Social media texts such as blog posts, comments, and tweets often contain offensive languages including racial hate speech comments, personal attacks, and sexual harassments. Detecting inappropriate use of language is, therefore, of utmost importance for the safety of the users as well as for suppressing hateful conduct and aggression. Existing approaches to this problem are mostly available for resource-rich languages such as English and German. In this paper, we characterize the offensive language in Nepali, a low-resource language, highlighting the challenges that need to be addressed for processing Nepali social media text. We also present experiments for detecting offensive language using supervised machine learning. Besides contributing the first baseline approaches of detecting offensive language in Nepali, we also release human annotated data sets to encourage future research on this crucial topic.
Service discovery is a crucial component in today's massively distributed applications. In this paper, we propose NDNSD -a fully distributed and general-purpose service discovery protocol for Named Data Networking (NDN). By leveraging NDN's data synchronization capability, NDNSD offers a highlevel API for service publishing and discovery. We present NDNSD's main design features including hierarchical naming, service information specification, and service accessibility. We also implemented two other discovery schemes, one reactive and one proactive, and compared them with NDNSD. Our evaluation shows that NDNSD achieves (a) lower latency, lower overhead, and same reliability compared to the reactive scheme, and (b) comparable latency, lower overhead at larger scale, and higher reliability compared to the proactive scheme.
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