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Objectives: Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers., Methods: The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields., Results: The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper., Conclusions: This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. This study delineates AI's dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI's capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI's involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. AI's incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. As the scientific community treads the path of AI integration, a balanced symbiosis between AI's efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing. [ABSTRACT FROM AUTHOR]

The term "Artificial Intelligence" (AI) is increasingly permeating public consciousness as it has gained more popularity in recent years, especially within the landscape of academia and libraries. AI in libraries has been a trending subject of interest for some time, as within the library there are numerous departments that serve a role in collectively contributing to the library's mission. Consequently, it is imperative to consider AI's influence on the digital preservation of historic documents. This paper delves into the historical evolution of preservation methods driven by technological advancements as, throughout history, libraries, archives, and museums have grappled with the challenge of preserving historical collections, while many of the traditional preservation methods are costly and involve a lot of manual (human) effort. AI being the catalyst for transformation could change this reality and perhaps redefine the process of preservation; thus, this paper explores the emerging trend of incorporating AI technology into preservation practices and provides predictions regarding the transformative role of Artificial Intelligence in preservation for the future. With that in mind, this paper addresses the following questions: could AI be what changes or creates a paradigm shift in how preservation is done?; and could it be the thing that will change the way history is safeguarded? [ABSTRACT FROM AUTHOR]

In the last decade, smart phone technology has turned research interests into point-of-care (POC) biosensors. Smart phones have changed our lives in area ssuch as entertainment, banking, shopping and health. With iCloud technology, digital technologies and machine learning intelligence, smart phone-based medical applications are predicted to overthrow conventional diagnostic methods. Useful properties of smart phones such as internal memory, high quality cameras and operating systems, wireless connectivity with other devices and its user-friendly interface utilizing easy operation for everyone resulted in more researchers to focus on the smart phones when designing portable POC biosensing devices. The criteria of an ideal POC deviceby WHO is affordable, sensitive and specific, userfriendly, rapid and robust, equipment-free, and deliverable to those in need, ASSURRED. To observe the interactions between the targets and their specific detection agents, optical, as colorimetric or fluorescence-based sensors, or electrochemical methods are currently being used. Since smart phone technology has evolved rapidly over the last decade in both hardware and software, smart phones have naturally become the obvious choice to replace complex instruments as optical or electrochemical interfaces or the sensor itself. With the overall improvements in smartphone software, camera design and quality, and wireless information sharing, this new emerging market is of particular interest for researchers hoping to create a new bridge between personalized POC diagnostics and resource-limitedsettings. [ABSTRACT FROM AUTHOR]

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In the framework of the CUNE-IIIF-ORM project the aim is to train an Artificial Intelligence Optical Character Recognition (AI-OCR) model that can automatically locate and identify cuneiform signs on photorealistic representations of Old Babylonian texts (c. 2000–1600 B.C.E.). In order to train the model, c. 200 documentary clay tablets have been selected. They are manually annotated by specialist cuneiformists on a set of 12 still raster images generated from interactive Multi-Light Reflectance images. This image set includes visualisations with varying light angles and simplifications based on the dept information on the impressed signs in the surface. In the Cuneur Cuneiform Annotator, a Gitlab-based web application, the identified cuneiform signs are annotated with polygons and enriched with metadata. This methodology builds a qualitative annotated training corpus of approximately 20,000 cropped signs (i.e. 240,000 visualizations), all with their unicode codepoint and conventional sign name. It will act as a multi-layerd core dataset for the further development and fine-tuning of a machine learning OCR training model for the Old Babylonian cuneiform script. This paper discusses how the physical nature of handwritten inscribed Old Babylonian documentary clay tablets challenges the annotation and metadating task, and how these have been addressed within the CUNE-IIIF-ORM project to achieve an effective training corpus to support the training of a machine learning OCR model. Applied computing → Document management and text processing → Document capture → Optical character recognition; Applied computing → Arts and humanities → Language translation. [ABSTRACT FROM AUTHOR]

This article explores the theme of human-machine convergence as portrayed in modern science fiction movies and TV/streaming series and compares them to real-world advancements in robotics, artificial intelligence (AI), and virtual reality (VR). It examines how science fiction often depicts humanoid robots and AI with human-like emotions and intentions, contrasting with the actual technological challenges and ethical considerations in developing intelligent machines. The text discusses the evolution of humanoid robots from fictional portrayals to real-life examples like Boston Dynamics' Atlas and Tesla's Optimus. The paper also explores the reverse interaction, where humans become avatars in virtual worlds, and briefly discusses the ethical implications of simulating deceased individuals in digital form. Through this examination, the paper emphasizes the complexity of human-machine convergence and the importance of considering social, ethical, and emotional aspects in technological progress. It concludes by suggesting that while science fiction provides insights into societal fears and hopes regarding technology and thus into ethical and regulative necessities, the real trajectory of human-machine convergence cannot be predicted through film but will be determined by ongoing and after all incidental developments in the real world. [ABSTRACT FROM AUTHOR]

