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This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decision-making, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement. [ABSTRACT FROM AUTHOR]

Abstract This article is a review paper, which examines the place of gender in hospitality research and education and discusses the significance of hospitality for women's employment and entrepreneurship opportunities. The paper's main purpose, however, is to speculate on the future of hospitality gender research and women's employment prospects in the industry. It suggests that, despite the emergence of gender perspectives in hospitality research and the recent rise of popular feminism in response to high-profile sexual harassment cases and a well-publicised gender pay gap, gender still does not occupy mainstream territory in the field. The paper stresses the continued missed opportunities of neglecting female talent and calls on female and male leaders to shape a gender-just future in hospitality study and practice by mentoring and holding hierarchies to account. The paper identifies AI and robotics and sexual exploitation and harassment as two areas in which hospitality gender scholars could lead scholarship and policy debates on gendered human experiences. [ABSTRACT FROM AUTHOR]

The use of digital technologies in managing bowel conditions has been a topic of interest among healthcare practitioners. The objectives of this paper were to provide information about the types of digital technologies that have been used for bowel management and the context of the studies; identify the gaps and challenges in digital technologies for bowel management and propose new methods and techniques for the application of digital technologies in bowel management. A scoping review was conducted following the principles of Preferred Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A search was conducted on six academic databases. 1891 papers were retrieved from the initial search; however, 6 papers were included based on the inclusion and exclusion criteria. The findings suggest that published work focused mainly on a research context and with a narrow focus targeting sub-categories of bowel conditions and not implemented in the context of everyday use. The findings also illustrate the variety of early-stage developments focused on increasing support for severe bowel dysfunction, for example, through biofeedback to aid muscle control training, or the placement of artificial anal sphincters to increase rectal perception. However, technology to support bowel management for broader populations with less severe or variable symptoms appears limited. Future work would be to conduct empirical research in the application of advanced technologies such as on-organ sensors in managing bowel conditions. [ABSTRACT FROM AUTHOR]

Copyright of EurAmerica is the property of EurAmerica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Industrial maintenance plays a crucial role in manufacturing by significantly reducing machine failure time and minimizing costs, especially in the revolution of Industry 4.0. Consequently, researchers and industrial engineers have continuously focused on this area. Manufacturing companies possess extensive maintenance reports or logs containing valuable textual information, which offers a new avenue for exploring effective industrial maintenance methods. Natural Language Processing (NLP), a subfield of Artificial Intelligence, has demonstrated remarkable potential in analyzing maintenance reports and achieving promising results in various tasks. This paper presents a comprehensive systematic literature review that specifically concentrates on the applications of NLP approaches employed in the field of industrial maintenance. Additionally, this review analyzed the datasets utilized in previous studies and the evaluation measures adopted, which can serve as a valuable resource for other researchers seeking potential solutions in maintenance. Furthermore, the paper discusses the challenges encountered in applying NLP to industrial maintenance and outlines future research directions in this domain. By conducting this systematic literature review, we provide a comprehensive understanding of the current state of NLP applications in industrial maintenance, identify gaps in the existing literature, and guide future research efforts in leveraging NLP techniques for enhanced maintenance practices. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

Information and communication technologies have moved us from an industrialized society to an information society, driven and regulated by the information content. The content of the website, the automation and robotisation as technologies used by hospitality organizations have become necessary conditions, providing a warranty that the company will be recognized on one hand. Tourism and hospitality are industries that have been most significantly affected by the information age in which we live. Due to digitization, the tourist has the opportunity to obtain preliminary information about the given destination, to take a virtual tour and to create expectations about the future destination he/she plans to visit. The digital technologies also support and help the tourist in the preplanning, planning stage as well during the trip and after it. In other words, the digitalization of tourism facilitates and contributes to the quality service of tourists. The current paper addresses the essence of digitalization through application of the artificial intelligence in the example of tourism and hospitality. What is more, it discusses how the digitalization can impact on the tourism industry. Best practices for the application of digitization in the sphere of hospitality and tourism have been also presented in the current paper. [ABSTRACT FROM AUTHOR]

