Search

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]

• In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. • We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. • We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. • Infrastructure considerations and population modeling in two European countries will be described. • This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. [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]

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]

Cameras are rapidly becoming the choice for on-board sensors towards space rendezvous due to their small form factor and inexpensive power, mass, and volume costs. When it comes to docking, however, they typically serve a secondary role, whereas the main work is done by active sensors such as lidar. This paper documents the development of a proposed AI-based (artificial intelligence) navigation algorithm intending to mature the use of on-board visible wavelength cameras as a main sensor for docking and on-orbit servicing (OOS), reducing the dependency on lidar and greatly reducing costs. Specifically, the use of AI enables the expansion of the relative navigation solution towards multiple classes of scenarios, e.g., in terms of targets or illumination conditions, which would otherwise have to be crafted on a case-by-case manner using classical image processing methods. Multiple convolutional neural network (CNN) backbone architectures are benchmarked on synthetically generated data of docking manoeuvres with the International Space Station (ISS), achieving position and attitude estimates close to 1 % range-normalised and 1 deg, respectively, an established rule of thumb for the navigation measurement accuracy during final approach. The integration of the solution with a physical prototype of the refuelling mechanism is validated in laboratory using a robotic arm to simulate a berthing procedure. • A novel AI-based solution for spacecraft docking and refuelling is presented. • First demonstration of deep learning for vision-based pose estimation in docking. • Various CNN backbones are benchmarked on photorealistic ISS refuelling datasets. • ResNet-101 reports average 1.19% error in position, 0.29 ∘ in attitude per trajectory. • Prototypes of the developed mechanism are validated in lab with 7-DOF manipulator. [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]

Data-driven method has been widely used in Fluid Catalytic Cracking (FCC) process modeling. However, due to the complexity of chemical process both in time and spatial domain, how to reflect the time and spatial characteristics of FCC units and build corresponding model is important to construct a better model for the gasoline yield prediction. In this paper, a special neural network structure was developed to deal with the input variables with different time scales considering the collection characteristics of various variables, as well as the time continuity of large-scale process manufacturing units, LSTMs with different time scales are stacked to extract temporal and spatial features to help capture the relationship between influencing factors and product yield. The characteristics of FCC process are also fully reflected in data processing and building model. It is demonstrated from the conclusions that the new model developed in this paper performs better than the traditional LSTM networks, which will be of great help to the intelligent upgrading of the FCC process. • A special network structure is built to deal with the difference of data recording frequency. • A new neural network model is proposed to predict the product yield of FCCU,. • Analyzing the relationship between the product yield and the features extracted by our new neural network. [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]

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]

This paper introduces a novel methodology, the Neural Network framework for the Discrete Element Method (NN4DEM), as part of a broader initiative to harness specialised AI hardware and software environments, marking a transition from traditional computational physics programming approaches. NN4DEM enables GPU-parallelised computations by mapping particle data (coordinates and velocities) onto uniform grids as solution fields and computing contact forces by applying mathematical operations that can be found in convolutional neural networks (CNN). Essentially, this framework transforms a DEM problem into a series of layered "images" composed of pixels, using stencil operations to compute the DEM physics, which is inherently local. The method revolves around custom kernels, with operations prescribed by the laws of physics for contact detection and interaction. Therefore, unlike conventional AI methods, it eliminates the need for training data to determine network weights. NN4DEM utilises libraries such as PyTorch for relatively easier programmability and platform interoperability. This paper presents the theoretical foundations, implementation and validation of NN4DEM through hopper test benchmarks. An analysis of the results from random packing cases highlights the ability of NN4DEM to scale to 3D models with millions of particles. The paper concludes with potential research directions, including further integration with other physics-based models and applications across various multidisciplinary fields. [Display omitted] • A novel integration: neural networks with DEM principles. • Physical laws dictate network weights. • Neural Network solver benefits without training data. • GPU parallelisation with PyTorch. • Random packing demonstrates computational capacity. [ABSTRACT FROM AUTHOR]

