Fuzz testing techniques are becoming pervasive for their ever-improving ability to generate crashing trial cases for programs. Memory safety violations however can lead to silent corruptions and errors, and a fuzzer may recognize them only in the presence of sanitization machinery. For closed-...
04b Atto di convegno in volume
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Model Predictive Control for Collision-free Spacecraft Formation with Artificial Potential FunctionsA collision-free formation control strategy for flying in formation is presented. A linear control law is developed by means of Model Predictive Control (MPC) via the dual-mode paradigm [1]. Collision avoidance is dealt with by using Artificial Potential Functions (APFs) to keep a desired safe...
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Federated Learning is a distributed learning solution for machine learning problems without the need of collecting the available data in a single centralized data centre. With the standard FL approaches, model training is performed locally and a centralized server collects and elaborates the...
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Federated Learning is a distributed and privacy-preserving machine learning technique that allows local clients to learn a model without sharing their own data by coordinating with a global server. In this work, we present the Adaptive Federated Learning (AdaFed) algorithm, which aims at improving...
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In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available.In...
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Part of the design of many blockchains and cryptocurrencies includes a treasury, which periodically allocates collected funds to various projects that could be beneficial to their ecosystem. These projects are then voted on and selected by the users of the respective cryptocurrency. To better...
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Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions...
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In most social choice settings, the participating agents express their preferences over the different alternatives in the form of linear orderings. While this clearly simplifies preference elicitation, it inevitably leads to poor performance with respect to optimizing a cardinal objective, such as...
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Motivated by governance models adopted in blockchain applications, we study the problem of selecting appropriate system updates in a decentralized way. Contrary to most existing voting approaches, we use the input of a set of motivated experts of varying levels of expertise. In particular, we...
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We consider the one-sided matching problem, where n agents have preferences over n items, and these preferences are induced by underlying cardinal valuation functions. The goal is to match every agent to a single item so as to maximize the social welfare. Most of the related literature, however,...