Reviewerly/exHarmony huggingface dataset:
Physically-Based Face Rendering for NIR-VIS Face Recognition

Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant domain gaps as well as a lack of sufficient data for cross-modality model training. To overcome this problem, we propose a novel method for paired NIR-VIS facial image generation. Specifically, we reconstruct 3D face shape and reflectance from a large 2D facial dataset and introduce a novel method of transforming the VIS reflectance to NIR reflectance. We then use a physically-based renderer to generate a vast, high-resolution and photorealistic dataset consisting of various poses and identities in the NIR and VIS spectra. Moreover, to facilitate the identity feature learning, we propose an IDentity-based Maximum Mean Discrepancy (ID-MMD) loss, which not only reduces the modality gap between NIR and VIS images at the domain level but encourages the network to focus on the identity features instead of facial details, such as poses and accessories. Extensive experiments conducted on four challenging NIR-VIS face recognition benchmarks demonstrate that the proposed method can achieve comparable performance with the state-of-the-art (SOTA) methods without requiring any existing NIR-VIS face recognition datasets. With slightly fine-tuning on the target NIR-VIS face recognition datasets, our method can significantly surpass the SOTA performance. Code and pretrained models are released under the insightface (https://github.com/deepinsight/insightface/tree/master/recognition).
A Sustainable Model for Deployment of M-Learning in Kenyan Universities

Institutions of higher learning around the world are considering the use of technology as alternative methods of instruction. Mobile learning technologies and wireless networks are becoming increasingly popular tools of facilitating learning in higher education institutions. Mobile learning (or m-learning) has the ability to connect learners to information anytime and anywhere. While some studies have explored the challenges and the issues of implementing this technology, a majority have focused on acceptance alone. However, little research has been conducted to comprehend factors for sustainable deployment of m-learning in higher education. This study was conducted with the intention of identifying the factors that affect the deployment of m-learning, in order to propose and evaluate a sustainable model for mobile learning deployment in university education. Objective: The main objective of the study was to develop a deployment model for use of m-learning in HEI in Kenya. Another objective was to find out the factors that affect deployment of HEI. Methodology: The study adopted a mixed method of research design where both qualitative and quantitative approaches were used. The study was A survey was conducted with a total of 148 students and 28 lecturers. The findings of this study indicate that a conceptual framework and model clearly delineating pre and post-deployment processes can provide a roadmap for sustainable deployment of m-learning. Furthermore, Education Organization and schools can use the model for blending mobile learning with traditional learning using a planned approach to improve education and learning
Comprehensive evaluation of operation reliability of IWCDSS for large water supply engineering based on cloud model

The operation reliability of the intelligent water conservancy decision support system (IWCDSS) of large-scale water supply projects will directly affect the safety of the water supply. The operating environment and system composition of the IWCDSS will have a certain impact on its operational reliability. This article takes the reliable operation of the IWCDSS of large-scale water supply projects as the goal. It analyzes the factors affecting the operation reliability from the four aspects of the computer room environment, hardware, communication and network, and software, and establishes the operation reliability evaluation index system of the decision support system (DSS). The index system is used to construct a comprehensive evaluation model for the operation reliability of the IWCDSS of water supply engineering based on the cloud model. Taking an IWCDSS of a water supply project as an example, this paper verifies the feasibility of the comprehensive evaluation model, which provides a reference for the operation reliability evaluation of the IWCDSS of water supply projects.
Development of Podcast-Based Irrational Belief Disputing Media to Increase Confidence of Body Shaming Victims in High School Students

