Priyanka Sinha

Priyanka Sinha, Dilys Thomas

Network partitions pose fundamental challenges to distributed name resolution in mobile ad-hoc networks (MANETs) and edge computing. Existing solutions either require active coordination that fails to scale, or use unstructured gossip with excessive overhead. We present \textit{Structured Gossip DNS}, exploiting DHT finger tables to achieve partition resilience through \textbf{passive stabilization}. Our approach reduces message complexity from $O(n)$ to $O(n/\log n)$ while maintaining $O(\log^2 n)$ convergence. Unlike active protocols requiring synchronous agreement, our passive approach guarantees eventual consistency through commutative operations that converge regardless of message ordering. The system handles arbitrary concurrent partitions via version vectors, eliminating global coordination and enabling billion-node deployments.

Priyanka Sinha, Pabitra Mitra, Antonio Anastasio Bruto da Costa et al.

Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. The outcome of a conversation may be positive or negative. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis as well as future collaboration design. We propose an explainable time series mining algorithm for such analysis. A dialogue is represented as an attributed time series of occurrences of keywords, EMPATH categories, and inferred sentiments at various points in its progress. A special decision tree, with decision metrics that take into account temporal relationships between dialogue events, is used for predicting the cause of the outcome sentiment. Interpretable rules mined from the classifier are used to explain the prediction. Experimental results are presented for the enterprise social media posts in a large company.