Reviewerly/exHarmony huggingface dataset:
Exploring the Potential Implications of Artificial Intelligence in Esports and related Video Games

The integration of advanced artificial intelligence (AI) systems and large language models (LLMs) has the potential to revolutionize the esports ecosystem by affecting game publishers, professional players, and the nature of esports games themselves. This article examines the relevant advancements in generative AI, their capabilities and limitations in context of gaming. It explores how AI-driven innovations can streamline game development processes, foster fair competition, and enable new types of esports games, including open-world and story focused collaborative games. Furthermore, the article delves into the potential impact of AI on game control mechanisms, the introduction of smart non-player characters (NPCs), and the emergence of human-AI esports.
Detection and Comparison of Abusive and Hate Speech in English and Hinglish with Emojis using Deep Learning and Non-Deep Learning Techniques

Abusive language and hate speech in tweets and comments on various social media platforms have become ubiquitous. This paper focuses on classifying an input text in English, Hindi and Hinglish (Hindi written in English) along with emojis and emoticons into three categories - Abusive, Hate Speech or Neither based on the confidence scores of each class. Abusive language on social media is textual content that contains disparaging words. Hate speech can be threatening and can hurt the sentiments of a person or a community. To gain better insights, results are captured with and without lemmatization and stemming. Accuracy is used as the metric to evaluate the classification models. Deep learning models BERT and RoBERTa are trained separately on plain English data (PE), Hinglish data along with emojis (HEmo) and the combined full dataset (CF). The same process is repeated for five non-deep learning models - Decision trees, k-Nearest Neighbours (kNN), Support Vector Machines (SVM), Random Forest and Naïve Bayes. The deep learning model RoBERTa results in the highest accuracy of 0.9938 on the PE dataset, 0.9896 on the EHEmo dataset and 0.9899 on the CF dataset. SVM model performed best among traditional classifiers with an accuracy of 0.7996 on the PE dataset, 0.7899 on the EHEmo dataset and 0.7913 on the CF dataset.
Automation of Decellularization Process Using Artificial Neural Networks

A neural network architecture inspired by AlexNet was created and adapted for regression, which predicts continuous values instead of classifying images. The network was trained on images with a resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1024}\times \mathbf{768}$</tex> pixels, taken during the decellularization processes. This database was created along with the experiments carried out within the OncoGen Research Institute, from Tlmişoara, using the modified Langerdorff device for rat heart decellularization. The biochemical values, DNA, and protein concentrations were measured at 30-minute intervals using a micro-spectrophotometer and correlated with the corresponding times of the acquired images. The model was trained on 46.938 images using data augmentation techniques such as rotation, flipping, and zooming. The model was tested on a separate set of 2000 images, and the performance was evaluated by comparing the predicted values of DNA and protein concentrations to the actual values. The created convolutional neural network architecture is efficient in detecting and predicting continuous values for DNA and protein concentration.
Analysis of the Energy Crisis in the Content of Users' Posts on Twitter

The emergence of social networks has facilitated the development of new ways of communication and social engagement. The dynamics of the conversation on social networks on a given topic help determine how users who post messages are affected. Since people's opinions are important factors influencing human behavior, we were concerned with extracting relevant information from social networks on one of the big global concerns, namely the energy crisis. In this context, the aim of our study was to identify and analyze the most important Twitter posts from April to September 2022 regarding the energy crisis using text mining methods. We focused on determining the most frequent words found in the extracted tweets with API V2 method and we analyzed the posts in relation to the identified opinion/sentiment using VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm. The paper presents the results we obtained. Determining the impact of the sentiment expressed in the Twitter posts on the “Energy Crisis” and correlating it with the analysis of the pandemic Covid-19's effects on people can help decision-makers develop policies that would also take public opinion into account.
Adversarial examples in random neural networks with general activations

A substantial body of empirical work documents the lack of robustness in deep learning models to adversarial examples.Recent theoretical work proved that adversarial examples are ubiquitous in two-layers networks with sub-exponential width and ReLU or smooth activations, and multi-layer ReLU networks with sub-exponential width.We present a result of the same type, with no restriction on width and for general locally Lipschitz continuous activations.More precisely, given a neural network f .I Â/ with random weights Â, and feature vector x, we show that an adversarial example x 0 can be found with high probability along the direction of the gradient r x f .xIÂ/.Our proof is based on a Gaussian conditioning technique.Instead of proving that f is approximately linear in a neighborhood of x, we characterize the joint distribution of f .xIÂ/ and f .x 0I Â/ for x 0 D x s.x/r x f .xIÂ/, where s.x/ D sign.f .xIÂ// s d for some positive step size s d .
Integrated Intelligent Services Accelerator Platform

