79 research outputs found
Impact of Social Networking Sites on Post-Partum Depression in Women: An Analysis in the Context of Bangladesh
Postpartum Depression (PPD) refers to moderate or severe depression in a woman after childbirth. It is strikingly common in new mothers from all regions of the world with a prevalence of around 10-15%. PPD can have severe adverse effects on maternal and child health, such as suicidal tendency of the mother, infanticide as well as poor cognitive and developmental growth of the child. Despite this, few women seek medical attention due to ignorance, negligence and financial limitations; the latter is especially true for those who live in developing countries. Nowadays, social networking sites (SNS) e.g., Facebook can act as accessible and effective tools for the prevention and treatment of PPD. In this paper, we analyze the opinions and awareness level of Bangladeshi people about PPD and impact of using SNS during postpartum period on reducing PPD based on our survey (N = 93). We also discuss possible SNS-based interventions and design implications that can effectively and feasibly reduce PPD in women in developing countries
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Intent-based management will play a critical role in achieving customers'
expectations in the next-generation mobile networks. Traditional methods cannot
perform efficient resource management since they tend to handle each
expectation independently. Existing approaches, e.g., based on multi-agent
reinforcement learning (MARL) allocate resources in an efficient fashion when
there are conflicting expectations on the network slice. However, in reality,
systems are often far more complex to be addressed by a standalone MARL
formulation. Often there exists a hierarchical structure of intent fulfilment
where multiple pre-trained, self-interested agents may need to be further
orchestrated by a supervisor or controller agent. Such agents may arrive in the
system adhoc, which then needs to be orchestrated along with other available
agents. Retraining the whole system every time is often infeasible given the
associated time and cost. Given the challenges, such adhoc coordination of
pre-trained systems could be achieved through an intelligent supervisor agent
which incentivizes pre-trained RL/MARL agents through sets of dynamic contracts
(goals or bonuses) and encourages them to act as a cohesive unit towards
fulfilling a global expectation. Some approaches use a rule-based supervisor
agent and deploy the hierarchical constituent agents sequentially, based on
human-coded rules.
In the current work, we propose a framework whereby pre-trained agents can be
orchestrated in parallel leveraging an AI-based supervisor agent. For this, we
propose to use Adhoc-Teaming approaches which assign optimal goals to the MARL
agents and incentivize them to exhibit certain desired behaviours. Results on
the network emulator show that the proposed approach results in faster and
improved fulfilment of expectations when compared to rule-based approaches and
even generalizes to changes in environments.Comment: Accepted for publication in IEEE CCNC 2024 conferenc
Person re-identification via efficient inference in fully connected CRF
In this paper, we address the problem of person re-identification problem,
i.e., retrieving instances from gallery which are generated by the same person
as the given probe image. This is very challenging because the person's
appearance usually undergoes significant variations due to changes in
illumination, camera angle and view, background clutter, and occlusion over the
camera network. In this paper, we assume that the matched gallery images should
not only be similar to the probe, but also be similar to each other, under
suitable metric. We express this assumption with a fully connected CRF model in
which each node corresponds to a gallery and every pair of nodes are connected
by an edge. A label variable is associated with each node to indicate whether
the corresponding image is from target person. We define unary potential for
each node using existing feature calculation and matching techniques, which
reflect the similarity between probe and gallery image, and define pairwise
potential for each edge in terms of a weighed combination of Gaussian kernels,
which encode appearance similarity between pair of gallery images. The specific
form of pairwise potential allows us to exploit an efficient inference
algorithm to calculate the marginal distribution of each label variable for
this dense connected CRF. We show the superiority of our method by applying it
to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation
Partial domain adaptation which assumes that the unknown target label space
is a subset of the source label space has attracted much attention in computer
vision. Despite recent progress, existing methods often suffer from three key
problems: negative transfer, lack of discriminability and domain invariance in
the latent space. To alleviate the above issues, we develop a novel 'Select,
Label, and Mix' (SLM) framework that aims to learn discriminative invariant
feature representations for partial domain adaptation. First, we present a
simple yet efficient "select" module that automatically filters out the outlier
source samples to avoid negative transfer while aligning distributions across
both domains. Second, the "label" module iteratively trains the classifier
using both the labeled source domain data and the generated pseudo-labels for
the target domain to enhance the discriminability of the latent space. Finally,
the "mix" module utilizes domain mixup regularization jointly with the other
two modules to explore more intrinsic structures across domains leading to a
domain-invariant latent space for partial domain adaptation. Extensive
experiments on several benchmark datasets demonstrate the superiority of our
proposed framework over state-of-the-art methods
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