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The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]

This paper presents the case of an adjunct university professor to illustrate the dilemma of using artificial intelligence (AI) technology to grade student papers. The hypothetical case discusses the benefits of using a commercial AI service to grade student papers—including discretion, convenience, pedagogical merits of consistent feedback for students, and advances made in the field that yield high-quality work—all of which are achieved quickly. Arguments against using AI to grade student papers involve cost, privacy, legality, and ethics. The paper discusses career implications for faculty members in both situations and concludes with implications for researchers within the discourse on academic integrity. [ABSTRACT FROM AUTHOR]

In the development of physical theories, thought experiments play a crucial role. Research on this topic began in 1976 and has continued to the present. This study aims to provide a more complete picture of the progress of thought experiments over the past two decades. To achieve this, this study employs bibliometric mapping methods. A total of 679 published papers were analyzed, including articles (504), conference papers (92), and book chapters (83). This data was retrieved from the Scopus database. The study's findings reveal that research and publications on thought experiments are highly valued and have received significant attention over the past eight years. According to the findings, 90% of the top 20 source titles contributing to thought experiments are from journals in the first and second quartiles (Q1 and Q2). This quartile ranking shows the quality and significant influence of a journal. The geographical distribution indicates that the United States contributes the most to thought experiments research, with 213 documents, 2592 citations, and 47 links. We also identified several prospective keywords that could be the focus of future research, including artificial intelligence, physics education, fiction, God, theology, productive imagination, technology, speculative design, and critical design. Therefore, this study provides a thorough picture of thought experiment research trends and future directions of potential topics that can be the focus of future researchers. [ABSTRACT FROM AUTHOR]

This paper shares results from a pedagogical experiment that assigns undergraduates to "cheat" on a final class essay by requiring their use of text-generating AI software. For this assignment, students harvested content from an installation of GPT-2, then wove that content into their final essay. At the end, students offered a "revealed" version of the essay as well as their own reflections on the experiment. In this assignment, students were specifically asked to confront the oncoming availability of AI as a writing tool. What are the ethics of using AI this way? What counts as plagiarism? What are the conditions, if any, we should place on AI assistance for student writing? And how might working with AI change the way we think about writing, authenticity, and creativity? While students (and sometimes GPT-2) offered thoughtful reflections on these initial questions, actually composing with GPT-2 opened their perspectives more broadly on the ethics and practice of writing with AI. In this paper, I share how students experienced those issues, connect their insights to broader conversations in the humanities about writing and communication, and explain their relevance for the ethical use and evaluation of language models. [ABSTRACT FROM AUTHOR]

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians' preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine. While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists' workflow. The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due—policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Key points 1. Various national societies have established initiatives for structured reporting in radiology. 2. Almost no monetary or structural incentives exist that favor structured reporting. 3. A consensus on technical standards for structured reporting is still missing. 4. The application of large language models may help structuring radiological reports. 5. Policy makers should incentivize the usage of structured radiological reporting. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]

This document is a special issue of the Journal of Intelligent Information Systems, focusing on the selected papers from the International Symposium on Methodologies for Intelligent Systems (ISMIS 2022). The symposium, held in Cosenza, Italy, showcased research on various topics related to artificial intelligence, including decision support, knowledge representation, machine learning, computer vision, and more. The special issue includes eleven papers that have undergone rigorous peer-reviewing and cover a wide range of research topics, such as deep learning, anomaly detection, malware detection, sentiment classification, and healthcare professionals' burnout. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]

To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology. [ABSTRACT FROM AUTHOR]

The article discusses the use of artificial intelligence (AI) in creating artwork and explores the future possibilities of AI in combining physics and images to stimulate reality for humans. The author examines the evolution of images and the growing relationship between humans and machines. They raise ethical questions about AI monitoring human actions independently and the potential risks of AI becoming uncontrollable. The article emphasizes the need for ongoing conversations between technology and society and urges AI developers and policymakers to consider the future implications of AI. [Extracted from the article]

