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Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in artificial intelligence (AI) technology. In today's digital era, the ability to decipher emotions from facial expressions has evolved into a fundamental mode of human interaction and communication. As a result, FER has penetrated diverse domains, including but not limited to medical diagnosis, customer feedback analysis, the automation of automobile driver systems, and the evaluation of student comprehension. Furthermore, it has matured into a captivating and dynamic research field, capturing the attention and curiosity of contemporary scholars and scientists. The primary objective of this paper is to provide an exhaustive review of FER systems. Its significance goes beyond offering a comprehensive resource; it also serves as a valuable guide for emerging researchers in the FER domain. Through a meticulous examination of existing FER systems and methodologies, this review equips them with essential insights and guidance for their future research pursuits. Moreover, this comprehensive review contributes to the expansion of their knowledge base, facilitating a profound understanding of this rapidly evolving field. In a world increasingly dependent on technology for communication and interaction, the study of FER holds a pivotal role in human‐computer interaction (HCI). It not only provides valuable insights but also unlocks a multitude of possibilities for future innovations and applications. As we continue to integrate AI and facial emotion recognition into our daily lives, the importance of comprehending and enhancing FER systems becomes increasingly evident. This paper serves as a stepping stone for researchers, nurturing their involvement in this exciting and ever‐evolving field. [ABSTRACT FROM AUTHOR]

The amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead‐times of producing high‐quality peer‐reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health‐related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health‐ and computer‐science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single‐screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health‐related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research. [ABSTRACT FROM AUTHOR]

Technology is being developed to support the peer review processes of journals, conferences, funders, universities, and national research evaluations. This literature and software summary discusses the partial or complete automation of several publishing‐related tasks: suggesting appropriate journals for an article, providing quality control for submitted papers, finding suitable reviewers for submitted papers or grant proposals, reviewing, and review evaluation. It also discusses attempts to estimate article quality from peer review text and scores as well as from post‐publication scores but not from bibliometric data. The literature and existing examples of working technology show that automation is useful for helping to find reviewers and there is good evidence that it can sometimes help with initial quality control of submitted manuscripts. Much other software supporting publishing and editorial work exists and is being used, but without published academic evaluations of its efficacy. The value of artificial intelligence (AI) to support reviewing has not been clearly demonstrated yet, however. Finally, whilst peer review text and scores can theoretically have value for post‐publication research assessment, it is not yet widely enough available to be a practical evidence source for systematic automation. [ABSTRACT FROM AUTHOR]

As full AI‐based automation remains out of reach in most real‐world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human‐AI systems. Our conceptual model is centered around three high‐level aspects ‐ agency, interaction, and adaptation ‐ and is developed through a multi‐step process. Firstly, an initial design space is proposed by surveying the literature and consolidating existing definitions and conceptual frameworks. Secondly, this model is iteratively refined and validated by conducting semi‐structured interviews with nine researchers in this field. Lastly, to illustrate the applicability of our design space, we utilize it to provide a structured description of selected human‐AI systems. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information‐theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data. [ABSTRACT FROM AUTHOR]

Summary: Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‐growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‐society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Epilepsy is a common non‐communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state‐of‐the‐art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start‐ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA advancements have shown immense potential and paved a path to successful real‐time detection, prediction, and management of epileptic seizures. [ABSTRACT FROM AUTHOR]

The so‐called age of AI, industry 4.0, the fourth industrial revolution, etc. all attempt to conjure into existence a new technological paradigm. Should we believe the hype? This paper draws on neo‐Schumpeterian and régulation theory to widen the scope of this debate and examine techno‐economic and institutional discontinuities. In exploring these discontinuities, this paper argues, first, that growth regimes are not necessarily tenable as indicators of new paradigms, and, second, that there are (infra)structural discontinuities between the ICT/post‐Fordist era and those of the AI/platform era. Platformisation entails a distinct institutional logic, regime of accumulation (RA) and mode of social régulation (MSR). The clusters of technological and institutional changes behind this shift have not yet been sufficiently addressed by economic geography and associated literatures. In reconceptualising the shift in terms of (infra)structural discontinuity, the paper synthesises neo‐Schumpeterian and régulation theory to identify both technological and institutional changes in the régulation of capitalist accumulation. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) technologies become more ubiquitous for streamlining and optimizing work, they are entering fields representing organizational logics at odds with the efficiency logic of automation. One such field is journalism, an industry defined by a logic enacted through professional norms, practices, and values. This paper examines the experience of technologists developing and employing natural language generation (NLG) in news organizations, looking at how they situate themselves and their technology in relation to newswork. Drawing on institutional logics, a theoretical framework from organizational theory, we show how technologists shape their logic for building these emerging technologies based on a theory of rationalizing news organizations, a frame of optimizing newswork, and a narrative of news organizations misinterpreting the technology. Our interviews reveal technologists mitigating tensions with journalistic logic and newswork by labeling stories generated by their systems as nonjournalistic content, seeing their technology as a solution for improving journalism, enabling newswork to move away from routine tasks. We also find that as technologists interact with news organizations, they assimilate elements from journalistic logic beneficial for benchmarking their technology for more lucrative industries. [ABSTRACT FROM AUTHOR]

