Reviewerly/exHarmony huggingface dataset:
The 2015 Biometric Voting System and the Politics of Free, Fair and Sustainable Democracy in Nigeria

Abstract The need for Nigerians to record a free, fair and credible election to sustain the democracy led to the use of biometric voting method. This method is expected to reduce political violence and post election petitions. This study examined the relationship between biometric identification technology and the challenges of 2015 general elections in Nigeria. The qualitative methods of data collection and analysis were adopted for the study. The chaos theory was adopted as a framework of analysis. This study therefore established that the use of biometric voting technology did not contribute to significant reduction in post-election petitions in the 2015 general elections in Nigeria. Limited or non-verification of voters' fingerprints even after authenticating their PVCs and over-voting were rampant. The use of the card readers was evidently problematic, with 18% malfunctioning and 91% not being able to consistently verify fingerprints resulting in significant disenfranchisement. Over 2.3 million of those that were accredited (7.3%) did not finally cast their ballot. This weakened the integrity of the process. A record of 610 postelection petitions were filed after the 2015 general elections which was less than 9 percent reduction compared to the 731 recorded in the preceding 2011 election. Again, the study established that the malfunctioning of the Smart Card Readers occasioned high increase in inconclusive elections during the 2015 general election in Nigeria. About 18% of smart card readers malfunctioned and 91% could not consistently verify fingerprints, and consequently manual voter identification was undertaken which increased enfranchisement. In more than 12% of polling units no attempt was made, to verify fingerprints. In 13% of polling units, card readers were not always able to read PVCs, and in 94% could not always verify fingerprints. This resulted in postponed elections in 13 House of Representative constituencies in three states. After the 2015 general elections, INEC has conducted 2 governorship re-run elections, one council election and aver 80 re-run legislative elections across the country. It is therefore recommended that INEC improve on training for regular and ad-hoc staff on the operation of biometric voting system. The number of judges that attend to post-election petitions should be increased to ensure speedy dispensation of justice. The Federal Government should provide accommodation and transportation logistics for INEC field staff during elections to guarantee timely and effective coverage of the designated polling units. It is expected that the findings in this paper will be used to improve subsequent election in Nigeria. Keywords: Biometric Voting; General elections, Progress, Politics, Pitfalls.
Understanding Interpretability: Explainable AI Approaches for Hate Speech Classifiers

Cyberbullying and hate speech are two of the most significant problems in today’s cyberspace. Automated artificial intelligence models might be used to find and remove online hate speech, which would address a critical problem. A variety of explainable AI strategies are being developed to make model judgments and justifications intelligible to people as artificial intelligence continues to permeate numerous industries and make critical change. Our study focuses on mixed code languages (a mix of Hindi and English) and the Indian sub-continent. This language combination is extensively used in SARRAC nations. Three transformer-based models and one machine learning model was trained and fine-tuned on the modified HASOC-Identification of Conversational Hate-Speech in Code-Mixed Languages (ICHCL) data for hate speech classification. Several types of explainability techniques have been explored on the respective models, such as Local interpretable model-agnostic explanations (LIME), Shapley additive explanations (SHAP), and model attention, to analyze model behavior. The analysis suggests that better trained models and comparison of Explainable Artificial Intelligence (XAI) techniques would provide better insight.
Semantic Incompleteness of Hilbert system for a Combination of Classical and Intuitionistic Propositional Logic

This paper shows Hilbert system (C+J)-, given by del Cerro and Herzig (1996) is semantically incomplete. This system is proposed as a proof theory for Kripke semantics for a combination of intuitionistic and classical propositional logic, which is obtained by adding the natural semantic clause of classical implication into intuitionistic Kripke semantics. Although Hilbert system (C+J)- contains intuitionistic modus ponens as a rule, it does not contain classical modus ponens. This paper gives an argument ensuring that the system (C+J)- is semantically incomplete because of the absence of classical modus ponens. Our method is based on the logic of paradox, which is a paraconsistent logic proposed by Priest (1979).
The Effects of Early Childcare Teachers’ Playfulness and social support within the workplace on Their Play Teaching Efficacy

