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In this short response to the papers appearing in this special issue (Technology, AI Bots and Psychotherapy After Covid), psychotherapist and author Aaron Balick draws on the variety of themes that have arisen within the contributed papers to reflect on the wider issue of computer mediated human relations. In it he makes a distinction between the papers that focus on the therapeutic process mediated by technology and those that look more broadly at the paradigm of therapy practice in this context. Framing technology as a tool, the author pulls together both strands to explore what psychotherapy research may say about the broader issues of societies mediated by technology and how therapeutic research may contribute to these larger social issues. [ABSTRACT FROM AUTHOR]

AbstractOpenAI’s ChatGPT is an advanced large language model AI that has caused both excitement and concern in academia. TurnItIn and similar software programs are widely used in higher education to detect potential plagiarism. However, the extent to which such software can identify papers produced by ChatGPT remains unclear. The current study partially addressed this question by submitting five versions of short essays about criminological topics generated from ChatGPT to the TurnItIn software. Overall, the results indicated that TurnItIn adequately detected that the essays were not original works (mean percent plagiarized score = 31%). The analyses further illustrated that ChatGPT wrote at an exceedingly high level (mean Flesch-Kincaid Grade Level = 15.1) atypical of essays in higher education. Consequently, it appears that detection of AI-generated writing may be easier than initially assumed although the technology is bound to improve. Accordingly, possible strategies for approaching AI in higher education are proposed. [ABSTRACT FROM AUTHOR]

With the increasing integration of AI tools like Yewno Discover, Scholarcy, and Grammarly in academic libraries, undergraduate research has witnessed transformative changes. These tools, while elevating the research process, also bring forth challenges rooted in ethics and application. This paper explores the synergy between modern technology and academic exploration, highlighting the benefits and potential pitfalls of using AI in the research workflow. It emphasizes that while Yewno Discover and similar tools offer streamlined navigation of vast information databases, it is imperative for undergraduates to remain cognizant of potential biases and other ethical considerations. This paper underscores the need for proactive measures in academic settings, including specialized training and policy development, to ensure that undergraduate researchers harness the power of AI responsibly and efficiently. [ABSTRACT FROM AUTHOR]

Though a computer professional, Meredith Broussard feels "that the way people talk about technology is out of touch with what digital technology can actually do", and that seeing the latter as a panacea has "resulted in a tremendous amount of poorly designed technology" (6), which needlessly complicates instead of improving or making life easier. Artificial Unintelligence: How Computers Misunderstand the World: by Meredith Broussard, Cambridge, MA, MIT Press, 2019, 248 pp., $15.95T/£12.99 (paper) Supported by extensive research, I Artificial Unintelligence i cogently challenges the prevailing technophile hype extolling the unlimited ways in which technology supposedly can "change the world for the better" and create a digital utopia with infinite benefits in every area of life. [Extracted from the article]

This paper highlights the importance of involving academic librarians in the development of ethical AI policies. The Academic Librarian Framework for Ethical AI Policy Development (ALF Framework) is introduced, recognizing librarians' unique skills and expertise. The paper discusses the benefits of their involvement, including expertise in information ethics and privacy, practical experience with AI tools, and collaborations. It also addresses challenges, such as limited awareness, institutional resistance, resource constraints, interdisciplinary collaboration, and evolving AI technologies, offering practical solutions. By actively involving librarians, institutions can develop comprehensive and ethical AI policies that prioritize social responsibility and respect for human rights. [ABSTRACT FROM AUTHOR]

The associability of artificial intelligence (AI) as a dual-use technology for nuclear material production (NMP) within the academic and practitioner communities remains widely neglected, and so a widening opportunity for AI to aid in illicit and covert non-peaceful applications exists. To address this emerging gap, this paper investigates the evolving and applicable uses of AI and finds broad evidence of its use to optimize performance, promote innovation, reduce costs, and enhance safety associated with the development and production of nuclear material. AI's use in this arena will, thereby, facilitate broader accessibility of peaceful uses of nuclear science and technology, while at the same time cause concerns that said improvements can aid the illicit development of nuclear weapons. As such, this paper advocates for a three-dimensional solution to manage the evolving dual-use concern of AI that involves advancing states-centric monitoring and regulation, promoting intellectual exchange between the nonproliferation sector and the AI industry, and encouraging AI industrial contributions. [ABSTRACT FROM AUTHOR]

The article presents a call for papers on artificial intelligence and robots for the library and information professions.

