Reviewerly/exHarmony huggingface dataset:
Enhancing AutoNLP with Fine-Tuned BERT Models: An Evaluation of Text Representation Methods for AutoPyTorch

Recent advancements in Automated Machine Learning (AutoML) have led to the emergence of Automated Natural Language Processing (AutoNLP), a subfield focused on automating NLP model development. Existing NLP toolkits provide various tools and modules but lack a free AutoNLP version. To this end, architecting the design decisions and tuning knobs of AutoNLP is still essential for enhancing performance in various industries and applications. Therefore, analyzing how different text representation methods affect the performance of AutoML systems is an essential starting point for investigating AutoNLP. In this paper, we present a comprehensive study on the performance of AutoPyTorch, an open-source AutoML framework with various text representation methods for binary text classification tasks. The novelty of our research lies in investigating the impact of different text representation methods on AutoPyTorch’s performance, which is an essential step toward transforming AutoPyTorch to also support AutoNLP tasks. We conduct experiments on five diverse datasets to evaluate the performance of both contextual and noncontextual text representation methods, including onehot encoding, BERT (base uncased), fine-tuned BERT, LSA, and a method with no explicit text representation. Our results reveal that, depending on the tasks, different text representation methods may be the most suitable for extracting features to build a model with AutoPyTorch. Furthermore, the results indicate that fine-tuned BERT models consistently outperform other text representation methods across all tasks. However, during the fine-tuning process, the finetuned model had the advantage of benefiting from labels. Hence, these findings support the notion that integrating fine-tuned models or a model fine-tuned on open source large dataset, including all binary text classification tasks as text representation methods in AutoPyTorch, is a reasonable step toward developing AutoPyTorch for NLP tasks.
N-Gram-Based Serbian Text Classification

Natural language processing is an active area of research which finds many applications in variety of fields. Low-resource languages are a challenge as they lack curated datasets, stemmers and other elements used in text processing. Statistical approach is an alternative which can be used to bypass lack of rule-based implementations. The paper presents a model for classification of unstructured text in Serbian language. The model uses n-gram-based stemming to create document attributes vectors. Vectors are created on 3-, 4- and 5-grams. Vector reduction is tested on two criteria: n-gram entropy and number of occurrences, and two lengths: 1000 and 2000 n-grams. The support vector machine is used to classify documents. The model is trained and tested on a dataset collected from a Serbian news portal. Classification accuracy of over 80% is achieved. The presented model provides a good basis for range of applications in business decision automation for low-resource languages.
Gesture Recognition in Millimeter-Wave Radar based on Spatio-Temporal Feature Sequences

Gesture recognition is a pivotal technology in the realm of intelligent education, and millimeter-wave (mmWave) signals possess advantages such as high resolution and strong penetration capability. This paper introduces a highly accurate and robust gesture recognition method using mmWave radar. The method involves capturing the raw signals of hand movements with the mmWave radar module and preprocessing the received radar signals, including Fourier transformation, distance compression, Doppler processing, and noise reduction through moving target indication (MTI). The preprocessed signals are then fed into the Convolutional Neural Network-Time Domain Convolutional Network (CNN-TCN) model to extract spatio-temporal features, with recognition performance evaluated through classification. Experimental results demonstrate that this method achieves an accuracy rate of 98.2% in domain-specific recognition and maintains a consistently high recognition rate across different neural networks, showcasing exceptional recognition performance and robustness
Thai culture image classification with transfer learning

Classifying images of Thai culture is important for a variety of applications, such as tourism, education, and cultural preservation. However, building a Machine learning model from scratch to classify Thai cultural images can be challenging due to the limited availability of annotated data. In this study, we investigate the use of transfer learning for the task of image classification on a dataset of Thai cultural images. We utilize three popular convolutional neural network models, namely MobileNet, EfficientNet, and residual network (ResNet) as baseline pre-trained models. Their performances were evaluated when they were trained from random initialization, used as a feature extractor, and fully fine-tuned. The results showed that all three models performed better in terms of accuracy and training time when they were used as a feature extractor, with EfficientNet achieving the highest accuracy of 95.87% while maintaining the training time of 24 ms/iteration. To better understand the reasoning behind the predictions made by the models, we deployed the gradient-weighted class activation mapping (Grad-CAM) visualization technique to generate heatmaps that the models attend to when making predictions. Both our quantitative and qualitative experiments demonstrated that transfer learning is an effective approach to image classification on Thai cultural images.
Image noise reduction by deep learning methods

