A review of genre-specific writing scales in ESL/EFL testing contexts

As an important part of language assessment, assessing English as a Second or Foreign Language (ESL/EFL) writing has long been the topic of interest among researchers. Writing scales have been used for quite some time in order to decrease the subjectivity of raters' evaluations. While most of the available scales are generic, there are also genre-specific scales which have been developed to be sensitive to the variations that are caused by different genres in the content, organization and structure of the written works to be evaluated. Having discussed the merits and challenges of using writing scales for evaluation purposes, the current paper presents some possible ways in which the usefulness of writing scales can be improved. The paper reviews a number of genre-specific writing scales and concludes with a discussion on their pedagogical implications

An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners

An intrusion detection system (IDS) is a vital security component of modern computer networks. With the increasing amount of sensitive services that use computer network-based infrastructures, IDSs need to be more intelligent and autonomous. Aside from autonomy, another important feature for an IDS is its ability to detect zero-day attacks. To address these issues, in this paper, we propose an IDS which reduces the amount of manual interaction and needed expert knowledge and is able to yield acceptable performance under zero-day attacks. Our approach is to use three learning techniques in parallel: gated recurrent unit (GRU), convolutional neural network as deep techniques and random forest as an ensemble technique. These systems are trained in parallel and the results are combined under two logics: majority vote and "OR" logic. We use the NSL-KDD dataset to verify the proficiency of our proposed system. Simulation results show that the system has the potential to operate with a very low technician interaction under the zero-day attacks. We achieved 87:28% accuracy on the NSL-KDD's "KDDTest+" dataset and 76:61% accuracy on the challenging "KDDTest-21" with lower training time and lower needed computational resources.Comment: 5 page