Reviewerly/exHarmony huggingface dataset:
Practical Evaluation of Federated Learning in Edge AI for IoT

AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This paper evaluates the FL regarding practical CPU usage and training time. Additionally, the paper presents how biased IoT Edge clients affect the performance of an AI model. Existing literature on the performance of FL indicates that it is sensitive to imbalanced data distributions and does not easily converge in the presence of heterogeneous data. Furthermore, model training uses significant on-device resources, and low-power IoT devices cannot train complex ML models. This paper investigates optimal training parameters to make FL more performant and researches the use of model compression to make FL more accessible to IoT Edge devices. First, a flexible test environment is created that can emulate clients with biased data samples. Each compressed version of the ML model is used for FL. Evaluation is done regarding resources used and the overall ML model performance. Our current study shows an accuracy improvement of 1.16% from modifying training parameters, but a balance is needed to prevent overfitting. Model compression can reduce resource usage by 5.42% but tends to accelerate overfitting and increase model loss by 9.35%.
Creating zoning plans with pass theory approach, example of 1/5000 zoning plan

Zoning plans express the process of arrangement and taxonomy in areas where people live together as a community. They occur in a region on a different scale from general to specific. Master and implementation zoning plans are plans made especially in this sense. In these plans, areas such as housing, and commercial and social equipment needed by the people living in the relevant region are created. These are the zoning plans that show the general lines of the plans before the implementation in the 5000 plans called development. From this point of view, we can evaluate the question of how the zoning plan will be formed with different approaches. One of these approaches is pass theory. Pass theory refers to the brain's process of perceiving different functional elements. That is, it emerges in the form of attention, information, simultaneous and successive cognitive processes, planning, and results while making a plan. In our study, a city-based study was carried out on a scale of 5000 in Turkey. In the revised zoning plan of the city of Elazığ, the formation of areas open to development and the planning process that can meet the needs of the increasing population was examined with the pass theory.
An Approach to Preliminary Design of a Quadrotor Cargo UAV

Unmanned aerial vehicles (UAV) are widely used in many different application areas today. UAVs are very cheaper than the other aerial platform and also their give opportunity to users in operating flexibility. It can be mentioned that the cargo transportation of UAV application area has high potential to grow. Compared to traditional highway delivery methods, cargo drones can offer cost savings by eliminating the need for human drivers, reducing fuel consumption, and optimizing delivery routes. Cargo drones can revolutionize the e-commerce industry by offering same-day or even same-hour delivery options. This can improve customer satisfaction, reduce shipping costs, and enable businesses to expand their operations. The main purpose of the study is to develop a preliminary design method for a quadrotor Cargo UAV to be used for cargo transportation. Within the scope of preliminary design activities, the initial sizing, optimizations and performance analyzes of the components were made. By using these results of the design, it was observed that the number of design iterations and the design time were significantly reduced.
COUNTERFEIT CURRENCY DETECTION USING MACHINE LEARNING

This research addresses the pervasive issue of counterfeit currency through a comprehensive approach integrating advanced image processing techniques and machine learning algorithms. The methodology encompasses crucial stages, including image comparison, segmentation, edge detection, feature extraction, and grayscale conversion, coupled with the implementation of machine learning models such as K-Nearest Neighbors (KNN),and theefficient MobileNetV2. In tackling the challenge of counterfeit currency, image processing techniques play a pivotal role by enabling the extraction and analysis of distinct features. From isolating patterns through segmentation to refining with edge detection and feature extraction, these techniques enhance the identification of intricate characteristics inherent in legitimate banknotes. Grayscale conversion further standardizes the representation for effective processing. Keywords: KNN, MobileNetV2, Image Processing, Machine learning algorithms, Feature extraction.
PERFORMANCE COMPARISON OF SARIMA INTERVENTION AND PROPHET MODELS FOR FORECASTING THE NUMBER OF AIRLINE PASSENGER AT SOEKARNO-HATTA INTERNATIONAL AIRPORT

The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.
A PRELIMINARY STUDY OF SENTIMENT ANALYSIS ON COVID-19 NEWS: LESSON LEARNED FROM DATA ACQUISITION, PRE-PROCESSING, AND DESCRIPTIVE ANALYTICS

