Unsupervised Active Visual Search With Monte Carlo Planning Under Uncertain Detections.

We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more plausible and robust to detector failures compared to state-of-the-art methods: i) it is unsupervised as it does not need any training sessions. ii) During the exploration, a probability distribution on the 2D floor map is updated according to an intuitive mechanism, while an improved belief update increases the effectiveness of the agent's exploration.

Adversarial Data Augmentation for HMM-Based Anomaly Detection.

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for generating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs).

Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots.

We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions.

Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset.

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring.

MP-GeneticSynth: inferring biological network regulations from time series.

MP-GeneticSynth is a Java tool for discovering the logic and regulation mechanisms responsible for observed biological dynamics in terms of finite difference recurrent equations. The software makes use of: (i) metabolic P systems as a modeling framework, (ii) an evolutionary approach to discover flux regulation functions as linear combinations of given primitive functions, (iii) a suitable reformulation of the least squares method to estimate function parameters considering simultaneously all the reactions involved in complex dynamics. The tool is available as a plugin for the virtual laboratory MetaPlab.

From time series to biological network regulations: an evolutionary approach.

In this paper we present a new methodology, based on genetic algorithms and multiple linear regression, for discovering regulation mechanisms responsible for observed time series in biological networks. The modeling framework employed is called Metabolic P systems; they are deterministic and time-discrete dynamical systems proposed as an effective alternative to ordinary differential equations for modeling biochemical systems. Our methodology is here successfully applied to the mitotic oscillator in early amphibian embryos.

A dictionary based informational genome analysis.

Background: In the post-genomic era several methods of computational genomics are emerging to understand how the whole information is structured within genomes. Literature of last five years accounts for several alignment-free methods, arisen as alternative metrics for dissimilarity of biological sequences. Among the others, recent approaches are based on empirical frequencies of DNA k-mers in whole genomes.