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This paper addresses the evaluation of multi-step point forecasting models. Currently, deep learning models for multi-step forecasting are evaluated on datasets by selecting one error metric that is aggregated across the time series and the forecast horizon. This approach hides insights that would otherwise be useful for practitioners when evaluating and selecting forecasting models. We propose four novel metrics to provide additional insights when evaluating models: 1) a win-loss metric that shows how models perform across time series in the dataset , allowing the practitioner to check whether the model is superior for all series or just a subset of series. 2) a variance weighted metric that accounts for differences in variance across the seasonal period. It can be used to evaluate models for seasonal datasets such as rush hour traffic prediction, where it is desirable to select the model that performs best during the periods of high uncertainty. 3) a delta horizon metric measuring how much models update their forecast for a period in the future over the forecast horizon. Less change to the forecast means more stability over time and is desirable for most forecasting applications. 4) decomposed errors that relate the forecasting error to trend, seasonality, and noise. Decomposing the errors allows the practitioners to identify for which components the model is making more errors and adjust the model accordingly. To show the applicability of the proposed metrics, we implement four deep learning architectures and conduct experiments on five benchmark datasets. We highlight several use cases for the proposed metrics and discuss the applicability in light of the empirical results. [ABSTRACT FROM AUTHOR]

For decades, the data-centric digital circuits of global finance have been driving financial markets toward algorithmic management. The penetration of algorithms into finance dates back to the 1980s, well before becoming a significant trend in Internet and social media companies. AI is now considered the new frontier of this process. This paper examines Brazil, the most important financial center in Latin America, as a case study to explore Machine Learning applications in capital markets, providing new evidence on the penetration of automated trading systems in the region. Grounded in a historical overview of the digitalization of capital markets in Brazil, the paper draws on the STS definition of infrastructures and the political economy of digitalized finance to analyze AI as an emerging financial infrastructure. Beyond its physical and material aspects, this approach also addresses how digitalization and AI relate to financialization and the emergence of new financial risks. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has emerged as a transformative force in various sectors, offering both opportunities and challenges, particularly in a rapidly developing nation like India. This paper explores the dual aspects of AI as a boon and a bane, analysing its impact on economic growth, employment, healthcare, education, and ethical concerns. While AI promises enhanced productivity and innovation, it also poses risks such as job displacement, privacy violations, and algorithmic bias. This study aims to provide a balanced perspective on the implications of AI in India, highlighting the need for strategic frameworks that maximize benefits while mitigating risks. [ABSTRACT FROM AUTHOR]

This paper looks at the way that Bing AI uses tone policing and racial gaslighting in its conversations with users as a method of disciplining them away from critical anti-racist ideological engagement and towards an ethos of white supremacy. We use a critical discourse analysis to examine the conversations produced through our use of Microsoft's Bing AI chatbot to find that through both the content it generates and the mechanisms through which it responds the Bing AI chatbot polices the tone of its users in a variety of ways. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model's performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter's efficiency compared to the inverter's power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. [ABSTRACT FROM AUTHOR]

We are confronted with the concept of intelligence every day. Starting with human intelligence to artificial intelligence. Some animals are also attested to be intelligent based on specific problems they solve. We also come across terms such as swarm intelligence, emotional intelligence or even physical intelligence. But there is still a lack of a clear definition of what intelligence actually is and, in particular, how it could be measured. Intelligence tests that provide quantitative information have so far only been available from psychology and only for people. There is a lack of criteria for what makes a system an intelligent system. This became particularly clear with the question of whether generative AI, such as ChatGPT, can be considered intelligent at all. So can intelligence be derived from the cognitive abilities of a system, or is it ultimately decisive how these abilities come about? This paper suggests a definition of the term intelligence and suggests an explanation for what constitutes intelligence and to what extent intelligence is required to gain knowledge. And finally it is questioned whether artificial systems are intelligent and have any knowledge at all. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