Abstract: This research and development aims to produce a podcast-based disputing irrational belief media to increase the self-confidence for victims of body shaming in high school students. This podcast is named the #PULIH podcast which is an audio visual medium and can be listened to independently through the Youtube platform. The #PULIH podcast consists of five episodes with different topics. Each episode discusses the aspect that will be changed, namely the perception of the physical by using the technique of disputing irrational belief. This research and development adapts the five stages of the Borg and Gall development model, namely the information collecting stage, planning stage, prototype development stage, initial field trial, and product revision stage. The initial field trial has three stages, namely the material expert test, the media expert test and the prospective user (student) test. The purpose of the assessment of experts and prospective users to determine the acceptability of the media which includes aspects of accuracy, usability, convenience, and attractiveness. Data analysis using the inter-rater agreement. The results of the expert and prospective user tests overall show a very high validation index. It is concluded that the podcast-based disputing irrational belief media is very appropriate, useful, easy, and interesting to increase the self-confidence for victims of body shaming in high school students.Abstrak: Penelitian dan pengembangan ini bertujuan menghasilkan media disputing irrational belief berbasis podcast untuk meningkatkan kepercayaan diri korban body shaming pada siswa sekolah menengah atas. Podcast ini ini diberi nama podcast #PULIH yang merupakan media audio visual dan dapat didengarkan secara mandiri melalui platform Youtube. Podcast #PULIH terdiri dari lima episode dengan pembahasan yang berbeda-beda. Setiap episodenya membahas aspek persepsi tentang fisik dengan menggunakan teknik disputing irrational belief. Penelitian dan pengembangan ini mengadaptasi lima tahapan dari model pengembangan Borg dan Gall yaitu tahap pengumpulan informasi, tahap perencanaan, tahap pengembangan prototype, uji coba lapangan awal, dan tahap revisi produk. Uji coba lapangan awal terdapat tiga tahap yaitu uji ahli materi, uji ahli media dan uji calon pengguna (siswa). Analisis data menggunakan inter-rater agreement. Hasil penilaian uji ahli dan calon pengguna secara keseluruhan menunjukkan indeks validasi yang sangat tinggi. Hal ini menunjukkan bahwa media disputing irrational belief berbasis podcast sangat tepat, berguna, mudah, dan menarik untuk meningkatkan kepercayaan diri korban body shaming pada siswa sekolah menengah atas.
Impact of Technologies in Learning &amp;Teaching Processes during Covid -19 in Teaching Applied Chemistry in Polytechnic System

This article is a proposal for an empirical study planned to study the impact of social media in learning and teaching processes during COVID-19 and its expected impact on post COVID-19. This study will be exclusively focus on teaching applied chemistry in Polytechnic system using the help of Technology blended learning at secondary level.
Education 4.0 in the Era of Fourth Industrial Revolution

The current study facilitates the discussion on implications of the Fourth Industrial Revolution (4IR) on education. World technological development and digitalization are unmistakable tokens of the 4IR; they will undoubtedly have a positive impact on transition to Education 4.0. Authors examine the key challenges and features of the 4IR for the Russian educational system. The fourth industrial revolution (IR4.0) is changing the world around us. IR4.0 has a key role in the evolution of education system. Within range of this article, the author focuses on researching important impacts of the fourth industrial revolution in coming time: training activities of higher education institutions; curriculum content, teaching methods , learning environment, methods, materials, equipment, demand and process of learners have many changes. On that basis, the author analyzes some orientations for the higher education development in the context of the fourth industrial revolution.
Development of COVID-19 Isolation Facility Management System with Scrum Framework

A COVID-19 pandemic hit Indonesia in early 2020, and on the 31st of March 2020, President Joko Widodo declared a public health emergency. By June 2021, the Delta variant hit Indonesia, causing shortages of hospital beds and resources. People who were tested positive for COVID-19 were asked to self-isolate at home. However, many houses in Indonesia are not suitable for self-isolation. Meanwhile, President University’s and President Community College’s students’ dormitories were empty as students returned to their homes and resumed their studies remotely using online classes. Therefore, the President University Foundation decided to repurpose the students’ dormitories as COVID-19 isolation facilities. To support its daily operation, an isolation facility management system was developed. To ensure the timely delivery of the system, Scrum was chosen as its development framework. Ten (10) participants tested the system for its usability, and the system scored an average of 94.5. This indicates that the developed system is easy to use and highly usable. The system was completed within a month, according to the planned schedule. The use of the Scrum framework has allowed the development team to produce a useful and effective information system in the shortest amount of time possible. Therefore, the system developed by this research provides services and facilities that are not only important in helping COVID-19 patients but also a better environment and has an integrated information system with various parties involved in handling COVID-19 patients.
Blur and Motion Blur Influence on Recognition Performance of Color Face

