Reviewerly/exHarmony huggingface dataset:
SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING AN ENHANCED ENCODER-DECODER ARCHITECTURE

Abstract. Semantic segmentation is one of most the important computer vision tasks for the analysis of aerial imagery in many remote sensing applications, such as resource surveys, disaster detection, and urban planning. This area of research still faces unsolved challenges, especially in cluttered environments and complex sceneries. This study presents a repurposed Robust UNet (RUNet) architecture for semantic segmentation, and embeds the architecture with attention mechanism in order to enhance feature extraction and construction of segmentation maps. The attention mechanism is achieved using Squeeze-and-Excitation (SE) block. The resulting network is referred to as SE-RUNet. SE is also tested with the classical UNet, termed SE-UNet, to verify the efficiency of introducing SE. The proposed approach is trained and tested using “Semantic Segmentation of Aerial Imagery” dataset. The results are evaluated using Accuracy, Precision, Recall, F-score and mean Intersection over Union (mIoU) metrics. Comparative evaluation and experimental results show that using SE to embed attention mechanism into UNet and RUNet significantly improves the overall performance.
Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach

Abstract Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.
A Study on the Healing Function of Healing Dramas Focusing on the K-Drama My Liberation Notes

This study explores the role of healing dramas as a conduit for disseminating cultural ideals and influencing collective behaviors. As society advances, dramas have seamlessly integrated into daily life, becoming a cultural touchstone. The contemporary emphasis on mental well-being has given rise to healing dramas, rooted in Japanese Shinto traditions. In the fast-paced modern urban life, healing has gained renewed significance, evident in the popularity of healing dramas and literature, offering solace. This paper investigates the healing potential of dramas through the K-drama My Liberation Notes. It is chosen for its resonant themes, such as I hope we were happy and Liberate ourselves from our individual lives! Dramas, as cultural assets, entertain, educate, and reflect societal values. Healing dramas surpass mere entertainment, fulfilling emotional needs, and enhancing well-being. Their popularity mirrors a modern desire for emotional engagement. Analyzing their impact deepens understanding. Beyond commercial success, healing dramas fulfill societal needs and promote positivity. This investigation of My Liberation Notes enriches drama development, offering insights through comprehensive analysis, advancing the field.
ANALISIS EKOEFISIENSI DAUR ULANG AIR LIMBAH MESIN WASHING IP DI PT X

The amount of water usage is not comparable to the amount flowed to WWTP due to the remote location and indications of leaks in the wastewater piping system, so an ecoefficient analysis of wastewater recycling of IP washing machines at PT X is needed. The type of data in this study consists of primary and secondary data. The primary data needed are the amount of clean water usage per machine, the amount of wastewater generation, and the effluent quality of wastewater. The results showed that the efficiency process was carried out by analyzing clean water usage, measuring wastewater generation, calculating the cost of using clean water and wastewater treatment costs, as well as measuring effluent quality on color parameters and TDS. It is obtained that the effluent quality results still meet government regulatory standards, so that it can be used as raw water in applying wastewater recycling, and wastewater can be flowed directly through sand filter media and carbon filters contained in IP plants, without being treated first at wastewater treatment plants. The amount of savings in implementing ecoefficiency in reducing groundwater use is 87% or 2331.8 m3 / month from the use of IP washing process water and wastewater treatment cost savings of Rp 3,807,370 / month.
For human-like models, train on human-like tasks

Abstract Bowers et al. express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs' failures to account for results from psychological research. We argue that to fairly assess DNNs, we must first train them on more human-like tasks which we hypothesize will induce more human-like behaviors and representations.
A Study of Identification of Corporate Financial Fraud Using Neural Network Algorithms in an Information-based Environment

This paper provides a brief overview of corporate financial fraud behavior and the initial feature indicators utilized for detecting financial fraud. Principal Component Analysis (PCA) was employed to refine these feature indicators. Subsequently, the Back -P ropagation Neural Network (BP NN ) algorithm was applied for identification . S imulation experiments were conducted to test the BP NN algorithm's parameters. Additionally, a comparative analysis was conducted to compare the BPNN algorithm with the decision tree and Support Vector Machine (SVM) algorithms. The results demonstrate d that PCA effectively reduce d the initial set of 30 indicators to 20, retaining 90.64% of the essential information. The optimal configuration for the BP NN algorithm was seven hidden nodes and the application of the ReLU activation function. Furthermore, the BP NN algorithm outperform ed the decision tree and SVM algorithms in the context of financial fraud recognition.
Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks’ data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system’s development and validation processes. Rigorous validation confirms the system’s accuracy, performance, and security through an experimental testbed. The results demonstrate the system’s dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system’s robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.
Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation

A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications.
Efficiency Improvement of the Implementation of Research Work on the Basis of Higher Educational Institutions

The article deals with issues related to the implementation of research work on the basis of higher educational institutions. Approaches of efficiency improvement for the development of science in a higher educational institution in modern economic conditions are determined. The points of cooperation between higher educational institutions and enterprises of the real sector of the economy are shown. The directions for the development of applied scientific research and development of higher educational institutions are determined. Methods of sustainable development of science in higher education institutions and proposals for the creation of a unified research and development management system are presented.
Data sharing in academic publications: The case of education journals

While there is a rich literature reporting the prevalence of data sharing in many academic disciplines, and particularly STEM-related ones, the extent of data sharing in journals in Social Science fields has been subject to only little empirical enquiry, hitherto. Focusing on a particular Social Science discipline, Education, this research examines empirically two related issues associated with data sharing in Education. First, journal data sharing policies were scrutinized via a search of the websites of 47 randomly selected Education journals. Over half of the journals in the representative sample had issued statements on websites encouraging authors to make the data underlying published research, generally available to the academic community, though only a handful of journals make such sharing mandatory. Thus, while the importance of data sharing is well recognized by journals in the Education field, a sizeable minority seems not to have taken a stand on this issue. The second issue related to the efficacy of the positive stance taken by journals, in eliciting the desired response from authors, leading to the sharing of their data. This was probed in a limited, mainly qualitative, survey of the authors of papers published in journals that encouraged data sharing through their websites. It was found that not a single author had made data available – indeed, some authors were even unaware of the journal’s policy on this matter. Thus, journals’ well-intentioned procedures to encourage greater data sharing are seen to be markedly ineffective. Two main sets of reasons were offered to justify author reticence to data share: either authors did not regard it as being in their interest or data sharing was seen to be inappropriate or not possible for the data set in question. However, these fears relating to engaging in data sharing may not necessarily present insurmountable barriers to its wider adoption, as measures are available to circumvent, at least partially, or to meliorate their effect.
Mathematical Modeling of Solar Systems

The photoelectric effect is the emission of electrons from metals under the action of light. In 1921, Einstein won the Nobel Prize for explaining the photoelectric effect, which says that light has nature of particulate or light extends in quanta called photons. More photons eject more electrons, but the energy of the ejected electrons can only increase if the photon's energy rises. The photovoltaic effect and photoelectric effect are not the same concept. These are two directly related terms but the difference is that the photoelectric effect releases electrons from the surface of the material, after exposure to a sufficient amount of energy from sunlight. The photovoltaic effect releases electrons pass between molecules of different material compounds, which causes an increase in voltage between the two electrodes. This discovery is the basis for the development of electricity generation from solar energy. Further development and achievement are given further in the work.
DWA-based Dynamic Obstacle Avoidance Planning Method for USVs

To solve the problem of environment perceived instability in the process of autonomous navigation of USVs, a local planning method based on DYNAMIC WINDOWS APPROACH is proposed for the known static obstacles and unknown dynamic obstacles collision avoidance planning in the navigation area of USVs, and combined with the Velocity Obstacle method, the velocity obstacle region is incorporated in the dynamic window to further optimize the speed constrained It also sets the fusion to start distance and limit distance in the evaluation function to take into account the safety and economy of the planning and improves the efficiency of the dynamic window method to evaluate the predicted trajectory for finding the best. The simulation results verify that the improved DWA can achieve automatic obstacle avoidance in complex environments, and ensure the safety of obstacle avoidance navigation while the planning path is more reasonable and smooth, avoiding the USVs from bypassing outside the dense obstacle cluster, and the number of iterations and running time are reduced by more than 15%. The research results have more obvious scientific value and practical significance to the development of autonomous obstacle avoidance technology for unmanned vehicles.
Analysis of Acute Lymphoblastic Leukemia Detection Methods Using Deep Learning