Objectives The purposes of this study is to investigate the effect of childcare teachers’ Playfulness and social support within the workplace on their play teaching efficacy.&#x0D; Methods This research was conducted with 233 childcare teachers. Childcare teachers responded to the playful-ness scale, the social support within the workplace scale, and the play teaching efficacy scale. The collected data were analyzed by using the SPSS 25.0 program.&#x0D; Results The childcare teachers’ playfulness and social support were the major factors that explained the play teaching efficacy. It was found that the playfulness of childcare teachers is high and the play teaching efficacy in-creases with a lot of social support. Especially the childcare teachers’ social support had much greater effect on their play teaching efficacy than other factors did.&#x0D; Conclusions This study has academic significance in considering the effects of early childcare teachers’ playful-ness and social support within the workplace on their play teaching efficacy. These results are significant in that they can be used in the development of childcare teachers’ education programs to improve play teaching efficacy.
A Case Study on Global Citizenship Education Program Development for University Curriculum: Global citizenship in the closet

Objectives The purpose of this study is to examine the effectiveness of college students' perception of global cit-izenship and change through case studies that combine global citizenship education programs as university liberal arts education with clothing life curriculum.&#x0D; Methods To this end, the global citizenship education program “Global citizenship in the closet” was developed by applying the ADDIE teaching and learning model through literature and prior research analysis. By setting class goals and topics for each week, and structuring the class environment mixed with non-face-to-face and face-to- face classes centered on teaching-learning course plans and class materials, researchers operated it as a liberal arts course at C University in Gwangju. It was intended to derive the possibility and effectiveness of global citizen-ship education fused with other subjects through discussions on the results obtained through post-class surveys and open question analysis.&#x0D; Results As a result, students actively participated as global citizens in the global citizenship education program fused with the clothing life curriculum developed in this study. In addition, it is effective to actively utilize an online class platform to communicate with students when conducting activity classes through pros and cons discussions and thoughts sharing, and students' confidence in organizing and speaking their opinions has been increased through several discussions and thoughts sharing classes. The application of the flip learning teaching method that provides video data before class during face-to-face practice classes was evaluated as a positive way to con-trol the speed of practice among students. Special lectures for students' perception and content expansion have the scope of clothing life education in their majors, but they were able to expand the depth of the course without losing the direction of the course by conducting three special lectures based on global issues. Finally, the results of the survey to change students' class satisfaction and global citizenship perception showed that most of the stu-dents who took this class clearly showed changes in the “technology” and “will to practice” areas compared to the “knowledge” and “attitude” areas.&#x0D; Conclusions Considering these points, it is expected that it will be possible to include the core of global citizenship education in other liberal arts curriculum as a university liberal arts curriculum, and it will be more effective if it is operated in combination with career, society, and design education.
FOO: An Upper-Level Ontology for the Forest Observatory

Wildlife and preservation research activities in the tropical forest of Sabah, Malaysia, can generate a wide variety of data. However, each research activity manages its data independently. Since these data are disparate, gaining unified access to them remains a challenge. We propose the Forest Observatory Ontology (FOO) as a basis for integrating different datasets. FOO comprises a novel upper-level ontology that integrates wildlife data generated by sensors. We used existing ontological resources from various domains (i.e., wildlife) to model FOO’s concepts and establish their relationships. FOO was then populated with multiple semantically modelled datasets. FOO structure and utility are subsequently evaluated using specialised software and task-based methods. The evaluation results demonstrate that FOO can be used to answer complex use-case questions promptly and correctly.
Enhancing Usability Through Cross-Enterprise Design Standards: The Luxce Model

Managing a large, cross-enterprise portfolio raises challenges of usability at scale. Usable systems incorporate consistency and standards across the various components, with the sum being a seamless user experience even when developed by different teams. While this usability principle is well-known to user experience practitioners, implementing design standards for consistency is a continual challenge in the Department of Defense, with both government and private contractors working separately on different pieces of software. The Lean User Experience Center for Excellence (LUXCE) is a living example of a cross-institution collaboration aimed at identifying and applying standards to next-generation mission planning software. Our successful model of government-industry collaboration results in design standards applied daily to interfaces across a large, multi-faceted enterprise suite. We describe our process of establishing and maintaining LUXCE design standards with an emphasis on practical advice for enterprise-level government system development.
A Framework for Assessing the Complexity of Auto Generated Questions from Ontologies

