Reviewerly/exHarmony huggingface dataset:
New U-Net for Image Deblurring Using Deep Learning

Many studies have been conducted for image deblurring, which is classified into non-blind and blind image deblurring techniques. Many iterative methods have been studied based on the maximum-a-posteriori (MAP) framework for image deblurring. Recently, deep learning methods for blind image deblurring have attracted a lot of attention for their excellent performance. In this paper, a method for improving the performance of the blind image deblurring using deep learning is proposed by introducing a new structure of U-Net. U-Net is used as a deep neural network for deep learning in various image processing fields. We propose a new U-Net by using short cut and parallel structure in each stage of contractive and expansive path for U-Net, and pre-processing and post-processing are used for the proposed new U-Net to improve the deblurring performance. Extensive computer simulations are performed to evaluate the image deblurring performance for motion blur and Gaussian blur, and it is shown that the proposed U-Net shows superior image deblurring performance compared to the conventional U-Net.
Modern Metrics (MM): Software size estimation using function points for artificial intelligence and data analytics applications and finding the effort modifiers of the functional units using indian software industry

SPM, Software Project Management is an engineering process for scheduling, developing, testing, and maintaining a software system. The size of a software is one of the important attributes in determining all the activities of SPM. Price, time, and effort are the basic factors for developing software in a successful and profitable manner. The fast explosions in technological development of software systems attracted all the fields that existed in the universe. The modern software system uses many programming languages, domains, operating systems, technologies, development cycles, topologies, etc. So, the industry is expecting a new, versatile, and highly updated sizing technique for modern software. Modern Metrics (MM) is a new anticipated size estimation technique for present software systems with updated functional values and metrics using Function Point (FP). This article analyzed the functional unit metric values (effort modifiers) of the MM with real world applications. MM is a novel method for determining the solution to the SPM challenges.
Retracted: Construction of Intelligent Nursing System Based on Visual Action Recognition Algorithm

[This retracts the article DOI: 10.1155/2022/8311371.].
Pengaruh Model Pembelajaran Role Playing dan Jigsaw terhadap Hasil Belajar Ips Ditinjau dari Motivasi Belajar

This research aims to determine the difference in the influence of the cooperative learning model Role Playing &amp; Jigsaw methode on Social Science Major (IPS) learning result, differences in the influence of students who have high, middle, and low learning motivation on IPS learning result, and is there any interaction between learning models and learning motivation of students on Social Science Major (IPS) learning result. This research used 2*3 factorial design. Sampling is taken by purposive sampling technique. Sample of this research is student of SDIP Al Madinah Nogosari 5th grade and SDIP Al Madinah Cepogo 5th grade. The two population samples were then tested for prerequisite analysis through the normality test with the Kolmogorov-Smirnov test, the homogeneity test with Levene's test, and the balance test with the independent sample t-test. The research was continued by identifying the learning motivation of the students in the sample through a questionnaire instrument that had been tested for validity and reliability, and validated by experts. The questionnaire was made with a Likert scale of 1-4 with the results of the identification of learning motivation. Learning outcomes are obtained with test instruments that have been tested for validity, reliability, difficulty, and discriminating power, and validated by experts. Hypothesis testing was carried out using a two-way ANOVA and further testing using the Scheffe’s method using the SPSS Statistic 26 application. The results of the research showed; 1) There are no difference in the influence of the Role Playing learning model &amp; Jigsaw on Social Science Major (IPS) learning result.; 2) There are differences in the influence of students who have high learning motivation, middle learning motivation, and low learning motivation on IPS learning result.; 3) There are no interaction of learning models and learning motivatin of students on IPS learning result.
Public Proofs of Data Replication and Retrievability with User-friendly Replication

Proofs of Retrievability (PoRs) and Provable Data Possession (PDP) are efficient cryptographic solutions to monitoring the state of storage, enabling integrity verification without the necessity of retrieving data. However, such protocols fail to guarantee data availability, since it is too late for cloud clients to recover the corrupted data when notified. To remedy it, these protocols are extended to verify integrity of both the stored data and their redundant copies in the multi-replica environment. Most of the existing multi-replica PDP protocols not only consume considerable computation and bandwidth resources of clients to generate and upload replicas, but also lead economic losses to service providers due to abused replica discounts. Though the other few considered these problems and proposed some countermeasures, they imposed the linear cost of generating and uploading copy parameters on clients in replication algorithms and fail to realize public verifiability. In this paper, we propose a public proofs of data replication and retrievability protocol with user-friendly replication for the first time. One distinguishable property is the simultaneous achievement of user-friendly replication and public verifiability. Specifically, we design a novel replication algorithm, which imposes nearly no computation and a constant-size communication cost on clients. Subsequently, we employ non-interactive succinct proofs to make the verification public. Furthermore, we present a comprehensive analysis and extensive experiments to demonstrate the security and efficiency of the proposed protocol.
Research on Online Loan Default Prediction Model Based on Ensemble Learning