Background: Auditory–perceptual assessment of voice is a subjective procedure. Artificial intelligence with deep learning (DL) may improve the consistency and accessibility of this task. It is unclear how a DL model performs on different acoustic features. Aims: To develop a generalizable DL framework for identifying dysphonia using a multidimensional acoustic feature. Methods & Procedures: Recordings of sustained phonations of /a/ and /i/ were retrospectively collected from a clinical database. Subjects contained 238 dysphonic and 223 vocally healthy speakers of Chinese Mandarin. All audio clips were split into multiple 1.5‐s segments and normalized to the same loudness level. Mel frequency cepstral coefficients and mel‐spectrogram were extracted from these standardized segments. Each set of features was used in a convolutional neural network (CNN) to perform a binary classification task. The best feature was obtained through a five‐fold cross‐validation on a random selection of 80% data. The resultant DL framework was tested on the remaining 20% data and a public German voice database. The performance of the DL framework was compared with those of two baseline machine‐learning models. Outcomes & Results: The mel‐spectrogram yielded the best model performance, with a mean area under the receiver operating characteristic curve of 0.972 and an accuracy of 92% in classifying audio segments. The resultant DL framework significantly outperformed both baseline models in detecting dysphonic subjects on both test sets. The best outcomes were achieved when classifications were made based on all segments of both vowels, with 95% accuracy, 92% recall, 98% precision and 98% specificity on the Chinese test set, and 92%, 95%, 90% and 89%, respectively, on the German set. Conclusions & Implications: This study demonstrates the feasibility of DL for automatic detection of dysphonia. The mel‐spectrogram is a preferred acoustic feature for the task. This framework may be used for vocal health screening and facilitate automatic perceptual evaluation of voice in the era of big data. WHAT THIS PAPER ADDS: What is already known on this subject: Auditory–perceptual assessment is the current gold standard in clinical evaluation of voice quality, but its value may be limited by the rater's reliability and accessibility. DL is a new method of artificial intelligence that can overcome these disadvantages and promote automatic voice assessment. This study explored the feasibility of a DL approach for automatic detection of dysphonia, along with a quantitative comparison of two common sets of acoustic features. What this study adds to existing knowledge: A CNN model is excellent at decoding multidimensional acoustic features, outperforming the baseline parameter‐based models in identifying dysphonic voices. The first 13 mel‐frequency cepstral coefficients (MFCCs) are sufficient for this task. The mel‐spectrogram results in greater performance, indicating the acoustic features are presented in a more favourable way than the MFCCs to the CNN model. What are the potential or actual clinical implications of this work?: DL is a feasible method for the detection of dysphonia. The current DL framework may be used for remote vocal health screening or documenting voice recovery after treatment. In future, DL models may potentially be used to perform auditory–perceptual tasks in an automatic, efficient, reliable and low‐cost manner. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is at the heart of modern society with computers now capable of making process decisions in many spheres of human activity. In education, there has been intensive growth in systems that make formal and informal learning an anytime, anywhere activity for billions of people through online open educational resources and massive online open courses. Moreover, new developments in Artificial Intelligence‐related educational assessment are attracting increasing interest as means of improving assessment efficacy and validity, with much attention focusing on the analysis of the large volumes of process data being captured from digital assessment contexts. In evaluating the state of play of Artificial Intelligence in formative and summative educational assessment, this paper offers a critical perspective on the two core applications: automated essay scoring systems and computerized adaptive tests, along with the Big Data analysis approaches to machine learning that underpin them. Lay Description: What is already known about this topic?: AI and machine learning are established in summative assessments (SA) of learningThe main types are Automated Essay Scoring (AES) and Computerised Adaptive Testing (CAT)Machine learning, based on Big Data and Learning Analytics, offers exciting possibilities in formative assessment (FA) What this paper adds?: A review of current AI and machine learning platforms in FA and SAInsights into the structure and design of CATs and AESsA review of current research on usage of Big Data/Learning Analytics for FA and SAA critique of the future for AI and machine learning in assessing higher order learning Implications for practice and/or policy: The use of AI for the assessment of higher order learning is not yet a feasible realitySophisticated learning analytics can now offer closer alignment of AI to human judgment'Trained' machine feedback can support self‐regulated learning in online environmentsIndicators are emerging from process data analysis on a learner's behaviour and affective state [ABSTRACT FROM AUTHOR]

