Reviewerly/exHarmony huggingface dataset:
Accompany Children's Learning for You: An Intelligent Companion Learning System

Abstract Nowadays, parents attach importance to their children's primary education but often lack time and correct pedagogical principles to accompany their children's learning. Besides, existing learning systems cannot perceive children's emotional changes. They may also cause children's self‐control and cognitive problems due to smart devices such as mobile phones and tablets. To tackle these issues, we propose an intelligent companion learning system to accompany children in learning English words, namely the Intelligent Augmented Reality Educator (IARE) . The IARE realizes the perception and feedback of children's engagement through the intelligent agent (IA) module, and presents the humanized interaction based on projective Augmented Reality (AR). Specifically, IA perceives the children's learning engagement change and spelling status in real‐time through our online lightweight temporal multiple instance attention module and character recognition module, based on which analyses the performance of the individual learning process and gives appropriate feedback and guidance. We allow children to interact with physical letters, thus avoiding the excessive interference of electronic devices. To test the efficacy of our system, we conduct a pilot study with 14 English learning children. The results show that our system can significantly improve children's intrinsic motivation and self‐efficacy.
Factor Augmented Inverse Regression and its Application to Microbiome Data Analysis

AbstractWe investigate the relationship between count data that inform the relative abundance of features of a composition, and factors that influence the composition. Our work is motivated from microbiome studies aiming to extract microbial signatures that are predictive of host phenotypes based on data collected from a group of individuals harboring radically different microbial communities. We introduce multinomial Factor Augmented Inverse Regression (FAIR) of the count vector onto response factors as a general framework for obtaining low-dimensional summaries of the count vector that preserve information relevant to the response. By augmenting known response factors with random latent factors, FAIR extends multinomial logistic regression to account for overdispersion and general correlations among counts. The projections of the count vector onto the loading vectors represent additional contribution, in addition to the projections that result from response factors. The method of maximum variational likelihood and a fast variational expectation-maximization algorithm are proposed for approximate inference based on variational approximation, and the asymptotic properties of the resulting estimator are derived. Moreover, a hybrid information criterion and a group-lasso penalized criterion are proposed for model selection. The effectiveness of FAIR is illustrated through simulations and application to a microbiome dataset. Supplementary materials for this article are available online.Keywords: Factor regressionLatent confoundingSequence readsSufficient dimension reductionVariational inference Supplementary MaterialsAppendix: It includes details on variational EM algorithm for FAIR and derivation of EN2(d), details on proofs of theoretical properties, and additional simulations. (Appendix.pdf)AcknowledgmentsThe authors would like to thank the Editor, the Associate Editor, and anonymous referees for their constructive comments that greatly improved this manuscript.Disclosure StatementThe authors report there are no competing interests to declare.Additional informationFundingThis research was supported in part by the National Natural Science Foundation of China10.13039/501100001809 (12222111, 11971017), the Fundamental Research Funds for the Central Universities, and Neil Shen’s SJTU Medical Research Fund of Shanghai Jiao Tong University.
A Fast Object Detection-Based Framework for Via Modeling on PCB X-Ray CT Images

For successful printed circuit board (PCB) reverse engineering (RE), the resulting device must retain the physical characteristics and functionality of the original. Although the applications of RE are within the discretion of the executing party, establishing a viable, non-destructive framework for analysis is vital for any stakeholder in the PCB industry. A widely regarded approach in PCB RE uses non-destructive x-ray computed tomography (CT) to produce three-dimensional volumes with several slices of data corresponding to multi-layered PCBs. However, the noise sources specific to x-ray CT and variability from designers hampers the thorough acquisition of features necessary for successful RE. This article investigates a deep learning approach as a successor to the current state-of-the-art for detecting vias on PCB x-ray CT images; vias are a key building block of PCB designs. During RE, vias offer an understanding of the PCB’s electrical connections across multiple layers. Our method is an improvement on an earlier iteration which demonstrates significantly faster runtime with quality of results comparable to or better than the current state-of-the-art, unsupervised iterative Hough-based method. Compared with the Hough-based method, the current framework is 4.5 times faster for the discrete image scenario and 24.1 times faster for the volumetric image scenario. The upgrades to the prior deep learning version include faster feature-based detection for real-world usability and adaptive post-processing methods to improve the quality of detections.
Instagram Text Sentiment Analysis Combining Machine Learning and NLP

research-article Free Access Share on Instagram Text Sentiment Analysis Combining Machine Learning and NLPJust Accepted Authors: Chia-Pang Chan Department of Information Management, Cheng Shiu University, Taiwan 10001, China Department of Information Management, Cheng Shiu University, Taiwan 10001, China 0000-0003-0016-8674Search about this author , Jun-He Yang Department of E-Sport Technology Management, Cheng Shiu University, Taiwan 10001, China Department of E-Sport Technology Management, Cheng Shiu University, Taiwan 10001, China 0000-0002-4008-4785Search about this author Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingAccepted on June 2023https://doi.org/10.1145/3606370Published:03 July 2023Publication History 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months69Last 6 weeks69 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
TechMiner: Analysis of bibliographic datasets using Python

