Papers

Large language models (LLMs) exhibit impressive capabilities in generating
realistic text across diverse subjects. Concerns have been raised that they
could be utilized to produce fake content with a deceptive intention, although
evidence thus far remains anecdotal. This paper presents a case study about a
Twitter botnet that appears to employ ChatGPT to generate human-like content.
Through heuristics, we identify 1,140 accounts and validate them via manual
annotation. These accounts form a dense cluster of fake personas that exhibit
similar behaviors, including posting machine-generated content and stolen
images, and engage with each other through replies and retweets.
ChatGPT-generated content promotes suspicious websites and spreads harmful
comments. While the accounts in the AI botnet can be detected through their
coordination patterns, current state-of-the-art LLM content classifiers fail to
discriminate between them and human accounts in the wild. These findings
highlight the threats posed by AI-enabled social bots.

Read the full article at: arxiv.org

There are many scenarios where we wish to imitate a specific author's pen-on-paper handwriting style. Rendering new text in someone's handwriting is difficult because natural handwriting is highly variable, yet follows both intentional and involuntary structure that makes a person's style self-consistent.
We present an algorithm that renders a desired input string in an author's handwriting. An annotated sample of the author's handwriting is required; the system is flexible enough that historical documents can usually be used with only a little extra effort. Experiments show that our glyph-centric approach, with learned parameters for spacing, line thickness, and pressure, produces novel images of handwriting that look hand-made to casual observers, even when printed on paper.

My Text in Your Handwriting
Tom S.F. Haines, Oisin Mac Aodha, and Gabriel J. Brostow
University College London
Transactions on Graphics 2016

Saad Alqithami
Computers 2021, 10(1), 12

Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus. Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing. People naturally exist in dynamically cascading networks of social/spatial interactions. Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis. Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc. Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns. We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread. Such properties are essential in managing the rate of spread of viral infections. Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network. We derive mathematical formulations to demonstrate the viral spread in the network structures.

Read the full article at: www.mdpi.com