Laser beam welding is the most modern and promising process for the automatic or robotized welding of structures of the highest Execution Class, EXC3-4, which are made of a variety of weldable structural materials, mainly steel, titanium, and nickel alloys, but also a limited range of aluminum, magnesium, and copper alloys, reactive materials, and even thermoplastics. This paper presents a systematic review and analysis of the author's research results, research articles, industrial catalogs, technical notes, etc., regarding laser beam welding (LBW) and laser hybrid welding (LHW) processes. Examples of industrial applications of the melt-in-mode and keyhole-mode laser welding techniques for low-alloy and high-alloy steel joints are analyzed. The influence of basic LBW and LHW parameters on the quality of welded joints proves that the laser beam power, welding speed, and Gas Metal Arc (GMA) welding current firmly decide the quality of welded joints. A brief review of the artificial intelligence (AI)-supported online quality-monitoring systems for LBW and LHW processes indicates the decisive influence on the quality control of welded joints. [ABSTRACT FROM AUTHOR]

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2's high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models. [ABSTRACT FROM AUTHOR]

This is a critique of how designers deal with temporality in design to speculate about socio-technical futures. The paper unpacks how embedded definitions and assumptions of temporality in current design tools contribute to coloniality in designed futures. Based on this critique, we reject the notion that it is only AI that needs fixing, as design practice becomes implicated in how oppression extends from physical systems to global digital platforms. To make these issues visible, we dissect the Futures Cone model used in speculative design. As an alternative, the paper then presents hauntology as a vocabulary that can aid designers in accommodating pluriversal histories in anticipatory futures and reorienting their speculative tools. To illustrate the benefits of the proposed metaphors, the paper highlights examples of coloniality in digital spaces and emphasizes the failure of speculative design to decolonize future imaginaries. Using points of reference from hauntology, ones that engage with states of lingering or spectrality, and notions of nostalgia, absence, and anticipation, the paper contributes to rethinking the role that design tools play in colonizing future imaginaries, especially those pertaining to potentially disruptive technologies. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a devastating immune-mediated disorder of the central nervous system resulting in progressive disability accumulation. As there is no cure available yet for MS, the primary therapeutic objective is to reduce relapses and to slow down disability progression as early as possible during the disease to maintain and/or improve health-related quality of life. However, optimizing treatment for people with MS (pwMS) is complex and challenging due to the many factors involved and in particular, the high degree of clinical and subclinical heterogeneity in disease progression among pwMS. In this paper, we discuss these many different challenges complicating treatment optimization for pwMS as well as how a shift towards a more pro-active, data-driven and personalized medicine approach could potentially improve patient outcomes for pwMS. We describe how the ‘Clinical Impact through AI-assisted MS Care’ (CLAIMS) project serves as a recent example of how to realize such a shift towards personalized treatment optimization for pwMS through the development of a platform that offers a holistic view of all relevant patient data and biomarkers, and then using this data to enable AI-supported prognostic modelling. [ABSTRACT FROM AUTHOR]

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process. [ABSTRACT FROM AUTHOR]

The profound digitization of public administration over recent decades has not eliminated information exchange via paper or electronic documents and certificates. We argue that a paradigm shift from document-centric to data-centric public service provision is needed and is feasible today with the exploitation of emerging technologies. We explore frameworks, architectures, benefits, and challenges in transforming document-centric administration processes into integrated, granular data exchange. A conceptual architecture for public service provision is proposed to extract preconditions from legislation, map the needed evidence to the service requirements, standardize evidence types, and integrate authoritative data sources. While promoting efficiency, privacy, and innovation, this shift faces technical and organizational challenges as the analysis of the "National Registry of Administrative Public Services" in Greece reveals. Further research on aligning policies, upholding trust, and coordinating institutional processes is warranted. [ABSTRACT FROM AUTHOR]

While artificial intelligence has successful and innovative applications in common medicine, could its application facilitate research on rare diseases? This study explores the application of artificial intelligence (AI) in orphan drug research, focusing on how AI can address three major barriers: high financial risk, development complexity, and low trialability. This paper begins with an overview of orphan drug development and AI applications, defining key concepts and providing a background on the regulatory framework of and AI's role in medical research. Next, it examines how AI can lower financial risks by streamlining drug discovery and development processes, analyzing complex data, and predicting outcomes to improve our understanding of rare diseases. This study then explores how AI can enhance clinical trials through simulations and virtual trials, compensating for the limited patient populations available for rare disease research. Finally, it discusses the broader implications of integrating AI in orphan drug development, emphasizing the potential for AI to accelerate drug discovery and improve treatment success rates, and highlights the need for ongoing innovation and regulatory support to maximize the benefits of AI-driven research in healthcare. Based on those results, we discuss the implications for traditional and AI-powered business in the drug industry. [ABSTRACT FROM AUTHOR]

