Reviewerly/exHarmony huggingface dataset:
Microscopic Plasmodium classification (MPC) using robust deep learning strategies for malaria detection

Pathogenic microbes cause harm to human lives. Very specific mention is Plasmodium species which belongs to such category of pathogen, biologically known as malaria parasite, the devastating cause of life loss. The presence of pathogenic Plasmodium microbes are done by optical blood sample analysis through microscope. Manual identification of microscopic pathogen is a challenging and time-consuming task with respect to their similar structural formation. Even through digital microscope, identification of such pathogenic structures depends on several complex factors which is always dominated by human limitation over a long period of sample scanning. Machine-driven identification of such pathogenic microbes will be an additive benefit to speedy identification of probable presence of Plasmodium in human RBC. The machine-driven classification, segmentation and identification by applying deep learning techniques can be incorporated for obtaining near-perfect identification solution. The closed approximation inference has been carried out by applying convolutional neural network models for proper classification followed by precise identification of variant Plasmodium species in a single slide. Classification models such as SE_ResNet, ResNeXt, MobileNet and XceptionNet are studied extensively and applied on taken dataset after data preprocessing, augmentation and regularization, with state of art comparison. Resultant analysis has been done graphically and numerically as well for attaining desired parametric conditions. The aforesaid models are mainly considered here for their confirmed reliability and consistency in producing saturated results relative to the concerned data type and constrained parameterized structure. During experiment the proposed methodology has resulted in the identification of pathogenic Plasmodium microbes in an optimum amount of time and classifying the type of Plasmodium parasite to its exact class, working as a state of decision support system in medical pathology.
Optimization algorithms and regularization techniques using deep learning

Artificial intelligence is going to be the driving force in the near future, so it is necessary to know and spread this information. The two major components of artificial intelligence are machine learning and deep learning. In this book chapter, we studied how deep learning is used in optimization and regularization techniques. We have discussed the approaches of deep learning and also the types of deep neural networks in this chapter. Some basic optimization algorithms are discussed next. Regularization techniques and their types have also been explained. A comparative study of all the main deep learning approaches and all the types of deep neural networks has been explained in this chapter. At the end, the application of deep learning in today&apos;s era has also been discussed.
Potential Benefits and Risks Given by the Virtual Reality of the Central Nervous System

The rapid growth and proliferation within healthcare of Virtual Reality (VR) technology's make it a promising platform for treating central nervous system (CNS)-related ailments. From neurorehabilitation to pain management and even cognitive enhancement, there are numerous benefits that VR offers this portion of medicine. Nevertheless, multiple risks among motion sickness and eye strain, exist with utilizing this new tech for CNS treatments. Beyond immediate physical harm concerns like these - researching the long-term impact on this area remains paramount regarding future implementation. The utilization of VR technology in the field of CNS presents considerable promise, the same its potential risks necessitate a meticulous analysis and resolution before its extensive acceptance across clinical settings.
Artistic training of young students

The article shows ways of forming the artistic culture of the future in higher education institutions. Having singled out the structural and functional elements of artistic culture, which represents a system of measures aimed at improving and developing in the student the ability to aesthetically perceive the world, correctly evaluate, understand, and create the high and beautiful in art and life and is an integral part of the educational process, we considered the main indicators and factors of the level of the artistic culture of the individual, the main stages of the formation of the artistic culture of the individual are highlighted. The effectiveness of the described principles of forming the artistic culture of student youth in institutions of higher education (general didactic and specific principles) was tested experimentally. The significance of the global Internet network for the formation of an individual's artistic culture has been proven. The selected components of the artistic culture of the future specialist in the professional training system the directions of aesthetic transformation of reality and the mechanism of forming the artistic culture of student youth during experimental testing showed the effectiveness of the system of forming the artistic culture of the future specialist.
Sentiment analysis in social network data using multilayer perceptron neural network with hill-climbing meta-heuristic optimisation

Social networks such as Twitter, Facebook, and Instagram are the most widely used communication media in which users share their feelings and opinions about current events in society, for example, the occurrence of COVID19, its causes, symptoms, precautions, safety measures, prohibitions, etc. This work proposes a multi-class sentiment classification model to classify the tweets under various polarisation categories of the social network data. For Twitter data classification, this work proposes a model based on a multilayer perceptron neural network with hill-climbing optimisation. The heuristic hill climbing is used at the backpropagation for learning. TF-ID method is used for feature extraction from the dataset. This work compares the proposed method with the existing sentiment classification models of social network data. The proposed multi-class sentiment classification model has shown improvements in performance with the measures namely f1-score, precision, accuracy and recall over the existing sentiment classification models.
Megalithic Culture In Suboh Sub District Situbondo Regency