This paper conducts a study on closed-loop control of engine performance parameters during mode transition process of TBCC engine based on artificial intelligence method. Firstly, a composite modeling method based on stepwise regression analysis and batch normalization-depth neural network is proposed to establish the on-board model during mode transition to estimate the thrust and inlet airflow in real-time. Secondly, based on the hybrid penalty function-particle swarm optimization algorithm, a mode transition control schedule applicable to the closed-loop control of thrust and inlet airflow is developed. Finally, a data processing method based on similarity conversion is proposed to extend the applicable envelope range of the mode transition control system. The transition time is shortened by 33.3 %, and the fluctuations of thrust and inlet airflow are reduced by 1.33 % and 10.77 %, respectively. When the control system is applied to the off-design mode transition process, a satisfactory mode transition performance is also obtained. [ABSTRACT FROM AUTHOR]

The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of in vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning in vitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference "A vision to the future: value-based laboratory medicine" (Padova, Italy; September 23–24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future. [ABSTRACT FROM AUTHOR]

This paper examines the problem of hallucinations in four scientific articles generated by ChatGPT-4 from various sub-fields of business administration. Specifically, the aim is to check whether the references generated by ChatGPT exist and, if this is the case, whether they are correct. A first surprising result is that nearly 40 Prozent of the 174 sources referenced in the articles, whose bibliographic details were listed after the individual main chapters, were not included in the specially for each article generated bibliography. A check of the bibliographic entries revealed that between 18 % and 100 % of the sources in the individual articles could not be found with Google Scholar. Furthermore, half of the sources found were incorrect to varying degrees (wrong document type, wrong journal name, wrong page reference). On average, only a quarter of the bibliographic references were completely correct. In Web of Science, only a quarter of the sources could be verified, but most of them were correct. The above results show that ChatGPT-4 – besides ethical and legal concerns – should definitely not be used for writing scientific papers, as a large proportion of the references generated by ChatGPT (valid at the time of writing this paper) does not exist or is partially incorrect. Furthermore, it can be assumed that some of the article texts generated are also "invented" or come from unreferenced sources (plagiarism). Texts generated by ChatGPT must therefore be subject to a comprehensive review, which may not always be easy. The authors therefore agree with the recommendation of Gimpel et al. (2023) that ChatGPT should primarily be used as a supplement to writing (e. g., spell-checking, creating summaries, supporting the generation of ideas). [ABSTRACT FROM AUTHOR]

The paper provides a critical assessment of the EU's AI Act, highlighting its deficiencies in addressing the complex challenges posed by AI technologies. The analysis underscores that while transparency and evaluation measures are essential components of AI governance, the Act's over-reliance on these measures alone is insufficient to effectively regulate the rapid proliferation of AI applications. Criticizing the AI Act's emphasis on innovation at the expense of genuinely beneficial technological development, Molavi Vasse'i argues for governance reforms that not only adapt to the evolving nature of AI, but also prioritize its impact on individuals and society. The paper calls for a rethinking of tech governance to create regulatory frameworks that are as adaptive as the technologies they aim to govern, with a primary focus on enhancing societal well-being, which should be the fundamental goal of all economic and technological progress. [ABSTRACT FROM AUTHOR]

Molecular communication is a novel approach for data transmission between miniaturised devices, especially in contexts where electrical signals are to be avoided. The communication is based on sending molecules (or other particles) at nanoscale through a typically fluid channel instead of the "classical" approach of sending electrons over a wire. Molecular communication devices have a large potential in future medical applications as they offer an alternative to antenna-based transmission systems that may not be applicable due to size, temperature, or radiation constraints. The communication is achieved by transforming a digital signal into concentrations of molecules that represent the signal. These molecules are then detected at the other end of the communication channel and transformed back into a digital signal. Accurately modeling the transmission channel is often not possible which may be due to a lack of data or time-varying parameters of the channel (e.g., the movements of a person wearing a medical device). This makes the process of demodulating the signal (i.e., signal classification) very difficult. Many approaches for demodulation have been discussed in the literature with one particular approach having tremendous success – artificial neural networks. These artificial networks imitate the decision process in the human brain and are capable of reliably classifying even rather noisy input data. Training such a network relies on a large set of training data. As molecular communication as a technology is still in its early development phase, this data is not always readily available. In this paper, we discuss neural network-based demodulation approaches relying on synthetic simulation data based on theoretical channel models as well as works that base their network on actual measurements produced by a prototype test bed. In this work, we give a general overview over the field molecular communication, discuss the challenges in the demodulations process of transmitted signals, and present approaches to these challenges that are based on artificial neural networks. [ABSTRACT FROM AUTHOR]

Despite substantial gains facilitated by Artificial Intelligence (AI) in recent years, it has to be applied very cautiously in sensitive domains like medicine due to the lack of explainability of many methods in this field. We aim to provide a system to overcome these issues of medical AI applications by means of our concept of medical operational AI detailed in this paper. We make use of various methods of AI and utilize knowledge graphs in particular. The latter is continuously updated by medical experts based on medical literature such as peer-reviewed papers and standard online sources such as UpToDate. We thoroughly derive a multi-level system tackling the corresponding challenges. In particular, its design encompasses (i) holistic diagnostic assistance on a macro level, (ii) predicitions and detailed suggestions for specific medical domains on a micro level, as well as (iii) AI-based optimizations of the overall system on a meta level. We detail practical merits of medical operational AI and discuss the state of the art beyond our solution. [ABSTRACT FROM AUTHOR]

