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Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation

Autor: Materwala, Huned, Naik, Shraddha M., Taha, Aya, Abed, Tala Abdulrahman, Svetinovic, Davor

Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exp

Externí odkaz: http://arxiv.org/abs/2411.03327

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Learning Patterns from Biological Networks: A Compounded Burr Probability Model

Autor: Chakraborty, Tanujit, Naik, Shraddha M., Chattopadhyay, Swarup, Das, Suchismita

Complex biological networks, comprising metabolic reactions, gene interactions, and protein interactions, often exhibit scale-free characteristics with power-law degree distributions. However, empirical studies have revealed discrepancies between obs

Externí odkaz: http://arxiv.org/abs/2407.04465

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Skew Probabilistic Neural Networks for Learning from Imbalanced Data

Autor: Naik, Shraddha M., Chakraborty, Tanujit, Hadid, Abdenour, Chakraborty, Bibhas

Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for t

Externí odkaz: http://arxiv.org/abs/2312.05878

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Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art

Autor: Chakraborty, Tanujit, S, Ujjwal Reddy K, Naik, Shraddha M., Panja, Madhurima, Manvitha, Bayapureddy

Since their inception in 2014, Generative Adversarial Networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas. Consisting of a discrimi

Externí odkaz: http://arxiv.org/abs/2308.16316

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