Automatic difficulty calibration (ADC) is the application of computational techniques to estimate the difficulty levels of assessment questions before administering them. Compared to traditional difficulty calibration approaches, ADC eliminates the need for pretesting, and minimises the time and efforts typically involved in manual calibration. In recent years, Ontology-based Automatic Question Generation (OAQG) has emerged as a powerful tool to generate assessment questions effortlessly and in massive numbers with minimal human intervention. Despite these benefits, not being able to control the characteristics of generated questions hinders their suitability to be used in pedagogical settings. However, much of the research up to now has tended to focus on the quantity of the questions rather than their quality. Indeed, most current automatic question generators produce simple questions which consist of a few facts, and simply test the recall of knowledge. Furthermore, the majority of existing frameworks are mostly technical and are not supported with a strong theoretical underpinning. In this paper, we propose a novel framework to assess the complexity of ontology-based, automatically generated questions. We discuss various aspects that are involved in determining the complexity of assessment questions, and attempt to quantify important characteristics of question complexity through the use of novel ontological metrics. To further support the plausibility of our computational framework, we shed light on its consistency with theories from education and cognitive psychology. This will provide us with a solid theoretical foundation which ensures that questions are generated according to principled methods that are grounded in theories of learning and cognition. The proposed approach is agnostic to different domains and independent of the question format, therefore, is highly general and applicable to a variety of contexts.
Hacker community detection in online social network: a case study

Online social networks serve as platforms facilitating communication, collaboration, and information exchange among hackers. Consequently, the detection and monitoring of hacker communities within these networks have emerged as significant research areas in the field of cybersecurity. Numerous methods have been employed to detect these communities, with some primarily utilizing network structural information and others incorporating user-generated content topics. However, most existing methods rely on probabilistic models such as TF-IDF or LDA to analyze textual similarity, resulting in limited accuracy. In this paper, we introduce a Siamese Attention-augmented Recurrent Convolutional Neural Network aimed at analyzing text similarity among users and evaluating the significance of different users by comparing their text similarity with seed users. Subsequently, a weighted network is constructed, and community detection is performed using a modularity maximization algorithm. To validate the proposed framework, we collected Twitter friendship data and user-generated content from a pre-existing regional hacker group. A comprehensive series of experiments were conducted on this dataset. The experimental results provide strong evidence of the effectiveness of our proposed framework, showcasing its remarkable performance across various indicators such as community quality and topic relevance. Consequently, our framework exhibits promising prospects for practical applications in the field of hacker community detection.
Binary search of the optimal cut-point value in ROC analysis using the F<sub>1</sub> score

Abstract This paper introduces a binary search algorithm for determining the optimal probability cut-point value ( C ) of binary classifiers. Cut-points are operating points on the receiver operating characteristic curve that divide positive and negative predictions. Compared to the traditional exhaustive search for optimal C value, the proposed method offers execution time efficiency ( O (log 2 ( k ))) and a small cut-point error of 1/2 n after k steps of binary search. Traditionally, the optimal C value is determined by stepping through all possible C values. This search is uninformed because there is no indication of the search direction. To address this issue, we derive the expectation of the F-Measure (aka F 1 score); and use it to guide the search process. Specifically, by comparing the F-Measure at the current cut-point with the F-Measure at expected cut-point, we can use the information to adjust C dynamically towards the optimal cut-point, resulting in optimal model performance. Our results on two classifiers trained from disease classification datasets suggest that the algorithm is robust and efficient, as compared to the traditional methods.
Multispectral Camera Calibration Using Convolutional Neural Networks

A methodology for multispectral camera calibration using convolutional neural networks is presented. RGB images were captured from the multispectral camera for each of the standards, the samples are taken under the same lighting conditions and with the same capture angle. The images are fragmented into small matrix sizes added to a specific class, and saved with a special label to distinguish it from the entire class database, the same process takes the remaining 7 Lucideon Std color tiles. One of the tiles will correspond to a particular class with an equal dimension for all classes. Finally, based on the presented methodology, it is possible to calibrate the camera with respect to the references.
Desain Bahan Pembelajaran Matematika Berbasis M-Learning Berorientasi Pada Kemampuan Berpikir Kreatif Dan Kemandirian Belajar

