Reviewerly/exHarmony huggingface dataset:
Introduction

This chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review how to use MATLAB, TensorFlow, software R, and WEKA from New Zealand, etc., as typical platforms for developing deep learning applications.
Deep Learning Platforms

There are a plethora of deep learning platforms available at present. The famous one is MATLAB deep learning toolbox developed by MathWorks which simplifies deep learning computations and reduces the workload of debugging in programming. Other platforms include Python-based platforms such as Google Colaboratory “Colab” and TensorFlow. The platforms such as software R and software WEKA also could be applied to the deep learning computations. In this chapter, we will introduce these platforms one by one.
Generative Adversarial Networks and Siamese Nets

In this chapter, we will emphasize computational iterations in GANs (i.e., generative adversarial networks) [46]Generative Adversarial Network (GAN) and Siamese nets [3, 6, 15]Siamese. In deep learning, these models are named as contrastive networks [3]. Additionally, we will deeply learn deep learning and interpret how information theory has been applied to the implementations of deep learning models.
Convolutional Neural Networks and Recurrent Neural Networks

In this chapter, we will introduce the typical deep neural networks from the viewpoint of Convolutional Neural Network (CNN or ConvNet)Convolutional Neural Network (CNN) family, especially Region-based CNN (R-CNN)Region-based CNN (R-CNN), Single Shot MultiBox Detector (SSD)Single Shot multibox Detector (SSD), and You Only Look Once (YOLO)YOLO. Capsule Neural Network (CapsNet)CapsNet has taken a topological structureTopological structure of a scene into consideration. The output will be a vector to reflect this geometric relationship.
Abduction as Deductive Saturation: a Proof-Theoretic Inquiry

Abstract Abductive reasoning involves finding the missing premise of an “unsaturated” deductive inference, thereby selecting a possible explanans for a conclusion based on a set of previously accepted premises. In this paper, we explore abductive reasoning from a structural proof-theory perspective. We present a hybrid sequent calculus for classical propositional logic that uses sequents and antisequents to define a procedure for identifying the set of analytic hypotheses that a rational agent would be expected to select as explanans when presented with an abductive problem. Specifically, we show that this set may not include the deductively minimal hypothesis due to the presence of redundant information. We also establish that the set of all analytic hypotheses exhausts all possible solutions to the given problem. Finally, we propose a deductive criterion for differentiating between the best explanans candidates and other hypotheses.
Manifold Learning and Graph Neural Network

In this chapter, we will introduce manifold learning and graph neural networks. We hope to introduce graphical probability models as the starting point of basestone. We need to introduce our readers why we should study graphs, what we can benefit from the graphs. Furthermore, we will introduce graph neural networks (GNN) and how to combine GNN with manifold learning together.
Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis

In education, space exists for a tool that valorizes generic student course evaluation formats by organizing and recapitulating students’ views on the pedagogical practices to which they are exposed. Often, student opinions about a course are gathered using a general comment section that does not solicit feedback concerning specific course components. Herein, we show a novel approach to summarizing and organizing students’ opinions as a function of the language used in their course evaluations, specifically focusing on developing software that outputs actionable, specific feedback about course components in large-enrollment STEM contexts. Our approach augments existing course review formats, which rely heavily on unstructured text data, with a tool built from Python, LaTeX, and Google’s Natural Language API. The result is quantitative, summative sentiment analysis reports that have general and component-specific sections, aiming to address some of the challenges faced by educators when teaching large physical science courses.
Eliciting Informative Priors by Modeling Expert Decision Making

There are significant limitations to current methods for eliciting the prior beliefs of experts. To combat some of these limitations, this paper proposes an alternative approach that infers an expert’s prior beliefs about an uncertain event, A, from the expert’s past decisions. We show that an analyst can use past information on an expert’s decision-making task, contingent on an expert’s prior of A, to model the decision-making process and infer an approximation of the prior for A. This concept is illustrated by an application to recidivism. We conclude this work by highlighting important directions for future research. Funding: J. R. Falconer’s research is funded through the University of Waikato Doctoral Scholarship.
Vulnerability to Contamination of the Olón Aquifer Using the GOD Method, Olón-Ecuador