The paper aims to examine the evolving retail sector in recent years, specifically how digitalisation and technological innovations have transformed it. All actors have had to adapt to remain competitive. Notably, a new innovation in the retail sector, namely the checkout-free or cashierless store, emerged in 2018. Systematic literature is relied upon to achieve the study's objectives. The significance of this study lies in the use of multiple IT tools such as AI, cameras, sensors, and self-organising shelves to replace human intervention in the retail sector. Globally, several startup companies have developed this new unmanned solution, and Amazon Go stands out as one of the most well-known among them. The primary objective of this pioneering concept is to enhance efficiency by saving time and reducing queues. The aim is to enable customers to enter and exit the store with minimal human contact as quickly as possible. This paper presents the recent trend of the cashierless concept, its evolution, and proliferation. A systematic literature review and data analysis from the Crunchbase Database were conducted. The findings demonstrate that this recent concept is altering both consumers' purchasing behaviours and companies' business models. This paper provides novel perspectives and insights into the wider literature on cashierless concepts and smart retail in the context of digital business. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technology has been extensively researched in various fields, including the field of malware detection. AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles. The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI. Although various adversarial attack and defense methods are actively being studied, there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks. An AI model's robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall. Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks. In this paper, a Sophisticated Adversarial Robustness Score (SARS) is proposed for AI model robustness evaluation. SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process. This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators. Moreover, the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks. This paper proposed using SARS, calculated based on adversarial attacks, to identify data groups with robustness vulnerability and improve robustness through adversarial training. Through SARS, it is possible to evaluate the level of robustness, which can help developers identify areas for improvement. To validate the proposed method, experiments were conducted using a malware dataset. Through adversarial training, it was confirmed that SARS increased by 70.59%, and the recall reduction rate improved by 64.96%. Through SARS, it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types. In addition, it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of education, the pivotal axis around which transformation revolves is human-AI interaction. In this sense, this paper adopts a data mining and analytic approach to understand what the related literature tells us regarding the trends and patterns of generative AI research in educational praxis. Accordingly, this systematic exploration spotlights the following research themes: Interaction and communication with generative AI-powered chatbots; impact of the LLMs and generative AI on teaching and learning, conversational educational agents and their opportunities, challenges, and implications; leveraging Generative AI for enhancing social and cognitive learning processes; promoting AI literacy for unleashing future opportunities; harnessing Generative AI to expand academic capabilities, and lastly, augmenting educational experiences through human-AI interaction. Beyond the identified research themes and patterns, this paper argues that emotional intelligence, AI literacy, and prompt engineering are the trending research topics that require further exploration. Accordingly, it's in this praxis that emotional intelligence emerges as a pivotal attribute, as AI technologies often struggle to comprehend and respond to the nuanced emotional cues. Generative AI literacy then takes center stage, becoming an indispensable asset in an era permeated with AI technologies, equipping students with the tools to critically engage with AI systems, thereby ensuring they become active, discerning users of these powerful tools. Concurrently, prompt engineering, the art of crafting queries that yield precise and valuable responses from AI systems, empowers both educators and students to maximize the utility of AI-driven educational resources. [ABSTRACT FROM AUTHOR]

The expansive development of AI technologies challenges our conventional understanding of agency, which has traditionally been anchored in the human capacity for autonomous action and decisionmaking. As human-AI interactions become increasingly complex, the boundary between human and machine agency is continuously breached, prompting a reconsideration of the concept of agency as potentially no longer a human proprium. This paper offers preliminary reflections on the prerequisites and conditions for a post-anthropocentric theory of agency in the age of AI, beginning with a historical reconstruction and conceptual validation of the evolving notion of agency. [ABSTRACT FROM AUTHOR]

Copyright of RED - Revista de Educación a Distancia is the property of Universidad de Murcia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The safety of people's lives and property is the primary factor for the success of urban construction. Therefore, in order to better maintain social stability and harmony, relying on computer technology to effectively detect violence and to make decision support has important theoretical and practical significance. Aiming at the shortcomings of traditional manual design feature extraction methods, this paper proposes a super automatic violence detection method based on the combination of Deep Learning and trajectory in AI systems. Firstly, aiming at the problem of complex time and high accuracy of traditional manual feature extraction, a deep spatiotemporal violence detection method based on three-dimensional convolution and trajectory in AI systems is proposed. We improve the IDT algorithm to extract the target trajectory, and carry out three-dimensional convolution and pooling operation to calculate the deep-seated temporal and spatial information in the video frame, so as to realize peer-to-peer detection in AI systems. Secondly, in order to further improve the acquired deep-seated time and space information and utilization rate and achieve high detection rate, the feature fusion of double stream convolution and three-dimensional convolution is proposed, and the feature extraction of continuous video frame sequence is carried out by three-dimensional convolution neural network (C3D), which can effectively extract the fusion feature information of time and space in the classification layer, so as to obtain the final classification result. Finally, in order to solve the problem of too deep network level and slow convergence, dense convolution is introduced, which reduces the parameters of the network model and time complexity. Experimental results show that compared with other mainstream algorithms, this method is more effective and stable, and can be applied to the detection of violent abnormal behavior in video. Meanwhile, the method proposed in this paper has important theoretical value and practical significance for decision support of video surveillance system in AI systems. [ABSTRACT FROM AUTHOR]