In this research work we designed and implemented a new generation of Internet services, based on modern technologies used in the nowadays context of worldwide communications, based on TCP/IP protocol stack. The goal is that users to benefit from the integration of modern technologies (mobility, virtual reality, augmented reality, artificial intelligence), having easy access to quality content, certified by the entire community, and as personalized as possible for each client. We also designed a recommendation generation algorithm, based on artificial intelligence, and on users' interactions in the proposed platform. Research and development of automatic methods for classifying service providers and storing information in public ledger were performed as part of this project.
CUformer: A Channel-wise Unet-based Transformer for Semantic Segmentation of Remote Sensing

Remote sensing semantic segmentation is applied to land cover mapping and urban change detection. Recently, some methods introduced CNN and Vit hybrid methods to solve the remote sensing semantic segmentation. Furthermore, to reduce the computation cost, they mainly introduce the window-self-attention or not the villain self-attention to extract the global semantic feature. However, the window-self-attention ignores the channel interaction information. To fix the problem, we propose Channel-wise Unet-based Transformer (CUformer). The CUformer introduces channel attention to add the channel interaction information into the window-self-attention. Furthermore, previous works have proved that semantic segmentation needs to fine-grain the global semantic feature. Therefore, we design a simple fine-grained head, which introduces a local feature to fine grain the global semantic feature. Extensive experiments show that our model achieves state-of-the-art performance in Potsdam and Vaihingen datasets.
A Novel Approach to Recognize Handwritten Telugu Words Using Character Level CNN

A computer can interpret and receive handwritten input from various sources, such as documents and photos. This is an area of great interest in document text recognition and image analysis, especially in the field of Indic languages. The language of Telugu is a part of the Dravidian language family, and it has primary status in multiple states. It has 52 core characters, consisting of 36 consonants and 16 vowels. This paper aims to provide a model to recognize convert handwritten Telugu documents at word level. The dataset consists of words of varying length between one letter word to seven letter words. The recognition accuracy varies according to the length of the word.
Due CRediT: Identifying Potential Biases through Contributor Role Taxonomy Analysis

<p class="first" id="d19820324e58">The contributor role taxonomy (CRediT) is an important tool in ensuring transparency and equity in publishing. The taxonomy consists of 14 ways authors contribute to research and its products, assigned by the submitting author, when submitting a scientific manuscript for peer review/publication. In 2017, the American Geophysical Union (AGU) adopted CRediT for all of its journals. This poster analyzes the adoption of CRediT in AGU journal submissions focusing on submissions in 2021 and 2022. It explores author team size and the use of CRediT, how the number of CRediT roles differ by gender and region, how author position affects roles assigned, and how roles differ between men and women. Finally, it discusses how to act on the outcomes of this analysis to improve implementation and uptake of CRediT and reduce bias.
Edmodo as a 21st Century Learning Tool to Enhance 4C Skills of Malay Language Management Undergraduates

This study aimed to identify the level of 4C under the 21st century learning skills and to determine the impact of Edmodo’s application in enhancing 4C skills. A total of 15 Malay Language Management undergraduate students were selected as respondents for the study, along with the course lecturer and three peer reviews (among lecturers) were also involved in the study. Using qualitative approach, this study employed scholarship of teaching and learning (SoTL) research design during lessons in the 14-week semester. As reflection is the main element of SoTL, a thematic analysis was used to analyze data from the students, course lecturer and peer reviews reflections together with focus group interview. The preliminary findings illustrated that the level of 4C skills among undergraduate students are very weak. However, with intervention implemented by the course lecturer through Edmodo, students have managed to improve their level of 4C skills. From the reflections that reflect positive impact towards the usage of Edmodo, it was found that students were able to boost self-confidence, deliver relevant opinions, generate new ideas upon thinking critically and actively participate in the discussion. This study clearly indicates that the Edmodo is an effective alternative tool for educators to enhance 4C skills among students.
Exploring the Influential Factors Affecting Staff Willingness to Adopt Augmented Reality