The British Army is regularly ranked as the best training provider in the UK, with the Royal Navy and Royal Air Force also in the top ten. The Military train a lot of people, from the most basic infantry training to advanced skills and leadership courses. At the pinnacle of this is the Defence Academy, which provides world-class professional defence and security education for UK military, civil service and the defence industry and for allies and partners from across the world. This article is based on the Prince Philip Lecture 2024 delivered by Major General Rowell at the Royal United Services Institute for Defence Studies at its Whitehall, London, headquarters on 10 July 2024. An abridged version of the first part of this paper appeared in Education Journal No. 568, 17 July 2024. [ABSTRACT FROM AUTHOR]

This article seeks to conduct a bibliometric analysis focusing on exponential technologies such as big data, internet of thing (IoT), artificial intelligence (AI), blockchain and cloud computing. It aims to outline research trends in this domain and explore their correlation with resilience. The study aims to track the evolution of research trends in this field over time and identify less explored dimensions of exponential technologies. Leveraging performance analysis and science mapping techniques, the paper highlights the significant growth and potential in these areas, considering them as pivotal agendas of the twenty-first century. By examining scientific productivity metrics such as publications, authors, institutions, countries and keywords, the article offers insights into emerging areas within exponential technologies. As the first comprehensive study of its kind, it provides a broad overview of the main trends and patterns in resilience research encompassing big data, IoT, AI, blockchain and cloud computing, consolidating them into a single cohesive narrative. [ABSTRACT FROM AUTHOR]

The pace of technological advancement over the last three decades has led to a slew of new companies adopting the latest technologies and marrying them to innovative new business models to threaten more traditional businesses. Start-ups such as Google, Meta and Amazon, to name a few, have revolutionised the way consumers engage with service providers, consume information and purchase goods. Fintech start-ups have also threatened to change the way financial services are provided, albeit with varying degrees of success due to barriers such as consumer trust in new brands, regulatory compliance and the financial strength of banks to build those same services internally. There is no denying, however, that custodian banks today face many challenges that are slowly eroding margins. Regulators are demanding shorter settlement times, clients are demanding greater control over their accounts, staff costs are rising and cyber security threats are increasing. This paper seeks to highlight some of the threats the industry is facing while exploring the role that artificial intelligence (AI) may be able to play in addressing some of these challenges. It offers a broad overview of not just areas of application but also weaknesses of the technology that the bank needs to be aware of and also possible issues with implementation. It also seeks to highlight the fact that AI is not a single technology, unlike distributed ledger systems. There are many nuances to AI, such as convolution neural networks, natural language processing and generative AI, and the judicious application of the right nuance of AI to the problem will be key to a successful implementation. [ABSTRACT FROM AUTHOR]

The functioning of modern urban environments relies heavily on the public transport system. Given spatial, economic, and sustainability criteria, public transport in larger urban areas is unrivaled. The system's role depends on the quality of service it offers. Achieving the desired service quality requires a design that meets transport demands. This paper uses a data-driven approach to address headway deviations in public transport lines and explores ways to improve regularity during the design phase. Headway is a critical dynamic element for transport organization and passenger quality. Deviations between planned and actual headways represent disturbances. On lines with headways under 15 min, passengers typically do not consult schedules, making punctuality less crucial. Reduced headway regularity affects the average travel time, travel time uncertainty, and passenger comfort. Ideally, the public transport system operates with regular headways. However, disturbances can spread and affect subsequent departures, leading to vehicle bunching. While previous research focused on single primary disturbances, this study, with the help of AI (reinforcement learning), examines multiple primary disturbances in the cities of Belgrade, Novi Sad, and Niš. The goal is to model the cumulative impact of these disturbances on vehicle movement. By ranking parameter influences and using the automatic optimization of static line elements, this research aims to improve headway regularity and increase system resilience to disturbances. The results of this research could also be useful in developing adaptive public transport management systems that leverage AI and IoT technologies to continuously optimize headway regularity in response to real-time data, ultimately enhancing service quality and passenger satisfaction. [ABSTRACT FROM AUTHOR]