This document is a summary of a special issue in the Journal of Intelligent Manufacturing titled "Intelligent cobot systems: human-cobot collaboration in manufacturing." The special issue includes 10 papers that cover various aspects of cobot systems design and management from a human perspective. Topics covered include adaptive safety constraints, augmented reality programming, task allocation, human-robot collaboration disassembly, role transitions and adaptation, intelligent assistance systems, fault detection and diagnostics, automated visual inspection, and intelligent digital twins. The papers highlight the advantages and challenges of cobot systems in manufacturing and provide insights for future research. The authors of the document are Maurizio Faccio and Yuval Cohen. [Extracted from the article]

Keeping up with the hastily growing economy implies undergoing unremitting transformation permanently. In the field of supply chain, such progress can only be guaranteed via the exploration of new horizons and innovative solutions in response to the constraints of the global market. Emerging technologies, particularly artificial intelligence, offer promising avenues for enhancing supply chain processes, with sustainability ascending as a critical consideration. Despite the recent surfacing of AI-driven applications, scant attention has been devoted to exploring their full potential within supply chain operations, particularly in conjunction with SDGs. Recognizing the untapped opportunities presented by the implementation of AI for a sustainable supply chain this study undertakes a bibliometric analysis of 236 research papers sourced from the Web of science database. The analysis utilizes R language BiblioShiny to examine the extracted papers, and dissect patterns, trends, and relationships among key concepts and themes as well as prominent topics, impactful authors, and leading journals and countries in this domain. The findings reveal substantial growth in research related to SCM, AI, and sustainability as the UK leads this field of study with 132 articles followed by India, China and the USA. Eventually, the National University of Singapore came first in terms of paper affiliations, followed by De La Salle University, and London Metropolitan University. These results only prove that sustainability is becoming more critical in the equation of AI-driven supply chains especially with the current socio-political and economic circumstances, constituting a solid base for further academic research and more innovations in the managerial and business-related policies in this field. [ABSTRACT FROM AUTHOR]

Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different "authors" including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging. [ABSTRACT FROM AUTHOR]

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow. [ABSTRACT FROM AUTHOR]

India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]

Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal Friction, focusing on the special issue of Artificial Intelligence (AI) and emerging computational approaches for tribology. Tribology research, which studies friction and wear, has traditionally relied on extensive experimentation. However, AI and computational approaches offer new opportunities to explore complex processes in tribology and push the boundaries of research. The special issue includes 15 papers covering various aspects of AI and machine learning in tribology, such as wear assessment, lubrication performance, contact wear prediction, and composite materials design. These articles highlight the transformative impact of AI in tribology research and provide valuable insights for future innovations. [Extracted from the article]

This correction notice acknowledges the contributions of several contributors to the original article on standardized nomenclature for litigational legal prompting in generative language models. Saman Andalib and Aidin Spina, along with their team at UC Irvine, created the concept of 'variables' and 'clauses' that were iterated upon in the paper. They also helped with the organization and presentation of information in the early stages of the paper. The original article has been updated to include these acknowledgements. The publisher, Springer Nature, remains neutral in terms of jurisdictional claims and institutional affiliations. [Extracted from the article]

The fourth paper which is about "Emotional AI and Crime" takes the argument into the key area of how good or bad are AI techniques that are designed for facial and related recognition. To an extent, the focus on AI is wider than what might find in any discussion of AI in the narrower technical field for context is all important to see urban AI in context. Artificial intelligence (AI) emerged alongside the development of the digital computer more than 80 years ago during the second world war. [Extracted from the article]

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

Summary: Due to the convenience of virtualization, the live migration of virtual machines is widely used to fulfill optimization objectives in cloud/edge computing. However, live migration may lead to side effects and performance degradation when migration is overused or an unreasonable migration process is carried out. One pressing challenge is how to capture the best opportunity for virtual machine migration. Leveraging rough sets and AI, this paper provides an innovative strategy based on Q-learning that is designed for migration decisions. The highlight of our strategy is the harmonious mechanism for applying rough sets and Q-learning. For the ABDS (adaptive boundary decision system) strategy in this paper, the exploration space of Q learning is confined by the boundary region of rough sets, while the thresholds of the boundary region can be dynamically adjusted by the reaction results from the computing cluster. The structure and mechanism of the ABDS strategy are described in this paper. The corresponding experiments show a firm advantage for the cooperation of rough sets and reinforcement learning algorithms. Considering both the energy consumption and application performance, the ABDS strategy in this paper outperforms the benchmark strategies in comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the applications of Convolutional Neural Networks (CNN) in the railway maintenance industry. Our research covers specifically the subdomain of railway track maintenance. In this study, we have analyzed the state-of-the-art of CNNs applied to railway track maintenance by conducting an extensive literature review, summarizing different tasks and problems related to the topic and presenting solutions based on CNNs with a special emphasis on the data used to create these models. The results of our research show different applications of CNNs within the scope, including the detection of defects in the surface of railway rails and railway track components, such as fasteners, joints, sleepers, switches and crossings, as well as the recognition of track components, and the continuous monitoring of railway tracks. The architecture of CNNs is fitting to learning spatial hierarchies of features directly from the input data, making them of great use for Computer Vision and other applications related to the topic at hand. The implementation of IoT devices and smart sensors aid the collection of real-time data which can be used to feed powerful CNN models to recognize patterns and identify complex events related to the maintenance of railway tracks. This and more insights are discussed in detail within the contents of this paper. [ABSTRACT FROM AUTHOR]

Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem. Highlights: Automated support for literature choice and solution selection for any use case. A generalized keyword selection scheme for literature database queries. Trends in literature: detecting AI methods for a case study using Scopus and OpenAI. A better understanding of the tool by sensitivity analyzes for Scopus and OpenAI. Robust tool for different domains with promising OpenAI performance results. [ABSTRACT FROM AUTHOR]

Online education, while not a new phenomenon, underwent a monumental shift during the COVID-19 pandemic, pushing educators and students alike into the uncharted waters of full-time digital learning. With this shift came renewed concerns about the integrity of online assessments. Amidst a landscape rapidly being reshaped by online exam/homework assistance platforms, which witnessed soaring stocks as students availed its questionable exam assistance, and the emergence of sophisticated artificial intelligence tools like ChatGPT, the traditional methods of assessment faced unprecedented challenges. This paper presents the results of an observational study, using data from an introductory statistics course taught every semester by the author, and delves into the proliferation of cheating methods. Analyzing exam score results from the pre and post introduction of ChatGPT periods, the research unpacks the extent of cheating and provides strategies to counteract this trend. The findings starkly illustrate significant increases in exam scores from when exams of similar difficulty were administered in person (pre-Covid) versus online. The format, difficulty, and grading of the exams was the same throughout. Although randomized controlled experiments are generally more effective than observational studies, we will indicate when we present the data why experiments would not be feasible for this research. In addition to presenting experimental findings, the paper offers some insights, based on the author's extensive experience, to guide educators in crafting more secure online assessments in this new era, both for courses at the introductory level and more advances courses The results and findings are relevant to introductory courses that can use multiple choice exams in any subject but the recommendations for upper-level courses will be relevant primarily to STEM subjects. The research underscores the pressing need for reinventing assessment techniques to uphold the sanctity of online education. [ABSTRACT FROM AUTHOR]

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. [ABSTRACT FROM AUTHOR]

The paper introduces a natural language processing system designed to answer natural language questions across knowledge graphs for scientific data applications where no prior training data in the form of question-answer pairs is available. 59 papers, submitted by 243 authors from over 20 countries/regions to SSDBM 2021, have showcased the pervasiveness of AI/ML in the field of scientific and statistical data management, which covered a variety of related topics (among others) including: In-database machine learning. Recent advancement in deep neural networks, combined with the high performance computing power and Big Data, has profoundly brought artificial intelligence (AI) to nearly all fields of scientific disciplines. [Extracted from the article]

Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]

Automatic Speech Emotion Recognition (ASER) has recently garnered attention across various fields including artificial intelligence, pattern recognition, and human–computer interaction. However, ASER encounters numerous challenges such as a shortage of diverse datasets, appropriate feature selection, and suitable intelligent recognition techniques. To address these challenges, a systematic literature review (SLR) was conducted following established guidelines. A total of 60 primary research papers spanning from 2011 to 2023 were reviewed to investigate, interpret, and analyze the related literature by addressing five key research questions. Despite being an emerging area with applications in real-life scenarios, ASER still grapples with limitations in existing techniques. This SLR provides a comprehensive overview of existing techniques, datasets, and feature extraction tools in the ASER domain, shedding light on the weaknesses of current research studies. Additionally, it outlines a list of limitations for consideration in future work. [ABSTRACT FROM AUTHOR]

