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1. Fun with F24

   https://doi.org/10.1007/JHEP02(2021)039  

   Harrison, Sarah M.; Paquette, Natalie M.; Persson, Daniel; Volpato, Roberto (February 2021, Journal of High Energy Physics)

   null (Ed.)

   A bstract We study some special features of F 24 , the holomorphic c = 12 superconformal field theory (SCFT) given by 24 chiral free fermions. We construct eight different Lie superalgebras of “physical” states of a chiral superstring compactified on F 24 , and we prove that they all have the structure of Borcherds-Kac-Moody superalgebras. This produces a family of new examples of such superalgebras. The models depend on the choice of an $$ \mathcal{N} $$ N = 1 supercurrent on F 24 , with the admissible choices labeled by the semisimple Lie algebras of dimension 24. We also discuss how F 24 , with any such choice of supercurrent, can be obtained via orbifolding from another distinguished c = 12 holomorphic SCFT, the $$ \mathcal{N} $$ N = 1 supersymmetric version of the chiral CFT based on the E 8 lattice. 

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2. Fun and Engaging Pre-CS1 Programming Languages

   https://doi.org/10.1145/3408877.3432570  

   Garcia, Daniel D.; Rogers, Michael P.; Stefik, Andreas (March 2021, Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE '21))

   The CSforALL movement to bring computational thinking to K-12 has been a boon for practitioners and language developers. This panel features three educators passionate about a particular lan- guage that has been successful with K-12 audiences. Each will demonstrate their language, describe what makes it unique, and share some of the fun and engaging projects students have created. 

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3. Fraud De-Anonymization for Fun and Profit

   https://doi.org/10.1145/3243734.3243770  

   Hernandez, Nestor; Rahman, Mizanur; Recabarren, Ruben; Carbunar, Bogdan (October 2018, Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security)

   The persistence of search rank fraud in online, peer-opinion systems, made possible by crowdsourcing sites and specialized fraud workers, shows that the current approach of detecting and filtering fraud is inefficient. We introduce a fraud de-anonymization approach to disincentivize search rank fraud: attribute user accounts flagged by fraud detection algorithms in online peer-opinion systems, to the human workers in crowdsourcing sites, who control them. We model fraud de-anonymization as a maximum likelihood estimation problem, and introduce UODA, an unconstrained optimization solution. We develop a graph based deep learning approach to predict ownership of account pairs by the same fraudster and use it to build discriminative fraud de-anonymization (DDA) and pseudonymous fraudster discovery algorithms (PFD). To address the lack of ground truth fraud data and its pernicious impacts on online systems that employ fraud detection, we propose the first cheating-resistant fraud de-anonymization validation protocol, that transforms human fraud workers into ground truth, performance evaluation oracles. In a user study with 16 human fraud workers, UODA achieved a precision of 91%. On ground truth data that we collected starting from other 23 fraud workers, our co-ownership predictor significantly outperformed a state-of-the-art competitor, and enabled DDA and PFD to discover tens of new fraud workers, and attribute thousands of suspicious user accounts to existing and newly discovered fraudsters. 

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4. Trojaning Language Models for Fun and Profit

   Zhang, Xinyang; Zhang, Z.; Ji, S.; Wang, T. (January 2021, Proceedings of IEEE European Symposium on Security and Privacy)

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5. GPU Memory Exploitation for Fun and Profit

   Guo, Yanan; Zhang, Zhenkai; Yang, Jun (August 2024, USENIX Association)