In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful embedding features (black-box) with supervised learning algorithms including Deep Neural Networks, Random Forests, and Extreme Gradient Boosting to achieve this differentiation with an impressive accuracy of 98 % . For the successful combinations, we also examine their model calibration, showing that some of the models are extremely well calibrated. Additionally, we present white-box features and an interpretable Bayes classifier to elucidate critical differences between the code sources, enhancing the explainability and transparency of our approach. Both approaches work well, but provide at most 85–88% accuracy. Tests on a small sample of untrained humans suggest that humans do not solve the task much better than random guessing. This study is crucial in understanding and mitigating the potential risks associated with using AI in code generation, particularly in the context of higher education, software development, and competitive programming. [ABSTRACT FROM AUTHOR]

Recent developments in AI and robotics enable people to create personalised digital duplicates – these are artificial, at least partial, recreations or simulations of real people. The advent of such duplicates enables people to overcome their individual scarcity. But this comes at a cost. There is a common view among ethicists and value theorists suggesting that individual scarcity contributes to or heightens the value of a life or parts of a life. In this paper, we address this topic. We make five main points. First, that there is a plausible prima facie case for the scarcity threat: AI may undermine the value of an individual human life by making us less scarce. Second, notwithstanding this prima facie threat, the role of scarcity in individual value is disputable and always exists in tension with the contrasting view that scarcity is a tragedy that limits our value. Third, there are two distinct forms of scarcity – instrumental and intrinsic – and they contribute to value in different ways. Fourth, digital duplication technology may undermine instrumental scarcity, to at least some extent, but the axiological consequences of this are highly variable. Fifth, digital duplication technology does not affect intrinsic scarcity, and may actually heighten it. [ABSTRACT FROM AUTHOR]

This paper argues that individual propositional knowledge, as traditionally analyzed in terms of true-justified-ungettiered belief, does not require phenomenal experience. Accordingly, those who are satisfied with the traditional conception need to come to terms with the possibility that AI and other zombies that lack phenomenal experience possess knowledge. Alternatively, those who resist attributing knowledge to AI based on the assumption that knowledge requires phenomenal experience need to modify or replace the traditional conception of knowledge to incorporate this requirement. [ABSTRACT FROM AUTHOR]

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the four best club options based on the player's maximum shot data for each club. The player picks a club, shot, and target and a probabilistic classification model identifies whether the shot represents a birdie opportunity, par zone, bogey zone, or worse. The results of our model show that taking into account factors such as terrain and atmospheric features increases the likelihood of a better shot outcome. [ABSTRACT FROM AUTHOR]

The emergence and fast proliferation of chatbot solutions have reshaped how we interact with customer services, professionals and organizations providing advisory services. Law firms and legal professionals have also been affected by chatbots, which have become an integral part of the legal market. They are used for training, discovery, legal research and various other tasks by multinational law firms and sole practitioners alike. Besides their benefits, however, chatbot solutions also face a number of limitations and their use could raise both legal and ethical concerns. Unsupervised use of such solutions can lead to serious professional responsibility issues, while their use in certain cases, such as in cases involving acting as a defense counsel or advising on sensitive matters, can also raise ethical concerns, or endanger the trust in lawyers built by generations of legal professionals. The processing of certain data or confidential information can further raise privacy or confidentiality issues, especially with respect to the need for artificial intelligence (AI) based solutions to constantly rely on huge datasets. Bearing the above in mind, the regulation of chatbots in the legal market is certainly a complex topic with many challenges. In this paper, I provide an overview of the use of chatbots in the legal market, summarize the main concerns regarding their use (especially including professional liability, privacy and ethical concerns) and also highlight the main challenges concerning AI and chatbot regulation and the potential approaches regulators could follow to prevent or minimize risks associated with the unlawful or unethical use of technology and disperse unnecessary fears by also supporting technological development and by preserving the positive effects of the use of chatbots in the legal market. [ABSTRACT FROM AUTHOR]

