The Effect of Knowledge Sharing on Open Source Contribution: A Multi-platform Perspective

Open source software (OSS) community plays a key role in contemporary software development. However, there is a need to better understand the factors which influence individuals’ voluntary contribution on open source platforms. In this paper, we investigate how different types of knowledge sharing affect an individuals’ contribution towards open source projects. We further refine knowledge sharing taxonomy by classifying explicit knowledge sharing into two sub-types – strong explicit knowledge sharing and weak explicit knowledge sharing, depending on the extent of interpersonal interaction required for knowledge transfer. In this paper, we take a multi-platform perspective – we collect data from GitHub – the biggest online platform to host open source software development, and Gitter – an open source instant messaging and chat room application designed for developers. We map the user identities across these two platforms. We analyze monthly panel data for the year 2017 consisting of 3,695 individuals. The results demonstrate that both strong and weak explicit knowledge sharing have positive relationship with open source contribution. Moreover, the tacit knowledge sharing positively moderates these relationships. Our paper extends the theoretical understanding of different knowledge sharing types and their inter-relationship, and their respective impact on contribution. Our findings have important implications for the OSS community, and especially help OSS platform designers get a better understanding of the symbiosis between different OSS platforms

Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation

Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.Comment: Accepted as a Short Paper in the 19th International Conference on Natural Language Processing (ICON) 202

Renewing membership in three community-based health insurance schemes in rural India

Low renewal rate is a key challenge facing the sustainability of Community-based Health Insurance (CBHI) schemes. While there is a large literature on initial enrolment into such schemes, there is limited evidence on the factors that impede renewal. This paper uses longitudinal data to analyse what determines renewal, both one and two years after the introduction of three CBHI schemes, which have been operating in rural Bihar and Uttar Pradesh since 2011. We find that initial scheme uptake is about 23-24 % and that two years after scheme operation, only about 20 % of the initial enrolees maintain their membership. A household’s socio-economic status does not seem to play a large role in impeding renewal. In some instances, a greater understanding of the scheme boosts renewal. The link between health status and use of health care in maintaining renewal is mixed. The clearest effect is that individuals living in households that have received benefits from the scheme are substantially more likely to renew their contracts. We find that having access to a national health insurance scheme is not a substitute for the CBHI. We conclude that the low retention rates may be attributed to limited benefit packages, slow claims processing times and the gaps between the amounts claimed and amounts paid out by insurance

Enrollment in community based health insurance schemes in rural Bihar and Uttar Pradesh, India

This paper assesses insurance uptake in three community based health insurance (CBHI) schemes located in rural parts of two of India’s poorest states and offered through women’s self-help groups (SHGs). We examine what drives uptake, the degree of inclusive practices of the schemes, and the influence of health status on enrollment. The most important finding is that a household’s socio-economic status does not appear to substantially inhibit uptake. In some cases Scheduled Caste/ Scheduled Tribe (SC/ST) households are more likely to enroll. Second, households with greater financial liabilities find insurance more attractive. Third, access to the hospital insurance scheme (RSBY) does not dampen CBHI uptake, suggesting that the potential for greater development of insurance markets and products beyond existing ones would respond to a need. Fourth, recent episodes of illness and selfassessed health status do not influence uptake. Fifth, insurance coverage is prioritized within households, with the household head, the spouse of the household head and both male and female children of the household head, more likely to be insured as compared to other relatives. Sixth, offering insurance through women’s SHGs appears to mitigate concerns about the inclusiveness and sustainability of CBHI schemes. Given the pan-Indian spread of SHGs, offering insurance through such groups offers the potential to scale-up CBHI

Selective inhibition of miR-21 by phage display screened peptide

miRNAs are nodal regulators of gene expression and deregulation of miRNAs is causally associated with different diseases, including cancer. Modulation of miRNA expression is thus of therapeutic importance. Small molecules are currently being explored for their potential to downregulate miRNAs. Peptides have shown to have better potency and selectivity toward their targets but their potential in targeting and modulating miRNAs remain unexplored. Herein, using phage display we found a very selective peptide against pre-miR-21. Interestingly, the peptide has the potential to downregulate miR-21, by binding to pre-miR-21 and hindering Dicer processing. It is selective towards miR-21 inside the cell. By antagonising miR-21 function, the peptide is able to increase the expression of its target proteins and thereby increase apoptosis and suppress cell proliferation, invasion and migration. This peptide can further be explored for its anti-cancer activity in vivo and may be even extended to clinical studies