Vulnerability is an indicator of foreseeable risks in aquifer’s quality, considering the environment and anthropogenic implications. This measure is determined by different conditions and realities of a system, such as air and water quality, economic studies, health and hygiene and landslides. Aquifers are prone to contamination by extrinsic and extrinsic factors affecting water quality. This work focuses on determining the vulnerability to contamination of the Olón aquifer, using the Groundwater Overlaying Depth (GOD) method for zoning the areas susceptible to contamination. The methodology consisted of three phases: (i) gathering of basic hydrogeological information; (ii) Application of geographic information systems according to the GOD method in the Olón aquifer; (iii) Analysis of the findings and elaboration of the vulnerability map. The results indicate that the aquifer, in its entire domain, has a high vulnerability index, mainly due to its intrinsic conditions and their relationship with anthropic activities in the sector. This methodology has made it possible to detect predominant factors that influence contamination and provides details for strategic planning of measures to be considered to preserve the resource and its nature. The extreme vulnerability near the coastline is caused by human settlement and tourist activities. This methodology made it possible to detect the predominant factors polluting the aquifer.; therefore, measures for protecting the community and its resources must be taken.
Deep Learning Models for Moving Vehicle Detection in Traffic Surveillance Video for Smart City Applications

In the last few decades, CCTV surveillance has become a piece of unavoidable equipment in public places. The most important application is for traffic monitoring. Recently, accident cases have been rapidly increasing day by day. So, continuous analysis of vehicles is a challenging task. Developing an algorithm to find a specific vehicle or pedestrian is highly beneficial in such cases. Such a procedure can be done by video processing. Video processing and its applications have improved over the last few years. The advancements in this area are helpful in numerous fields. At the same time, the intervention of artificial intelligence also has a relevant role in this area. Machine learning has also become essential and valuable in various complex cases. Image classification using artificial intelligence provides a highly accurate result rather than any other classification method. This paper mainly compares different convolutional neural networks on moving objects detected by background subtraction and GMM with the morphological operation.
Usability Evaluation of an Intelligent Mobile Chatbot for Crop Farmers

The need for a mobile chatbot for crop farmers has been established by many as one major contributor to the sustainable development goals. Most of these chatbots adopt machine learning (ML) approaches. We previously developed a chabot with the ontology-based representation of domain knowledge along with word shuffling and ML-based Jacquard Similarity algorithm. However, its usability evaluation was not considered. This paper aims at evaluating the effectiveness of the developed mobile chatbot through a usability testing method called the Think-aloud Protocol. Using this approach, participants in testing express their thoughts on the applicationwith ontology-based representation while executing set tasks. The users who had the chatbot already running, query the chatbot with randomly selected thirty (30) questions asked in different ways. The result of the experiment shows that twenty-five (25) questions were answered correctly. The user's intent was also considered. The percentage of accuracy of the chatbot is calculated to be 83.3%. The usability evaluation of the developed chatbot gives an acceptable satisfactory average score of 78.5%, being a clear indication that the chatbot is perceived positively and liked by the users as it is efficient, effective, user-friendly, flexible and easy to use. The chatbot usability evaluation results can guide decision makers on the right policies that will affect farmers positively through quality agricultural services for improved agricultural productivity and food security.
Integration III

This chapter addresses the process of integration by parts, by substitution, with power series, and using published tables. We try integration by parts if the integral is a product and one term of the product gives a constant when differentiated (one or more times). Integration by parts allows us to integrate products that cannot be integrated more straightforwardly. Occasionally, we need to integrate by parts more than once; we call this procedure ‘successive reduction’. Meanwhile, we try integration by substitution if the integral is a function of a function. We can also check if the integral is a standard function available from published tables or can be rearranged to give such an integral.
The strength of digital transition in higher education as a success factor

This accelerated societal transformation forced students, academic staff, academic organizations, and society in general to make a rapid transition to digital. In addition, the physical mobility of students, teachers, and nonteaching staff was replaced, in many cases, by digital mobility. Thus, new forms of virtual mobility were developed that continued to allow learning between people from institutions in different countries, preventing the breaking of healthy international academic ties. Likewise, conferences and other scientific meetings took place online. The high speed at which this digital transition is taking place has shown that we are facing a new world where communication and the individual are at the heart of the equation, allowing for economic, environmental, time, and reflection gains, among other factors. On the other hand, social and educational relationships are rapidly changing, acting on and reacting to the digital transition. Perhaps it is not incorrect to say that, socially and technologically, the 21st century just began, forced by the pandemic originated by SARS-CoV-2. Methodologically, this chapter presents a literature review of the digital transition, mainly in European higher education institutions, without forgetting experiences in other countries. To this end, it uses various official and scientific publications on the subject in question, complementing it with a broader reading, using articles from newspapers. This complementarity allows us to launch the discussion on how this complex subjects reaches the general population in 2021.
A Bidirectional LSTM approach for written script auto evaluation using keywords-based pattern matching