Universal Bibliographic Control was formulated in the 1960s and 1970s, and was at the core of international bibliographic productions and exchange in the subsequent decades. However, in a digital ecosystem that is very different from the context in which it was born and thrived, it is important to examine what Universal Bibliographic Control means to the international bibliographic community, that is, the producers and managers of bibliographic - and authority - metadata, today. This paper is meant to invite discussion and reflections and to resonate with the various papers from the International conference on Bibliographic control in the digital ecosystem, organised by the University of Florence in February 2021. It focuses on the future of interoperability and the role of UBC in a democratic society, in the context of mass digital information, and its companion technologies. [ABSTRACT FROM AUTHOR]

Many global companies specializing in complex technological systems use forms of group decision making to select a combination of solutions from suppliers. This requires technical expertise and up-to-date awareness of what is available within and outside the company. The use of artificial intelligence seems like an obvious progression but is fraught with difficulties. As a step in this longer-term direction, this paper looks to a methodology that uses the idea of functionality to first list abstract requirements before finding potential solutions with appropriate performance characteristics. This paper re-examines a methodology called value engineering, which mixes measurable and immeasurable concepts in its foundational idea. This paper reasons and deduces a new way to conceive this foundational idea so that it can be modelled mathematically and provide a useful step toward a database ontology and schema that would suit artificial intelligence. It also provides an immediate benefit to value engineering practitioners in workshops. [ABSTRACT FROM AUTHOR]

The research paper contends that Artificial Intelligence (AI) serves as a collaborative partner in architectural design, rather than merely a utility tool. To substantiate this argument, a three-phase, nine-test investigation evaluating the strengths and limitations of two prominent AI platforms: Midjourney AI and Stable Diffusion was undertaken. These platforms synergize human creativity and AI capabilities through features like text prompts and image references, thereby fostering innovative avenues in architecture. Our analysis indicates that Midjourney AI is proficient in generating initial design concepts, largely thanks to its extensive data libraries, but is deficient in design refinement and user control. Conversely, Stable Diffusion empowers designers with greater control via features like ControlNet but sacrifices visual clarity due to its smaller generative models. Both platforms share a common flaw: an overemphasis on aesthetics and shape at the expense of functional understanding. Building upon these empirical observations, the paper outlines strategies for designers to reasonably leverage AI in optimising workflows. It confirms two key hypotheses concerning the interplay of creativity, control, and collaboration, emphasising that both human architects and AI systems benefit from iterative feedback and continuous adaptation. In summary, the study posits that AI is not just an adjunct technology but a transformative force with the capacity to fundamentally alter architectural design processes, paving the way for a new paradigm where human expertise and machine capabilities converge for enriched design outcomes.

It is a network system for teaching English through a wireless communication (WC) premised distance teaching system. This is a process of education that is capable of encouraging students' concerns to acquire knowledge voluntarily. The paper is designed to develop and implement an online intelligent English training system using artificial intelligence (AI) that helps students improve their English learning efficiency in line with knowledge and personality. The system's numerous sensor nodes may create a variety of topologies. The gathered information is transmitted over the global system for mobile communication (GSM) network to the user interface. The operator can manage the remote sensor node via the GSM network. Nevertheless, there are certain derivative aspects such as the absence of verbal judgment, the actual evaluation and signaling system, the interactive educational platform teachers and learners need. The paper is based on the above issues. It contains a whole talk-based system where teachers, students, and English teaching can be revised together — AIWC (ET-AIWC) systems are designed to improve and advance the genetic algorithm based on an encoding technique for dynamic parameter adjustment of the iterative process based on these problems. In combination with an AI expert system, suitable learning techniques were created to enable students to double the learning effect by half the amount of work. An online teaching assistant system was designed to monitor, regulate, and engage with students throughout the learning process and a modified scoring system that provides real-time evaluation of student speakers to improve students' oral competence in English better and more efficiently, achieving 95.2%. [ABSTRACT FROM AUTHOR]

