Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants’ expectations regarding credibili...

Software testing is often hindered where it impossible or impractical to determine the correctness of behaviour output software under test (SUT), a situation known as oracle problem. An example an area facing problem automatic image classification, using machine learning classify input one set predefined classes. approach that alleviates metamorphic (MT). While traditional examines individual c...

As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up propose multiple mathematical definitions, algorithms, systems ensure different notions fairness in applications. Given multitude propositions, it become imperative formally verify metrics satisfied by algorithms on datasets. In this paper, we stochastic satisfiability (SSAT) fra...

The era of big data has brought with it a richer understanding user behavior through massive datasets, which can help organizations optimize the quality their services. In context transportation research, mobility provide municipal authorities (MAs) insights on how to operate, regulate, or improve network. Mobility data, however, may contain sensitive information about end users and trade secre...

in exploration seismology, migration refers to a multi-channel processing step that attempts to spatially re-position events and improve focusing. before migration, seismic data is usually displayed with traces plotted at the surface location of the receivers and with a vertical time axis. this means that dipping reflections are systematically mispositioned in the lateral coordinate and the ver...