Reviewerly/exHarmony huggingface dataset:
Coping with the Challenges of Teaching English Online: Lessons from the Newbies

The COVID-19 school closure has forced teachers to move classes to virtual settings.For teachers who have never experienced teaching courses online, this technology-mediated teaching presents several challenges that could prevent them from delivering the teaching material effectively.This study employed an open-ended questionnaire to explore the main barriers faced by four English teachers who were new to remote teaching and the steps they did to transform faceto-face (F2F) classes into functioning e-learning courses.The paper showed the teachers' perspectives that might be useful for other novices in preparing for virtual classes.Findings showed three major points related to participants' attempts to overcome the limitations of technological competency and social barriers, the instructional strategies they used to develop the content for online classes through multimedia integration, and their reflections on teaching English online.Finally, the findings shed light on how to prepare and implement effective online teaching that meets the needs of beginning online teachers.
One or many project offices in educational organization of tertiary education: advantages and disadvantages

The article is devoted to the creating and management expediency of several project offices in an educational organization of tertiary education (hereinafter – OTE). The topic relevance is due to the growing number and types of ongoing projects that require proper organization and management and, consequently, governance of project management offices. The article presents the features of educational projects and the role of the project management office in the OTE system. The main types of project management offices at universities and their functions and tasks are described. Common elements of successful project management offices from around the world are highlighted based on a global study conducted by the Project Management Institute (PMI) together with PricewaterhouseCoopers (PwC) in 2021. The advantages and disadvantages of creating a single system of several project offices of different orientation and a single project management office at the university are discussed. Based on the information from the scientific literature and the conducted research, the authors conclude that in order to use all the advantages of the project office in full, it is necessary to create one project office in OTE. However, the authors also claim the need for further research to assess the feasibility of the practical implementation of their proposed solution.
The Construct Effects of Governance Indicators and Its Implication on Doing Business in Africa: Expatriates Perception. Using PLS-SEM Approach

There are many factors that entrepreneurs might consider before venturing into business.In the case of Africa, Africa's expatriates usually consider factors such as control of corruption, peace, stability, and other control indicators are necessary conditions for building one's confidence in venturing into doing business.Developing and implementing appropriate regulatory policies are excellent approaches to attracting business-oriented ex-pats.This will enhance business effectiveness and efficiency to boost economic growth in Africa.The study used the PLS-SEM approach to determine which variables had a significant impact on the ease of doing business in Africa.Findings make a significant contribution to existing knowledge on doing business.The research is in support of the United Nations Sustainable Development Goal 8.The results back country-specific policies to impel doing business and economic growth in Africa.
Small target location algorithm based on optimized template matching

Abstract At present, few published template matching algorithms can achieve the performance of commercial software. This paper studies and optimizes the existing geometric feature template matching algorithm, and combines it with the improved Fourier Merlin transform registration algorithm, and finally obtains a fast small target location algorithm. The matching time of the algorithm is 1-4ms, its positioning accuracy is sub-pixel level, the angle accuracy is less than 1 °, and it has good robustness to illumination. The experimental data show that the algorithm meets the requirements of real-time and accurate industrial automation production.
On a Scientific Approach for Deep Time Investigations

Abstract I came to geology through a metaphoric back door and was gifted a series of visionary mentors who imbued me with an outside‐the‐box view of Earth history. Coupled with remarkable advances in microscale analytical capabilities that arose just as I was graduating into the field, that unorthodox view permitted my research group to explore an alternate conception of the longstanding paradigm of earliest Earth history. As I look back on how the field has evolved in my 40 year career, I see a mixed record of significant advances in our understanding alongside a persistent groupthink that discourages creativity and individual responsibility. I've come to understand this as reflecting different intellectual requirements of deep time geology relative to the study of modern processes. This difference between the general understanding of the scientific method and our needs has given cover to an unconstructive habit. My field is ripe for a fundamental change in the way we address the meager and almost certainly biased geologic record. This disruption could lead younger scientists and future generations away from poorly justified consensus models toward a more complete understanding of the darkest age of our planet's history.
Hyperparameter optimization of tapping center machines model using robust whale optimization algorithm

