Reviewerly/exHarmony huggingface dataset:
Exploration-Aided Downstream Graph Learning Tasks: A Survey on Exploratory Graph Learning

Graph learning is crucial for extracting meaningful information from graph-structured data, enabling effective solutions to various downstream tasks. However, existing methods for solving downstream graph learning tasks often rely on the availability of the graph structure, which may not always be accessible in real-world applications. To overcome this limitation, recent approaches have introduced exploratory learning techniques, which aim to tackle graph learning tasks on graphs with unknown topology. In this article, we provide a comprehensive overview of exploratory graph learning applied to two widely studied graph learning tasks: 1) influence maximization and 2) community detection. We delve into the problem formulation of both tasks concerning graphs with unknown topological information. Additionally, we explore the application of exploratory learning techniques to address these problems effectively.
Facilitating Students’ Abstract and Computational Thinking Skills Using Virtual Reality

As Virtual reality (VR) devices become more widespread, they are being recognized as efficient teaching tools for many subjects. In the 21st century, learners are expected to master important skills, which are difficult to teach in conventional lectures, such as computational thinking, critical thinking, creativity, and abstract reasoning. [1] Therefore, computer science or information technology courses often limit the discussion of recursion to calculating a factorial or a Fibonacci number, even though recursion is presented as an academic-type oddity in most cases. [2] To elucidate how VR can improve students’ computational thinking skills and abstract thinking associated with solving problems that require recursive algorithms, this paper examines the use of VR in computer science education to teach the abstract concept of recursion. The results of learners using VR Tower of Hanoi software are compared to those using the conventional Hanoi puzzle. Most learners show an improved understanding after experiencing the gameplay in the VR software, and some demonstrate a better understanding of recursion. In the post-survey, most participants acknowledge that the immersive environment is helpful when learning abstract subjects.
A Study on Few-shot Object Detection for Warships Based on Data Generation Using Image Outpainting

With the advent of hyperscale AI based on large amounts of data and supercomputing infrastructure to process them, AI has made remarkable progress in many areas. Among them, AI-based image generation technology has recently developed rapidly, and research on the use of AI training data is also active. Augmenting training data with synthetic image generation can be of great help in AI training for defense and medical applications, where data collection is difficult. In particular, in marine environments, where data collection is more difficult than on land, it is difficult to collect data on various weather conditions, so the application of AI-based image generation technology is very effective. In this paper, several images are generated by image outpainting based on objects in a real image, and the generated images are intended to be used as training data for an object detector.
Multi-Lingual DALL-E Storytime

Visualizations are a vital tool in the process of education, playing a critical role in helping individuals comprehend and retain information. With the recent advancements in artificial intelligence and automatic visualization tools, such as OpenAI’s DALL-E, the ability to generate images based on text prompts has been greatly improved. However, a major drawback of the majority of text-to-image tools is their limited ability to create a series of consecutive coherent frames that tell a story or illustrate a process that changes over time. Rather, they are limited to producing only a few isolated images based on the input prompt. Furthermore, these existing text-to-image tools present an added challenge for populations with limited proficiency in the English language. This serves to widen the educational divide between children from diverse backgrounds and restricts their access to innovative technology. Here, we introduce a DALL-E storytelling framework designed to facilitate the fast and coherent visualization of non-English songs, stories, and biblical texts. Our framework extends the original DALL-E model to handle non-English input and allows users to specify constraints on story elements, such as a specific location or context. The key advantage of our framework over manual editing of DALL-E images is that it offers a more seamless and intuitive experience for the user, as well as automates the process, thus eliminating the time-consuming and technical-expertise-requiring manual editing process. The visualization masks are automatically adjusted to form a coherent story, ensuring that the figures and objects in each frame are consistent and maintain their meaning throughout the visualization, allowing for a much smoother experience for the viewer. Our results demonstrate that our framework is capable of effectively and quickly visualizing stories in a coherent way, conveying changes in the plot over time, and creating a narrative with a consistent style throughout the visualization. By enabling the visualization of non-English texts, our framework helps bridge the gap between populations and promotes equal access to technology and education, particularly for children and individuals who struggle with understanding complex narrative texts, such as fast-paced songs and biblical stories. This has the potential to significantly enhance literacy and foster a deeper understanding of texts.
Virtual Reality Museum Application for the Arts

