Reviewerly/exHarmony huggingface dataset:
Re-defining the Climatic Zones over Nigeria

Aims: This study aimed at identifying climatic zones over Nigeria.&#x0D; Duration of Study: Daily air temperature data for the 36 states and FCT in Nigeria were obtained from National Aeronautic and Space Administration (NASA) for the period of thirty-seven (37) years (1984-2020).&#x0D; Method: Several hierarchical clustering procedures–single linkages, complete linkage, average distance within clusters, average distance between clusters, centroid clustering, median linkage and Ward’s method were used in this study.&#x0D; Results: Based on the findings, median linkage, complete linkage and Ward’s clustering method solutions seems more realistic. The application of cluster analysis, revealed five climate zones (cluster) over Nigeria. Cluster 1 covers Plateau state only while cluster 2 covers three south-west states and one north-west state (Kaduna). Cluster 3 covers all the states in south-east, south-south (except Cross River), North-central (except Kogi and Kwara), Bauchi in North-west and ogun and Oyo in South-west. Cluster 4 covers one state in south-south (Cross River), South-south (Lagos), two states in North-central (Kogi and Kwara), three states in North-east (Adamawa, Gombe and Taraba) and four states in North-west (Jigawa, Kano, Kastina and Zamfara). Lastly cluster 5 covers two states in North-east (Borno and Yobe) and North-west (Kebbi and Sokoto).&#x0D; Conclusion: Our findings clearly show that cluster analysis can be applied to identify similar weather/climate state from air temperature data.
Management Information System and Corporate Decision Making in a Digital Economy: A Study of Nigeria’s Power and Energy Firms

Information has become a valuable resource for corporate management in today’s digital and knowledge-based economy. This study was carried out to examine the relationship between management information system and corporate decision making among selected energy and power companies in Delta State, Nigeria. The objectives of the study include to ascertain the influence of MIS on quality of decision making, business innovativeness, and service delivery. The study adopted a survey research design as primary data were collected through deployment of structured questionnaires as research instruments. The study population was drawn from staff of three (3) selected energy and power transmission companies in Delta state. The hypotheses formulated for the study were tested using the chi square analysis at 5% level of significance. Findings of the study indicate that implementation of MIS influences the decision-making process has a positive impact on business innovativeness. Further, the study shows a positive and significant effect of MIS on the service delivery of energy and power firms in Delta State, Nigeria. The study concludes that implementation of MIS is imperative to enhance informed corporate decision making in a digital economy and recommends amongst others that companies should use modern ICT that will enhance business innovativeness and by extension service delivery.
Using Poems in Learning English as a Foreign Language at the Secondary Level of Education in Bangladesh

In Bangladesh, English is a compulsory subject at the secondary level, where it is taught and learned as a foreign language. Literary texts have long been used as a tool for it. Generally, two types of textbooks are used in classrooms at the secondary level: compulsory NCTB textbooks and Grammar books of various authors. The purpose of this research was to investigate the use of poetry in NCTB textbooks and its effects on the language learning process at the secondary level of education. This study also tried to find out the range and usability of the poems that are included in the textbook. For this study, data was collected from both the learners and the instructors of Secondary Level education through a set of questionnaires. Then the data was analyzed to find out the appropriateness, usability, understandability, and effectiveness of poems on the overall language learning process. Finally, this study attempted to explore the way out of the existing and probable obstacles to ensure the intended outcome of using poetry in learning English as a foreign language.
Improving 3D Pose Estimation for Sign Language