The existence of AI application examples is by itself an admirable achievement of engineers and professionals working on them, and certainly there is an interest to move forward to design AI algorithms to make it write proficiently with originality. Artificial Intelligence (AI) has become a trendy topic through advertisements, notes and even warnings about it, claiming that it can be used to answer almost any kind of question. Generating new content requires that AI conduct research work starting from, in principle, human proposed hypothesis, and then getting analyzable results to formulate conclusions. [Extracted from the article]

This paper explores how regulatory sandboxes can be used to promote the development of trustworthy artificial intelligence (AI) systems. The analysis focus on the experiences of Data Protection Authorities (DPAs) who have been experimenting with sandboxes to foster the implementation of privacy by design principles in AI systems. Throughout bibliographic research, this study (i) highlights privacy-related elements on international AI regulatory frameworks; (ii) explores the concept of privacy by design (PbD) and some of its strategies, patterns and techniques, such as Privacy Enhancing Technologies (PETs); (iii) reflects upon the impact of regulatory sandboxes; and (iv) analyses how regulatory sandboxes are being discussed in the context of AI regulatory frameworks. While this study brings global perspective on regulatory sandboxes, it also does relevant analysis in the Latin American context by presenting different stages of development of initiatives in the region. One of the main findings of this paper is that literature currently lacks an analysis of how privacy by design could be used in the context of AI systems design and governance. It also suggests that regulatory sandboxes may be able to integrate PbD principles to foster the development of trustworthy AI, although further research would be needed on that matter. [ABSTRACT FROM AUTHOR]

The United Nations Educational, Scientific and Cultural Organization (UNESCO) launched its Futures of Education policy initiative in early 2020. The process sought to open a sustained global policy debate on educational futures in a world typified by climatological instability, social inequity, and political unrest. Drawing on Deleuze and Guattari’s theory of assemblages, this paper explores how the Futures of Education initiative simultaneously produces, captures, and reroutes flows of desire by opening its policymaking process to global consultation. Following this line of thinking, the paper introduces a speculative method for critical policy analysis that utilises AI-powered tools to generate expressive images and imaginaries of education futures based on UNESCO’s three stages of policy development. The authors argue that this method enables a step beyond conventional interpretations of a policy’s discursive content toward a speculative analysis of its processual dynamics and expressive potentials. Ultimately, the paper underscores the generative possibilities of AI-assisted methods for tracking flows and expressions of desire within education policy assemblages under conditions of climatological and political upheaval. The authors encourage further experimentation with plugging emerging technologies into global policy assemblages to better understand how desire is invested into particular images and imaginaries of educational futures. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [ABSTRACT FROM AUTHOR]

This discussion article examines the potential integration of Generative Artificial Intelligence (Gen-AI), including advanced Large-Language Models like the popular platform ChatGPT into subject English education. Following the significant public and academic attention in response to these technologies through 2023, this paper considers the transformative potential and challenges posed by Gen-AI in educational settings. Central to the discussion is the exploration of how English teachers can leverage Gen-AI to enrich student learning beyond the obvious domain of writing skills. Instead, the article foregrounds the necessity for students' understanding of Gen-AI as an essential component of digital literacy. While acknowledging ethical concerns such as plagiarism, equity, and access, the paper presents an argument for the productive use of Gen-AI in the classroom to augment reading, viewing, and interpretation lessons. Avoiding an evangelical or dystopian view of AI, this discussion piece explores the time-critical and urgent issue of how, when, and why English can engage with the technology. [ABSTRACT FROM AUTHOR]

Learning around artificial intelligence (AI)-powered technologies that attends to power is an urgent and widely felt priority among the learning sciences and CS ed broadly. Popular approaches to AI education focus on technical skills, with far less theoretical and practical work around critical and justice-centered AI learning. Adding to this literature, we discuss tool design and observed interactions in Drag vs AI workshops, where participants use hands-on makeup art as a medium for fooling, subverting, and refusing facial recognition. Our broader analysis asks how participants make sense of the technical and political aspects of AI, as they interact with AI through the Drag vs AI workshops' modes of aesthetic transformation, tinkering, and resistance. In this paper, we focus on participants' embodied algorithmic tinkering with AI and affordances for justice-centered computing education. Our analysis highlights how tinkering and play modes of interaction with AI materials can promote critical and agentive learning. [ABSTRACT FROM AUTHOR]

Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation. [ABSTRACT FROM AUTHOR]

The lack of explainability of Artificial Intelligence (AI) is one of the first obstacles that the industry and regulators must overcome to mitigate the risks associated with the technology. The need for 'eXplainable AI' (XAI) is evident in fields where accountability, ethics and fairness are critical, such as healthcare, credit scoring, policing and the criminal justice system. At the EU level, the notion of explainability is one of the fundamental principles that underpin the AI Act, though the exact XAI techniques and requirements are still to be determined and tested in practice. This paper explores various approaches and techniques that promise to advance XAI, as well as the challenges of implementing the principle of explainability in AI governance and policies. Finally, the paper examines the integration of XAI into EU law, emphasising the issues of standard setting, oversight, and enforcement. [ABSTRACT FROM AUTHOR]

