Reviewerly/exHarmony huggingface dataset:
A Study of Tibetan-Chinese Machine Translation Based on Bidirectional Training Strategy

The lack of Tibetan-Chinese parallel corpus is the main reason that affects the effectiveness of Tibetan-Chinese neural machine translation. In this paper, bidirectional training strategy is invoked for the study of Tibetan-Chinese Transformer machine translation model, and the quality of translation is improved by combining with back translation method. The experimental results show that the BLEU values of the translations obtained by this method are improved by 1.27%, 1.25%, 1.09%, 0.90%, 1.21%, 1.16%, respectively, compared with the baseline experiments.
Experimental Data Analysis

Scientific experiments are used to evaluate a hypothesis or to measure a certain scientific quantity. They are crucial in advancing modern sciences and technologies. In particular, physics experiments are usually employed to establish a quantitative relationship among different physical parameters (e.g., the Cavendish experiment for measuring the force of gravity between masses, the measurement of electrostatic force as a function of test charge, etc.), to prove or confirm a particular theoretical prediction or hypothesis (e.g., general relativity, the existence of black holes, etc.), or to measure important physical constants (e.g., the speed of light in vacuum, the charge of an electron, Planck’s constant, etc.). In the 400 years of modern science (since Galileo Galilei, 1564–1642), rigorous guiding principles have been adopted for scientific experiments.
Web Links and Smartphone Applications

Listed in the table in this section are some software programs, applications, and program languages that are used in the experiments described throughout this book. Links for downloading or purchasing them are provided.
Exploring the use of a WhatsApp Bot to Influence Online Continuous Professional Development of Teachers in Rwanda

The shift from in-person to blended and online delivery of Continuous Professional Development (CPD) of teachers has created various challenges. First, blended and online learning can impact the quality of learning. Some participants find it difficult to self-regulate, plan their learning and complete activities within the set deadlines. Secondly, blended and online CPD can influence the intensity of learning. Lastly, offering CPD programmes online may have equity implications. VVOB – education for development in partnership with Rwandan education authorities, experimented with a conversational learning pilot (WhatsApp bot) to strengthen teachers’ involvement. A treatment-control group research design was used in two blended CPD programmes.The total sample consisted of 417 educators. Findings reveal that additional WhatsApp input helps teachers to move through the online courses, positively influencing their learning. Almost all participants indicated that the additional WhatsApp support was crucial for their timely delivery of tasks. No significant differences in learning outcomes were identified when comparing with a counterfactual. Regarding equity-related challenges, positive effects were identified on the inclusion of older age groups. // By discussing these findings, VVOB wants to explore with the conference audience how semi-automated systems can be further deployed to support teachers and school leaders in their lifelong learning.
Promoting Python Code Clubs in Greece: A Teacher Training Program and a Case Study

We present the design, implementation and evaluation of a training programme for Computer Science teachers on the educational use of the Python programming language inside and outside school. The programme used pre-existing educational resources centered on meaningful, self-contained programming projects. The training programme followed a blended-learning approach thus offering an opportunity to many computer science teachers make their first steps towards the educational use of the Python language within a very promising learner-centered pedagogical framework. After the initial training phase, the participating teachers were supported to establish and run local Python code clubs. The paper presents the experience from establishing, running and evaluating one of them, established at Chania, Crete by three of the authors. The evaluation clearly demonstrates the effectiveness of the code club approach to learning to code in a manner that is engaging, fun and meaningful for the students and opens up new creative opportunities for them.
Diffraction from Gratings

In this experiment, the screen of an old iPhone 3G is used as a 2D grating and another smartphone is used as a photography device to record the diffraction pattern. To quantitatively analyze the diffraction pattern, one must ensure that <i>y<sub>m</sub></i> ≪ <i>L</i>. The distance between the screen and the surface of the iPhone 3G as well as the exact separation between diffraction bright spots will be measured. If the distance between the photography phone and the screen is fixed, one can use a printed ruler with known dimensions to calibrate the image pixel size.
Smartphones and Their Optical Sensors

