bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction to Security and Privacy
Authors: Weippl Edgar , Schrittwieser Sebastian 
Published: December 2023

AbstractThis chapter on Security and Privacy builds on two aspects central to digital humanism: (1) self-determination and (2) humans in the center.Security refers to a system being in a state of protection against potential threats and risks; what this means specifically depends on the so-called security requirements (Bishop, IEEE Security and Privacy, 1(1), 67–69, 2003). Typically, this pertains to protecting data’s (1) confidentiality, (2) integrity, and (3) availability. Thus, security mechanisms are designed to prevent unauthorized access, use, disclosure, disruption, modification, or destruction.Privacy is the individual’s fundamental right to determine and limit who has access to their personal information and experiences, ensuring their ability to maintain personal boundaries, confidentiality, and freedom from unauthorized surveillance (Bélanger and Crossler, Privacy in the digital age: A review of information privacy research in information systems. MIS Quarterly, pp. 1017–1041, 2011). Security and privacy are of utmost importance in this increasingly connected world, as they can help protect individuals, companies, and organizations from data breaches, identity theft, and other malicious attacks.The goals of digital humanism are to shape technologies in accordance with human values and needs, instead of allowing technologies to shape humans. Our task is not only to rein in the downsides of information and communication technologies but to encourage human-centered innovation (Werthner, The Vienna manifesto on digital humanism. In Digital transformation and ethics (pp. 338–357). Ecowin, 2020).In the following sections, we will analyze security requirements that can compromise these goals and show which security mechanisms can be employed to protect them. Both security and even more privacy are central to digital humanism—also mentioned as one of its principles.
Bias and the Web
Authors: Baeza-Yates Ricardo , Murgai Leena 
Published: December 2023

AbstractBias is everywhere, sometimes blatantly explicit, but most of the time it’s hidden, as it often arises from that which is missing, the gaps in our knowledge or data. In this chapter, we cover what bias is and its different sources: how it arises, persists, feeds back into a system, and can be amplified through algorithms. To exemplify the problem, we use the Web, the largest information repository created by humankind. The first countermeasure against bias is awareness – to understand what is represented—so that we may identify what is not. So, we systematically explore a wide variety of biases which originate at different points on the Web’s information production and consumption cycle. Today, many if not all the predictive algorithms we interact with online rely on vast amounts of data harvested from the Web. Biased data will of course lead to biased algorithms, but those biases need not be replicated precisely. Without intervention, typically they are amplified. We start with engagement bias, that is, the difference in rates at which people produce content versus passively consume it. We then move onto data bias: who is producing data on the Web, in what language, and the associated measurement and cultural biases. Algorithmic bias and fairness are intertwined. We discuss the difficulty in defining fairness and provide examples of algorithmic bias in predictive systems. Lastly, we look at biases in user interactions. We discuss how position bias can be mitigated by distributing visuals across results and shared information about other users can lead to different social biases. We discuss how biases continually feed back into the Web and grow through content creation and diffusion.
Copyright Enforcement on Social Media Platforms: Implications for Freedom of Expression in the Digital Public Sphere
Authors: Mendis Sunimal 
Published: December 2023

AbstractOnline social media platforms (e.g., Facebook, Twitter, YouTube, TikTok) constitute a core component of the contemporary digital public sphere. As such, their regulation should be designed in a manner that enables these platforms to flourish as digital spaces for robust democratic discourse. Ensuring effective protection of users’ fundamental right to freedom of expression is critical toward achieving this aim.This chapter explores how the current EU legal framework on copyright enforcement—the seminal provision of which is Article 17 of the Copyright in the Digital Single Market Directive (DSM) [2019]—can undermine the freedom of expression on social media platforms by limiting users’ ability to reuse and reinterpret copyright-protected content in ways that promote democratic discourse. Thus, the chapter focuses on legal aspects of social media platform regulation.The chapter concludes by outlining several proposals presented by copyright law scholars for ensuring a fair balance between user and copyright holder interests in a way that promotes robust democratic discourse in the digital public sphere.
Evolution of Computing
Authors: Larus James R. 
Published: December 2023

