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Found 17 papers, 4 papers with code
Modeling Resilience of Collaborative AI Systems
We tested our framework on a real-world case study of a robot collaborating online with the human, when the system is experiencing a disruptive event.
GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems
A Collaborative Artificial Intelligence System (CAIS) works with humans in a shared environment to achieve a common goal.
Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks
Predicting the behavior of real-time traffic (e. g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources.
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks
Herein we demonstrate how a feature-fusion strategy of the orientation components leads to further improving visual recognition accuracy to 97\%.
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review
Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats.
Correlation analysis of node and edge centrality measures in artificial complex networks
The importance of a node in a social network is identified through a set of measures called centrality.
Social and Information Networks
Graph and Network Theory for the analysis of Criminal Networks
This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study.
Social and Information Networks Physics and Society
Experimental Review of Neural-based approaches for Network Intrusion Management
This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.
Disrupting Resilient Criminal Networks through Data Analysis: The case of Sicilian Mafia
Herein, we borrow methods and tools from Social Network Analysis to (i) unveil the structure of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently disrupt them.
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium.
On-line Building Energy Optimization using Deep Reinforcement Learning
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure.
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods.
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data
Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences.
Predictive No-Reference Assessment of Video Quality
Among the various means to evaluate the quality of video streams, No-Reference (NR) methods have low computation and may be executed on thin clients.
A topological insight into restricted Boltzmann machines
Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on.
Relevance As a Metric for Evaluating Machine Learning Algorithms
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical.