In October 2021, the Secure Knowledge Management (SKM) Conference convened many of scholars and practitioners to discuss and share ideas about the use and development of AI for SKM and cybersecurity. Over the past half-decade, numerous federal funding agencies such as the National Science Foundation (NSF), National Academies of Science (NAS), and National Security and Technology Council (NSTC) have noted the significant role that Artificial Intelligence (AI)-enabled analytics techniques such as deep learning, machine learning, network science, generative models, reinforcement learning, text analytics, and other techniques could play for Secure Knowledge Management (SKM), and more broadly, cybersecurity. Papers in each theme had a component of AI that was based in one or more of the major themes of extant AI-enabled cybersecurity and SKM research summarized in the previous section. [Extracted from the article]

However, the use of artificial intelligence (AI) tools like ChatGPT has generated significant concern among researchers about their potential misuse and ethical implications (Curtis [1]). The artificial abstracts produced by ChatGPT and the original abstracts were passed through AI output detector, and also reviewed by blinded human reviewers. B Commentary b ChatGPT, is an artificial intelligence language model developed by OpenAI, is a well-known technology that requires no introduction in today's world. [Extracted from the article]

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. Critical relevance statement: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. Key Points: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects. [ABSTRACT FROM AUTHOR]

In the past, manual inspection was often used for equipment inspection in indoor environments such as substation rooms and chemical plant rooms. This way often accompanies high labor intensity, low inspection efficiency, and low safety, which is difficult to meet the increasingly stringent requirements of indoor equipment operation and maintenance management. For dealing with these issues, a VIORB-SLAM2 algorithm based on the integration of IMU and visual information, was proposed by this paper. Firstly, the IMU data and image data were integrated to restore scale information of cameras, and then an error function was established to enhance the algorithm's robustness. Secondly, in order to improve the accuracy of the algorithm, the random sampling consensus method was used to eliminate the wrong matching points in feature point matching, and the normalized cross-correlation matching was employed to constrain key frame matching conditions. Finally, through the iterative closest point method to stitch the point clouds, a dense map for navigation was constructed. The experimental results show that the algorithm designed by this paper has solved the shortcomings of applying the ORB-SLAM2 algorithm to indoor inspection robots while achieving high positioning accuracy, which can be combined with other algorithms in the field of artificial intelligence for object detection and semantic map construction in the future. [ABSTRACT FROM AUTHOR]

Language agents are AI systems capable of understanding and responding to natural language, potentially facilitating the process of encoding human goals into AI systems. However, this paper argues that if language agents can achieve easy alignment, they also increase the risk of malevolent agents building harmful AI systems aligned with destructive intentions. The paper contends that if training AI becomes sufficiently easy or is perceived as such, it enables malicious actors, including rogue states, terrorists, and criminal organizations, to create powerful AI systems devoted to their nefarious aims. Given the strong incentives for such groups and the rapid progress in AI capabilities, this risk demands serious attention. In addition, the paper highlights considerations suggesting that the negative impacts of language agents may outweigh the positive ones, including the potential irreversibility of certain negative AI impacts. The overarching lesson is that various AI-related issues are intimately connected with each other, and we must recognize this interconnected nature when addressing those issues. [ABSTRACT FROM AUTHOR]

As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks. [ABSTRACT FROM AUTHOR]