Face recognition is an existing and one of the most prominent biometrics techniques, including the processing of images. It is widely used in many applications. The performance of such systems is directly due to face image quality. Since blur and motion blur are common imagery problems, this paper explores the influence of such disturbances on color face recognition performance. The research described in this paper compares the performance of the face recognition algorithm based on the Haar features and Local Binary Patterns Histograms when it uses color face images of good quality, images with added Gaussian blur and motion blur, as well as enhanced images.
Application of Unscented Kalman Filter Algorithm for Assessing the Aero Engine Performance Degradation

Kalman Filter and Extended Kalman Filter (EKF) have been widely applied for aero engine performance assessment. Aiming at improving upon the Kalman Filter based methods, this investigation explores applying Unscented Kalman Filter (UKF) algorithm for aero engine performance degradation evaluation. Improvements on the UKF algorithm are made for optimal performance, by adding a factor or adjusting the prediction variance matrix, as well as finding the optimal distribution of Sigma points. The simulation result shows that compared with EKF algorithm and with multi-dimensional degradation, the UKF algorithm proposed in this study improves the performance degradation assessment error by a large margin(33%), while suffering from prolonged numerical time and sensitivity to the initial error.
Reducing Barriers to Open Science by Standardizing Practices and Realigning Incentives

Open science, the practice of sharing findings and resources towards the collaborative pursuit of scientific progress and societal good, can accelerate the pace of research and contribute to a more equitable society. However, the current culture of scientific research is not optimally structured to promote extensive sharing of a range of outputs. In this policy position paper, we outline current open science practices and key bottlenecks in their broader adoption. We propose that national science agencies create a digital infrastructure framework that would standardize open science principles and make them actionable. We also suggest ways of redefining research success to align better with open science, and to incentivize a system where sharing various research outputs is beneficial to researchers.
MIAR: A Context-Aware Approach for App Review Intention Mining

Due to the highly competitive and dynamic mobile application (app) market, app developers need to release new versions regularly to improve existing features and provide new features for users. To accomplish the maintenance and evolution tasks more effectively and efficiently, app developers should collect and analyze user reviews, which contain a rich source of information from user perspective. Although there are many approaches based on intention mining that can automatically predict the intention of reviews for better understanding valuable information, those approaches are limited since contextual information of the whole review text may be lost. In this paper, we propose Mining Intention from App Reviews (MIAR), a novel deep learning model to predict the intention of app reviews automatically. We adopt a Contextual Feature Extractor to capture the context semantic information and fuse it with the local feature through a fusion mechanism. The experiment results demonstrate that MIAR has made significant improvement over the baseline approaches in Precision, Recall and [Formula: see text]-score evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in other intention mining tasks, proving its generalization ability and robustness.
Research on adaptive soft sensing modeling method of photovoltaic power generation process based on online semi-supervised selective ensemble learning

Soft sensors often estimate product quality variables that are difficult to measure in real time. However, strong nonlinearity and dynamic and time-varying characteristics lead to poor prediction performance of soft sensors for the photovoltaic power generation process. This paper proposes an adaptive soft sensor model construction strategy based on extreme learning machine online semi-supervised selective ensemble learning (SEMI-SEL-ELM). Firstly, a local learning strategy based on the Spatio-temporal criterion is proposed to limit the impact of nonlinear dynamic characteristics. The unlabeled samples are then examined to provide a semi-supervised sample set reconstruction. After that, using the extreme learning machine method, a local soft sensor model is created. Furthermore, the statistical hypothesis test is used to create the statistical information of the test samples, and the t-test is utilized to acquire the quantitative local model satisfaction. Additionally, the adaptive computation of mixed weights of distinct sub-models is performed using the improved information entropy. The black hole algorithm determines the parameters of this approach automatically. Finally, the method is applied to the historical data set of a photovoltaic power station in Australia. The results show that the method effectively deals with nonlinear, dynamic, and time-varying regression problems in PV power prediction.
Design of Bad Information Filtering System for Web Pages

The openness and globalization of the Internet make the information spread on the network mixed, and all kinds of advertisements, spam, reactionary, obscene, violence and other bad information seriously interfere with the normal use of the Internet by Internet users. This paper studies the workflow of the web page bad information filtering system, analyzes the functional modules of the filtering system, and designs the network data processing, text data processing and adaptive processing modules in detail.
Exploring the causes of college students' English writing anxiety