To build a synchronization error prediction model for the machine tool efficiently, a robust whale optimization algorithm (RWOA) method proposed in this study is applied to the hyperparameter optimization of its model. The proposed RWOA method integrated a non-linear time-invariant inertia weighting (NTIW) method and a Taguchi-based adaptive parameter exploration (ATPE) to improve the performance of WOA and promote robustness. The NTIW method can improve the performance of other algorithms, so this study used the NTIW method in the WOA. In addition, the Taguchi method can get an excellent combination of variables with optimal values and stable performance, making the WOA robust. First, to verify the validity of the proposed RWOA method, 13 benchmark functions were used in this study. The results of the benchmark function tests include the mean, standard deviation, and p-value of the t-distribution test. The results show that 11 of the 13 functions differ significantly. In other non-significant difference functions, the means and standard deviations obtained by the proposed RWOA are considerably better than those obtained by WOA. Since the product cost of machine tools is higher, if a prediction model can be built effectively, it can reduce the cost. Therefore, in this study, the proposed RWOA was used to explore the best hyperparameter combination for the model. From the results, the model’s average MAPE (mean absolute percentage error) was 7.2604% for training data and 9.2603% for the testing data under 30 modeling runs. For the best one in 30 models, the MAPE was 6.8384% for the training data and 6.7372% for the testing data. This model was also introduced into the actual machine in this study, and the experimental results showed the MAPE 6.3447%. The proposed RWOA method effectively explores a suitable synchronization error model for the tool machine.
Improving Logical-level Natural Language Generation with Topic-conditioned Data Augmentation and Logical Form Generation

Logical Natural Language Generation, i.e., generating textual descriptions that can be logically entailed by a table, has been a challenge due to the low fidelity of the generation. Previous works have addressed this problem by annotating interim logical programs to control the generation contents and semantics, and presented the task of table-aware logical form to text (Logic2text) generation. However, although table instances are abundant in the real world, logical forms paired with textual descriptions require costly human labor, which limits the parallel data size. To mitigate this, we propose topic-conditioned data augmentation (TopicDA), which employs controlled sequence-to-sequence generation as auxiliary tasks to augment logical forms and textual descriptions directly from tables. We further introduce logical form generation (LG), a dual task of Logic2text that requires generating a valid logical form based on a text description of a table. We hence propose a semi-supervised learning approach to jointly train a Logic2text and an LG model with both labeled and augmented data. Experimental results on both Logic2text and LG demonstrate that our approach can effectively utilize the augmented data and outperform supervised baselines by a substantial margin.
Causal Structural Learning via Local Graphs

.We consider the problem of learning causal structures in sparse high-dimensional settings that may be subject to the presence of (potentially many) unmeasured confounders, as well as selection bias. Based on structure found in common families of large random networks, we propose a new local notion of sparsity for structure learning in the presence of latent and selection variables, and develop a new version of the fast causal inference (FCI) algorithm, which we refer to as local FCI (lFCI). Under the new sparsity condition and an additional assumption that ensures that conditional dependencies can be determined locally, lFCI is consistent and offers reduced computational and sample complexity when compared to standard FCI algorithms. The new notion of sparsity allows the presence of highly connected hub nodes, which are common in real-world networks but problematic for existing methods. Our numerical experiments indicate that the lFCI algorithm achieves state-of-the-art performance across many classes of large random networks, and its performance is superior to that of existing methods for networks containing hub nodes.Keywordscausal inferencestructural learninggraphical modelslatent variablehigh dimensionalalgorithmMSC codes62F9962H9962P10
Integrating Machine Learning Algorithms for Predicting Solar Power Generation

In recent years, there has been a growing interest in using artificial intelligence (AI) techniques to predict solar power generation. One such technique is the use of an artificial neural network (ANN) with a genetic algorithm (GA) to optimize its parameters. This approach involves training an ANN to predict solar power generation based on historical data and using a GA to optimize the ANN’s architecture and activation function. The GA searches for the best combination of hidden layers and activation functions to minimize the error between the predicted and actual solar power generation. This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.
Smart Resume Analyzer

Smart Resume Analyzer is a project aimed at simplifying the job recruitment process by automating the initial screening of job applications. The system utilizes natural language processing and machine learning techniques to analyze and extract relevant information from a candidate's resume. The extracted information is then used to evaluate the candidate's qualifications against the job requirements, providing a score that reflects their suitability for the position. The system also offers personalized feedback to candidates to help them improve their resumes and increase their chances of landing a job. Smart Resume Analyzer aims to streamline the recruitment process for employers and candidates alike, saving time and increasing the likelihood of finding the best-fit candidates for open positions. The software will be able to extract relevant information such as work experience, education, skills, and achievements, and match them against job descriptions to identify the best fit for the job
Compensated Attention Feature Fusion and Hierarchical Multiplication Decoder Network for RGB-D Salient Object Detection

Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when the object is not salient in the depth maps. Furthermore, most existing (UNet-based) methods cannot fully exploit high-level abstract features to guide low-level features in a coarse-to-fine fashion. In this paper, we propose a compensated attention feature fusion and hierarchical multiplication decoder network (CAF-HMNet) for RGB-D SOD. Specifically, we first propose a compensated attention feature fusion module to fuse multi-modal features based on the complementarity between depth and RGB features. Then, we propose a hierarchical multiplication decoder to refine the multi-level features from top down. Additionally, a contour-aware module is applied to enhance object contour. Experimental results show that our model achieves satisfactory performance on five challenging SOD datasets, including NJU2K, NLPR, STERE, DES, and SIP, which verifies the effectiveness of the proposed CAF-HMNet.
Categorizing Simulation Models Using Convolutional Neural Networks

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Whether as an optimization problem or a development tool, neural networks help engineers to work more efficiently. This paper’s central aspect is to add metadata to the core files of the project simulation data. To understand the project and its simulation models, a pre-processing methodology and convolutional neural network architecture are presented. With the added labels, it is possible to access the content of the model files of an engine performance simulation tool without examining them. At first, a pre-processing approach and its design are introduced to extract and filter the desired data from the XML data structure. Then, the data is split into sequences and paired with labels. Expert knowledge is used to label the models. These labels are further paired with the extracted sequences. In addition, a convolutional neural network design with a single convolutional layer and three dense layers is presented to add the defined labels that characterize the powertrain architecture to these sequences. Furthermore, this paper shows the advantages and disadvantages of the pre-processing methodology and the architecture of the convolutional neural network. The results show that the model can categorize the extracted sequences with a very good accuracy. At last, suggestions for improvement for the pre-processing and additional studies are presented.&lt;/div&gt;&lt;/div&gt;
A Study on Korean Grammar Education for Vietnamese Korean Language Learners

한국과 베트남이 우호 관계를 수립한 지 30여 년, 한국 내 베트남인 한국어 학습자는 중국인 학습자의 뒤를 이어 그 수가 급증하였다. 고급의 한국어 학습자에 대한 수요 역시 늘어나 한국어 문법교육의 중요성이 점차 강조되고 있는 현 상황에서 문법적 차이가 큰 베트남인 한국어 학습자를 위한 문법교육 연구 성과에 대한 검토와 베트남에서의 한국어교육 현황에 대한 비판적 연구는 한국어 문법교육의 과제와 방향을 제시하는 데 필수적이다. 한국의 ‘학술정보연구서비스’(RISS)에서 검색한 자료와 베트남의 베트남 한국학술연구회(KRAV)의 학술지 ‘HAN QUOC’(한국 잡지)에 수록된 논문들을 분석하고, 베트남 주요 대학 9곳의 한국어 문법교육 현황을 조사한 결과 베트남인 한국어 학습자를 위한 한국어 문법 연구와 교육은 다음과 같은 문제가 나타났다. 한국어 문법교육의 연구 주제가 다양하지 않고, 새로운 교수 모형을 개발하거나 교수 방법에 따른 실질적 효과를 검증하는 등 수준 높은 연구를 찾아보기 어렵다. 또한 베트남인 학습자를 위한, 현지화된 한국어 문법 교재가 없고 교수 방법도 많이 개선되었다고는 하나 교육 여건상 교사 중심의 수업이 주를 이루고 있다. 본 연구에서 베트남인 한국어 학습자를 위한 한국어 문법교육의 발전된 미래를 기대하면서 공동 연구 등 몇 가지 개선 방안을 제시하였다. Recently, many exchange activities have been promoted in various fields between Viet Nam and Korea with the aim to comprehend as well as deepen the partnership of the two countries, especially in the aspect of education. Viet Nam ranks second in the number of Korean learners in South Korea after China. During the process of studying Korean, there is no room for doubt that grammar plays an important role, that’s why I started this study with curiosity about how Korean grammar education and research are conducted for Vietnamese Korean learners while there are many differences between Korean and Vietnamese grammar. After analyzing data searched by Korea"s “Academic Information Research Service”(RISS) and papers contained in Vietnam"s “HAN QUOC”(Korean magazine), and investigating data about the current status of Korean grammar education at 9 Vietnamese universities, I found that there are several problems remain. The research topics of Korean grammar education are not diverse enough. In addition, it is difficult to find high-quality studies such as developing a new teaching model or verifying the practical effects of teaching methods. Although teaching methods have improved a lot, there are no localized Korean grammar textbooks for Vietnamese learners and teacher-centered classes are dominated due to educational conditions. In this study, several improvement measures are also proposed in anticipation of the developed future of Korean grammar education for Vietnamese Korean learners.
Facial emotion recognition using convolutional neural network based krill head optimisation