Conventional m-learning environments have typically exploited only the connectivity and programming capabilities of smartphones/tablets, whereas the devices’ advanced opto-electronic/micro-electromechanical systems (MEMS)- sensing and geo-referencing capabilities have been underexplored. Outside of learning environments, there has been a surge of recent research on the use of smartphone/tablet hardware and interfaces to create point-of-care diagnostic tools or portable medical devices [18]. For example, smartphones have been integrated into powerful microscopes for image analysis, colorimetric detection, blood testing, and disease diagnostics [19–30]. In combination with nanotechnology, smartphones have also been modified for use as spectrometers for chemical and biological sensing [31]. Many do-it-yourself (DIY) enthusiast websites have also demonstrated the use of smartphones/tablets in electronics projects [32]. It is evident that besides using the communication and software capabilities of smartphones/tablets for conventional m-learning, their opto-electronic/MEMS-sensing capabilities can be exploited to build laboratory instruments for hands-on lab education.
Radiographic image enhancement based on a triple constraint U-Net network

Radiographic testing (RT) images of complex components are affected by several factors, including low greyscale levels, low contrast and blur. These factors can significantly restrict the accuracy and effectiveness of defect recognition. To address this issue, this paper proposes a radiographic image enhancement method based on a triple constraint U-Net network. Firstly, a radiographic image preprocessing target dataset is constructed based on conventional image preprocessing technology and previous experience. The U-Net model is then used to design a model loss function, including the parameters of image consistency, texture consistency and structural similarity, in order to achieve structure preservation and noise removal in the images. Finally, radiographic images of actual complex components are used to illustrate and verify the effectiveness of the proposed method. The results show that the proposed method can effectively convert an original image to a target image, enhance the details of the defect area and improve the accuracy of defect recognition by 5.2%.
The Moderating Effect of Self-Efficacy in the Effects of Corona-Stress on Depression of College Students

This study was conducted to confirm the effects of stress experienced by COVID-19 oncollege students' feelings of depression, and to verify the effect of self-efficacy as a way forrelieving their depression. For this study, data collected from 223 students from 2 and 4-yearcolleges in Seoul, Gyeonggi and Chungcheong Province and the SPSS 21.0 were used. As aresult of the study, it was found that corona-stress factors, such as infection anxiety, socialdistancing, and anger towards others due to COVID-19, had a positive (+) effect on collegestudents' feelings of depression. It was confirmed that self-efficacy had a negative (-) effect onthe depression of college students, while relieving depression caused by corona stress. Therefore, the results of this study suggest that there is a need to be interested in improvingself-efficacy as a way to increase resistance to depression experiences of college students insimilar pandemic situations including COVID-19.
Exploration on the construction of innovative mathematics and Applied Mathematics Specialty

the specialty of mathematics and applied mathematics has a wide range of uses.If you want to learn the specialty well, you need to create a suitable classroom atmosphere to help students improve their innovation ability and make students study efficiently in a relaxed environment.This article focuses on the construction of innovative mathematics and applied mathematics, hoping to provide some reference for relevant education industries.
Performance Portability of an SpMV Kernel Across Scientific Computing and Data Science Applications .

Opteron “Interlagos” processors, but with IBM Power 9 processors, performance remains within an acceptable range for small batch sizes. These kernels provide a test for achieving performance portability with compiler directives for problems with memory-intensive components as are often found in scientific applications.
Incremental Delivery and Continuous Improvement

This chapter covers delivery, reporting, and continuous improvement. It provides examples of Agile metrics for Scrum and Kanban teams and reviews software delivery and product satisfaction metrics. It discusses team empowerment, feedback loops, and Retrospective techniques. In addition, it discusses the concept of a product life cycle and how it affects incremental delivery.
Efficient low-rank federated learning based on singular value decomposition

In this paper, we propose a low-rank federated learning (FL) algorithm based on singular value decomposition (SVD). The SVD factorizes the global parameters that need to be exchanged between a global server and clients for distributed model training, significantly reducing the associated communication cost. Experiment results confirm that the number of transmissions is significantly reduced while maintaining the accuracy performance of the local model using the approximately recovered parameters.
Interactive Web Service and Risk Perception: Implication Toward Virtual Try-On (VTO) Service