This paper explores how AI-based game design affects the game development process and the role structure of game development teams, with a particular focus on the changes it brings during the early game project proposal phase. We present a comparison of game design proposal cases conducted by a game designer over a span of four years, showcasing how the utilization of artificial intelligence is re-empowering game designers. We envision a empowered game designer capable of market sensing, artwork creation, playable prototype development, subsequent game analysis, and operations all by oneself or with a very small team, a "super game designer". This paper illustrates how AI is ushering in a new era for game designers, and discusses the potential to stimulate innovation within the game industry by fostering creativity and imagination of game designers. [ABSTRACT FROM AUTHOR]

MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T 2 -weighted (T 2 w) images, reflect not only the biologically relevant T 2 of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T 2 mapping avoids these confounds because it quantitatively isolates the biological parameter of interest, thus representing a universal standardization across sites. However, efforts to incorporate quantitative mapping sequences into routine clinical practice have seen slow adoption. Here we show, for the first time, that the routine T 2 w complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints. While previous constrained reconstruction methods are unable to reconstruct a T 2 map based on this data, the expanding-constrained alternating minimization for parameter mapping (e-CAMP), which employs stepwise initialization, a linearized version of the exponential model and a phase conjugacy constraint, is demonstrated to provide useful quantitative maps directly from a vendor T 2 w single image data. This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T 2 w images taken on both phantom and brain. Because T 2 w scans are included in nearly every protocol, this approach could open the door to creating large, standardized datasets without requiring widespread changes in clinical protocols. [ABSTRACT FROM AUTHOR]

Copyright of Historia y Comunicación Social is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The second-leading cause of death for women is breast cancer. Consequently, a precise early diagnosis is essential. With the rapid development of artificial intelligence, computer-aided diagnosis can efficiently assist radiologists in diagnosing breast problems. Mammography images, breast thermal images, and breast ultrasound images are the three ways to diagnose breast cancer. The paper will discuss some recent developments in machine learning and deep learning in three different breast cancer diagnosis methods. The three components of conventional machine learning methods are image preprocessing, segmentation, feature extraction, and image classification. Deep learning includes convolutional neural networks, transfer learning, and other methods. Additionally, the benefits and drawbacks of different methods are thoroughly contrasted. Finally, we also provide a summary of the challenges and potential futures for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Filosofia UNISINOS is the property of Universidade do Vale do Rio dos Sinos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As artificial intelligence becomes pervasive, therapists might be left wondering about its implications for narrative practice. This paper explores an unexpected discovery about the power of artificial intelligence in re-imagining a story of injustice. Lucy (the therapist) and Miles (the client) used an AI image creator to assist in the externalisation of problems. Creating imagery representing Miles’s story of injustice and sharing the images with outsider witnesses became acts of justice and healing. The process of narrative justice using AI has implications for the practice of narrative therapy. This article finishes with an opportunity for readers and viewers to respond to Miles’s online gallery of imagery. [ABSTRACT FROM AUTHOR]

The advancements in deep neural network design have led to a significant increase in the possibilities and functioning of AI-assisted medical hardware. To make use of this progress in the field of mobile applications or even as wearable devices, a suitable hardware-software ecosystem must be identified to meet the high computation and memory demands of neural networks with minimal energy consumption. In this paper, we analyze an up-to-date heterogenous embedded platform employing a deep convolutional network for hand position recognition through electromyography signals. Our evaluation aimed to determine the optimization efforts required for the architecture to function as a human wearable device and identify the most suitable accelerators on the given platform for this task. [ABSTRACT FROM AUTHOR]

AI's impact on jobs varies by gender, with women facing higher replacement risks. This discrepancy is intensified by the digital divide, insufficient knowledge and skills, and a less open attitude towards AI among women, thereby limiting women's access to AIdriven opportunities. This paper's primary objective is to scrutinize women's selfassessment of digital competencies when utilizing AI in professional settings. Drawing from a survey conducted among Polish women, our findings reveal that the adoption of AI is linked to factors such as age, education, work experience, and location of residence. Women with higher levels of education residing in larger cities, as well as younger individuals with less work experience, are more likely to recognize the advantages of utilizing AI. Their concerns are less about the necessity for continuous development of digital skills, but rather about the potential impact of AI on their work-life balance and the possibility of a decrease in earnings, despite the anticipated improvements in productivity and time management efficiency. [ABSTRACT FROM AUTHOR]