Abstract Emotion recognition from facial image expression is always a problem for computer vision algorithms in classifying the human facial emotions. On other hand, over fitting is considered as a fundamental problem that prevents the deep learning model to fit the observed data on training data and unseen data on the test data. In this paper, we develop a convolutional neural network (CNN) based emotion recognition from facial images. Initially the study uses proper background removal tool to remove the image background and secondly, we use CNN to classify the facial features based mainly on the extraction of facial feature vector. The CNN is trained with huge number of facial features from the input images causes over fitting. This is mainly due to noise presence, limited training data size and classifier complexity and to avoid this, the study uses krill herd optimisation (KHO) that enables the fitting of the classifier. The simulation is conducted to test the efficacy of the model with expressional vector that recognizes the five types of facial features. The database consists of 20,000 images collected from 300 persons and the study obtains an accuracy of 98.6% during training and 98.45% during testing. The comparative analysis between various other deep learning models show that the proposed CNN‐KHO has higher rate of accuracy than other methods.
The effect of using entry-level keyboards in improving user-adaptability to new keyboards: Case of the Central-Bantu keyboards

Current trends in keyboard design show that QWERTY-similarity is a key factor for high user-adaptability keyboard design. This design approach has the challenge that the higher the similarity the lower the text-entry rate for the optimized keyboard. This article reports on the findings of an empirical study which we conducted on QWERTY-users to measure the effect of using an entry-level keyboard in improving user-ability to adapt to a new keyboard. The study used two Central-Bantu physical keyboards (entry-level: with high QWERTY-similarity, and advanced-level: with low similarity) which we had designed in an earlier study. The empirical study obtained learning-curves of the Advanced-level keyboard, of a 12-participant group which was first introduced to an entry-level keyboard, against a control-group of similar size, in a longitudinal study design. A two-sample t-test on the empirical results showed that the entry-level approach caused a marginally significant text-entry-rate improvement of 9.4% with p < .09. A two-sample U-test on word-error rates indicated a non-significant improvement of 8.4%. Our study has shown that the use of entry keyboards is an effective strategy in improving keyboard user-adaptability.
Wheel drive control system of an under-ice robot for monitoring underwater objects

The aim of the study is to develop a design and to study the control system of a wheeled underwater robot with adjustable buoyancy for under-ice monitoring of underwater objects.As part of the study, the task is to generate a mathematical model and its computer implementation for the controlled movement of a wheeled robot on the inner surface of ice, as well as to set up computational experiments demonstrating the operability of the proposed automatic control system for an anti-slip device.The work uses both classical methods for constructing and studying mathematical models and control systems for mobile robots, as well as original methods for numerically integrating systems of nonlinear differential equations that describe the effects of slipping the robot wheels relative to the supporting ice surface.The novelty of the work lies in using an original way of moving a wheeled robot at the interface of media with regulating the normal reaction by controlling the volume of wheeled air cylinders and controlling slippage using an automatic control system that improves the drive energy efficiency while accelerating.In the course of the study, the authors developed: an original design and a method of moving a robot for monitoring underwater objects, a design diagram of the device and a schematic design of the robot drive control system. A mathematical model is created that describes the object controlled motion and a set of computational experiments is performed to prove the applicability of the proposed methods for implementing movement without slipping the driving wheels of a mobile robot.The method of moving proposed in the paper has a number of key advantages in monitoring underwater objects; however, implementing the advantages in practice is associated with the necessity to use a system for controlling slippage of the robot wheels with a supporting surface. The paper proposes the structure and algorithms of the control system to ensure movement with minimal slippage, and computational experiments on the mathematical model of the device prove its applicability and effectiveness.
A Study on the Public Opinion on Online Banking in India

These papers discuss the Financial balance in the online banking . The executives Framework is an application for keeping up an individual's record in a bank. In this task I attempted to demonstrate the workings of a financial record framework and spread the fundamental usefulness of a Ledger The executives Framework. To build up an undertaking for explaining budgetary utilizations of a client in banking condition so as to sustain the necessities of an end banking client by giving different approaches to perform banking assignments. Likewise to empower the client's work space to have extra functionalities which are not given under a traditional financial venture. The Financial balance The board Framework attempted as a venture depends on important innovations. The principle point of this undertaking is to create programming for Financial balance The executives Framework. This venture has been created to do the procedures effectively and rapidly, which is absurd with the manuals frameworks, which are overwhelmed by this product. The sample size is 839.The primary information is collected from the public.The statistic used are chi square,Anova and the independent variables are correlated with the dependent variable and analyzed.the paper concluded that the online banking in India is in neutral positions between good and bad