Non-linear Convolution Filters for CNN-based Learning

During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.Comment: 9 pages, 5 figures, code link, ICCV 201

Affect state recognition for adaptive human robot interaction in learning environments

Previous studies of robots used in learning environments suggest that the interaction between learner and robot is able to enhance the learning procedure towards a better engagement of the learner. Moreover, intelligent robots can also adapt their behavior during a learning process according to certain criteria resulting in increasing cognitive learning gains. Motivated by these results, we propose a novel Human Robot Interaction framework where the robot adjusts its behavior to the affect state of the learner. Our framework uses the theory of flow to label different affect states (i.e., engagement, boredom and frustration) and adapt the robot's actions. Based on the automatic recognition of these states, through visual cues, our method adapt the learning actions taking place at this moment and performed by the robot. This results in keeping the learner at most times engaged in the learning process. In order to recognizing the affect state of the user a two step approach is followed. Initially we recognize the facial expressions of the learner and therefore we map these to an affect state. Our algorithm perform well even in situations where the environment is noisy due to the presence of more than one person and/or situations where the face is partially occluded

Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans

Recently there has been an explosion in the use of Deep Learning (DL) methods for medical image segmentation. However the field's reliability is hindered by the lack of a common base of reference for accuracy/performance evaluation and the fact that previous research uses different datasets for evaluation. In this paper, an extensive comparison of DL models for lung and COVID-19 lesion segmentation in Computerized Tomography (CT) scans is presented, which can also be used as a benchmark for testing medical image segmentation models. Four DL architectures (Unet, Linknet, FPN, PSPNet) are combined with 25 randomly initialized and pretrained encoders (variations of VGG, DenseNet, ResNet, ResNext, DPN, MobileNet, Xception, Inception-v4, EfficientNet), to construct 200 tested models. Three experimental setups are conducted for lung segmentation, lesion segmentation and lesion segmentation using the original lung masks. A public COVID-19 dataset with 100 CT scan images (80 for train, 20 for validation) is used for training/validation and a different public dataset consisting of 829 images from 9 CT scan volumes for testing. Multiple findings are provided including the best architecture-encoder models for each experiment as well as mean Dice results for each experiment, architecture and encoder independently. Finally, the upper bounds improvements when using lung masks as a preprocessing step or when using pretrained models are quantified. The source code and 600 pretrained models for the three experiments are provided, suitable for fine-tuning in experimental setups without GPU capabilities.Comment: 10 pages, 8 figures, 2 table

NEW 3D METHOD TO ESTIMATE THE CYCLING FRONTAL AREA DURING PEDALLING

Aerodynamic drag represents a high percentage of the resistive forces in cycling. The frontal area has been traditionally measured through 2D digitalization, which requires a capture and an analysis processes. In the present study, the 2D method has been compared with a new real time 3D method to measure the frontal area in three different cycling positions. High reliability (ICC\u3e0.9) was found on both measurement methods. However there were differences between methods at the most aerodynamic positions. These differences might appear due to the capacity of the new method to measure the depth dimension, which supposes an improvement on the frontal area analysis. Furthermore, the new 3D method allows feedback of the frontal area in real time

Using Tobit Kalman filtering in order to improve the Motion recorded by Microsoft Kinect

In this paper, we analyze data from Microsoft Kinect v2 camera using Kalman Tobit and Kalman filters so as to minimize noise. The data concern three-dimensional spatial coordinates recording movements of a persons’A joints, which are subject to measurement errors. The noise variances of the process and the measurements are estimated using the maximum likelihood function. In order to include into the model restrictive conditions based on anthropometric data (e.g. the distances between various joints) we apply the Tobit Kalman Filter. Additionally, restrictions for the joints displacements per fame based on real data can be used in order to get better results. Finally simulations of skeleton before and after using Kalman filtering are presented

Two examples of online eHealth platforms for supporting people living with cognitive impairments and their caregivers

This paper compares two methodological approaches derived from the EU Horizon 2020 funded projects CAREGIVERSPRO-MMD (C-MMD)1 and ICT4LIFE2. Both approaches were initiated in 2016 with the ambition to provide new integrated care services to people living with cognitive impairments, including Dementia, Alzheimer and Parkinson disease, as well as to their home caregivers towards a long-term increase in quality of life and autonomy at home. An outline of the disparities and similarities related to non-pharmacological interventions introduced by the two projects to foster treatment adherence was made. Both approaches have developed software solutions, including social platforms, notifications, Serious Games, user monitoring and support services aimed at developing the concepts of self-care, active patients and integrated care. Besides their differences, both projects can be benefited by knowledge and technology exchange, pilot results sharing and possible user's exchange if possible in the near future.Peer ReviewedPostprint (published version

Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks

The undeniable computational power of artificial neural networks has granted the scientific community the ability to exploit the available data in ways previously inconceivable. However, deep neural networks require an overwhelming quantity of data in order to interpret the underlying connections between them, and therefore, be able to complete the specific task that they have been assigned to. Feeding a deep neural network with vast amounts of data usually ensures efficiency, but may, however, harm the network’s ability to generalize. To tackle this, numerous regularization techniques have been proposed, with dropout being one of the most dominant. This paper proposes a selective gradient dropout method, which, instead of relying on dropping random weights, learns to freeze the training process of specific connections, thereby increasing the overall network’s sparsity in an adaptive manner, by driving it to utilize more salient weights. The experimental results show that the produced sparse network outperforms the baseline on numerous image classification datasets, and additionally, the yielded results occurred after significantly less training epochs

Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks

The undeniable computational power of artificial neural networks has granted the scientific community the ability to exploit the available data in ways previously inconceivable. However, deep neural networks require an overwhelming quantity of data in order to interpret the underlying connections between them, and therefore, be able to complete the specific task that they have been assigned to. Feeding a deep neural network with vast amounts of data usually ensures efficiency, but may, however, harm the network’s ability to generalize. To tackle this, numerous regularization techniques have been proposed, with dropout being one of the most dominant. This paper proposes a selective gradient dropout method, which, instead of relying on dropping random weights, learns to freeze the training process of specific connections, thereby increasing the overall network’s sparsity in an adaptive manner, by driving it to utilize more salient weights. The experimental results show that the produced sparse network outperforms the baseline on numerous image classification datasets, and additionally, the yielded results occurred after significantly less training epochs