This work addresses 3D human pose reconstruction in single images. We present a method that combines Forward Kinematics (FK) with neural networks to ensure a fast and valid prediction of 3D pose. Pose is represented as a hierarchical tree/graph with nodes corresponding to human joints that model their physical limits. Given a 2D detection of keypoints in the image, we lift the skeleton to 3D using neural networks to predict both the joint rotations and bone lengths. These predictions are then combined with skeletal constraints using an FK layer implemented as a network layer in PyTorch. The result is a fast and accurate approach to the estimation of 3D skeletal pose. Through quantitative and qualitative evaluation, we demonstrate the method is significantly more accurate than MediaPipe in terms of both per joint positional error and visual appearance. Furthermore, we demonstrate generalization over different datasets. The implementation in PyTorch runs at between 100-200 milliseconds per image (including CNN detection) using CPU only.
Learning and reasoning with graph data

Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields ranging from logic-oriented knowledge representation and reasoning to graph kernels and statistical relational learning. In this review we outline a broad map and inventory of the field of learning and reasoning with graphs that spans the spectrum from reasoning in the form of logical deduction to learning node embeddings. To obtain a unified perspective on such a diverse landscape we introduce a simple and general semantic concept of a model that covers logic knowledge bases, graph neural networks, kernel support vector machines, and many other types of frameworks. Still at a high semantic level, we survey common strategies for model specification using probabilistic factorization and standard feature construction techniques. Based on this semantic foundation we introduce a taxonomy of reasoning tasks that casts problems ranging from transductive link prediction to asymptotic analysis of random graph models as queries of different complexities for a given model. Similarly, we express learning in different frameworks and settings in terms of a common statistical maximum likelihood principle. Overall, this review aims to provide a coherent conceptual framework that provides a basis for further theoretical analyses of respective strengths and limitations of different approaches to handling graph data, and that facilitates combination and integration of different modeling paradigms.
CONTRIBUTION OF SOCIAL ATTITUDE, LEARNING INTEREST, AND TEACHER COMPETENCE TO STUDENT ADAPTABILITY

This study aimed to determine the contribution of social attitude, learning interest, and teacher competence to the adaptability of fifth grade students at SD Semester II Cluster 3, Tabanan District, Tabanan Regency. This research was an ex post-facto study with a population of 137 students. Sampling applied saturated sampling technique. Research data was collected using a questionnaire instrument. Research data were analysed using multiple regression and partial correlation. The results showed that (1) the significant contribution of social attitudes to adaptive skills was 23.50% and the effective contribution was 8.52%; (2) a significant contribution of interest in learning to adapt skills of 32.9% and an effective contribution of 15.60%; (3) the significant contribution of teacher competence to adaptive skills was 37.50% and the effective contribution was 24.97%; and (4) the combination of the contribution of social attitudes, learning interest, and teacher competence to students' adaptive skills was 49.10%.
Developing an Online Game-Based Curriculum System for Martial Artists in Public Sports Using Big Data Technology

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
An Analysis of Students’ Difficulties In Listening Comprehension at English Education Department in Faculty of Teacher Training And Education of Universitas Ekasakti

Listening is part of the English language skills component which is quite complex in EFL (English as Foreign Language) learning practices. Difficulties in this skill is experienced by students in the Indonesian context consisting of various ethnicities, languages and cultures. Listening skill would enable people to improve their ability in communication. However, in some cases of EFL classes, college students still find difficulties in learning this skill since it needs more attentions and concentrations in order to comprehend the language materials. This study tries to discover the listening difficulties encountered by English Education Department students of faculty of teacher training and education in Universitas Ekasakti while taking the listening courses. The design of this research was descriptive research with population were the second-year students who have taken the Listening comprehension. They were 17 members . The researcher used total sampling technique to take the sample so all population was sample. The instrument used was listening comprehension test. The results of the study showed that there are four main categories of students’ difficulties in listening, namely: difficulties related to the learning materials (unfamiliar topics, topic that doesn’t exist in students’ cultures, too many unfamiliar words, and the length of the spoken texts); difficulties related to the speakers (unclear pronunciation, varieties of accents, and the speed of speaking); difficulties related to the listeners (inferring the meanings of unknown words, losing concentration, memory, and the length of the questions); and difficulties related to the physical settings (noises around, the online learning media used, and the internet connection).
Metastasis Detection of Breast Cancer using Ensemble Deep Learning

