Reviewerly/exHarmony huggingface dataset:
Big Book Storytelling: Stimulation Strategies in Early Childhood Literacy

Background: Early childhood literacy plays an important role in overall personality development. Literacy can be developed in various ways, one of which is by using various interesting learning tools such as big books and storytelling. Aim: The purpose of this research is to analyze the application of big book storytelling media to early childhood literacy. Method: The type of research is experimental research with a one-group-pretest-posttest design. In the sampling technique, purposive sampling was used. The research was conducted at TK Negeri Pembina Ngimbang, Lamongan Regency with a total sample of 85 children. Data collection techniques used observation sheets and documentation. The data analysis technique used the Wilcoxon Test. Result: The results of the Wilcoxon test mean the rank between the pretest and posttest is 9.50, with a Z of -4.243 and a significance of 0.000 &lt;0.05 indicating that there is a significant difference between the pretest and posttest. Conclusion: The results of this study big book storytelling media can stimulate early childhood literacy. This has implications that PAUD educators can maximize the use of big book storytelling media to increase early childhood literacy.
A Comparative Analysis of Blockchain-Based Authentication Models for IoT Networks

Nowadays, the authentication security of users and devices in the Internet of Things (IoT) is a significant challenge. The use of blockchain technology for authentication in IoT networks has emerged as an efficient method to enhance the network's security. This paper analyzes recent studies on this topic to determine whether or not applying the blockchain approach to IoT networks is beneficial and what issues need to be considered and resolved.
Federated Learning for Linux Malware Detection: An Experimental Study

The key to the success of Artificial Intelligent models is data. However, a major challenge is that data security and privacy issues must be considered. Federated Learning (FL) is considered an approach to overcoming challenges of data sensibility. In this study, we perform experiments with the Federated Learning model on detecting malware in the Linux operating system. We compare Federated Learning with distributed data and traditional Fully Connected Models with centralized data. Although the Federated Learning model has not been optimized for the configured parameters, the Linux malware detection results are very positive compared to the traditional model. In particular, the Federated Learning model has very high data security.
Producción de territorios virtuales en la educación a distancia en tiempos de pandemia, una propuesta de investigación desde la etnografía virtual

Social confinement as a measure to contain the COVID-19 pandemic forced many educational centers and institutions to make available all possible tools and resources to provide continuity to educational programs. Such measures led to a massive increase in the use of the Internet and with it the unprecedented opening of virtual communication that maintained the continuity of academic life, work, social and economic life. This text focuses on what happened at the Faculty of Humanities, of the Autonomous University of Chiapas, and proposes digital ethnography as a way to study it, based on paying attention to a group of students of the Bachelor's Degree in Communication from several elements such as the study of experiences, relationships, social worlds and the study of localities, as well as some topics to discuss, such as the production of territory, symbolization, the polarization of forces, the rise of virtuality and its legitimation in everyday life.
On Thunderclouds

Landscapes and atmospheric processes are rapidly changing, and so are the technologies we use to depict and detect them. How to ponder these transformations through artistic thinking and making? In the artwork On Thunderclouds , I examine the thundercloud as aesthetic motif, meteorological phenomenon, and as an ominous sign of climate change. In the making of the work, the tension between prediction and unpredictability on a larger level is mirrored in my working process, a balancing act between setting up systems and allowing elements of chance, even chaos. In the work various predictions, speculations and uncertainties thus vibrate.
Chatbots in Online Marketing: A work in progressNajlae Zhani

