Reviewerly/exHarmony huggingface dataset:
Indonesian Twitter Emotion Recognition Model using Feature Engineering

Twitter is a social media platform that has a large amount of unstructured natural language text. The content of Twitter can be utilized to capture human behavior via emphasized emotions located in tweets. In their tweets, people commonly express emotions to show their feelings. Hence, it is crucial to recognize the text’s underlined emotions to understand the message’s meaning. Feature engineering is the process of improving raw data into often overlooked features. This research explores feature engineering techniques to find the best features for building an emotion recognition model on the Indonesian Twitter dataset. Two different text data representations were used, namely, TF-IDF and word embedding. This research proposed 12 feature engineering configurations in TF-IDF by combining data stemming, data augmentation, and machine learning classifiers. Moreover, this research proposed 27 feature engineering configurations in word embedding by combining three-word embedding models, three pooling techniques, and three machine-learning classifiers. In total, there are 39 feature engineering combinations. The configuration with the best F1 score is TF-IDF with logistic regression, stemmed dataset, and augmented dataset. The model achieved 65.27% accuracy and 66.09% F1 score. The detailed characteristics from the top seven models in TF-IDF also follow the same feature engineering configuration. Lastly, this work improves performance from the previous research by 1.44% and 2.01% on the word2vec and fastText approaches, respectively.
Advanced Techniques for Recognizing Emotions: A Unified Approach using Facial Patterns, Speech Attributes, and Multimedia Descriptors

The inability to efficiently store distinguishing edges, local appearance-based textured descriptions generally have limited performance in detecting facial expression analysis. The existing technology has certain drawbacks, such as the potential for poor edge-related disturbance in face photos and the reliance on present sets of characteristics that might fail to adequately represent the subtleties of emotions and thoughts in a variety of contexts. In order to overcome the difficulties associated with identifying facial expressions identification and emotion categorization, this study presents an innovative structure that combines three different information sets: a new multimedia descriptors, prosodic functions, and Local Differential Pattern (LDP). The principal driving force is the existence of noise-induced warped and weak edges in face pictures, which result in inaccurate expressions characteristic assessment. By identifying and encoding only greater edge reactions, as opposed to standard local descriptors that the LDP approach improves the endurance of face feature extraction. Robinson Compass and Kirsch Compass Masks are used for recognising edges, and the LDP formulation encodes each pixel with seven bits of information to reduce code repetition. The last category comprises Long-Term Average Spectrum (LTAS) obtained from signals related to speech, Mel-Frequency Cepstral Coefficients (MFCC), and Forming agents. Fisher Criterion is used to reduce dimensionality, and unpredictable characteristics are used in picking features. Emotion prediction is achieved by classifying two distinct circumstances using Support Vector Machine (SVM) and Decision Tree (DT) algorithms, and combining the obtained data. The research also presents a unique Visual or audio Descriptors that gives priority to key structure selections and face positioning for Audio-visual input. A concise depiction of expression is offered by the suggested Self-Similarity Distance Matrix (SSDM), which uses facial highlight points to estimate both time and space correlations. Formant frequency range, energy sources, probabilistic properties, and spectroscopic aspects define the acoustic signal. The 98% accuracy rate is attained by the emotion recognition algorithm. Major improvements upon cutting-edge techniques are shown in validation studies on the SAVEE and RML information sets, highlighting the usefulness of the suggested model in identifying and categorising emotions and facial movements in a variety of contexts. The implementation of this research is done by using Python tool.
Comparison of the Application of Weighted Cosine Similarity and Minkowski Distance Similarity Methods in Stroke Diagnostic Systems