The growing integration of AI in agriculture necessitates access to diverse data sets for training artificial intelligence (AI) systems. Manufacturers often face challenges due to a perceived shortage of valid data sets, highlighting the need for centralized platforms offering high-quality data. While global AI regulations are yet to be established, existing laws govern data collection, creation, labeling, and processing for AI training in various legal systems. The absence of explicit AI laws prompts a call for platforms supplying data to adhere to relevant regulations, especially in areas where AI controls devices interacting with humans, such as collision warning systems in yard tractors and robots. Compliance is crucial, given the complexity of rules governing data sets featuring people, animals, and buildings. The paper focuses on how to check the compliant data sets in Germany's legal context, focusing on situations in farm yards. People are often visible on such data sets; thus, the data set must preserve the right to informational self-determination and one's image, which is prevalent in many legal systems. Images portraying people typically qualify as personal data, requiring careful anonymization to address privacy concerns. The paper uses artificial neural networks (ANN) to remove images that contradict people's rights on one's image; thus, data sets that comply with data protection laws can be created. It also addresses the lesser-explored issue of the right to one's image in German law. Though AI training may not be impeded, dissemination via digital platforms must adhere to legal boundaries, even if metadata is absent. The paper suggests an AI solution to identify images within legal limits, detect violations, and modify them to create a legally compliant data set respecting the right to one's image. • Discussing legal conform data sets for the agricultural application domain. • Providing a data set of 2694 images covering the categories: – class (I) not legally compliant and – class (II) legally compliant on the right to one's image; cf. Fig. 1. • Identifying the gap in ANN data sets and models for law compliance checks. • Train and benchmark ANN models for checking law compliance – on the right to one's image; – cf. Tab. 1, Tab. 2, and Fig. 3. • Promoting the importance of AI-based law compliance checks for – detecting violations against the law and – developing compliant AI systems in agriculture. [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]

Healthcare organizations have realized that Artificial intelligence (AI) can provide a competitive edge through personalized patient experiences, improved patient outcomes, early diagnosis, augmented clinician capabilities, enhanced operational efficiencies, or improved medical service accessibility. However, deploying AI-driven tools in the healthcare ecosystem could be challenging. This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale. As AI continues to make strides in healthcare, its integration presents various challenges, including production timelines, trust generation, privacy concerns, algorithmic biases, and data scarcity. The paper highlights that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools. Healthcare organizations should re-evaluate root problems such as misaligned financial incentives (e.g., fee-for-service models), dysfunctional medical workflows (e.g., high rates of patient readmissions), poor care coordination between different providers, fragmented electronic health records systems, and inadequate patient education and engagement models in tandem with AI adoption. This study also explores the need for a cultural shift in viewing AI not as a threat but as an enabler that can enhance healthcare delivery and create new employment opportunities while emphasizing the importance of addressing underlying operational issues. The necessity of investments beyond finance is discussed, emphasizing the importance of human capital, continuous learning, and a supportive environment for AI integration. The paper also highlights the crucial role of clear regulations in building trust, ensuring safety, and guiding the ethical use of AI, calling for coherent frameworks addressing transparency, model accuracy, data quality control, liability, and ethics. Furthermore, this paper underscores the importance of advancing AI literacy within academia to prepare future healthcare professionals for an AI-driven landscape. Through careful navigation and proactive measures addressing these challenges, the healthcare community can harness AI's transformative power responsibly and effectively, revolutionizing healthcare delivery and patient care. The paper concludes with a vision and strategic suggestions for the future of healthcare with AI, emphasizing thoughtful, responsible, and innovative engagement as the pathway to realizing its full potential to unlock immense benefits for healthcare organizations, physicians, nurses, and patients while proactively mitigating risks. • This study categorizes AI applications in healthcare and analyzes their deployment challenges at scale in medical practices. • The paper emphasizes a cultural shift to view AI as a healthcare delivery enhancer and job creator, not a threat. • To integrate AI, investments in finance and human capital, continuous learning, and a supportive environment are needed. • Clear regulatory frameworks should be developed to build trust, ensure safety, and guide the ethical use of AI in healthcare. • The study provides strategic suggestions for responsible AI use in healthcare to maximize benefits and minimize risks. [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 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]

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]

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]

The application of autonomous technology in food supply chains gives rise to a number of ethical considerations associated with the interaction between human and technology, human-technology-plant and human-technology-animal. These considerations and their implications influence technology design, the ways in which technology is applied, how the technology changes food supply chain practices, decision-making and the associated ethical aspects and outcomes. Using the concept of reflexive governance, this paper has critiqued existing reflective food-related ethical assessment tools and proposed the structural elements required for reflexive governance architectures which address both the sharing of data, and the use of artificial intelligence (AI) and machine learning in food supply chains. Considering the ethical implications of using autonomous technology in real life contexts is challenging. The current approach, focusing on discrete ethical elements in isolation e.g., ethical aspects or outcomes, normative standards or ethically orientated compliance-based business strategies, is not sufficient in itself. Alternatively, the application of more holistic, reflexive governance architectures can inform consideration of ethical aspects, potential ethical outcomes, in particular how they are interlinked and/or interdependent, and the need for mitigation at all lifecycle stages of technology and food product conceptualisation, design, realisation and adoption in the food supply chain. This research is of interest to those who are undertaking ethical deliberation on data sharing, and the use of AI and machine learning in food supply chains. • Autonomous technology can support decision-making in food supply chains. • The use of autonomous technology has ethical implications. • Ethical considerations focus on both aspects and outcomes of technology use. • Existing governance approaches are limited by being reflective, normative, rule-based. • Reflexive governance architectures for technology 'concept to realisation' are essential. [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]

