Reviewerly/exHarmony huggingface dataset:
Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting

The proliferation of classification-capable artificial intelligence (AI) across a wide range of domains (e.g., agriculture, construction, etc.) has been allowed to optimize and complement several tasks, typically operationalized by humans. The computational training that allows providing such support is frequently hindered by various challenges related to datasets, including the scarcity of examples and imbalanced class distributions, which have detrimental effects on the production of accurate models. For a proper approach to these challenges, strategies smarter than the traditional brute force-based K-fold cross-validation or the naivety of hold-out are required, with the following main goals in mind: (1) carrying out one-shot, close-to-optimal data arrangements, accelerating conventional training optimization; and (2) aiming at maximizing the capacity of inference models to its fullest extent while relieving computational burden. To that end, in this paper, two image-based feature-aware dataset splitting approaches are proposed, hypothesizing a contribution towards attaining classification models that are closer to their full inference potential. Both rely on strategic image harvesting: while one of them hinges on weighted random selection out of a feature-based clusters set, the other involves a balanced picking process from a sorted list that stores data features’ distances to the centroid of a whole feature space. Comparative tests on datasets related to grapevine leaves phenotyping and bridge defects showcase promising results, highlighting a viable alternative to K-fold cross-validation and hold-out methods.
An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.
Ethical compass: The need for Corporate Digital Responsibility in the use of Artificial Intelligence in financial services

Service research and business ethics literature intersect concerning the question of artificial intelligence (AI) service robot accountability. In financial services, there is a broad spectrum of potential ethical issues, from data usage to customer vulnerabilities. This article scrutinizes the impact of morality and where accountability resides in the use of AI service robots in financial services. To address this challenge, we discuss the role of Corporate Digital Responsibility (CDR) for firms and illustrate how to implement a conceptual framework on the ethical implications of AI service robot applications, drawing on normative ethical theory. The framework elaborates on how the locus of morality (from human to AI agency) and moral intensity combine within context-specific AI service robot applications, and how this might influence associated accountability. We provide examples of AI robots’ use for different purposes, differentiating between four 'accountability clusters': (1) professional norms, (2) business responsibility, (3) inter-institutional normativity, and (4) supra-territorial regulations cluster. We also discuss the CDR implications in different clusters. Ethical implications of using AI service robots and associated accountability challenges are relevant for a network of actors—from customers and designers to firms and the government. Implementation of the framework incorporates a range of internal and external stakeholders that firms need to consider. We also provide a CDR roadmap to incorporate a time perspective and to inform implementation efforts.
Machine Learning Training Optimization using the Barycentric Correction Procedure

Machine learning (ML) algorithms are predictively competitive algorithms with many humanimpact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.
Application of self-supervised learning in natural language processing

Self-supervised learning uses the label-free data learning model and has a significant impact on the NLP task. It reduces data annotation costs and improves performance. The main applications include pre-training models such as BERT and GPT, contrast learning, and pseudo-supervised and semi-supervised methods. It has been successfully applied in text classification, emotion analysis and other fields. Future research directions include mixed unsupervised learning, cross-modal learning and improving interpretability of models while focusing on ethical social issues.
Lexical Stance Markers in High-Density Passages

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Compiling Inventories of Lexical Stance Markers

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Connectives: Meanings and Functions

This chapter first considers the functional and semantic overlap between discourse connectives and discourse markers, where the latter is presented as including the former. Since the two categories share most protypical features, the fuzzy boundaries between the two categories are explained in terms of partial overlap.
Automata Theory

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
The Evolution of Connectives’ Meanings

In this chapter, we first sketch a number of assumptions underlying diachronic research in order to understand how researchers sketch the emergence of discourse connectives (and discourse markers) in language. We then review the discussion about the theoretical framework underlying the diachronic evolution of discourse connectives, that is, in which conceptual terms this linguistic process is best accounted for, grammaticalization or pragmaticalization. We then turn to a general description of the evolution from clause combining strategies to coordinating and subordinating connectives. Different case studies are presented in order to illustrate typical and less typical cases of language change in the area of connectives: the semantic evolution from temporal meaning to concessive meaning of French cependant ('yet'), the peculiar semantic evolution from cause to contrast of Italian però ('but') (5.3.2), and a diachronic account of the synchronic polysemy of French alors.
Validation of Converged Teaching Materials of Artificial Intelligence and English for High School Students to Improve Machine Translation Literacy

