Reviewerly/exHarmony huggingface dataset:
Solar Tracking System Utilizing Internet of Things Technologies for Enhanced Power Generation

Solar energy is a valuable and sustainable source of power. Researchers are exploring various methods to optimize its utilization, including solar tracking systems. These systems aim to increase power generation by aligning solar panels with the sun's position. Traditional solar tracking approaches have shown 30-40% improvements compared to static panels. However, loT (Internet of Things) technology advancements have opened doors for intelligent solar tracking systems with increased functionality compared to sun-sensing systems. This study presents an innovative, smart solar tracking system that leverages loT technologies. By utilizing low-cost, compact computers like Raspberry Pi Zero 2 W, the system gathers real-time data from the internet to accurately track the sun's movement. Additionally, loT capabilities allow the system to access weather information, enhancing the possibilities for panel protection. The proposed loT solar tracking system offers a comprehensive and efficient solution. Measurements and comparative analyses are conducted to evaluate the performance of the solar power efficiency system using the loT solar tracking system, comparing it to traditional static systems and sensor- based tracking systems. This research aims to contribute to solar energy optimization and highlight the benefits of integrating loT technologies into solar tracking systems. The findings of this study will demonstrate the potential for maximizing power output and fostering sustainable energy solutions. Adopting loT solar tracking systems can increase solar energy utilization, accelerating the transition toward a cleaner and more efficient future.
Hybrid Firefly Algorithm with Rao Algorithm for Optimization Problems

This paper proposes an improvement of firefly algorithm for optimization problems. In this study, the rao algorithm was applied in crossover and mutation operators instead of procedures of moving firefly i towards j. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), JDE self-adaptive algorithm, and intersection mutation differential evolution (IMDE) algorithm. The computation results illustrated that the proposed algorithm can produce optimal solutions for two of six functions. The proposed algorithm has provided the best results compared to the other three algorithms. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function and the discontinuous step function.
A Machine Learning and Deep Learning Integrated Model to Detect Criminal Activities

Crime is considered an intended execution of an act that is harmful to society and punishable under criminal law. Bangladesh as a developing country has experienced severe crime-related issues in recent years due to the constantly growing criminal activities. The law enforcement sector of Bangladesh is dedicated to implementing the law and reducing fear of offense to make Bangladesh a secure place to live. Despite this, the traditional ways of detecting crimes in Bangladesh are currently slow and less effective because they are not getting enough technological support. As a result, most of the time the crimes get reported after the occurrence. So to attain efficiency in policing activities it has become significant to develop such a system that can notify the police department before the occurrence of a crime. In our proposed model, we develop a system that can prevent crime by detecting and notifying real-time criminal activities beforehand. To carry out this concept, we build a central system consisting of three modules i.e. criminal activity identification module, weapon detection module, and criminal detection module. Each of the modules is centrally connected and can give an alert when any type of abnormality occurs. We use Deep Learning and Machine Learning algorithms to perform criminal activity detection and face recognition of criminals. For each of the modules, we collect and prepare our own data set. We perform various experiments to investigate the effectiveness of the method. The evaluation result shows that our proposed method performs impressively in identifying criminal activities and criminals.
Performance Analysis of TSP Training Instance Generation Methods in Policy-Based Hyper-Heuristic Optimization Framework

The Policy-Based Hyper-Heuristic Optimization framework (PHH), which utilizes reinforcement learning to solve combinatorial optimization problems, has demonstrated promising results in prior studies. However, training PHH models with real problem instances can be resource-intensive and challenging to obtain. To address this issue, this paper explores the effects of different randomization methods for generating training instances in the context of the traveling salesman problem (TSP). Seven randomization methods, namely grid, uniform, square, circle, Gaussian, wave, and mixed, were employed to generate TSP instances. The PHH models were trained using these instances, and their performance was evaluated on unseen TSP instances from TSPLIB. Experimental results indicate that training with TSP instances generated by the square method consistently achieved the lowest travel distances across small, medium, and large test instances. This research contributes to the understanding of instance-generation techniques for combinatorial optimization problems and provides insights for designing efficient hyper-heuristic frameworks.
Breast Cancer Prediction of Benign and Malignant Tumors by Classification Algorithms

Breast cancer is a disease that affects both males and females, but it is more commonly found in females. It is considered the most common form of cancer among women worldwide. Unfortunately, breast cancer can be fatal and is the most invasive cancer in women globally. The absence of symptoms in the early stages of the disease often leads to a delayed diagnosis, rendering medical treatment ineffective. However, early screenings can reduce the mortality rate associated with breast cancer. In addition to traditional medical diagnosis methods, machine learning techniques can be utilized to predict breast cancer risk using health data. In this study, we attempted to compare various machine learning classification algorithms, such as the Decision Tree Classifier, Logistic Regression, KNeighbors Classifier, and Naïve Bayes Classifier. The results indicate that the Naïve Bayes Classifier outperformed the other algorithms in terms of performance. Experimental results show that the Naïve Bayes Classifier yields the highest accuracy (93%). Future research will explore fuzzy logic, rule-based methods for accurate breast cancer diagnosis, and expert validation of the model.
Masked Face Recognition Using Deep Learning Techniques