Such behaviour is to observe in non-volatile memory (NVM) elements, e.g., in resistive RAMs (ReRAMs), spin-torque transfer (STT)-MRAMs, phase change memory (PCM)-RAMs or polarization-based ferroelectric RAMs (FeRAM). It was in 1971 when Leon Chua concluded by a plausibility consideration based on symmetry that there should exist a fourth fundamental element in electronics in addition to resistance, capacitance and inductance. These properties include, e.g., a pinched hysteresis curve of the current/voltage waveform, i.e., the curve passes through the point of origin in contrast to a usual hysteresis curve (according to a paper title from Leon Chua "if it's pinched, it's a memristor"). [Extracted from the article]

This paper presents classification and analysis of the mathematical models of the spread of COVID-19 in different groups of population such as family, school, office (3–100 people), town (100–5000 people), city, region (0.5–15 million people), country, continent, and the world. The classification covers major types of models (time-series, differential, imitation ones, neural networks models and their combinations). The time-series models are based on analysis of time series using filtration, regression and network methods. The differential models are those derived from systems of ordinary and stochastic differential equations as well as partial differential equations. The imitation models include cellular automata and agent-based models. The fourth group in the classification consists of combinations of nonlinear Markov chains and optimal control theory, derived by methods of the mean-field game theory. COVID-19 is a novel and complicated disease, and the parameters of most models are, as a rule, unknown and estimated by solving inverse problems. The paper contains an analysis of major algorithms of solving inverse problems: stochastic optimization, nature-inspired algorithms (genetic, differential evolution, particle swarm, etc.), assimilation methods, big-data analysis, and machine learning. [ABSTRACT FROM AUTHOR]

This paper aims to carry out empirical analysis of the viability of large language models (LLMs), specifically ChatGPT, in simulating the common law system and facilitating its evolutionary processes. Drawing on the Theory of Rules Evolution, it is understood that common law generates efficient rules by natural selection through constant litigation. Nonetheless, this evolutionary mechanism faces several hindrances. The process of change is typically slow and incremental. Courts often have to wait for a case that's deemed 'appropriate' before they can change the law, leading to extended delays. Additionally, courts frequently struggle to make efficient decisions due to limited information. Other factors that decelerate the creation of efficient rules include judicial bias, unequal distribution of resources among litigating parties, and the diminishing presence of a competitive legal order. This study first assesses ChatGPT's capability to embrace the essence of the common law system, namely the doctrine of stare decisis. We then assess its potential to overcome the hindrances in common law development and promote efficient rules. Through a series of meticulously designed hypothetical cases set in a virtual jurisdiction called the "Matrix Kingdom," we observed that ChatGPT mimic the functions of a common law court by citing, following, and distinguishing its own precedents, but it accomplishes this with significantly fewer resources and in less time. This implies that humans can introduce hypothetical legal situations, enabling LLMs to replicate the natural selection process observed in the common law system but with a significantly accelerated pace. Given that LLMs are trained with diverse information sources, not just the factual contexts of cases, they could potentially lower the informational constraints in decision-making. As such, LLMs might significantly contribute to the evolutionary processes of common law development. However, it is important to remain cautious of certain limitations, such as the potential for AI Hallucination and inherent biases in LLMs, which require careful consideration and management. [ABSTRACT FROM AUTHOR]

This paper focused on the novel design of receiver side of wavelength division multiplexing passive optical network (WDM-PON). It serves the need for a dramatic change in demand for high data rate beyond 10 gigabits per second (Gbps) at the last mile for a larger coverage area or distance. This increase in demand for higher bandwidth is due to the emerging future technologies like Internet of things (IoT), silicon on chip (SoC), cloud computing, machine learning (ML), and artificial intelligence (AI). Simulation results demonstrated for the different coverage areas which compare the bit error rate (BER), quality (Q)-Factor, and eye height as per the guidelines of ITU-T. It has been predicted that the proposed WDM-PON design supports up to a distance of 60 km. Results have been verified for 40 km, 50 km, 60 km, and 70 km, respectively, at 100 GHz frequency spacing with acceptable BER and better quality (Q)-factor as defined by ITU. [ABSTRACT FROM AUTHOR]

Reasonableness and Risk offers two main claims that might open up fruitful avenues for further reflection. The first claim has to do with the need to abandon form and turn our attention to substance. The argument is that we cannot understand or justify the law of torts without attending to the interests that it protects. The second claim is related to the need to abandon the habit of treating torts in isolation and instead study its integration within a web of institutions which share responsibility for protecting the urgent interests of members of society. The argument here is that our urgent interest in physical integrity exceeds (goes beyond and is greater than) the law of torts. In engaging with these claims, this paper points out potential connections with some pressing questions for both tort law and risk regulation brought to the fore by the recent emergence of Artificial Intelligence (AI) systems. [ABSTRACT FROM AUTHOR]

