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Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for...
An effective silicon debug technique uses a trace buffer to monitor and capture a portion of the circuit response during its functional, post-silicon operation. Due to the limited space of the available trace buffer, selection of the critical trace signals plays an important role in both minimizing the number of signals traced and maximizing the observability/restorability of other untraced signals...
Data uncertainty is common in real-world applications due to various causes, including imprecise measurement, network latency, outdated sources and sampling errors. These kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we propose a new rule-based classification and prediction algorithm called uRule for classifying uncertain...
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