Adopting modern learning technology such as Augmented Reality (AR) technology in higher education has become a demand to enhance teaching and learning performance and motivate students to acquire effective learning processes. Yet, the readiness of universities to adopt modern learning technology, such as AR applications, in developing countries, particularly in Saudi Arabia, is considered one of the critical issues to ensure the AR system’s success. This study aims to explore the factors that influence the academic members’ willingness in Saudi Arabian higher education to adopt modern learning techniques such as AR technology. To test the model, a quantitative survey using a questionnaire with a five-point Likert scale was applied in this study to collect the data. The study was conducted among a sample of 228 academic members and e-learning staff. Based on the analysis, the study found that perceived usefulness and perceived pedagogical contribution are important predicting factors for academic members’ willingness to employ AR in Saudi Arabian higher education teaching. The findings from this study provide insights that assist further studies regarding AR integration factors in higher education. This study contributes to the literature by developing a theoretical and conceptual framework of critical success factors for AR incorporation in Saudi Arabian (SA) universities.
(Weak) Pairwise Justifiability as a common root of Arrow’s and the Gibbard-Satterthwaite theorems

We introduce a novel principle that we call pairwise justifiability,which applies to a large class of collective choice rules, including the social choice functions and the social welfare functions about which the Gibbard-Satterthwaite theorem and Arrow’s impossibility theorem are predicated, respectively. We prove that, under appropriate qualifications, our principle is a common root for these two classical results, when applied to rules defined over the full domain of weak preference orders.
Diamonds, Abaci, and Hexagrams: Exploring the Pitch Surface of the Guitar Fretboard

In this presentation, I will analyze the idiosyncrasies, challenges, limits, and affordances of the guitar’s "pitch surface": its fretboard and tuning. Whatever the context—staff notation, music theory, improvisation, performance, and other arenas of guitar activity—the layout of the fretboard exerts a profound influence on how guitarists learn, play, and compose. Inspired by De Souza, Goodrick, and Martino, I will also explore the benefits of creative constraints: scordatura, capo, MIDI tools, and other ways of remapping the guitar’s pitch matrix. These constraints can be used to break the guitarist’s tight auditory-motor link, revitalizing the fretboard as a primary musical space and insightful musical "abacus."
School Resources Management and Improvement of Elementary Schools in Sto. Angel District, Division of San Pablo City

The school has different resources that are needed in the pursuit of achieving its goal as an institution.However, due to the vast array of responsibilities of the school, particularly the school heads, other areas of resources have been neglected.Thus, this study determined the relationship between school resources management and the improvement of elementary schools in Sto.Angel District.Through administering a researcher-made questionnaire and data gathering were made and treated using simple descriptive statistics such as Mean, Standard Deviation, and Pearson Product Moment Correlation, it has come to a revelation that school resources management correlates with the school improvement.Particularly, the study found out that material resources, fiscal resources, and human resources, were positively correlated with school culture, school partnerships, and teachers' performance.Additionally, the study found that partnership and tie-ups were positively correlated with all four aspects of school improvement.This finding suggests that school partnerships and collaboration with external organizations can be valuable resources in promoting school improvement.The findings also highlight the need for schools to ensure that they have adequate material, fiscal, and human resources, as well as to engage in partnerships and collaborations with external organizations.However, further research is needed to fully understand the complex relationships between school resources and students' performance.
A Proactive Approach to Addressing Security Challenges in Cloud Migration

In today's digital landscape, organizations are increasingly migrating their operations to the cloud. However, during this shift comes the crucial need for robust security measures. This paper explores the major security challenges organizations face during cloud migration and proposes an effective strategy to mitigate these risks. By building upon existing research, the paper identifies key security challenges and focuses on a proactive approach to address them. The paper delves into the fundamental principles of cloud security, emphasizing the shared responsibility model and the importance of encryption, access controls, identity management, and cloud governance. It highlights the specific security issues encountered during cloud migration. To address these challenges, it advocates for the application of threat modeling—a systematic approach to identify and evaluate potential threats, vulnerabilities, and risks. The benefits of threat modeling include early risk identification, risk prioritization, a security-by-design approach, alignment with standards and best practices, and ongoing security assurance. Practical guidance on implementing threat modeling for a secure cloud migration is provided particularly based on the Microsoft threat modelling approach by identifying and prioritizing potential security threats, implementing effective controls, and validating their effectiveness. Keyword: Proactive Approach, Security Challenges, Cloud Migration, Security, Risks, Models Proceedings Citation Format Njoku, J. (2023): A Proactive Approach to Addressing Security Challenges in Cloud Migration. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 89-96. https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P11
TICS IN THE YVU PORA RENDA INDIGENOUS COMMUNITY

The following investigative work allowed us to analyze the lack of influence of Information and Communication Technologies -ICT- in the Yvu Pòra Renda Indigenous Community, ICTs have already reached the most remote places through, for the most part, the smart phones. This reality must be read properly from school, to take advantage of these technologies for the benefit of learning with cultural and linguistic relevance. The methodology used is mixed research under the Quantitative and Qualitative approach, data collection was carried out through interviews and surveys. The results made it possible to identify shortcomings in the use of ICTs in the teaching-learning processes, difficulties in the indigenous community regarding the use of ICTs, and interaction problems, which could be overcome with the appropriate and oriented use of ICTs. ICT, this facilitated the generation of a series of recommendations regarding their use, with a view to improving the indigenous community and thus favoring the general academic results and the interculturality present in the community, actions that could be equally beneficial in other institutions or countries. Through the use of ICTs, the indigenous community can make good use of drones, GPS, software, digital mapping platforms, and data management. Several indigenous peoples in the region have found an ally in technology to strengthen their territories and their identity.
Cybersecurity Certificate Selection Process: A Data Envelopment Analysis Approach

Organizations that face the threat of cyberattacks protect against those threats, in part, by hiring cybersecurity professionals who have both relevant experience and information security/cybersecurity certifications. Cybersecurity certificates vary in many aspects, involve costs, and offer associated potential financial returns. Given the large number of cybersecurity certificates available, the decision of which one(s) to pursue may not necessarily be straightforward. In this paper, we illustrate the use of Data Envelopment Analysis (DEA) for cybersecurity certificate selection. Our analysis identifies six of 18 certifications considered that demonstrate a maximal relative efficiency, and we analyze why the other certifications are relatively inefficient.
A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information

Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I.
A Study on Nursing Students’ Satisfaction of Community Health Nursing Class Applying Blended Learning

The purpose of this study is to analyze the effect of blended learning in community health nursing classes on the learning satisfaction of nursing students. The subjects were 210 nursing department seniors. Data were analyzed with descriptive statistics, t-test, and ANOVA using SPSS/WIN version 21.0. Satisfaction according to class type was highest for blended learning class (3.89), followed by online class (3.66) and offline class (3.58). There was a difference in satisfaction toward blended learning class depending on the general characteristics of the subjects such as satisfaction toward major, interest, and media literacy. The students had the following opinions about blended learning. They responded that the most useful part about blended learning is that online contents are interesting and easy to understand, and also the fact that they can repeatedly watch the videos. They pointed out that time shortage and learning burden are the things that must be improved. This study is significant in that it explored specific methods to use blended learning in community health nursing classes and verified the effects through a survey on the satisfaction of nursing students.
STRUCTURAL–FUNCTIONAL MODEL OF FORMING THE TEACHERS’ READINESS TO MANAGE THE DEVELOPMENT OF INTELLECTUALLY GIFTED STUDENTS

STRUCTURAL–FUNCTIONAL MODEL OF FORMING THE TEACHERS’ READINESS TO MANAGE THE DEVELOPMENT OF INTELLECTUALLY GIFTED STUDENTS
AREAS OF EVALUATION OF VOCATIONAL HIGH SCHOOL TEACHERS

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 1247-1251ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0412Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
ENHANCED AUTHORIZATION FOR SECURE MANAGEMENT OF SENSITIVE DATA IN HYBRID APPLICATIONS

Sensitive data is often managed by cloud-based applications, which can be vulnerable to attackers who seek unauthorized access to this data. Traditional approaches to authorization may not be sufficient to protect sensitive data from such attacks. In this article, we propose an enhanced authorization approach that uses a combination of symmetric and asymmetric cryptography to secure sensitive data. Specifically, we propose generating a unique encryption key per file and a set of public and private keys per user, which are used to encrypt and decrypt the data. We demonstrate the feasibility of our approach with examples in Node.js, showing how to generate public and private keys, encrypt and decrypt files, and store encrypted data on a drive. Our approach provides an effective solution to the problem of managing sensitive data in hybrid applications, while preserving user and developer convenience.