Most students are monetarily or physically inhibited from visiting private or public institutions. The Metaphysical Exhibit project goal is to give all ages a modern, technological take on the museum experience by providing a Virtual Reality alternative. By lowering the barrier of entry to a one-time purchase for the hardware and free software, any classroom or consumer can experience masterworks in an immersive environment. A rich collection of art pieces across the eras can be displayed under one roof by compiling public information on historical works. By utilizing Virtual Reality, the museum is easily distributed and portable. In this paper, we aim to develop a virtual museum with artworks and observe its effects on users. The developed application is capable of running on modern headsets, specifically the Meta Quest 2. To analyze the viability of the application in a classroom and personal setting, we especially find answers to the following questions: (i) Does experiencing the museum in this format feel analogous to prior exhibit experiences? (ii) What is the level of interest in exploratory, self-guided Virtual Reality content used in education from a student and teacher perspective? (iii) How can the experience be improved? The results show that the project was received positively by students and teachers as an introductory experience for the arts.
Implementation of the A<sup>*</sup> Algorithm in a First Aid Educational Game Application for Elementary School Students

First aid, immediate assistance for individuals facing illness, injury, or accidents, plays a crucial role in saving lives, preventing physical disabilities, and providing comfort before being referred to a healthcare center. This research presents comprehension and cultivates learning interest by developing an educational first-aid game in the Indonesian language, utilizing Object-Oriented Programming (OOP) concepts and the $\mathrm{A}^{*}$ algorithm. The selection of the $\mathrm{A}^{*}$ algorithm is based on its efficiency in optimizing pathfinding through a heuristic-based approach. By employing heuristics, the number of nodes requiring evaluation can be significantly reduced, enabling Non-Playable Characters (NPC) or the ambulance character to swiftly and effectively determine the best routes for providing aid. This efficiency is particularly critical in illustrating the context of first aid, where every second has a profound impact on safety and health. The functionality of the game operates effectively, demonstrated by the successful independent $\mathrm{A}^{*}$ path testing. Additionally, the results from beta testing indicate a user satisfaction level of 92%, affirming the value of this game as a viable option for children or students unable to engage in extracurricular first aid activities at school. Essentially, this game facilitates the interactive, efficient, and comprehensible delivery of first aid knowledge, providing substantial benefits to society.
Concurrency Performance Analysis on MySQL Relational Database Management System in Virtual Machine Environment

Relational Database Management Systems (RDBMS) constitute a class of software applications designed to organize and manage data in a structured, tabular format. Within this paradigm, data is stored in tables, each comprising rows and columns. The specific performance of RDBMS concurrency handling has not been studied as extensively as other aspects of RDBMS performance. This study aims to bridge this knowledge gap by evaluating the concurrency performance of RDBMS. It contributes to the understanding of RDBMS concurrency handling performance and offers insights for designing and configuring RDBMS systems for applications that demand effective concurrency handling. Data gathered through controlled experiments with hardware and software configurations to assess the impact of varying configuration settings on performance. MySQL 8.0 installed on a virtual machine running the Ubuntu Server 22.04 operating system. The results showed that MySQL 8.0 has good concurrency handling, with the ratio of the increase in query execution duration when there is concurrency being smaller than the ratio of the increase in the number of records. Additionally, the study found that the increase in RAM size and the number of CPUs does not linearly improve concurrency performance.
Implementation of Convolutional Neural Network – Extreme Learning Machine for Handwriting Recognition of Sundanese Script

Sundanese script is one of the cultural heritages in West Java. Apart from Sundanese which is the official regional language in West Java, Sundanese also has a special script. However, currently very few children understand or can write Sundanese script so it needs to be protected and preserved. In this research, the CNN-ELM method is proposed for recognizing Sundanese script. The CNN-ELM method is a model that utilizes the Convolutional Neural Network (CNN) method to extract image features and the Extreme Learning Machine (ELM) for image classification. This method performs well in terms of training speed and accuracy, and can also work with smaller data sets. The aim of this research is to measure accuracy in recognizing Sundanese script. After testing Sundanese script handwriting several times using CNN-ELM, the best results were obtained with an accuracy of 88% with a total of 30,000 hidden neurons.
Enhancing Fine-Tuning in Low Data Regime by Increasing Representation Entropy During Pre-Training Phase