In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students' Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed. [ABSTRACT FROM AUTHOR]

AbstractWith the rapid advancement of technology, assistive robotic entities have arisen as indispensable instruments within diverse Human-Robot Interaction (HRI)-based health-care applications. By integrating Artificial Intelligence (AI) into these assistive robotic entities, they gain the capacity to autonomously perceive, engage in sophisticated reasoning, and execute actions within highly dynamic and complex environments. In light of these impressive achievements, this paper highlights a three-stage exploratory overview of the literature on the remarkable advancement of assistive robotics in HRI-based health-care applications. The first stage initiates an assessment of assistive robotics spanning historical epochs from ancient to modern times. Following this, the second stage comprehensively explores assistive robotics investigations in the realm of HRI-based health-care with its four sub-fields including rehabilitation, geriatric-care, pediatric-care, and nursing. Finally, the third stage entails a thorough analysis of the common challenges encountered in these pertinent investigations and provides a set of recommendations. This comprehensive paper not only provides an abundance of studies for each concept, method, and application in HRI, but it also presents their theoretical foundations, strengths, gaps, critical challenges, and recommendations. The results of the conducted exploratory overview shed light on the noteworthy prominence of assistive robotic entities within the HRI-based health-care field. The acquired findings emphasize the positive impact of such entities on human health, affirming their pivotal role in contributing to the advancement and effectiveness of health-care interventions. Furthermore, this paper provides an opportunity for scholars and researchers actively engaged in the pertinent field to obtain comprehensive additional insights, serving as a guiding resource for their academic endeavors. [ABSTRACT FROM AUTHOR]

The recent advances in educational technology enabled the development of solutions that collect and analyse data from learning scenarios to inform the decision-making processes. Research fields like Learning Analytics (LA) and Artificial Intelligence (AI) aim at supporting teaching and learning by using such solutions. However, their adoption in authentic settings is still limited, among other reasons, derived from ignoring the stakeholders' needs, a lack of pedagogical contextualisation, and a low trust in new technologies. Thus, the research fields of Human-Centered LA (HCLA) and Human-Centered AI (HCAI) recently emerged, aiming to understand the active involvement of stakeholders in the creation of such proposals. This paper presents a systematic literature review of 47 empirical research studies on the topic. The results show that more than two-thirds of the papers involve stakeholders in the design of the solutions, while fewer papers involved them during the ideation and prototyping, and the majority do not report any evaluation. Interestingly, while multiple techniques were used to collect data (mainly interviews, focus groups and workshops), few papers explicitly mentioned the adoption of existing HC design guidelines. Further evidence is needed to show the real impact of HCLA/HCAI approaches (e.g., in terms of user satisfaction and adoption). [ABSTRACT FROM AUTHOR]

The paper examines modern re-conceptualisations and re-contextualisations of publicness, proposing theoretical and empirical advancements in its conceptualisation. It critically analyses two significant developments following the English translation of Habermas's seminal work, "Strukturwandel der Öffentlichkeit." On the one hand, the book (re)affirmed the key role of communication and media in democratic politics and instigated a broad acceptance of the public sphere as a fundamental concept in academic discourse, leading to renewed research efforts and innovative developments. However, the subsequent conceptual fragmentation of (the concept of) the public sphere raised concerns about the loss of its original critical epistemic value. On the other hand, Habermas's book obscured important sociological traditions, contributing to a divide between normative theory and sociological analysis, exemplified by a neglect of the habitual roots of public opinion and contractual bonds in the evolution of publics. These aspects gain relevance within the context of the platform economy and artificial intelligence governing internet communication. The paper concludes by introducing the concept of the contractual public, which draws on the evolving dynamics between public and private spheres, and proposing four strategies to revitalise publicness. [ABSTRACT FROM AUTHOR]

