Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence for Innovative Customer Services in the Hospitality Industry of India

Artificial intelligence (AI) is a new technology that has been applied in tourism and hospitality businesses to enhance customer experiences. In hospitality industry, there have been researches about AI applications, yet it is not widely applied in hotels in Indian aspect. Therefore, the purpose of this chapter is to identify the customer experience from use of AI in hotels in India. The research methodology used in the study was quantitative research with field survey. The target respondents were contacted in national capital region (NCR) of India for data collection. The survey results came out quite surprising with differing opinions of respondents. Most respondents agreed that applying AI is a modern trend to follow. Some of the important points to respondents were always-on service, fast access to service and cleanliness. Many respondents would prefer to interact with employees more than AI machines. Due to the wide knowledge of AI and limitation of the research, further research should be done to gain better perspectives on AI approaches in hotels.
Federated Learning and Artificial Intelligence in E-Healthcare

Federated Learning (FL), a novel distributed interactive AI paradigm, holds particular promise for smart healthcare since it enables many clients including hospitals to take part in AI training while ensuring data privacy. Each participant's data that is sent to the server is really a trained sub-model rather than original data. FL benefits from better privacy features and dispersed data processing. Analysis of very sensitive data has substantially improved because to the combination of Federated Learning with healthcare data informatics. By utilizing the advantages of FL, the clients' data is preserved safely with their own model, and data leakage is avoided to prevent any malicious data modification in the system. Horizontal FL takes data from all devices with a comparable trait space suggests that Clients A and B are using the same features. Vertical Federated Learning uses a number of datasets from various feature domains to train a global model. A successful FL implementation could thus hold a significant potential for enabling precision medicine on a large scale.
AI and Robots in Science Fiction Movies

Artificial intelligence has revolved around the ideological core of social sciences as never before. Public opinion shows exegetes of AI and detractors who feared the advance of robots and AI in human life. Although AI makes life for humans faster and easier, no less true is that it reduces human autonomy, probably threatening future jobs. Quite aside from this, this chapter interrogates the impacts of AI and technology in science fiction movies. The authors analyze the critical position of different scholars regarding movies or Sagas such as The Terminator, Matrix, and HBO Saga Westworld. From a sociological perspective, they approach the problem of technology while placing it under the critical lens of scrutiny. In so doing, seminal voices like Paul Virilio, Jean Baudrillard, and Jacques Ellul are put in the foreground. Plausibly discussing the advance of AI in daily life is interrogating the future of digital society.
Facial Recognition in Law Enforcement

Abstract This chapter discusses the use of facial recognition technology (FRT) by law enforcement with a special focus on machine learning applications. First, FRT is situated in the larger context of the rise of video surveillance and a shift in Western security culture. The chapter shows how the promise of FRT addresses the problems of video surveillance. Then, it explains how FRT works and how machine learning techniques such as eigenface and convolutional neural networks come into play. Afterward, it explains how the accuracy and effectiveness of FRT are evaluated and why it is important to take the interplay of human operators and FRT systems into account. Finally, several controversies around the police use of FRT are presented and the limits of technological solutions to the problem of bias are demonstrated.
Visual sentiment analysis with semantic correlation enhancement

Abstract Visual sentiment analysis is in great demand as it provides a computational method to recognize sentiment information in abundant visual contents from social media sites. Most of existing methods use CNNs to extract varying visual attributes for image sentiment prediction, but they failed to comprehensively consider the correlation among visual components, and are limited by the receptive field of convolutional layers as a result. In this work, we propose a visual semantic correlation network VSCNet, a Transformer-based visual sentiment prediction model. Precisely, global visual features are captured through an extended attention network stacked by a well-designed extended attention mechanism like Transformer. An off-the-shelf object query tool is used to determine the local candidates of potential affective regions, by which redundant and noisy visual proposals are filtered out. All candidates considered affective are embedded into a computable semantic space. Finally, a fusion strategy integrates semantic representations and visual features for sentiment analysis. Extensive experiments reveal that our method outperforms previous studies on 5 annotated public image sentiment datasets without any training tricks. More specifically, it achieves 1.8% higher accuracy on FI benchmark compared with other state-of-the-art methods.
Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.
THE ANALYSIS OF CLASSROOM INTERACTION IN ENGLISH BETWEEN TEACHERS AND STUDENTS AT A KINDERGARTEN IN JAMBI