Die Öffnung ihrer digitalen Sammlungen für die Nachnutzung ist ein wichtiges strategisches Ziel der Österreichischen Nationalbibliothek. Der vorliegende Beitrag beschäftigt sich mit Fragen der Digitalisierung und der Öffnung digitaler Sammlungen und diskutiert, wie diese am besten als Daten für die Forschung zugänglich gemacht werden können. Ein Schwerpunkt liegt dabei auf historischen Zeitungen im Forschungskontext. Weiters wird auf das Konzept von „Collections as Data" und die Rolle von GLAM Labs eingegangen, die in jüngster Zeit in vielen Bibliotheken, darunter auch in der Österreichischen Nationalbibliothek, eingerichtet wurden. Opening its digital collections for reuse is an important strategic goal of the Austrian National Library. This paper deals with questions of digitisation and opening digital collections and discusses how these can best be made accessible as data for research. One focus is on historical newspapers in a research context. This paper also discusses the concept of "Collections as Data" and the role of GLAM Labs, which have recently been established in many libraries, including the Austrian National Library. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is having a tremendous impact upon commercial activities, socio-economic activities, as well as technological progress. However, there is lack of clarity about the appropriate mechanics of AI regulation. A variety of proposals have been floated, but with little agreement concerning their implementation and interaction. This paper builds a framework to address these concerns. It first identifies the scope of AI for regulatory purposes by establishing the defining characteristic of this technology. In the second part, it identifies four different interpretations of the question, 'why regulate AI?', each with a bearing on the purpose of AI regulation. It treats each one of these as an 'imperative of regulation'. The scope of each imperative is delineated. The last part situates the proposed mechanics of AI regulation within the imperative driven framework. Four broad categories of the mechanics of AI regulation are first described in detail in order to understand their features and are subsequently tested on the touchstone of the four imperatives, thereby ascertaining which of the imperatives drive the mechanics and which are irrelevant. The findings provide theoretical clarity to the AI regulation proposals being mulled over across the globe. The paper concludes with an argument for proactive regulation of AI. [ABSTRACT FROM AUTHOR]

Until today, a common understanding of technology is still shaped by the industrialisation as a progress that facilitates our everyday life, making it more predictable, interesting and diverse too. They demonstrate their research results in an social VR game and support self-embodiment for non-HMD users. The authors of the paper "Supporting Self-Embodiment for Non-HMD Users in Asymmetric Social VR Games" discuss the feasibility of supporting Self-Embodiment in an asymmetric VR interaction mode. [Extracted from the article]

In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently. [ABSTRACT FROM AUTHOR]

There has been tremendous progress in artificial neural networks (ANNs) over the past decade; however, the gap between ANNs and the biological brain as a learning device remains large. With the goal of closing this gap, this paper reviews learning mechanisms in the brain by focusing on three important issues in ANN research: efficiency, continuity, and generalization. We first discuss the method by which the brain utilizes a variety of self-organizing mechanisms to maximize learning efficiency, with a focus on the role of spontaneous activity of the brain in shaping synaptic connections to facilitate spatiotemporal learning and numerical processing. Then, we examined the neuronal mechanisms that enable lifelong continual learning, with a focus on memory replay during sleep and its implementation in brain-inspired ANNs. Finally, we explored the method by which the brain generalizes learned knowledge in new situations, particularly from the mathematical generalization perspective of topology. Besides a systematic comparison in learning mechanisms between the brain and ANNs, we propose "Mental Schema 2.0," a new computational property underlying the brain's unique learning ability that can be implemented in ANNs. [ABSTRACT FROM AUTHOR]

SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in an unusual fashion. Indeed, organisers asked world renowned scientists to prepare two videos: a short video presenting a question of interest to them and a longer one presenting their point of view on the question. The first video served as a "teaser" for the conference and the second were presented during the conference as an introduction to the round-table. These videos and this round-table generated original scientific insights and discussion worthy of being shared with the community which we do by means of this paper. [ABSTRACT FROM AUTHOR]

