AIMC Topic: Recognition, Psychology

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An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition.

Sensors (Basel, Switzerland)
Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may ...

Confidence-Calibrated Human Activity Recognition.

Sensors (Basel, Switzerland)
Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sens...

Unsupervised cross-lingual model transfer for named entity recognition with contextualized word representations.

PloS one
Named entity recognition (NER) is one fundamental task in the natural language processing (NLP) community. Supervised neural network models based on contextualized word representations can achieve highly-competitive performance, which requires a larg...

Boosting Intelligent Data Analysis in Smart Sensors by Integrating Knowledge and Machine Learning.

Sensors (Basel, Switzerland)
The presented paper proposes a hybrid neural architecture that enables intelligent data analysis efficacy to be boosted in smart sensor devices, which are typically resource-constrained and application-specific. The postulated concept integrates prio...

A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps.

Sensors (Basel, Switzerland)
Neuromorphic hardware systems have been gaining ever-increasing focus in many embedded applications as they use a brain-inspired, energy-efficient spiking neural network (SNN) model that closely mimics the human cortex mechanism by communicating and ...

Skeleton-Based Action Recognition Based on Distance Vector and Multihigh View Adaptive Networks.

Computational intelligence and neuroscience
Skeleton-based human action recognition has attracted much attention in the field of computer vision. Most of the previous studies are based on fixed skeleton graphs so that only the local physical dependencies among joints can be captured, resulting...

Dynamic graph convolutional networks with attention mechanism for rumor detection on social media.

PloS one
Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spr...

Student Behavior Recognition System for the Classroom Environment Based on Skeleton Pose Estimation and Person Detection.

Sensors (Basel, Switzerland)
Human action recognition has attracted considerable research attention in the field of computer vision, especially for classroom environments. However, most relevant studies have focused on one specific behavior of students. Therefore, this paper pro...

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

Annual review of vision science
Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organi...

A computational model of familiarity detection for natural pictures, abstract images, and random patterns: Combination of deep learning and anti-Hebbian training.

Neural networks : the official journal of the International Neural Network Society
We present a neural network model for familiarity recognition of different types of images in the perirhinal cortex (the FaRe model). The model is designed as a two-stage system. At the first stage, the parameters of an image are extracted by a pretr...