Since the COVID-19 pandemic, people have been wearing masks in public, which poses a huge challenge to facial recognition. This paper introduces a new solution which is a masked face recognition method based on FaceNet and Inception-ResNet which are deep learning techniques. A well-known benchmark dataset (RWMFD) is employed to train the face recognition model. Furthermore, simulating masks and data augmentation are used to further improve the classification precision of the model. Our four experimental cases on the synthetic and the real-mask face datasets show that our method can effectively process face recognition and identification task with and without masks, with very high performance in terms of accuracy, precision, recall and F1-score.
KNN And Naïve Bayes Algorithms for Improving Prediction of Indonesian Film Ratings using Feature Selection Techniques

This electronic document is a "live" template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract) A survey conducted by IDNTimes with 411 respondents shows that audiences in Indonesia choose which films to watch based on several factors. People chose films to watch in theatres based on the genre by 16.4%, actors or actors by 14.5%, and directors by 12.7%. There needs to be more interest from researchers regarding Indonesian film ratings. Most of them discussed foreign films more than Indonesian films. So research is required that discusses Indonesian film ratings. This research will use data mining tools, namely RapidMiner, with the CRISP-DM framework method. The study compared two algorithms, Naïve Bayes and KNN, to predict Indonesian film ratings using the feature selection technique. This research yielded that the KNN algorithm performs better than Naïve Bayes with or without feature selection techniques. Of the three models made for KNN, namely with Forward Selection, Backward Elimination, and without feature selection, all produce the same accuracy value, namely 86.65%. At the same time, Naïve Bayes gets an accuracy value of 85.13% with forward selection, 84.91% with backward elimination, and 71.32% without feature selection technique.
An Integrated Approach for Sentiment Analysis and Topic Modeling of a Digital Bank in Indonesia using Naïve Bayes and Latent Dirichlet Allocation Algorithms on Social Media Data

Social media provides a public platform for expressing complaints and opinions. Researchers can use text mining techniques such as sentiment analysis and topic modeling on social media data to compare features and gauge public opinion on competing digital banks in Indonesia. The aim of this study is to classify sentiments and identify topics in social media data related to a specific digital bank. To accomplish this, the Naïve Bayes algorithm is used for sentiment analysis, while Latent Dirichlet Allocation is used for topic modeling. The social media data is sourced from Twitter and Instagram for Line Bank digital bank. The study finds that the Naïve Bayes algorithm performs well in classifying sentiments, achieving a maximum F1 score of 0.863. Positive sentiments are more prevalent in Twitter data, while negative sentiments are more prevalent on Instagram. Topic modeling using Latent Dirichlet Allocation algorithm identifies four optimal topics for positive sentiment and five for negative sentiment. The coherence value obtained is 0.426279 for positive sentiment and 0.397232 for negative sentiment.
An Analysis of Bangla Tweets on Social Media Platform for Polarity Detection Using Machine Learning Algorithms

Nowadays, social media is a platform that is gaining popularity. Public and private thinking on a wide range of issues is continually expressed and transmitted via social media. So, the sentiment analysis of those tweets is a topic of the study. Bangla sentiment analysis is one of those research studies. In this regard, this paper proposes a supervised machine learning method for sentiment polarity detection in Bangla social media tweets, whether they are depressive or non-depressive. For analysis, we first preprocessed the data by removing stopwords (punctuation and less important words) from the corpus. For assigning the importance of words, we used TF-IDF feature extraction with NGrams (Unigram, Bigram, and Trigram) concepts. Eight machine learning algorithms, including logistic regression, decision tree, random forest, K-nearest neighbor, multinomial naive bayes, linear SVM, radial basis function SVM, and stochastic gradient descent, are used to detect polarity in social media tweets. The confusion matrix calculates accuracy, precision, recall, and f1score. Using Unigram TF-IDF, the highest score achieved by Random Forest (RF) was 99.65%; using Bigram TF-IDF, the highest score achieved by Logistic Regression (LR) was 98.72%; and using Trigram TF-IDF, the highest score achieved by Radial Basis Function SVM (RBFSVM) was 99.19%. This study might make a small addition to Bangla textual analysis’s model in natural language processing (NLP).
A Hybrid Deep Learning Neural Network for Recognizing Exercise Activity Using Inertial Sensor and Motion Capture System