How ›literary mediation‹ is observed from the perspective of literature is discussed in this paper on the basis of Clemens J. Setz' Bot. Gespräch ohne Autor. It is described here as a network of multiple operations and interconnections that take up excerpts of what has already been published and combine them with something new, and, at the same time, it is made recognizable as a fundamental moment of literature. What is reconstructed here on the basis of and from an exemplum is systematically relevant. The systematic connections that are of interest here, in turn, can only be made plausible by means of the text. This constellation is theoretically indissoluble. This paper discusses this using both the notion of ›epitext‹ and incorporating the concept of ›mediation‹ unfolded by Bruno Latour. It brings the two together and opens the theoretical territory of ›literary mediation and promotion‹. It follows that mediation is defined as an operation that transforms, that is, not conserves and preserves, transferred into terms of literary mediation: not simply explains and comments, but transforms by inscribing and imprinting itself on what it mediates, is emphasized here. For the understanding of literary mediation, it follows that – instead of being in the service of a literary text conceived as an unchanging entity – it is always modifying and translating it in order to continually bring it forth as something new. While peritexts, however supplementary, constitute compact units, the epitextual perspective brings about their spatial and temporal dispersion. Literature is to be grasped epitextually not as a unity, but as an ensemble or network of different elements, references, and functions that project into a virtually expanded environment of a text. With such a reformulation of the concept of literature, it is stated that epitexts are not attributed to the mediation of literature, but to literature, and that the boundary between these areas is thought to be permeable. The article examines how a text file becomes a printed text and how this shapes the understanding of ›digital literature‹. This also addresses the problem of big data, which requires distant reading procedures and to which Bot. Gespräch ohne Author reacts in a specific way, by capturing context-independent »word distributions« (Piper 2018, 43) to use them for new connectivities. The article reveals the shifts between the possibilities of digitization, its literary adaptations, and a literature oriented to the categories of work, author, and book. It is not concerned with replacing texts designed according to traditional criteria with digital surfaces, but rather with pointing out the untranslatability of one system into the other. An untranslatability, however, that can only be demonstrated in the process of translation, the médiation. By taking up concepts of digital culture and incorporating them by quoting, reflecting, and parodying them, the book, consisting of printed paper bound between two covers, allows them to emerge in a disguise as mediators who participate in its shaping. On the one hand, it suggests that there can be no non-digital literature in a digital ecology, even if it ultimately presents itself in paper form; but, on the other hand, it also suggests that an artificial intelligence can only be described as text or code. It can show that and how literature subjects its mediations or the institutional and medial processes linked to literary mediation to (literary) scrutiny and thereby continually negotiates its own literariness. Where mediation meets the concept of literature, it is also challenged as a literary-theoretical category. With the help of the conceptual pair peri- and epitext, which corresponds to the distinction between literature and literary mediation, as well as with the inclusion of Bruno Latour's concept of the ›médiateur‹, not only the category of the work in the sense of a stable entity distinguishable from its context is questioned, but also – directly related to this – the authorial function as a collectivity of technical operations was traced. A questioning that takes on a particular urgency under the auspices and with the instruments of digitality, bringing to light the traditional concepts of literature as (re)translations, which is exemplified by the transfer of a digital data set into a printed book. [ABSTRACT FROM AUTHOR]

The contribution of laboratory medicine in delivering value-based care depends on active cooperation and trust between pathologist and clinician. The effectiveness of medicine more in general depends in turn on active cooperation and trust between clinician and patient. From the second half of the 20th century, the art of medicine is challenged by the spread of artificial intelligence (AI) technologies, recently showing comparable performances to flesh-and-bone doctors in some diagnostic specialties. Being the principle source of data in medicine, the laboratory is a natural ground where AI technologies can disclose the best of their potential. In order to maximize the expected outcomes and minimize risks, it is crucial to define ethical requirements for data collection and interpretation by-design, clarify whether they are enhanced or challenged by specific uses of AI technologies, and preserve these data under rigorous but feasible norms. From 2018 onwards, the European Commission (EC) is making efforts to lay the foundations of sustainable AI development among European countries and partners, both from a cultural and a normative perspective. Alongside with the work of the EC, the United Kingdom provided worthy-considering complementary advice in order to put science and technology at the service of patients and doctors. In this paper we discuss the main ethical challenges associated with the use of AI technologies in pathology and laboratory medicine, and summarize the most pertaining key-points from the guidelines and frameworks before-mentioned. [ABSTRACT FROM AUTHOR]

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices. [ABSTRACT FROM AUTHOR]

Part 1 of this paper (Beardwood, CRi 2024, 97) provided an update on the progress of AIDA and the EU AI Act (I), outlined a summary roadmap of the base similarities and differences between the two items of legislation (II), reviewed the objectives of AIDA in contrast to the EU AI Act (III), compared their respective jurisdictional scope (IV), reviewed their respective definitions of AI systems (V), outlined new definitions/concepts which have been introduced into the legislation (VI), outlined the extent to which there are exclusions for the public sector (VII) and for research (VIII), and provided an overview of their respective risk-based approaches (IX). This Part 2 compares in detail the obligations for High-Impact Systems and General-Purpose Systems (AIDA) (X), and for High-Risk AI Systems and General-Purpose AI Systems (EU AI Act) (XI), and finally reviews the penalties and offences for noncompliance imposed by AIDA and EU AI Act (XII) before concluding (XIII). [ABSTRACT FROM AUTHOR]

This paper explores the evolving landscape of User-Centric Artificial Intelligence, particularly in light of the challenges posed by systems that are powerful but not fully transparent or comprehensible to their users. Despite advances in AI, significant gaps remain in aligning system actions with user understanding, prompting a reevaluation of what "user-centric" really means. We argue that current XAI efforts are often too much focused on system developers rather than end users, and fail to address the comprehensibility of the explanations provided. Instead, we propose a broader, more dynamic conceptualization of human-AI interaction that emphasizes the need for AI not only to explain, but also to co-create and cognitively resonate with users. We examine the evolution of a communication-centric paradigm of human-AI interaction, underscoring the need for AI systems to enhance rather than mimic human interactions. We argue for a shift toward more meaningful and adaptive exchanges in which AI's role is understood as facilitative rather than autonomous. Finally, we outline how future UCAI may leverage AI's growing capabilities to foster a genuine co-evolution of human and machine intelligence, while ensuring that such interactions remain grounded in ethical and user-centered principles. [ABSTRACT FROM AUTHOR]

