Reviewerly/exHarmony huggingface dataset:
Status of Selected Heavy Metals in Soils Samples Obtained From Crown Estate Dumpsite, Okada, Edo State, Nigeria

This research work focused on the status of selected heavy metals in dumpsite soil compared with soil samples obtained from a green park area as a control. The research results were also related to threshold limits set by the National Environmental Standards and Regulations Enforcement Agency (NESREA), World Health Organization (WHO) and Food and Agriculture Organization (FAO). Soil samples were collected from a dumpsite inside Crown Estate (made up of residential quarters for both staff and students of Igbinedion University) and a green area of Igbinedion University's permanent site, Okada, Benin City, Edo State in Nigeria. The soil samples were air-dried, ground, sieved, digested and subjected to heavy metal analysis using atomic absorption spectroscopy. The analyzed metal profiles in the dumpsite soil samples are as follows: concentration of Fe, Pb, Cu, Zn, Mn and Ni ranged from 453.26 – 569.28, 0.85 – 1.16, 20.17 – 32.35, 60.98 – 74.26, 5.15 – 9.28, and 5.92 – 9.57 mg/Kg, with mean concentrations of 501.33 mg/Kg, 0.96 mg/Kg, 25.38 mg/Kg, 66.33 mg/Kg, 6.89mg/Kg and 7.90 mg/Kg respectfully, while the status of heavy metal concentration in soil samples collected from the control site ranged from 386.21 – 463.95, 0.82 – 1.32, 20.05 – 26.27, 47.75 – 65.22, 5.77 – 8.73, and 6.35 – 9.87 mg/Kg, with mean concentration of 401.76 mg/Kg, 1.05 mg/Kg, 23.05 mg/Kg, 54.84, 7.39 mg/Kg and 7.78 mg/Kg for Fe, Pb, Cu, Zn, Mn and Ni respectfully. Of all the metals analyzed only the level of Fe in this study did exceed the tolerance limits of NESREA, WHO and FAO. The results also revealed higher heavy metal content in the dumpsite justifiably due to the difference in anthropogenic activities carried out on the two sites.
Formation of professional and aesthetic readiness and media literacy of pedagogical university students

The processes taking place in the cultural aspect of the last decade purposefully change the traditional ideas about the role of aesthetic education in the formation of the teacher's personality. Therefore, at the moment there are questions of developing and implementing a holistic program for the formation of professional and aesthetic readiness of the future teacher. Also, the situation with the coronavirus has shown that professional and aesthetic readiness should be supported by the media literacy of future teachers of fine arts.The main di-rections and ideas of scientific research: the article presents the results of the formation of professional and aesthetic readiness of future teachers of fine arts, studies the directions for the formation of professional and aesthetic readiness. The formation of professional and aesthetic readiness is applied through information technology as the basis for subsequent literacy in the use of Internet resources in teaching activities.A brief description of the scientific and practical significance of the work: this work will be useful to teachers of a pedagogical universityas a tool for checking the professional and aesthetic readiness of students.A brief de-scription of the research methodology: to determine the level of formation of professional and aesthetic readi-ness of the study “Definition of the formation of value orientations”; the test “Learn emotions”; the test “Cat-egories of analysis of the artistic content of works of fine art”; the test “Motivation of professional activity”was applied.The main results and analysis, conclusions of the research work: in general,the results demon-strate a low overall level of knowledge of aesthetic knowledge, methodological skills to use aesthetic baggage in the educational process, the desire for creative self-realization in the artistic and pedagogical sphere. The students showed medium and low levels of formation of emotional-value, cognitive, reflexive-constructive components of professional and aesthetic readiness of future teachers of fine arts.The value of the conducted research (the contribution of this work to the relevant field of knowledge): parametric characteristics and a set of diagnostic tools for evaluating the training of future teachers of fine arts are proposed.The practical signif-icance of the results of the work: the selected components allow us to diagnose the degree of formation of each component of the professional and aesthetic preparedness of teachers of the educational direction “Art”
Contextualization of functional literacy assessment tasks with LDA topic modeling method

