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Automated writing evaluation (AWE) has become increasingly popular in the assessment of writing. The study in this chapter examines the extent to which EFL learners' overall narrative writing performance improves through the AWE feedback system (i.e., Pigai). Eighteen university participants were required to write one paragraph narratives on the web-based Pigai system every week over the course of a month. Findings show a significant improvement in overall scores between the first and last writing task. The analysis of lexical profile further shows a significant improvement in lexical richness, clause density, and paragraph length between the first and last narrative task. The study also reported that the primary error types that occurred in learner narrative writing were lexical, mechanical, and syntactic errors. Results of post-writing interviews also showed a positive attitude towards Pigai. Finally, a positive correlation was observed between automated Pigai scores and human rating scores, supporting the reliability of the AWE system.

With the shrinking size of circuits and the scaling of Network-on-Chip (NoC), the on-chip components will have a higher chance to fail. The on-chip failures can cause traffic congestion and even system crash. To overcome this problem, the NoC routing algorithm should be implemented with fault-tolerant capability. Inspired by the fault-tolerant behavior of ant colony consisting of three steps: Encounter, Search, and Select, we propose Ant Colony Optimizationbased Fault-aware Routing (ACO-FAR) algorithm for traffic balancing. To effectively forward the packets to a non-faulty region, three mechanisms of ACO-FAR correspond to the three-step behaviors of ants are proposed in this work. The simulation results show that proposed ACO-FAR has higher throughput than related works by 12.5%-77.7%. Also, this routing method improves the reachable packet ratio to 99.50%-99.98% and the distribution of traffic load in the faulty network.