Stroke is a critical medical condition requiring prompt intervention due to its multifaceted symptoms and causes influenced by various factors, including psychological aspects and the patient's lifestyle or daily habits that impact risk factors. The recovery process involves consistent medical care and lifestyle adjustments tailored to the individual case. Expert Systems, a scientific field focused on studying and developing diagnostic systems, can employ the Case-based Reasoning method to identify the type of stroke based on similarities with prior patient cases, considering specific causes and symptoms. This study utilizes the Weighted Cosine, Jaccard Coefficient, and Minkowski Distance methods to assess the similarity of stroke cases. The evaluation is based on input data such as patient causes or symptoms and risk factors from medical records. The analysis of case similarity and solutions involves applying the Weighted Cosine, Jaccard Coefficient, and Minkowski Distance methods, with a defined threshold value. The highest similarity values from previous patient cases are selected for each method. The test outcomes suggest that employing the Minkowski Distance method with a threshold value of 75 and an r value of three or four yields the highest levels of accuracy, recall, and precision. The Minkowski Distance achieves an accuracy and recall rate of more than 88 percent with 100 percent precision.
BASIC SKILLS OF HANDWRITING IN THE PROJECT WORK OF GRAPHIC DESIGN STUDENTS

The purpose of this article is to highlight the methodology of initial assignments in handwriting and calligraphy for the specialization of graphic design. To systematize the co-authors' experience in teaching handwriting and to outline a common vision of the directions of realization of calligraphy in the professional education of graphic design. Methods. The study is based on the methods of observation, experimentation and systematization, which made it possible to structure the methodology for teaching basic handwriting skills and applying them in students' project activities. Results. By analyzing the applied resource of handwritten font, the path from basic exercises to the implementation of a specific design project is traced. The results of the study accumulate the co-authors' many years of experience in teaching handwriting and calligraphy and outline a common vision of the directions of calligraphy implementation in graphic design professional education. Conclusions. The creation and development of a national font school is especially relevant for our time. A font is one of the most essential tools of a graphic de signer, which is used in all areas of his or her activity. Therefore, it is worth studying a typeface from its handwritten basis in order to use the full potential of its capabilities, which will help to explore its place in the overall development of Ukrainian design. If the technique of execution, the craft level of first-year students often requires more practice and improvement, then creative solutions, courage in the interpretation of topics, and the found figurative language demonstrate the creative level of students. Also, students acquire the ability to competently imitate calligraphic writing with the help of other means, computer programs, etc., which is an important and valuable experience in further work with literature or the development of typefaces. In general, to look at your work with letters more critically. This combination of course tasks contributes to the improvement of professional training and allows for the successful implementation of creative ideas of a future graphic design specialist.
Towards a Reference Architecture for Semantic Interoperability in Multi-Cloud Platforms

This paper focuses on semantic interoperability as one of the most significant issues in multi-cloud platforms. Organizations and individuals that adopt the multi-cloud strategy often use various cloud services and platforms. On top of that, cloud service providers may offer a range of services with unique data formats, structures, and semantics. Hence, semantic interoperability is required to enable applications and services to understand and use data consistently, regardless of the cloud service providers. The main goal of this study is to propose a reference architecture for semantic interoperability in multi-cloud platforms. Towards achieving the main goal, this paper presents two main contributions. First contribution is an extended cloud computing interoperability taxonomy, with semantic approach as one of the solutions for facilitating semantic cloud interoperability. Two fundamental semantic approaches have been identified, namely semantic technologies and frameworks which will be adopted as the main building blocks. Semantic technologies, such as ontologies, can be used to represent the semantics or meanings of data. Data may be reliably represented across multiple cloud platforms by employing a common ontology. This promotes semantic interoperability by ensuring that data is interpreted and processed uniformly within diverse cloud platforms. On the other hand, a framework offers a standardized and organized way for managing, exchanging, and representing data and services. For the second contribution of this paper, a review of recent (2018-2023) related works has been conducted by investigating the state-of-the-art of semantic interoperability in multi-cloud platforms. As a result, the proposed solution will be implemented in the context of a reference architecture. The reference architecture will act as a blueprint to systematically represent semantic interoperability in multi-cloud platforms using a hybrid approach of role-based and layer-based. Additionally, a semantic layer will be extended to the reference architecture to facilitate semantic interoperability.
Research on Efficient CNN Acceleration Through Mixed Precision Quantization: A Comprehensive Methodology