<p style="margin:0in;text-indent:.25in"><span style="color:black">Fashione-retailers have adopted Virtual Try-On (VTO) services based on 3D Avatarscreated with consumers’ individual body measurements. As avatar-based VTOservices provide user-friendly and interactive experiences online, it is necessary to examine their concern about providing personal body information for enhancing consumer experience that ultimately influences their decision to use 3D body-scanning-basedVTO services in future. This study collected consumer responses toward using the Avatar-basedVTO service, and structural education modeling was conducted to analyze the dataset. Results indicate that the direct and indirect roles of consumer experience in explaining the behavior of adopting 3D body-scanning-based VTO services in future within the S-O-R framework. Results also indicate that consumers’ experience with the interactive services (i.e., perceived personalization and responsiveness)can buffer their negative perception of information security risk. Results provide practical suggestions to e-fashion retailers that adopt VTO services.<o:p></o:p></span>
Generative Models

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Color Video Compression and Encryption with Hybrid Method

A method including intermodulation, diffractive encoding and transformation algorithms has been developed to encode the video with sound in a single plane. A video consisting of 3600 color images is successfully encoded into the plane.
Image Enhancement Method Based on Dark Channel Prior

According to the characteristics of foggy images, such as high noise, low resolution and uneven illumination, an improved foggy image enhancement method based on dark channel priority is proposed, which solves the problems of that the color of large area is uneven and the overall color of the image is dark when the traditional dark channel prior method is used to remove fog. Firstly, the new algorithm refines the transmittance and optimizes the atmospheric light value, and converts the restored image to HSV space. Secondly, the brightness V component is enhanced by MSRCR algorithm improved by bilateral filtering, and the saturation S is improved by adaptive stretching algorithm. Finally, the image is converted from HSV space to RGB space to complete image enhancement. The experimental results show that from subjective evaluation and quantitative analysis the new algorithm overcomes the shortcomings of noise amplification and edge blur when the conventional enhancement algorithm enhances the image. It can improve image darkening and avoid image distortion. It has a good effect on preserving the edge information and has good adaptability and stability for fog image enhancement.
CLASSIFICATION APPROACHES FOR MULTI-LEVEL BREAST CANCER PREDICTION: A MACHINE LEARNING PERSPECTIVE

This research compares the performance of machine learning and deep learning algorithms for breast cancer prediction in order to identify the most effective strategy for handling large data sets while maintaining high prediction accuracy. The study focuses on the use of machine intelligence techniques, such as machine learning and deep learning, for breast cancer prediction and classification. The paper reviews previous research on machine learning algorithms, segmentation techniques, and classification approaches used for multi-level breast cancer prediction. The results of the comparison show that using Long Short-Term Memory (LSTM) Recurrent Neural Networks for multi-disease prediction results in improved classification performance for breast cancer predictions.
A Survey on Text Description to Image Generation Using GAN

The introduction of generative adversarial networks (GANs) and increasing number of images generated from text descriptions paves way for building a novel and active research area in the field of computer vision (CV). The GANs are emerging as a more adaptable and innovative approach in the imaging research and have significantly improved in terms of visualization, classification, and semantic segmentation. However, the fields within the complicated area requires more attention. This can be achieved by including additional human-like vision on complex images. The current research works evaluate the area and growth in the field of CV-based image processing operations ranging from image construction to image fusion model. This paper discusses about the different techniques adopted by GANs to generate image from the text description. Different datasets and improvements have been made to the current architecture and also the new trends are also discussed.
Enhanced Crime and Threat Intelligence Hunter with Named Entity Recognition and Sentiment Analysis

Collecting cybercrime evidence on the Internet typically involves reconnaissance and analyses of information extracted. Scouring the Internet, especially the Deep Web, often requires manual effort and is time-consuming. Hence, it is imperative to have an efficient framework for an intelligent tool to gather cyber intel automatically according to programmed directives. In this paper, we present an updated design to our prior threat intelligence hunter. We make use of machine learning for parsing linguistic intel.
Methods of Application Control in Cloud Computing Systems

In this article, solutions to the problem of authentication data protection are given, in particular, authentication methods based on one-time use (single sign-on and single sign-on) are studied, and the protocol for using single sign-on technology is analyzed. The scheme of the hidden protocol (implicit flow) and the main authentication protocol (authorization code flow) are shown.
Explainable AI for Sentiment Analysis