Makkah has developed unique architectural vocabularies for centuries. The Roushan, Qalalib, Kharja, and Shwabir responded to topography, climate conditions, and socio-cultural aspects. However, demolishing heritage buildings to expand the Holy Mosque has led to the disappearance of many traditional architectural vocabularies. Then, the imposition of modern architectural elements by modernity increased a feeling of discontinuity with the past. In addition, globalization has branded Makkah as a city of skyscrapers and high-rise buildings. Megaprojects have emerged since Saudi Arabia announced its Vision 2030, necessitating creative architectural solutions. Although the literature on the architecture of Makkah introduced varied approaches to developing traditional architectural vocabularies, the results were far from anticipated. Most studies assumed that traditional elements should shape the future of architecture in Makkah. The field of architecture suggests analyzing physical forms in reality, while the philosophical attempt proposes exploring them in virtuality. Reality and virtuality must be dynamic in space as methods for innovation; this space should be detached from architecture to challenge physical norms and separated from philosophy to promote practicality. This paper maps the literature to evaluate the approaches that examine the architecture of Makkah. It suggests a third space, outside architecture, and philosophy, utilizing AI to generate Makkah's architectural elements. The aim is to employ technology, i.e., AI, not to document but to generate architectural vocabulary based on rising potentials and differentiations, which are the bases of innovation, hence establishing continuity with the past to shape the future. [ABSTRACT FROM AUTHOR]

Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI‐powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets. [ABSTRACT FROM AUTHOR]

The vast volume of currently available unstructured text data, such as research papers, news, and technical report data, shows great potential for ecological research. However, manual processing of such data is labour-intensive, posing a significant challenge. In this study, we aimed to assess the application of three state-of-the-art prompt-based large language models (LLMs), GPT-3.5, GPT-4, and LLaMA-2-70B, to automate the identification, interpretation, extraction, and structuring of relevant ecological information from unstructured textual sources. We focused on species distribution data from two sources: news outlets and research papers. We assessed the LLMs for four key tasks: classification of documents with species distribution data, identification of regions where species are recorded, generation of geographical coordinates for these regions, and supply of results in a structured format. GPT-4 consistently outperformed the other models, demonstrating a high capacity to interpret textual data and extract relevant information, with the percentage of correct outputs often exceeding 90% (average accuracy across tasks: 87–100%). Its performance also depended on the data source type and task, with better results achieved with news reports, in the identification of regions with species reports and presentation of structured output. Its predecessor, GPT-3.5, exhibited slightly lower accuracy across all tasks and data sources (average accuracy across tasks: 81–97%), whereas LLaMA-2-70B showed the worst performance (37–73%). These results demonstrate the potential benefit of integrating prompt-based LLMs into ecological data assimilation workflows as essential tools to efficiently process large volumes of textual data. • Large Language Models (LLMs) are tested for extracting biodiversity data from text. • Tasks include identification of relevant sources, geocoding, and data structuring. • LLMs, especially GPT-4, show over 90% accuracy on multiple tasks. • LLMs promise effective data assimilation from unstructured text in ecology. [ABSTRACT FROM AUTHOR]

The launch of ChatGPT created a tidal wave of interest, rapid uptake of the app and a surge of capital investment. It quickly became clear that what was now being called 'generative AI' would have multiple impacts and implications across the board. The paper begins by outlining aspects of the context from which these innovations arose. They include Neoliberalism and the 'playbook' of careless, 'non-legal' innovation that occurred during the early rise of social media platforms. The paper also critiques Silicon Valley, both for its one-sided culture and its obsession with marketing. It specifically draws attention to the characteristics and limitations of 'technoscientific' outlooks and the way they conceal, or edit out, key aspects of human existence. A summary of the attributes of technoscience suggests a number of possible responses. Behind these, however, is a widely ignored dilemma - the proposition that a continuation of further stages of high-tech innovation benefits the human enterprise. Yet many high-tech developments are unambiguously sociopathic. They project extreme danger and dysfunction out onto innocent people and unprepared social environments. The paper summarises recent attempts to come to terms with a radically transformed, technology saturated environment and concludes with suggestions for enhancing human agency. [ABSTRACT FROM AUTHOR]