When assessing the functional literacy of university graduates in Kazakhstan, the context in which the tasks are placed plays an important role. This context is due to the very importance of the development of function-al literacy as the basis for the development of human capital. In this article, the issue of contexts is studied in detail by analyzing methodological documents of authoritative international comparative studies in the field of functional literacy assessment, a thematic analysis of tasks of standardized studies that are in open access with usingmachine learning algorithms (thematic modeling based on the Dirichlet latent placement method) is carried out. When analyzing methodological documents and reports of international comparative studies in the field of assessing the functional literacy of the adult population in context, technically, the names of the-matic areas of contexts, although slightly changed, the main semantic core of contexts in all considered inter-national comparative studies are similar. The main major thematic areas of contexts in the field of assessing functional literacy of adults are: “society and communities”, “work”, “education and training”and everyday situations. Thematic analysis of databases of tasks is in the public domain from the perspective of determin-ing the context by the method of thematic modeling. In addition, as a general conclusion, it can be concluded that the contexts of tasks are closely related to the main goal of the development of functional literacy: the ef-fective functioning of a person in society. It follows from this that when assessing the functional literacy of university graduates in Kazakhstan, tasks should be placed in such contexts that are associated with the effec-tive functioning of graduates in society.
SWOT analysis as a means ofcritical thinking skills development of IT students in professional Englishclass

his article focuses on theoretical and practical issues of using SWOT analysis as a means of critical thinking skills development of IT students at the professional Englishlanguage lessons. The authors emphasize the importance of close-knit teaching of critical thinking skills and SWOT analysis that form the core of an Eng-lish lesson for future IT specialists. Currently, the problem of developing critical thinking of students and schoolchildren is relevant, since developing thinking means enhancing the ability to think. The use of SWOT analysisgives an opportunity to build learners’ skills and abilities to process and sort out necessary infor-mation, find strengths and weaknesses, opportunities and threatsthat arise in the workplace.Thanks to a per-son's competence to think “outside the box”, new ways of solving problems will be revealed, difficult tasks will be solved, discoveries will be made, inventions will appear. The research is based on the works by well-known home and foreign methodologists studying the issues of critical thinking.
Aspect-Based Sentiment Analysis of Indonesian Restaurant Reviews in Bahasa Indonesia using Word2Vec and SVM

Restaurant reviews on social media are one of the main sources of information for customers before making a decision. Since user opinions vary, it requires sentiment analysis for processing reviews. This research aims to evaluate the performance of sentiment analysis on restaurant reviews in Bahasa Indonesia. This research explains how sentiment analysis can help in processing restaurant reviews, and provide a summary of reviews in the form of sentiment results for customers who will make decisions. Using aspect-based sentiment analysis in the form of five aspects, FOOD, AMBIENCE, MISCELLANEOUS, PRICE, and SERVICE. Word2Vec is a word embedding that learns word vector representations through neural networks. This study employs the Skip-Gram method, one of two types of Word2Vec methods available: Continuous Bag of Words (CBOW) and Skip-Gram. Word2Vec was chosen because of its ability to produce a vector representation of words that properly describes their meaning and context, which is crucial in sentiment analysis of restaurant reviews. Then, SVM is used as a classification technique for sentiment prediction by optimizing the dataset. SVM is used in this research because it can accurately solve various complex problems. The result is SVM with Word2Vec has the most optimal macro average f1-score of 86.33%.
Naïve Bayes Classifier and Word2Vec for Sentiment Analysis of Aspect Based on Indonesian Restaurant Review

The development of technology and information has significantly led to enhanced accessibility of certain types of media, specifically reviews on the internet. These reviews can be valuable for in assisting customers in making informed purchasing decisions while providing business owners with valuable insights to refine their operations. To analyze customer opinions, one approach is to use Sentiment Analysis. This method incorporates techniques like Natural Language Processing and Artificial Intelligence to categorize opinions into positive or negative sentiments. This analytical process can be applied to various forms of text, such as reviews. The goal is to understand the emotional tone or opinion conveyed in the reviews. In the realm of sentiment analysis, there exists a specialized method known as aspect-based sentiment analysis. Reviews are dissected into relevant aspects. For example, in a restaurant review, the aspects that might be analyzed are food, price, service, and ambience. Several methodologies can be employed for sentiment analysis, including Naïve Bayes classification and using Word2Vec as a feature extraction tool. The highest performance result by using Naïve Bayes and Word2Vec on this research produces an performance 85.27% for overall, 83.96% for food aspect, 87.89% for price aspect, 86.87% for service aspect, and 87.33% for ambience aspect.
Internet of Things for Seismographs using Spline Interpolation

