Reviewerly/exHarmony huggingface dataset:
ITAQ: Image Tag Recommendation Framework for Aquatic Species Integrating Semantic Intelligence via Knowledge Graphs

In the era of Web 3.0, there is an increasing demand for social image tagging that incorporates knowledge-centric paradigms and adheres to semantic web standards. This paper introduces the ITAQ framework, a recommendation framework specifically designed for tagging images of aquatic species. The framework continuously integrates strategic knowledge curation and addition at various levels, encompassing topic modelling, metadata generation, metadata classification, ontology integration, and enrichment using knowledge graphs and sub graphs. The ITAQ framework calculates context trees from the enriched knowledge dataset using AdaBoost classifier which is a lightweight machine learning classifier. The CNN classifier handles the metadata, ensuring a well-balanced fusion of learning paradigms while maintaining computational feasibility. The intermediate derivation of context trees, computation of KL divergence, and Second Order Co-occurrence PMI contribute to semantic-oriented reasoning by leveraging semantic relatedness. The Ant Lion optimization is utilized to compute the most optimal solution by building upon the initial intermediate solution. Finally, the optimal solution is correlated with image tags and categories, leading to the finalization of labels and annotations. An overall precision of 94.07% with the lowest value of FDR of 0.06% and accuracy of 95.315 % has been achieved by the proposed work.
The role of Digital Twins in future transportation challenges

Students enrolled in technical education programs, such as Aeronautical Engineering Technology, pursue careers in aviation, aerospace, and commercial space industries, encompassing physical and digital work environments. Gaining knowledge in a broader range of subjects, including digital twin settings, is crucial for contextual learning and generating graduates with the level of proficiency the industry demands. Crow and Dabars (2020) highlight the significance of instructional innovation in their analysis of the fifth-wave history of American colleges. There is a demand for the modification of conventional academic processes in order to better cater to the external needs of retraining and upskilling both current workers and recent graduates. The aviation and aerospace sectors have consistently incorporated the Industrial Internet of Things (IIoT), Digital Twins, Big Data frameworks, automation, and robotics in diverse capacities into their routine operations over an extended period. The rapid progress in computer and sensor capabilities has facilitated the widespread adoption of many data science methods, such as the digital thread, digital twin, edge computing, machine-to-machine learning (M2M), and Artificial Intelligence (AI). Educational institutions, including prominent colleges like Purdue, must integrate digital thread and digital twin technologies into the learning framework beyond the introductory levels. For graduates to possess the necessary skills to join the workforce with a high level of preparedness, it is imperative to integrate Digital Twins into the learning cycle. The Purdue University CREATE approach for Augmented, Virtual, and Mixed Reality Simulation and Digital Twins is centered around enhancing the efficacy and efficiency of aviation training programs on a global scale, specifically the Competency-Based Training and Assessment (CBTA) framework. This approach aims to achieve this by offering a more authentic and immersive learning experience proposal, streamlining the training and certification processes, and facilitating the transition to an Artificial Intelligence (AI) – Digital Twins environment. The present study also focuses on reducing residual risk within the 'AI black box.' The analysis and evaluation of the difficulties of implementation of Artificial Intelligence (AI) were conducted within the framework of Digital Twins.
Exploring Anime Character Image Generation Based on User Preferences

This paper presents a method for generating images of anime characters based on user preferences. A questionnaire is developed to measure user preferences, inspired by Rubin's "Love and Liking Scale". Specifically, user preferences are collected by presenting anime character images to participants through a crowd survey and collecting their responses. The model responsible for generating the anime character images is trained using deep learning techniques, using the survey data as a training set. However, attempts to generate images using the trained model did not produce the expected results. When analyzing the survey data, it was found that there was limited variability in the "Love and Liking" scales for each anime character. This suggests that the trained models may not adequately reflect user preferences. Future work will focus on improving the model to accurately capture user preferences and developing a more appropriate model. This study provides fundamental knowledge and essential insights for the development and advancement of anime character image generation methods tailored to individual user preferences.
Optimal Composite Likelihood Estimation and Prediction for Distributed Gaussian Process Modeling

