This study explores the use and functions of discourse markers (DMs) in narrative essays 85 Jordanian high school students adopting Frasers’s (2006) framework. The results show that most frequently used DMs were elaborative (55.1%), followed by temporal (31.4%), inferential (9.3%) contrastive (4%) respectively. Regarding DMs, reveal participants employed to express addition information, sequenc...

Which facial characteristics do people rely on when forming personality impressions? Previous research has uncovered an array of features that influence people’s impressions. Even though some (classes of) features, such as resemblances to emotional expressions or width-to-height ratio (fWHR), play a central role in theories social perception, their relative importance impression formation remai...

medication errors (mes) are the most common error in icus. in fact, 78% of all serious errors in icus are due to mes. therefore, detecting mes has vital significance. the goal of this study was to investigate the frequency, type and consequences of different types of errors including prescribing, transcribing and administration errors in an icu of a large teaching hospital. disguised direct obs...

We study the learnability of enumerable families L of uniformly recursive languages in dependence on the number of allowed mind changes, i.e., with respect to a well{studied measure of e ciency. We distinguish between exact learnability (L has to be inferred w.r.t. L) and class preserving learning (L has to be inferred w.r.t. some suitable chosen enumeration of all the languages from L) as well...

It is now well-known that the feasibility of inductive learning is ruled by statistical properties linking the empirical risk minimization principle and the “capacity” of the hypothesis space. The discovery, a few years ago, of a phase transition phenomenon in inductive logic programming proves that other fundamental characteristics of the learning problems may similarly affect the very possibi...

In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of im...