Interaction is a fundamental aspect of human life, with communication serving as a key component. In educational settings, classroom interaction plays a crucial role in facilitating effective knowledge transmission and fostering learning experiences. This study focuses on examining the patterns and quality of verbal interaction among English as a Foreign Language (EFL) students in a kindergarten classroom. This qualitative case study investigates the dynamics of classroom interaction in English between teachers and students at a kindergarten in Jambi. The research aims to gain a comprehensive understanding of the patterns of interaction, describe the types of teacher talk, and explore the characteristics of student talk. Through close observation and analysis, the study reveals compelling findings regarding the dominance of teacher talk, the varied functions of both teacher and student talk, and the emergence of significant themes within the interactions. The results demonstrate that teacher talk holds a prominent role in the classroom, influencing the learning process and managing classroom activities. However, the level of dominance displayed by the teacher varies across different learning activities. Furthermore, the study uncovers distinct types and functions of teacher talk, encompassing accepting feelings, encouraging, accepting students' ideas, asking questions, lecturing, giving directions, criticizing, and silencing. Student talk involved responding, initiating, and silencing.
A Secure Anonymous Identity-Based Virtual-Space Agreement Method for Crowds-Based Anonymous Communicate Scheme

Anonymous data exchange is in great demand in many situations, especially in remote control systems, in which a stable, secure, and secret data channel must be established between the controlling and controlled parties to distribute control commands and return data. In the previous work, we built a two-level Virtual-Space anonymous communication scheme based on the Crowds System for performing secret data exchange in remote control systems. However, as an essential part of security and anonymity, participating nodes’ identity declaration and session key agreement phases were not well designed. In this paper, we redesign the identity agreement and declaration process and design an identity-based Virtual-Space agreement method using the extended Chebyshev Chaotic Maps. In this approach, we transform the identity declaration process into a multilevel Virtual-Space agreement problem, where a series of security-progressive Virtual-Space addresses are negotiated between the controller and the controlled nodes. The protocol can handle the case where there are multiple controllers in the system, and the negotiated Virtual-Space depends on the identity of the controller and the controlled node, so different controllers do not affect each other. The designed protocol is verified on Freenet, and we conclude this paper with a detailed security analysis of the method to prove that the method satisfies forward security.
Corpus Modeling and the Geometries of Text

Abstract This chapter explores the theoretical implications of spatial metaphors in the field of computational text analysis and inspects how the properties of topologies aid and inhibit our theories of textual meaning. Rather than mining for “ground truth,” machine learning algorithms for text, especially word embedding models, provide a selectively simplistic map of the semantic space. The representation of that textual map depends not only on the choice of algorithm but also on the composition of the corpora used to train them. Along with reviewing the technical aspects of embedding text into space, this chapter surveys the consequences of training algorithms with internal and external objectives. The implications of different types of training corpora are enumerated, with particular attention to ethical considerations. More scholarship, institutional support, and technical infrastructure directed toward the careful building, documenting, and sharing of corpora as well as machine learning models trained on those corpora are recommended.
Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model (CHISM)

Abstract Recent advances in Artificial Intelligence (AI) have paved the way for the integration of text-based and voice-enabled chatbots as adaptive virtual tutors in education. Despite the increasing use of AI-powered chatbots in language learning, there is a lack of studies exploring the attitudes and perceptions of teachers and students towards these intelligent tutors. This study aims to compare several linguistic and technological aspects of four App-Integrated Chatbots (AICs) and to examine the perceptions among English as a Foreign Language (EFL) teacher candidates. In this mixed-methods research based on convenience sampling, 237 college students from Spain (n = 155) and the Czech Republic (n = 82) interacted with four AICs over a month, and evaluated them following a rubric based on the Chatbot-Human Interaction Satisfaction Model. This scale was specifically designed to assess different linguistic and technological features of AICs such as response interval, semantic coherence, sentence length, and user interface. Quantitative and qualitative data were gathered through a pre-post-survey, based on the CHISM model and student assessment reports. Quantitative data were analyzed using SPSS statistics software, while qualitative data were examined using QDA Miner software, focusing on identifying recurring themes through frequency analysis. The findings indicated a moderate level of satisfaction with AICs, suggesting that enhancements in areas such as better adapting to learner needs, integrating interactive multimedia, and improving speech technologies are necessary for a more human-like user interaction.
The Research on Missing Data Imputation Method of Aero-Engine’s ACARS Based on GAN-Attention

The ACARS data is an important data source for Real-time flight status monitoring. However, ACARS data is affected by unstable factors during transmission and storage, resulting in complex missing situations. We focus on a data-driven class of methods based on deep learning applied to the reconstruction of ACARS data. The main work and innovation points are as follows: We categorized four missing patterns and generated corresponding datasets for training and testing models. The GAN architecture is adopted to deal with the problem that the ACARS data are dimensionally wide and difficult to be extracted. Encoder and decoder are used to form the generator of GAN, allowing the model to compress existing information into deep features and then generate a complete data set. And the attention mechanism is introduced to enhance the learning ability of the model for long-distance information. Finally, the effectiveness of the proposed method is verified by comparing it with different reconstruction models under four missing modes and ablation experiments.
Landing Pad Detection and Computing Direction of Motion for Autonomous Precision Landing Quadcopter