In recent years, with the rise of internet finance and the promotion of early consumption awareness, personal credit services have witnessed rapid development. Traditional banks, due to reasons such as complex borrowing process and long cycle, can no longer meet the growing demand for loans from sma
Project Work in Literature Teaching in the First Stage of Primary Education

The subject of study presented in the article are the educational projects included in the readers for 1st – 4th grade levels, their varieties and methods of working on a certain project. The objective is to outline the status of the educational project in the readers for primary literature education and reveal the possibilities of application of project-based technologies through the main teaching device. The nature and specific features of project work in the first stage of education are revealed. It is established that there are a variety of project types in the readers. Creative, role-play and information-cognitive projects prevail. Group and collective projects are mainly included, but there is a room also for individual projects. Priority is given to short-term projects, but opportunities for implementation of long-term projects are also presented. The stages in the implementation of the project activity are not developed to the maximum.
SISTEM PAKAR DIAGNOSIS PENYAKIT PADA BALITA DENGAN METODE FORWARD CHAINING

Children under the age of 1 year (toddlers) are more susceptible to disease, which causes the environment to greatly affect the health of toddlers. Children's health is one of the most important things, especially when the child is in its infancy. The importance of SOP is one of the assessments of the quality of health services for both government and private agencies. There is input from patients as service users so that the importance of SOP for General Hospital services as service guidelines so that patient satisfaction is obtained. The purpose of this study was to see how useful the SOPs were compiled and the compliance of doctors in providing services based on SOPs on the quality of service received by patients. The method used is the Forward Chaining Method which is a method for reasoning from a problem by providing a solution. This research is an applied technology product that is expected to provide benefits as a media for consultation or instructor for the community in general, as well as for doctors and paramedics at public hospitals. The design of this system has been carried out through data collection activities, design rules, process design and system testing. From the collection of data and information found disease facts, complaints, symptoms and suggestions for treatment. The results obtained from testing the system using the PHP MySQl application show that the results of diagnoses and diseases of children at Tanjung Balai General Hospital have as many as 10 patient data that have been studied by experts and have carried out system testing to achieve an accuracy of 80%. Keywords: SOP, Forward chaining, rules, PHP MySQl, Expert System, disease diagnosis.
Online curricula –students’ perception on the quality of digital tools (the online course and the educational websites)

During the last year, due to the evolution of the global pandemic, the educational system has been reorganized, and e-learning, with its adjacent forms of development, has been explored as the main means of organizing the educational process. The purpose
Experimental Comparison of Strategies for Quantum State Overlap Estimation

We experimentally compare the performance of different quantum state overlap estimation strategies with photonic setups. The results show overlap-dependent estimation precision and inspire designs of experimentally efficient strategy for various quantum information processing applications.
PENERAPAN PENGGUNAAN PAPAN TULIS DIGITAL TERHADAP HASIL BELAJAR SISWA PADA OPERASI HITUNG PECAHAN DI KELAS 5C SDN KEBRAON 2 SURABAYA

The purpose of this classroom action research is to determine the level of effectiveness of using virtual whiteboards in online learning and also students' perceptions after using virtual whiteboards during learning. The subjects in this study were 5th grade students of SDN Kebraon 2 which consisted of all students who took part in online learning. This research is a research that uses a quantitative approach. The data used were taken from the results of the pre-test and post-test of students in working on 20 questions of fractional counting operations. The use of a digital whiteboard has an effect on increasing grades in learning fractional arithmetic operations. Based on the pre-test results that have been given, the results have increased by an average of 4.5 points. Students also feel more able to enjoy the lesson and understand the lesson more deeply.
Pengembangan Media Pembelajaran Pop-Up Book Materi Perkembangan Teknologi Transportasi Bahasa Indonesia Kelas III SD

The author is writing describe development of 3D Learning media in the form of pop-up book for Bahasa Indonesia lesson with lesson material development of transportation technology for third grader elementary school. The learning media development in this study obtained its feasibility throughh media expert and material expert. This writting is in the form of qualitative using the ADDIE method (Analysis, Design, Development, Implementation, Evaluation). The author uses data collection techniques based on validity tests conducted by media masterful and material masterful. The results of writing a 3D learning media pop-up book for Indonesian subjects material development of third-grade elementary school technology that is included in the category of feasible/valid with the results of the media expert test reaches a percentage of 83.92% with validd criteria. While the results of the expert material test I reached a percentage of 77.08% with sufficient criteria valid/feasible and the results of the expert test material II reached a percentage of 100% with valid criteria / feasible. Based on the validity test, it can be summarized that the Pop-Up media that was developed entered into the criteria is valid/feasible so that this media is feasible and can be implemented in the classroom prac tice.Keywords: 3D Learning media, Pop-Up Book, Bahasa Indonesia, Development of Transportation Technology
Estrategia metodológica basada en TIC para mejorar las competencias digitales de los docentes