To overcome challenges associated with deploying Convolutional Neural Networks (CNNs) on edge computing devices with limited memory and computing resources, we propose a mixed-precision CNN calculation method on a Field Programmable Gate Array (FPGA). This approach involves a collaborative design encompassing both software and hardware aspects. Initially, we devised a CNN quantization method tailored for the fixed-point operation characteristics of FPGA, addressing the computational challenges posed by floating-point parameters. We introduce a bit-width strategy search algorithm that assigns bit-widths to each layer based on CNN loss variation induced by quantization. Through retraining, this strategy mitigates the degradation in CNN inference accuracy. For FPGA acceleration design, we employ a flow processing architecture with multiple Processing Elements (PEs) to support mixed-precision CNNs. Our approach incorporates a folding design method to implement shared PEs between layers, significantly reducing FPGA resource usage. Furthermore, we designed a data reading method, incorporating a register set buffer between memory and processing elements to alleviate issues related to mismatched data reading and computing speeds. Our implementation of the mixed-precision ResNet20 model on the Kintex-7 Eco R2 development board achieves an inference accuracy of 91.68% and a computing speed 4.27 times faster than the Central Processing Unit (CPU) on the CIFAR-10 dataset, with an accuracy drop of only 1.21%. Compared to a unified 16-bit FPGA accelerator design method, our proposed approach demonstrates an 89-fold increase in computing speed while maintaining similar accuracy.
Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.
ENHANCING IMBALANCED DATA CLASSIFICATION: A CASE STUDY OF PORTUGUESE BANK MARKETING

In classification tasks, it is presumed that the number of classes of observations is balanced. While classification models usually give a heavily biased weight to the class that has higher occurrence, building an efficient classification model is likely a challenge when feeding it with imbalanced dataset observations or samples of data. This study introduces a new method for addressing imbalanced datasets in classification tasks, particularly focusing on predicting long-term deposits in banking institutions. The method involves systematic evaluation and comparison of random oversampling (ROS) and synthetic minority over-sampling technique (SMOTE) while employing meticulous feature selection to optimize classification precision. This new methodology showcased competitive performance, notably achieving an accuracy of 89.1% and a G-mean of 0.61 with SMOTE at a 500% ratio encompassing all features in Experiment 2 and an accuracy of 87.2% and a G-mean of 0.677 with ROS at a 500% ratio using the top 15 features in Experiment 3. Keywords: Synthetic Minority Oversampling Technique, Random Oversampling, Imbalanced Data, Feature Selection, Random Forest DOI： https://doi.org/10.35741/issn.0258-2724.58.6.21
Analysis and Application of Matrix-Form Neural Networks for Fast Matrix-Variable Convex Optimization

Matrix-variable optimization is a generalization of vector-variable optimization and has been found to have many important applications. To reduce computation time and storage requirement, this article presents two matrix-form recurrent neural networks (RNNs), one continuous-time model and another discrete-time model, for solving matrix-variable optimization problems with linear constraints. The two proposed matrix-form RNNs have low complexity and are suitable for parallel implementation in terms of matrix state space. The proposed continuous-time matrix-form RNN can significantly generalize existing continuous-time vector-form RNN. The proposed discrete-time matrix-form RNN can be effectively used in blind image restoration, where the storage requirement and computational cost are largely reduced. Theoretically, the two proposed matrix-form RNNs are guaranteed to be globally convergent to the optimal solution under mild conditions. Computed results show that the proposed matrix-form RNN-based algorithm is superior to related vector-form RNN and matrix-form RNN-based algorithms, in terms of computation time.
Variable-length Differential Evolution for Numerical and Discrete Association Rule Mining

This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Multi-Screen Adaptive Displaying With Augmented Reality Manipulation Based on the Internet of People

This paper proposes a multi-screen adaptive displaying (MSAD) framework that can enable the assembling of heterogeneous mobile devices into one multi-screen displaying equipment based on the Internet of People. According to our review of relevant research, this is the first framework that provides the following features: (1) the static image, dynamic video, or running text can be properly divided into multiple parts based on the relative positions and screen sizes of different mobile devices, (2) the splitting image, video, or running text can be immediately translated, resized, and previewed on the mobile device in an augmented reality manner, and (3) the different parts of a video or running text can be synchronically played on multiple mobile devices with different computing speeds and screen resolutions. An Android-based prototype with a splitting server is implemented to verify the feasibility and superiority of our MSAD framework. Experimental results show that our framework manipulates the desired content much faster than existing methods/systems and can significantly reduce the total operating time. Moreover, MSAD can effectively alleviate the displaying time inconsistency and unsynchronized content gap on heterogeneous mobile devices.
Detection of endangered species proboscis monkey using computer vision technique on low compute device