This paper introduces a new research area called Explainable Robotics, which studies explainability in the context of human-robot interactions. The focus is on developing novel computational models, methods and algorithms for generating explanations that allow robots to operate at different levels of autonomy and communicate with humans in a trustworthy and human-friendly way. Individuals may need explanations during human-robot interactions for different reasons, which depend heavily on the context and human users involved. Therefore, the research challenge is identifying what needs to be explained at each level of autonomy and how these issues should be explained to different individuals. The paper presents the case for Explainable Robotics using a scenario involving the provision of medical health care to elderly patients with dementia with the help of technology. The paper highlights the main research challenges of Explainable Robotics. The first challenge is the need for new algorithms for generating explanations that use past experiences, analogies and real-time data to adapt to particular audiences and purposes. The second research challenge is developing novel computational models of situational and learned trust and new algorithms for the real-time sensing of trust. Finally, more research is needed to understand whether trust can be used as a control variable in Explainable Robotics. [ABSTRACT FROM AUTHOR]

AI is transforming healthcare system with many innovations in diagnosis, drug research and advancement in medical treatments. But there are several concerns and dilemmas related to data misuse, AI efficiency for critical diagnostic services, users' resistance, investment costs, funding issues, and so on that have been raised by many previous studies on the effective integration of AI in clinical settings. Using paradox theory in the organisational settings, the present study discusses several technological paradoxes associated with the adoption of AI in healthcare. In this regard, the study examines the views of diverse medical practitioners about using AI services for several medical needs. The study analyses the efficacy and limitations of AI services which develop several ethical dilemmas in the mind of medical practitioners and also suggest a few strategies for the adoption. Using grounded theory approach, the study collected views of 62 medical practitioners on these dimensions. The primary drivers to the adoption identified in the present study are: ease of use, automation efficacy, diagnostic accuracy, and cost efficiency. A lack of training and education, cultural and religious considerations, privacy issues and work insecurity are some of the concerns highlighted by the medical staffs. The study inferred a few paradoxes or ethical dilemmas of practitioners which need attentions. The study contributes to the existing literature on paradox theory and AI, and identifies a few under-discussed areas, drivers, and barriers of AI services are highlighted in the paper, which may lead to ethical concerns and steer AI adoption in healthcare. • This paper explores several technological paradoxes related to AI deployment in healthcare using paradox theory. • The paper examines several drivers and barriers associated with AI services, which create ethical dilemma. • The study also suggests a few strategies for the smooth adoption of AI services in healthcare. • The study surveyed 62 medical practitioners using grounded theory. • The study examines the diverse views of medical practitioners about using AI services for several medical needs. [ABSTRACT FROM AUTHOR]

This paper investigates the specific properties of Explainable Artificial Intelligence (xAI), particularly when implemented in AI/ML models across high-stakes sectors, in this case cybersecurity. The authors execute a comprehensive systematic review of xAI properties, various evaluation metrics, and existing frameworks to assess their utility and relevance. Subsequently, the experimental sections evaluate selected xAI techniques against these metrics, delivering key insights into their practical utility and effectiveness. The findings highlight that the proliferation of metrics enhances the understanding of xAI systems but simultaneously exposes challenges such as metric duplication, inefficacy, and confusion. These issues underscore the pressing need for standardized evaluation frameworks to streamline their application and strengthen their effectiveness, thereby improving the overall utility of xAI in critical domains. • Bridging xAI theory and practice. • Systematic review of xAI metrics and frameworks. • Experimental evaluation of various xAI explanations. • The results show many metrics are ine2ective. • The abundance of metrics has pros and cons. [ABSTRACT FROM AUTHOR]