Objectives This study aimed to validate AI and English Integrated Teaching Materials for High School Students to Improve Machine Translation Literacy.&#x0D; Methods To accomplish this, we organized a group of experts in the field of English education and artificial in-telligence integrated education and conducted a Delphi survey. We collected opinions on the necessity of strengthening translator utilization skills, that is, machine translation literacy, as well as the goal, structure, and content of the classes, in order to validate foundational materials for lessons that enhance the utilization skills of artificial intelligence translation systems. After revising the contents, the second Delphi survey was conducted.&#x0D; Results The validity of each sub-question was analyzed based on .49 which is the standard value for securing content validity when the number of participants in the expert group was 15. As a result of the 1st Delpi survey, it was found that it was not valid in the content structure(.33), the content of the third class(.33), and the content of the third class worksheet(.46). After revising the contents, the second Delphi survey was conducted. Statistically significant figures were secured in all items such as class necessity, design, structure, and content of each class. The validity of the artificial intelligence convergence English education contents was secured, and the Delphi survey was completed in the second round.&#x0D; Conclusions AI-English integrated education is essential to improve Machine Translation Literacy. This study is of great significance in that it has validated AI integrated education contents that can strengthen Machine Translation Literacy by understanding the principles and differences of translators, identifying the causes of trans-lation differences, and judging translation errors. However, since Machine Translation Literacy and students' English skills are inseparable concepts, continuous research will be needed on integrated education methods that can improve them together.
Designing of Goods Separation Conveyor using GM65 QR Code Scanner Sensor

The sorting or separation process is crucial in the delivery of goods or packages, especially when viewed from the many regions in Indonesia. From the management system of separating goods which is generally still done manually, causing some goods to experience destination errors and delivery delays or commonly called Criss-Cross (CC). Delivery delays are also influenced by human error factors, so special tools are needed to separate goods based on the destination city of the goods, one of which uses a conveyor with a GM65 QR Code Scanner sensor. This sensor works based on commands from Arduino which has been programmed in such a way that it can control all inputs and outputs used in the system. In this system, the goods to be separated are made in the form of 3D mode boxes, where each box is affixed with a different QR code, namely A, B, and C. Each QR code represents the destination city of the goods to be separated, namely city A and city B, but for QR code C indicates data other than city A and city B which will be separated as foreign goods so that in this case it does not match any separation. After testing, the tool can work properly and obtain the results of separating goods in the form of a type of QR code that is read with the destination city of each item that has been separated.
Evaluasi Layanan Referensi Perpustakaan Universitas Bung Hatta

The aim of this research is to describe the evaluation of reference services which is then seen from the CIPP Evaluation Model theory. This type of research is quantitative with descriptive methods. The variable in this research is library reference services with sub variables, namely library service evaluation using the CIPP evaluation model. The population in this study is visitors to reference services at the Bung Hatta University Library from January 2023 to November 2023 with sampling using the non-probability silling formula with an accidental sampling technique. The number of samples is 61 users. The main instrument used is a research questionnaire which has been tested for validity and reliability. Data collection was carried out through questionnaires. The data were analyzed through the stages of examination, tabulation, and drawing conclusions. The results of the research are First, in the evaluation of the context, namely the reference service function, there are 3 statements which have an average score of 3.11. This score is on a scale of 2.50-3.24 which means good. Second, in the input evaluation there are 3 sub-indicators, (1) librarian which consists of 4 statements and has an average score of 3.24. This score is on a scale of 2.50-3.24 which means good; (2) a collection consisting of 5 statements and having an average score of 3.24. This score is on a scale of 2.50-3.24 which means good; (3) a catalog consisting of 3 statements and having an average score of 3.09. This score is on a scale of 2.50-3.24 which means good. Third, in the evaluation process there are 2 sub-indicators, (1) library reference services which consist of 5 statements and have an average score of 3.25. This score is on an interval scale of 3.25-4.00 which means very good; (2) information search service which consists of 3 statements and has an average score of 3.16. This score is on a scale of 2.50-3.24 which means good. Fourth, in product evaluation there are 2 sub-indicators, (1) visitor data which consists of 3 statements and has an average score of 3.20. This score is on a scale of 2.50-3.24 which means good; (2) the level of user satisfaction which consists of 3 statements and has an average score of 3.20. This score is on a scale of 2.50-3.24 which means good.&#x0D; &#x0D;
Rethinking Smart Keyboard Layout to Aid Strong Password Creation

In an era marked by increasing digitization and the omnipresence of smartphones, the importance of robustpassword security cannot be overstated. With the ever-growing threat of cyberattacks, there is a pressing needfor user-friendly tools that facilitate the creation of strong and unique passwords. Traditional alphanumerickeyboard layouts (physical or virtual) have remained largely unchanged for decades, relying on the sameQWERTY layout initially designed for typewriters. However, these layouts may not be optimal for generatingstrong passwords. This paper focuses on tailoring virtual keyboard layouts on smartphones specifically forstrong password creation. For this, we have performed extensive user surveys to see if the presence ofdedicated rows for digits and special characters (essential in any strong password) allows users to createstronger passwords compared to regular smartphone keyboard layout. Apart from that, we also investigatedthe optimal assignment of characters, digits, and special characters and their groupings in a single soft key.The findings from the detailed user experiment suggested optimal settings for a smartphone virtual keyboard(for Android) like- diagonal length for good typing speed (approximately between 8.38 and 9.41 cm), andkey density (0.88 to 1.21 keys/cm2) which produces the least error without sacrificing the strength ofpasswords created using those layouts. We hope the outcome of this paper will help designers to aid virtualkeyboard layouts for smartphones that can motivate and create strong passwords without sacrificing usability.
An alternative for kernel SVM when stacked with a neural network

