Astral Body: A Virtual Reality Game for Body Ownership Investigation

As one of the most disruptive human-computer interaction techniques, Virtual Reality (VR) provides a novel way to examine human movements, e.g. when investigating Body Ownership (BO) in the field of cognitive sciences, especially when the visual output diverges from real-world actions. Previous research in BO uses questionnaires and brain...

EMGSense: A Low-Effort Self-Supervised Domain Adaptation Framework for EMG Sensing

This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and...

Characteristics of fly ash blended mortars under ammonium chloride solution leaching

Characteristics of fly ash(FA) blended mortars (50%, 70% and 90%) at different curing ages (28 days and 180 days) under ammonium chloride solution(ACS) leaching were investigated. Mass loss under leaching, initial CaO content, Ca(OH)2 content(CH), content of chemical bonded water, and hydration degree of blended pastes were tested by XRF and DSC...

Matching images and text with multi-modal tensor fusion and re-ranking

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on embedding or classification, the first one mapping image and text instances into a common embedding space for distance measuring, and the second one regarding...

Recurrent knowledge graph embedding for effective recommendation

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities...

MRLR: Multi-level representation learning for personalized ranking in recommendation

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem...