses for a long time. Immersive and interactive experiences with alternate realities have been around for more than 150 years. We may never declare “the metaverse” to be here because we keep demanding more fidelity as the technologies advance.
VCFN: Virtual Cloth Fitting Try-On Network

Virtual cloth fitting network has an increasing demand with a growing online shopping trend to map target clothes on reference subject. Previous research depicts limitations in the generation of promising deformed clothes on the wearer's body while retaining the design features of cloth-like logo, text, and wrinkles. The proposed model first learns thin-plate spline transformations to warp images according to body shape, followed by a try-on module. The former model combines deformed cloth with a rendered image to generate a composition mask and outputs target body without blurry clothes while preserving critical requirements of the wearer. Experiments are performed on the Zalando dataset and the model produces fine richer details and promised generalized results.
Blockchain Technology – Delivering Trust to Global Pharmaceutical Distribution

With up to 30% of donated pharmaceutical aid products being diverted, stolen or going to waste in this session we will learn how blockchain technology is addressing some of the biggest trust challenges in the global supply chain. Supply chain issues may prevent people that are in greatest need of life-saving medicines from being treated. Through the use of a Decentralized Knowledge Graph and blockchain technology firms and governmental aid organizations now have visibility to supply chain traceability and transparency of donated medicines. The session will additionally cover ancillary benefits with the implementation of such a solution.
The Evaluation Result of Online Master Course During Pandemic: Case of Industrial Management Course at Mongolian University of Science and Technology

This paper describes shortly about Mongolia, its higher education in current days, teaching during pandemic in Mongolia and evaluation of online master course at Mongolian University of Science and Technology (MUST). Mongolia is a developing country and the engineering education is one of the important issues of country development. There are comparatively high number of higher education institutions in country which proves need of families to support education of their children. The MUST is a public university which offers the most engineering study courses. The MUST offers bachelor, master and doctor programs in different fields. This paper covers teaching of master courses for “Industrial Management” professions at Graduate School of Business. Due to the pandemic situation the teaching format switched from traditional to fully online mode without any preparation time. Since winter semester of 2022 teaching format returned to face-to-face mode but it is still important to figure out feedback from students who attended master courses fully online during pandemic. Therefore, this paper describes evaluation model and evaluation results for the master course “Industrial Management”.
Vibration Analysis and Digital Image Correlation Techniques for a Suspension Bridge

Bridge inspection is critical to safe transportation, especially in highly urbanized areas. Frequent inspection invoked by the un-scheduled maintenance for bridges is often subjected to the availability of NDT service providers. Hence the objective of the current research is to provide an economical NDT technique for the maintenance crew. Digital image correlation techniques can be cost-effective for the displacement measurement of highway bridges using merely one high-resolution camera. The vibration analysis based on the remote measurements of high-speed camera systems is not only challenging but also prohibitively expensive. An alternative approach using two consumer-grade digital cameras has been developed. The field test has been successfully carried out for a 50-meter-span suspension bridge. Satisfactory results have been obtained in both the vertical displacement caused by a moving load and the mode shapes of the first and second vibration modes.