This paper considers inequivalent monotone Boolean functions of an arbitrary number of variables, two monotone Boolean functions are equivalent if one can be obtained from the other by permuting the variables. It focuses on some inequivalent monotone Boolean functions with three and four types of equivalent variables, where the variables are either dominant or dominated. The paper provides closed formulas for their enumeration as a function of the number of variables. The problem we deal with is very versatile since inequivalent monotone Boolean functions are monotonic simple games, structures that are used in many fields such as game theory, neural networks, artificial intelligence, reliability or multiple-criteria decision-making. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of using ChatGPT in higher education, and discusses the potential risks and rewards of these tools. The paper also considers the difficulties of detecting and preventing academic dishonesty, and suggests strategies that universities can adopt to ensure ethical and responsible use of these tools. These strategies include developing policies and procedures, providing training and support, and using various methods to detect and prevent cheating. The paper concludes that while the use of AI in higher education presents both opportunities and challenges, universities can effectively address these concerns by taking a proactive and ethical approach to the use of these tools. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as an innovative technology with the potential to revolutionize various industries including libraries and information science. Academic libraries are increasingly adopting artificial intelligence (AI) to enhance services, improve efficiency, and enhance user experience. This study utilizes a bibliometric approach to comprehensively analyze current research on AI in academic libraries (AI in ALs). This study employed bibliometric indicators to identify key trends, patterns, and research gaps in the existing literature. A comprehensive dataset of 373 research papers on AI in ALs published between 2002 and 2022 was collected and analyzed using the Scopus database. Various bibliometric tools, such as Biblioshiny, VOSviewer, and BibExcel, have enhanced this analysis. The findings of this study provide important insights. By 2022, there were 64 publications, constituting 17.16% of the total corpus, accompanied by 65 citations. In contrast, 2019 witnessed only 33 publications yet accumulated a substantial number of citations, amounting to 294, representing 8.85% of the overall citations. Conference papers exhibited the highest frequency among different publication types, with 165 publications, whereas journal articles had the highest citation count, accumulating 217 citations. Geographically, China emerged as the leading contributor with 119 publications, and Wuhan University stood out as the most prominent affiliation. Notably, the "Lecture Notes in Computer Science" series emerged as the most prolific source title, publishing 15 articles, of which eight were cited. The authors Wang J., Wang C., and Wang X. from China demonstrated significant contributions, consistently publishing four papers annually from 2010 to 2022. [ABSTRACT FROM AUTHOR]

This paper examines the potential impact of AI-based language models on education, with a specific focus on the ChatGPT model developed by OpenAI. The study begins by providing an overview of the current state of AI in education, including the challenges and opportunities presented by these models. It then delves into an analysis of ChatGPT, including its capabilities, strengths and limitations. The paper also conducts a thorough analysis of potential implications and offers suggestions for assessment design to prevent students from utilising AI-based language models in educational environments. Finally, the paper concludes with a set of recommendations for assessment design, considering the specific characteristics of ChatGPT and the potential impact of AI-based language models on tourism education. [ABSTRACT FROM AUTHOR]

Despite the complex punching shear behavior of reinforced concrete slabs have been comprehensively addressed in the literature, it is further essential to develop a universal design model comprising high accuracy and the simplicity for design practicability, adaptable to diverse conditions encountered in practice. Artificial intelligence applications, artificial neural networks (ANN), and more recently, various machine learning (ML) and deep learning (DL) techniques veer off in a new direction in structural engineering context with improved accuracy and efficiency. The paper begins with the assessment of the capabilities of various artificial intelligence applications in predicting the punching shear strength of slab-column connections without shear reinforcement through the extensive database using 650 punching shear experiments from the literature. Critical parameters influencing the punching shear strength as well as the precision of the current code provisions in predicting this feature were then thoroughly examined in the paper. The results shown in this paper validated the competency of artificial intelligence applications in predicting the punching shear strength of such connections with increased accuracy and improved simplicity in practical terms. The proposed models utilizing the artificial intelligence applications encourage the ultimate rehabilitation policies to be proposed and improved code provisions to be developed for contemporary structures. [ABSTRACT FROM AUTHOR]

There is considerable uncertainty about the role that Artificial Intelligence (AI) might play in Environmental Impact Assessment (EIA), including into research. AI large language model (LLM) chatbots have the potential to increase the efficiency of EIA research, but their outputs can create concerns. This paper investigates the potential time savings achievable using LLM chatbots to undertake a critical review of literature focussing on the use of AI in EIA. Using a combination of ChatGPT and Elicit, literature was reviewed to identify 12 key issues associated with the use of AI in EIA and this paper was prepared in three and a half days from initial conception. A protocol is developed to assist researchers in fact checking evidence delivered through Elicit (or other machine learning tools) which serves as a novel outcome of this research. Using comments from three peer reviewers allowed some more objective reflection on the credibility of the LLM chatbot-derived output, on the appropriateness of the time savings, and on the future research needed on the application of LLM chatbots in this context. [ABSTRACT FROM AUTHOR]

