Effectiveness of a Robot-Assisted Psychological Intervention for Children with Autism Spectrum Disorder.

Difficulties with social interaction characterise children with Autism Spectrum Disorders and have a negative impact in their everyday life. Integrating a social-humanoid robot within the standard clinical treatment has been proven promising. The main aim of this randomised controlled study was to evaluate the effectiveness of a robot-assisted psychosocial intervention and the secondary aim was to investigate potential differences between a robot-assisted intervention group and a control group receiving intervention by humans only. The analysis of the results showed that robot-assisted intervention could be beneficial by improving children's psychosocial skills. This improvement was highlighted by neuropsychological testing and parent reporting. Group comparison only presented minimal statistically significant differences. The study underpins the potential of robot-assisted interventions to augment standard care.

A proposal of Texture Features for interactive CTA Segmentation by Active Learning.

Our objective is to create an interactive image segmentation system of the abdominal area for quick volumetric segmentation of the aorta requiring minimal intervention of the human operator. The aforementioned goal is to be achieved by an Active Learning image segmentation system over enhanced image texture features, obtained from the standard Gray Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). The process iterates the following steps: first, image segmentation is produced by a Random Forest (RF) classifier trained on a set of image texture features for labeled voxels. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. The approach will be applied to the segmentation of the thrombus in Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients. A priori knowledge on the expected shape of the target structures is used to filter out undesired detections. On going preliminary experiments on datasets containing diverse number of CT slices (between 216 and 560), each one consisting a real human contrast-enhanced sample of the abdominal area, are underway. The segmentation results obtained with simple image features were promising and highlight the capacity of the used texture features to describe the local variation of the AAA thrombus and thus to provide useful information to the classifier.

Fuzzy lattice neurocomputing (FLN) models.

In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely 'sigma-FLN' for competitive clustering, and 'FLN with tightest fits (FLNtf)' for supervised clustering. Learning by the sigma-FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf, is incremental, data order independent, polynomial theta(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The sigma-FLN is presented here as fuzzy-ART's extension in the FL-framework such that sigma-FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with 'missing' and/or 'don't care' attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL-framework in computing is also delineated.

Estimation of the stapes-bone thickness in the stapedotomy surgical procedure using a machine-learning technique.

Stapedotomy is a surgical procedure aimed at the treatment of hearing impairment due to otosclerosis. The treatment consists of drilling a hole through the stapes bone in the inner ear in order to insert a prosthesis. Safety precautions require knowledge of the nonmeasurable stapes thickness. The technical goal herein has been the design of high-level controls for an intelligent mechatronics drilling tool in order to enable the estimation of stapes thickness from measurable drilling data. The goal has been met by learning a map between drilling features, hence no model of the physical system has been necessary. Learning has been achieved as explained in this paper by a scheme, namely the d-sigma Fuzzy Lattice Neurocomputing (d sigma-FLN) scheme for classification, within the framework of fuzzy lattices. The successful application of the d sigma-FLN scheme is demonstrated in estimating the thickness of a stapes bone "on-line" using drilling data obtained experimentally in the laboratory.

Fuzzy lattice neural network (FLNN): a hybrid model for learning.

This paper proposes two hierarchical schemes for learning, one for clustering and the other for classification problems. Both schemes can be implemented on a fuzzy lattice neural network (FLNN) architecture, to be introduced herein. The corresponding two learning models draw on adaptive resonance theory (ART) and min-max neurocomputing principles but their application domain is a mathematical lattice. Therefore they can handle more general types of data in addition to N-dimensional vectors. The FLNN neural model stems from a cross-fertilization of lattice theory and fuzzy set theory. Hence a novel theoretical foundation is introduced in this paper, that is the framework of fuzzy lattices or FL-framework, based on the concepts fuzzy lattice and inclusion measure. Sufficient conditions for the existence of an inclusion measure in a mathematical lattice are shown. The performance of the two FLNN schemes, that is for clustering and for classification, compares quite well with other methods and it is demonstrated by examples on various data sets including several benchmark data sets.