Bibliometric analyses and tech-mining studies are assuming a crucial role in research, covering the need to synthetize current knowledge; however, they can be difficult to conduct due to several analyses employ different software tools, some of them available only under commercial licenses, and in some cases, requiring advanced programming skills for their use. This article introduces a graphical Python-based application that performs bibliometric and statistical analyses of data sets extracted from Scopus. TechMiner allows the user to run basic task as data cleaning and author disambiguation and advanced analyses as keyword clustering or citation analysis.
WhatsApp as a Platform of Community of Practice and Its Affordances Toward Capacity Building of Teachers in Online Teaching

The current study examined the efficiency of Whatsapp as a mobile-based Community of Practice (CoP) toward capacity building of teachers in acquiring online teaching skills during the COVID-19 pandemic. The data were collected using mixed- methods in which both survey and in-depth interviews were used to for data collection. The sample for the study were secondary school teachers from six private schools in Chennai, India. The authors collected data from 170 secondary school teachers via a web-based survey using a questionnaire adapted from the Community of Practice Evaluation Questionnaire (CoPeval). To elicit the best practices for utilising Whatsapp as a CoP platform for professional learning, qualitative data were gathered through in-depth interviews with six participants, including two teachers, subject headteachers, and principals. The findings confirmed that Whatsapp CoP is a viable mobile-based platform for upskilling and capacity building of teachers in online teaching skills. The suggestions and recommendations of the study are presented.
Augmented Reality Applications and Usage Examples in the Metaverse Age

The aim of this chapter is to identify and introduce the most preferred applications by analyzing augmented reality applications used in master's theses and doctoral dissertations in Turkey. In order to achieve this aim, first of all, the concepts related to the Metaverse, which constituted the conceptual framework of the chapter, were explained by supporting them with the research findings obtained from the literature. Then, the AR development platforms, AR software development kits, AR applications, and the devices on which AR applications were run were examined in thesis studies within the scope of augmented reality accessed from the Council of Higher Education Thesis Archive. At the end of this chapter, Vuforia, ARCore, and Artivive Bridge software development kits are briefly introduced as the first three most frequently used AR software development kits in thesis studies accessed from the National Thesis Center Archive of the Council of Higher Education of Turkey.
AR/VR Technologies in the Metaverse Ecosystem

Imagine living in a virtual environment where millions of individuals can communicate among themselves, explore, shop, and have a comfortable life on their couches. In this universe, the computer displays have transformed portals into a physical, three-dimensional digital realm that is superior and more expansive than the real world. Digital avatars that are representations of us roam between experiences while carrying our identities. Putting the sensationalism aside, this is known as the “metaverse.” The metaverse is an idea that describes a virtual universe composed of a couple of interconnected virtual spaces or worlds. It is an imaginative immersion in which the digital and bodily worlds converge, creating a widespread and interconnected network of virtual environments that can be accessed globally. It can boost employment opportunities by supplementing individuals with practical training and ensures that the individuals with disabilities can also be incorporated in this marathon of growth by leveraging these technologies.
Generative Adversarial Networks

Generative adversarial networks (GANs) have transformed machine learning and created new research and application areas. GANs are now used for data augmentation, picture, audio, text-to-image, and 3D object production thanks to IoT. These applications could make IoT devices more personalized, efficient, and productive by collecting and using data. GANs are also employed in healthcare and finance for IoT applications, enabling new research. GANs have many benefits, however stability issues during training, interpretability issues, ethical concerns, and more must be addressed. GANs could transform the IoT market, and hardware and infrastructure improvements are projected to increase their influence. GANs will open up many new research and development avenues for IoT devices.
Hybrid Optimization Techniques for Data Privacy Preserving in the Metaverse Ecosystem