There is a life after breast cancer. The prerequisite is early detection. Breast cancer is curable when detected early, tiny, and has not spread – regular screening aids in early detection. Clinical Thermography and artificial intelligence are potentially a good fit for early breast cancer screening. This survey paper presents a systematic review of artificial intelligence-based breast cancer screening using thermal infrared cameras. Initially, we will present the qualitative analysis of the existing literature regarding the trend and distribution. This review manuscript will then explore the literature about infrared thermal image acquisition and storage techniques. We will then highlight various segmentation techniques used for processing infrared thermal images. This paper presents the experimental results of the traditional image processing and deep learning-based segmentation techniques available in the literature using infrared breast thermal images. We then summarize the works that have used artificial intelligence to segment and classify infrared thermal images. The existing literature shows opportunities to explore the area of explainable artificial intelligence (AI). Explainable AI will make clinical Thermography into assistive technology for the medical community. [ABSTRACT FROM AUTHOR]

With the progression of deep-learning techniques, digital media recording and synthesis media generation have become exceptionally easy. Due to open access of user-friendly deepfaking applications generated using Artificial Intelligence methods especially Generative Adversarial Networks (GANs) and Variational Auto-encoders (VAEs), synthesis of recorded media has been made more effortless. Digital media synthesized using such applications is termed as deepfake. Generation of realistic fake audio/video content poses critical threats to an individual and society at large. To curb the threat of deepfaking, numerous deepfake detection algorithms have been proposed. This paper presents a survey on state-of-the-art deepfake generation techniques, categorized into face swap, attribute manipulation, and lip-sync manipulation. An analysis of some recently developed techniques that can generate images from text is also presented in the proposed paper. In addition, state-of-art image and video datasets released in the domain of deepfake detection are also discussed. The analysis provided in this survey reveals that every new deepfake generation technique calls for the development of novel deepfake detection techniques. Among deepfake detection techniques proposed so far, MobileNet-CNN model, multi-scale temporal CNN model, and GAN-based technique outperform good for face swap, lip-syncing, and attribute manipulation detection with an accuracy of 99.28%, 97.1% ,and 100%, respectively. This survey would help researchers to understand the literature on deepfake generation and detection which is required for future development in this field. [ABSTRACT FROM AUTHOR]

The concept of smart port is still disputed in the literature, appearing in dispersed contributions, often not aligned with its intelligent systems background. In this sense, this paper proposes a theoretical framework for smart port terminals by presenting a literature review and conceptual definition, proposing a maturity model, and exploring a research agenda. We investigate defining characteristics and relations between enablers, applications, and outcomes for smart port terminals. The proposed maturity model evaluates these main aspects in six areas of interest, providing maturity indexes that enable identifying the current state of intelligence in a port terminal, hence aiding its improvement. The application of the maturity model in two Brazilian container terminals provided feedback to the theoretical proposition and supports its use as a tool for general assessment of smart port terminal maturity. As such, the paper can serve as a comprehensive guide for researchers and port managers to understand the field, fostering the development of roadmaps and smart applications in port terminals. [ABSTRACT FROM AUTHOR]

The evolution of digital technologies has frequently been hailed as a 'game-changer' in education. However, like previous technological innovations, such as television, these recent developments have failed thus far to demonstrate any significant large-scale improvement in the quality of educational provision or in educational outcomes. The papers in this special issue suggest that there is potential to change this scenario. Digital platforms such as Binogi have been able to exploit technological advances such as vastly improved crosslinguistic machine translation ushered in by artificial intelligence to make curriculum content much more accessible to multilingual students. Drawing on the papers in this special issue, I highlight three dimensions of digital learning environments that have demonstrated pedagogical credibility to enhance multilingual learners' development of literacy and their acquisition of academic content in the target language: (a) they provide extensive access to and promote engagement with written (and oral) input in the target language, (b) they provide instructional scaffolds within the digital environment to promote both awareness of how language works and intentional learning of academic concepts and subject matter content, and (c) they encourage and enable students to become autonomous learners who are capable of self-regulating and evaluating their own learning. [ABSTRACT FROM AUTHOR]

Germany's role in global geopolitics is being renegotiated within current debates about future security architecture, weapons technology capabilities, and the spatiality of future battlefields. This paper introduces a critical geopolitics of technological futures approach and analyses weapons technology discourses through poststructuralist discourse theory and scholarship on technological expectations. It investigates German security policy documents' construction of future threat scenarios, technological visions, battlefields, and the material-technological consequences associated with these futures. It identifies how the geopolitical scenario of 'Westlessness' discursively shifted after Russia's war in Ukraine. It also considers the socio-technical imagination of 'hyperwar', which justifies the development of artificial intelligence-based weapons technologies. Such socio-technical imaginations construct desirable weapons and influence geopolitical leitmotifs. In turn, geopolitical imaginaries affect expectations of future weapons technologies and legitimate the acquisition of specific technologies. Therefore, this paper offers an approach to examining geopolitical power-knowledge constructions in relation to (new) technologies of killing. [ABSTRACT FROM AUTHOR]