The strategy of training a new model through the fine-tuning of pre-trained model has gained considerable prominence due to its potential to reduce training costs or enhance performance. However, accomplishing both of these objectives concurrently presents a noteworthy challenge. To address the challenge, this study adopts two entropy metrics, which can be seamlessly integrated into the cross entropy loss. Our approach aims at increasing representation entropy during pre-training phase, leading to an increased information encoded in the representation of a pre-trained model. This increased information can then be effectively harnessed during subsequent fine-tuning phase. In experiments, we substantiate the reliability of the two adopted entropy metrics in accurately quantifying representation entropy. Moreover, we demonstrate the effectiveness of the two metrics in enhancing the performance of fine-tuning, particularly in low data regime.
Unidirectional-Edge Detection based Background Subtraction method for Real-time Object Detection in Restricted Environments

Although many studies focus on the deep learning algorithms, traditional image processing and machine learning technologies are still being developed and used as an auxiliary means. For examples, there is background subtraction based object localization. This can reduce the number of deep learning model inference. The most famous background method for subtraction is background subtraction using the GMM-derived MOG, KNN, and MOG2 algorithms. However, these algorithms still use a non-trivial of computing resources on lightweight single board computers. To alleviate this problem, we propose a unidirectional edge detection based background subtraction algorithm in restricted environments. In terms of processing time, proposed algorithm outperformed others. Although the processing time improved significantly, the precision (77.8927%) was only about 1% lower than the best method. These improvements will enable the video surveillance system to be implemented on lightweight single board computer, such as NVIDIA Jetson boards.
The Effect of Data Augmentation Techniques on Persian Sentiment Analysis

The growth of the Internet and increased use of social media have led to the production of a large amount of unstructured data. These data have many variations, such as text, image, video, and audio. Among them, textual data has the largest share. Considerably textual data comprises the viewpoints of users regarding a wide range of services and products. In order to achieve high accuracy in sentiment classification, Data Augmentation techniques have been used in this paper, which has significantly increased the volume and variety of data. Seventeen different Deep learning models have been trained on the final dataset. The results show that using the Data Augmentation approach has increased the accuracy by more than 6% in Persian sentiment analysis. Methods used in this paper include translating sentences from Farsi to English and vice versa, sentence displacement, replacing synonyms, and data balancing. Also, in this paper, two new Lexicons have been manually created for Persian sentiment analysis. The first is a sentiment lexicon containing 660 positive and negative words, and the second is a lexicon of 390 stop words. Finally, the accuracy obtained using the Data Augmentation approach has reached 96.73%, which is sufficient compared to previous research.
DR based Sentence &amp; SPO Tuple Pair Generation for Open Information Extraction

This paper presents a method for generating sentence and SPO (Subject-Predicate-Object) tuple pairs based on dependency relations (DR) to train a DNN model for open information extraction. The proposed system extracts sentences from the OLLIE dataset that contain seed tuples in NL-based sentences with SPO tuples, performs dependency parsing on the NL-based sentences, and generates DR-based sentence and SPO tuple pairs. Consequently, approximately 230,000 DR-based sentence and SPO tuple pairs were generated from the 3 million data. To demonstrate the effectiveness of the generated DR-based learning data in natural language processing, experiments were conducted using the BERT model to recognize SPO tuples. The results showed that the performance of SPO tuple extraction was better when using DR-based learning data compared to NL-based learning data. Specifically, the average accuracy for top-1, top-3, and top-5 was 0.08, 0.25, and 0.22 higher, respectively, when using DR-based learning data compared to NL-based learning data.
Scalable Semi-Supervised Learning through Combined Anchor-based Graph and Flexible Manifold Embedding

This paper focuses on graph-based semi-supervised learning, specifically for large-scale graphs used in inductive multi-class classification. The proposed method aims to overcome limitations in current scalable graph-based semi-supervised learning techniques. The key innovation is integrating the anchor graph calculation into the learning model, rather than treating it as a separate, offline step. This approach involves several essential tasks, including simultaneously estimating unlabeled samples, mapping the feature space to the label space, creating an affinity matrix for the anchor graph, and using labels and features associated with anchor points to construct the graph. The experimental results, conducted with large datasets, demonstrate a positive trend, showing higher accuracy and greater stability compared to existing scalable graph-based semi-supervised learning methods.
Dynamic Image Representations for Crowd Anomaly Detection using Generative Adversarial Networks

Anomaly detection within crowded environments is a key challenge in the crowd behaviour understanding and computer vision fields. Application of crowd anomaly detection has improved recently, however, advancements of accuracy and computation (processing power and time) are still required. In this paper, we present an approach to crowd behaviour anomaly detection using dynamic image representations as an alternative to optical flow extractions for temporal development feature extraction. The features are used in conjunction with image-to-image translation using CGANs for anomaly detection within crowds, the proposed framework is evaluated on standard benchmark datasets. The experimental results obtained have demonstrated the efficacy of this approach in comparison to the state-of-the-art crowd anomaly detection methods.
Comparison of Transformer-based Architectures for Product Categorization