Since the implementation of "Teaching Guide" for two years, there is a lack of relevant research to test the implementation effect from the perspective of college students' feedback. This paper actively explores the causes of English writing anxiety from the perspective of English writing learners, and conducts a survey in the form of semi-structured interviews, so as to explore the significance of the "Teaching Guide" in reducing English writing anxiety and ensure the implementation of special English writing teaching. Effect. We found that English writing anxiety is still prevalent, which provides teachers with a new perspective on targeted teaching activities.
A Study on the Influence of Middle School Students ' Classroom Participation on English Achievement: Based on the Data of a Middle School in Foshan

This paper takes 120 students of Grade 8 from a middle school in Foshan as the research object, and analyzes the correlation between classroom participation and English learning achievement. It is found that there are fewer active verbal activities in middle school students ' classroom participation, mainly in the form of nonverbal participation. Pearson correlation analysis showed that ' classroom questioning ',' active answer ' and ' taking notes ' had significant positive correlation with English achievement, and ' taking notes ' had the highest correlation coefficient with English achievement. There is a significant negative correlation between ' questioning students ', ' listening but silence ' and English achievement, and there is no significant correlation between students ' entrance scores and English achievement. The results of multiple regression analysis show that ' taking notes ' has the most significant effect on English achievement, and ' active answer ' is the second important factor. In addition, ' classroom questioning ', ' asking students ', ' listening but silent ' and ' entrance score ' have no significant effect on English achievement. In view of this, teachers should improve the classroom teaching mode in middle school students ' English teaching, so that students can participate in classroom verbal activities more actively.
Toward expanding the scope of radiology report summarization to multiple anatomies and modalities

Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric.
Towards efficient privacy-enhancing protocols for highly distributed systems

In the current state of the World Wide Web, both the ownership and control over data of highly available systems also becomes distributed and sometimes even decentralized. From a business perspective, combining this data as part of partnerships can lead to new business opportunities; however, this also introduces security and privacy issues. More specifically, additional research is required regarding efficient and privacy-enhancing protocols. Therefore, as part of this doctoral study, we dive into existing decentralized access control solutions and secure multi-party operations. The ultimate goal of this study is to adjust the state-of-the-art solutions for decentralized access control to be used for these highly available systems. Further, we would also like to address the multi-party operations to impose additional privacy constraints on the corresponding data.
On the integrated mean squared error of wavelet density estimation for linear processes

Let $$\{X_n: n\in {{\mathbb {N}}}\}$$ be a linear process with density function $$f(x)\in L^2({{\mathbb {R}}})$$ . We study wavelet density estimation of f(x). Under some regular conditions on the characteristic function of innovations, we achieve, based on the number of nonzero coefficients in the linear process, the minimax optimal convergence rate of the integrated mean squared error of density estimation. Considered wavelets have compact support and are twice continuously differentiable. The number of vanishing moments of mother wavelet is proportional to the number of nonzero coefficients in the linear process and to the rate of decay of characteristic function of innovations. Theoretical results are illustrated by simulation studies with innovations following Gaussian, Cauchy and chi-squared distributions.
Mobile Robot Path Planning in 2D Space: A Survey

Robot path planning is task of navigating a mobile robot around a space in which lie a number of obstacles that have to be avoided. Path-planning can be static or dynamic.It is also an important primitive for autonomous mobile robots that lets robots find the shortest or otherwise optimal path between two points. Here we deal with static path-planning. We proposed 8 methods for path planning on this topic.
Model Comparison in Sentiment Analysis: A Case Study of Amazon Product Reviews

Sentiment analysis is essential in NLP, especially in businesses because it can improve customer services. This paper focuses on a particular case of sentiment analysis, a case study of Amazon reviews of books on kindle. Firstly, this paper applies several non-deep-learning algorithms including Logistic Regression, Naïve Bayes, Support Vector Machine, Convolutional Neural Network, and Recurrent Neural Network, and compares their accuracies. Especially, for deep learning methods, this paper studies the slope of accuracies concerning the number of hidden layers. Secondly, as a multi-class text classification problem, the product review data set has five labels ranging from one star to five stars, a new method called Hybrid Sequential Binary Classification (HSBC) is introduced in this paper, which improves the behavior of classical binary classifiers on a multi-class text classification problem. Moreover, a comparison of HSBC and multi-class classification models is presented.