The key target of Distributed Denial-of-Service (DDoS) attacks is to interrupt and suspend any available online services either executed for professional or personal gains. These attacks originate from the fast advancement in the number of insecure technologies. The attacks are caused due to the easy access to internet and advent of technology resulting to exponential growth of traffic volumes. DDoS attack remains most leading security risks to provisioning services. Also, the current embraced security mechanism for defense lacks flexibility and adequate resources to combat these attacks. Hence, there is need to embrace various other critical resources, where they can share the problem of mitigation. In addition, emerging technologies for instance smart contracts and blockchain offers for the sharing of these potential attacks information in an entirely automated and distributed manner. This paper recommends for a blockchain design which combines smart contracts and Machine Learning (ML) technologies, by presenting new ideal opportunities towards efficient DDoS mitigation solutions in variety of cooperative domains. Furthermore, the key advantage and benefits of this structure is deployment of still existing distributed and public infrastructure to blacklisted IP address or even advertise white, and the application of such an infrastructure with further defense mechanisms to current attacks of DDoS, deprived of considering distribution mechanisms or specialized registries, which facilitates the implementation of procedures across diverse domains. This paper further presents the demonstration and implementation features of this blockchain structure, discussion and study findings over these smart contracts and ML technologies. The study further concludes by recommending use of smart contract in collaborative block-chain design with ML for mitigating future attack of DDoS. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) are making headlines in medical research, especially in drug discovery, digital imaging, disease diagnostics, genetic testing, and optimal care pathway (personalized care). However, the potential uses and benefits of AI/ML applications need to be distinguished from hype. In the 2022 American Statistical Association Biopharmaceutical Section Regulatory-Industry Statistical Workshop, we convened a panel of experts from FDA and industry to talk about the challenges of successfully applying AI/ML in precision medicine and how to overcome those challenges. This paper provides a summary and expansion on the topics discussed in the panel: the application of AI/ML, bias, and data quality. [ABSTRACT FROM AUTHOR]

This paper presents a method for optimizing wireless networks by adjusting cell parameters that affect both the performance of the cell being optimized and the surrounding cells. The method uses multiple reinforcement learning agents that share a common policy and take into account information from neighboring cells to determine the state and reward. In order to avoid impairing network performance during the initial stages of learning, agents are pre-trained in an earlier phase of offline learning. During this phase, an initial policy is obtained using feedback from a static network simulator and considering a wide variety of scenarios. Finally, agents can intelligently tune the cell parameters of a test network by suggesting small incremental changes, slowly guiding the network toward an optimal configuration. The agents propose optimal changes using the experience gained with the simulator in the pre-training phase, but they can also continue to learn from current network readings after each change. The results show how the proposed approach significantly improves the performance gains already provided by expert system-based methods when applied to remote antenna tilt optimization. The significant gains of this approach have truly been observed when compared with a similar method in which the state and reward do not incorporate information from neighboring cells. [ABSTRACT FROM AUTHOR]

Integrating Artificial Intelligence (AI) technologies implied significant growth in various domains. Furthermore, many companies integrate AI technologies into their products to enhance the quality of their services. Chatbots are among the AI technologies widely used in several areas, especially E-learning. Chatbots support learners in their learning processes by helping them to find the appropriate answers to their questions. We aim to conduct a systematic literature review (SLR) to uncover the use of AI chatbots to offload teachers from repetitive and massive tasks. This article surveys the literature over the period 2016–2022 on the use of AI chatbots in the E-learning domain as they automatically answer learners’ questions. Thus, we identify, collect, and synthesize multiple research studies on the application of AI chatbots in the E-learning field. Based on the renowned frameworks, PRISMA and PICO, we have succeeded in (1) Developing our research questions and (2) Automatically implementing a solution based on Python language to analyze selected papers, highlighting research gaps, and opening new windows to guide our future works. Our study shows that chatbots effectively interact with learners. However, there are some drawbacks: (1) Educational chatbots are still limited in their local Knowledge Base (KB), which makes them unable to answer students’ questions correctly. Thus, Chatbot’s KB needs to be extended through external sources, enabling the chatbot to update its KB over time, making it rich and saving time. (2) Lack of reliable external sources to enrich the chatbot’s KB and make it up to date. (3) Lack of educational chatbots with smart services such as speech recognition and sentiment analysis to boost the user experience and make learning easier. In our SLR, we discuss these limitations and propose some solutions to fill the gap. [ABSTRACT FROM AUTHOR]

