The paper proposes a game-theoretic model of interaction between investors and innovators, taking into account the existence of so-called "fake" innovators offering knowingly unprofitable projects. The model is a Bayesian non-cooperative, repetitive game with recalculated payments and partly unobservable player types. It allows quantifying the parameters of the strategy for all player types to find equilibrium solutions. The model describes rational modes for screening "fake" innovators based on adjusting players' probabilistic estimates.
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http://dx.doi.org/10.1016/j.heliyon.2020.e05603 | DOI Listing |
Front Artif Intell
January 2025
Alma Sistemi Srl, Rome, Italy.
This study explores the evolving role of social media in the spread of misinformation during the Ukraine-Russia conflict, with a focus on how artificial intelligence (AI) contributes to the creation of deceptive war imagery. Specifically, the research examines the relationship between color patterns (LUTs) in war-related visuals and their perceived authenticity, highlighting the economic, political, and social ramifications of such manipulative practices. AI technologies have significantly advanced the production of highly convincing, yet artificial, war imagery, blurring the line between fact and fiction.
View Article and Find Full Text PDFSci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFHeliyon
December 2024
School of Economics and Management, Shanghai Maritime University, China.
The quality of corporate environmental information disclosure is concerned by all sectors of society. Using data on Chinese listed A-share companies in heavy polluting industries from 2015 to 2022, this study examines whether the "quantity" and "quality" dimensions of corporate environmental information disclosure have peer effects in the same region. Further, it compares the mechanism and economic consequences of the "real green" and "fake green" behaviors.
View Article and Find Full Text PDFPeerJ Comput Sci
October 2024
College of Media Engineering, Communication University of Zhejiang, Hangzhou, China.
The harm caused by deepfake face images is increasing. To proactively defend against this threat, this paper innovatively proposes a destructive active defense algorithm for deepfake face images (DADFI). This algorithm adds slight perturbations to the original face images to generate adversarial samples.
View Article and Find Full Text PDFPac Symp Biocomput
December 2024
College of Health and Human Development, Department of Biobehavioral Health, 219 Biobehavioral Health Building, 296 Henderson Drive, Pennsylvania State University, University Park, PA 16802, USA.
There is a disconnect between data practices in biomedicine and public understanding of those data practices, and this disconnect is expanding rapidly every day (with the emergence of synthetic data and digital twins and more widely adopted Artificial Intelligence (AI)/Machine Learning tools). Transparency alone is insufficient to bridge this gap. Concurrently, there is an increasingly complex landscape of laws, regulations, and institutional/ programmatic policies to navigate when engaged in biocomputing and digital health research, which makes it increasingly difficult for those wanting to "get it right" or "do the right thing.
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