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