In the digital era, chatbots have emerged as a transformative tool in online marketing (Crolic et al. 2022). This abstract introduces an ongoing research endeavor that specifically explores the role of chatbots in online marketing strategies. The research is a work in progress, aimed at understanding and contributing to the dynamic field of chatbot marketing. In today's dynamic digital landscape, chatbots have gained prominence as a powerful element of online marketing (Nwachukwu and Affen, 2023). This abstract serves as a preliminary exploration of the role of chatbots and conveys that our research is in progress, intended to provide insights into their evolving significance in online marketing. Chatbots, AI-driven virtual conversational agents, are becoming ubiquitous in digital marketing. Our research in progress focuses on understanding how these chatbots engage with customers, answer queries, and facilitate personalized interactions. We explore how chatbots enhance customer experience and drive engagement on websites and messaging platforms. A key aspect of our research in progress is dedicated to conversational marketing, wherein chatbots play a pivotal role in guiding customers through their journey. We investigate how chatbots create a seamless conversational experience and the impact on lead generation and conversions. Our research explores how chatbots collect and analyze data to offer personalized recommendations and insights. We aim to understand how chatbots leverage data-driven personalization to enhance user experience and provide tailored solutions to customers. As part of our research in progress, we delve into the future of chatbots in marketing. We anticipate discussing emerging trends and potential challenges, such as the integration of chatbots with emerging technologies, including voice assistants and augmented reality. Methodology and Data Collection Our ongoing research employs a mixed-method approach, combining quantitative and qualitative data collection techniques. We conduct surveys, interviews, and user behavior analysis to gather insights into how chatbots are perceived and utilized by businesses and consumers. Implications for Businesses Our research aims to offer practical insights for businesses aiming to leverage chatbots in their marketing strategies. We discuss potential benefits and challenges, providing guidance on the optimal use of chatbots to drive brand engagement and customer satisfaction. In conclusion, this abstract represents an ongoing research initiative focusing on the pivotal role of chatbots in online marketing. By emphasizing that our research is a work in progress, we signal our commitment to contributing to the evolving field of chatbot marketing. As we continue our research journey, we anticipate providing valuable insights to assist businesses in harnessing the potential of chatbots for online marketing success.
Technologies, Inbetweenness and Affordances

Abstract Categorization of technologies by the order of their inbetweenness is a useful device for parsing complex structures info fundamental parts and understanding the application of a technology. This promises a coherent foundation for explaining how we deploy technologies in design, in particular with respect to the affordances they create. By connecting the categorization of technologies to the matching of user effectivities to features of the environment in affordances, the paper proposes an approach to the transparent description of the assemblages produced by design in terms of which technologies are involved and how they connect to each other, to the wider environment and to users. For affordances, this improves specificity concerning the features of the environment that are directly relevant to an interaction and the connections between these features and the rest of the environment. With respect to technologies, it helps understand not only why a technology may be used under certain circumstances but also abuse and underperformance. Finally, it supports design by providing means for parsing complex situations into chains of technologies between animals and environments. This helps explain how technologies modify effectivities, environments or relations between the two and how this affects design performance.
On Giving Meanings to Programs

Abstract In a short section on the semantics of programs within his discussion of program correctness, Primiero seems to endorse the received view on the Scott-Strachey approach to denotational semantics as directly related to correctness. While this is true to some extent, I argue that the mathematical entities associated with programs play a lesser role in reasoning on program correctness, while the mathematical foundations of denotational semantics, namely the theory of domains, have contributed significantly to the conceptual understanding of programming and of computation in general
A Prediction Approach Based on Self-Training and Deep Learning for Biological Data

With the exponential growth of biological data, labeling this kind of data becomes difficult and costly. Although unlabeled data are comparatively more plentiful than labeled ones, most supervised learning methods are not designed to use unlabeled data. Semi-supervised learning methods are motivated by the availability of large unlabeled datasets rather than a small amount of labeled examples. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. This paper introduces an approach based on a model of semi-supervised learning, which is the self-training with a deep learning algorithm to predict missing classes from labeled and unlabeled data. In order to assess the performance of the proposed approach, two datasets are used with four performance measures: precision, recall, F-measure, and area under the ROC curve (AUC).
Implementing a Computing System: A Pluralistic Approach

In chapter eleven of "On The Foundation of Computing," Primiero takes on the implementation debate in computer science. He contrasts his theory with two other views—the Semantic and the specification—artifact. In this paper, I argue that there is a way to fine-tune the implementation concept further. Firstly, contrary to Primiero, I claim it is problematic to separate the implementation relationship from the conditions which make it correct. Secondly, by taking a pluralistic approach to implementation, I claim it is a mistake to try and provide a general theory of implementation for the entire production cycle of computational systems.
Robustness and Predictive Performance of Homogeneous Ensemble Feature Selection in Text Classification

The use of ensemble paradigm with classifiers is a proven approach that involves combining the outcomes of several classifiers. It has recently been extrapolated to feature selection methods to find the most relevant features. Earlier, ensemble feature selection has been used in high dimensional, low sample size datasets like bioinformatics. To one's knowledge there is no such endeavor in the text classification domain. In this work, the ensemble feature selection using data perturbation in the text classification domain has been used with an aim to enhance predictability and stability. This approach involves application of the same feature selector to different perturbed versions of training data, obtaining different ranks for a feature. Previous works focus only on one of the metrics, that is, stability or accuracy. In this work, a combined framework is adopted that assesses both the predictability and stability of the feature selection method by using feature selection ensemble. This approach has been explored on univariate and multivariate feature selectors, using two rank aggregators.
THE ROLE OF ECONOMIC SECURITY IN THE FORMATION AND IMPLEMENTATION OF SUSTAINABLE DEVELOPMENT STRATEGIES