AbstractComputers and computing emerged within a lifetime and completely changed our world. Although their history is brief, the change they precipitated has been rapid and constant. Today’s world would be unimaginably different without these machines. Not necessarily worse, but certainly slower, static, disconnected, and poorer. One has to look back to the steam engine in the nineteenth century or electricity in the early twentieth century to find technologies with similar rapid and far-reaching effects. This chapter briefly describes the evolution of computing and highlights how its growth is closely tied to concerns of digital humanism.
Platforms: Their Structure, Benefits, and Challenges
Authors: Parker Geoffrey , Van Alstyne Marshall 
Published: December 2023

AbstractIn this chapter, we describe platforms and their structure and how that structure differs from traditional linear value chains. We then discuss some of the key economic factors, including two-sided and multi-sided network effects, which underpin both the platform value proposition and the ability to create welfare for users. Platform and technology firms have grown to the point where their market capitalizations greatly exceed oil, gas, and financial services firms. We then explore some key governance and regulatory issues, including privacy, false information, and antitrust. We conclude with a discussion of emerging issues posed by Large Language Models such as ChatGPT, including their ability to create false information at scale and disrupt creative industries.
Sovereignty in the Digital Age
Authors: Timmers Paul 
Published: December 2023

AbstractThe century-old concept of state sovereignty is acquiring new and hotly debated meaning, due to digital disruption and technology-without-borders, dominance by powerful—often foreign-owned—global tech companies, and cyber-undermining by malicious states. Sovereignty, as we know it, is also threatened by rising geopolitical tensions, war, and global challenges such as climate change, pandemics, and global cyber-crime. This chapter deals with the future of sovereignty in a digital and geopolitically contested age. It starts with an introduction into international relations, sovereignty, and strategic autonomy thinking. It reflects on the impact of digital technology on the international system of states. Then the chapter provides an analysis and some practical guidance to tackle the challenges of developing public policy for sovereignty in the digital, and digital humanistic, age. Finally, two case studies and a set of questions invite the reader to a deeper dive.
Digital Transformation, Digital Humanism: What Needs to Be Done
Authors: Werthner Hannes 
Published: December 2023

AbstractInformation technology (IT) changes our society and world, from the individual level up to the ongoing geopolitical powerplay. From an ontological point of view, it influences how we perceive the world and how we think about it. This transformation only happened in the short time span of about 80 years, and it is continuing. We highlight some of the major features of this process. Besides its enormous achievements, this development has serious shortcomings. We will discuss some of them and describe our positive answer: Digital Humanism, an approach that describes, analyzes, and, most importantly, influences the complex interplay of technology and humankind, for a better society and life, fully respecting universal human rights. It is a proactive approach, focusing on the integration of technical and social innovation. Then we will present our Digital Humanism initiative, discuss its research and innovation roadmap, and finish with a general framework integrating the different dimensions of this initiative. [Some arguments have already been partly expressed in other publications such as Werthner (Electronic Markets, 32:145–151, 2022a) and Werthner et al. (IEEE Computer, 56(1):138–142, 2023).]
A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations
Authors: Heitzinger Clemens , Woltran Stefan 
Published: December 2023