As the pillar industry of coastal areas, aquaculture needs artificial intelligence technology to promote economic development. To realize the automation of the aquaculture industry, this paper proposes a new underwater object detection network: WBi-YOLOSF. It realizes the automatic classification and detection of aquatic products, improves the production efficiency of the aquaculture industry, and promotes its economic development. This paper creates an image dataset containing 15 aquatic products to lay the data foundation for model training. In the data preprocessing part, an underwater image enhancement algorithm is proposed to improve the quality of the data set effectively. Aiming at the problem of high false detection rate and missed detection rate of underwater dense small targets, a new data enhancement method was proposed to improve the training set's data quality comprehensively. Inspired by the weighted bidirectional feature pyramid network, this paper proposes a new feature extraction network: AU-BiFPN, which solves the gradient problem caused by the network hierarchy's deepening on enhancing the network's multi-scale feature fusion. The AU-BiFPN network structure is embedded into the YOLO series network framework, significantly improving the basic network's feature extraction and feature fusion ability and dramatically improving the prediction accuracy without affecting the network inference speed. Here, the swarm intelligence algorithm is introduced to optimize the model hyperparameters, accelerating the convergence speed of model training and significantly reducing the computational cost. At the same time, the model's accuracy is improved by a cliff. In addition, the Funnel Activation is introduced in the network's backbone, and the simple, parameter-free attention module is integrated, effectively improving the accuracy and speed of the model prediction. Ablation and comparison experiments show the effectiveness and superiority of the proposed model. Verified by the mean average precision and frame rate evaluation indicators, the experimental results of the WBi-YOLOSF target detection network can reach 0.982 and 203 frames per second, which are 1.4% and five frames per second higher than the network with the second score. In summary, this method can quickly and accurately identify aquatic products, realize real-time target detection of aquatic products, and lay the foundation for developing an aquaculture automation system. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered panic-buying behavior around the globe. As a result, many essential supplies were consistently out-of-stock at common point-of-sale locations. Even though most retailers were aware of this problem, they were caught off guard and are still lacking the technical capabilities to address this issue. The primary objective of this paper is to develop a framework that can systematically alleviate this issue by leveraging AI models and techniques. We exploit both internal and external data sources and show that using external data enhances the predictability and interpretability of our model. Our data-driven framework can help retailers detect demand anomalies as they occur, allowing them to react strategically. We collaborate with a large retailer and apply our models to three categories of products using a dataset with more than 15 million observations. We first show that our proposed anomaly detection model can successfully detect anomalies related to panic buying. We then present a prescriptive analytics simulation tool that can help retailers improve essential product distribution in uncertain times. Using data from the March 2020 panic-buying wave, we show that our prescriptive tool can help retailers increase access to essential products by 56.74%. [ABSTRACT FROM AUTHOR]

Facial recognition is an artificial intelligence-based technology that, like many other forms of artificial intelligence, suffers from an accuracy deficit. This paper focuses on one particular use of facial recognition, namely identification, both as authentication and as recognition. Despite technological advances, facial recognition technology can still produce erroneous identifications. This paper addresses algorithmic identification failures from an upstream perspective by identifying the main causes of misidentifications (in particular, the probabilistic character of this technology, its 'black box' nature and its algorithmic bias) and from a downstream perspective, highlighting the possible legal consequences of such failures in various scenarios (namely liability lawsuits). In addition to presenting the causes and effects of such errors, the paper also presents measures that can be deployed to reduce errors and avoid liabilities. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is becoming part of the everyday. During this transition, people's intention to use AI technologies is still unclear and emotions such as fear are influencing it. In this paper, we focus on autonomous cars to first verify empirically the extent to which people fear AI and then examine the impact that fear has on their intention to use AI-driven vehicles. Our research is based on a systematic survey and it reveals that while individuals are largely afraid of cars that are driven by AI, they are nonetheless willing to adopt this technology as soon as possible. To explain this tension, we extend our analysis beyond just fear and show that people also believe that AI-driven cars will generate many individual, urban and global benefits. Subsequently, we employ our empirical findings as the foundations of a theoretical framework meant to illustrate the main factors that people ponder when they consider the use of AI tech. In addition to offering a comprehensive theoretical framework for the study of AI technology acceptance, this paper provides a nuanced understanding of the tension that exists between the fear and adoption of AI, capturing what exactly people fear and intend to do. [ABSTRACT FROM AUTHOR]

The performance improvement of CMOS computer fails to meet the enormous data processing requirement of artificial intelligence progressively. The memristive neural network is one of the most promising circuit hardwares to make a breakthrough. This paper proposes a novel memristive synaptic circuit that is composed of four MOS transistors and one memristor (4T1M). The 4T1M synaptic circuit provides flexible control strategies to change memristance or respond to the input signal. Applying the 4T1M synaptic circuit as the cell of memristive crossbar array, based on the structure and algorithm of the back-propagation (BP) neural network, this paper proposes circuit design of the memristive crossbar-based BP neural network. By reusing the 4T1M memristive crossbar array, the computations in the forward-propagation process and back-propagation process of BP neural network are accomplished on the memristive crossbar-based circuit to accelerate the computing speed. The 4T1M memristive crossbar array can change all the cells' memristance at a time, accordingly, the memristive crossbar-based BP neural network can realize synchronous memristance adjustment. The proposed memristive crossbar-based BP neural network is then evaluated through experiments involving XOR logic operation, iris classification, and MNIST handwritten digit recognition. The experimental results present fewer iterations or higher classification accuracies. Further, the comprehensive comparisons with the existing memristive BP neural networks highlight the advantages of the proposed memristive crossbar-based BP neural network, which achieves the fastest memristance adjustment speed using relatively few components. [ABSTRACT FROM AUTHOR]

