Athira Varma Jayakumar

Anthony Couthures, Athira Varma Jayakumar, Vineeth Satheeskumar Varma et al.

Conventional community detection requires centralized network data, making it unsuitable for distributed or privacy-preserving systems. In this paper, we demonstrate that macroscopic graph partitioning can emerge purely from strictly local, privacy preserving interactions driven by social learning. By reframing clustering as a symmetry-breaking process within nonlinear opinion dynamics, we show that exchanging saturated state dependent signal (like public actions) forces a network to naturally fracture along its sparsest cuts. We mathematically establish the spectral conditions under which dense core communities lock into stable, polarized states, robustly resisting external influence. To apply this mechanism, we propose three decentralized algorithms, leading up to the Score-based Edge Reliability (SER) framework. By evaluating network ties across multiple independent discussion topics, SER statistically bypasses the errors of traditional greedy bisections and naturally isolates structurally ambiguous frontier nodes. Validations on the ABCD benchmark and the real-world Ngogo chimpanzee network confirm that our fully decentralized approach matches the accuracy of globally optimized heuristics (e.g., Louvain, Leiden) up to a theoretical limit of detectable graphs.

Alexander Weiss, Smitha Gautham, Athira Varma Jayakumar et al.

Understanding fault types can lead to novel approaches to debugging and runtime verification. Dealing with complex faults, particularly in the challenging area of embedded systems, craves for more powerful tools, which are now becoming available to engineers.

Smitha Gautham, Abhilash Rajagopala, Athira Varma Jayakumar et al.

Advanced embedded system technology is one of the key driving forces behind the rapid growth of Cyber-Physical System (CPS) applications. Cyber-Physical Systems are comprised of multiple coordinating and cooperating components, which are often software intensive and interacting with each other to achieve unprecedented tasks. Such complex CPSs have multiple attack surfaces and attack vectors that we have to secure against. Towards this goal, we demonstrate a multilevel runtime safety and security monitor framework where there are monitors across the CPS for detection and isolation of attacks. We implement the runtime monitors on FPGA using a stream-based runtime verification tool called TeSSLa. We demonstrate our monitoring scheme for an Autonomous Emergency Braking (AEB) CPS system.