Breast cancer is one of the diseases which mainly affects women and is responsible for most of the deaths in Brazil, followed by skin and lung cancer. Among the consequences of occurrence, there are genetic predisposition, sedentarism, and late menopause, for example. The metastatic stage of this illness has a low survival rate because the disease spreads from the breast to other parts of the body, and the patients need the diagnosis as fast as possible to start the treatment. Moreover, state-of-art works claim that pathologists can reach 0.72 AUC in analyzing an exam composed of thousands of histopathologic images of lymph node sections. In this context, this work presents an Ensemble Convolutional Neural Network with Transfer Learning, called U-net VGG19, for detection using the PatchCamelyon dataset. Results indicate that the proposal reached an AUC of 0.9565 and a loss of 0.2869, reaching better results than state-of-the-art CNNs such as VGG16, VGG19, MobileNetV3Large, ConcatNet, and a custom-made CNN.
Development of Quizizz Game-Based Interactive Learning Media to Improve Learning Outcomes

In this digital era, it is very important to understand and use technology wisely to adapt to digital transformation. Technology affects the quality of education in these turbulent times. A game is an interesting learning medium with an interactive nature. Currently, educators can choose Quizizz as an interesting learning environment supporting the learning process. This study aims to provide teachers with interactive learning media interventions based on quizizz games in Economics class X SMA Darul Hadits, Sepulu District, Bangkalan Regency. This time, learning media development uses the research model (ADDIE). The research subjects included in the development of interactive learning media are validation tests by media validators, practitioner validators, and material validators, as well as tests aimed at students. Research data were obtained through observation techniques, questionnaires, and interviews with economics teachers and several class X SMA Darul Hadits regarding the learning process. The study results show that the teacher uses lecture and discussion methods using LKS and PowerPoint media in economics learning. So, with this, the researcher uses the Quizizz game application to support student learning enthusiasm so that later it will improve student learning outcomes. This study got a very good response from teachers and students.
Project Communication Management in a Micro Small Medium Enterprises (MSMES) in a Construction Project

ABSTRACTMicro, small, and medium enterprises (MSMEs) have become Indonesia's most essential and strategically important national economic drivers. This research focuses on an MSME involved in a construction project in 2021. This research aims to shed light on the project communication management of an MSME organization, PT Nata Kota Nusantara, simply Natakota, a micro-scale enterprise based in Bogor. Natakota was engaged in a civil construction project in a remote site area at PT GDE (GDE) in Ciwidey, Bandung. GDE is a state-owned enterprise focusing on geothermal energy and power generation. Virtual ethnography, observation, and discourse analysis were performed to reveal the project communication management process within the organization. It was unveiled that Natakota used Google Spreadsheet as a form of free and open-source software (FOSS) as their primary communication tool during the project execution. Actor-Network Theory (ANT) explains that the actor is the prime entity that shapes, drives, and accelerates the communication process within the network. This research observed the communication and collaboration between employees on several Google Spreadsheet files. In a project execution phase, communication is essential to obtain a productive project. Project communication plays a central part in communicating information flow to execute that project successfully. The results of this research show that several features in Google Spreadsheet play as actors in the ANT framework, which shapes, drives, and accelerates the internal communication process in the organization. Additionally, it appears that Google Spreadsheet could offer a competitive low-cost option for MSMEs looking to manage internal communications effectively and efficiently.Keywords: collaboration, communication, MSMEs, organization, project
Fine-Tuned PEGASUS: Exploring the Performance of the Transformer-Based Model on a Diverse Text Summarization Dataset