The megalithic culture in Suboh District, Situbondo Regency has its own uniqueness, namely the mortar stones, where the mortar stones in Suboh District have holes around the main hole, where the holes function to place crushed grain and there are also many archaeological remains that have not been recorded. The aim of this research is to add to the inventory of archaeological remains, social, economic and cultural systems. The type of research is qualitative research. The research methods are: (1) heuristics, (2) criticism, (3) interpretation, (4) historiography. Based on the research results, the typology of archaeological remains in Suboh District consists of three types, namely sarcophagi, stone mortars and stone monoliths with a hollow stone type. Community supporters of megalithic culture in Suboh District already know the concept of belief in the spirits of ancestors (acestor-worship). Social, economic and cultural life has recognized an egalitarian agricultural system.&#x0D; Keywords: Culture, Suboh, Megalithic
A Procedure of Comparative Analysis of Recursive Nonlinear Filtering Algorithms in Navigation Data Processing Based on Predictive Simulation

Abstract The paper describes a procedure based on predictive simulation for comparing the recursive suboptimal algorithms developed for nonlinear filtering problems including navigation data processing problems. The algorithms are compared in terms of accuracy, consistency, and computational complexity. The provided examples explain the procedure and illustrate its application.
A Secure Communication With One Time Pad Encryption And Steganography Method In Cloud

Cloud computing is a framework for suitable on demand network admittance to a mutual pool of computer resources. Cloud computing is a model for delivering services as per the demand related to information technology over the internet. It is a type of internet-based computing by which client pay for using the services. A client sends a request to the cloud server, server respond to his request and deliver on demand services. The inflamed attack in the cloud surface makes it extra susceptible to prevailing and emerging security threats. Predictable cloud data prevention and security approached have been initiated unable in anticipation of such threats and this distasteful trend has compelled the need of revolutionary approach to cloud data security. Blowfish Encryption and Steganography already had established techniques of data security. This Research work possess an amplify approach to safeguarding data security by combining both of the proven techniques. The united methodology is aimed to leveraging the strength of these two proven techniques to attain a robust mechanism of confirming security in cloud data.
Low-level circuit optimization for the quantum approximate optimization algorithm

Quantum computer systems require robust software integration to support quantum algorithm development towards addressing practical challenges in many important problem areas including logistics, chemistry, and AI/ML. Researchers predict quantum algorithms will achieve computational advantage over classical algorithms for these specific problem areas in the Noisy Intermediate Scale Quantum (NISQ) era [1]. We consider one such algorithm, the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm for solving combinatorial optimization problems [2]. QAOA promises to be a practical alternative to inefficient classical algorithms for large problem sizes, but quantum hardware capabilities lag behind the requirements for practical application. We show that the Superstaq quantum compiler can support performance improvements for QAOA by exploiting optimization opportunities only revealed at levels lower than logic gates, i.e., at the level of pulses, quantum hardware instructions.
Implementation of Big Data Information System Using Open-Source Metabase for Civil Registration and Vital Statistics Data Visualization in Surabaya

Civil registration involves the mandatory and continuous documentation of important life events of a country's population under local legal requirements. In many countries, these documents are required to access government services such as education, healthcare, social services, formal employment, insurance benefits, and inheritance rights. Indonesia should prioritize building a comprehensive Civil Registration and Vital Statistics (CRVS) using a big data information system to ensure every individual has a legal identity, can access government services, and collect accurate and reliable statistics on vital events through geospatial maps. Surabaya, a city in Indonesia, still needs a comprehensive Civil Registration and Vital Statistics (CRVS) system. We produce many informative visualizations from the query and modeling processes in Metabase. Based on the PIECES framework, this application's importance level is 4.56 or 91.25%, meaning the application is important, and the satisfaction level is 4.29 or 85.76%, meaning the application is satisfied for the respondents. This research provides a brief overview of how Metabase works and how it can be used to generate visualizations of job-type data. It demonstrates the ease with which visualizations can be changed and customized. It had a good affordability point, making its implementation easier and more beneficial. It also emphasizes the importance of having a powerful tool like Metabase for data analysis and decision-making, especially for the dispendukcapil as a civil registration agency.
INVESTIGATION OF THE CIRCUMSTANCES OF COMPUTER MODIFIED OF THE DOCUMENTS BY METHODS OF THE DIGITAL FORENSICS