The explainable artificial intelligence (XAI) is a set of practices and methods that allows the human users to understand and trust the results and outputs created by machine learning algorithms. The whole calculation process is converted to what is commonly called a “black box” that cannot be defined. Therefore, we use XAI to explain these models so that we can understand the working of an AI model or any neural network. We are going to use XAI for understanding an NLP model of sentiment analysis. The NLP of natural language processing model for sentiment analysis helps us to predict whether a given sentiment is positive or negative or irrelevant. We use a neural network to build this model. In this research paper, we are going to use the XAI tool on this NLP model so that we can understand the working of each layer of the neural network and how the model takes a decision at every step.
Blockchain for Wearable Internet of Things in Healthcare

Background: Security is one of the persistent challenges for the tiny devices embedded in the fabric of Internet of Things (IoT) particularly for the wearables equipped in the healthcare monitoring systems. Blockchain is the state-of-the-art technology to secure and revolutionize the healthcare industries. The choice of communication technologies, operating systems, and the data access technologies is very crucial in wearable IoT (WIoT). Device mobility, reliability, availability, scalability, and the data analytics are persistent challenges for WIoT. Objectives: To provide fair enough justification for the choice of communication technologies, operating systems, and the data access technologies based on the comparative analysis and to apply Blockchain for the proposed WIoT architecture. Methodology: Blockchain is applied to provide security and transparency. The immutable nature of Blockchain can be applied to wearable devices to track the mobility and ensure the secure and authorized access. The log 224record of the traverse can be utilized further to enhance the throughput and reduce the delay at each stage of traverse. Thus, proving the scope for enhancing the energy efficiency of the WIoT devices. Result: It is observed that the use of Blockchain has ensured security in the challenging environment of WIoT. Conclusion: With all the challenges, such as device constraints, energy constraints, and mobility, the Blockchain provides improved security and transparency of the traverse of WIoT devices. Using Blockchain, the challenges in WIoT can be solved and hence its overall performance can be improved.
PENGEMBANGAN POTENSI MASYARAKAT DENGAN PENERAPAN TEKNOLOGI MESIN PENCACAH SAMPAH ANORGANIK DI KELURAHAN TANJUNG PINGGIR

This community service activity aims to train and assist the Tanjung Pinggir village community in developing community potential through the use of inorganic waste counting machine technology. This activity's target audience consists of ten people from the Tanjung Pinggir village community in Pematangsiantar city. This activity is trained and mentored through lectures, demonstrations, and exercises, which are supplemented by questions and answers. The lecture method is used to explain business concepts and theories that are based on technology. The demonstration method is used to explain a future technology work process. While the training method is used to practice technology-based media, the question and answer method allows participants to consult on media development problems. The enthusiasm of the participants, pangulu, and activity funds aided in the implementation of this activity. The participants did not have prior knowledge of technology, and there was limited time for training. Participants can benefit from this service activity by increasing participant business sales
Evaluation of mobile applications in foreign language learning at early age

In the technology era that we are in, teaching with traditional method is not sufficient alone in increasing efficiency. Varied content, whether technological or not, has an important place to increase the effectiveness of teaching. The methods and models used in the selection and usage of these contents are also considered important in terms of achieving the targeted success. In this study, the effects of mobile applications used in foreign language learning were examined on students' academic achievement. A total of 87 students from the Experiment-1 (E1), Experiment-2 (E2) and Control (C) groups consist of the sampling of this study. The English Achievement Test (EAT) was applied to all three groups as pretest and posttest. C group has attended to the regular face-to-face classroom teaching while students in E1 and E2 groups additionally used Duolingo and Memrise, respectively. In addition, volunteer parents and students’ opinions about mobile applications were received after the implementation. In line with the results obtained by comparing the groups with respect to each other, it was concluded that the E2 group obtained higher scores from EAT than the E1 and C group. Particularly, there is a higher increase in the average score in the "Reading and Writing" section of the E2 group. According to the opinions of students and parents, both applications can be said to improve academic success positively.