The uncommon and endemic proboscis monkey, also known as Nasalis larvatus, is a unique member of Indonesia's diverse animal population that is in the list of endangered species. With the development of technology, the ability to detect this species can be proven useful to further look out this species on the dense forest, their inhabitant. This research paper presents the dataset and compares the performance and computational load to run on a low computation device from three state of the art object detection models: You Only Look Once version 7 (YOLOv7), Single Shot Multibox Detection (SSD) and Faster Region Convolutional Neural Network (Faster R-CNN). All the models are evaluated using low computing device with their respective backbone MobileNetV2 for SSD and tiny-version for YOLOv7. According to the report, Faster R-CNN give the best accuracy of 89.16 and 53.46 in AP0.5 and AP[0.5:0.95] on test dataset with the slowest time among other models with FPS of 1.26 s and huge memory usage. In the other hand, SSD give the fastest detection speed with FPS of 0.31s and the lightest model to run in low computing devices with a decent average precision of 81.35 and 42.99 in AP0.5 and AP[0.5:0.95] respectively. Hence, the best proposed model for real-time detection is SSD with MobileNetV2 as the backbone.
Intelligent Maximization of Eco-friendly Output Energy Based on Internal Photovoltaic Structure

This paper studies the effect of photovoltaic internal cell structure on the maximization of the overall panel output power. Photovoltaic (PV) power generation is one of the eco-friendly and sustainable electrical energy sources. On the other hand, due to the climate change harmful phenomena, the use of renewable energy resources is mandatory. Henceforward, the manuscript contribution is studying the influence of absorber layer thickness , buffer layer thickness , absorber layer doping , and buffer layer doping on the PV electrical energy management through examining their effect on cell efficiency and panel output power. The simulation is carried out using the SCAPS program. The maximum efficiency and output power are reached by applying three strategies of cell parameters variation ( , , and ). The simulation results are compared to a market-existing module. A Convolutional Neural Network (CNN) is used to model the PV behavior utilizing the SCAPS results. The obtained results indicate that the module area can be reduced by 22.39 % while maintaining the same power, thereby reducing the overall cost of energy production by the proportion of the land capital cost and maintenance. It indicates satisfying results
Analysis of Synthetic Data Utilization with Generative Adversarial Network in Flood Classification using K-Nearest Neighbor Algorithm

Indonesia is a country with a tropical climate that has high rainfall rates and is supported by the uncertainty of weather and climate conditions. With the uncertainty of weather and climate as well as flood events, minimal predictive information on flooding, and the lack of availability of data on the causes of flooding, a comparison of synthetic data generation from the minimal data available from BMKG with synthetic data generation from Kaggle online platform data in the form of temperature and humidity data, rainfall, and wind speed from BMKG and annual rain data from Kaggle was analyzed. This research aims to obtain the results of data comparison analysis of synthetic data generation from different datasets with the benchmark of classification system results using K-Nearest Neighbor (KNN) and accuracy evaluation with Confusion Matrix. The research process uses climate data from the BMKG DI Yogyakarta Climatology Station within 20 months, the Geophysical Station within 12 months, and Kerala data with a range of 1901–2018. Synthetic data generation is done using the Conditional Tabular Generative Adversarial Network (CTGAN) model. CTGAN produces quite good data in terms of distribution and data differences if the original data is large and the synthetic data generated is small. The KNN classification system on the BMKG data experienced overfitting, as indicated by the accuracy value in the evaluation increasing in the range of 85–94% and the validation decreasing in the range of 89%–65%. This is because there is no uniqueness in the data and too little original data made into synthetics, which affects the difficulty of the classification system in identifying data that is quite different in distance and data values generated by CTGAN. In Kerala, the accuracy value on evaluation is in the range of 92–95%, and validation is in the range of 0.7–0.83%, with Classifier k1 being the most optimal system.