The article considers groups of digital traces formed as a result of the preparation of documents by computer modified. Methodological recom­mendations for the detection and analysis of traces of each group are proposed, software for working with these traces is considered. The results of an experi­mental study based on the described recommendations, which consisted in performing computer modified of the document and subsequent analysis of the resulting digital traces, are presented.
E-research untuk Universitas Riset Studi Kasus Layanan Digital Perpustakaan Universitas Sumatera Utara

This study aims to determine e-research on the Digital Service Library of Universitas Sumatera Utara in supporting the mission of Universitas Sumatera Utara as a research university. This study uses a qualitative method with a descriptive approach. Research informants are users of Digital Services, namely Lecturers and Postgraduate Students who are conducting research. Informants were taken using snowball sampling. During the data collection process, eleven key informants were netted, namely six lecturers, five postgraduate students. One additional informant is the Digital Service Librarian. The results showed that e-research at the Digital Service Library of Universitas Sumatera Utara was first, information retrieval, namely the assistance provided by librarians to users in finding references relevant to the research topic. Second, e-resources, namely electronic information sources: e-repository, e-journal, e-thesis, and e-dissertation. Third, information literacy, namely training on the use of online databases, e-journal and e-book searches. Fourth, global network, namely internet facilities with bandwidth &gt;30 Mbps and wifi for library users who are doing research. The conclusion that can be drawn is that e-research in the Digital Services Library of Universitas Sumatera Utara has supported the mission of Universitas Sumatera Utara as a research university. Digital Services has acted as a mediator and facilitator in research activities at Universitas Sumatera Utara. The facilities in Digital Services are used to build research excellence and maintain the status of Universitas Sumatera Utara as a global research university.
ChatGPT as a Tool for Language Development: Investigating Its Impact on Proficiency and Self-Evaluation Accuracy in Indonesian Higher Education

English language learning plays a crucial role in today’s globalized world, and the integration of technology, particularly chatbots, has shown promise in enhancing language learning experiences. However, there is a noticeable gap in the literature regarding the application of chatbots in the specific context of Indonesia University. This research employs a mixed-methods approach, combining quantitative and qualitative data collection methods. A sample of 100 students from Indonesia University participated in pre-test and post-test assessments to measure language development. Additionally, the accuracy of students’ self-assessments was evaluated before and after using ChatGPT. The main findings of this study indicate that personalized feedback provided by ChatGPT significantly influenced students’ language development, as evidenced by the notable improvement in post-test scores. Furthermore, there was a considerable increase in self-assessment accuracy after students engaged with ChatGPT, demonstrating the effectiveness of personalized feedback in enhancing students’ ability to evaluate their language proficiency. Participants reported that ChatGPT facilitated language learning by providing interactive feedback and targeted guidance, allowing learners to practice and improve their language skills at their own pace. English language learners perceive benefits such as interactive feedback, increased motivation, and resource accessibility. While challenges exist, the advantages outweigh the limitations. These findings contribute to understanding the potential of chatbots, specifically ChatGPT, as effective language learning tools in self-directed learning environments. This finding offers valuable insights for educators and practitioners in implementing chatbots effectively within language pedagogy, ultimately contributing to more effective and engaging English language learning experiences in the Indonesian university context.
Exploring value and meaning of story sharing in the classroom

The purpose of this study is to explore the value and meaning of children by observing how story sharing between teacher and children, and between children and children in the classroom where childㆍplay-based curriculum is being operated. For this purpose, a researcher and class teacher observed the child’s story sharing and conducted participatory observation by sharing stories together depending on the situation. The subjects of observation were 17 5-year-old children attending Kindergarten A, and the observation situation was the daily routine including play, activities, and daily life in which story sharing was found in the kindergarten classroom. The research results are as follows. First, the story sharing found in kindergarten classrooms had value as a link that connected members of the conversation to each other. Looking at its meaning, we can see that children participating in story sharing were able to reveal ‘I’ as a speaker, remember ‘you’ as a listener, and share the world with members as "we." Second, the story sharing that children share with other people in the classroom was a normal and natural occurrence in all daily routines. It had value as a link between play, activities, and daily life. Through the story sharing, the child suggested breaking down the boundaries between play and activity, and expressed the lingering feelings of play and activity, and realized the meaning of an extension that connects daily life. It is necessary to find appropriate guidance measures in the field so that children's story sharing can be activated, which has the same value as the above research results.
Novel Quantum Circuit Designs for the Oracle of Grover’s Algorithm to Solve the Vertex Cover Problem

