Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition

Automated Human Activity Recognition has long been a problem of great interest in human-centered and ubiquitous computing. In the last years, a plethora of supervised learning algorithms based on deep neural networks has been suggested to address this problem using various modalities. While every modality has its own limitations, there is one common challenge. Namely, supervised learning requires vast amounts of annotated data which is practically hard to collect. In this paper, we benefit from the self-supervised learning paradigm (SSL) that is typically used to learn deep feature representations from unlabeled data. Moreover, we upgrade a contrastive SSL framework, namely SimCLR, widely used in various applications by introducing a temporal feature alignment procedure for Human Activity Recognition. Specifically, we propose integrating a dynamic time warping (DTW) algorithm in a latent space to force features to be aligned in a temporal dimension. Extensive experiments have been conducted for the unimodal scenario with inertial modality as well as in multimodal settings using inertial and skeleton data. According to the obtained results, the proposed approach has a great potential in learning robust feature representations compared to the recent SSL baselines, and clearly outperforms supervised models in semi-supervised learning. The code for the unimodal case is available via the following link: https://github.com/bulatkh/csshar_tfa.Comment: Accepted to IJCB 202

Recent results of the research for preseismic phenomena on the underground water and temperature in Pieria, northern Greece

International audienceThe recent results of the research for earthquake precursory phenomena on the underground water level and temperature at the area Pieria of northern Greece are presented. The analysis of our observations in relation to the local microseismicity indicate that underground water level variations may be considered as precursory phenomena connected to the local microseismic activity in the area of Pieria. Base on these results, it can be supported that monitoring the shallow underground water level and temperature for detecting earthquake precursory phenomena may be proved to be a useful method in the framework of an interdisciplinary research for earthquake prediction

Skeleton-based Human Action Recognition using Basis Vectors

Automatic human action recognition is a research topic that has attracted significant attention lately, mainly due to the advancements in sensing technologies and the improvements in computational systems’ power. However, complexity in human movements, input devices’ noise and person-specific pattern variability impose a series of challenges that still remain to be overcome. In the proposed work, a novel human action recognition method using Microsoft Kinect depth sensing technology is presented for handling the above mentioned issues. Each action is represented as a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors. Using simple kernel regressors for computing the affinity matrix, complexity is reduced and robust low-dimensional representations are achieved. The proposed scheme loosens action detection accuracy demands, while it can be extended for accommodating multiple modalities, in a dynamic fashion

Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations

An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human understandable concepts. In this work, we expand on previous works in the literature that use annotated concept datasets to extract interpretable feature space directions and propose an unsupervised post-hoc method to extract a disentangling interpretable basis by looking for the rotation of the feature space that explains sparse one-hot thresholded transformed representations of pixel activations. We do experimentation with existing popular CNNs and demonstrate the effectiveness of our method in extracting an interpretable basis across network architectures and training datasets. We make extensions to the existing basis interpretability metrics found in the literature and show that, intermediate layer representations become more interpretable when transformed to the bases extracted with our method. Finally, using the basis interpretability metrics, we compare the bases extracted with our method with the bases derived with a supervised approach and find that, in one aspect, the proposed unsupervised approach has a strength that constitutes a limitation of the supervised one and give potential directions for future research.Comment: 15 pages, Accepted in IEEE Transactions on Artificial Intelligence, Special Issue on New Developments in Explainable and Interpretable A

Being the center of attention: A Person-Context CNN framework for Personality Recognition

This paper proposes a novel study on personality recognition using video data from different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. Therefore, we build a novel multi-stream Convolutional Neural Network framework (CNN), which considers multiple sources of information. From a given scenario, we extract spatio-temporal motion descriptors from every individual in the scene, spatio-temporal motion descriptors encoding social group dynamics, and proxemics descriptors to encode the interaction with the surrounding context. All the proposed descriptors are mapped to the same feature space facilitating the overall learning effort. Experiments on two public datasets demonstrate the effectiveness of jointly modeling the mutual Person-Context information, outperforming the state-of-the art-results for personality recognition in two different scenarios. Lastly, we present CNN class activation maps for each personality trait, shedding light on behavioral patterns linked with personality attributes

Bremsstrahlung from the Cosmic Neutrino Background

In this paper we discuss a detection method for the Cosmic Neutrino Background using bremsstrahlung from a neutrino scattering process which has no kinematic threshold, does not rely on a resonance and would in principle allow to measure the velocity distribution of the relic neutrinos. As a concrete example we calculate the rate for solar neutrinos scattering from a relic neutrino emitting a photon. We also provide the energy and angular distributions of the emitted photons.Comment: 4 pages, 2 figure

NNLO QCD corrections to weak boson fusion Higgs boson production in the H → bb‾\overline{b} and H → WW* → 4l decay channels

We compute the next-to-next-to-leading order QCD corrections to Higgs boson production in weak boson fusion followed by its decay to a b$\overline{b}$ pair or to a pair of leptonically-decaying W bosons. Our calculation allows us to compute realistic fiducial cross sections and assess the impact of fiducial cuts applied to the Higgs boson decay products on the magnitude of QCD radiative corrections in weak boson fusion