Reviewerly/exHarmony huggingface dataset:
Cyber-Crime and Cyber-Security are Two Sides of the Same Coin. A Critical Discussion on these Concepts based on Practical Examples which Happened in Zimbabwe Over the Past 6 Years.

Cyber-crime and cyber-security are contemporary broad areas that handle issues to deal with computers and networks. Many people suggest that “cyber-crime and cyber-security are two sides of the same coin” and this paper intends to dissect and give informed comments concerning this statement. Cybercrime is a 21st criminal behaviour, an anti-social behaviour that manifests itself in the cyberspace where as the cyber security is a concerted effort to combat the cybercrime. The concepts are divergent in the sense that when cybercrime is on the increase it entails failure on the part of cybersecurity. On the other hand, cybersecurity effectiveness can be seen through decrease in cybercrime. This write up will deliberate on the relationship between cyber-crime and cyber-security mentioning areas where they converge as well as outlining their main differences. This stems from the fact that global connectedness requires in-depth understanding of the consequences of cybercrimes, this will trigger attention to detail in respect of mechanisms that should be put in place to improve cybersecurity. Cybersecurity is one of the major contemporary facets in the world that has to be providing high end solutions to the ever changing technological and cyberspace. The paper provides the key definitions, provides overview of the concept of cybercrime and cybersecurity. It will then provide a discussion on the cybersecurity and cybercrime, highlighting their divergence as well as providing relevant examples. The paper then provides a comprehensive conclusion and provides recommendations as predicted by the discussion.
Extending OCR Model for Font and Style Classification

Font and style classification aims to recognize which font and which style the characters in the input image belong to. The con- junction of font and style classification with traditional OCR systems is important in the reconstruction of visually-rich documents. However, the current text recognition systems have yet to take into account these tasks and focus solely on the recognition of characters from input images. The separation of these tasks makes the document reconstruction systems computationally expensive. In this paper, we propose a new approach that extends the current text recognition model to include font and style classification. We also present a dataset comprising input images and corresponding characters, fonts, and styles in Vietnamese. We evaluate the effectiveness of this extension on multiple recent OCR models, including VST [10], CRNN [8], ViSTR [1], TROCR [7], SVTR [4] . Our results demonstrate that our extension achieves decent accuracy rates of 98.1% and 90% for font and style classification, respectively. Moreover, our extension can even boost the performance of the original OCR models.
PENGENALAN WAJAH PEGAWAI KANTOR DENGAN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK BERBASIS ANDROID

Face is one of the elements used to identify identity between humans. The purpose of making this thesis is as a basic basis for developing an attendance system and making artificial intelligence that can identify humans through their faces. How to do data processing, the data taken comes from a video of office employees which lasts approximately 10 seconds. To make a program that can recognize the faces of office employees, the Convolutional Neural Network (CNN) method is used which will be trained to be able to distinguish each unique feature on the face to distinguish and recognize humans specifically. In performing facial recognition, office employees can provide input in the form of facial photos of office employees who have been trained and use the camera on a smartphone to perform face recognition directly. The faces of office employees used as targets for this CNN training came from Pt Eternal Indonesia, Faculty of Information Technology, Tarumanagara University, and Kekar ​​Clinic. The output of the application is the accuracy of each photo of the office employee's face given. The results of the confusion matrix test show that the trained model has an accuracy of 80.39%, a precision of 80%, a recall of 80%, and an f1-score of 80%.
Role of Multi-experience Developmental Platforms (MXDP) in Futuristic Classroom Education