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. [ABSTRACT FROM AUTHOR]

The conceptualization of work and careers has evolved with technological advancements. From the steam engine to the internet, and now to digital technologies like AI and robotics, each era has redefined employment. These innovations offer safer, more meaningful work and broaden access. However, they also bring challenges such as the need for new skills and constant adaptation. This special issue includes nine papers exploring themes like lifelong learning, access to work, human agency, and ethical dimensions. The research highlights the need for continuous skill development, equitable access, and informed policy-making to harness the benefits of technology while mitigating its risks. • The special issue includes diverse and imaginative research on topics like exoskeletons, AI, lifelong learning, and surveillance technology. • The papers emphasize the potential of technology to expand work opportunities. • The research underscores the importance of informing ethical and policy discussions with social science insights. [ABSTRACT FROM AUTHOR]

This study explores the impact and potential of generative AI, specifically Open AI's ChatGPT, on the transformation of higher education, particularly in the domain of business education. Adopting a qualitative mixed-methods research strategy, this study employs a three-phased methodology consisting of a systematic literature review, semi-structured interviews with academics, and text analysis of opinion posts on professional platforms. This comprehensive approach lends a holistic perspective, integrating insights from both national and global contexts, thereby enhancing the study's methodological rigor and ensuring a nuanced understanding of AI's implications in the educational landscape. ChatGPT, with its personalized and interactive features, enhances pedagogical innovation, academic integrity, and experiential engagements in the teaching-learning-assessment (TLA) process, as revealed by these methodologies' central themes. However, potential obstacles such as plagiarism and diminished development of interpersonal skills were also identified. The findings have significant ramifications for academia and the evolution of AI in education, casting light on the practical benefits and obstacles associated with AI implementation. The study identifies unexplored research avenues to encourage further investigation in this field. This paper contributes to the emerging discourse on AI-enhanced education in business institutions by providing a comprehensive and multifaceted examination of the impact of generative AI on higher education. • Examined the potential of generative AI, specifically Open AI's ChatGPT, in business education. • three-phased methodology is applied to triangulate the findings. • findings have significant ramifications for academia and the evolution of AI in education. • paper contributes to the emerging discourse on AI-enhanced education in business management. [ABSTRACT FROM AUTHOR]

This paper suggests a new business model based on modern technology for retail. In the age of digitalization, stationary retail is losing market shares to online retail. Therefore, there is an obvious need for change in businesses. The concept developed in this paper combines the strengths of online and stationary retail to benefit stationary retail. In the approach taken in this paper, the basis for change is modern technology. Finding innovative ways to use technologies like NFC, AI, and robotics is regarded as the key factor to sustainable success. As the implementation of modern technologies entails a particular investment, the customers’ opinion on structural changes like these has been included in the consideration. Therefore, a survey has been conducted to find out which level of innovation current customers are willing to accept thus emphasizing the need for certain changes and dissuading specific others. The result of this paper is the modern showroom concept which takes the customers’ opinion into account while implementing the right amount of technology that should pave the way to a sustainable future for stationary retail. [ABSTRACT FROM AUTHOR]

The landscape of libraries is rapidly changing with technological advancement, and the integration of Artificial Intelligence (AI) and the National Education Policy 2020 (NEP 2020) have created an unprecedented opportunity for libraries to revolutionize their services. This paper explores the role of AI and NEP 2020 in transforming libraries and how they can improve library services in terms of personalized learning, content curation, digital preservation, resource optimization, and learning analytics. The paper provides an overview of AI and NEP 2020 and its importance for libraries. AI technologies are existing in libraries to automate tasks, provide personalized learning experiences, and optimize resource management. Furthermore, NEP 2020 provides a framework for the integration of technology in education, and libraries can play a key role in achieving the policy's goals through the implementation of AI technologies. The paper also examines case studies of libraries that have implemented AI technologies to enhance their services, such as the British Library, which has used AI to curate their digital collections and the University of Liverpool Library, which has used AI to optimize the discovery of resources. These examples demonstrate the potential for AI to revolutionize library services and improve user experiences. The paper also highlights how AI can support NEP 2020 goals, such as personalized learning, digital preservation, and learning analytics, and provides examples of libraries that have used AI to implement these recommendations, such as the Indian Institute of Technology Ropar Library, which has used AI to provide personalized reading recommendations to users. In conclusion, this paper emphasizes the need for libraries to embrace AI and NEP 2020 initiatives to remain relevant in the rapidly evolving digital age. The integration of AI technologies can enhance library services, improve user experiences, and support the goals of NEP 2020. Libraries that adopt AI technologies can position themselves as leaders in the information field and provide valuable services to their users. [ABSTRACT FROM AUTHOR]