Over the last 50 years education has been transformed by digital technologies. Many efforts have been made to create and apply "digital" teaching and learning methods, tools and platforms. The last 25 years of computer-based education can be characterized by the availability of digital information sources and the implementation and operation of digital learning management platforms based on the Internet. The question about further meaningful and effective progress in the next decades is openly discussed. Many expect strong influences and changes from artificial intelligence systems that generate contextualized information from various sources. Some see interactive virtual worlds expanding and partially replacing the physical world. Many believe in the further development of learning management and communication platforms. Others do not expect much valuable change at all due to the slow pace of complex educational systems with recent studies even showing a decline in the quality of educational outcomes during the last five years as a result of even too much digitalization in education. This paper discusses these positions with an emphasis on the roles of humans and computers and their interfaces, i.e. Human-Computer Interaction, for future learning and teaching with rapidly changing information technologies in the next 25 and 50 years. [ABSTRACT FROM AUTHOR]

People are increasingly eager to know more about themselves through technology. To date, technology has primarily provided information on our physiology. Yet, with advances in wearable technology and artificial intelligence, the current advent of consumer neurotechnology will enable users to measure their cognitive activity. We see an opportunity for research in Human-Computer Interaction (HCI) in the development of these devices. Neurotechnology offers new insights into user experiences and facilitates the development of novel methods in HCI. Researchers will be able to create innovative interactive systems based on the ability to measure cognitive activity at scale in real-world settings. In this paper, we contribute a vision of how neurotechnology will transform HCI research and practice. We discuss how neurotechnology prompts a discussion about ethics, privacy, and trust. This trend highlights HCI's crucial role in ensuring that neurotechnology is developed and utilised in ways that truly benefit people. [ABSTRACT FROM AUTHOR]

This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

Der digitale Wandel hat in den letzten Jahrzehnten die Geisteswissenschaften transformiert. Jede Art von Tätigkeit in Forschung und Lehre hat zumindest einen digitalen Anteil und sei es, dass dieser nur in der Nutzung digitaler Informationsinfrastrukturen für die Recherche besteht. In den historisch arbeitenden Wissenschaften mag der Beharrungswille größer sein als in anderen Disziplinen, aber auch hier haben sich in den vergangenen Jahren zwei Tendenzen herauskristallisiert: Erstens nimmt die Arbeit mit digitalen Surrogaten analoger Quellen zu – und wird noch mehr an Bedeutung gewinnen, je mehr die Fächer mit digital geborenen Quellen arbeiten. Zweitens werden die historischen Fächer selbst zu Produzenten von Forschungsdaten. Der Beitrag zeichnet diese Veränderungen nach und zeigt exemplarisch aktuelle Verfahren, Workflows und deren Voraussetzungen. Für die Gedächtniseinrichtungen argumentiert er, dass diese die sich abzeichnende Entwicklung nicht nur nicht ignorieren dürfen, sondern dass sie bereits jetzt ein wichtiger Teil des digitalen Umbruchs sind, der sich durch eine Konvergenz von Datenkompetenzen, aber auch von Verfahren und Modellierungen über die Grenzen der Fächer hinweg auszeichnet. Abschließend reflektiert der Beitrag, auf welche Weise Gedächtniseinrichtungen den Anforderungen dieser Veränderung und neuer Gruppen von Nutzenden gerecht werden können und welche Herausforderungen auch epistemischer Art in den bestehenden Sammlungen ruhen. The digital turn has shaped the humanities in recent decades. Every kind of activity in research and teaching now has at least a digital component, even if this only implies using digital information infrastructures for research. In the historical scholarship, the will to persevere time honed approaches may be greater than in other disciplines, yet even here two trends have emerged in recent years: firstly, scholarship based on datafied surrogates of analogue sources is increasing–and will become even more important the more scholars work with digitally born sources. Secondly, the historical disciplines themselves are becoming producers of research data. This paper traces the transformation, illustrating current techniques, workflows, and their prerequisites arguing that memory institutions should not only not ignore the development, but that they form already an important part of the digital transformation. For memory institutions the digital turn is characterised by a convergence of data competencies as well as procedures and models across the traditional boundaries of the disciplines. Finally, the article reflects on how memory institutions can meet the requirements of this transformation and new groups of users and what challenges, of an epistemic nature as well, are inherent to the existing collections. [ABSTRACT FROM AUTHOR]

