Month: October 2023

Abstract: Modern distributed systems also deal with uncertain scenarios where environments, infrastructures, and applications are widely diverse. In the scope of IoT-Edge-Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. A captivating set of hypotheses from the field of neuroscience suggests that human… Continue reading Distributed Intelligence in the Computing Continuum

Abstract: In multiwinner voting, voters report their preferences over the available alternatives, and the goal is to select a fixed-size subset of alternatives, usually referred to as a committee; this model captures a variety of real-life scenarios, from selecting a representative governing body to deciding which search results should appear on the first page of… Continue reading Fairness in Collective Decision-Making: From Multiwinner Voting to Participatory Budgeting

Abstract：Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. We propose a specific type of adversarial attack that can cheat classifiers by significant changes. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence… Continue reading Research on adversarial attack and robustness of deep neural networks