Generating fake news has become a prevalent issue in the digital age, with profound consequences on society, politics, and media. Due to an exponential increase in the use of unverified information, spread on social media by users with different backgrounds, a significant task facing natural language processing is real-time recognition of generated fake news. The objective of this paper is to investigate the feasibility of utilizing artificial intelligence (AI) for the automatic generation of fake news that possesses a sufficiently high level of quality to serve as a synthetic corpus. These automatically generated texts are subsequently used to improve fake news detectors, evaluated on different publicly available datasets fake news datasets, but also against a novel fake news corpus, especially developed for this research. [ABSTRACT FROM AUTHOR]

The primary purpose of the article is to identify strategic risks from two points of view: a) general, which fundaments are the nine pillars of Industry 4.0, and b) specific - which is the context of AI. This analysis is additionally extended to include the category of scalability, which refers to the system's ability to accommodate an increasing number of elements. The evaluation of risks should be followed by the identification of their types, as well as the risk catalog, which was also presented in the paper. [ABSTRACT FROM AUTHOR]

Novel usages of brain stimulation combined with artificially intelligent (AI) systems promise to address a large range of diseases. These new conjoined technologies, such as brain-computer interfaces (BCI), are increasingly used in experimental and clinical settings to predict and alleviate symptoms of various neurological and psychiatric disorders. Due to their reliance on AI algorithms for feature extraction and classification, these BCI systems enable a novel, unprecedented, and direct connection between human cognition and artificial information processing. In this paper, we present the results of a study that investigates the phenomenology of human-machine symbiosis during a first-in-human experimental BCI trial designed to predict epileptic seizures. We employed qualitative semi-structured interviews to collect user experience data from a participant over a six-years period. We report on a clinical case where a specific embodied phenomenology emerged: namely, after BCI implantation, the patient reported experiences of increased agential capacity and continuity; and after device explantation, the patient reported persistent traumatic harms linked to agential discontinuity. To our knowledge, this is the first reported clinical case of a patient experiencing persistent agential discontinuity due to BCI explantation and potential evidence of an infringement on patient right, where the implanted person was robbed of her de novo agential capacities when the device was removed. • Some brain-computer interfaces predict seizures by monitoring neural data, some advise users based on specific neural data thresholds. • In the ethical literature, advisory devices are perceived as encompassing a stronger degree of autonomy because they keep users in the decisional loop. • We report on a clinical case where a BCI implantation yielded enhanced agential capacity and continuity, while device explantation caused ongoing traumatic harm and agential discontinuity • To our knowledge, the first reported case of a patient experiencing ongoing agential discontinuity due to BCI removal, raising concerns about potential patient rights violation. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) empowers Business Intelligence (BI) by enhancing data analysis, automating data integration, enabling predictive analytics, providing natural language interfaces, detecting anomalies, delivering personalized recommendations, and enhancing data visualization. AI-driven systems process large datasets, identify patterns, and extract valuable insights. They automate data integration, cleansing, and transformation. Predictive analytics forecasts future trends, while natural language interfaces make data access easier. This paper gives an overview of the perspectives of intelligent empowerment of BI by AI. It focuses on presenting the ways AI can complement or even enhance the different processes of BI systems used for driving decision-making. Therefore, contextualizing the use of AI in business empowerment, we analyzed the perspectives of making intelligent empowerment of BI based on AI. We further exhibit a flowchart showing how AI Chatbots can assist BI processes to achieve the highest level of contextual autonomy and creative capability with continual improvement. [ABSTRACT FROM AUTHOR]

Trust management has become a fundamental requirement for Social Internet of Things (SIoT) to enable a trustworthy social network of smart objects necessary for enhancing the security and reliability of cyber-physical systems. To increase the credibility scores in trust management, AI (Artificial Intelligence) has been adopted. However, the current need for digital acceleration has brought ethical concerns related to the smartness and social consciousness of autonomous objects, which leads to a question whether AI-based trust management is ready to deal with these concerns. In this paper, we consider 11 ethical dimensions within the context of trust management in SIoT. Then, we examine the existing AI-based trust models in the context of SIoT and its related application domains to assess their maturity in terms of the 11 ethical dimensions. The evaluation results show how trust management can be improved by AI ethical principles in vehicular networks and underwater acoustic sensor networks. [ABSTRACT FROM AUTHOR]