Cloud computing brings important benefits and it is expected to play a key role in facilitating the uptake of emerging technologies and applications, including artificial intelligence, blockchain, and high-performance computing. Despite its potential to deliver cost and time-efficient services, the majority of businesses in the EU have still not implemented cloud computing. This illustrates the need for a more widespread adoption of the technology. Yet, recent regulatory initiatives may obstruct the uptake of cloud services. This is arguably because such initiatives do not reflect a proper understanding of the market, which our paper intends to provide. To that end, the paper examines what cloud computing is and how it works. It subsequently discusses the EU's attempts to regulate cloud computing, including the Digital Markets Act, the Digital Services Act, and the Data Act proposal. Our analysis demonstrates that the logic of these instruments and the obligations they establish do not fit the characteristics and workings of cloud computing. The paper concludes by noting that future regulation must mirror the specificities of the cloud, which has a value chain and traits that differ significantly from other digital services, most notably online platforms. [ABSTRACT FROM AUTHOR]

AbstractPrediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis. [ABSTRACT FROM AUTHOR]

Whatever one makes of the relationship between free will and moral responsibility – e.g. whether it’s the case that we can have the latter without the former and, if so, what conditions must be met; whatever one thinks about whether artificially intelligent agents might ever meet such conditions, one still faces the following questions. What is the value of moral responsibility? If we take moral responsibility to be a matter of being a fitting target of moral blame or praise, what are the goods attached to them? The debate concerning ‘machine morality’ is often hinged on whether artificial agents are or could ever be morally responsible, and it is generally taken for granted (following Matthias 2004) that if they cannot, they pose a threat to the moral responsibility system and associated goods. In this paper, I challenge this assumption by asking what the goods of this system, if any, are, and what happens to them in the face of artificially intelligent agents. I will argue that they neither introduce new problems for the moral responsibility system nor do they threaten what we really (ought to) care about. I conclude the paper with a proposal for how to secure this objective. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated methodology to enhance the efficiency of patch panel assembly lines by harnessing advanced artificial intelligence (AI) techniques. The primary objective is to minimise the total completion time of patch panels, a pivotal metric for manufacturing efficiency. The study employs the Assembly Line Production Optimisation (ALPO) model to analyze and enhance the production capacity of the bottleneck station. Existing challenges related to patch panel assembly line task distribution, process content, and tooling layout are scrutinised against production process standards for operational measurements. To achieve improvement, Deep Neural Network (DNN) and Recurrent Neural Network (RNN) models are employed in hidden layers, supplemented by deep feed-forward (DFF) layers for output, in adherence to engineering design principles. The bottleneck station's load rate is successfully reduced to below 80%, contributing to an overall balance rate increase from 73% to 88%. These outcomes signify a notable enhancement in production efficiency and employee satisfaction. The research utilises a dataset derived from the Microsoft Azure AI-based PdM dataset. The paper illustrates how AI techniques offer a comprehensive solution to address the inherent complexities in patch panel assembly line optimisation. [ABSTRACT FROM AUTHOR]

When it comes to computation in design, architecture, and the built environment, practices, methods, and tools frequently offer "neutral" and "optimized" techno-solutions to (social) design problems. We welcome contributions that seek to understand and uncover the power relations between (commercial) CAD systems, computational design practices, technology infrastructures, knowledge, education, and their reproductions of bias at multiple scales. This narrative of neutrality conceals power that computer-aided design (CAD) software monopolies and technology providers hold (Cardoso Llach [3]). [Extracted from the article]

Horizon scanning is a process to generate foresight – insight into how the future could unfold. Horizon scanning involves systematically searching a wide range of information sources for early signals of change, collecting those signals in a database, and exploring their possible implications for the future. The focus is often on external change in the broad contextual environment of a field or organization, i.e., the social, economic, technological, and governance domains. External change could be highly disruptive and is unlikely to be on the radar of decision makers. Early awareness of changes and monitoring their development over time can help natural resource planners, managers, and policymakers prepare for emerging challenges and opportunities. This paper introduces horizon scanning, characterizes different approaches and techniques, outlines the basic process, and briefly describes artificial intelligence in horizon scanning. [ABSTRACT FROM AUTHOR]

In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it. [ABSTRACT FROM AUTHOR]