Background: Different types of assessments influence learning and learning behaviour. Multiple‐choice questions (MCQs) reward partial knowledge and encourage surface learning, while open‐ended questions (OEQs) promote deeper learning. Currently, MCQs is part of team‐based learning (TBL) curriculum, and it is challenging to implement OEQs as immediate feedback is necessary. Objectives: We asked if MCQ and OEQs affect student achievement, student learning and student engagement differently in a TBL classroom. Methods: MCQs and OEQs test scores of N = 66 students were automatically captured in Learning Activity Management System (LAMS) and were compared using a switching replications quasi‐experimental design with pre‐ and post‐tests to answer the research questions. Student learning approaches and engagement in the team activities were assessed using the study process questionnaire and the structure of observed learning outcomes taxonomy respectively. Results and Conclusions: Students get significantly higher MCQ scores than OEQs for the same set of questions, but the reverse is true for application exercises (AEs), which focus on higher‐level application. Most students significantly deepened their learning approaches before OEQs, while poorly prepared students were less engaged during OEQ discussions. Interestingly students subjected to OEQs took less time and scored higher in AE discussions, suggesting better focus on higher‐level thinking. Implications: This project is significant as it bridges our understanding of the value of OEQs and TBL. Our approach is transferable to other courses, and thus it can improve the quality of teaching and learning in tertiary education. Lay Description: What is already known about this topic?: Multiple‐choice questions (MCQs) do not promote deep learning as much as open‐ended questions (OEQ) do.Team‐based learning (TBL) requires MCQs because of the need for immediate feedback.Automated marking can make it more efficient to provide immediate feedback on OEQs as required by the TBL approach. What this paper adds?: OEQs changed student‐learning approaches in the TBL context.OEQs changed the quality of team discussions during the team‐readiness assessment.Test (tRAT) and application exercise phases.Students were concerned about the validity of automated grading. Implications for practitioners: Consider using OEQs in TBL context for deepening student learning.Consider how to defuse students' scepticism of new technology.Consider forming teams with a mix of deep and surface learners. [ABSTRACT FROM AUTHOR]

Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non‐reproducible and lack accuracy. The following review describes methods that diagnose and measure the extent of eye dryness, enabling clinicians to quantify its severity. Our aim with this paper is to review classical methods as well as those that incorporate automation. For only four ways of quantifying DED, we take a deeper look into what main elements can benefit from automation and the different ways studies have incorporated it. Like numerous medical fields, Artificial Intelligence (AI) appears to be the path towards quality DED diagnosis. This review categorises diagnostic methods into the following: classical, semi‐automated and promising AI‐based automated methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how data about public preferences may be used to inform policy around the use of controversial novel technologies, using public preferences about autonomous vehicles (AVs) as a case study. We first summarize the recent 'Moral Machine' study, which generated preference data from millions of people regarding how they think AVs should respond to emergency situations. We argue that while such preferences cannot be used to directly inform policy, they should not be disregarded. We defend an approach that we call 'Collective Reflective Equilibrium in Practice' (CREP). In CREP, data on public attitudes function as an input into a deliberative process that looks for coherence between attitudes, behaviours and competing ethical principles. We argue that in cases of reasonable moral disagreement, data on public attitudes should play a much greater role in shaping policies than in areas of ethical consensus. We apply CREP to some of the global preferences about AVs uncovered by the Moral Machines study. We intend this discussion both as a substantive contribution to the debate about the programming of ethical AVs, and as an illustration of how CREP works. We argue that CREP provides a principled way of using some public preferences as an input for policy, while justifiably disregarding others. [ABSTRACT FROM AUTHOR]

This paper proposed a saturated adaptive barrier sliding mode control (SABSMC) for disturbed systems with parameter uncertainty, external disturbance, and input saturation. One distinctive feature of the proposed method is that even under the influence of input saturation, it is able to drive the system trajectory into a prespecified neighbourhood of the sliding surface, independent of the upper limit of lumped disturbance. Moreover, attributing to the designed adaptive laws, the switching gain of the proposed method can be adaptively adjusted in a wise manner. Another attractive feature of the proposed method is that it removes any presumption of constant upper limit on the lumped disturbance. Instead, SABSMC utilises a more rational state‐dependent upper limit assumption. The system dynamics of the closed‐loop system is analysed via the Lyapunov method in the presence of parameter uncertainty, external disturbance, and input saturation. The effectiveness of the proposed method is confirmed through numerical simulations. [ABSTRACT FROM AUTHOR]