• The use of facial expression recognition (FER) in sound evaluation in urban open space. • Clustering the visual environments for FER evaluation. • The interactive effect of visual and sound environments. • Data processing methods of dynamic FER data. The quality of soundscape in urban open spaces is important to wellbeing. An AI-based facial expression recognition method has the potential to be a useful tool for predicting urban sound perception since it can correctly and in real time gather human perception data from facial photos. The purpose of this study is to provide classification criteria, time dimension data processing and indicator screening methods for facial expression recognition-based soundscape prediction. The study captured typical urban audiovisual environments and recorded facial expression data from subjects in a laboratory setting. The results of the study showed that there were significant differences in the effects of the three types of visual environments and the three types of acoustic environments on the subjects' facial expressions, and that the effect of acoustic environments on visual perception (η2 0.067 on average) was generally greater than the effect of visual environments on acoustic perception (η2 0.035 on average). A linear regression model for predicting acoustic perception was developed based on audiovisual interactions, dynamic change patterns, and indicator selection. The results of this paper help to realize another perceptual dimension in the establishment of smart city and help achieve sustainability. [ABSTRACT FROM AUTHOR]

Radiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. A Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. 86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. Responses indicate that AI will have a strong impact on reporting radiographers' decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. This survey clarifies UK reporting radiographers' perceptions of AI, used for image interpretation, highlighting key issues with AI integration. [Display omitted] [ABSTRACT FROM AUTHOR]

In the last couple of years, videoconferencing platforms have become very popular and have captured the attention of researchers and the public, particularly due to the Covid-19 pandemic. Immediately after the pandemic started in the spring of 2020, demand for video conferencing apps has grown exponentially. This demand, and the emergence of new needs, have forced manufacturers to adapt to the new context by improving the services offered and adding new features to existing applications. Many new video conferencing applications have also emerged with this demand. This paper presents a series of statistics on the evolution during the pandemic and the current status of the main video conferencing systems. The different ways in which these systems have integrated artificial intelligence technologies to address different identified problems and user needs are also presented. [ABSTRACT FROM AUTHOR]

Hospitals and General Practitioner (GP) surgeries within National Health Services (NHS), collect patient information on a routine basis to create personal health records such as family medical history, chronic diseases, medications and dosing. The collected information could be used to build and model various machine learning algorithms, to simplify the task of those working within the NHS. However, such Electronic Health Records are not made publicly available due to privacy concerns. In our paper, we propose a privacy-preserving Generative Adversarial Network (pGAN), which can generate synthetic data of high quality, while preserving the privacy and statistical properties of the source data. pGAN is evaluated on two distinct datasets, one posing as a Classification task, and the other as a Regression task. Privacy score of generated data is calculated using the Nearest Neighbour Adversarial Accuracy. Cosine similarity scores of synthetic data from our proposed model indicate that the data generated is similar in nature, but not identical. Additionally, our proposed model was able to preserve privacy while maintaining high utility. Machine learning models trained on both synthetic data and original data have achieved accuracies of 74.3% and 74.5% respectively on the classification dataset; while they have attained an R2-Score of 0.84 and 0.85 on synthetic and original data of the regression task respectively. Our results, therefore, indicate that synthetic data from the proposed model could replace the use of original data for machine learning while preserving privacy. [ABSTRACT FROM AUTHOR]

The easy accessibility and simplicity of Short Message Services (SMS) have made it attractive to malicious users thereby incurring unnecessary costing on the mobile users and the Network providers' resources. The aim of this paper is to identify and review existing state of the art methodology for SMS spam based on some certain metrics: AI methods and techniques, approaches and deployed environment and the overall acceptability of existing SMS applications. This study explored eleven databases which include IEEE, Science Direct, Springer, Wiley, ACM, DBLP, Emerald, SU, Sage, Google Scholar, and Taylor and Francis, a total number of 1198 publications were found. Several screening criteria were conducted for relevant papers such as duplicate removal, removal based on irrelevancy, abstract eligibility based on the removal of papers with ambiguity (undefined methodology). Finally, 83 papers were identified for depth analysis and relevance. A quantitative evaluation was conducted on the selected studies using seven search strategies (SS): source, methods/ techniques, AI approach, architecture, status, datasets and SMS spam mobile applications. A Quantitative Analysis (QA) was conducted on the selected studies and the result based on existing methodology for classification shows that machine learning gave the highest result with 49% with algorithms such as Bayesian and support vector machines showing highest usage. Unlike statistical analysis with 39% and evolutionary algorithms gave 12%. However, the QA for feature selection methods shows that more studies utilized document frequency, term frequency and n-grams techniques for effective features selection process. Result based on existing approaches for content-based, non-content and hybrid approaches is 83%, 5%, and 12% respectively. The QA based on architecture shows that 25% of existing solutions are deployed on the client side, 19% on server-side, 6% collaborative and 50% unspecified. This survey was able to identify the status of existing SMS spam research as 35% of existing study was based on proposed new methods using existing algorithms and 29% based on only evaluation of existing algorithms, 20% was based on proposed methods only. This study concludes with very interesting findings which shows that the majority of existing SMS spam filtering solutions are still between the "Proposed" status or "Proposed and Evaluated" status. In addition, the taxonomy of existing state of the art methodologies is developed and it is concluded that 8.23% of Android users actually utilize this existing SMS anti-spam applications. Our study also concludes that there is a need for researchers to exploit all security methods and algorithm to secure SMS thus enhancing further classification in other short message platforms. A new English SMS spam dataset is also generated for future research efforts in Text mining, Tele-marketing for reducing global spam activities. • A comprehensive review of Soft Techniques in SMS Spam Classification (upto May'19). • Overview of the most exploited methods used in feature selection & classification. • Taxonomy of existing state of the art methodologies is developed. • User's acceptability of SMS spam applications on Android App store was evaluated. [ABSTRACT FROM AUTHOR]