The open-pit production system is a highly dynamic and uncertain environment with complex interactions between haulage and loading equipment on a shared road network. One of the key decisions in open-pit short-term planning is the allocation sequence of shovels to mining faces to meet the production targets established by long- and medium-term strategic plans. Deep Reinforcement Learning(DRL) techniques are commonly used in dynamic production environments. In this approach, an agent is trained on a simulation of the production system to learn the optimal decisions based on the system's current state. This paper proposes a DRL approach based on the Deep Q-Learning algorithm to obtain a robust shovel allocation plan for open-pit short-term planning. First, a discrete-event simulation of the mining production system incorporating trucks, shovels, crushers, waste dumps, and the road network is created. This simulation models the uncertainties of each component's operating cycle based on historical activity records, and it is used to train the DRL agent. The goal is to learn a robust shovel allocation strategy for the next production quarter, 3 months, to meet the tonnes per hour (TPH) production target to be delivered to the crusher feeds by interacting with the production simulator. The framework is tested in an iron ore open-pit mine case study where the shovel allocation agent successfully learns a strategy that consistently delivers the desired production target. [ABSTRACT FROM AUTHOR]

In the past, decisions on wastewater treatment methods have predominantly rested on expert opinions, utilizing the Delphi method. Yet, with an anticipated increase in diversification and customization, especially in the "small-batch and diverse" market over the next decade, addressing the formulation and execution of wastewater treatment for these non-traditional production processes will present substantial challenges. Relying solely on Delphi experts' decision-making within a short and time-constrained production planning window is expected to prove inadequate. Predominantly relies on the authors' over 15 years of industry experience in wastewater treatment, this perspective paper proposes an inventive solution that integrates Membrane Bioreactors (MBRs) with Artificial Intelligence (AI) applications. This approach signifies a more advanced method for industrial wastewater treatment compared to conventional methods, with the intention of garnering increased interest for future research endeavors. [ABSTRACT FROM AUTHOR]

A large number of ancient mosaics are no longer available to us because they have been destroyed by erosion, earthquakes, looting, or even used as materials in newer construction. To make things worse, among the small fraction of mosaics that have been recovered, many are damaged or incomplete. Therefore, it is of interest to explore how the mosaics may have appeared originally by using virtual reconstruction. This has traditionally been done manually and more recently using computer graphics programs but always by humans. In the last few years, artificial intelligence (AI) has made impressive progress in the generation of images from text descriptions and reference images. State-of-the-art AI tools such as DALL-E2 can generate high-quality images from text prompts and can take a reference image to guide the process. In August 2022, DALL-E2 launched a new feature called outpainting that takes as input an incomplete image and a text prompt and then generates a complete image filling in the missing parts. In this paper, we explore whether this innovative technology can be used to perform virtual reconstruction of mosaics with missing parts. Hence, a set of ancient mosaics have been selected and DALL-E2 has been used to create virtual reconstructions; the results are promising, showing that AI is able to interpret the key features of the mosaics and is able to produce virtual reconstructions that capture the essence of the scene. However, in some cases AI does not reproduce some details or geometric forms or introduces elements that are not consistent with the rest of the mosaic. This suggests that when AI image generation technology matures in the next few years, it could be a valuable tool to create virtual reconstructions of mosaics in the future. [ABSTRACT FROM AUTHOR]

Mental health concerns due to excess use of technology, accelerated by the COVID-19 pandemic, require increased research studies that explore the role of technology in users' wellbeing. This article aims to provide a conceptualization of digital wellbeing (DWB) in hospitality, or wellbeing in digital settings. An integrative literature review was used to identify three main enablers of DWB in hospitality: digital devices, digital interfaces, and digital applications. A conceptual model of DWB in the hospitality industry was created emphasizing the importance of designing digital experiences that foster positive emotions, engagement, relationships, meaning, and achievement of users to support their hedonic and eudaimonic wellbeing. The paper provides insights for the hospitality sector to enhance DWB. Future research should focus on empirical analyses of users' perceptions to refine and validate the proposed model and examine ethical issues concerning digital wellbeing. [ABSTRACT FROM AUTHOR]

Fashion image retrieval is one of the important services of e-commerce platforms, and it is also the basis of various fashion-related AI applications. Studies have shown that in a multi-modal environment (images + attribute labels), embedding items into specific attribute spaces can support more fine-grained similarity measures, which is especially suitable for fashion retrieval tasks. In this paper, we propose an attention-based attribute-guided similarity learning network (AttnFashion) for fashion image retrieval. The core of this network is an attribute-guided spatial attention module and an attribute-guided channel attention module, which correspond to the mapping between attributes and image regions, and the mapping between attributes and high-level image semantics, respectively. To make these two modules interact deeply, we design a parallel structure that allows them to share attribute embeddings and guide each other to extract specific features, which also helps to reduce the network parameters of the attention modules. An adaptive feature fusion strategy is proposed to synthesise the features extracted by the two modules. Extensive experiments show that the proposed AttnFashion performs better than current competitive networks in the field of fine-grained attribute-based fashion retrieval. [ABSTRACT FROM AUTHOR]