Der Beitrag erörtert Spannungsfelder, die sich aus der fortwährenden digitalen Transformation der Gesellschaft und dem „ChatGPT-Schock" derzeit neu formieren. Anhand des Beispiels der Erschließung handschriftlicher Dokumente werden diese Felder analysiert und es wird versucht, die verschiedenen sich ändernden Rollen und Verständnisse auszuloten. Dies umfasst die Anteilseigner aus Bibliothek als traditionellem Informationsdienstleister, Plattformbetreiber als Mediatoren, Wissenschaft wie auch Gesellschaft, wobei sich die Technik zunehmend vom passiven Werkzeug zum handelnden Akteur wandelt. Aus diesen Überlegungen heraus wird eine Zukunftsperspektive einer prozess-orientierten Wissensschaffung abgeleitet, zu der alle genannten Akteure als „Co-Produzenten" beitragen. The paper discusses fields of tension that are currently reshaping themselves from the ongoing digital transformation of society and the "ChatGPT shock". Using the example of manuscript exploration, these fields are analysed, and an attempt is made to explore the various changing roles and understandings. This includes the stakeholders from library as traditional information service provider, platform operators as mediators, academia as well as society, where technology is increasingly changing from a passive tool to an acting participant. Based on these considerations, a future perspective of process-oriented knowledge creation is derived, to which all the actors contribute as "co-producers". [ABSTRACT FROM AUTHOR]

This paper deals with applications of digital imaging total stations in a geodetic context using artificial intelligence (AI). We present two different use cases. The first is to minimise manual intervention by the operator by classifying images with different backgrounds. We use a developed software to control a total station extended by an industrial camera, which is used for the in-situ calibration of the camera. We show that the AI successfully tests the captured image for its suitability for further use and under which circumstances the AI fails. The second case is the detection of different geodetic targets (reflective and non-reflective). Captured images of an imaging total station are automatically checked to see whether a supposed target is shown in the image, identify it and localise it in the image. Already implemented applications for target identification are to be supported in this way and extended by further information. [ABSTRACT FROM AUTHOR]

This paper addresses the interrelation between AI and language studies in the (digital) humanities in the context of knowledge exchange economies. We propose a three-dimensional semiotic model (pyramid) that includes AI as an active agent in meaning-creation into the sign system of communication and test its validity on autocompletion and predictive texting. We, therefore, analyzed 360 million tweets on COVID-19 over a period of 2 years and focused on the time-dependent shift of language use in the positive and negative extremes correlating to significant external events. The data suggest that digital language use has significantly changed over the course of the pandemic. Feedback loops are created between AI, utterance, and external reality that fossilize preconceived notions into textual isoglosses by suggesting and completing text entries. We thus argue that AI subtly changes the perception of reality by influencing the choice of wording. To mitigate the impact of AI in meaning-creation, we highlight the structural precautions undertaken by providers; we argue, however, that the (digital) humanities may re-emphasize their intrinsic value within the economy of knowledge exchange according to the subject-specific insights they may provide for language use and AI. [ABSTRACT FROM AUTHOR]

In the age of artificial intelligence, writing machines or robot authors have already begun to produce narrative texts in a variety of genres, including short stories and poetry, as well as journalistic articles. This article is based on the prospect that the narrative ecosystem is in a transitional period of decisive disconnection as it enters the era of artificial intelligence. The primary force driving this transition is the formidable execution of artificial intelligence algorithms, which fully automate narrative communication and narrative works. This article attempts to lay the groundwork for building a new paradigm of post-narrativity through a critical examination of several detailed themes in narrative semiotics and non-anthropocentric narratology. The process of narrative creation based on artificial intelligence algorithms is a key condition that constitutes post-narrativity. This presupposes a non-anthropocentric view. In the landscape of post-narrativity, human writers, paper books, computer screens, and invisible narrative algorithms are all agents with equal influence. Automated narrative production by algorithms accelerates the repositioning of other existing media and actors, and changes the narrative ecosystem by incorporating new elements into activities such as production, distribution, and reception of narratives. [ABSTRACT FROM AUTHOR]

The amount of data generated in the field of laboratory medicine has grown to an extent that conventional laboratory information systems (LISs) are struggling to manage and analyze this complex, entangled information ("Big Data"). Statistical learning, a generalized framework from machine learning (ML) and artificial intelligence (AI) is predestined for processing "Big Data" and holds the potential to revolutionize the field of laboratory medicine. Personalized medicine may in particular benefit from AI-based systems, especially when coupled with readily available wearables and smartphones which can collect health data from individual patients and offer new, cost-effective access routes to healthcare for patients worldwide. The amount of personal data collected, however, also raises concerns about patient-privacy and calls for clear ethical guidelines for "Big Data" research, including rigorous quality checks of data and algorithms to eliminate underlying bias and enable transparency. Likewise, novel federated privacy-preserving data processing approaches may reduce the need for centralized data storage. Generative AI-systems including large language models such as ChatGPT currently enter the stage to reshape clinical research, clinical decision-support systems, and healthcare delivery. In our opinion, AI-based systems have a tremendous potential to transform laboratory medicine, however, their opportunities should be weighed against the risks carefully. Despite all enthusiasm, we advocate for stringent added-value assessments, just as for any new drug or treatment. Human experts should carefully validate AI-based systems, including patient-privacy protection, to ensure quality, transparency, and public acceptance. In this opinion paper, data prerequisites, recent developments, chances, and limitations of statistical learning approaches are highlighted. [ABSTRACT FROM AUTHOR]