This research work puts forward a comparative study of four prominent deep learning models - ResNet, InceptionNet, MobileNet and EfficientNet — for the classification and detection of Acute Lymphoblastic Leukemia (ALL) from microscopic single blood cell images. Leukemia, a critical hematological malignancy, demands accurate and swift diagnosis to facilitate effective treatment. The advent of deep learning has revolutionized medical image analysis, enabling automated and efficient disease detection. In this work, we evaluate the performance of ResNet, InceptionNet, MobileNet, and EfficientNet, all of which have demonstrated exceptional capabilities in various computer vision tasks. The proposed study involves the construction of a dataset containing diverse blood cell images, which then undergoes preprocessing and augmentation to ensure model robustness and generalization. Subsequently, the four deep learning architectures are implemented, pretrained on large-scale image datasets, and fine-tuned on the leukemia dataset. Training, validation, and testing phases are conducted under controlled experimental conditions. The results reveal nuanced differences in the performance of ResNet, InceptionNet, MobileNet, and EfficientNet for leukemia detection and classification. The evaluation metrics provide insights into their strengths and limitations, helping guide selection based on specific application requirements. This study clarifies how various architectures impact model performance in the context of medical image analysis.
Multilabel Classification of Student Feedback Data Using BERT and Machine Learning Methods

Studying student feedback is essential for educational institutions to provide good services to their students. The main purpose of evaluation is to improve the services offered to students through the interest monitoring information system. The guardian of each learner should provide feedback on infrastructure and learning services so that the educational institution can improve its services. In addition, the purpose of evaluation is to investigate student inquiries and receive responses from the appropriate departments to ensure the successful delivery of student services. Automatic classification of student feedback is necessary to improve response time and service quality. Student feedback should immediately follow the service to the relevant department, therefore the automated system classifies the feedback according to the unit handling it, prioritizing the most rapid development of the system. Each student feedback can be handled by more than two units, so the problem includes multilabel classification. This study aims at multi-label classification of student feedback data. This study uses a Bidirectional Encoder Representation from Transformers (BERT) to derive word vectors from student feedback data. In this study, several machine learning methods such as Support Vector Machines(SVM), K-Nearest Neighbors(KNN), Random Forests(RF), and Decision Trees(DT) are used to classify multi-label student feedback and compare their performances. This dataset consists of an assessment of the guardianship information system for 3323 students with the composition of the experiment using a comparison of 80% training data and 20% testing data. The SVM method with linear kernel has the best performance as evidenced by the accuracy of 82% and F1 value of 90%.
Batik Style Transfer into Fractal Shape

Image style transfer is a technique that blends the features of two images. The application of neural style transfer extends to Indonesian batik. Previous studies worked on creating new batik patterns by transferring the batik style to another image. The goal of this paper is to generate batik-like motifs by forming fractal shapes colored with migrated batik style. This was done by having fractal acts as the content image and batik as the style image. Unlike previous works, an evaluation procedure was performed in this study to assess the result of the applied style transfer model. The assessment was done to both methods of previous studies and the proposed method from this paper. VGG19 architecture was chosen to act as the style transfer model for this study. Though this network is one of the oldest architectures, it has the best convolutional layer to extract features of batik images. The architecture was equipped with different loss functions. Total variation was added to the function to help denoise the quality of the generated image. The generated image was also given an image-filtering procedure to capture the formation of the fractal. Some questionnaires were used to help evaluate the results of this study. The filtered image with the proposed method yielded slightly more positive opinions than the generated image without further filtering. However, the hypothesis tests showed that the different treatments did not produce significant results. The questionnaires also showed style transfer result was easily identifiable as not man-made, distinguishing it from traditional batik.
Classification of Illustrated Question for Indonesian National Assessment with Deep Learning

Reading literacy is a part of the Indonesian National Assessment. Each question has an illustrative image to help the reader understand the information conveyed. The dataset in this study was collected manually from official government platforms. However, in this study, we encountered a small dataset problem, so data augmentation was needed to increase the variation of the training data. This research focuses on the use of deep learning, namely the Convolutional Neural Network (CNN) to classify illustrative images into information or literature classes. We use five backbones, Visual Geometry Group 16 (VGG-16), MobileNetV2, Residual Network 50 (ResNet-50), Dense Network (DenseNet-121), and EfficientNetB4. Then we compared accuracy, F1 score, loss function to our evaluation metrics. We apply the transfer and fitting learning method to solve small data set problems. The best result in this study is using the ResNet-50 model and the fastest is using the Adam optimizer but it easily causes overfitting.