A Study on Emergency Response Management for System Failures using Project Management Knowledge

Atsushi Shimoda — Mon, 12 Jan 2026 02:21:05 +0000

Many companies prepare action procedures for emergency responses to system failures. Traditionally, these are based on procedures stipulated in international standards and best practices. However, they present only procedures and do not provide specific perspectives that organizations should consider in uncertain situations. Therefore, if an event that is not described in the action procedure manual occurs, there is a possibility that the site will be confused and rational judgment will not be possible. In this research, by utilizing project management knowledge, specific viewpoints that organizations should keep in mind in uncertain situations are presented. The effectiveness of the proposed method is shown by a case study of system failure.

Offensive Language Detection on Social Media Using Three Language Models and Three Datasets

ZHENMING LI, Kazutaka Shimada — Mon, 12 Jan 2026 02:24:12 +0000

There are more and more offensive posts on Social Media nowadays. Those posts are harmful and should be treated seriously. The most efficient way to detect offensive posts is to fine-tune a Large Language Model (LLM) on an offensive language dataset. In our research, we focus on maximizing the capacity of LLMs on offensive language detection tasks on Social Media. We select three LLMs with different attributes (DeepMoji, Bert, and HateBert) and three offensive language datasets (OLID, Curious Cat, and Ask FM). We mainly discuss achieving the best performance by configuring the LLMs and datasets. Experimental results show that simply fine-tuning an LLM with larger data can not always achieve the best performance. The combination of LLMs was effective, especially the combination of DeepMoji and HateBert.

Information Engineering Express

A Study on Emergency Response Management for System Failures using Project Management Knowledge

Offensive Language Detection on Social Media Using Three Language Models and Three Datasets