Reviewerly/exHarmony huggingface dataset:
Characterization and Geological Meaning of the Crystalline Basement Occurrence in the Unaí Region, Minas Gerais State (Central Brasilia Belt)

Granitoid rocks petrographically and compositionally characterized as tonalite, monzonite granodiorite and granite were observed in restricted areas in the central portion of the external zone of the Brasilia Belt. These outcrops of peraluminous rocks were interpreted as the sialic basement of the region, associated with paleogeographic highs prior to the deposition of the Proterozoic sedimentary cover. U-Pb zircon age of 2.14 Ga and geochemical analysis are compatible with other Paleoproterozoic bedrock areas observed mainly in the north Brasilia Belt. The existence of a basement bulkhead conditioned the Neoproterozoic deformation, causing the inflection of regional structures from NNW to N60-70W and over again to the NNW regional trend. The absence of contact metamorphism along the adjacent supracrustal rocks, regional foliation attitude that is different from that observed in the granitoids (tonalite, granotiorite, monzonite and granite) and the older age, show that these rocks represent basement windows and not younger intrusive bodies, as previously interpreted. The rocks studied in this research are correlated to the Aurumina Suite, which represents the main basement rock set in the North Brasília Belt.
Developing a Model for Offline Signature Verification Using CNN Architectures and Genetic Algorithm

The signature verification process has many applications, such as its use in financial operations, providing the electronic signature of documents, and providing an additional confidentiality standard to verify the identity of users in computer systems. This process has the advantage of being accepted by the community and is less intrusive compared to other biological methods. Deep learning (DL) and CNN (Convolutional Neural Network) are widely used by bioinformaticians. Due to the difficulty in extracting features in other systems or models, DL and CNN-based signature verification systems have been significantly improved. Yet Hyperparameter optimization for CNN models remains a challenging problem in designing highly efficient models with the most accurate results. It is often convolutional neural network (CNN) models that are manually designed. The proposed method is focused on a genetic algorithm that develops a population of CNN models in order to find the best fit architecture for designing an offline signature verification model. Our model is tested on more than one dataset, BHSig260-Bengali, BHSig260-Hindiin, GPDS, and CEDAR. The result of the approach proposed in this paper has the highest discrimination rate of FRR of 2.5, FAR 3.2, EER 2.35, and 97.73 %-accuracy rate.
Blockchain and Machine Learning in Internet of Vehicles: Applications, Challenges, and Opportunities

The Internet of Vehicles (IoV) has emerged as a promising technology for transforming transportation systems by leveraging intelligent services and data-driven decision-making. Leveraging machine learning (ML) techniques, IoV data offers various benefits, including enhanced traffic management, improved road safety, and personalized user experiences. However, centralized ML methods face challenges in scalability and security, hampering their effectiveness in large-scale IoV deployments. This article presents a scalable and secure framework that incorporates distributed machine learning and blockchain technologies into the IoV ecosystem to overcome these limitations. The proposed framework enables the distribution of ML algorithms among participating vehicles, with each vehicle training a local model using its data. By executing a consensus algorithm, Roadside Units (RSUs) aggregate local models to provide more personalized and intelligent services in a scalable manner. Furthermore, the integration of blockchain ensures safety, transparency, and untampered features, thereby enhancing the overall security of the IoV system. This framework holds the potential to advance the efficiency, scalability, and security of IoV applications, paving the way for the widespread adoption of intelligent services in the transportation domain.
Enabling AI/ML-based Incumbent Detection in a CBRS Experimental Network Through OpenSAS