Sentiment analysis is a method used to analyze opinions and feelings. The goal of sentiment analysis is to determine whether a document contains a positive or negative emotion. Along with the spread of Covid-19 cases, news related to Covid-19 has often become a trending topic in the mass media. Conducting sentiment analysis using all news becomes more challenging because it might take time and cost. Therefore, the sampling method is needed to obtain representative news for the analysis. Web scraping was employed to obtain the news article about Covid-19 in Indonesia. In order to select the representative news, two-step sampling was employed by using stratified and systematic random sampling. According to the topic modelling results using lambda 0.6, news articles are grouped into three topics: updating Covid-19 cases, vaccination, and government policy. In addition, based on the number of positive and negative words, news articles are grouped into news dominated by positive words, news dominated by negative words, and news with the same number of positive and negative words. Methods for representing text in numerical form have been developed. Some of them use tf-idf weighting and word embedding. It does not pay attention to word order or meaning, only based on the frequency of words both locally and globally. Furthermore, this method will form a vector size as large as the number of unique words in the document, so it is less effective when many documents are used. Meanwhile, the vector size generated from the word2vec method is not as much as the number of unique words in the corpus. In addition, word2vec considers the context of the words in the corpus.
INTEGRATED OF WEB APPLICATION RSHINY FOR MARKOV CHAIN AND ITS APPLICATION TO THE DAILY CASES OF COVID-19 IN WEST SUMATERA

Discrete-time of Markov chains, starting now referred to as Markov chains, have been widely used by previous researchers in predicting the phenomenon. The predictions were made by manual calculations and using separate software, including Maple, Matlab, and Microsoft Excel. The analysis takes a relatively long time, especially in calculating the number of transitions from each state. This research built an integrated R script for the Markov chain based on the web application RShiny to quickly, easily, and accurately predict a phenomenon. The Markov chain integrated R script is built via command-command to predict the day-n distribution with the n-step distribution and long-term probability using a stationary distribution. The RShiny web application built is limited to state two and three. The integrated web application RShiny for the Markov chain is used to predict the daily cases of COVID-19 in West Sumatra. Based on the analysis carried out in predicting the daily cases of COVID-19 in West Sumatra from March 26, 2020, to October 20, 2020, for the next three days and in the long term, the results show that there is a 51.2% probability of an increase in COVID-19 cases, a 43% probability that cases will decrease, and 5.8% chance of stagnant cases
SENTIMENT ANALYSIS OF LEAGUE OF LEGENDS: WILD RIFT REVIEWS ON GOOGLE PLAY USING NAÃVE BAYES CLASSIFIER

League of Legends: Wild Rift is a mobile game with more than 48 million downloads worldwide. The game publishers could earn profit from selling item in the game (in-app purchases). Performance of player and players' impressions during the first week usually determine whether players would made in-app purchases or not. Therefore, it is important to understand the player opinions so that the game publisher could encourage the players to increase the in-app purchases. Therefore, this research utilized sentiment analysis to study the player opinions about the League of Legends: Wild Rift game based on the reviews given by the players on the Google Play Store. The sentiment analysis was applied by using Naive Bayes Classifier (NBC) algorithm which was well known for achieving good accuracy in the sentiment analysis task. In addition, data preprocessing and feature extraction should be carried out properly to increase the accuracy of the classifier. Therefore, this research investigated the impact of using stemming and transformation of informal words into formal words in the preprocessing stages, then compared two feature extraction algorithms, namely Term Frequency â€“ Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). From the experiment, it was found that the use of stemming could decrease the accuracy of the classifier, but the use of transformation of non-standard words into standard words could improve the performance of the classifier, for both feature extractions, BOW and TF-IDF. In this case, BOW feature extraction was able to achieve better performance, compared to TF-IDF. The best model was achieved when not using stemming, applying the transformation of informal words into formal words, and using BOW bigram feature extraction, with the accuracy of 79,3%, precision of 82.10%, recall of 83.50%, and f1-score of 82,8.10%.
Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning

Cross-lingual summarization (CLS) is the task of generating a summary in a target language from a document in a source language. Recently, end-to-end CLS models have achieved impressive results using large-scale, high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora. However, due to the limited performance of low-resource language translation models, translation noise can seriously degrade the performance of these models. In this paper, we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data. We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary. Specifically, we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary, and combine it with cross-entropy loss to optimize the CLS model. To validate the performance of our proposed model, we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets. Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.
FINDINGS FROM THE DEEP MAGNETOTELLURIC STUDIES IN THE AREA OF THE SLOBODA GEODYNAMIC INTERSECTION (EAST EUROPEAN PLATFORM)