Technological advancements in recent years have raised the need for transformation in traditional education making way for e-learning to provide instant information and learning, causing attention deficiency and leading to diminished receptive powers of the brain. This leads to the most important question; how can we provide and promote immersive and panoptic solutions for reaching new heights in the realms of education and learning experiences? The inclusion of mixed reality experiences has encouraged innovators and thinkers to derive a more nuanced approach to catering to educational requirements. Psychological responses of the brain toward interactive learning are highly advantageous as all four systems within the brain synchronize for deeper engagement, the four systems of the brain being, cognitive, behavioral, experiential, and emotional learning systems. Through this paper, researchers wish to highlight the gaps and potential opportunities in the world of immersive experiences and technological advancements that may be adapted into modern-day education and explore possible innovations. As futuristic classrooms are the topic of discussion and recognition, what role does the top technology trend, multi-experience play?
PENGENALAN AKTIVITAS MANUSIA DI SUPERMARKET DENGAN METODE LONG SHORT TERM MEMORY

Since a long time ago, supermarkets have become people's destinations for shopping for various things such as food, cooking ingredients, cleaning products and others. Supermarkets are known for their very large and crowded places, making it difficult to monitor. Therefore, supermarkets need a system to help monitoring. With the development of technology, monitoring systems are increasingly advanced and one of the results of these technological developments is a system for recognizing human activities. By using OpenPose to obtain human skeleton data on the image and using the Long Short Term Memory method to perform recognition, testing of the training data was carried out so as to produce a precision value of 99%, recall 99%, and f1-score 99%. And real-time testing using a camera resulted in an accuracy value of 73% for the picking class, 87% for the standing class and 81% for the walking class.
Sistem Pengenalan Covid-19 Berdasarkan Foto X-ray Paru dengan Metode EfficientNet-B0

Covid-19 is a viral infection disease severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Covid-19 is a group of viruses that attack the respiratory system in humans which can cause symptoms ranging from mild symptoms to severe symptoms. Currently, to detect whether a person is infected with the Covid-19 virus or not, several tests can be carried out, one of which is the polymerse chain reaction (PCR) examination. This type of examination has a high level of accuracy but this examination requires quite expensive costs, adequate laboratories and requires a long time. So from these problems there is another alternative, namely radiological examination. From these problems, a system was built that can perform classification based on x-ray images of the lungs using the convolutional neural network (CNN) method of Efficientnet-B0 architecture. This system is expected to assist medical personnel in pre-diagnosing a patient's lung condition based on their lung x-ray without changing the role of the medical personnel. After successfully building a Covid-19 recognition system, the system will be tested using the confusion matrix method where in this test there are 2 scenarios. In the first scenario, the data trained using the CLAHE preprocessing method obtained an accuracy rate of 98%, while in the second scenario the data was trained without using the CLAHE preprocessing method, the results obtained an accuracy rate of 97%. Previous research was conducted using the resnet-18 method and obtained an accuracy rate of 92%. From the results obtained prove that Efficientnet is able to increase the level of accuracy from previous studies.
An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

Dear Editor, This letter deals with a solution for time-varying problems using an intelligent computational (IC) algorithm driven by a novel decentralized machine learning approach called isomerism learning. In order to meet the challenges of the model's privacy and security brought by traditional centralized learning models, a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination, thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process. Moreover, nodes in the network are equipped with different models to meet many challenges caused by the model silos. Furthermore, an integration scheme is introduced to efficiently obtain the global solutions of time-varying problems. In this letter, the convergence of the proposed model is theoretically proven, where its efficiency is validated via experiments, which shows that it outperforms many state-of-the-art models using centralized processing.
Developing Professional Skills of a Performing Musician in a Higher Music Institution

"The aim of the research is to determine the problem of developing the professional skills of a performing musician in a higher education music institution. The study involved the following methods: comparative analysis, systematization and grouping, derivation of categories. The relevance of this study is determined by the need to modernize and expand the boundaries of the educational environment when teaching performing musicians. The results of the development of this problem represented it as a complex, multifaceted and, at the same time, integral system, which includes components that differ in content and function, ensuring the effectiveness of the educational program. The conclusions testify to a wide scope of the problem of the formation of a performing musician, its focus on various spheres in the life of society, and the importance of each of its components. The prospects of further research in this field of art are in its flexibility, the ability to be updated, replenished with the achievements of time, and respond to the needs and mood of society. Keywords: music education, integration, art, performing musician, dual education"
Evaluating the Risks of Human Factors Associated with Social Media Cybersecurity Threats

