Search results

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

The perpetration of sexual coercion is a complex public health problem associated with many kinds of deficits. The literature has shown that women also perpetrate sexually coercive behaviours. Recent work has suggested that this kind of behaviour could be explained by two distinct developmental pathways. However, this model does not allow the authors to identify how the individual processes social information in situ and may decide to resort to coercive behaviours. This study aimed to investigate the role of social information processing in women’s sexual coercion.

A sample of 125 French-speaking women from the general population were recruited to complete online questionnaires pertaining to dark triad personality traits, emotion abilities, alexithymia and antecedents of sexual coercion.

Results revealed that women with a history of sexual coercion had a significantly higher narcissistic traits score and more emotion regulation (ER) deficits than those without a history. For women with a history of sexual coercion perpetration, correlational analyses showed positive correlations, respectively, between psychopathic traits and alexithymia and between Machiavellianism and deficits in ER.

These results contribute to identifying the deficits relating to SIP in terms of sexual coercion perpetrated by women. Women with a history of sexual coercion perpetration appear to endorse more dark triad traits and to have ER issues. Certain level of these deficits could be a trigger and affect the SIP of women and increase the likelihood behaving in a sexually coercive manner.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

This is a general review study aiming to specify the key customer-based factors and technologies that influence the value co-creation (VCC) process through artificial intelligence (AI) and automation in the hospitality and tourism industry.

The study uses a theory-based general literature review approach to explore key customer-based factors and technologies influencing VCC in the tourism industry. By reviewing the relevant literature, the authors conclude a theoretical framework postulating the determinants of VCC in the AI-driven tourism industry.

This paper identifies customers' perceptions, attitudes, trust, social influence, hedonic motivations, anthropomorphism and prior experience as customer-based factors to VCC through the use of AI. Service robots, AI-enabled self-service kiosks, chatbots, metaversal tourism and new reality, machine learning (ML) and natural language processing (NLP) are technologies that influence VCC.

The results of this research inform a theoretical framework articulating the human and AI elements for future research set to expand the models predicting VCC in the tourism industry.

Few studies have examined consumer-related factors that influence their participation in the VCC process through automation and AI.

The purpose of this research is to introduce the Layar augmented reality (AR) application among library users and to understand the user’s satisfaction towards the information services provided by the Layar application with the help of the structural equation model (SEM).

According to Thomas (2016), action research is mainly undertaken to develop new skills or new approaches and to solve issues and problems with direct application to any applied setting. The present study helps to develop new skills and approaches to repackaging information using AR applications. Researchers have identified the question of what could be done to increase the awareness of Layar AR applications among students. Because the Layar augmented application is one of the new tools for an academic library to repackage information for mass accessibility. Therefore, in the present action research approach, researchers encompass two activities action and research. Researchers have used participatory action research methods by collecting data from 17 MBA institute libraries affiliated with Savitribai Phule Pune University. Researchers have systematically used the Layar application in the library by obtaining permission from each higher authority. Researchers have designed a Layar satisfaction model using the SEM with AMOS and SPSS.

The researcher found that the relationship between experience, performance and service quality is positively significant. The user’s experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study tested Layar AR application in MBA libraries affiliated with Savitribai Phule Pune University in the Pune and Pimpri Chinchwad areas.

The Layar app helps the academic library to convert selected print collections into an AR feel for library users. This is an additional method of providing information services to users through mobile devices. A total of 157 students downloaded the Layar application from their handsets and provided feedback through a questionnaire. Researchers have found that the relationships between users and Layar experience, performance and service quality are positively significant. The user experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study examined the performance, service quality and user experience of Layar applications. Structural equation and Modelling theories were used to examine the relationship between user satisfaction and information services using the Layar application.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

Based on the cognition–affect–conation pattern, this study explores the factors that affect the intention to use facial recognition services (FRS). The study adopts the driving factor perspective to examine how network externalities influence FRS use intention through the mediating role of satisfaction and the barrier factor perspective to analyze how perceived privacy risk affects FRS use intention through the mediating role of privacy cynicism.

The data collected from 478 Chinese FRS users are analyzed via partial least squares-based structural equation modeling (PLS-SEM).