The Sloboda Geodynamic Intersection is considered as an area of convergence for major segments, aulacogens, faults, and suture zones of the East European Platform.Between 2020 and 2022, researchers from the Geological Faculty of Lomonosov Moscow State University and the Geoelectromagnetic Research Center (GEMRC IPE RAS) conducted deep magnetotelluric surveys in this region. The surveys were performed along the Pushkiny Gory – Andreapol, Sebezh – Velikiye Luki – Rzhev, and Ostrov – Ostashkov profiles. Through qualitative analysis of the collected data, the dimensionality of the studied medium, the total longitudinal conductance of the sedimentary cover, and the dominant strike direction of conductive structures were assessed. Quantitative interpretation using 1D, 2D, and 3D inversions resulted in geoelectric models comprising a conductive sedimentary cover and a highly resistive basement. Within the depth range of 10–40 km, transcrustal zones of enhanced electrical conductivity were consistently identified, interpreted as the southwest extension of the Ilmen-Ladoga anomaly. These conductivity anomalies are attributed to the deep submergence of heavily tectonized and metamorphosed sedimentary rocks, initially saturated with organic and carbonate matter.
ADAPTATION OF FIRST-YEAR STUDENTS TO THE CONDITIONS OF DISTANCE LEARNING IN HIGHER EDUCATION INSTITUTIONS

The article deals with the issue of first-year students’ adaptation to the conditions of distance educational environment in a higher education institution. The theoretical aspects of the problem under study are revealed. The concept of "adaptation of first-year students" is clarified as a complex process of integration into a new social environment, which involves the assimilation and reproduction of relevant norms, values and roles, building new social relations for the purpose of harmonious coexistence and establishing effective interaction with participants in the educational process of a higher education institution (administration, teaching stuff, curator, parents, members of the academic group). The classification of types (forms) of adaptation is presented; the following forms are singled out: active, passive, social, didactic, psychological, formal, socio-psychological, physiological and professional. The characteristic problems of first-year students’ adaptation in the conditions of distance learning in higher education institutions are identified related to self-organization of activities and mastering new types of it, disorientation in the organizational issues of the distance educational process, lack of skills to independently process a large amount of material, the ability to use educational platforms, Google applications, video conferencing, social networks, general student communities, etc. The program "I am a student" is designed; it is aimed at optimizing the process of adaptation of first-year students in the conditions of distance learning in higher education and preventing their maladjustment. Its conceptual and content aspects are characterized. The directions (organizational, informational, psychological, preventive) and stages (organizational and diagnostic, motivational and content, final and reflective) of the program implementation are indicated. The content of each block of the program is described in detail; the following blocks are singled out: educational and orientation, social and orientation, social and adaptation, and sociocultural. The expected results of the program implementation are predicted at the level of each first-year student (activity, mobility, independence, stress resistance, self-control, self-organization, readiness to implement their own educational trajectory) and at the level of the student group (absence of conflicts, involvement in the academic community, academic performance, integrity of the student group, readiness to provide support and assistance, etc.)
METASEZGİSEL ALGORİTMALARIN FARKLI PERFORMANS KRİTERLERİ İLE KARŞILAŞTIRILMASI

Nature-inspired metaheuristic algorithms are widely used because they achieve successful results in difficult optimization problems. Their popularity has led to the development of new metaheuristics for solving different engineering problems. New metaheuristics lead scientific research by providing faster and more efficient results. In this study, Artificial Rabbit Algorithm (ARO), Dwarf Mongoose Algorithm (DMO) and Genetic Algorithm (GA), which are recently developed metaheuristics, are compared. According to the literature review, the performances of these three algorithms are compared for the first time. Single and multi-modal standard quality test functions were used to evaluate the algorithms. The results of the algorithms were checked by t-test to see if there is a significant difference in terms of the functions used. According to the results obtained, it was observed that ARO produced more successful results than the other algorithms compared. This shows that the newly developed metaheuristics can be used in many engineering problems.
A MODEL OF THE TWO-LEVEL SYSTEM FOR FORMING INFORMATION AND COMMUNICATION COMPETENCE OF STUDENTS IN GENERAL SECONDARY EDUCATION INSTITUTIONS