Background: The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production. Methods: We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration. Results: The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support. Conclusions: The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved. Plain English Summary: Modern technology offers new treatment options for patients and novel avenues of research. However, there is limited available information in easily understandable language for the public explaining how technology relates to them and could influence their healthcare. The researchers, healthcare professionals and public members worked together collaboratively to address this problem by creating new materials for the public. Our paper explores that project through creative methods. Firstly, everyone involved was offered an opportunity to attend training sessions. Then, people took photos and described them to illustrate to others what is their experience of working together. Finally, we all met to use included photos as building blocks to present a shared experience in the project. Afterwards, the professional artist included it as one circular illustration with six interlinked layers. These layers present everyone's experiences (from inside) (1) is about the opportunity to build confidence in a new topic, (2) relationships with others, (3) working remotely during the pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) expectation that the project needs be inclusive and accessible, (6) ethical principles that researchers using new technology should follow. We showed that it is possible for researchers, healthcare professionals and members of the public to feel joint ownership of the project and that working together can be meaningful to everyone. [ABSTRACT FROM AUTHOR]

In the paper of ‘Natural Selection Favors AIs over Humans,’ Dan Hendrycks applies principles of Darwinian evolution to forecast potential trajectories of AI development. He proposes that competitive pressures within corporate and military realms could lead to AI replacing human roles and exhibiting self-interested behaviors. However, such claims carry the risk of oversimplifying the complex issues of competition and natural selection without clear criteria for judging whether AI is selfish or altruistic, necessitating a more in-depth analysis and critique. Other studies, such as ‘'The Threat of AI and Our Response: The AI Charter of Ethics in South Korea,’ offer diverse opinions on the natural selection of artificial intelligence, examining major threats that may arise from AI, including AI’s value judgment and malicious use, and emphasizing the need for immediate discussions on social solutions. Such contemplation is not merely a technical issue but also significant from an ethical standpoint, requiring thoughtful consideration of how the development of AI harmonizes with human welfare and values. It is also essential to emphasize the importance of cooperation between artificial intelligence and humans. Hendrycks’s work, while speculative, is supported by historical observations of inevitable evolution given the right conditions, and it prompts deep contemplation of these issues, setting the stage for future research focused on AI safety, regulation, and ethical considerations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is often seen as a key technology for future economic growth. However, its concrete effects on the emergence of radical innovations and the associated socio-economic impacts, through increasing divergence between smaller and larger firms, have not yet been systematically researched. This paper addresses this by investigating the extent to which AI-related knowledge influences the emergence of radical innovations and differentiates between SMEs and large firms. Based on a unique dataset of European firms combining firm-level data with patent data, we find a nuanced influence from AI. While AI applications assert a positive influence, AI techniques negatively influence the emergence of radical innovations. Being an SME significantly moderates these effects. Larger firms gain from AI applications, whereas SMEs gain from AI techniques. Therefore, AI knowledge in itself is not a general answer to increase the likelihood of creating radical innovation. Instead, a more differentiated view on AI is needed. Plain English Summary: Possessing knowledge in AI techniques decreases the chance of creating radical knowledge in general, but SMEs benefit from such knowledge. Conversely, knowledge in AI applications is more beneficial for large firms. Artificial intelligence (AI) is often seen as a key technology for future economic growth. However, some firms may have an advantage in utilizing AI knowledge to create radical innovation. This paper investigates the influence of AI-related knowledge in firms on the emergence of radical innovations with a specific focus on the differences between SMEs and large firms. We find that application-related AI knowledge increases the likelihood for radical innovations, while technique-related AI knowledge decreases it. Nevertheless, SMEs have an advantage in utilizing AI techniques to generate radical innovations. Thus, the principal implication of this study is that SMEs should focus on AI techniques, allowing them to capture unseen technological opportunities which cannot be obtained in a formalized R&D process within a large firm. [ABSTRACT FROM AUTHOR]

The power of converging and coupling Artificial Intelligence (AI), Fifth Generation Cellular(5G), Internet of Things (IoT), Big Data analytics, and Cyber-security has been touted to be the next revolutionary technologies. If applied correctly, the synergy of the aforementioned technologies is poised to unravel, transform and yield unparalleled economic efficiency and productivity globally across various sectors. It is important to note that each of these technologies has an enormous impact as a standalone in various industries/sectors but when properly combined, the technologies will create an array of opportunities that hugely impact widely all sectors of global economy. This paper will carry out a systematic review of applications of the above-mentioned technologies towards improving Africa's multi-sectoral economy, and their global impact. The findings of this paper will guide the development of techniques of converging various technologies towards enhancing efficiency and impacting sustainability in virtually all sectors of human endeavors. [ABSTRACT FROM AUTHOR]

