We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human...

Nowadays crowdsourcing is widely used in supervised machine learning to facilitate the collection of ratings for unlabelled training sets. In order to get good quality results it is worth rejecting results from noisy/unreliable raters, as soon as they are discovered. Many techniques for filtering unreliable raters rely on the presentation of training instances to the raters identified as most a...

This article presents our work on constructing a corpus of news articles in which events are annotated for estimated bounds on their duration, and automatically learning from this corpus. We describe the annotation guidelines, the event classes we categorized to reduce gross discrepancies in inter-annotator judgments, and our use of normal distributions to model vague and implicit temporal info...

This paper proposes a newmethod of manual evaluation for statistical machine translation, the so-called quiz-based evaluation, estimating whether people are able to extract information from machine-translated texts reliably. We apply the method to two commercial and two experimental MT systems that participated in WMT 2010 in English-to-Czech translation. We report inter-annotator agreement for...

In addition to human-to-human (H2H) and machine-to-human (M2H) communications, an emerging technology enabling full mechanical automation that may change living styles is being developed. Communications among machine-type devices are known as machine-to-machine (M2M) communications. Current market penetration and recent predictions confirm that M2M system deployments are increasing exponentiall...

Ensuring reliability as the electrical grid morphs into the “smart grid” will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance; in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning and data mining) techniques. To help in meetin...

This paper presents an economic production quantity (EPQ) model with a periodic order quantity (POQ) policy, product reliability and periodic demand. The machine reliability has decreased over time; therefore, the rates of perfect and defective products reduce and increase over time, respectively. A fixed percentage of these products are reworked while the rest is wasted. Some equipment in thei...

Modern Power systems are smart, interconnected, interdependent, load sharing and phased mission systems. Reliability of such complex power systems is very important in design, planning, installation and maintenance to provide electrical energy as economical as possible with an acceptable degree of reliability. In this thesis four new methods for reliability evaluation and enhancement of power s...