The fast & continuous evolution of e-commerce search engines globally has encouraged researchers & industry specialists alike to improve product categorization models to elevate user experience. Also, the revolutionary emergence of large language models has given us the opportunity to capture the complex relationships and dependencies found in large datasets that we were not able to discover with traditional techniques, and not to mention the multitude of other natural language understanding tasks it performs. In this paper, we aim to evaluate different transformer-based architectures on the Amazon Shopping Queries dataset to determine which architecture is well-suited for the task of product categorization within an e-commerce platform.
Developing Mini VR Game Engines as an Engaging Learning Method for Digital Arts &amp; Sciences

Digital Arts and Sciences curricula have been known for combining topics of emerging technologies and artistic creativity for the professional preparation of future technical artists and other creative media professionals. One of the key challenges in such an interdisciplinary curriculum is the instruction of complex technical concepts to an audience that lacks prior computer science background. This paper discusses how developing small custom virtual and augmented reality game engines can become an effective and engaging method for teaching various fundamental technical topics from Digital Arts and Sciences curricula. Based on empirical evidence, we demonstrate examples that integrate concepts from geometry, linear algebra, and computer programming to 3D modeling, animation, and procedural art. The paper also introduces an open-source framework for implementing such a curriculum in Quest VR headsets, and we provide examples of small-scale focused exercises and learning activities.
Enhancing Hate Speech Detection in Social Media Using IndoBERT Model: A Study of Sentiment Analysis during the 2024 Indonesia Presidential Election

Legislative elections, presidential elections, and village head elections are all on the schedule for Indonesia in 2024. Law No. 7 of 2017 governs these elections, emphasizing the ideals of direct, public, free, confidential, honest, and fair elections. With the growth of social media, political personalities are employing these channels for campaign activity, resulting in a variety of user responses, including hate speech. Detecting hate speech is critical for preventing and mitigating the bad impact it may have on society. The purpose of this article is to investigate the usage of IndoBERT, a fine-tuned BERT model, to improve hate-speech identification during the 2024 Indonesia Presidential Election. The research focuses on hate speech in the context of the presidential election and the challenges raised by social media. In this paper, we utilized Twitter as a case study to develop effective hate-speech identification techniques. The method comprises fine-tuning IndoBERT using a dataset of Indonesian tweets on the election that have been preprocessed to reduce noise and classified as hate speech or non-hate speech using a pre-trained model. The accuracy, precision, recall, and F1-score of the model are used to assess its performance. The results reveal that the IndoBERT model detects hate speech in Indonesian Twitter data with high accuracy. A future study might look towards fixing typos in the dataset and examining expert recognition of hate speech scenarios.
Preventing Fraud in E-tickets Validation Using The 2FA Approach

Electronic ticketing (e-ticket) systems employing QR codes or barcodes are essential in the digital era but come with cybersecurity risks like fraud, counterfeiting, and ticket scalping. This study explores the cybersecurity implications of large-scale event e-ticketing, with a focus on comprehending these technologies and improving security. It introduces the Ticket_Events application, which combines online registration and the secure generation of QR-coded electronic tickets. The app mandates QR code scanning at the event venue and encourages the use of location services for enhanced verification, preventing ticket swapping and potential fraud. The primary objective is to create a dependable e-ticketing application centered on ownership verification. In conclusion, the study delves into the intricacies of e-ticketing and barcode scanning, pinpoints challenges, and proposes solutions to fortify the security of digital ticket delivery.
Semi-supervised Classification through Data and Label Graph Fusion

This study introduces a groundbreaking structure for semi-supervised learning based on graphs. Our technique provides an all-encompassing strategy that simultaneously tackles the challenges of label prediction and linear transformation. Specifically, the linear transformation we advocate is designed to forge a distinguishing subspace, thereby significantly compressing the data’s dimensionality.In advancing semi-supervised learning techniques, our framework particularly focuses on effectively utilizing the intrinsic data configuration and the provisional labels related to the unlabeled examples in our possession. This distinctive methodology leads to a more sophisticated and discriminative form of linear transformation. Tests carried out on authentic image datasets clearly validate the efficiency of the method we advocate. These tests repeatedly show enhanced performance in contrast to semi-supervised strategies that address the fusion of data and label deduction in isolation.