AbstractThis chapter gives an overview of the most important methods in artificial intelligence (AI). The methods of symbolic AI are rooted in logic, and finding possible solutions by search is a central aspect. The main challenge is the combinatorial explosion in search, but the focus on the satisfiability problem of propositional logic (SAT) since the 1990s and the accompanying algorithmic improvements have made it possible to solve problems on the scale needed in industrial applications. In machine learning (ML), self-learning algorithms extract information from data and represent the solutions in convenient forms. ML broadly consists of supervised learning, unsupervised learning, and reinforcement learning. Successes in the 2010s and early 2020s such as solving Go, chess, and many computer games as well as large language models such as ChatGPT are due to huge computational resources and algorithmic advances in ML. Finally, we reflect on current developments and draw conclusions.
Cardiovascular and mortality outcomes with GLP-1 receptor agonists vs other glucose-lowering drugs in individuals with NAFLD and type 2 diabetes: a large population-based matched cohort study
Authors: Krishnan Arunkumar , Schneider Carolin V. , Hadi Yousaf , Mukherjee Diptasree , AlShehri Bandar , Alqahtani Saleh A. 
Published: December 2023

Abstract
Aims/hypothesis
We aimed to determine whether the use of glucagon-like peptide-1 receptor agonists (GLP-1RA) in individuals with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus decreases the risk of new-onset adverse cardiovascular events (CVEs) and mortality rate compared with other glucose-lowering drugs in a real setting at a population level.

Methods
We conducted a population-based propensity-matched retrospective cohort study using TriNetX. The cohort comprised patients over 20 years old who were newly treated with glucose-lowering drugs between 1 January 2013 and 31 December 2021, and followed until 30 September 2022. New users of GLP-1RAs were matched based on age, demographics, comorbidities and medication use by using 1:1 propensity matching with other glucose-lowering drugs. The primary outcome was the new onset of adverse CVEs, including heart failure, composite incidence of major adverse cardiovascular events (MACE; defined as unstable angina, myocardial infarction, or coronary artery procedures or surgeries) and composite cerebrovascular events (defined as the first occurrence of stroke, transient ischaemic attack, cerebral infarction, carotid intervention or surgery), and the secondary outcome was all-cause mortality. Cox proportional hazards models were used to estimate HRs.

Results
The study involved 2,835,398 patients with both NAFLD and type 2 diabetes. When compared with the sodium–glucose cotransporter 2 (SGLT2) inhibitors group, the GLP-1RAs group showed no evidence of a difference in terms of new-onset heart failure (HR 0.97; 95% CI 0.93, 1.01), MACE (HR 0.95; 95% CI 0.90, 1.01) and cerebrovascular events (HR 0.99; 95% CI 0.94, 1.03). Furthermore, the two groups had no evidence of a difference in mortality rate (HR 1.06; 95% CI 0.97, 1.15). Similar results were observed across sensitivity analyses. Compared with other second- or third-line glucose-lowering medications, the GLP-1RAs demonstrated a lower rate of adverse CVEs, including heart failure (HR 0.88; 95% CI 0.85, 0.92), MACE (HR 0.89; 95% CI 0.85, 0.94), cerebrovascular events (HR 0.93; 95% CI 0.89, 0.96) and all-cause mortality rate (HR 0.70; 95% CI 0.66, 0.75).

Conclusions/interpretation
In individuals with NAFLD and type 2 diabetes, GLP-1RAs are associated with lower incidences of adverse CVEs and all-cause mortality compared with metformin or other second- and third-line glucose-lowering medications. However, there was no significant difference in adverse CVEs or all-cause mortality when compared with those taking SGLT2 inhibitors.

Graphical Abstract
The role of fire in the Medieval and Early Modern landscape of Bad Waldsee within the broader context of the pre-Alpine forelands of south-western Germany
Authors: Saeidi Ghavi Andam Sara , Marinova Elena , Wick Lucia , Haas Kristin , Lemmes Claudia , Rösch Manfred , Schiedek Thomas , Krahn Kim J. , Nelle Oliver , Hirbodian Sigrid , Rückert Peter , Schwalb Antje , Hinderer Matthias 
Published: December 2023

