A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure
In this paper, we propose a gamification approach as a novel framework for smart building
infrastructure with the goal of motivating human occupants to consider personal energy …
infrastructure with the goal of motivating human occupants to consider personal energy …
Regional homogeneity: Towards learning transferable universal adversarial perturbations against defenses
This paper focuses on learning transferable adversarial examples specifically against
defense models (models to defense adversarial attacks). In particular, we show that a simple …
defense models (models to defense adversarial attacks). In particular, we show that a simple …
[图书][B] Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …
despite recent advances in deep learning. Deep learning is not provably secure. A critical …
Inverse open-loop noncooperative differential games and inverse optimal control
We consider the problem of computing parameters of player cost functionals such that given
state and control trajectories constitute an open-loop Nash equilibrium for a noncooperative …
state and control trajectories constitute an open-loop Nash equilibrium for a noncooperative …
Design, benchmarking and explainability analysis of a game-theoretic framework towards energy efficiency in smart infrastructure
In this paper, we propose a gamification approach as a novel framework for smart building
infrastructure with the goal of motivating human occupants to reconsider personal energy …
infrastructure with the goal of motivating human occupants to reconsider personal energy …
A novel graphical lasso based approach towards segmentation analysis in energy game-theoretic frameworks
HP Das, IC Konstantakopoulos… - 2019 18th IEEE …, 2019 - ieeexplore.ieee.org
Energy game-theoretic frameworks have emerged to be a successful strategy to encourage
energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number …
energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number …
On solution functions of optimization: Universal approximation and covering number bounds
We study the expressibility and learnability of solution functions of convex optimization and
their multi-layer architectural extension. The main results are:(1) the class of solution …
their multi-layer architectural extension. The main results are:(1) the class of solution …
Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems
In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …
tailored for solving a certain class of non-convex distributionally robust optimisation …
[图书][B] Digital Forensics in the Era of Artificial Intelligence
N Moustafa - 2022 - taylorfrancis.com
Digital forensics plays a crucial role in identifying, analysing, and presenting cyber threats as
evidence in a court of law. Artificial intelligence, particularly machine learning and deep …
evidence in a court of law. Artificial intelligence, particularly machine learning and deep …
Game theoretical adversarial deep learning
This chapter summarizes the game theoretical strategies for generating adversarial
manipulations. The adversarial learning objective for our adversaries is assumed to be to …
manipulations. The adversarial learning objective for our adversaries is assumed to be to …