This paper takes the occasion of French DJ David Guetta's use of generative AI tools to create lyrics and a voice in the style of Eminem, which he then used in one of his concerts, as the basis for an exploration of the shifting meaning of creativity and originality in the age of generative AI. Our main contention is that the Guetta form of creativity with generative AI tools differs in certain important respects from what has come before. The paper describes an iterative, dynamic process of conception, prompting, generation, refining, and deployment to characterise creativity in this context. Nevertheless, we contend that copyright – specifically the concept of originality as articulated in US federal law – is a sufficiently durable legal mechanism that can manage these new cultural forms, and that the two basic requirements of modern copyright law (a tangible medium of expression and a modest degree of creativity) remain relevant in identifying the scope of legal protection. The paper argues that the David Guetta story reveals something more general about creativity in a digital age, namely that while hybrid-networked (i.e., human – corporate – machine) creators have always created hybrid-networked cultural forms (i.e., creations that blend human and technology-constituted elements), such hybridity becomes increasingly visible and complex in the context of a new world of generative AI. At the very least, earlier – and influential – models of creativity as human-driven involving creation ex nihilo become harder to sustain in a new age of generative AI. But this does not mean copyright or notions of originality are redundant or that copyright law cannot accommodate Guetta and other cases. Such an account seems important as it challenges the hegemonic and reductive view that AI "generates" artistic works autonomously and avoids reducing the copyright issues raised by such creative works to the related but distinct question of whether learning models rely on copyrighted data. As such, copyright law should remain an important mechanism to facilitate genuine creators who are using AI systems in innovative and unique ways to push the boundaries of their creativity. [ABSTRACT FROM AUTHOR]

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide. • Of the 52 "neuroalgorithms" studied, primary literature could be identified for 26 of them. • Fifteen of the algorithms had primary literature reporting algorithmic performance metrics. • Heterogeneity in study design between papers made definitive assessment of algorithmic performance difficult. • Clinical adoption of algorithms was hampered by the relative lack of patient-centered research testing each algorithm. • Few papers provided a comprehensive overview of all notable limitations for a single algorithm. [ABSTRACT FROM AUTHOR]

• AI-based Auto Optical Inspection (AOI) method reduces OLED panel production costs at the TFT substrate stage. • XAI prediction model improves stain detection accuracy from 50% to 80% by identifying H diffusion and Stain correlation. • New inspection method enables earlier risk detection, saving costs and speeding up decision-making. This paper attempts to implement an auto-optical inspection (AOI) system using artificial intelligence (AI) technology for cost reduction in the production of organic light emitting diode (OLED) panels, specifically at the production stage of the thin film transistor (TFT). Further, to improve the accuracy of mura detection, the possible causes of mura were properly identified, and a model to control and predict mura occurrence was realized based on the sufficient analysis of these causes. More specifically, an explainable AI (XAI) prediction model was developed using the fab image and test element group (TEG) engineering methods, which could be applied as input data for the circuit simulations to improve the accuracy of the overall simulations. Initially, we attempted to predict backplane stain using only the TFT width, length dimension, and resistance–capacitance (RC) extraction data, but the results were not accurate. Consequently, we identified, via sufficient analysis, that the correlation between the dehydrogenation and stain, and introduced an AI model. As a result, the accuracy was improved from 50 to 80%, which is more effective in terms of time and cost, compared to conventional simulation through the TCAD analysis. Overall, by implementing the inspection method described in this paper, it was possible to detect stains at the backplane stage, which was only possible during the final test stage, thereby resulting in significant cost savings. [ABSTRACT FROM AUTHOR]