Business operations have entered the digital era in which artificial intelligence (AI), machine learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML, deep learning is a critical area. In this paper, we aim to investigate how deep learning and BKC together can help improve business operations. We first examine the operations research (OR) literature related to the applications of deep learning for business operations. Then, we discuss the prior studies on using BKC for operations. After that, we explore deep learning's applications for BKC, BKC's applications for deep learning as well as how deep learning and BKC have been used together for business operations. Then, we construct a research framework and propose future research directions. [ABSTRACT FROM AUTHOR]

While the advancement of Internet Technology generates massive amounts of data that can facilitate decision makings of financial markets, it also arouses new challenges to financial activities, such as the acquisition, processing and analysis of multiple information resources. The paper "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination" predicts the hourly movement directions of eight banking stocks in Borsa Istanbul using historical stock prices and technical indicators as features. The paper "Detecting conflicts of interest in credit rating changes: a distribution dynamics approach" uses a novel distribution dynamics approach to compare the adjustments of credit ratings by an investor-paid credit rating agency (CRA) and an issuer-paid CRA. [Extracted from the article]

Currently, much of the world's IVF data is stuck in paper charts, owned and sold by private IVF clinics, with images taken on different microscopes, and cropped any number of ways to remove patient data. Image data (if collected at all) is almost never connected to the IVF lab KPI data, which is almost never connected to the clinical data. Artificial intelligence (AI) - systems that program themselves to listen, think, predict, anticipate, prognose, diagnose, and RESPOND better than people - are the future of reproductive medicine. [Extracted from the article]

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. [ABSTRACT FROM AUTHOR]

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

The first issue of Artificial Intelligence and Law journal was published in 1992. This special issue marks the 30th anniversary of the journal by reviewing the progress of the field through thirty commentaries on landmark papers and groups of papers from that journal. [ABSTRACT FROM AUTHOR]

These days, Internet coverage and technologies are growing rapidly, hence, it makes the network more complex and heterogeneous. Software defined network (SDN) revolutionized the network architecture and simplified the network by separating the control and data plane. On the other hand, machine learning (ML) and its derivations have made the systems more intelligent. Many pieces of research papers have addressed ML and SDN. In this survey, we collected the papers published in Springer, Elsevier, IEEE, and ACM and addressed SDN and ML between 2016 and 2023. The research papers are organized based on the solutions, evaluation parameters, and evaluation environments to help those working on SDN and ML for improving the target functional or non-functional parameters. The research papers will be analyzed to extract the solutions, evaluation parameters and environments. The extracted solutions, evaluation parameters and environments will be clustered in this review paper. The research gap and future research directions will be stated in this work. This survey is completely useful for those who working on SDN and want to improve the functional and non-functional parameters using machine learning. [ABSTRACT FROM AUTHOR]

This paper uses speculative methods as a way of imagining futures for higher education in open, non-predictive ways. The complexity and 'unknowability' of the highly technologised, environmentally damaged and politically degraded futures we seem to be facing can mean that our conversations about the future of higher education have a tendency to spiral too quickly into dystopianism and hopelessness. Speculative methods can help open up new kinds of conversation capable of supporting active and fundamental hope. Working within a postqualitative framework, we argue that such approaches support the collaborative imagining of multiple alternatives, and represent a way of advocating for those that are preferable. The paper presents a series of speculative scenarios and microfictions focusing on worlds ruptured by climate change, artificial intelligence, revolution and the technological enhancement of humans, connecting each of these to current critical research focused on climate crisis, 'big tech', rising global injustice and 'big pharma'. It emphasises the vital contribution and place of higher education within such futures, and advocates for speculative methods as an approach to maintaining hope. [ABSTRACT FROM AUTHOR]

The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]