This study focused on revolutionizing earthquake monitoring through the utilization of IoT-based seismographs, harnessing the power of Internet of Things (IoT) technology for real-time data collection. Despite the promising potential of these devices, technical glitches often lead to data gaps, posing significant challenges in seismic data analysis. To mitigate this issue, the study implemented cubic spline interpolation to effectively fill the missing data points, and a rigorous comparison with quadratic interpolation was undertaken to gauge accuracy levels. The results underscore the success of cubic spline interpolation in closely mimicking the seismic data pattern. Although slight deviations from the original data were noted, the method exhibited an impressively low Root Mean Square Error (RMSE) of 0.033902 and Mean Square Error (MSE) of 0.001149, highlighting its prowess in addressing incomplete or missing data in seismic recordings. Conversely, another result illustrates the outcomes of quadratic interpolation, demonstrating its ability to approximate the seismic data pattern more accurately than cubic spline. However, similar minor deviations were observed, with an RMSE of 0.033721 and MSE of 0.001137. These findings illuminate the significance of cubic spline interpolation in enhancing earthquake monitoring techniques, showcasing its immense potential in seismic data analysis and paving the way for more robust and accurate earthquake predictions in the future.
GPT-4 versus Bard and Bing: LLMs for Fake Image Detection

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deepfakes – a task that has become critically important in a world increasingly reliant on digital media. Our investigation rigorously assesses the capabilities of these advanced LLMs in identifying and differentiating manipulated imagery. We explore how these models process visual data, their effectiveness in recognizing subtle alterations, and their potential in safeguarding against misleading representations. The implications of our findings are far-reaching, impacting areas such as security, media integrity, and the trustworthiness of information in digital platforms. Moreover, the study sheds light on the limitations and strengths of current LLMs in handling complex tasks like image verification, thereby contributing valuable insights to the ongoing discourse on AI ethics and digital media reliability.
Imbalanced Multi-label Classification of Hadith of Bukhari (Indonesian Language Translation) Using Ensemble Stacking

This paper discusses handling imbalanced data in classifying multi-label text in the Bukhari hadith. This research uses the ensemble stacking method because it provides good evaluation results in cases of imbalanced data compared to the single classifier. The proposed method generally combines several predictions from the base learners or a single classifier method to get better classification prediction results. This research begins with preprocessing data, which includes punctuation removal, case folding, stop word removal, and n-gram. At the same time, TF-IDF is used for text representation. To evaluate the classification results, the accuracy and F1-score measures are applied. In the evaluation, several experiment scenarios are applied to observe the impact of base learner combinations as well as the number of features used in the classification. In addition to observing the improvement level of the proposed method, an experiment scenario was done either by applying or not applying the proposed method. The proposed method consistently improves evaluation results, particularly in imbalanced multi-label classification. It effectively addresses data imbalance, enhancing accuracy for minority labels. The experiment results show that the combination of three base learners (Random Forest, Gaussian NB, and SVM), which is applied to two thousand max features, outperformed compared to the other combinations. This combination of base learners resulted in 83,14 % accuracy and 42,92 % of F1-score.
Implementation of Network Security Using a Cloud Computing-Based Firewall on the PukulEnam Company Website

In our modern digital landscape, websites serve as the primary means for individuals and organizations to communicate and share information online. However, this convenience also brings forth potential security vulnerabilities that malicious actors can exploit, jeopardizing confidential data. Therefore, a robust network security system is imperative to safeguard this sensitive information. PukulEnam, a company specializing in news and AI consulting, is acutely aware of the looming threat of cyberattacks that could compromise its valuable proprietary data. To proactively mitigate such risks, we are embarking on the essential first step of implementing a firewall within PukulEnam's website security framework. Firewalls, when effectively deployed, act as a formidable deterrent against potential threats and reduce the likelihood of confidential data breaches. Our project's core objective is to deploy Google's cloud-based firewall onto the PukulEnam website. Through the meticulous configuration of firewall rules tailored to our company's unique requirements, we aim to fortify our website's defenses against potential cyberattacks. The results obtained in implementing this firewall are in the form of correct responses and in accordance with the firewall rules that have been set. and with the highest response latency value below 1.4 ms, which means the response speed provided by the website is at a good value. Utilization of cloud application engine firewalls makes a positive contribution to the network security of the PukulEnam website.
Interpreting Decision of Self-Attention Network’s in the Context of Image Recognition by Efficiently Utilizing Attention Scores

Deep Learning has risen to success exceeding human level in different tasks of computer vision in the last decade. Despite the gain in performance, the black box nature and lack of interpretation have always been a criticism against deep networks and in many cases a barrier to its application. Even though convolutional neural networks are the dominant, most successful architecture in vision problems, recently it has been shown that fully self-attention based networks can outperform convolutional networks in terms of performance and complexity. Despite being proposed initially in the language domain, self-attention based networks have been slowly finding their application and importance in vision problems as well. One of the key aspects of attention-based networks is the attention scores they provide. Visualizing attention scores to understand the inside of the deep network’s working is a common practice in different problems such as image captioning, machine translation, etc. In this paper, we propose an efficient framework to utilize attention scores to generate an explanation of the model’s decision when self-attention networks are implemented in the context of image recognition. Using our approach we show different visualizations of the original image and regions of the image that the network finds as important. We also compare the importance heatmap produced by our approach and other standard gradient-based approaches. We show that when the network assigns importance to a large cluster of region gradient-based methods fail to integrate the total pattern, a problem that our suggested strategy is able to solve.
Approaches in Using Python for Image Processing