Human behaviors and attitudes play a significant role in cybersecurity. However, studies to quantify the impact of such behaviors and attitudes are scarce, and they are not always considered when developing mitigation strategies. To compensate for this, we have looked into a large sample of employees with different levels of expertise and backgrounds across a variety of industrial sectors and organizations. We have found that age and job role constitute the main human factors associated with social media cybersecurity risks. We can confirm that the youngest employees are the most risk prone within an organization, and the employees working in the business and financial sectors are the ones who face the highest amount of cybersecurity risk. In addition, our investigation shows that employees with less than two years of working experience, and those who are at least of age 55, need more cybersecurity training, due to their lack of awareness on the subject. Our work has led us to formulate a risk equation which can assist policymakers and training providers in defining countermeasures against risks and prioritize the training for those who need it the most.
Deteksi Onset Gamelan Bebasis DWPT dan BLSTM

Gamelan consists of various kinds of instruments that have different characteristics. Each has characteristics in terms of the basic frequency, amplitude, signal envelope, and different ways of playing it, resulting in differences in the sustain power of the signal. These characteristics cause the problem of vanishing gradient in the Elman Network model which was used in previous studies in studying the onset detection in the Saron instrument signal which has an average interval of more than 0.6 seconds. This study uses BLSTM (Bidirectional Long Short Term Memory) as a model for training and Wavelet Packet Transformation to design a psychoacoustic critical bandwidth as a model for feature extraction. For the peak picking method, this study uses a fixed threshold method with a value of 0.25. The use of the BLSTM model supported by the Wavelet Packet Transform is expected to overcome the vanishing gradient that exists in a simple RNN architecture. The model was tested based on 3 evaluation parameters, namely precision, recall and F-Measure. Based on the test scenario carried out, the model can overcome the vanishing gradient problem on the Saron instrument which has an average interval between onset of 600 ms. Out of a total of 428 onsets on the Saron instrument, the model successfully detected 426 correctly, with 4 incorrectly detected onsets and 2 undetected onsets. A thorough evaluation for each of the precision, recall, and F1-Measure algorithms obtained 0.975, 0.945 and 0.960.
Klasifikasi Kekuatan Struktur Beton Menggunakan Convolutional Neural Networks

Concrete is one of the most important elements in building a building construction. Concrete is widely used because it has advantages compared to other construction materials. In addition, the development of concrete construction has increased rapidly compared to other constructions, especially in the way of making concrete to the technology and use of materials used. In its development, materials will increase so that experiments in the laboratory make the costs swell. Therefore, a research is proposed which is intended to help researchers as well as to provide a comparison of the use of the model used.&#x0D; The method used to classify will use the CNN model by producing output that will display the class categories on the variables that have been inputted. The test results on training data resulted in an accuracy of 86.04% and testing on test or validation data was 82.14% on the Adam optimizer and 83.25% on training data and 80.35% on test or validation data on RMSprop. After determining the model to be used, it is continued with the use of K-fold validation.
A Qualitative Study on Ecology-related education experience of high school students from the perspective of the New Ecological Paradigm (NEP)