Development of Teaching Materials Based on M-Learning on Material Opportunities for Vocational High School Students Oriented to Mathematical Creative Thinking Ability and Learning Independence. This development research refers to the development steps by Allesi &amp; Trollip with procedures that include planning, design and development. The purpose of this development research; (1) Producing M-Learning-based teaching materials on opportunity material for SMK students, (2) Producing M-Learning-based teaching materials that are oriented towards creative thinking skills, (3) Producing M-Learning-based teaching materials that are oriented towards independent learning. Data collection techniques used interview guidelines, material expert questionnaires, media expert questionnaires and student response questionnaires as well as test instruments. Product testing consists of alpha testing and beta testing. This research produces teaching materials based on m-learning in the form of applications on Android smartphones, namely mastery of mathematics. The product developed is considered suitable for use with an average value of material expert validators 4.4, media expert assessment 3.89 with a feasible category and teaching materials are considered oriented to creative thinking skills with an average validation of 4 with a very feasible category, and self-oriented learning with an average validity of 4.43 with a very feasible category.
Digital Literacy Competencies: A Study of Distance Learners of Higher Education Regulatory Authority Khyber Pakhtunkhwa

With the use of technology, students can store, share, and record what they learned in a given session or day of remote learning. This study aimed to assess distance learners’ abilities in digital literacy by examining their attitudes, concepts, and methods. The quantitative descriptive survey research method was used. For this purpose, 95 distance learners were selected from the different private colleges in district Swabi that were affiliated with Higher
Multi-Scale Geo-Localization Based on Local Similarity Area Distance Measurement Method

Cross-view geo-localization is to match remote sensing images from different platforms. The UAV-view image is matched with the satellite-view image with geographical localization information, so as to determine the specific geographical localization of the UAV. Cross-view geo-localization can be applied in many fields. For example, cross-view geo-localization can assist traditional satellite positioning to improve accuracy or perform positioning tasks independently. The main challenge now is that there are great differences in remote sensing images obtained by satellites and UAVs, such as changes in viewpoints and differences in scales. The current method mainly studies the influence of viewpoint change, while ignoring the scale difference between cross-source images caused by different resolutions. In order to reduce the influence of scale differences, We propose a network structure called local similarity network (LSNet) based on the siamese network and multi-scale sliding windows. LSNet adopts a new distance measurement method based on the most similar area. Experiments show that our method has excellent performance in multi-scale remote sensing geo-localization.
Sistem Penjualan Point of Sale Berbasis Web Pada Toko Bangunan

Easy Jaya Building Store is a building shop that sells various kinds of tools and building material supplies. This shop is located on Jalan Cristophel Mihing No 123 and has been established since 2017. Based on the process the business that is currently running at the store that becomes an obstacle is when making notes, checking prices of goods and stock of goods. In overcoming this problem, a Point Of Sale sales system was created so that problems that arise such as making notes, prices of goods and stock of goods are more structured and more detailed which will certainly make it easier in the store. Based on the results of the trial of this program which has been implemented at the Easy Jaya Building Store, the results obtained are very good and also received a good response from the shop owner because the program created has been very helpful. As for suggestions in its development such as adding accounting, barcode scanners and also making the user interface look even better
Powerful Encoding and Decoding Computation of Reservoir Computing

Reservoir computing (RC) has been widely applied in the fields of time series data processing and time series prediction due to its powerful data representation capability. The reservoir exhibits nonlinear dynamics, and its internal dynamics have infinitely long correlations when the system settles at the edge of chaos, rendering the system to achieve excellent computational performance. However, the encoding and decoding performance of RC is still unclear. This paper investigates the encoding and decoding abilities of the classic RC model, Echo State Network (ESN), on an image reconstruction task. The results show that ESN could greatly reconstruct grey images as well as color images, and demonstrate excellent generalization. Furthermore, a deep neural network based on ESN is proposed to resist the attacks on the trained model in experiments, showing that ESN enables the model to have excellent privacy protection ability. Our results demonstrate that the ESN’s powerful encoding and decoding computational performance makes it highly promising in facilitating tasks, such as few-shot learning and privacy computing.