Knowledge inference through analysis of human activities

Monitoring human activities provides context data to be used by computational systems, aiming a better understanding of users and their surroundings. Uncertainty still is an obstacle to overcome when dealing with context-aware systems. The origin of it may be related to incomplete or outdated data. Attribute Grammars emerge as a consistent approach to deal with this problem due to their formal nature, allowing the definition of rules to validate context. In this paper, a model to validate human daily activities based on an Attribute Grammar is presented. Context data is analysed through the execution of rules that implement semantic statements. This processing, called semantic analysis, will highlight problems that can be raised up by uncertain situations. The main contribution of this paper is the proposal of a rigorous approach to deal with context-aware decisions (decisions that depend on the data collected from the sensors in the environment) in such a way that uncertainty can be detected and its harmful effects can be minimized.This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia ˆ within the Project Scope: UID/CEC/00319/2019

A nonlinear approach to NN interactions using self-interacting meson fields

Motivated by the success of models based on chiral symmetry in NN interactions we investigate self-interacting scalar, pseudoscalar and vector meson fields and their impact for NN forces. We parametrize the corresponding nonlinear field equations and get analytic wavelike solutions. A probability amplitude for the propagation of particle states is calculated and applied in the framework of a boson-exchange NN potential. Using a proper normalization of the meson fields makes all self-scattering amplitudes finite. The same normalization is able to substitute for the phenomenological form factors used in conventional boson exchange potentials and thus yields an phenomenological understanding of this part of the NN interaction. We find an empirical scaling law which relates the meson self-interaction couplings to the pion mass and self-interaction coupling constant. Our model yields np phase shifts comparable to the Bonn B potential results and deuteron properties, in excellent agreement with experimental data.Comment: Reviewed version, 25 pages REVTeX, more info at http://i04ktha.desy.d

Local realizations of contact interactions in two- and three-body problems

Mathematically rigorous theory of the two-body contact interaction in three dimension is reviewed. Local potential realizations of this proper contact interaction are given in terms of Poschl-Teller, exponential and square-well potentials. Three body calculation is carried out for the halo nucleus 11Li using adequately represented contact interaction.Comment: submitted to Phys. Rev.

Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment

International audienceConvolutional Neural Networks (CNN) are very useful for fully automatic extraction of discriminative features from raw sensor data. This is an important problem in activity recognition, which is of enormous interest in ambient sensor environments due to its universality on various applications. Activity recognition in smart homes uses large amounts of time-series sensor data to infer daily living activities and to extract effective features from those activities, which is a challenging task. In this paper we demonstrate the use of the CNN and a comparison of results, which has been performed with Long Short Term Memory (LSTM), recurrent neural networks and other machine learning algorithms, including Naive Bayes, Hidden Markov Models, Hidden Semi-Markov Models and Conditional Random Fields. The experimental results on publicly available smart home datasets demonstrate that the performance of 1D-CNN is similar to LSTM and better than the other probabilistic models

San Bernardino Cave (Italy) and the appearance of Levallois technology in Europe: results of a radiometric and technological reassessment.

The introduction of Levallois technology in Europe marked the transition from the Lower to the early Middle Paleolithic. This new method of flake production was accompanied by significant behavioral changes in hominin populations. The emergence of this technological advance is considered homogeneous in the European archaeological record at the Marine isotopic stage (MIS) 9/MIS 8 boundary. In this paper we report a series of combined electron spin resonance/U-series dates on mammal bones and teeth recovered from the lower units of San Bernardino Cave, Italy, and the technological analyses of the lithic assemblages. San Bernardino Cave has yielded the earliest evidence of Levallois production on the Italian Peninsula recovered to date. In addition to our results and the review of the archaeological record, we describe the chronological and geographical differences between European territories and diversities in terms of technological developments. The belated emergence of Levallois technology in Italy compared to western Europe corresponds to the late Italian Neanderthal speciation event. The new radiometric dates and the technological analyses of San Bernardino Cave raise the issue of the different roles of glacial refugia in the peopling and the spread of innovative flaking strategies in Europe during the late Middle Pleistocene