This article emphasizes ICT in the educational field since they seek to improve its quality and equity at different levels worldwide, which is why its implementation in the teaching-learning process must be fundamental, the objective proposed is to design a methodological strategy based on ICT to improve the digital skills of high school teachers of the Educational Unit "Santa Maria" of the Canton El Carmen. For the purpose of this research, we work with a mixed approach of a descriptive type with a non-experimental design, for the collection of data, the techniques of surveys and interviews are used, applying the pertinent instruments; The population considered was 15 high school teachers and 2 authorities of the institution, being a probabilistic sampling for convenience. It was possible to demonstrate that the design of a methodological strategy based on ICT is of great importance to improve the digital competences of teachers in which they can update, enrich, and develop them in an easy and dynamic way, therefore, it is concluded that Teachers must be in constant learning in terms of the development of skills and abilities that allow them an effective mastery of ICT.
UNDERGRADUATE ENGINEERING STUDENTS’ ATTITUDE TOWARDS THE USE OF PHYPHOX IN PHYSICS EXPERIMENT

During the pandemic, the faculty of engineering at Trisakti University still used old apparatus and onsite learning in practical lectures, albeit with some COVID restrictions. Nowadays, when the pandemic is going out the faculty is trying to implement a new method in practical lectures using a smartphone-based application to enhance the effectiveness of physics experiments. This research aims to discover the students’ attitudes toward the use of Phyphox in physics experiments and to examine the differences in the attitude between students with various backgrounds, such as gender, department, smartphone use, and prior experience. Two hundred self-developed questionnaires were delivered to seven departments in the faculty of engineering that have participated in this research. The findings indicated that: gender correlated to significant differences in some factors of attitude; smartphone use did not affect attitude; students with prior experience showed more positive attitude; and the major taken by students correlated with majority of factors of attitude towards the use of Phyphox in physics experiment. This research article presents a case study of the integration of the Phyphox application, that utilises the smartphones' integrated sensors in hands-on physics experiments, in an inquiry framework.
Improved Blind Image Denoising with DnCNN

Denoising convolutional neural networks (DnCNN) has been proposed recently for image denoising for additive white Gaussian noise with both blind and non-blind versions. For blind DnCNN, the networks are trained with noise levels from 0 to 55, which is not perfect for other noise levels. In this paper, we train the DnCNN with three noise ranges [0, 40], [40, 80], and [80, 120] separately to obtain three network models so that better denoising results can be achieved. The training of our new models can be done in parallel by taking advantages of GPUs. We choose the suitable network model according to the estimated noise level from the noisy images. Experimental results demonstrate that our proposed method outperforms the standard DnCNN for image denoising for almost all testing cases with all six testing images.
Geometry-Based End-to-End Segmentation of Coronary Artery in Computed Tomography Angiography

Coronary artery segmentation has great significance in providing morphological information and treatment guidance in clinics. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Limited by the low resolution and poor contrast of medical images, voxel-based segmentation methods could potentially lead to fragmentation of segmented vessels and surface voids are commonly found in the reconstructed mesh. Therefore, we propose a geometry-based end-to-end segmentation method for the coronary artery in computed tomography angiography. A U-shaped network is applied to extract image features, which are projected to mesh space, driving the geometry-based network to deform the mesh. Integrating the ability of geometric deformation, the proposed network could output mesh results of the coronary artery directly. Besides, the centerline-based approach is utilized to produce the ground truth of the mesh instead of the traditional marching cube method. Extensive experiments on our collected dataset CCA-520 demonstrate the feasibility and robustness of our method. Quantitatively, our model achieves Dice of 0.779 and HD of 0.299, exceeding other methods in our dataset. Especially, our geometry-based model generates an accurate, intact and smooth mesh of the coronary artery, devoid of any fragmentations of segmented vessels.
Transfer Learning in Breast Mass Detection on the OMI-DB Dataset: A Preliminary Study