We propose new quantum circuit designs for the Grover oracle, i.e., the oracle of Grover’s algorithm, to solve the vertex cover problem (VCP) efficiently. Grover’s algorithm is an unstructured quantum search algorithm to search a target data item out of N data pieces by calling the oracle $\mathrm{O}(\sqrt{N})$ times. It represents a quadratic speedup compared to the classical unstructured data sequential search algorithm, which requires O(N) oracle calls. We are in the noisy intermediate-scale quantum (NISQ) era in which quantum computers have a limited number of qubits, short decoherence time, and low gate fidelity. It is thus desirable to design quantum circuits with three properties: (1) a reduced number of qubits, (2) shorter quantum depth, and (3) fewer gates. We created novel designs of a quantum counter and a quantum semaphore to be applied to the Grover oracle to have the three properties. Through the IBM Quantum Lab service, we executed Grover’s algorithm with the novel quantum circuit designs to solve the VCP to validate the design correctness.
Impact of Online Chat Language on Second Language Learners' written composition: A Corpus Based-Study

This study investigates the impact of online chat language on second language learners and has gained significant attention in the field of language acquisition. The study examines how online chat language influences learners' proficiency, accuracy, vocabulary acquisition, and sociolinguistic competence. By analyzing relevant research studies conducted using the corpus-based methodology, the study aims to provide insights into the advantages and challenges associated with online chat language for second language acquisition. A sample of the corpus of online chat conversations via WhatsApp chat will be compiled, representing various language learners' interactions and finding out the KWIC through online software programmer Sketch Engine. The corpus analysis will be complemented questionnaire conducted with a subset of participants. The findings of this study will contribute to our understanding of the role of online chat language in second language acquisition and provide insights for language learners.
PERANCANGAN PLTS ON GRID SEBAGAI SISTEM CATU DAYA KAWASAN JATILUWIH

Jatiluwih is a tourist village that was declared by UNESCO as one of the locations of world cultural heritage in 2012. Being recognized as a World Heritage Site, the Jatiluwih Area is not allowed to undergo any development that could damage the area's image. To support Jatiluwih as a green and sustainable area, this study proposes the installation of Grid-Connected Solar Photovoltaic Systems (PLTS) on rooftops to serve as the power supply system for the area. The objective of this study is to assess the rooftop solar power plant's capability and its energy production to meet the energy needs in the Jatiluwih WBD Area.Based on the survey, there are 45 buildings to support tourism activities in the Jatiluwih WBD Area, with a total daily energy requirement of 225.37 kWh. According to the established criteria, five buildings meet the requirements for installing rooftop PLTS. In the design of the rooftop PLTS, two scenarios are used: the load requirement scenario and the roof area scenario. The research results indicate that the installed rooftop PLTS capacity based on the load requirement for the Dukuh Baturan Villa, Warung Dhea, and Bhuana Agung Restaurant buildings is 2.05 kWp, for the Restoran Iga Lawar building is 2.46 kWp, and for the Teras Subak Homestay building is 1.2 kWp. Based on the roof area, the Dukuh Baturan Villa building has a capacity of 9.8 kWp, Teras Subak Homestay has 23.4 kWp, Warung Dhea has 10.6 kWp, Restoran Iga Lawar has 16 kWp, and Bhuana Agung Restaurant has 6.5 kWp.The rooftop PLTS system on these five buildings in Jatiluwih, using the roof area scenario, can generate an energy output of 270.35 kWh/day, thereby fulfilling the energy requirements of the 45 buildings.
Unsupervised Video Deraining with An Event Camera

Current unsupervised video deraining methods are inefficient in modeling the intricate spatio-temporal properties of rain, which leads to unsatisfactory results. In this paper, we propose a novel approach by integrating a bio-inspired event camera into the unsupervised video deraining pipeline, which enables us to capture high temporal resolution information and model complex rain characteristics. Specifically, we first design an end-to-end learning-based network consisting of two modules, the asymmetric separation module and the cross-modal fusion module. The two modules are responsible for segregating the features of the rain-background layer, and for positive enhancement and negative suppression from a cross-modal perspective, respectively. Second, to regularize the network training, we elaborately design a cross-modal contrastive learning method that leverages the complementary information from event cameras, exploring the mutual exclusion and similarity of rain-background layers in different domains. This encourages the deraining network to focus on the distinctive characteristics of each layer and learn a more discriminative representation. Moreover, we construct the first real-world dataset comprising rainy videos and events using a hybrid imaging system. Extensive experiments demonstrate the superior performance of our method on both synthetic and real-world datasets.