The evaluation process necessitates significant work in order to effectively and impartially assess the growing number of new subjects and interests in courses. This paper aims at auto-evaluating and setting scores for individuals similar to those provided by humans using deep learning models. This system is built purely to decipher the English characters and numbers from images, convert them into text format, and match the existing written scripts or custom keywords provided by the invigilators to check the answers. The Handwritten Text Recognition (HTR) model fervors and implements an algorithm that is capable of evaluating written scripts based on handwriting and comparing it with the custom keywords provided, whereas the existing models using Convolutional Neural networks (CNN) or Recurrent Neural networks (RNN) suffer from the Vanishing Gradient problem. The core objective of this model is to reduce manual paper checking using Bidirectional Long Short Term Memory (BiLSTM) and CRNN (Convolutional Recurrent Neural Networks). It has been implemented more than the models built on conventional approaches in aspects of performance, efficiency, and better text recognition. The inputs given to the model are in the form of custom keywords; the system processes them through HTR and image processing techniques of segmentation; and the output formats the percentage obtained by the student, word error rate, number of words misspelt, synonyms produced, and the effective outcome. The system has the capability to identify and highlight errors made by students. This feature is advantageous for both students and teachers, as it saves a significant amount of time. Even if the keywords used by students do not align perfectly, the advanced processing models employed by the system possess the intelligence to provide a reasonable number of marks.
Development of creativity as a supra-professional skill in adult learners in business English classes

The aim of the study is to substantiate the expediency of using the communicative types of educational activities that require divergent thinking among postgraduate students within the framework of the business English programme. The paper considers the specifics of using the effective pedagogical technologies in working with adult learners (case technology, role-playing and intellectual games, problem-and-search-based and project activities) in order to provide the most favourable creative educational environment; describes the experience of developing the skill of creativity among postgraduate students within the framework of the business English programme. The scientific novelty lies in the fact that teaching English for specific purposes is considered in the context of creative andragogy and the following andragogical conditions for the implementation of the principle of educational process creativisation are identified: adaptability of teaching, individual approach, variability of teaching, as well as a balance between creative and traditional types of work. As a result of analysing scientific and methodological literature, observing and summarising accumulated pedagogical experience in this field, the authors present practical recommendations on the development of creativity in adults as an important supra-professional skill.
Integrated learning as a promising form of organising literary education at a university

The aim of the research is to characterise the main factors in the design of the educational process at the faculty of philology of a pedagogical university taking into account the dominant aspects of future teachers’ professional training. The paper considers interdisciplinary integration as an important trajectory for the effective acquisition of professional competencies and personal self-realisation by philology students in the process of studying literary disciplines. Special attention is paid to the conditions for the formation of literary competence in the aspect of integrating subject, technological and information-resource training, which meets the requirements of the Federal State Educational Standard of Higher Education for the competencies of future teachers. The scientific novelty of the work lies in determining the pedagogical conditions for the successful implementation of interdisciplinary integration as a meaningful complementarity of different academic disciplines in the training of future teachers and philologists at a university. As a result, it has been proved that interdisciplinary integration implemented in the teaching of literary disciplines at a university can become a fundamental methodological principle that resolves the contradiction between the separate subject training of a particular discipline and the formation of a holistic view of the profession without losing the quality of education.
Exploring and visualizing temporal relations in multivariate time series

This paper introduces an approach to analysing multivariate time series (MVTS) data through progressive temporal abstraction of the data into patterns characterizing behavior of the studied dynamic phenomenon. The paper focuses on two core challenges: identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior. The proposed approach combines existing methods for univariate pattern extraction, computation of temporal relations according to the Allen's time interval algebra, visual displays of the temporal relations, and interactive query operations into a cohesive visual analytics workflow. The paper describes application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match, illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.
Short-Term Forecasting of Daily Pan Evaporation Using Corrected Numerical Weather Forecasts Products