Large-scale electronic databases are being maintained by businesses and can be accessed via the internet or intranet. Employing data mining techniques, significant information was extracted from the data. The privacy of the data is inherently at risk while data mining operations are being carried out. All users shouldn't have access to the private information stored in the database. Methods for protecting privacy have been suggested in the literature. Algorithms used in privacy-preserving data mining (PPDM) on private data are unknown even to the algorithm operator. Personal information about users and data on their collective behaviour are the two main aspects of privacy preservation. The majority of privacy-preserving techniques rely on reducing the level of granularity used to represent the data. Although privacy is improved, information is lost as a result. As a result, with PPDM, there is a trade-off between privacy and information loss. Effective methods that don't undermine the security defences are needed.
Cyber Security in the Metaverse

Security in the Metaverse is a critical concern as virtual worlds continue to expand and evolve. With an increasing number of users engaging in immersive experiences, safeguarding personal information, assets, and digital identities becomes paramount. Robust security measures are essential to protect against hacking, identity theft, and fraudulent activities. Technologies like blockchain and encryption play a crucial role in ensuring secure transactions, while identity verification systems help authenticate users. Additionally, monitoring and moderation tools are necessary to combat harassment, hate speech, and inappropriate content. As the Metaverse becomes more integrated into our lives, a comprehensive approach to security is vital to foster trust and safeguard the virtual realm. This chapter aims to provide an overview of cybersecurity in Metaverse. It discussed the various cyber stacks in detail along with solutions to manage these attacks. The authors have also discussed the various challenges in securing metaverse, and possible future research directions.
Application of Mathematical Models in Linear Algebra to the Metaverse Ecosystem

Linear algebra is a branch of mathematics that is widely used throughout science and engineering. Linear algebra includes arithmetic operations with notation sharing. We can be able to have a better understanding of machine learning algorithms only after having a good understanding of linear algebra. Sometimes, machine learning might be pure linear algebra, involving many matrix operations; a dataset itself is often represented as a matrix. Linear algebra is used in data pre-processing, data transformations, and model evaluation. In this chapter, the basic importance of linear algebra has been discussed, and the close liaison of the subject with current research domain in machine learning and data science has been explored in the light of application of the same in solving some critical issues.
Enhancing Traceability in Food Supply Chains Using Blockchain and IoT Technologies

The work centers around a connected viewpoint intended for the problematic information interface between blockchain and IoT for further developing information provenance and uprightness. The examination draws on the author's industry-based experience driving a two-year AUD 1.5 million undertaking that elaborates on the coordination of IoT and blockchain to follow and safeguard the legitimacy of Australian meat in the quickly developing Chinese market. The plan drove the system to direct the improvement of the food production network project as a particular mix of IoT and blockchain to a specific food store network context. The presentation can change the prophet's personality. The information legitimacy rehearses instead of looking for the unadulterated fact of the matter and crypto financial aspects. The suggestion uses a different methodology to enhance the traceability of the product. It uses a Merkle tree to generate hash keys. It detects security breaches by 2.93% and improves client satisfaction by 9.96% compared to previous work.
Unleashing the Power of the Metaverse in Intelligent Libraries

The emergence of Metaverse has presented new opportunities and possibilities for the advancement of intelligent libraries. However, it also brings new challenges to the professional competencies of librarians. This chapter examines the potential of Metaverse as an enabling technology for intelligent libraries, focusing on how it can be used to drive innovation and improve library services. The chapter also delves into the advantages and underlying technologies of Metaverse, as well as its theoretical foundations for application in libraries. It is argued that Metaverse and its related technologies are crucial for transforming libraries into fully intelligent ones, and that the concept of Metaverse can also provide benefits for library collection management, reading spaces, cultural promotion, reading experiences, and special user services. Intelligent libraries must seize this opportunity to create a new form of immersive experience and achieve breakthroughs in their digital transformations, driven by the diverse technologies of Metaverse.
Data Privacy in the Metaverse Ecosystem

This chapter introduces data privacy and its significance in the metaverse to the readers. It introduces several data privacy policies, privacy enhancing techniques (PET), and discusses some existing literatures on data privacy issues in the metaverse. This chapter seeks to educate readers on the various methods and technologies available to protect personal and sensitive data throughout the entire data lifecycle (collection, storage, use, and transmission). In addition, it discusses how these privacy-preserving techniques can be used to secure metaverse data and describes the most recent findings from studies conducted on the PET methods to improve these practices. The chapter also includes suggestions for additional research and development that will help readers take the first step toward enhancing data privacy techniques in the metaverse or incorporating existing technologies into privacy-enhancing metaverse applications.
Future Outlier Detection Algorithm for Smarter Industry Application Using ML and AI