Objectives To explore the role of education in the transition to ecological civilization, this paper analyzed the current ecological-related educational experience in school education, focusing on the ecological paradigm (NEP).&#x0D; Methods The research method used in this study is a qualitative study using interviews. In order to understand the ecological paradigm of students formed through school education, 21 second-year high school students were interviewed with ecological education experiences and ecological perceptions such as ecological crises, fragility of nature's balance, anti-anthropocentrism, anti-exemptionalism and growth limits.&#x0D; Results Students' ecological education experiences remained in narrow understanding and individual-level practice, such as “personal practice-oriented education,” and “science and technology to overcome the ecological crisis” were considered important. It also understood humans as the “top predator of the ecosystem,” the most superior being in the Earth's ecosystem. Underlying these experiences and perceptions was the “university entrance examination, which is more important than the ecological crisis,” making it difficult to actively pay attention to and practice the ecological crisis. Focusing on the new ecological paradigm (NEP), the meaning of their ecological-related educational experiences was analyzed as ‘a peripheral understanding of the ecological crisis’, ‘inclusion to growth ideology’, ‘solid human exceptionalism’, and ‘human-centered dualism’.&#x0D; Conclusions In the process of transition to ecological civilization, education should begin with criticism and reflection on the overall structure of society, economy, and culture based on the modern paradigm.
Dark side of blockchain technology adoption in SMEs: an Indian perspective

Purpose The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature. Design/methodology/approach A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers. Findings This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities. Research limitations/implications This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling. Originality/value The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.
Design of Context-Aware Information Systems in Manufacturing Industries: Overview and Challenges

In the last 30 years, several academic and commercial projects have explored the context-awareness concept in multiple domains. Ubiquitous computing and ambient intelligence are features associated with the 4th generation industry empowering space to interact and respond appropriately according to context. In the scope of Industry 4.0, context-aware systems aim to improve productivity in smart factories and offer assistance to workers through services, applications, and devices, delivering functionalities and contextualised content. This article, through descriptive research, discusses the concepts related to context, presents and analyses projects related to ubiquitous computing and associated with Industry 4.0, and discusses the main challenges in systems and applications development to support intelligent environments for increased productivity, supporting informed decision-making in the factories of the future. The study results indicate that many research questions regarding the analysed projects remain the same, leading the research in the context-aware systems area to minimise issues related to context-aware features, improving the incorporation of Industry 4.0 paradigm concepts.
Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity

&lt;abstract&gt; &lt;p&gt;Predicting the risk of mortality of hospitalized patients in the ICU is essential for timely identification of high-risk patients and formulate and adjustment of treatment strategies when patients are hospitalized. Traditional machine learning methods usually ignore the similarity between patients and make it difficult to uncover the hidden relationships between patients, resulting in poor accuracy of prediction models. In this paper, we propose a new model named PS-DGAT to solve the above problem. First, we construct a patient-weighted similarity network by calculating the similarity of patient clinical data to represent the similarity relationship between patients; second, we fill in the missing features and reconstruct the patient similarity network based on the data of neighboring patients in the network; finally, from the reconstructed patient similarity network after feature completion, we use the dynamic attention mechanism to extract and learn the structural features of the nodes to obtain a vector representation of each patient node in the low-dimensional embedding The vector representation of each patient node in the low-dimensional embedding space is used to achieve patient mortality risk prediction. The experimental results show that the accuracy is improved by about 1.8% compared with the basic GAT and about 8% compared with the traditional machine learning methods.&lt;/p&gt; &lt;/abstract&gt;
Fast $K$-Medoids With the $l\_{1}$-Norm

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> , and usually the quality of the obtained solutions strongly depends upon their initialization phase. In this work, we propose an algorithm for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -medoids problem on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l\_{1}$</tex-math></inline-formula> -norm/Manhattan distance with a computational complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{O}(n \cdot \max\{\log\; n,\; K\} \cdot d)$</tex-math></inline-formula> , along with theoretical guarantees in terms of the accuracy of the obtained approximation. In addition, we propose a cheap split-merge mechanism that can be used to re-start the proposed algorithm after its convergence to a fixed point. Under some mild assumptions, we prove that such a re-start procedure reduces the error of the given fixed point. The work also includes an extensive experimentation, in which we compare our method to the most popular approaches for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -medoids problem: PAM, CLARA and Park's <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -medoids. The obtained empirical results show the proposed algorithm to consistently converge to the solutions with the lowest errors, up to two orders of magnitude of relative error lower than the previously mentioned methods, while also requiring the lowest computational running times among them: up to three orders of magnitude lower.