In the movement toward industry 4.0, there is a significant change in ISO Geometrical Product Specification and Verification standards (GPS), The latest version of GPS empowers an engineer to describe his/her requirements precisely and unambiguously. However, it also considerably increases the difficulty of ISO GPS education. To this end, the paper proposes an AI-based learning method through a Tiktok-Style education app for vocational training, specifically in the delivery of ISO GPS. Tiktok is a short-form video-sharing app empowered by machine learning. Based on the key concepts learned from Tiktok, the framework of an education APP for ISO GPS, namely GPS-SV, is developed. The framework is detailed and illustrated via a case study. [ABSTRACT FROM AUTHOR]

The increasing demands on the quality of high-precision optical components mean that established manufacturing processes are reaching their technical limits. While individual processes can therefore hardly be improved any more, networking production machines digitally throughout the entire process chain offers high potential for meeting the tight tolerances in the optics industry and increasing the production output. To gain these benefits, first, an infrastructure is needed, containing a flexible and module data structure to satisfy different requirements from the processes. Second, the idea of offering services such as initial data analytics, artificial intelligence (AI) and machine learning (ML) must be integrated. In this paper, we present a digital infrastructure, that contains data from the entire process chain of glass and polymer optics production including simulation, process and measurement data. Services to optimize the production process are integrated and provide an outlook on how AI/ML can be added. [ABSTRACT FROM AUTHOR]

This research proposal explores how citizen-centered learning and career advancement can benefit from artificial intelligence, occupational classification frameworks, and the concept of proactive services. In the current literature, there are a lot of machine learning methods used in various job and training recommendation systems to tackle scientific or real-life problems. However, only a few use the existing occupational classifications to classify job and training advertisements or enable cross-regional labor mobility. Additionally, the quality of public e-services regarding labour market services in each European country varies greatly. For example, even though Estonia is typically referred to as being at the forefront of public service digitization and automation, it has not implemented machine learning methods to match job offers or training with candidates. The matching process between vacancy and job seeker is currently carried out with outdated International Standard Classification of Occupations (ISCO) codes, altered to the Estonian Unemployment Insurance Fund's needs. The ISCO code is assigned to each job vacancy by a company and job wish by a citizen manually. Such functionality is rigid and requires the users to define an accurate ISCO code. Even when the filled-in CV consists of detailed previous work experience and educational background, if the ISCO code is not accurate, the e-service will not help the citizen find a new job. Moreover, in today's public employment service portals, there is typically no option to insert specific skills that a citizen has to receive an increased number of accurate job or training recommendations. Consequently, despite many well-established frameworks dealing with competencies and occupations, citizen-centered public services supporting upskilling and finding a new job are inefficient. In this paper, we put forth a research roadmap for investigating how to enable a technical ecosystem using occupational classification frameworks so that citizens, both employed and unemployed, can receive proactive recommendations about upcoming training events and job vacancies. Such a system should be tailored to support citizen life events. For example, it could consider citizens' previous work and educational background to help with retraining, upskilling, or changing one's career path. Therefore we have initiated a project in collaboration with the Estonian Unemployment Insurance Fund, the Estonian Qualifications Authority, other Estonian public organizations, and partner universities in Latvia and Finland. The research is planned as an action design research to design an artificial intelligence-enabled Virtual Competence Assistant (VCA) for the EU labour market. [ABSTRACT FROM AUTHOR]

Detecting fake news and disinformation is a challenging and complex task. With the proliferation of the transformer architectures, researchers and practitioners have started actively using them to combat this phenomenon. In that regard, different approaches have been proposed by the community. Unfortunately, a significant number of solutions have started to treat misinformation in a similar way as other natural language processing challenges (e.g., sentiment classification). However, the reality is more complex, and the fake news detection problem often require additional context that may be essential to provide a complete assessment. Another pitfall in combating fake news is treating it as a binary classification challenge (e.g., fake vs. legitimate news). Combining such a simplified response with a deep black-box neural system effectively impacts the interpretability and eventual trustworthiness of the detection system. In this paper, the phenomenon of fake news is approached from a multi-factor perspective. Therefore, the proposed innovative approach does not limit news content rating to a one-dimensional binary response. In particular, the authors utilize a vast spectrum of processing techniques, including text embedding, machine translation, and various users' preferences to provide content assessment. The results are presented using a Polish digital media use-case. • Novel approach to content assessment. • Combination of text embedding, machine translation and users' preferences. • Innovative, complex approach to annotation. • New dataset of content in Polish language. • New pipeline including information about the annotator. [ABSTRACT FROM AUTHOR]

