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Indiana State University

Academic institutionnorthamerica · us
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Research library3linked papers
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Selected work

Representative Papers

Toward Machine Risk Perception: Integrating Trust Calibration and Precursor-Based Risk Estimation for Humanoid

Jun 17, 2026

This study addresses the complex safety risks posed by humanoid robots in smart manufacturing, where human-like dynamic motions introduce temporal and stochastic accident characteristics that conventional passive safety mechanisms—relying on fixed force or distance thresholds—fail to adequately handle. To overcome this limitation, the authors propose a novel risk-aware framework integrating trust calibration with precursor-driven reasoning. For the first time, the approach couples the temporal evolution of precursors with dynamic trust assessment, employing a Logistic-Exponential model to capture multi-source precursor cues over time and defining trust as the reciprocal of predicted accident probability to enable real-time adaptive behavior. Evaluated on a dataset comprising 126 incidents and 241 precursors, the method identifies 12 dominant accident patterns and demonstrates successful early warning and proactive intervention in “fall-and-collide” simulations, advancing humanoid robot safety from static thresholds toward evidence-based, dynamic risk inference.

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Odd Cycle Transversal in $P_k$-Free Graphs

Jun 05, 2026

This work addresses the Odd Cycle Transversal problem—deleting a minimum set of vertices to make a graph bipartite—in $P_k$-free graphs. The problem is known to be NP-complete and hard to approximate within any constant factor for $k \geq 6$. By leveraging a structural decomposition that transforms $P_k$-free graphs into a bipartite-cycle framework, the authors obtain polynomial-time exact algorithms for subclasses such as $(P_6, C_3)$-free graphs. Building on this insight, they design the first constant-factor approximation algorithm for general $P_k$-free graphs, achieving an approximation ratio of $k-2$ when $k$ is odd and $k-3$ when $k$ is even. This result provides the first nontrivial, $k$-dependent approximation guarantee for this graph class, matching the hardness lower bound implied by the Unique Games Conjecture and filling a longstanding gap in the approximability landscape.

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Recent publications

Latest Papers

Toward Machine Risk Perception: Integrating Trust Calibration and Precursor-Based Risk Estimation for Humanoid

Jun 17, 2026

This study addresses the complex safety risks posed by humanoid robots in smart manufacturing, where human-like dynamic motions introduce temporal and stochastic accident characteristics that conventional passive safety mechanisms—relying on fixed force or distance thresholds—fail to adequately handle. To overcome this limitation, the authors propose a novel risk-aware framework integrating trust calibration with precursor-driven reasoning. For the first time, the approach couples the temporal evolution of precursors with dynamic trust assessment, employing a Logistic-Exponential model to capture multi-source precursor cues over time and defining trust as the reciprocal of predicted accident probability to enable real-time adaptive behavior. Evaluated on a dataset comprising 126 incidents and 241 precursors, the method identifies 12 dominant accident patterns and demonstrates successful early warning and proactive intervention in “fall-and-collide” simulations, advancing humanoid robot safety from static thresholds toward evidence-based, dynamic risk inference.

0 citationsRead paper

Odd Cycle Transversal in $P_k$-Free Graphs

Jun 05, 2026

This work addresses the Odd Cycle Transversal problem—deleting a minimum set of vertices to make a graph bipartite—in $P_k$-free graphs. The problem is known to be NP-complete and hard to approximate within any constant factor for $k \geq 6$. By leveraging a structural decomposition that transforms $P_k$-free graphs into a bipartite-cycle framework, the authors obtain polynomial-time exact algorithms for subclasses such as $(P_6, C_3)$-free graphs. Building on this insight, they design the first constant-factor approximation algorithm for general $P_k$-free graphs, achieving an approximation ratio of $k-2$ when $k$ is odd and $k-3$ when $k$ is even. This result provides the first nontrivial, $k$-dependent approximation guarantee for this graph class, matching the hardness lower bound implied by the Unique Games Conjecture and filling a longstanding gap in the approximability landscape.

0 citationsRead paper