Andrea Lodi

Mehdi Taobane

Cloud networks are the backbone of the modern distributed internet infrastructure as they provision most of the on-demand resources organiza… (voir plus)tions and individuals use daily. However, any abrupt cyber-attack could disrupt the provisioning of some of the cloud resources fulfilling the needs of customers, industries, and governments. In this work, we introduce a game-theoretic model that assesses the cyber-security risk of cloud networks and informs security experts on the optimal security strategies. Our approach combines game theory, combinatorial optimization, and cyber-security and aims at minimizing the unexpected network disruptions caused by malicious cyber-attacks under uncertainty. Methodologically, our approach consists of a simultaneous and non-cooperative attacker-defender game where each player solves a combinatorial optimization problem parametrized in the variables of the other player. Practically, our approach enables security experts to (i.) assess the security posture of the cloud network, and (ii.) dynamically adapt the level of cyber-protection deployed on the network. We provide a detailed analysis of a real-world cloud network and demonstrate the efficacy of our approach through extensive computational tests.

2023-03-10

ArXiv (prépublication)

A Convex Reformulation and an Outer Approximation for a Large Class of Binary Quadratic Programs

Borzou Rostami

Fausto Errico

2023-03-01

Operations Research (publié)

Game theoretical analysis of Kidney Exchange Programs

Margarida Carvalho

2023-02-01

European Journal of Operational Research (publié)

Cutting Planes from the Branch-and-Bound Tree: Challenges and Opportunities

Claudio Contardo

Andrea Tramontani

2023-01-01

INFORMS J. Comput. (publié)

Optimizing Fairness over Time with Homogeneous Workers (Short Paper).

Bart-jan Van Rossum

Rui Chen

2023-01-01

ATMOS (publié)

Lookback for Learning to Branch

Prateek Gupta

Elias Boutros Khalil

Didier Chételat

Maxime Gasse

Yoshua Bengio

M. Pawan Kumar

2022-10-10

TMLR (accepté)

openreview.net

OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations

Leandro R. Costa

Daniel Aloise

Luca G. Gianoli

A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). Whil… (voir plus)e the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.

2022-06-01

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS) (publié)

Capacity Variation in the Many-to-one Stable Matching

Federico Bobbio

Margarida Carvalho

Alfredo Torrico

2022-05-03

ArXiv (prépublication)

OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations

Leandro Rincon Costa

Daniel Aloise

Luca Giovanni Gianoli

2022-05-01

International Conference on Distributed Computing in Sensor Systems (published)

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

L'ea Ricard

Guy Desaulniers

Louis-Martin Rousseau

2022-05-01

Transportation Research Part C: Emerging Technologies (publié)

Predicting the probability distribution of bus travel time to measure the reliability of public transport services

L. Ricard

Guy Desaulniers

Louis-Martin Rousseau

2022-05-01

Transportation Research Part C: Emerging Technologies (published)

On the estimation of discrete choice models to capture irrational customer behaviors

Sanjay Dominik Jena

Claudio Sole

The random utility maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economi… (voir plus)cs has provided strong empirical evidence of irrational choice behaviors, such as halo effects, that are incompatible with this framework. Models belonging to the random utility maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. In particular, we propose a column-generation method to gradually refine the discrete choice model based on partially ranked preference sequences. Extensive computational experiments indicate that our model, explicitly accounting for irrational preferences, can significantly boost the predictive accuracy on both synthetic and real-world data instances. Summary of Contribution: In this work, we propose an estimation method for the recently proposed generalized stochastic preference choice model, which subsumes the family of random utility maximization models and is capable of capturing halo effects. Specifically, we show how to use partially ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a treelike data structure containing the customer behaviors. Furthermore, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach by comparing it against rank-based methods with only rational preferences and with more general benchmarks from the literature. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average when tested on a real-world data set from a large chain of grocery and drug stores.

2022-05-01

INFORMS Journal on Computing (publié)