• Optimal RT outcomes requires accurate robust predictive radiation oncology models. • Present challenges in model and data quality must be addressed to improve RO models. • Mechanistic understanding of radiobiological concepts can inform RO models. • Integrating novel computational and modeling methods will advance model development. • Interdisciplinary RO model development leverages the expertise of all stakeholders. Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines. [ABSTRACT FROM AUTHOR]

This article embarks on a comprehensive examination of two research questions. The first question is "What are the AI-enabled applications, which are developed and deployed in Greece in the border management field?". The goal is to provide to the reader a thorough listing of these technologies, as well as information regarding the companies that develop them, and the EU funding schemes that support them. By investigating this question, the paper assesses whether there exists transparent information regarding the procurement, development, and deployment phases of such AI tools in Greece, with a keen focus on the accessibility of related documents and data to civil society actors.The second question is "To what extent are the development and deployment of these AI-enabled border management applications in compliance with the applicable data protection provisions?". There the goal is to register the different breaches of data protection provisions when such AI tools are developed and deployed in practice, taking into account the findings of civil society actors that have challenged the lawful use of such applications in accordance with the related national legal framework enforcing Regulation 2016/679 (GDPR) and transposing the Directive 2016/680. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of finding the part of the observations that is useful for the diagnosis. We define a sub-observation as an abstraction of the observations. We then argue that a sub-observation is sufficient if it allows a diagnoser to derive the same minimal diagnosis as the original observations; and we define critical observations as a maximally abstracted sufficient sub-observation. We show how to compute a critical observation, and discuss a number of algorithmic improvements that also shed light on the theory of critical observations. Finally, we illustrate this framework on both state-based and event-based observations. [ABSTRACT FROM AUTHOR]

Solar energy has emerged as a vital renewable alternative to fossil fuels, enhancing environmental sustainability in response to the pressing need to reduce carbon emissions. However, the integration of solar power into the electrical grid faces challenges due to its unpredictable nature, as a result of solar energy production variability. This research presents an advanced Explainable Artificial Intelligence (XAI) framework to explicate machine learning models decision-making processes, thereby improving the predictability and management of solar energy distribution. The influence of critical parameters such as solar irradiance, module temperature, and ambient temperature on energy yield is studied using the Local Interpretable Model-Agnostic Explainer (LIME). Rigorous testing using four advanced regression models identified Random Forest Regressor as the superior model, with an R2 score of 0.9999 and a low Root Mean Square Error (RMSE) of 0.0061. Furthermore, Partial Dependency Plots (PDP) are used to emphasize the intricate dependencies and interactions among features in the dataset. The application of XAI techniques for solar power generation extends beyond explainability, addressing challenges due to various parameters in solar radiation pattern analysis, error estimation in solar performance, degradation of the battery function, and also provides interpretable insights for enhancing the lifespan of solar panels, contributing to advancements in sustainable energy technologies. The results of this study show how XAI has the potential to transform power system management (PSM) and strategic planning, propelling us toward a future of energy that is more resilient, efficient, and environmentally friendly. • The Purpose of the paper is to provide an Explainability to the productivity of solar energy to the end-user and to perform solar energy predictions. • This work performs regression analysis of various parameters related to solar power distribution and picks the model that is more reliable and delivers the maximum performance which is the Random Forest Regressor. • The selected model is applied with the Explainable Artificial intelligence with tools like Local Interpretable Model Agonistic Explainer (LIME) and the Partial Dependency plots (PDP). • They not only provide the prediction but also provide the feature importance, feature weights, and what features provide positive and negative impact towards the target prediction. • The future work includes the application of federated learning for Solar power generation and distribution prediction. [ABSTRACT FROM AUTHOR]

Traditionally, an organization had to have in-house servers and hardware to build a web application. This evolved into Cloud computing where the possibility for cost reduction and scalable data storage became a reality. With the introduction of cloud computing came a concept known as NoOps, or No Operations. This paper aims to take a closer look into what NoOps is and the benefits and challenges of NoOps. The authors identified three RQs that could help to give more insight into NoOps. Further we discussed the findings and RQs and lay out the way forward for future studies into NoOps. We also looked at artificial intelligence (AI) and how AI seems to be heavily linked with a true NoOps environment. With the lack of scientific studies into NoOps, a Multivocal literature review was selected as the method used to investigate the concept and its implications. We try to show voices both for and against NoOps. Further, we try to look at a misconception of what NoOps really is, what true NoOps could be. Finally we look at what requirements there are for companies wanting to go NoOps, and discuss the possibility that many companies unknowingly are moving towards a NoOps environment. [ABSTRACT FROM AUTHOR]

