Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

Clinical decision making and diagnosis in healthcare is enabled by learning causal relationships among entities. In order to answer the question, ’Will changing the treatment regimen lead to quicker recovery of a patient?’ requires predicting counterfactuals using survival analysis. We seek to reliably estimate the average treatment effect in terms of survival probabilities of the factual outcomes as compared to survival probabilities of the unobserved counterfactual population. Due to the nature of observational data, this framework is prone to treatment selection bias and censoring bias. We address these issues and propose the novel SurvCI framework which consists of a counterfactual inference (CI) algorithm for data with time-to-event outcomes. We investigate the performance of the proposed framework on synthetic and semi-synthetic data and employ survival and causal metrics to empirically confirm that our method outperforms baselines.

Summary Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics.

Social robots have been used in mental health care interventions not only to increase access to mental health treatments, but also to complement the support provided by practitioners. We propose incorporating social robots in conventional treatments for children and adolescents with Social Anxiety Disorder (SAD). Although non-robotic, evidence-based interventions for social anxiety are already available, factors such as embarrassment, and anticipatory anxiety have led to treatment delay and avoidance among this clinical population. To encourage treatment and to further improve treatment outcomes, in this work-in-progress article we propose the incorporation of social robots in conventional treatments for SAD. Social robots offer many advantages, such as adaptability, being non-judgmental, and providing interaction capabilities, which could make them a useful tool in the hands of practitioners working with children and adolescents with SAD. We discuss the different roles that social robots could play in helping children with social anxiety make the most of conventional treatments. We also present preliminary results (68 participants) on adolescents’ preferences for using intelligent agents in promoting mental well-being. We conclude by summarizing the potential benefits and limitations of using social robots in conventional treatments for social anxiety.

Digital advertising companies typically conduct several advertising campaigns in parallel while being constrained by a fixed overall advertising budget. This gives rise to the problem of distributing the budget across the different campaigns dynamically so as to optimize the overall return on investment (ROI) (or some other metric) within a specified time duration. In this paper, we propose an RL formulation called BCQ4DCA for dynamic optimization of budget-constrained campaign allocation. The formulation is model-free and uses a novel cumulative reward model that is learned alongside a Deep Q-Network. We utilize a real-world Criteo user interaction dataset to evaluate BCQ4DCA in terms of conversion rate, budget utilization, cost per conversion, and ROI, finding that it outperforms current heuristic, attribution based approaches like DARNN, DNAMTA across a common time window.

Computing with Words (CW) is a concept which solves problems when input is provided in form of natural language. CW is at its initial stages and is not at its full potential Medicine is a pivotal field and CW has barely been explored here. This paper concentrates on how to use CW methods to overcome challenging problems in medicine. It especially focuses on finding a solution to help Dementia affected people whose symptoms are not curable. The latter part lists down some more ways in which CW can affect areas in medicine.