AbstractFire, an important element of human land use strategies, was part of many early industrial activities but also of burning incidents that significantly impacted settlement structures and development. The spatial proximity of the historically well-documented medieval town and sediment archive deposited in its central lake (Stadtsee) was taken to investigate the interplay of fire and socio-environmental developments during the preindustrial phase of 750−150 bp (ad 1200–1800). The burning episodes were detected using the sediment macroscopic charcoal record (> 150 μm) and were interpreted considering sedimentological, palynological, and historical evidence. Macro-charcoal analysis revealed two main phases of biomass burning: a late Medieval one (653−533 cal bp ), followed by a distinct fire-free interval, and a second Modern Times phase (313 cal bp until today). During the late Medieval times and after ad 1750 (200 cal bp) low-magnitude local fires coincided with high-intensity land use pressure. Major historical events like the Thirty Years’ War and the fire incident near the town (ad 1386, 174 cal bp) were also documented by the charcoal sedimentary record. The specific terrain morphology and the town microclimate impacted the chance of whether certain local burning patterns were detectable at all. This study demonstrates that during the Medieval period to the present day, fire events, mainly anthropogenic, were coupled with the main vegetation developments. Anthropogenic fire was an inseparable part of the Bad Waldsee landscape, as also recorded historically by the town archives.
Optimizing anomaly-based attack detection using classification machine learning
Authors: Gouda Hany Abdelghany , Ahmed Mohamed Abdelslam , Roushdy Mohamed Ismail 
Published: December 2023

AbstractOne of the significant aspects of our digital world is that data are literally everywhere, and it is increasing. On the other hand, the number of cyberattacks aiming to seize this data and use it illegally is increasing at an exponential rate, and this is the challenge. Therefore, intrusion detection systems (IDS) have attracted considerable interest from researchers and industries. In this regard, machine learning (ML) techniques are playing a pivotal role as they put the responsibility of analyzing enormous amounts of data, finding patterns, classifying intrusions, and solving issues on computers instead of humans. This paper implements two separate classification layers of ML-based algorithms with the recently published NF-UQ-NIDS-v2 dataset, preprocessing two volumes of sample records (100 k and 10 million), utilizing MinMaxScaler, LabelEncoder, selecting superlative features by recursive feature elimination, normalizing the data, and optimizing hyper-parameters for classical algorithms and neural networks. With a small dataset volume, the results of the classical algorithms layer show high detection accuracy rates for support vector (98.26%), decision tree (98.78%), random forest (99.07%), K-nearest neighbors (98.16%), CatBoost (99.04%), and gradient boosting (98.80%). In addition, the layer of neural network algorithms has proven to be a very powerful technology when using deep learning, particularly due to its unique ability to effectively handle enormous amounts of data and detect hidden correlations and patterns; it showed high detection results, which were (98.87%) for long short-term memory and (98.56%) for convolutional neural networks.
Arbitrage problems with reflected geometric Brownian motion
Authors: Buckner Dean , Dowd Kevin , Hulley Hardy 
Published: December 2023

AbstractContrary to the claims made by several authors, a financial market model in which the price of a risky security follows a reflected geometric Brownian motion is not arbitrage-free. In fact, such models violate even the weakest no-arbitrage condition considered in the literature. Consequently, they do not admit numéraire portfolios or equivalent risk-neutral probability measures, which makes them unsuitable for contingent claim valuation. Unsurprisingly, the published option pricing formulae for such models violate classical no-arbitrage bounds.
Enhanced simulated moving bed reactor performance for the synthesis of solketal by implementing the ModiCon strategy
Authors: Corrêa Isabella , Faria Rui P. V. , Rodrigues Alírio E. 
Published: December 2023