Text summarization is the art of succinctly capturing the essence of a lengthy text document through a concise summary.The intricate craft of text summarization involves distilling a voluminous text document into a brief and elegant summation that conveys its core message.Ultimately, the goal of text summarization is to help on grasping the essence of a text without having to wade through its entire length.In this research, we propose to fine-tune and explore the quality performance of the deep learning and transformer-based PEGASUS model for abstractive text summarization on a diverse dataset.The diversity of the dataset is expected to challenge the model and test its capabilities in generating summaries for a wide range of text types and styles.Our experimental results indeed indicate that the model's performance varies based on the topic and category of the text reaching as high as 88.03 F1 (ROUGE-1) score with some topics and as low as 81.22 with others.This is crucial as texts, such as political, economic, literary, legal, and medical, have distinctive writing conventions and styles, and a model that performs well on a diverse dataset is more likely to adapt to other text types.
Real-Time Controllable Denoising for Image and Video

Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising strength requires performing network inference each time, making it almost impossible for real-time user interaction. In this paper, we introduce Real-time Controllable Denoising (RCD), the first deep image and video denoising pipeline that provides a fully controllable user interface to edit arbitrary denoising levels in real-time with only one-time network inference. Unlike existing controllable denoising methods that require multiple denoisers and training stages, RCD replaces the last output layer (which usually outputs a single noise map) of an existing CNN-based model with a lightweight module that outputs multiple noise maps. We propose a novel Noise Decorrelation process to enforce the orthogonality of the noise feature maps, allowing arbitrary noise level control through noise map interpolation. This process is network-free and does not require network inference. Our experiments show that RCD can enable real-time editable image and video denoising for various existing heavy-weight models without sacrificing their original performance.
Are Data-Driven Explanations Robust Against Out-of-Distribution Data?

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts.
Automatic High Resolution Wire Segmentation and Removal

Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds. We observe several unique challenges: wires are thin, lengthy, and sparse. These are rare properties of subjects that common segmentation tasks cannot handle, especially in high-resolution images. We thus propose a two-stage method that leverages both global and local contexts to accurately segment wires in high-resolution images efficiently, and a tile-based inpainting strategy to remove the wires given our predicted segmentation masks. We also introduce the first wire segmentation benchmark dataset, WireSegHR. Finally, we demonstrate quantitatively and qualitatively that our wire clean-up system enables fully automated wire removal with great generalization to various wire appearances.
Methodology as the Guide of the Cognitive Process (Feedback on of the Textbook by I.V. Ponkin and A.I. Lapteva: Methodology of Research and Applied Analytics. 3rd edition, 2022)

The publication presents a review of the new edition of the textbook: Igor V. Ponkin, Alena I. Lapteva. Methodology of Scientific Research and Practical Analytics: Third edition, 2022.
The Role of Technology Continuance Theory in Willingness to Continue Online Learning Among Fisheries and Aquaculture Students During The COVID-19 Pandemic

Higher education institutions have been forced to close campuses and shift towards online learning to prevent the spread of the coronavirus disease (COVID-19). Although the curriculum requires laboratory sessions, fisheries and aquaculture students from a public university in Malaysia shifted their training online, a completely different and unexpected scenario. Due to the virus's rapid spread, the online sessions were not properly prepared by the university but still needs to be performed for the students. As the pandemic forces higher institutions to continue with online learning, understanding the factors influencing students' willingness to continue online learning is crucial for both parties. The study adopted the Technology Continuance Theory and used a purposive sampling method, gathering 294 respondents from the online survey. The study also examined the online learning service quality, conformation, perceived usefulness, perceived ease of use, satisfaction, attitude, performance and fisheries and aquaculture students’ willingness to continue using online learning during the pandemic. Moreover, the study extended the predictive power by introducing the serial mediation of perceived usefulness, perceived ease of use, attitude, performance and satisfaction. Using SmartPLS, the results revealed that all of the hypotheses were supported. Ultimately, the findings provided essential information to understand further the student’s willingness to continue online learning during the pandemic. Furthermore, the results help the top management craft a better strategy and policy on online learning and develop a proper plan to allow students to return to campus life.