Efficient spectrum management is crucial for the Citizen Broadband Radio Service (CBRS), promoting shared radio frequency spectrum use. The US Federal Communications Commission (FCC) designated the frequency range of 3550 MHz to 3700 MHz for three types of users: incumbent users, Priority Access License (PAL) users, and General Authorized Access (GAA) users. The Spectrum Access System (SAS) coordinates spectrum sharing among user tiers by using Environment Sensing Capabilities (ESC) sensors to detect incumbent users and prioritize their access while operating in uninformed incumbent detection mode. Machine Learning (ML) based techniques can be used for incumbent detection and enforce incumbent protection through SAS. Virginia Tech researchers have developed an open-source SAS for spectrum-sharing experimentation. In this work, we leverage the open-source SAS by enabling and testing Wireless Innovation Forum (WInnForum) standards (.i.e CBRS Base Station Device (CBSD) interface), implementing an ML-based incumbent radar detection mechanism using a feedforward neural network in an Software-defined Radio (SDR)-based experimental CBRS network deployed at CCI xG testbed and naming it "OpenSAS". If the presence of the incumbent is determined with at least a certainty of 85%, the incumbent protection is triggered. In 500 trials, the model achieves 95.83% validation accuracy in detecting the radar signal, and in Over the air (OTA) tests, it achieves 85.35% accuracy.
THE REPRESENTATION OF THE IDEAL SUNDANESE, ACCORDING TO KANG DEDI MULYADI YOUTUBE CHANNEL

Celebrity, including politicians, is often seen as the epitome of one specific culture. Dedi Mulyadi attempts to present himself as an ideal Sundanese for his viewers through his YouTube channel. With almost 4 million subscribers, the politician has become an internet sensation as his Sundanese characteristics reach many viewers. This article tries to elaborate on the idea of being Sundanese according to Dedi Mulyadi's YouTube Channel. There are many ways to elaborate on this idea; this article focuses on his utterances in interacting with certain people. Thus, the pragmatic approach covering mixing code, speech act, and the representation issue is applied in this article to break down the selected conversation. Eventually, the findings show that Dedi Mulyadi uses conversational pragmatics to represent himself as an ideal Sundanese. According to his videos, the caring, compassionate, yet assertive leader is what it means to be an ideal Sundanese.
An improved K-Means clustering based on differential evolution

Abstract Randomly selecting the initial cluster center point in the K-means clustering algorithm can lead to the issue of local optima. The paper introduces an enhanced differential evolution algorithm for optimizing K-means clustering. Experimental results demonstrate the superiority of the improved K-means clustering, based on the proposed differential evolution, over other comparison algorithms. Notably, the optimized version exhibits exceptional performance in clustering Iris, Wine, and Glass datasets. The initial clustering center can improve the clustering accuracy in the later stage. Therefore, the proposed algorithm is more superior.
Key technologies research and application of green power traceability based on blockchain

Abstract Green power traceability is a crucial link to ensure the green power transactions’ authenticity, effectiveness and credibility. It’s an essential means to promote the large-scale development of green power. However, there are many links in the whole chain of green power. The data involves multiple departments and institutions. So it is not easy to trace the authenticity. This paper first introduces the entire chain of green power traceability and critical information. Secondly, it proposes the green power traceability scheme and key technologies. Then, it designs the system and functional architecture of green power traceability based on blockchain. Finally, it analyses the application effect of the system in green power trading and the Winter Olympics. This research provides the technical reference for the authenticity and traceability of green power. It helps the green-low-carbon transformation of energy consumption structure.
Improving CAPTCHA Recognition for Enhanced Web Scraping

Abstract: Web scraping capabilities are greatly improved by CAPTCHA recognition, which enables automated systems to get past security barriers and access valuable data. This journal presents a comprehensive study and analysis of techniques aimed at improving split CAPTCHA recognition for effective web scraping. The research investigates two approaches, including optical character recognition, and image processing techniques, to tackle the challenges posed by increasingly complex CAPTCHAs.
Readability

Chapter 8 gives a brief introduction to the concept of readability. It also covers how you should use personal pronouns A readable text is one that is easy to understand with the minimal mental effort. It is also a text that is inclusive and does not offend anyone.
A SOLUTION TO THE DIFFICULTY OF VOCABULARY MASTERY TROUGH MEMRISE

The purpose of conducting research on the effectiveness of the Memrise application is to improve students' English vocabulary skills. Research on the effectiveness of this application has been carried out at MAN 3 BLITAR which is located in Blitar Regency. The method used in this study is a quantitative method by providing pre-test and post-test Memrise applications. This application will compare students who use the application with those who do not. The results of research on the effectiveness of this application are shown from the results of the post-test. The results of the post test at Man 3 Blitar still show a low average value, the data shows the number 47.77. This value shows that it is still far from the KKM score, which is 75.00. after using the memrise application in learning there is a significant increase in value, namely 85.00. It can be seen that the memrise application plays a role in increasing teacher knowledge, especially in terms of increasing students' English vocabulary skills.
Human Object Detection for Real-Time Camera using Mobilenet-SSD