Image processing is an increasingly widespread field in computer science. On the other hand, it is increasingly used both in modern business and in social communication. We will look at image manipulation methods, emphasizing approaches for comparing them. We will offer ways to process images included in popular specialized applications. We emphasize the use of the popular Python programming language, which is becoming more and more popular due to its ease of use and large range of tools. We will look at some of the image processing and analysis libraries used in Python.
Review of Transform Domain Medical Image Watermarking Techniques

Nowadays, to enhance patient care and streamline the management of medical information, medical images obtained in hospitals or imaging centers can be readily shared among multiple healthcare professionals. However, this sharing often takes place over insecure networks, exposing medical images to various security threats where attackers aim to manipulate, steal, or destroy sensitive information, any variation to the medical record might result in an incorrect diagnosis and hence harm the patient's life. This highlights the increased need for security measures to ensure confidentiality, availability, and reliability, which encompass integrity and authenticity. Watermarking techniques have emerged as a popular and effective means of protecting patient data from these attacks and problems. Watermarking techniques exhibit a range of categorizations, encompassing factors such as their classification based on robustness, imperceptibility, capacity, and the transform domain. Transform domain watermarking techniques have garnered significant interest due to their ability to offer a high level of robustness against attacks, all while ensuring the imperceptibility of the watermark. In this review, we explore a variety of transform domain watermarking techniques specifically designed for medical images. Additionally, we provide a comparative analysis of different kinds of transform domains commonly employed in medical image watermarking.
Supernets Based Image Classification

Designing of Deep Neural Networks (DNN) is accelerated through an efficient approach known as Neural Architecture Search (NAS). A search space with various neural network architectures for performing a deep learning task is sampled through this approach. In practice, designing of neural networks is automated by NAS approach which drastically reduces the training time as compared to hand design. But critical problems tend to arise as the datasets become larger. The complexity in the hidden layers and computational cost is increased due to which vanishing gradient comes into picture. In this paper, an optimized approach based on NAS algorithms is proposed. Neural architectures in the search space are combined to form a single model called the SuperNet. A candidate architecture also known as the ensemble, is generated from the SuperNet by training the SuperNet once and sampling it. A wide range of parameters are evaluated with the validation set for the ensemble. The proposed algorithm is compared with existing NAS approaches and a conclusion is drawn that the SuperNet approach for NAS is effective in automating the neural network design. For CIFAR-10 dataset, the proposed algorithm provides 93.40% accuracy with GPU time of 8.78 hours compared to 75.82% accuracy with 17 days computation for conventional NAS algorithm.
Neoteric Furtherance of Artificial Intelligence in Kidney Disease – a Purview

Artificial intelligence cites the brainpower exhibited by the contraption, as antagonistic to spontaneous brainpower disposed of by personage conviction. Artificial Intelligence is machine-demonstrated intelligence and human thoughts, which include psychology, philosophy, computer science, information technology, and linguistics, which is opposed to natural thoughts and intelligence. AI is widely used in the medical field. Such as early detection of disease, diagnosis, management, and treatment. As compared to other fields, kidney disease diagnosis and treatment remains a global health issue. Kidney disease diagnosis and treatment is still a dare to us. Artificial Intelligence can input patients' positions; suitable decisions can output and augment the possibilities in kidney disease management. Here, this paper gives a comprehensive study of the scope of artificial intelligence / current studies of AI in kidney disease.
High-Resolution Face Image Enhancement via Generative Adversarial Networks

Image upscaling, also known as image super-resolution, is a technique that boosts an image's resolution in order to enhance its quality. A subclass of super-resolution called face image super-resolution focuses on enhancing the quality of face images for biometrics, face tracking, surveillance, and recognition of faces. This paper introduces High-Resolution Face image GAN (HRFGAN) which is a deep leaning GAN based solution for face image super-resolution. HRFGAN balances performance and computational efficiency. This modified architecture achieves comparable PSNR and SSIM compared to the base architecture of SRGAN with fewer parameters. To assess perceptual quality, the model is evaluated using LPIPS, which measures perceptual similarity. Compared to low-resolution inputs, HRFGAN generates visually beautiful super-resolved face images with better details. The evaluation using LPIPS confirms the perceptual quality improvement achieved by HRFGAN.