Early screening for breast cancer is an effective tool to detect tumors and decrease mortality among women. However, COVID restrictions made screening difficult in recent years due to a decrease in screening tests, reduction of routine procedures, and their delay. This preliminary study aimed to investigate mass detection in a large-scale OMI-DB dataset with three Transfer Learning settings in the early screening. We considered a subset of the OMI-DB dataset consisting of 6,000 cases, where we extracted 3,525 images with masses of Hologic Inc. manufacturer. This paper proposes to use the RetinaNet model with ResNet50 backbone to detect tumors in Full-Field Digital Mammograms. The model was initialized with ImageNet weights, COCO weights, and from scratch. We applied True Positive Rate at False Positive per Image evaluation metric with Free-Response Receiver Operating Characteristic curve to visualize the distributions of the detections. The proposed framework obtained 0.93 TPR at 0.84 FPPI with COCO weights initialization. ImageNet weights gave comparable results of 0.93 at 0.84 FPPI and from scratch demonstrated 0.84 at 0.84 FPPI.
Infrastructure for MLOps

This chapter is about infrastructure. You might think of buildings and roads when you hear the word infrastructure, but in MLOps, infrastructure refers to the most fundamental services we need to build more complex systems like training, inference, and model deployment pipelines. For example, we need a way to create data stores that can store features for model training and servers with compute and memory resources for hosting training pipelines. In the next section, we will look at a way we can simplify the process of creating infrastructure by using containers to package up software that can easily be maintained, deployed, and reproduced.
Practical Applications of What Information Variables Predict Bitcoin Returns? A Dimension-Reduction Approach

In <ext-link><bold><italic>What Information Variables Predict Bitcoin Returns? A Dimension-Reduction Approach</italic></bold></ext-link> from the Spring 2023 issue of <bold><italic>The Journal of Alternative Investments</italic></bold>, Sang Baum Kang of the <bold>Illinois Institute of Technology</bold>, <bold>Yao Xie</bold> of <bold>Morningstar</bold>, and <bold>Jialin Zhao</bold> of <bold>St. Mary’s University</bold> found that blockchain technology, stress level, and investor sentiment play important roles in predicting bitcoin returns. The authors use 25 theoretically motivated explanatory variables falling within these three categories and two others, including macroeconomic variables and other assets, such as gold, to predict bitcoin returns with several dimension-reduction techniques. These techniques are used to eliminate variables with redundant information and avoid problems associated with multicollinearity and noise. Given the recent growth in data availability, dimension reduction is an increasingly relevant issue. The importance of variables within the five categories varied over time. Interestingly, macroeconomic variables and variables in the “other assets” category were unimportant, except for during the Covid-19 period. Simulating dynamic trading strategies based on predictions, the authors show that the “three-pass regression filter” performed best relative to their other dimension-reduction approaches.
Foundations for MLOps Systems

In this chapter, we will discuss foundations for MLOps systems by breaking down the topic into fundamental building blocks that you will apply in future chapters. While we will discuss programming nondeterministic systems, data structures and algorithmic thinking for data science, and how to translate thoughts into executable code, the goal is not to give a fully comprehensive introduction to these areas in a single chapter but instead provide further resources to point you in the right direction and answer an important question: Why do you need to understand mathematics to develop and deploy MLOps systems?
Nurturing Foreign Language Talents of the New Era with a New Academic Style

A new pattern of foreign language talents development has been built with the changing of international situation and the developing of the artificial intelligence.Foreign language talents in the new era should possess three core qualities: capability of mastering new technology, capability of international communication and capability of mastering various skills.Therefore, the academic style construction of foreign language majors in colleges and universities should uphold fundamental principles and break new ground.That is, the academic style should gradually realize the optimal transformation to an intelligence-oriented, conversation-oriented and integration-oriented academic style to nurture the foreign language talents in the new era.
Performance of domestic investment in Tanzania: an empirical analysis over the 1980 – 2020 period

Domestic investment is a key component of the economic growth of any country. In recent years, the growth of domestic investment has not been optimistic. This study aims at analyzing the factors affecting domestic investment in Tanzania. Time-series data of the 1980- 2020 period is used to empirically analyse the performance of the domestic investment. The study applies the Error Correction Model (ECM) to estimate the parameters. Factors affecting domestic investment in Tanzania are critically analysed. The factors influencing domestic investment includes money supply, interest rate, savings rate, and government expenditure. The regression results of this study show that government expenditure is insignificant in influencing domestic investment while money supply, interest rate, and savings rate are statistically significant. To this end, measures that influence domestic investment are indeed called for, implying a need for policies that can control interest rates, savings rates, and money supply . This paper recommends putting in place a conducive environment that could accelerate financial intermediation and domestic savings mobilization in order to improve domestic investment. Furthermore, the study suggests the encouragement of investment incentives and the establishment of vibrant investment promotion agencies.