In modern conditions, the national economy of Ukraine faces complex challenges caused by Russia's aggression and occupation of territories. This conflict has led to serious consequences for the economic security of Ukraine, including a decrease in investment activity, an increase in business risks, and a decrease in foreign trade. In addition, the state of the environment and ecological problems are becoming a serious task for the country, requiring a balanced approach to development. The article examines the role of economic security in the formation and implementation of sustainable development strategies. Economic security is an important foundation for achieving sustainable development. The article examines how economic security can affect a society's ability to achieve sustainable development, and how these two concepts interact with each other. Sustainable development involves meeting the current needs of society without harming future generations, and includes the rational use of resources, environmental protection, the development of socially responsible business practices, and ensuring economic stability. The study emphasizes the importance of international cooperation and global interaction in the context of achieving sustainable development. The emphasis of the work is on the fact that environmental safety is becoming more and more relevant, and solving environmental problems is becoming a task for the entire society. The paper also examines key aspects of economic security and their impact on sustainable development strategies, including aspects of financial stability, resource provision, infrastructure and social justice. Thanks to this research, it is possible to identify key aspects that need to be taken into account in the development and implementation of sustainable development strategies, in particular, the need for a balanced approach to economic security, effective management of resources and preservation of social justice. The article proposes a comprehensive approach to strengthening economic security, which takes into account all aspects of economic security and ensures the harmonious development of society and nature. This is the only way to ensure sustainable economic growth and preserve our planet for future generations.
AN AUTOMATIC CYBERBULLYING DETECTION MODEL IN TWITTER SOCIAL MEDIA- BUSINESS APPLICATION BASED ON BIDIRECTIONAL COOT OPTIMIZED GATED RECURRENT UNIT

Cyberbullying (CB) is an electronic type of bullying in which a group or a person engages in purposeful and aggressive behaviour towards another group or individual on social media platforms. It contains hate messages that are spread by social media, emails, and other means on personal or public computers, as well as personal mobile phones. The hypothesized differences between CB and traditional bullying show that CB findings from traditional bullying are insufficient. With its rising frequency, CB has had a psychological and physical impact on victims. To limit the risk in smart cities, it is critical to recognize the CB context and its applications. However, from the perspective of the cyber world, the application using CB has challenges such as a lack of awareness of aggressors and their identities, a lack of direct communication, and linking repercussions to others. Hence, this work gives a new cyberbullying detection model to automatically classify the tweets using optimized deep learning.
Edge preserving image denoising with deep networks and ensemble convolutional neural network architectures

In the field of visual perception, the edges of images tend to be rich in effective visual stimuli, which contribute to the neural network's understanding of various scenes. Image smoothing is an image processing method used to highlight the wide area, low-frequency components, and main part of the image or to suppress image noise and high-frequency interference components, which can make the image's brightness smooth and gradual, reduce the abrupt gradient, and improve the image quality. Reducing noise is treated as one of the important problems in image processing. At the same time, preserving the edges of objects is of critical importance to protect the visual appearance of the objects. Deep networks have marked a trend in computer vision applications and this paper presents a customized Gaussian noise minimizing network with edge preserving filers. Here, ensemble architecture of convolutional neural network is used in minimizing the Gaussian noise and image denoising. The ensemble architecture is combined with VGG-19 and Xception design of CNN. The ensemble Convolutional Neural Networks (CNNs) are classified for Gaussian noisy images, real noisy images, blind denoising, and hybrid noisy images, representing the combination of noisy, blurred, and lowresolution images. Following the classification, motivations and principles of various deep learning methods are analyzed. Subsequently, a comparison of state-of-the-art methods on public denoising datasets is conducted, considering both quantitative and qualitative analyses. The experimental analysis is carried out in terms of PSNR, accuracy, precision, recall and F-measure.
DEVELOPMENT OF INTERACTIVE E-BOOK TEACHING MATERIALS BASED ON LOCAL WISDOM USING WORDWALL EDUCATIONAL GAMES