The study produces the following results. (1) FRS use intention is motivated directly by the positive affective factor of satisfaction and the negative affective factor of privacy cynicism. (2) Satisfaction is affected by cognitive factors related to network externalities. Perceived complementarity and perceived compatibility, two indirect network externalities, positively affect satisfaction, whereas perceived critical mass, a direct network externality, does not significantly affect satisfaction. In addition, perceived privacy risk generates privacy cynicism. (3) Resistance to change positively moderates the relationship between privacy cynicism and intention to use FRS.

This study extends knowledge on people's use of FRS by exploring affect- and cognitive-based factors and finding that the affect-based factors (satisfaction and privacy cynicism) play fully mediating roles in the relationship between the cognitive-based factors and use intention. This study also expands the cognitive boundaries of FRS use by exploring the functional condition between affect-based factors and use intention, that is, the moderating role of resistance to use.

This study aims to investigate how message sidedness affects the impact of fake news posted on social media on consumers' emotional responses.

The study involves a face-tracking experiment in which 198 participants were exposed to different fake news messages concerning the COVID-19 vaccine. Specifically, participants were exposed to fake news using (1) a one-sided negative fake news message in which the message was entirely unfavorable and (2) a two-sided fake news message in which the negative message was mixed with favorable information. Noldus FaceReader 7, an automatic facial expression recognition system, was used to recognize participants' emotions as they read fake news. The authors sampled 17,450 observations of participants' emotional responses.

The results provide evidence of the significant influence of message sidedness on consumers' emotional valence and arousal. Specifically, two-sided fake news positively influences emotional valence, while one-sided fake news positively influences emotional arousal.

The current study demonstrates that research on fake news posted on social media may particularly benefit from insights regarding the potential but often overlooked importance of strategic design choices in fake news messages and their impact on consumers' emotional responses.

The purpose of this paper is to systematically analyze existing studies related to the adoption of smart technologies in cruise tourism services, particularly robots, artificial intelligence, service automation and virtual reality. More specifically, the authors intend to highlight the current state of research on this topic, present the findings within a conceptual framework and propose a research agenda.

The relevant literature was extracted using two major electronic databases, web of science (WoS) and Scopus. The authors identified 31 articles from high-quality journals and used a systematic review and the VOSviewer software to analyze them.

Since 2014, there has been an increase in the number of studies related to smart technologies in cruise tourism services. At first, researchers focused on Royal Caribbean’s robotic bartender arm, whereas other technologies such as digital signage, self-service options, facial recognition and virtual culinary experiences received less attention. However, the interest in exploring these last smart technologies has grown significantly since 2019. The adoption of RAISA in the cruise tourism service (ASCT) framework was proposed, identifying five major domains: cruise robotic technology, technology innovation, cruise passengers’ engagement behavior, cruise passengers’ technology readiness and privacy perception and knowledge expertise. These domains provide valuable guidance for future research in this field.

To the best of the authors’ knowledge, this is the first study to systematically analyze literature on the adoption of new technologies in cruise tourism services, specifically focusing on the major technologies available on cruise ships.

本文的主要目标是系统地分析与邮轮旅游服务中智能技术采用（RAISA）相关的现有研究, 特别是关于机器人、人工智能、服务自动化和虚拟现实等方面。具体而言, 我们意图突出此主题的当前研究现状, 将研究结果呈现在一个概念框架中, 并提出一个研究议程。

我们使用了两个主要的电子数据库 WoS 和 Scopus 提取相关文献。我们从高质量期刊中找到了 31 篇文章, 并采用系统综述和 VOSviewer 软件对它们进行了分析。

自 2014 年以来, 与邮轮旅游服务中智能技术相关的研究数量不断增加。最初, 研究人员着重关注皇家加勒比邮轮公司的机器人调酒师臂, 而其他技术, 如数字标识、自助选项、面部识别和虚拟烹饪体验等, 得到的关注较少。然而, 自 2019 年以来, 探索这些智能技术的兴趣显著增长。提出了 ASCT 框架, 识别了五个主要领域：邮轮机器人技术; 技术创新; 邮轮旅客参与行为; 邮轮旅客技术准备度; 以及隐私感知和专业知识。这些领域为该领域的未来研究提供了有价值的指导。

这是第一篇系统分析邮轮旅游服务中新技术采用文献的研究, 特别聚焦于邮轮上的主要技术。