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can benefit from this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is safety, namely in searching for people, which is still done manually. Searching for teams to find people is often challenging because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the MobilenetV2-SSD, where this algorithm has high detection and accuracy. Using the mobilenetV2-SSD simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Cataloging Beyond the Notes: Annotating Bibliographic Formats for Music Effectively in RDA: Examples Illustrating RDA in the Online Bibliographic Record, Second Edition of “Notes for Music Catalogers <b>Cataloging Beyond the Notes: Annotating Bibliographic Formats for Music Effectively in RDA: Examples Illustrating RDA in the Online Bibliographic Record, Second Edition of “Notes for Music Catalogers</b> , <b>”</b> by Ralph Hartsock and Peter Lisius, (Music Library Association Technical Reports …

"Cataloging Beyond the Notes: Annotating Bibliographic Formats for Music Effectively in RDA: Examples Illustrating RDA in the Online Bibliographic Record, Second Edition of “Notes for Music Catalogers." Music Reference Services Quarterly, ahead-of-print(ahead-of-print), pp. 1–2
"2 &lt; n &lt; infinity": A Multilayered "Phyllo Dough of the Analog and the Digital" in Alison Knowles and James Tenney's The House of Dust

Abstract: One of the stanzas in Alison Knowles and James Tenney's computer-generated poem "The House of Dust" suggests a sense of a shiny, metallic, unevenly reflective enclosure, filled with light characteristic of the desert environment, and yet one that somehow shelters both aquatic and avian species: "A HOUSE OF TIN / IN A DESERT / USING NATURAL LIGHT / INHABITED BY VARIOUS BIRDS AND FISH." One distinct mood, one world of atmospheric and affective effects opens up here through the specific juxtapositions of entities and materials. Following Eve Sedgwick's sense, in her book Touching Feeling , of an intimacy that "seems to subsist between textures and emotions," this essay enumerates the affective qualities generated through patterns of recombination that structure "The House of Dust." Seen through the lens of cybernetics and affect theory this early computer-generated poem offers an interesting example of the complex layering of the analogue and the digital, as it translates between the binary code of early computer languages and the discrete, analogue properties of specific configurations of materials and the concomitant affective states they evoke. As such, the poem and the concomitant performances it has generated over time in which aspects of the poem were frequently recreated out of elements of the material universe complicate any kind of a binary relationship between the analogue and digital, materiality and code, or even original creation and reception.
Actual problems of creative activity and new cognitive possibilities: a transdisciplinary approach

Digitalization of education and science is a unique opportunity for creative activity, as well as new problems of the functioning of complex dynamic systems, including the human body, in extreme conditions (technogenic, environmental, epidemics and wars). New tools are proposed, based on new phenomena, analogies and principles, as well as metamodels of thought structures and relationships between key sciences and their metasciences, the complementarity of which has cognitive and heuristic value. It is shown that the heuristic significance of successful activity is associated with the emotional perception of the harmony of nature, the holographic nature of memory and the desire to generate useful ideas. The cognitive value of extreme principles in the natural sciences, in information theory, in psychology and design is considered. Attention is drawn to new possibilities for the use of signs and symbols, the versatility of which made it possible to propose: • apply the diversity of signs and symbols in the natural sciences to bridge the gap between the humanities and science cultures; • heuristic meta-models in the form of the Star of David, the complementarity of which increases their cognitive value; • new tools for optimizing creative activity, the use of which contributes to the harmonization of interaction in the digital environment. It is proposed to unify the criteria for evaluating different types of creative activity (scientific, technical and artistic), which are aimed at solving the actual problems of the individual (health, safety and development of emotional intelligence).
KESALAHAN PENGGUNAAN EJAAN BAHASA INDONESIA PADA BROSUR PENERIMAAN PESERTA DIDIK BARU JENJANG SMA DI KABUPATEN SINTANG