This research is based on an analysis of teaching materials that do not contain local wisdom values ​​and a lack of creativity to develop innovative teaching materials using technological developments in the learning process due to time and human resources constraints faced by teachers. This study aimed to assess the feasibility and practicality of using interactive e-book instructional resources that incorporate local knowledge, using wordwall educational games. This study's research and development methodology relates to utilizing the R&amp;D development model with the ADDIE step, which includes five stages: Analyzing, Designing, Developing, Implementing, and Evaluating. The results of the eligibility validation of media experts yielded a score of 82.2%, which indicated "Very Valid." The three expert validations show that using wisdom-based interactive e-book teaching materials using Wordwall educational games is feasible for testing. The results of the practicality of e-book interactive teaching materials products received good feedback from educators and students, respectively, at 98% (very good) and 100% (very interesting). Based on the findings presented, the conclusion is that using interactive e-books as teaching materials based on local wisdom with Wordwall educational games is suitable for implementing an educational learning framework. The three expert validations show that using wisdom-based interactive e-book teaching materials using Wordwall educational games is feasible for testing. The results of the practicality of e-book interactive teaching materials products received good feedback from educators and students, respectively, at 98% (very good) and 100% (very interesting). Based on the findings presented, the conclusion is that using interactive e-books as teaching materials based on local wisdom with Wordwall educational games is suitable for implementing an educational learning framework. The results of the practicality of e-book interactive teaching materials products received good feedback from educators and students at 98% (very good) and 100% (very interesting). Based on the findings presented, the conclusion is that using interactive e-books as teaching materials based on local wisdom with Wordwall educational games is suitable for implementing an educational learning framework. The results of the practicality of e-book interactive teaching materials products received good feedback from educators and students, respectively, at 98% (very good) and 100% (very interesting). Based on the findings presented, the conclusion is that using interactive e-books as teaching materials based on local wisdom with Wordwall educational games is suitable for implementing an educational learning framework.
TEACHER STRATEGIES USING QUANTUM LEARNING TO IMPROVE SCIENCE LEARNING OUTCOMES IN CLASS IV OF ELEMENTARY SCHOOL OF GMIM 1 KAKASKASEN

The research aims to improve science learning outcomes using the Quantum Learning strategy for fourth-grade students at Elementary School GMIM I Kakaskasen. This type of research uses Classroom Action Research (PTK), which consists of 4 stages, namely: 1) Planning, 2) Implementation, 3) observation, and 4) reflection. The subjects of this research were fourth-grade elementary school students. Techniques for collecting data are Observation and tests. These techniques are considered appropriate to the background of the science learning process with material on the structure and function of stems in plants using the Quantum Learning strategy. The research results showed that in the first cycle, the learning outcomes were 73.33% complete, and in the second cycle, it was 92.38%. So, it can be concluded that by implementing the Quantum Learning strategy.
APPLICATION OF THE PROJECT-BASED LEARNING MODEL TO IMPROVE INDONESIAN LANGUAGE LEARNING OUTCOMES FOR CLASS V ELEMNTARY SCHOOL STUDENTS

This research was conducted to address the issues that occur in the classroom and determine the extent of improvement in students' learning outcomes in the Indonesian language, particularly in the Pantun material, using the Project Based Learning model for the fifth-grade students at SD GMIM 4 Tomohon for the academic year 2022/2023, with a total of 31 students. The background of this research arises from the low learning outcomes of the fifth-grade students at SD GMIM 4 Tomohon, especially in the Pantun subject, due to the suboptimal application of the learning model. Based on observations conducted in the fifth-grade class at SD GMIM 4 Tomohon, out of 31 students, only eight have achieved the minimum completeness criteria (KKM) of 75. In contrast, 23 students did not meet the minimum completeness criteria set. This action research refers to the research design by Kemmis and Mc. Taggar has the following steps: Planning, Action, Observation, and Reflection, carried out in two cycles. Based on the percentage of learning outcomes obtained in Cycle I, which is 69%, it requires re-planning for Cycle II. In Cycle II, the learning outcomes have reached 86.2%, indicating an improvement and achievement of the completeness standard. With these results, the researcher can conclude that implementing the Project Based Learning model has increased students' learning outcomes. Based on the research and discussion, students' learning outcomes in the Indonesian language can be improved by applying the Project Based Learning model, especially in the Pantun material.