The work systematizes the authors’ experience of using information technologies in the educational process of a general secondary education institution and presents the authors’ model of effective development of students' information and communication competence based on it. A model of a two-level system of formation of information and communication competence of students in the educational process has been developed. At the first level of the model, teachers use information tools during lessons to form students' basic knowledge and skills. At the second level, information technologies are used by teachers in extracurricular activities as a tool for creating creative products of students (in particular, scientific research works, educational projects, posters, presentations, video demonstrations), as well as their own developments (pedagogical technologies, teaching aids, etc.), which contributes to the formation of students' comprehensive ability to apply acquired knowledge and skills in practice, and, therefore, information and communication competence. To implement the first level of the proposed model, a comparative analysis of the most common information tools was conducted, the results of which are presented using original schemes. Information tools that ensure high-quality educational process at the "Cossack Lyceum" were subject to analysis: learning management systems – Google Classroom and NZ.UA; video conferencing platforms - Zoom and Google Meet; online boards – Jamboard, IDroo and Flinga; tools for creating information content – Google Presentations, Canva, Prezi and Microsoft Sway; software for simulating natural processes – Phet and Mozaik Education; tools for creating interactive exercises – Mentimeter, Kahoot, Wordwall, Live Worksheets and Learning Apps. The implementation of the second level of the model is shown on the example of a student's creative product created by participating in the competition of the Small Academy of Sciences of Ukraine. We associate promising directions for further research with the development of methodological recommendations for the use of information and communication technologies at various stages of students' creative activity.
Designing a Human-centered AI-based Cognitive Learning Model for Industry 4.0 Applications

The aim of this chapter is to focus on the status of data mining approaches for e-learning and identify the gaps in them. It is seen that a predominant work has been done in e-learning through data mining techniques such as educational recommenders, association rules, clustering, profile-based approaches, personalization, prediction techniques, and decision support systems. However limited work is done on e-learning from a cognitive science perspective, which gives us the motivation to work in this field. The proposed work aims at proposing a cognitive model for e-learning systems using data mining for Industry 4.0 applications which shall usher in a new datum for e-learning in cyber-physical systems.
A Comprehensive Study of State-of-the-Art App- lications and Challenges in IoT, and Blockchain Technologies for Industry 4.0

The Internet of Things (IoT) is a network of smart and self-configuring devices that exchange data by interacting with the environment to make decisions without human intervention. Endowed to sense surrounding events, these physical objects generate large amounts of real-time data that need an acceptable architecture with better security to process and convert it into meaningful information. Implementation of blockchain in IoT offers a secure, transparent and efficient mechanism to store and manage data generated by connected IoT devices. Even though the integration of blockchain with IoT is pretty recent, there are at present a huge number of applications that include smart healthcare, smart homes, e-government, automotive industry, smart education, precision agriculture etc. However, there are several challenges encountered in Blockchain-IoT integration which include anonymity, standardization, interoperability, heterogeneity, data privacy, smart contracts, legal issues, transparency, storage capacity and scalability, security, etc. This chapter presents the current state-of-the-art Blockchain-IoT integration in order to examine how blockchain could possibly improve the IoT ecosystem catered towards Industry 4.0. This chapter investigates the various application domains of Blockchain-IoT integration. It also discusses the main challenges faced in the adoption of blockchain in IoT environments for I4.0.
A Collaborative Multi-objective Approach for Clustering Task Based on Distance Measures and Clustering Validity Indices

Clustering algorithm has the task of classifying a set of elements so that the elements within the same group are as similar as possible and, in the same way, that the elements of different groups (clusters) are as different as possible. This paper presents the Multi-objective Clustering Algorithm (MCA) combined with the NSGA-II, based on two intra- and three inter-clustering measures, combined 2-to-2, to define the optimal number of clusters and classify the elements among these clusters. As the NSGA-II is a multi-objective algorithm, the results are presented as a Pareto front in terms of the two measures considered in the objective functions. Moreover, a procedure named Cluster Collaborative Indices Procedure (CCIP) is proposed, which aims to analyze and compare the Pareto front solutions generated by different criteria (Elbow, Davies-Bouldin, Calinski-Harabasz, CS, and Dumn indices) in a collaborative way. The most appropriate solution is suggested for the decision-maker to support their final choice, considering all solutions provided by the measured combination. The methodology was tested in a benchmark dataset and also in a real dataset, and in both cases, the results were satisfactory to define the optimal number of clusters and to classify the elements of the dataset.