Large-scale Gaussian process (GP) modeling is becoming increasingly important in machine learning. However, the standard modeling method of GPs, which uses the maximum likelihood method and the best linear unbiased predictor, is designed to run on a single computer, which often has limited computing power. Therefore, there is a growing demand for approximate alternatives, such as composite likelihood methods, that can take advantage of the power of multiple computers. However, these alternative methods in the literature offer limited options for practitioners because most methods focus more on computational efficiency rather than statistical efficiency. Limited accurate solutions to the parameter estimation and prediction for fast GP modeling are available in the literature for supercomputing practitioners. Therefore, this study develops an optimal composite likelihood (OCL) scheme for distributed GP modeling that can minimize information loss in parameter estimation and model prediction. The proposed predictor, called the best linear unbiased block predictor (BLUBP), has the minimum prediction variance given the partitioned data. Numerical examples illustrate that both the proposed composite likelihood estimation and prediction methods provide more accurate performance than their traditional counterparts under various cases, and an extremely close approximation to the standard modeling method is observed.
A Case Study on the Integration of ‘Curriculum-Class-Evaluation-Recording’ in High school Art Education Using EduTech

Objectives This study aims to figure out possibility of applying EduTech in high school art classes. To find out the applicability, this study developed high school art classes with the purpose of integrating ‘curriculum-class-eval-uation-recording’, and examined the effectiveness by applying those classes in the school.&#x0D; Methods To achieve the objectives, based on achievement standards of high school art in 2015 revised curricu-lum, a “digital collage” class that can actively utilize EduTech was developed as a content element. The effective-ness was verified from art teachers who applied EduTech classes for second-year students of girls' high schools in Chungbuk. The data collected for analysis include participatory observation of classroom, digital performance records of students and teachers, and evaluation records from students record. After that, in-depth interviews with teachers and students were conducted to derive more detailed results.&#x0D; Results Art classes using EduTech made it possible for students to access data search and organization easily physically. Students participated in the classes challengingly and actively while organizing art activities on their own using accessible digital technology. Moreover, teachers were able to effectively manage the entire course through EduTech's digital platform, helping to achieve the integration of “curriculum-class-evaluation-recording.”&#x0D; Conclusions EduTech could provide a unique experience by giving an opportunity to effectively reveal educational goals for high school art classes and establishing novel communication relationships. Also, students were able to continuously record their own learning so students could provide the main resources for their student record. This role of EduTech helped student record become student-centered so that the student record could become more qualitatively substantial. Although this study unable to use high-level EduTech considering the environment and budget, it is meaningful to imply ideas on how the use of EduTech can be carried out in the school throughout art classes.
Development of a Virtual Experiment System for Transformer Characteristics Experiments in Student Laboratory

We have developed a virtual laboratory for student laboratory on transformer characteristics. In the past, the division of roles was fixed during student laboratory in our department. Many students felt that they could not wire even if they looked at the circuit diagram. Therefore, we developed a virtual laboratory where students can input wiring data while looking at a circuit diagram on the Web and automatically determine whether the wiring is correct or incorrect. In addition, a video on transformer experiments was also created on the Web. In fact, third-year students in this department were asked to use the virtual laboratory, and a questionnaire survey was conducted. As a result, it was found that the virtual experiment developed in this study is effective for learning because the students can see the circuit diagram and practice wiring as many times as they want until. The virtual experiment data can be displayed after the wiring is completed, which is considered to have improved the educational effect.
An Edge Computing-Enabled Track Obstacle Detection Method Based on YOLOv5

Rail transit is developing towards intelligence, which requires a lot of computing resources to carry out deep learning tasks. However, the limited computing power of on-board equipment hinders the operation of complex detection networks for the execution of complex deep learning tasks. And the existing railway inspection methods are mostly based on fixed equipment, their detection range is limited. In this article, we propose a mobile platform railway inspection method based on edge computing. Edge computing can reduce stress by offloading the workload to edge nodes. The mobile platform vehicle is responsible for acquiring real-time images. Edge computing servers are used to help execute object detection algorithms off-orbit and carry most of the computing power. The use of edge computing architecture for railway inspection can effectively reduce the pressure of the on-board server and reduce the network dependence. Aiming at the complex scene of track detection, we propose an improved YOLOv5-based target detection algorithm. By adjusting the network model and deploying it on the edge server, the method reduces the computing burden and realizes the collaborative reasoning with the whole edge computing architecture. We implement the collaborative reasoning scheme in practical experiments and find that the proposed improved target detection method based on edge computing can complete the target detection task with few computing resources, and meet the demand for precision and real-time performance.
An online multilingual numeral dataset on Devnagari and English languages for pattern recognition research

