AIMC Topic: Learning

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Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning.

Sensors (Basel, Switzerland)
Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human-Computer Interaction (HCI). Although this technology has been successful...

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor.

Computational intelligence and neuroscience
Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could he...

Differential mapping spiking neural network for sensor-based robot control.

Bioinspiration & biomimetics
In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor spac...

Enhancement of Target-Oriented Opinion Words Extraction with Multiview-Trained Machine Reading Comprehension Model.

Computational intelligence and neuroscience
Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by...

One-shot object parsing in newborn chicks.

Journal of experimental psychology. General
Controlled-rearing studies provide the unique opportunity to examine which psychological mechanisms are present at birth and which mechanisms emerge from experience. Here we show that one core component of visual perception-the ability to parse objec...

Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.

eLife
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete seq...

Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique.

Computational and mathematical methods in medicine
Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic F...

Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation.

Journal of biomedical informatics
Online healthcare consultation offers people a convenient way to consult doctors. In this paper, we aim at building a generative dialog system for Chinese healthcare consultation. As the original Seq2seq architecture tends to suffer the issue of gene...

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.

Journal of medical systems
In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysi...

A message-passing multi-task architecture for the implicit event and polarity detection.

PloS one
Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task...