Throughout many real-world investigations, outliers are prevalent. Even a few aberrant data points can cause modeling misspecification, biased parameter estimate, and poor forecasting. Outliers in a time series are typically created at unknown moments in time by dynamic intervention models. As a result, recognizing outliers is the starting point for every statistical investigation. Outlier detection has attracted significant attention in a variety of domains, most notably machine learning and artificial intelligence. Anomalies are classified as strong outliers into point, contextual, and collective outliers. The most significant difficulties in outlier detection include the narrow boundary between remote sites and natural areas, the propensity of fresh data and noise to resemble genuine data, unlabeled datasets, and varying interpretations of outliers in different applications.
Artificial Intelligence in Video Surveillance

Surveillance is an essential component of security, and e-surveillance is one of the primary goals of the Indian Government's Digital India development initiative. Video surveillance offers a wide range of applications to reduce ecological and economic losses and becomes one of the most effective means of ensuring security. This chapter addresses the problem of how artificial intelligence is powering video surveillance. There is a significant research focus on video analytics but comparatively less effort has been taken for surveillance videos. However, there is little evidence that researchers have approached the issue of intelligent video surveillance in terms of suspicious action detection, crime scene description, face detection, crowd counting, and the like. Most AI-powered surveillance is based on deep neural networks and deep learning techniques using analysis of video frames as images. Consequently, this chapter aims to provide an overview and significance of how artificial intelligence techniques are employed in video surveillance and image processing.
Revolutionizing the Farm-to-Table Journey

In recent years, blockchain technology has gained a lot of attention for its various applications in various fields, with agriculture being one of the most promising. The use of blockchain in agriculture covers areas such as food security, information systems, agribusiness, finance, crop certification, and insurance. In developing countries, many farmers are struggling to earn a living, while in developed countries, the agriculture industry is thriving. This disparity is largely due to poor supply chain management, which can be improved using blockchain technology. Blockchain provides a permanent, sharable, and auditable record of products, improving product traceability, authenticity, and legality in a cost-effective manner. This chapter aims to compile all existing research on blockchain technology in agriculture and analyze the methodologies and contributions of different blockchain technologies to the agricultural sector. It also highlights the latest trends in blockchain research in agriculture and provides guidelines for future research.
Content-Based Video Retrieval With Temporal Localization Using a Deep Bimodal Fusion Approach

Content-based video retrieval is a research field that aims to develop advanced techniques for automatically analyzing and retrieving video content. This process involves identifying and localizing specific moments in a video and retrieving videos with similar content. Deep bimodal fusion (DBF) is proposed that uses modified convolution neural networks (CNNs) to achieve considerable visual modality. This deep bimodal fusion approach relies on the integration of information from both visual and audio modalities. By combining information from both modalities, a more accurate model is developed for analyzing and retrieving video content. The main objective of this research is to improve the efficiency and effectiveness of video retrieval systems. By accurately identifying and localizing specific moments in videos, the proposed method has higher precision, recall, F1-score, and accuracy in precise searching that retrieves relevant videos more quickly and effectively.
Blockchain-Based Messaging System for Secure and Private Communication

In recent years, concerns about privacy and security in online communication have become increasingly prominent. To address these concerns, the authors propose a blockchain-based messaging system that provides secure and private communication using double AES encryption. The system utilizes the decentralized and tamper-resistant nature of the blockchain to ensure that messages are not modified or deleted by unauthorized parties. Additionally, they employ double AES encryption to ensure that the content of messages remains confidential even if the blockchain itself is compromised. They evaluate the performance of the system and show that it is scalable and efficient. The system provides a secure and private messaging solution that can be used by individuals and organizations alike.
Artificial Intelligence of Things for Smart Healthcare Development

Smart healthcare systems are the health services that use the technologies like wearable devices, internet of things (IoT), and mobile internet to access medical information dynamically. It connects people, materials, and institutions related to healthcare; actively manages; and automatically responds to medical ecosystem needs. It helps the traditional medical system in making healthcare more efficient, convenient, and personalized. This chapter proposed (1) a review of smart healthcare development using artificial intelligence, the internet of things, and smartphone Android apps; (2) an experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases; (3) the integration of IoT and AI algorithms. Artificial intelligence of things (AIoT) is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in international journals and proceedings between 2010 and June 2022 is considered for the study.