Communication is part of our everyday life and our ability to communicate can have a significant role in a variety of contexts in our personal, academic, and professional lives. For long, the characterization of what is a good communicator has been subject to research and debate by several areas, particularly in Education, with a focus on improving the performance of teachers. In this context, the literature suggests that the ability to communicate is not only defined by the verbal component, but also by a plethora of non-verbal contributions providing redundant or complementary information, and, sometimes, being the message itself. However, even though we can recognize a good or bad communicator, objectively, little is known about what aspects – and to what extent—define the quality of a presentation. The goal of this work is to create the grounds to support the study of the defining characteristics of a good communicator in a more systematic and objective form. To this end, we conceptualize and provide a first prototype for a computational approach to characterize the different elements that are involved in communication, from audiovisual data, illustrating the outcomes and applicability of the proposed methods on a video database of public speakers.
This short paper describes our contribution to the LNDb - Grand Challenge on automatic lung cancer patient management [1]. We only participated in Sub-Challenge A: Nodule Detection. The officially stated goal of this challenge is From chest CT scans, participants must detect pulmonary nodules. We developed a computer-aided detection (CAD) system for the identification of small pulmonary nodules in screening CT scans. The two main modules of our system consist of a CNN based nodule candidate detection, and a neural classifier for false positive reduction. The preliminary results obtained on the challenge database is discussed. In this work, we developed an Ensemble learning pipeline using state of the art convolutional neural networks (CNNs) as base detectors. In particular, we utilize the 3D versions of SE-ResNet18 and DPN68. Much like classical bagging, base learners were trained on 10 stratified data-set folds (the LUNA16 patient-level dataset splits) generated by bootstrapping both our training set (LUNA16) and the challenge provided training set. Furthermore, additional variation was introduced by using different CNN architectures. Particularly, we opted for an exhaustive search of the best detectors, consisting mostly of DPN68 [2] and SE-ResNet18 [3] architectures. We unfortunately joined the competition late, and we did not train our system on the corpus provided by the organizers and therefore we only run inference using our LIDC-IDRI trained model. We do realize this is not the best approach.
This paper proposes representing and detecting manufacturing defects at the micrometre scale using deep convolutional neural networks. The information theoretic notion of entropy is used to quantify the information gain or mutual information of filters throughout the network, where the deepest network layers are generally shown to exhibit the highest mutual information between filter responses and defects, and thus serve as the most discriminative features. Quantitative detection experiments based on the AlexNet architecture investigate a variety of design parameters pertaining to data preprocessing and network architecture, where the optimal architectures achieve an average accuracy of 98.54
In this work, we have carried out a performance analysis of chess players comparing a standard ranking measure with a novel one proposed here. Using the idea of treating participants eye movements, when answering several on-screen valid chess questions of distinguished complexities, as high-dimensional spatial attention patterns we have shown that expertise is consistently associated with the ability to process visual information holistically using fewer fixations rather than locally focusing on individual pieces. These findings might disclose new insights for predicting chess skills.
Identification and extraction of individual modes in non-stationary multicomponent signals is a challenging task which is shared by several applications, like micro-doppler human gait analysis, surveillance or medical data analysis. State-of-the-art methods are not capable yet to correctly estimate individual modes if their instantaneous frequencies laws are not separable. The knowledge of time instants where modes interference occurs could represent a useful information to use in separation strategies. To this aim, a novel time-domain method that is capable of locating interferences is investigated in this paper. Its main property is the use of multiscale energy for selecting the best analysis scale without requiring either the use of time-frequency representations or imaging methods. The performance of the proposed method is evaluated through several numerical simulations and comparative studies with the state of the art Rényi entropy based method. Finally, an example concerning a potential application to simulated micro-doppler human gait data is provided.