This study discusses the language errors in the brochure for New Student Admissions for the Sintang District High School level. The errors studied included errors in capital letters, italics, punctuation, diction, and prepositions. This research data is 6 brochures for New Student Admission to the high school level in Sintang Regency. This research is qualitative research with a descriptive method. Data analysis stages are carried out by collecting data, identifying, classifying, and evaluating errors. The basis used to analyze the data is PUEBI. Of the six brochures, the SMKN 1 Kelam Permai brochure found the most errors. Meanwhile, the fewest errors were found in the SMAN 1 Binjai Hulu brochure.
EVOLUTION OF INTERACTIVE CONTENT IN DIGITAL MEDIA

International scientific conference «The European development trends in journalism, PR, media and communication» : conference proceedings (September 6–7, 2023, Wloclawek, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 64 pages.
FEATURES THE EXPANSION INFORMATION IN THE TIMES OF A FULL-SCALE WAR

International scientific conference «The European development trends in journalism, PR, media and communication» : conference proceedings (September 6–7, 2023, Wloclawek, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 64 pages.
A Sustainable Digital Transformation in Healthcare and Well-Being: An Overview, Integration, Design and Security Challenges, Blockchain Technology, Applications, and Future Research Directions

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.
UNVEILING THE LABYRINTH OF INTERNET PHRASEOLOGY: NAVIGATING THE LINGUISTIC LANDSCAPE OF THE DIGITAL ERA

Internet phraseology, the unique language used in online communication, has become an essential aspect of the digital era. This article provides a comprehensive exploration of Internet phraseology, unveiling its evolution, linguistic characteristics, and sociocultural implications. Drawing upon existing research and scholarly contributions, the literature review highlights the role of Internet phraseology in enhancing communication efficiency, its cultural significance in identity construction, and its impact on language innovation and cross-cultural dynamics. The discussion section delves deeper into the implications of Internet phraseology, emphasizing the importance of digital literacy and understanding in navigating its linguistic labyrinth. By embracing Internet phraseology and its dynamic nature, individuals can effectively engage in online communication, bridge cultural divides, and navigate the linguistic landscape of the digital era with confidence and understanding.
Comparison of Transfer Learning Algorithm Performance in Hand Sign Language Digits Image Classification

Image hand sign classification has become an interesting topic in image processing and machine learning. However, to achieve optimal performance in hand sign image classification tasks, a large and diverse dataset as well as powerful learning algorithms are required. One popular technique for improving the performance of classification models is transfer learning, which allows the use of knowledge learned from previous models and applies it to new tasks. In this study, the performance of two different transfer learning algorithms, ResNet-50 and VGG-16, was compared on the Sign Language Digits Dataset, which consists of 10 different types of handwriting images. The results of the experiment showed that both tested transfer learning algorithms had good performance. However, VGG-16 provided the best results with an accuracy of 97,29%, precision of 97,38%, recall of 97,45%, and an F1 score of 97,36%, while ResNet-50 achieved an accuracy of 94,57%, precision of 94,75%, recall of 94,96%, and an F1 score of 94,78%. In conclusion, transfer learning algorithms are effective techniques for improving the performance of hand sign image classification models. Choosing the appropriate transfer learning algorithm and dataset can help generate more accurate classification models.
Identification Of Hijaiyah Letters Image Using Extreme Learning Machine Method

The development of digital image processing technology has many benefits, one of which is the identification of an object, such as the identification of the image of the hijaiyah letter. Hijaiyah letters are the letters of the arabic alphabet as the original language of the Qur'an. In essence, humans have the ability to recognize and distinguish hijaiyah letter patterns from one another, but this is not the case with computers, using digital images and machine learning, in this study an identification concept was built by recognizing the image of hijaiyah letters using one of the machine learning methods. namely the extreme learning machine (ELM) method. Extreme learning machine (ELM) is a feedforward neural network with one hidden layer or better known as single hidden layer feedforward neural networks (SLFNs). Therefore, the purpose of this study is that the computer can identify objects as well as human capabilities and see how accurate the results obtained in the ELM method are. The digital image identification process using the extreme learning machine (ELM) method is carried out in two stages, namely training and testing, where previously the preprocessing process was carried out first by changing the color of the RGB image to HSV and processing the color v, then segmentation was carried out with the aim of separating the objects (foreground) with the background, then to make it easier to recognize the pattern, a morphological process is carried out. From the simulation carried out on the test data, the results obtained an average accuracy of 90% with an error of 10%.