This research paper explores the use of AI, specifically ChatGPT, in interactive activities within online shopping and digital marketing. The paper examines the potential of AIpowered chatbots to enhance customer engagement, personalization, and marketing effectiveness. Through a literature review, case studies, and user feedback analysis, the research highlights the benefits of AI chatbots in providing real-time assistance, personalized recommendations, and improved customer experiences. The paper also addresses ethical and privacy concerns associated with AI implementation and discusses future research directions. The paper suggests that AI-powered chatbots have the potential to revolutionize online shopping and digital marketing by offering enhanced customer engagement, optimizing marketing strategies, and addressing customer needs. E-commerce preferences of human-mediated services regarding the application of AI in frontline services will be examined. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has had far-reaching effects in research and the academic world. It has been used in many ways by the scientific community within the context of qualitative research, such as literature and systematic reviews, for conceptualization purposes, thematic and content analysis. It has however prompted concerns and questions about the potential for unreliable research, bias, and unethical behavior in the outcomes of AI-produced research. The purpose this paper is to examine the current use of AI in research, its strengths and limitations, dilemmas and ethical considerations from theoretical critical perspective principles, while delivering five key considerations for the appropriate, rigorous, and reliable use of AI in research practice. The first step is to become acquainted with the data generated by AI systems. The second is concerned with removing biased content and addressing ethical concerns when using AI, while the third is concerned with cross-referencing information generated by AI. The fourth step is to control the analysis process. The fifth and most important key consideration is the demonstration of cognitive input and skills by the researcher throughout the process of using AI in any qualitative research study and in reaching conclusions. [ABSTRACT FROM AUTHOR]

The paper tries to investigate the connection between AI and the people who make the discourse in English is a complicated one with many facets The following objectives are set to achieve the aim of the paper: 1. Shed light on Ethics and the Use of Discourse Makers in English 2. Morals and new media The methods of the research are quantitative and qualitative. Frequency analysis, discourse analysis, and other methods will be used to study our corpora. [ABSTRACT FROM AUTHOR]

International students face unique challenges in pursuing higher education in a foreign country. To address these challenges and enhance their academic experience, higher education institutions are increasingly exploring the use of artificial intelligence (AI) applications. This research essay aims to investigate the impact of AI on the education of international students. Instead of a traditional literature review, it employs a research approach to examine the potential applications of AI and discuss associated concerns. The research paper explores various AI applications, such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research. By analyzing the role of AI in education for international students, this research paper sheds light on how AI can improve learning efficiency and provide customized educational support. Additionally, it identifies significant risks and limitations, including privacy concerns, cultural differences, language proficiency, and ethical implications, which must be effectively addressed. The findings contribute to a better understanding of the potential impact of AI on international students' educational experiences and offer insights into the integration of AI into educational administration and learning processes. [ABSTRACT FROM AUTHOR]

This paper will discuss how current challenges in airport management can be addressed by means of increased operational control. The paper will show that the availability and correct usage of operational data can help solve many of the headaches airport managers face today. The paper starts with an overview of the key developments that led to today's challenges, which will then be described. Next, how real-time and historical data can be used to address these challenges will be presented. Real-world examples will be used to illustrate that this is not just theory, but that tangible results are very much achievable. Finally, the paper will conclude with a summary of best practices that enable and facilitate the adoption of data-driven operations. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of the characteristics of 5G services on users' continuous intention to use, focusing on the technology acceptance model. With the start of the fourth industrial revolution in the 21st century, 5G is the best technology used in the Internet of Things, high-speed information and communication, artificial intelligence, big data, autonomous vehicles, virtual reality, augmented reality, robots, nanotechnology, and blockchain. The technical characteristics of 5G ultra-high-speed information communication are represented by ultra-high speed, ultra-high capacity, ultra-low delay, and ultra-high connectivity. 5G mobile communication technology is essential, and after the technology provided by 5G services is commercialized, it can play all its roles as a practical core new growth engine. 5G mobile communication (hereinafter referred to as 5G) is far superior to LTE, which is a 4G mobile communication, in terms of transmission speed, waiting time, and terminal capacity. 5G service is not just an axis of the process of developing mobile communication technology, but also the creation of innovative corporate value of technology. This is because higher network quality and innovation with 5G service technology will improve perceived usability, perceived ease of use, and perceived entertainment, which will ultimately have a positive impact on users' intention to use 5G services. Therefore, due to the lack of investment in information and communication bases, platforms, and applications, this paper can be used as the basis for establishing government policies. [ABSTRACT FROM AUTHOR]