In the digital era, human and thing behavioral patterns have been merged, which leads to the need for trust management to secure the relationship among people and things (e.g., driverless cars). Due to the dynamism and complexity of digital environments, trust management depends largely on indirect trust to support its reasoning by building the reputation of trustees based on recommendations reflected in the feedback of sentiment and non-sentiment objects. However, different biases are still affecting the accuracy of indirect trust that reflects a collective trustworthiness belief or societal stereotypes. This work focuses on enabling indirect trust management by leveraging deep learning in combination with synthetic data for bias management. Specifically, this paper proposes a reputation model to support decision-making in trust management by minimizing bias in indirect trust information and fostering fairly the relationship among sentiment and non-sentiment objects. Our experimental results show that the synthetic data can significantly improve the classification accuracy in trust management. [ABSTRACT FROM AUTHOR]

The technological revolutions greatly impact current and future Classrooms. These advances in technology include the revolution of artificial intelligence, virtual reality, and super highspeed internet. In the coming next generation (6G), the data rate will be very sufficient for live scenes in virtual reality applications such as telepresence and teleoperation. This paper review and discuss next-generation technologies in AI, VR, and communication. Moreover, we examine the motivation for establishing an Advanced Technology based Smart and Immersive Classroom (SIC) and the advantages of its availability to the virtual society. Recent advances in computer and communications technology have delivered capabilities to tomorrow's SIC. Advances in virtual reality and real-time streaming on the internet have created a revolution in curricula and classrooms. Index Terms--Virtual Reality in education; 6G for education, Artificial Intelligence in education, Immersive Classroom. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly transforming digital marketing practices. While the extant literature extensively covers AI applications that generally benefit businesses and customers, there is scant research on AI deployments that exacerbate problems for financially vulnerable customers. These customers have limited access to financial systems, services or technologies. To rectify this research deficit, this paper describes the challenges confronting businesses as they attempt to integrate AI into the digital marketing of their financial services. Ultimately, Al-enabled digital marketing is not as simple as collecting big data and using analytical algorithms; the technology may not always help businesses target their customers more effectively. This paper examines the relationships between AI, digital marketing, and financial services in relation to vulnerable customers, highlighting key implications in the collection, processing, and delivery of information, as well as the importance of human connection for optimal customer experience and engagement with financial services providers. Understanding ethical implications, as well as data and modelling challenges, is necessary for the successful deployment of AI. This study provides a theoretical framework to financial services providers, AI developers, marketers, policymakers, and academics, aiding the understanding of the precarious conditions facing vulnerable customers, and the ways in which they can more effectively be reached. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is not new, but recent years have seen a growing concern about the technology's political, economic and social impact, including debates about its governance. This paper describes how an analysis of the technology's governance should build on the understanding of AI as the creation of artificial agents, and that the challenge that governance seeks to address is best understood as one of material agency. It describes how relevant rules can be systematically analyzed across different applications by considering the fundamental properties of artificial agents. The paper concludes by describing how the framework can be applied for further governance studies, and as a means to bridge insights across social science and technical perspectives on AI. [ABSTRACT FROM AUTHOR]

Manufacturing evolved substantially over the years and with it the inherent complexity increased well. Currently organizations are transitioning towards smart manufacturing and implementation of industry 4.0 paradigms. Artificial intelligence and machine learning can help organizations of any size on this journey. The physics of manufacturing processes have been extensively studied and documented as relationships and equations. Machine learning algorithms rely on quality data to enable inferences and smart actions. At the intersection of these two methods lies hybrid analytics which captures the knowledge in the defined physics-based relationships and uses data-driven methods to fill the gaps that the physics-based model may have. The goal of this paper is to identify approaches and use cases of hybrid modeling wherein the term hybrid is used to indicate the combination of a physics- or first-principle-based modeling component and a data-driven modeling component. VOSViewer, Voyant Tools, and Microsoft Excel have been used to understand the relationships between various attributes associated with the articles on hybrid modeling. The papers cited the most, the organizations/regions that funded the research in this domain, the contents of recent papers, and what categories and keywords are frequently used are discussed. This work is the basis for an extended literature review to understand in detail the algorithms, use cases, and implementation issues of hybrid analytics. This bibliometric analysis uses the state of the art references and procedures mentioned in the documentation of VOSViewer and Voyant Tools. [ABSTRACT FROM AUTHOR]