Organisations can use artificial intelligence to make decisions about people for a variety of reasons, for instance, to select the best candidates from many job applications. However, AI systems can have discriminatory effects when used for decision-making. To illustrate, an AI system could reject applications of people with a certain ethnicity, while the organisation did not plan such ethnicity discrimination. But in Europe, an organisation runs into a problem when it wants to assess whether its AI system accidentally discriminates based on ethnicity: the organisation may not know the applicants' ethnicity. In principle, the GDPR bans the use of certain 'special categories of data' (sometimes called 'sensitive data'), which include data on ethnicity, religion, and sexual preference. The proposal for an AI Act of the European Commission includes a provision that would enable organisations to use special categories of data for auditing their AI systems. This paper asks whether the GDPR's rules on special categories of personal data hinder the prevention of AI-driven discrimination. We argue that the GDPR does prohibit such use of special category data in many circumstances. We also map out the arguments for and against creating an exception to the GDPR's ban on using special categories of personal data, to enable preventing discrimination by AI systems. The paper discusses European law, but the paper can be relevant outside Europe too, as many policymakers in the world grapple with the tension between privacy and non-discrimination policy. [ABSTRACT FROM AUTHOR]

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the system so that they can explore results and find patterns that can be compared with their expected solution. Reflecting features and patterns of human understanding of a domain against algorithmic results can create awareness of such patterns and may increase the trust that a user has in the solution. If expectations are not met, close inspection can be used to decide whether a solution conforms to the expectations or whether it goes beyond the expected. By either accepting or rejecting a solution, the user's set of expectations evolves and a learning process for the users is established. In this paper we present a conceptual framework that reflects and supports this process. The framework is the result of an analysis of two exemplary case studies from two different disciplines with information systems that assist experts in their complex tasks. [ABSTRACT FROM AUTHOR]

This paper outlines the experimental framework for an e-governance study by the AGI Laboratory. The goal of this research study is to identify indications of the relative performance of e-governance methodologies and how those methods might be improved by applying advanced agent and collective based AI software. The agent in this study will be based on the Independent Core Observer Model Cognitive Architecture modified with mASI (mediated Artificial Superintelligence) collective system architecture. The study will apply different groups and methods to a static set of questions analyzing the quality of those results. We hope to identify the best application model for e-governance using this kind of technology and help us identify additional paths for research with the mASI research program and systems as applied to e-governance. [ABSTRACT FROM AUTHOR]

The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. Machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics. Pattern recognition based on image processing, computer vision, and AI smelling are discussed by introducing the principles of operation, reviewing available tools and software solutions, and illustrating their potentials and limitations through selected applications. • Artificial Intelligence boost the informative potential of GC × GC. • Computer Vision based on chromatogram images. • AI smelling machine with GC × GC-MS predicts food aroma properties. • Advancing the explorative potential of comprehensive two-dimensional chromatography. • Augmented visualization combines visual features with chemical information. [ABSTRACT FROM AUTHOR]

Due to the large number of medical images generated each year, data-driven machine learning methods hold great promise in the field of medical image diagnosis and analysis. In recent years, the application of deep neural networks in oral and cranio-maxillofacial surgery has gradually increased, helping surgeons to obtain more comprehensive information and develop more accurate treatment planning, including segmentation and recognition of anatomical structures, disease diagnosis, and automatic surgical planning. This paper presents a new dataset of maxillofacial defect images, containing computed tomography (CT) data of 125 normal human cranial skeletal 3D reconstructions and 2625 corresponding craniomaxillofacial bone defects. The data are classified according to seven different types of defects which are anterior skull and forehead, zygomatic arch, zygomatic bone, Naso-orbital-ethmoidal (NOE), wall of the maxillary sinus, mandibular body, mandibular ramus, and condylar respectively. Each kind of defect is further divided into three categories according to the different locations, including left side, right side, and bilateral sides (or defect passes through the midline of the face). There are three main contributions: first, a dataset is publicly available to assist researchers in the development of data-based methods for generating missing portions of the skull; second, a repair method is proposed to evaluate the dataset; third, a review of various related datasets. The release of this dataset aims to advance artificial intelligence methods in the field of oral and cranio-maxillofacial surgery and plastic surgery helping physicians to reduce the difficulty and time consumption while improving the efficiency and accuracy of prosthesis design. [ABSTRACT FROM AUTHOR]