Looking back at 50 years of Journal of Consumer Research methods and interviewing some of the field's most respected methodologists, this article seeks to craft a core set of best practices for scholars in consumer research. From perennial issues like conceptual validity to emerging issues like data integrity and replicability, the advice offered by our experts can help scholars improve the way they approach their research questions, provide empirical evidence that instills confidence, use new tools to make research more inclusive or descriptive of the "real world," and seek to become thought leaders. [ABSTRACT FROM AUTHOR]

There have been giant leaps in the field of education in the past 1–2 years.. Schools and colleges are transitioning online to provide more resources to their students. The COVID-19 pandemic has provided students more opportunities to learn and improve themselves at their own pace. Online proctoring services (part of assessment) are also on the rise, and AI-based proctoring systems (henceforth called as AIPS) have taken the market by storm. Online proctoring systems (henceforth called as OPS), in general, makes use of online tools to maintain the sanctity of the examination. While most of this software uses various modules, the sensitive information they collect raises concerns among the student community. There are various psychological, cultural and technological parameters need to be considered while developing AIPS. This paper systematically reviews existing AI and non-AI-based proctoring systems. Through the systematic search on Scopus, Web of Science and ERIC repositories, 43 paper were listed out from the year 2015 to 2021. We addressed 4 primary research questions which were focusing on existing architecture of AIPS, Parameters to be considered for AIPS, trends and Issues in AIPS and Future of AIPS. Our 360-degree analysis on OPS and AIPS reveals that security issues associated with AIPS are multiplying and are a cause of legitimate concern. Major issues include Security and Privacy concerns, ethical concerns, Trust in AI-based technology, lack of training among usage of technology, cost and many more. It is difficult to know whether the benefits of these Online Proctoring technologies outweigh their risks. The most reasonable conclusion we can reach in the present is that the ethical justification of these technologies and their various capabilities requires us to rigorously ensure that a balance is struck between the concerns with the possible benefits to the best of our abilities. To the best of our knowledge, there is no such analysis on AIPS and OPS. Our work further addresses the issues in AIPS in human and technological aspect. It also lists out key points and new technologies that have only recently been introduced but could significantly impact online education and OPS in the years to come. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI. [ABSTRACT FROM AUTHOR]

In the era of generative AI and specifically large language models (LLMs), exemplified by ChatGPT, the intersection of artificial intelligence and human reasoning has become a focal point of global attention. Unlike conventional search engines, LLMs go beyond mere information retrieval, entering into the realm of discourse culture. Their outputs mimic well-considered, independent opinions or statements of facts, presenting a pretense of wisdom. This paper explores the potential transformative impact of LLMs on democratic societies. It delves into the concerns regarding the difficulty in distinguishing ChatGPT-generated texts from human output. The discussion emphasizes the essence of authorship, rooted in the unique human capacity for reason—a quality indispensable for democratic discourse and successful collaboration within free societies. Highlighting the potential threats to democracy, this paper presents three arguments: the Substitution argument, the Authenticity argument, and the Facts argument. These arguments highlight the potential risks that are associated with an overreliance on LLMs. The central thesis posits that widespread deployment of LLMs may adversely affect the fabric of a democracy if not comprehended and addressed proactively and properly. In proposing a solution, we advocate for an emphasis on education as a means to mitigate risks. We suggest cultivating thinking skills in children, fostering coherent thought formulation, and distinguishing between machine-generated output and genuine, i.e., human, reasoning. The focus should be on the responsible development and usage of LLMs, with the goal of augmenting human capacities in thinking, deliberating and decision-making rather than substituting them. [ABSTRACT FROM AUTHOR]

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI. [ABSTRACT FROM AUTHOR]

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at t and t − 1. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method. [ABSTRACT FROM AUTHOR]