167 citations · 169 across the 7 of their papers we have counts for
7 papers
Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition
Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker +1
Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild l…
Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations
Ioannis Ziogas, Ensieh Khazaei, Bilal Taha +4
Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing. However, existing ap…
Variational decomposition autoencoding improves disentanglement of latent representations
Ioannis Ziogas, Aamna Al Shehhi, Ahsan H. Khandoker +1
Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech an…
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Charalampos Lamprou, Aamna Alshehhi, Leontios J. Hadjileontiadis +1
Accounting for inter-individual variability in brain function is key to precision medicine. Here, by considering functional inter-individual variability as meaningful data rather t…
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition
Ioannis Ziogas, Hessa Alfalahi, Ahsan H. Khandoker +1
Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeli…
K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
Cheul Young Park, Narae Cha, Soowon Kang +6
Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of natural…
Dynamic Spectrum Sensing Through Accelerated Particle Swarm Optimization
Alexandros E. Paschos, Vasileios M. Kapinas, Georgia D. Ntouni +2
In this paper, a novel optimization algorithm, called the acceleration-aided particle swarm optimization (AAPSO), is proposed for reliable dynamic spectrum sensing in cognitive rad…