activity
20102021
most citedRuntime Monitoring Neuron Activation Patterns

7 citations · 25 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CC2021

Computational Complexity of Quadratic Unconstrained Binary Optimization

Hirotoshi Yasuoka

In this paper, we study the computational complexity of the quadratic unconstrained binary optimization (QUBO) problem under the functional problem FP^NP categorization. We focus o…

cs.LG2021

The missing link: Developing a safety case for perception components in automated driving

Rick Salay, Krzysztof Czarnecki, Hiroshi Kuwajima +6

Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine…

cs.SE2019★ 5 cited

Engineering problems in machine learning systems

Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles…

cs.CY2018★ 4 cited

Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems

Hiroshi Kuwajima, Hirotoshi Yasuoka, Toshihiro Nakae

Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Qual…

cs.LG2018★ 7 cited

Runtime Monitoring Neuron Activation Patterns

Chih-Hong Cheng, Georg Nührenberg, Hirotoshi Yasuoka

For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runt…

cs.LG2018★ 4 cited

Towards Dependability Metrics for Neural Networks

Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang +2

Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for…

cs.SE2018★ 2 cited

Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems

Chih-Hong Cheng, Chung-Hao Huang, Hirotoshi Yasuoka

Systematically testing models learned from neural networks remains a crucial unsolved barrier to successfully justify safety for autonomous vehicles engineered using data-driven ap…

cs.CR2012★ 2 cited

Quantitative Information Flow as Safety and Liveness Hyperproperties

Hirotoshi Yasuoka, Tachio Terauchi

We employ Clarkson and Schneider's "hyperproperties" to classify various verification problems of quantitative information flow. The results of this paper unify and extend the prev…

cs.CR2011

On Bounding Problems of Quantitative Information Flow

Hirotoshi Yasuoka, Tachio Terauchi

Researchers have proposed formal definitions of quantitative information flow based on information theoretic notions such as the Shannon entropy, the min entropy, the guessing entr…

cs.CR2010★ 1 cited

Quantitative Information Flow - Verification Hardness and Possibilities

Hirotoshi Yasuoka, Tachio Terauchi

Researchers have proposed formal definitions of quantitative information flow based on information theoretic notions such as the Shannon entropy, the min entropy, the guessing entr…