The recognition of human activities holds great significance in the fields of AI and human-computer interaction studies. Human activity recognition (HAR) finds extensive application, including in behavioral assessment, gesture identification, fall detection, and gait evaluation. Recent strides in wearable technology and AI have showcased the potential to accurately classify movement events for HAR, achieved by analyzing time-series data collected from wearable sensors. This study focuses on deep learning networks, particularly CNN and LSTM, to extract spatial and temporal features from wearable sensor data for efficient exercise activity classification. A comprehensive assessment compared two sensor data types: 3-axis linear acceleration from IMUs and 3D acceleration from MOCAP markers. Findings highlight comparable accuracy between IMUs and MOCAP in exercise identification. The hybrid CNN-LSTM demonstrated high efficacy, achieving remarkable F1-scores of 98.85% for IMU and 98.23% for MOCAP data.
Machine Learning Models for Micro-bubble Image Detection in Mosquito Sprayer Quality Control:Addressing Class and Scale Imbalance

Introduction: Vector-borne diseases (VBDs) account for around 17% of global infectious diseases, with malaria and dengue being the most prevalent. Effective VBD prevention involves controlling mosquito populations by spraying micro-bubble (MB) insecticide. Quality control measures for MB size are essential, but few centers can assess MB size measurement. Machine learning (ML) is a powerful tool for automating and enhancing image analysis tasks, including measuring MB size. In this study, we train and evaluate ML models using an MB image dataset to assess their performance.Methods: Our experimental study used an MB image dataset labeled by specialists, with images resized and randomly assigned to train, validate, and test datasets. We employed various evaluation metrics, such as recall, precision, and mean average precision (mAP). Image augmentation techniques were applied to increase the number of unqualified MBs. Pretrained models from Detectron2 (D2), MMDetection (MM), and YOLO libraries were used for training and evaluation.Results: We identified severe class and scale imbalances in the dataset. Single-stage and two-stage CNN models demonstrated superior performance in MB detection compared to DCN and transformer-based models. D2 outperformed MM, and training Faster-RCNN R-50 on an augmented dataset improved performance for unqualified MB detection. Considering multiple evaluation metrics, YOLO v7 exhibited the highest accuracy and effectiveness in detecting MBs.Conclusion: Our study revealed the effectiveness of CNN models in MB detection and the importance of selecting appropriate libraries and pretrained weights. Image augmentation improved unqualified MB detection while considering multiple evaluation metrics provided a more comprehensive understanding of model performance. Models like YOLO v7 and Faster-RCNN R-50 and X-101 are recommended for practical MB quality assessment tasks, as they demonstrate high precision and recall performance for qualified and unqualified MBs.
Data Security in Cloud Computing using a Hybrid Algorithm Approach

Cloud computing has become the next-generation paradigm of Information Technology. In this modern era, Cloud Computing is the current technology where people can share resources, services, and information at a very low cost between people. Moreover, Cloud computing has added a new dimension in big data. In hybrid cloud security, to accelerate this adoption, it is important to ensure the security and privacy of the cloud. In this paper, we combine symmetric & asymmetric encryption & decryption to ensure data confidentiality using multiple algorithmic approach providing a flexible and efficient security solution for cloud-based data security.
PHILOSOPHICAL FOUNDATIONS OF THE APPLICATION OF ANTON MAKARENKO'S THEORY AND PRACTICE OF PEDAGOGICAL DESIGN IN CONTINUING EDUCATION

The article provides an analysis of the development of the theory and practice of designing didactic systems, which was initiated by Anton Makarenko. Designing advanced training courses in continuing education is considered by the author as a purposeful educational activity of a teacher in an institution of continuing education regarding the creation of a project of advanced training courses as an innovative model of the continuing education system aimed at mass use.&#x0D; The solution of a number of problems of andragogy is planned for the development of the concept of designing advanced training courses: a) clarification of the essence of designing advanced training courses; b) distinguishing its subject, object, subject, product; c) selection of principles, methods, means, stages, mechanisms of design activity; d) improvement of the technology of designing advanced training courses based on international standards; e) identification of design features of each component of professional development courses – purpose, content, means, technologies of professional development of teachers, innovative and creative safe educational environment and their coordination; f) development of valid methods for operational and objective assessment of the levels of teacher training using modern information technologies, etc.&#x0D; A conclusion is made about the relevance of the author's proposed concept of advanced training course design for the continuous education system, which is based on the ideas of Anton Makarenko, Hryhoriy Vashchenko, Vasyl Sukhomlynskyi, Hryhoriy Kostyuk and other domestic researchers.
The Future of Industrial Statistics