&lt;span lang="EN-US"&gt;Image noise reduction is an important task in the field of computer vision and image processing. Traditional noise filtering methods may be limited by their ability to preserve image details. The purpose of this work is to study and apply deep learning methods to reduce noise in images. The main tasks of noise reduction in images are the removal of Gaussian noise, salt and pepper noise, noise of lines and stripes, noise caused by compression, and noise caused by equipment defects. In this paper, such noises as the removal of raindrops, dust, and traces of snow on the images were considered. In the work, complex patterns and high noise density were studied. A deep learning algorithm, such as the decomposition method with and without preprocessing, and their effectiveness in applying noise reduction are considered. It is expected that the results of the study will confirm the effectiveness of deep learning methods in reducing noise in images. This may lead to the development of more accurate and versatile image processing methods capable of preserving details and improving the visual quality of images in various fields, including medicine, photography, and video.&lt;/span&gt;
Generative adversarial deep learning in images using Nash equilibrium game theory

A generative adversarial learning (GAL) algorithm is presented to overcome the manipulations that take place in adversarial data and to result in a secured convolutional neural network (CNN). The main objective of the generative algorithm is to make some changes to initial data with positive and negative class labels in testing, hence the CNN results in misclassified data. An adversarial algorithm is used to manipulate the input data that represents the boundaries of learner’s decision-making process. The algorithm generates adversarial modifications to the test dataset using a multiplayer stochastic game approach, without learning how to manipulate the data during training. Then the manipulated data is passed through a CNN for evaluation. The multi-player game consists of an interaction between adversaries which generates manipulations and retrains the model by the learner. The Nash equilibrium game theory (NEGT) is applied to Canadian Institute for Advance Research (CIFAR) dataset. This was done to produce a secure CNN output that is more robust to adversarial data manipulations. The experimental results show that proposed NEGT-GAL achieved a grater mean value of 7.92 and takes less wall clock time of 25,243 sec. Therefore, the proposed NEGT-GAL outperforms the compared existing methods and achieves greater performance.
Hiding algorithm based fused images and Caesar cipher with intelligent security enhancement

&lt;span lang="EN-US"&gt;The process of sending confidential data through the communication media and in complete secrecy is now necessary, whether the data is related to patients, a particular military operation, or a specified office. On the other hand, with the development of various ciphering algorithms, and information hiding algorithms, there is a need to obtain ciphered and hidden data securely without the need to exchange secret keys between the two ends of the communication. In this paper, a hiding algorithm based on fused images and Caesar cipher with intelligent methods to strengthen the security of confidential information is proposed. Firstly, fused image scattering is obtained using 1’s complement and circularly shifting the bits of fused pixels by specified positions before the hiding process. Secondly, the keys for the Caesar cipher are derived from the length of secret information according to the mathematical equation. Thirdly, strengthen the security of Caesar’s cipher by taking a 1’s complement of each letter in the cipher data. The results guarantee the security of the presented algorithm.&lt;/span&gt;
A simplified predictive framework for cost evaluation to fault assessment using machine learning

&lt;span lang="EN-US"&gt;Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.&lt;/span&gt;
Enhancing speaker verification accuracy with deep ensemble learning and inclusion of multifaceted demographic factors

&lt;span lang="EN-US"&gt;Effective speaker identification is essential for achieving robust speaker recognition in real-world applications such as mobile devices, security, and entertainment while ensuring high accuracy. However, deep learning models trained on large datasets with diverse demographic and environmental factors may lead to increased misclassification and longer processing times. This study proposes incorporating ethnicity and gender information as critical parameters in a deep learning model to enhance accuracy. Two convolutional neural network (CNN) models classify gender and ethnicity, followed by a Siamese deep learning model trained with critical parameters and additional features for speaker verification. The proposed model was tested using the VoxCeleb 2 database, which includes over one million utterances from 6,112 celebrities. In an evaluation after 500 epochs, equal error rate (EER) and minimum decision cost function (minDCF) showed notable results, scoring 1.68 and 0.10, respectively. The proposed model outperforms existing deep learning models, demonstrating improved performance in terms of reduced misclassification errors and faster processing times.&lt;/span&gt;
Best-worst northern goshawk optimizer: a new stochastic optimization method

&lt;span lang="EN-US"&gt;This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.&lt;/span&gt;
Analisis Kesesuaian Lahan untuk Tanaman Jagung Menggunakan Citra Landsat 8 OLI/TIRS dan Sistem Informasi Geografis di Kabupaten Lampung Selatan