Many studies stack SVM and neural network by utilzing SVM as an output layer of the neural network. However, those studies use kernel before the SVM which is unnecessary. In this study, we proposed an alternative to kernel SVM and proved why kernel is unnecessary when the SVM is stacked on top of neural network. The experiments is done on Dublin City LiDAR data. In this study, we stack PointNet and SVM but instead of using kernel, we simply utilize the last hidden layer of the PointNet. As an alternative to the SVM kernel, this study performs dimension expansion by increasing the number of neurons in the last hidden layer. We proved that expanding the dimension by increasing the number of neurons in the last hidden layer can increase the F-Measure score and it performs better than RBF kernel both in term of F-Measure score and computation time.
Improving Classification Performance on Imbalanced Medical Data using Generative Adversarial Network

In many real-world applications, the problem of data imbalance is a common challenge that significantly affects the performance of machine learning algorithms. Data imbalance means each target of classes is not balanced. This problem often appears in medical data, where the positive cases of a disease or condition are much fewer than the negative cases. In this paper, we propose to explore the oversampling-based Generative Adversarial Networks (GAN) method to improve the performance of the classification algorithm over imbalanced medical datasets. We expect that GAN will be able to learn the actual data distribution and generate synthetic samples that are similar to the original ones. We evaluate our proposed methods on several metrics: Recall, Precision, F1 score, AUC score, and FP rate. These metrics measure the ability of the classifier to correctly identify the minority class and reduce the false positives and false negatives. Our experimental results show that the application of GAN performs better than other methods in several metrics across datasets and can be used as an alternative method to improve the performance of the classification model on imbalanced medical data.
Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm

Research on earthquakes has been extensively conducted by previous studies using various methods and specific discussions. Similarly, research to predict the magnitude of earthquakes that will occur in the future has also been conducted. This study employs the Prophet algorithm to test its capability in predicting a case study's magnitude using data with numerous missing values and outliers. The study is conducted without transformation and with Box-Cox and log-transformations. Transformations are applied to handle outliers. The results indicate that across the three experiments, the difference between the predicted and actual data ranges from 0.1 to 0.5 or even more. Performance metrics reveal that the log-transform is superior to the other two experiments, with a smaller MAE of 0.27 and a MAPE of 5.96%. Nevertheless, the use of the Prophet algorithm in this case study needs further investigation with different treatments to achieve more accurate results.
A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.
Antecedents of Employee Creativity and Its Influence on Job Performance

This study aims to determine the relationship between the antecedents of employee creativity and its effect on job performance in Police personnel in Surakarta Police Station. This research method uses quantitative methods with a total population of police personnel in Surakarta police station totaling 1083 personnel. The sampling technique of this study will refer to the criteria proposed by (Joseph F Hair et al., 2014) which are based on the Maximum Likelihood Estimation (MLE) technique. A good sample number according to MLE ranges from 100-200 samples, therefore the expected sample number is a minimum of 100 respondents and a maximum of 200 respondents (Joseph F Hair et al., 2014). Specifically for this study, the sample used was around 180-200 respondents at the Surakarta City Police Station. Data collection techniques use questionnaires that will be given randomly to the object of research. Data analysis tools using Smart-PLS applications. The results showed that Mindfulness has a significant effect on the creative process of members, Support perception of the organization has a significant effect on the meaning of work, Proactive personality has a significant effect on the meaning of work, Involvement of the creative process has a significant effect on employee creativity, Meaning of Work has a significant effect on employee creativity, Mindfulness has an indirect effect on employee creativity, in the mediation relationship First perception of support The organization has an indirect effect on employee creativity through the meaning of work, Both proactive personalities have an indirect effect on employee creativity through the meaning of work, Employee creativity has a significant effect on job performance
The Investigation of Students’ Writing Skill Improvement in GBA Implementation Using Procedure Text

Based on the implementation of the new curriculum with GBA, this study investigates the effectiveness of GBA implementation in students'' writing skills through procedure text. This study took 41 seventh grade students of SMPIT Al Falaah Simo. This research used descriptive analysis to serve data analysis. It applied quantitative method that collects data through pre-test and post-test. Students were given a task to make procedure text about food creation of instant noodle. From the findings of the T-Test and N-Gain results, it is known that the application of GBA through Procedure text provides a significant change at the intermediate level in students'' writing skills. This means that the increase in skills has increased by about 50% of the skills in writing procedure text before the treatment. The T-Test and N-Gain results showed that the application of GBA through Procedure text provided a significant change in students'' writing skills in moderate stage. Factors such as mental unpreparedness, lack of material books, lack of time, and limited media facilities caused the increase in skills.