AbstractW. Edwards Deming used the power of statistical thinking and ideas from experimental design, process control, and sampling theory to revolutionize the manufacturing industry. This article describes how Deming’s ideas can by translated from manufacturing to the knowledge economy and information technologies predominate today. Examples related to computational advertising and recommender systems are discussed in the article. Additional informationNotes on contributorsDavid BanksDavid Banks obtained an MS in applied mathematics from Virginia Tech in 1982, followed by a PhD in statistics in 1984. He won an NSF Postdoctoral Research Fellowship in the mathematical sciences, which he took at Berkeley. In 1986, he was a visiting assistant lecturer at the University of Cambridge, and then joined the Department of Statistics at Carnegie Mellon in 1987. In 1997, he joined the National Institute of Standards and Technology, then served as chief statistician of the U.S. Department of Transportation, and joined the U.S. Food and Drug Administration in 2002. In 2003, he returned to academics at Duke University.Banks was the coordinating editor of the Journal of the American Statistical Association; co-founded Statistics and Public Policy and served as its editor; and co-founded the ASA’s Section on National Defense and Homeland Security, and has chaired that section, as well as those on Risk Analysis and on Statistical Learning and Data Mining. He has published 80 refereed articles, edited eight books, and written four monographs. He is a fellow of the ASA, Institute of Mathematical Statistics, and American Association for the Advancement of Science, and recently received the ASA’s Founders Award. His research areas include models for dynamic networks, dynamic text networks, adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and topics in high-dimensional data analysis.
Timeless Temples—a Journey Through Emma Willard’s Chronographic Masterpieces

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsR J AndrewsRJ Andrews is a practicing data storyteller who helps organizations solve high-stakes problems by using visual metaphors and information graphics: charts, diagrams, and maps. He recently produced designs for the White House and MIT. His passion is studying the history of information graphics. He published Information Graphic Visionaries, a new book series celebrating three data visualization creators: Emma Willard, Florence Nightingale, and E.J. Marey. Each volume has new writing, complete visual catalogs, and discoveries never before seen by the public. A remastered version of his first book, Info We Trust, is due in early 2024. His books are available from Visionary Press.
Definability of Boolean Functions in Kripke Semantics

A set F of Boolean functions is said to be functionally complete if every Boolean function is definable by combining functions in F. Post clarified when a set of Boolean functions is functionally complete (with respect to classical semantics). In this paper, by extending Post's theorem, we clarify when a set of Boolean functions is functionally complete with respect to Kripke semantics.
Provocative AI: Beyond Calm Interactions

Whilst the uses and potential benefits of AI-enabled technologies are expanding, there is also increased recognition of their potential downsides. This short comment critiques approaches to AI-enabled technologies based around ‘calm computing’ for rendering both users and the technologies themselves as passive and thus doing little to challenge the unsustainable status quo. It highlights some recent efforts to develop more proactive forms of AI but argues that, whilst these may lend more agency to technologies, they do little to increase the agency of users or engage them in thinking more critically about the nature of contemporary inequalities and unsustainabilities. To move beyond this situation, it calls for new forms of experimentation around what might be termed “Provocative AI” technologies. Drawing on emerging speculative examples relating to gender inequality, energy, and surveillance, it suggests that, instead of minimising or solving problems for users, Provocative AI could instead work to create more actively engaged citizens by promoting deeper reflection on the nature of contemporary societal problems.
DataSEA: Mature, Modern Data Management Enabling Sustainable Data Strategy

We present DataSEA, a mature data management platform that emphasizes the role of metadata in contextualizing and finding data through an extended evidentiary cycle and complements existing report-oriented documentation practices to produce sustainable, long-lived data strategy.
AQUA: Adding Bandwidth Allocation to QUIC for Metaverse Multi-Stream Applications

Metaverse applications, such as Virtual Reality and E-Health, pose immense challenges for current networks. A single metaverse application may include multiple types of streams (e.g., video, audio, control, IoT and sensors measurements), each with different bandwidth requirements. Although recent protocols like the Quick UDP Internet Connections (QUIC) support multiple streams, they don’t address heterogeneous stream requirements, even when combined with the state-of-the-art congestion control (CC) schemes. Moreover, in most cases, increasing the network capacity, as expected in future networks, such as 6G, can not guarantee that bandwidth requirements are satisfied for all streams. Other challenges include updating stream requirements and changes of network conditions over time.We present AQUA, a novel adaptive bandwidth allocation scheme which improves QoS and supports unbounded number of stream requirements. AQUA is implemented as a QUIC module and was evaluated both in lab and in real-world experiments with different CC schemes. The experiments demonstrate that AQUA significantly outperforms QUIC and preserves utilization and stability, even under high network congestion. AQUA’s code is publicly shared to encourage adoption and contributions from the community.