Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...
ABSTRACT: A new nano-based architectural design of multiple-stream convolutional homeomorphic error-control coding will be conducted, and a corresponding hierarchical implementation of important class ...
ORLANDO, Fla. — Steering clinicians toward a cascade approach for thyroid function testing cut unnecessary orders by a monthly average of 15% and concurrent orders by 19% per month, according to ...
Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...
In the context of using DNSGA2 to solve dynamic multi-objective optimization problems (DMOPs), a critical issue arises regarding the timing of the callback function execution and its impact on ...
Abstract: Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall ...
ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...
As a very effective machine learning ML-born optimization setting, boosting requires one to efficiently learn arbitrarily good models using a weak learner oracle, which provides classifiers that ...
Despite being groundbreaking, smart contracts are not impervious to flaws that malevolent parties could exploit. Inadequate input validation is a prevalent weakness that enables attackers to affect ...
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