This paper presents an algorithm for an autonomous quadcopter to perform autonomous precision landing. This research focuses on designing the quadcopter so that it can land precisely on the landing pad using image processing algorithms. First, the captured image will be converted to grayscale, then the thresholding method is carried out and followed by a morphological process to eliminate noise and produce a clear image. The detected image will be displayed in a frame that will calculate the distance to the middle point. It will be used as Pulse Width Modulation (PWM) input to adjust the direction of motion of the quadcopter. so that it can land autonomously. The algorithm was tested in several color pads which are located in the grass, sand and cluttered ground. Testing is carried out to test the accuracy and precision of the designed algorithm. The results of the experiment show an accuracy rate of 94.76% and a precision level of 96.59% with an average landing time of 19 seconds and an average detection time is 8.55 milliseconds.
A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.
Application of Decimated Mathematical Equations and Polynomial Root-Finding Method in Protection of Text Messages

Cryptography is the process of transforming data so that only the recipient of the message can read it. It uses an algorithm and a key to convert an input into an encrypted output. In this study, we introduce a novel method for protecting readable messages through the utilization of a polynomial root-finding technique in conjunction with the decimated output of Zaslavsky equations. The innovative approach we have developed is a block encryption algorithm that offers both protection for sensitive information as well as adaptability to various block sizes, including dynamically changing block sizes. Precise security analysis is provided for the proposed algorithm using key space analysis and strong statistical tests. The presented results show that the novel block encryption protection provides a high level of security.
Homological commutation relation of electric and magnetic fluxes

Abstract To give a firm base to argument on quantization of an LC circuit, we derive a commutation relation of electric flux and magnetic flux from quantum electrodynamics. The electric flux is defined by integration of electric field on two-dimensional surface. The magnetic flux is defined by integration of magnetic field surrounded by one-dimensional loop. It is proved that the commutator of the electric flux and the magnetic flux is equal to a crossing number of the loop and the surface. It is also proved that the flux commutator is invariant under gauge transformations and homological deformations. It is also argued that the LC-circuit system can be used as a platform for realizing EPR entanglement.
What enhances or worsens the user-generated metaverse experience? An application of BERTopic to Roblox user eWOM

Purpose Given its growing economic potential and social impact, this study aims to understand the motivations and concerns regarding metaverse usage. It identifies user needs and risks around the metaverse grounded on uses and gratifications theory and perceived risk theory. Design/methodology/approach The authors analyzed user reviews and rating data from Roblox, a representative modern metaverse platform. They applied BERTopic modeling to extract topics from reviews, identifying key motivations and risk aspects related to metaverse usage. They further constructed an explanatory model to assess how those affect user satisfaction and changes in these effects over time. Findings This study discovered that gratifications like entertainment, escapism, social interaction and avatar-based self-expression significantly influence user satisfaction in the metaverse. It also highlighted that users find satisfaction in self-expression and self-actualization through creating virtual spaces, items and video content. However, factors such as identity theft, fraud and child safety were identified as potential detriments to satisfaction. These influences fluctuated over time, indicating the dynamic nature of user needs and risk perceptions. Research limitations/implications The novelty of this study lies in its dual application of the uses and gratifications theory and perceived risk theory to the metaverse. It provides a novel perspective on user motivations and concerns, shedding light on the distinct elements driving user satisfaction within the metaverse. This study unravels the metaverse’s unique capacity to assimilate features from established digital media while offering a distinctive user-generated experience. This research offers valuable insights for academics and practitioners in digital media and marketing. Originality/value This research pioneers the application of both uses and gratifications and perceived risk theories to understand factors influencing metaverse satisfaction. By establishing a comprehensive framework, it explores the metaverse’s unique value as a user-content creation platform, while encompassing existing digital platform characteristics. This study enriches the academic literature on the metaverse and offers invaluable insights for both metaverse platforms and brand marketers.
Hate Speech Detection and Bias in Supervised Text Classification

Abstract This chapter introduces hate speech detection, describing the process of developing annotated datasets, training machine learning models, and outlining the cutting-edge techniques used to improve these methods. The case of hate speech detection provides valuable insight into the promises and pitfalls of supervised text classification for sociological analyses. A key theme running through the chapter is that ostensibly minor methodological decisions—particularly those related to the development of training datasets—can have profound downstream impacts. In particular, the chapter examines racial bias in these systems, explaining why models intended to detect hate speech can discriminate against the groups they are designed to protect and discussing efforts to mitigate these problems. It argues that hate speech detection and other forms of content moderation should be an important topic of sociological inquiry as platforms increasingly use these tools to govern speech on a global scale.
CVII: Enhancing Interpretability in Intelligent Sensor Systems via Computer Vision Interpretability Index

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across domains. The CVII is rigorously evaluated using a range of computer vision models applied to the COCO (Common Objects in Context) dataset, a widely recognized benchmark in the field. The findings established a robust correlation between image interpretability, model selection, and CVII scores. This research makes a substantial contribution to enhancing interpretability for human comprehension, as well as within intelligent sensor applications. By promoting transparency and reliability in AI-driven decision-making, the CVII framework empowers its stakeholders to effectively harness the full potential of AI technologies.