175 research outputs found
Opportunities in Mental Health Support for Informal Dementia Caregivers Suffering from Verbal Agitation
People with dementia (PwD) often present verbal agitation such as cursing,
screaming, and persistently complaining. Verbal agitation can impose mental
distress on informal caregivers (e.g., family, friends), which may cause severe
mental illnesses, such as depression and anxiety disorders. To improve informal
caregivers' mental health, we explore design opportunities by interviewing 11
informal caregivers suffering from verbal agitation of PwD. In particular, we
first characterize how the predictability of verbal agitation impacts informal
caregivers' mental health and how caregivers' coping strategies vary before,
during, and after verbal agitation. Based on our findings, we propose design
opportunities to improve the mental health of informal caregivers suffering
from verbal agitation: distracting PwD (in-situ support; before), prompting
just-in-time maneuvers (information support; during), and comfort and education
(social & information support; after). We discuss our reflections on cultural
disparities between participants. Our work envisions a broader design space for
supporting informal caregivers' well-being and describes when and how that
support could be provided.Comment: 26 pages, 1 figure, 2 tables. Accepted to PACM HCI (CSCW 2024
Delivering Development to Gender-conscious Communities in S. Korea: What Factors Matter?
This paper aims to suggest an alternative monitoring framework for the WFC (Women-Friendly City) project that can have an impact on the performance of the WFC project. In particular, this study looks at the mutual relationships between the factors of the framework that was suggested through the analysis of the project-related interviewees accounts of qualitative research. Path analysis was carried out to analyze the relationship between the six variables – independent (Institutional Resources), parametric (PPCo or Public-Public Cooperation), Education, PPP (Public-Private Partnership) and dependent (Performance and (Gender) Representation). As a result, it was found that Institutional Resources, PPCo, and PPP had a significant influence on Performance and Representation. In particular, there were four statistical associations (paths): Institutional Resources → Performance, PPCo → Performance, PPCo → Representation, and PPP → Representation (p<0.05)
Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification
Graph neural networks (GNNs) are commonly used in semi-supervised settings.
Previous research has primarily focused on finding appropriate graph filters
(e.g. aggregation methods) to perform well on both homophilic and heterophilic
graphs. While these methods are effective, they can still suffer from the
sparsity of node features, where the initial data contain few non-zero
elements. This can lead to overfitting in certain dimensions in the first
projection matrix, as training samples may not cover the entire range of graph
filters (hyperplanes). To address this, we propose a novel data augmentation
strategy. Specifically, by flipping both the initial features and hyperplane,
we create additional space for training, which leads to more precise updates of
the learnable parameters and improved robustness for unseen features during
inference. To the best of our knowledge, this is the first attempt to mitigate
the overfitting caused by the initial features. Extensive experiments on
real-world datasets show that our proposed technique increases node
classification accuracy by up to 46.5% relatively
Is Signed Message Essential for Graph Neural Networks?
Message-passing Graph Neural Networks (GNNs), which collect information from
adjacent nodes, achieve satisfying results on homophilic graphs. However, their
performances are dismal in heterophilous graphs, and many researchers have
proposed a plethora of schemes to solve this problem. Especially, flipping the
sign of edges is rooted in a strong theoretical foundation, and attains
significant performance enhancements. Nonetheless, previous analyses assume a
binary class scenario and they may suffer from confined applicability. This
paper extends the prior understandings to multi-class scenarios and points out
two drawbacks: (1) the sign of multi-hop neighbors depends on the message
propagation paths and may incur inconsistency, (2) it also increases the
prediction uncertainty (e.g., conflict evidence) which can impede the stability
of the algorithm. Based on the theoretical understanding, we introduce a novel
strategy that is applicable to multi-class graphs. The proposed scheme combines
confidence calibration to secure robustness while reducing uncertainty. We show
the efficacy of our theorem through extensive experiments on six benchmark
graph datasets
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
A cross-domain recommendation has shown promising results in solving
data-sparsity and cold-start problems. Despite such progress, existing methods
focus on domain-shareable information (overlapped users or same contexts) for a
knowledge transfer, and they fail to generalize well without such requirements.
To deal with these problems, we suggest utilizing review texts that are general
to most e-commerce systems. Our model (named SER) uses three text analysis
modules, guided by a single domain discriminator for disentangled
representation learning. Here, we suggest a novel optimization strategy that
can enhance the quality of domain disentanglement, and also debilitates
detrimental information of a source domain. Also, we extend the encoding
network from a single to multiple domains, which has proven to be powerful for
review-based recommender systems. Extensive experiments and ablation studies
demonstrate that our method is efficient, robust, and scalable compared to the
state-of-the-art single and cross-domain recommendation methods
Structural Damping by the Use of Fibrous Materials
Because of the increasing concern with vehicle weight, there is an interest in lightweight materials that can serve several functions at once. Here we consider the vibration damping performance provided by an “acoustical” material (i.e., a fibrous layer that would normally be used for airborne noise control). It has been previously established that the vibration of panel structures creates a non-propagating nearfield in the region close to the panel. In that region, there is an oscillatory, incompressible fluid flow parallel to the panel whose strength decays exponentially with distance from the panel. When a fibrous medium is placed close to the panel in the region where the oscillatory nearfield is significant, energy is dissipated by the viscous interaction of the flow and the fibers, and hence the panel vibration is damped. The degree of panel damping is then proportional to the energy removed from the nearfield by the viscous interaction with the fibrous medium. In his paper, experiments are described that demonstrate this effect. Fibrous layers were placed next to a lightly damped panel driven by a shaker, and the vibration of the panel was quantified by using a scanning laser vibrometer. These experiments showed that it is possible to achieve a strong damping effect by using fibrous layers. In addition a new theory that can be used to predict the depth of treatment needed to achieve a damping effect is presented. The theory is based on analyzing the wave number transforms of the panel motion in terms of radiating and non-radiating components, and by using that approach to identify the spatial extent of the oscillatory nearfield, and hence the depth of the fibrous layer required to provide effective structural damping
Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data
- …