The real-time air-writing multilingual datasets are widely used for several purposes, such as handwriting character or numeral pattern recognition. The air-writing systems are commonly used in operation theatres, online education systems, banking sectors, reservation counters, etc. However, the air-written numeral datasets are less for Devanagari and English languages needed for detecting patterns. Therefore, the present article introduces novel datasets written in the air for Devanagari and English. In addition, this article proposes a systematic novel strategy to collect the air-written multilingual numeral dataset from 100 individuals ranging in 20-40 age groups. The Devanagari and English 0-9 digits were ten times written in the air by every individual resulting in 10000 images for each language. Thus, 20000 images were generated and stored in the databases. The proposed dataset is freely available and could be a good resource for pattern recognition research.
Raspberry Pi and grovepi with Python: Sensors, actuators and interfaces

Currently we have seen the launching and distribution of a wide variety of programmable boards, from the well-known Arduino to boards such as Raspberry Pi, Orange Pi, SparkFun RedBoard Artemis, among others. Each one of them presents differences not only in design but also in the language used to work with them, memory capacity, processor type, etc. Another characteristic that distinguishes them is in the extensions and complements such as hats, sensors and actuators that enhance their use. The cards are a fundamental element in the development of projects focused on robotics, monitoring systems, IoT technology applications to name a few, which is why this chapter will delve into one of the extensions compatible with Raspberry Pi, GrovePi+. This add-on presents advantages when using humidity, temperature, light, air quality sensors, as well as actuators of different types. In addition, graphical interfaces will be implemented taking advantage of the Python3 distribution available in Raspberry Pi; in this way, elements that maximize the use of the hat can be used. This with the purpose that the user can understand and manipulate the types of ports available (analog and digital mainly), presenting the Grove family modules from a didactic point of view.
Digital Visuality Is the Mirror of Cultural Milieu

2018–2019 saw the creation and propagation of new memes in online discourse with the growing use of digital modes of communication. Globally, new symbolic forms are generated through online participationonline participation and offer a multi-layered cultural critique that at times can be difficult to interpret. Symbolic use of coded or visual metaphorsvisual metaphors are culturally specific. New forms of communicating online indicate the loss and/or abridgement of written text and a preference for visual forms of communication to immediately reflect emotional states or beliefs.
The utilisation of open research data repositories for storing and sharing research data in higher learning institutions in Tanzania

Purpose The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania. Design/methodology/approach A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically. Findings Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints. Originality/value Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.
VISUAL ANALYSIS OF LOCAL EARTHQUAKE IN NORTH TAPANULI BASED ON DATA SCIENCE

Earthquakes are natural phenomena that occur when the Earth's tectonic plates move and release energy. Big Data's emergent epistemological and research paradigms, as well as data science, an increasingly integrated field of data research, are opening up new opportunities. Visualizing earthquake data is all about understanding earthquake characteristics such as size, location and depth. The result show that September was the quietest month in terms of earthquakes, and in this graph we can see the number of earthquakes for each month in 2022. The month of October is the one that has the highest number of earthquakes. We can see the average depth and magnitude of each year on the bubble chart. In addition, the size and color of the bubbles indicate the number of earthquakes that month. In general, most of the earthquakes occurred in the shallow earthquake range and the 1.8-3.85 magnitude range.
Perancangan Perpustakaan Dengan Tema “The Shape of Nature” di Grogol

Books are windows to the world. In the era of 4.0 which continues to develop and advance, easy access to information and reading books can not only be obtained from the library, now reading can be done via a cellphone from a pocket. This ease of access does not necessarily make people willing to read. According to Unesco, Indonesia's reading interest is in the 2nd lowest rank. UNESCO says Indonesia ranks second from the bottom in terms of world literacy, meaning that reading interest is very low. According to UNESCO data, the reading interest of the Indonesian people is very concerning, only 0.001%. This means that out of 1,000 Indonesians, only 1 person is an avid reader. In a different research titled World's Most Literate Nations Ranked conducted by Central Connecticut State University in March 2016, Indonesia was ranked 60th out of 61 countries in terms of reading interest, just below Thailand (59) and above Botswana (61).
Electronic Medical Records Using Blockchain Technology

Blockchain technology has emerged as a crucial tool for ensuring security and reliability in various domains, particularly in healthcare. In this study, we utilize blockchain to establish an append-only chain of transaction blocks, ensuring the integrity and security of patient medical records. By employing blockchain, we aim to safeguard patient data, grant specific clinicians’ access to medical records, and ensure user privacy. The doctor will only receive prescription information after the patient has granted access, ensuring comprehensive protection for both parties. Consensus mechanisms within the blockchain guarantee consistency among blocks and require agreement from existing nodes before adding new transactions. Traditional healthcare systems often result in delays in data exchange and strict restrictions on access due to concerns about sensitive data leakage. By integrating blockchain technology into healthcare records and data, this article seeks to enhance data sharing while mitigating the risks of data tampering and security breaches.
Pengembangan Media Papanca “Papan Panca Indera” pada Pembelajaran Bahasa Indonesia Keterampilan Berbicara Siswa Kelas 1 Sekolah Dasar

Media is a tool that teachers use to convey material during the learning process. Therefore, media is very important to use during the learning process. The aim of this research is to produce the PAPANCA media product 'Papan Panca Indra' which is suitable for use to improve the speaking skills of grade 1 elementary school students. The type of research used is development research or Research and Development (R&amp;D). The method used is experimental. The research model used is the Addie model from Dick and Carey. The data collection techniques used were observation and interviews. The results of material expert validation and media expert validation stated that the PAPANCA media 'Papan Panca Indra' was suitable for use and did not need further revision. The value obtained from material expert validation was 80% and from media expert validation a value of 80% was obtained. The PAPANCA media 'Papan Panca Senses' was declared very worthy by getting the very good category to be used and implemented to improve speaking skills in the Indonesian language subject chapter 1 "What Sounds?" The response of students when using PAPANCA 'Papan Panca Indra' media was very good, it can be seen that students felt enthusiastic when learning using PAPANCA 'Papan Panca Indra' media. Therefore, the PAPANCA media 'Papan Panca Senses' can be used to improve the speaking skills of grade 1 elementary school students.
Improving Unsupervised Domain Adaptation via Multiple Adversarial Learning

&lt;p&gt;Most machine learning methods assume the training and test sets to be independent and have identical distributions. However, this assumption does not always hold true in practical applications. Direct training usually induces poor performance if the training and test data have distribution shifts. To address this issue, a three-part model based on using a feature extractor, a classifier, and several domain discriminators is adopted herein. This unsupervised domain adaptation model is based on multiple adversarial learning with samples of different importance. A deep neural network is used for supervised classification learning of the source domain. Numerous adversarial networks are used to constitute the domain discriminators to align each category in the source and target domains and effectively transfer knowledge from the source domain to the target domain. Triplet loss functions&amp;mdash;classification loss, label credibility loss, and discrimination loss&amp;mdash;are presented to further optimize the model parameters. First, the label similarity metric is designed between the target and source domains data. Second, a credibility loss function is proposed to obtain an accurate label for the unlabeled data of the target domain under training iterations. Finally, a discrimination loss is designed for multiple adversarial domain discriminators to fully utilize the unlabeled data in the target domain during training. The discrimination loss function uses predicted label probabilities as dynamic weights for the train data. The proposed method is compared with mainstream domain adaptation approaches on four public datasets: Office-31, MNIST, USPS, and SVHN. Experimental results show that the proposed method can perform well in the target domain and improve generalization performance of the model.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;