Numerical weather prediction (NWP) can provide vital information for pan evaporation (Ep) forecasts for the 16 days ahead, which is of great help to water resources management. However, the information for forecasting Ep usually requires bias corrections. This study was based on three bias correction methods [the equidistant cumulative distribution function method (EDCDFm; M1), XGBoost (XGB) with a single meteorological factor input (M2), and XGB with multiple meteorological factor input (M3)] and the meteorological data from 18 weather stations in southern China, the bias correction of meteorological factors forecasted by the second-generation Global Ensemble Forecast System (GEFSv2) was carried out. The results indicated the bias correction ability of the M3 method for GEFSv2 outputs was better than that of the M1 and M2 methods. It was a model-data error between GEFSv2 outputs and the corresponding observation data. Solar radiation exhibited the lowest error, whereas minimum temperature exhibited the highest. However, the M3 method decreased the forecast model-data error. In addition, this study compared the ability of three tree-based models to forecast Ep, namely, M5Tree (M5T), random forest (RF), and XGB. The XGB model had the highest forecasting accuracy for Ep. When the NWP outputs corrected by M1, M2, and M3 methods were used as the input of the XGB model, the averages of mean absolute errors (MAEs) at the 18 stations during the 1–16 day period ranged at 0.99–1.69, 0.78–1.14, and 0.78–1.07 mm/day, respectively. EP forecast showed the most significant error in the summer. Further, the relative humidity contributed the most to the Ep forecasting error. By addressing the issue of NWP outputs applied to Ep forecast, this study improves understanding of the bias correction method of NWP outputs and tree-based models to forecast Ep. It also improves understanding of the seasonal performance of Ep forecast and the impact of meteorological factors on forecast error that can inform future studies and models.
Continual learning approaches for single cell RNA sequencing data

Single-cell RNA sequencing data is among the most interesting and impactful data of today and the sizes of the available datasets are increasing drastically. There is a substantial need for learning from large datasets, causing nontrivial challenges, especially in hardware. Loading even a single dataset into the memory of an ordinary, off-the-shelf computer can be infeasible, and using computing servers might not always be an option. This paper presents continual learning as a solution to such hardware bottlenecks. The findings of cell-type classification demonstrate that XGBoost and Catboost algorithms, when implemented in a continual learning framework, exhibit superior performance compared to the best-performing static classifier. We achieved up to 10% higher median F1 scores than the state-of-the-art on the most challenging datasets. On the other hand, these algorithms can suffer from variations in data characteristics across diverse datasets, pointing out indications of the catastrophic forgetting problem.
System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value

This paper describes an extended experiment on a system that converts sentences with toxic expressions into safe sentences, along with the evaluation and influence of the system. In recent years, toxicity on social media has created many problems. We evaluated the effectiveness of the proposed system for identifying toxic sentences using a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) and then converting them into safe sentences using attention values and a score that indicates whether the sentences are appropriate after the predictive conversion. Six patterns of methods were tested, with Pattern 6 being the most effective for mitigating toxicity. This pattern is a technique that changes the way to take the top sentences of a beam search for each number of treatments, in addition to converting words with an attention value above a threshold and their adjacent words and phrases and words registered in the toxic dictionary. We used multiple indicators to judge the effectiveness of this method and evaluated its ability to make the text safe while preserving its meaning.
Archives Metadata Text Information Extraction into CIDOC-CRM

This paper presents an Information Extraction approach to extract events and entities from ISAD(G) elements with semi-structured text descriptions. Natural Language processing is done by using two methodologies: the ANNIE system, by defining proper Gazetteers and Jape rules to process the text and extract the intended information; and a reduced Portuguese BERT Language model that is fine-tuned for Semantic Role Labelling. The evaluation of the Information Extraction processes is done in a sample of 1000 records, for each type of information, and a corresponding dataset is manually built for each type of information considered, baptism events and passport requisitions. The CIDOC-CRM knowledge base is automatically populated with newly linked events and entities, using several automatic information extraction processes. The use of SPARQL queries to explore the information represented in CIDOC-CRM, obtained from the migration of DigitArq records and extracted from text descriptions, allows new ways of visualising the archival records and retrieving information from different sources, including archives digital repositories.