Edge computing gateway automation system is integrated in edge computing gateway. One of the main functions of edge computing system is to connect industrial instruments and communication equipment in the process of industrial production. It provides real-time data monitoring and analysis, and initiates responses to predetermined logical events. The operation process includes separating and designing different processes in a certain order. The production and processing process is susceptible to problems such as long production and processing cycles, multiple types of monitoring data, large amounts of processing data, and data vulnerability to external interference, which leads to inaccurate and unsynchronized data. Based on this, this article investigated the analysis of data processing systems based on cloud computing, focusing on analyzing the system architecture and processing, and elaborating the design of data collectors. Then, this article analyzed the efficiency of AI (artificial intelligence) automatic control system and data processing unit. This article discussed the application of AI in collecting and processing data, the composition of the data management module of AI automatic control system, and the data processing in the data module of AI automatic control system. This paper also described the construction method and process of the automatic control system of edge computing gateway, and discussed from the following aspects: data preprocessing module, data classification processing module, data accumulation analysis module, automatic control algorithm logic module, and instruction execution control module. Experiments and investigations showed that the accuracy of data analysis by using the new AI automatic control system and data processing system was 0.11 higher than that of traditional automatic control systems and data processing systems. The data processing effectiveness of the new AI automatic control system and data processing system was 0.10 higher than that of the traditional automatic control system and data processing system. By using AI technology and edge computing technology to structure the automatic control system and data processing system, a new AI automatic control system and data processing system were constructed, which were 9 % more satisfied than the traditional automatic control system and data processing system. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the progress implemented during the AURA project, funded by the Creative-Europe program with project partners from Germany, Italy, and Ukraine. The project aims to create auralized applications for three music venues in each of the project countries, namely the Konzerthaus Berlin, the Teatro del Maggio in Florence, and the Opera House Lviv. Each will be digitally recreated and auralized before they are then used to conduct case studies. This paper gives insights into current digitalization and auralization techniques. The results of a digital survey will be laid out and the conception and implementation of a first auralized prototype using a hand-modeled 3D object from the Great Hall of the Konzerthaus Berlin will be demonstrated. Furthermore, the usage of auralization for touristic purposes will be investigated using artificial intelligence for an audience preference analysis. A conclusion will be drawn and a short outlook into the ongoing course of the AURA project will be given. [ABSTRACT FROM AUTHOR]

The German Society for Information and Knowledge (DGI) invites its members to the regular general meeting on November 8, 2024. At the meeting, a new board will be elected and the annual budget plan will be discussed and approved. DGI is also organizing a theme day where the working groups AKTS and IuG will present their work results and seek exchange with business, science, and administration. Training and further education opportunities on the use of Artificial Intelligence (AI) will also be offered. DGI mourns the loss of long-time member Dr. Robert Fugmann, who passed away at the age of 97. The German Society for Information Science and Information Practice (DGI) offers various services related to legal aspects, knowledge management, and information literacy. In 2023, the focus of the Information Literacy and Education (IKB) working group was on the anniversary symposium of DGI. Discussions on topics such as fake news and information literacy took place. DGI also plans to update a position paper on the topic of Artificial Intelligence and Education. Events to promote information literacy were held, and collaboration with other organizations was maintained. The FaMI conference in 2024 will be hosted by the Joseph-DuMont Vocational College in Cologne. Approximately 30 vocational school teachers for media and information services met for professional exchange and further education. The program included topics such as project work and podcast production. Excursions to various institutions provided insights into the practice and possible work environments of the apprentices. It was emphasized that the field of information and documentation services needs to be strengthened nationwide. [Extracted from the article]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article explores, first, what the central tenet of law and economics is and uses an example to illustrate. Then I turn to discussing behavioral law and economics and empirical law and economics. In the final section, I consider three topics that are likely to be central to the next era of law and economics–data collection, constitutional law and economics, and artificial intelligence. [ABSTRACT FROM AUTHOR]

Digital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption. [ABSTRACT FROM AUTHOR]

The real time transient stability assessment is the important steps of the dynamic security evaluation of power systems. To do this, intelligent systems (ISs) such as neural networks as an effective method to accurately and quickly estimate the critical clearing time (CCT) has been widely used. But, choosing the proper inputs for these systems remains a major challenge for researchers, still. Variables related to energy functions such as minimum kinetic energy, the slope of the variation of the minimum kinetic energy and maximum potential energy contain useful information in estimating CCT. Accordingly, in this paper, these variables are used as the IS inputs. However, the time-domain simulation of the power system response to obtain these inputs is time consuming. To be able to use the energy function-based inputs for real time stability assessment, in addition to the main IS used to estimate CCT, another ISs are used. By those ISs, a very limited period of system response is simulated to obtain proper inputs of the main IS. The method is simulated on the 10-generator New England test system. Simulation results show, the CCT can be found by simulation just 0.05 s of the considered power grid. [ABSTRACT FROM AUTHOR]