Information externalities and voluntary disclosure: Evidence from a major customer’s earnings announcement

Singapore Management University SOAR; Lee Kong Chian Fellowshi

GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

Content-based collaborative filtering (CCF) provides personalized item recommendations based on both users' interaction history and items' content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to finetune PLM in an end-to-end (E2E) manner in CCF due to its multi-modal nature: optimization involves redundant content encoding for interactions from users. For this, we propose GRAM (GRadient Accumulation for Multi-modality): (1) Single-step GRAM which aggregates gradients for each item while maintaining theoretical equivalence with E2E, and (2) Multi-step GRAM which further accumulates gradients across multiple training steps, with less than 40\% GPU memory footprint of E2E. We empirically confirm that GRAM achieves a remarkable boost in training efficiency based on five datasets from two task domains of Knowledge Tracing and News Recommendation, where single-step and multi-step GRAM achieve 4x and 45x training speedup on average, respectively.Comment: NAACL 2022 Main Conferenc

Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process

The Association between Audit Quality and Abnormal Audit Fees

Also presented at Korean Accounting Association Annual Conference 2006</p

Decision Making Method Based on Importance-Dangerousness Analysis for the Potential Risk Behavior of Construction Laborers

Unsafe behavior contributes to 90% of the causes of construction accidents. To prevent construction accidents, studies on existing unsafe behaviors have been regularly conducted. However, existing studies generally tend to average the survey results and conduct analyses thereon, and such a method cannot consider the potential risk as regards people’s anxiety about a certain unsafe behavior. Thus, this research suggests an Importance-Dangerousness Analysis (IDA) technique so that potential risks due to unsafe behaviors of laborers working in the construction sector could be evaluated. In order to verify the applicability of the suggested technique, an actual survey was conducted, and the results of Importance-Performance Analysis (IPA) and IDA were compared with each other. It was found that, unlike IPA, unsafe behaviors that could pose potential risks were confirmed by IDA. Further, unsafe behaviors in the construction sector that should be urgently addressed were also studied. Finally, the IDA suggested in this research could contribute to effective construction safety management on-site by supporting the decisions of the safety manager based on the unsafe behavior analysis of construction laborers