AbstractThe need to extend the range of potential uses for the Simulated Moving Bed Reactor (SMBR) leads the development of new operational strategies that enable the equipment to perform increasingly difficult reactions and separations. Solketal is the valorization product from the reaction between glycerol and acetone, a thermodynamic limited process that faces miscibility and selectivity issues. In this work, the ModiCon SMBR is proposed and optimized aiming to overcome the difficulties of solketal synthesis in the Conventional SMBR. The optimization is based on the “Reactive-Separation Volumes” methodology, a well-established procedure followed for optimizing numerous other SMBR processes. The results demonstrate that the new SMBR operating mode can produce solketal with a purity of 97%, reaching a productivity of over 9.69 $${\text{kg}}_{{{\text{Solk}}}} {\text{ L}}_{{{\text{Ads}}}}^{{ - {1}}} {\text{ day}}^{{ - {1}}}$$

kg
Solk

L

Ads

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1

day

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1

, 38% higher than the Multifeed, another non-conventional strategy proposed in the open literature for the synthesis of solketal. The desorbent consumption is 6.76 $${\text{L}}_{{{\text{Desorbent}}}} \,{\text{kg}}_{{{\text{Prod}}}}^{ - 1} ,$$

L
Desorbent

kg

Prod

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1

,

35% higher than the Multifeed. The most impressive result is the decrease in the reactants consumption of 12%. Such results demonstrate that this strategy intensifies the process even more than other non-conventional operating strategies proposed in the literature, which makes it more appealing to be implemented in the large scale solketal production.
The NMR signature of maltose-based glycation in full-length proteins
Authors: Defant Pauline , Regl Christof , Huber Christian G. , Schubert Mario 
Published: December 2023

AbstractReducing sugars can spontaneously react with free amines in protein side chains leading to posttranslational modifications (PTMs) called glycation. In contrast to glycosylation, glycation is a non-enzymatic modification with consequences on the overall charge, solubility, aggregation susceptibility and functionality of a protein. Glycation is a critical quality attribute of therapeutic monoclonal antibodies. In addition to glucose, also disaccharides like maltose can form glycation products. We present here a detailed NMR analysis of the Amadori product formed between proteins and maltose. For better comparison, data collection was done under denaturing conditions using 7 M urea-d4 in D2O. The here presented correlation patterns serve as a signature and can be used to identify maltose-based glycation in any protein that can be denatured. In addition to the model protein BSA, which can be readily glycated, we present data of the biotherapeutic abatacept containing maltose in its formulation buffer. With this contribution, we demonstrate that NMR spectroscopy is an independent method for detecting maltose-based glycation, that is suited for cross-validation with other methods.
On Computing Medians of Marked Point Process Data Under Edit Distance
Authors: Sukegawa Noriyoshi , Suzuki Shohei , Ikebe Yoshiko , Hirata Yoshito 
Published: December 2023

AbstractIn this paper, we consider the problem of computing a median of marked point process data under an edit distance. We formulate this problem as a binary linear program, and propose to solve it to optimality by software. We show results of numerical experiments to demonstrate the effectiveness of the proposed method and its application in earthquake prediction.
Comparing CNN-based and transformer-based models for identifying lung cancer: which is more effective?
Authors: Gai Lulu , Xing Mengmeng , Chen Wei , Zhang Yi , Qiao Xu 
Published: December 2023

AbstractLung cancer constitutes the most severe cause of cancer-related mortality. Recent evidence supports that early detection by means of computed tomography (CT) scans significantly reduces mortality rates. Given the remarkable progress of Vision Transformers (ViTs) in the field of computer vision, we have delved into comparing the performance of ViTs versus Convolutional Neural Networks (CNNs) for the automatic identification of lung cancer based on a dataset of 212 medical images. Importantly, neither ViTs nor CNNs require lung nodule annotations to predict the occurrence of cancer. To address the dataset limitations, we have trained both ViTs and CNNs with three advanced techniques: transfer learning, self-supervised learning, and sharpness-aware minimizer. Remarkably, we have found that CNNs achieve highly accurate prediction of a patient’s cancer status, with an outstanding recall (93.4%) and area under the Receiver Operating Characteristic curve (AUC) of 98.1%, when trained with self-supervised learning. Our study demonstrates that both CNNs and ViTs exhibit substantial potential with the three strategies. However, CNNs are more effective than ViTs with the insufficient quantities of dataset.