In this paper, a fractional adaptive backstepping sliding mode control system based on the Caputo-Fabrizio derivative for a 2-DoF mobile robot manipulator is designed and described. The fractional dynamics of the system are obtained using the Atangana-Baleanu derivative and the Euler-LaGrange formalism, which include external disturbances, parametric uncertainties, and nonholonomic constraints. A fractional sliding mode control strategy is designed for trajectory tracking tasks. In order to compensate for the chattering phenomenon, the proposed controller is combined with a fractional backstepping strategy. Additionally, the Caputo-Fabrizio derivative is introduced to further reduce the chattering effects and improve the driver performance. A fractional adaptive control law is used to cope with the parametric uncertainties, while adding robustness to external disturbances. To further improve system performance, a tracking control strategy based on a fractional neural network is added. For a comparative analysis, the conventional adaptive neural network backstepping sliding mode control is implemented. Numerical simulations are described and discussed to validate the effectiveness of our control system for trajectory tracking tasks under different operating conditions such as trajectory changes and external disturbances. Our proposed control system and its traditional counterpart were tuned using the Cuckoo optimization approach. [ABSTRACT FROM AUTHOR]

AbstractThis review explores the transformative potential of Artificial Intelligence (AI) in the light of evolving threats to planetary health, particularly the dangers posed by the climate crisis and its emerging mental health impacts, in the context of a climate-vulnerable country such as the Philippines. This paper describes the country’s mental health system, outlines the chronic systemic challenges that it faces, and discusses the intensifying and widening impacts of climate change on mental health. Integrated mental healthcare must be part of the climate adaptation response, particularly for vulnerable populations. AI holds promise for mental healthcare in the Philippines, and be a tool that can potentially aid in addressing the shortage of mental health professionals, improve service accessibility, and provide direct services in climate-affected communities. However, the incorporation of AI into mental healthcare also presents significant challenges, such as potentially worsening the existing mental health inequities due to unequal access to resources and technologies, data privacy concerns, and potential AI algorithm biases. It is crucial to approach AI integration with ethical consideration and responsible implementation to harness its benefits, mitigate potential risks, and ensure inclusivity in mental healthcare delivery, especially in the era of a warming planet. [ABSTRACT FROM AUTHOR]

AbstractThe widely accepted globalization of innovation entails two interrelated undertheorized aspects: (1) the capacity of certain firms to orchestrate transnational innovation systems appropriating successful results, which some have explained with the concept of corporate innovation systems (CIS), and (2) the co-existence of such globalization with those CIS and national innovation systems. I address these matters analysing US Big Tech artificial intelligence (AI) CIS showing that they combine multiple mechanisms to co-produce and appropriate AI. I propose ‘frenemy’ to describe Microsoft’s strategy because many Chinese organizations and even direct competitors integrate its CIS. ‘University’ symbolises Google’s strategy, given its focus on fundamental AI, its central place in the AI research field and appropriation mechanisms that are not translating into clear business advantages. ‘Secrecy’ defines Amazon’s strategy, maximizing knowledge inflows while minimizing outflows. Facebook, with the narrowest AI CIS, exhibits an ‘application-centred’ strategy. Ultimately, this paper contributes to understanding the multiple mechanisms used by leading corporations for controlling and shaping frontier transnational knowledge production and appropriation. By doing so, it advances our knowledge of the interplay between different innovation spheres (national, global and corporate) and highlights the dangers of CIS’s encroachment of national and global systems. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence applications based on machine learning is creating not only many opportunities but also risks. The recent regulatory and political debate, at the international level, emphasizes the urgent need to develop appropriate statistical methods that can measure the safety and the risks of AI applications. In line with this emerging need, the aim of this work is to launch a call for discussion papers in the Statistics journal on safe machine learning methods. To this aim, we define which metrics to develop and an example of a recent proposal, along with its advantages and disadvantages [ABSTRACT FROM AUTHOR]

Previous research in security studies contends that information warfare (IW) is becoming a critical element in states' overall security strategies. Additionally, many researchers posit that artificial intelligence (AI) is quickly emerging as an important component of digital communications and states' military applications worldwide. However, less is known regarding how states are incorporating AI in their information warfare and influence operations (IWIO). Thus, given the growing importance of AI and IW in global security, this paper examines how the United States, China, and Russia are incorporating AI in their IWIO strategies and tactics. We find that the US, China, and Russia are utilizing AI in their IWIO approaches in significant ways depending on each state's overall IW strategy, with important implications for international security. [ABSTRACT FROM AUTHOR]