Analysis of land suitability for corn plants is needed to increase production in the development of the agricultural sector. South Lampung Regency is the largest corn producer in Lampung Province with production reaching 598,032 tonnes in 2016. The aim of the study was to determine the potential for land development for corn plants using Landsat 8 OLI/TIRS image interpretation and Geographic Information Systems in South Lampung Regency by assessing the quality of the land and to determine the level of land suitability conditions for corn crops based on planting seasons 1, 2 and 3 in South Lampung Regency. The analysis is carried out by the matching method or by giving a class assessment to each of the required parameters. The results show that the 1st planting season has the greatest level of land suitability compared to the other growing seasons, 73% very suitable to suitable. Meanwhile, Planting Season 2 had the lowest suitability level of 60% due to less stable temperatures due to the transition from rainy to dry seasons. And Planting Season 3 has a moderate suitability level of 66%, because this season the temperature has stabilized for cultivating corn. Keywords: Corn, Geographic Information System, Class Assessment, Land Suitability, Landsat 8 OLI/TIRS Imagery, Matching.
Ontology engineering of automatic text processing methods

&lt;span lang="EN-US"&gt;Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.&lt;/span&gt;
Kecerdasan Emosional dalam Perspektif Daniel Goleman (Analisis Buku Emotional Intelligence)

Emotional Intelligence is a person’s ability to identify, understand and regulate the emotions of themselves and others. Emotional Intelligence must be trained and studied from an early age in order to obtain emotional intelligence because emotional intelligence does not come suddenly. This emotional Intelligence book offers several theories on how to be successful in emotional intelligence. The methodology used is descriptive qualitative research using primary and secondary data and using content analysis. The aim of this research is to improve emotional intelligence very well and explain what emotional intelligence is according to Daniel Goleman in his book Emotional Intelligence. Having emotional intelligence enables a person to recognize their own feelings and those of others as well as the ability to motivate themselves and the ability to manage emotions well in themselves and in relationships with others.
Pelatihan Pembuatan Desain Busana Secara Digital Bagi Peserta Didik SMK Tata Busana di Yogyakarta

This training activity aims to provide knowledge and skills in making digital fashion designs as a continuation of the process of making manual fashion designs for students of SMK Fashion Yogyakarta. The target of the activity is the third-year students of SMK (vocational high school) of the Fashion Expertise Program in Yogyakarta. The lecturers doing off-campus project activities related to fashion design provide training on making fashion designs digitally with the CLO 3D application. In the form of training, the speaker not only delivers material or theory but also conducts training directly to students. This activity is divided into three stages: the preparation stage, the implementation stage, and the evaluation stage. The methods used in this training activity are lectures to convey fashion design theory, demonstration methods to provide examples of the process of making fashion designs digitally, and the practice of making fashion designs using the CLO 3D application carried out by students accompanied by a presenters team. The activity results are (1) Implementing practical activities to make fashion designs with digital techniques attended by three classes, XII Busana 1, 2, and 3, consisting of 90 students. (2) Students are trained in making fashion designs with digital techniques using the CLO 3D application. Based on the evaluation of digitally making fashion designs, participants' attendance during the training reached 96%. While based on student practice results, 62% of student design result is categorized as 'good,' while 38% means fairly good.
Anxiety about English Public Speaking in the Students of English Education Department

Speaking in spoken English is the act of conveying information or expressing thoughts and feelings. This study described how to analyze students' speaking fear in English Public Speaking at eighth semester Muslim Nusantara University Al-Washliyah Medan English Education Department. The qualitative research method was applied in this study. The researcher employed observation, interview, and questionnaire to gather the data. Three main stages of data analysis were used by the researcher: data reduction, data display, and conclusion drawing verification. Following a study of the data, the researcher came to the following conclusions about speaking fear among students at eighth semester Muslim Nusantara Al-Washliyah Medan English Education Department: First, the researcher discovered that the following factors contributed to students' speaking anxiety in English Public Speaking: self-prediction of fear, illogical faith, oversensitivity to threat, anxiety sensitivity, incorrect attribution of body signal, and low self-efficacy. Second, students' lack of speaking confidence in English, fear of making mistakes when speaking, nervousness when the teacher asks them to speak up, and shyness to perform in front of the class are the causes of students' speaking anxiety.
Adversarial attack driven data augmentation for medical images

An important stage in medical image analysis is segmentation, which aids in focusing on the required area of an image and speeds up findings. Fortunately, deep learning models have taken over with their high-performing capabilities, making this process simpler. The deep learning model’s reliance on vast data, however, makes it difficult to utilize for medical image analysis due to the scarcity of data samples. Too far, a number of data augmentations techniques have been employed to address the issue of data unavailability. Here, we present a novel method of augmentation that enabled the UNet model to segment the input dataset with about 90% accuracy in just 30 epochs. We describe the us- age of fast gradient sign method (FGSM) as an augmentation tool for adversarial machine learning attack methods. Besides, we have developed the method of Inverse FGSM, which im- proves performance by operating in the opposite way from FGSM adversarial attacks. In comparison to the conventional FGSM methodology, our strategy boosted performance up to 6% to 7% on average. The model became more resilient to hostile attacks because to these two strategies. An innovative implementation of adversarial machine learning and resilience augmentation is revealed by the overall analysis of this study.
An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.