International Journal of Service and Knowledge Management

Evaluation of a Method to Support the Design and Evaluation of Rules that Take into Account the Mechanism of Functional Performance

NAOKI OKAMOTO — 2025-06-08

In recent years, there has been a need to take preventive measures against rule violations and rules becoming a dead letter, which is a major factor in organizational accidents and scandals. As one of the measures, a method has been proposed to support the design and evaluation of rules that take into account the mechanism of functional performance of rules. However, an evaluation including understandability and usability of the method has not yet been conducted. Therefore, the purpose of this study is to evaluate a method to support the design and evaluation of rules that take into account the mechanism of functional performance including the perspectives of understandability and usability. To evaluate the method, we conducted user evaluations using questionnaires on the method and the results of the method, experiments to compare the method with another method, and third-party evaluations by the regulated persons, and identified several problems with understandability and usability. We explain our results and conclusions of this study and our future research topics, including problems of the method, in the last part of the paper.

A Study of NLPDedup Efficiency for Small and Large Datasets

Hinata Yokoyama — 2025-11-29

Recently, the amount of data has grown rapidly. The deduplication functions reduce the amount of data by finding and deleting the redundant data. Meanwhile, to check data redundancy, the functions affect the performance because they issue many read I/Os and compare data. To mitigate the performance penalty, it is effective to narrow down processed files. Conventional methods use file metadata and hash values generated from file contents as indicators. However, if many files are stored in a file system, the methods are not efficient because of high load caused by checking metadata and hash value calculation. We propose a novel method, called NLPDedup, to narrow down files by using natural language processing for data deduplication functions. NLPDedup uses file names as indicators for narrowing down target files. This paper describes the overview of NLPDedup, how NLPDedup determines the target files, and the evaluation results of small and large datasets. From the results, the threshold of NLPDedup indicators needs to be set in terms of the natural language processing algorithms and the datasets. Consequently, we found that NLPDedup is effective in both datasets, and it is more effective by setting appropriate thresholds.

Proactive Internal Fraud Detection Strategies Utilizing UX Data Based on Multiple Sensors

Shigeaki Tanimoto — 2025-12-20

The COVID-19 pandemic forced companies to change how they work, promoting a work style unbound by time or location, such as teleworking. While convenient, this remote approach has been linked to increased internal fraud risks due to alienation, perceived unfairness, and reduced compliance awareness. In 2023, Japan's Information-technology Promotion Agency (IPA) ranked "information leaks due to internal misconduct" as the fourth most critical information security threat. Internal fraud is categorized into carelessness/negligence (60%) and intentional misconduct (40%), with the latter costing 1.34 times more to address, leading to significant financial losses. This paper explores user experience (UX) environments utilizing sensors to collect non-cyber information, such as facial expressions, to counter intentional fraud. A theoretical analysis indicates that combining two sensor types—environmental and biometric—offers the most cost-effective solution, significantly improving fraud detection rates. While adding more sensor types enhances accuracy, cost-effectiveness declines beyond three types. Additionally, the detection of rationalization remains consistently low, highlighting the need for complementary methods like text and speech analysis or long-term behavioral monitoring. These findings underscore the effectiveness of UX-based non-cyber information and its potential as an innovative approach to mitigating intentional internal fraud.

Time-series Keyword Extraction Method and Its Application to Discovery Japanese Key Technology Transition Insights

Fan Cheng — 2025-12-20

This paper presents a time-series keyword extraction method and its application to the discovery of key technological transition insights in Japan. In general, it is one of the most important issues in understanding trends in science and technology. To grasp these trends, it will be possible to visualize trends in science and technology from time to time if a method is established to extract important keywords from time to time using the White Paper on Science, Technology, and Innovation published every year by the Japanese government as an example. In this method, words are extracted from text data from time to time, and a new Importance Transition Discovering Score (WITD-Score) is proposed as an index representing the likelihood of the occurrence of each word at that time, following the concept of F-Score. By extracting the transition of keywords from time to time from the change in the WITD-Score for each word, we can extract the transition of keywords from time to time. By implementing this method, we can discover important keywords from time-series text data and visualize the transition of keywords.

Basic Income Stalls the Economy: Economic Conditions Affecting the Effects of Basic Income and the Response to Relative Income

Kosei Takashima — 2025-10-07

Basic income (BI) is a social welfare policy idea that has recently received much attention. BI implementation requires extensively transforming social and economic systems, possibly resulting in significant, unforeseeable impacts. This study focused on the impact of BI in terms of its effectiveness based on relative income and economic conditions at the time of its implementation. We analyzed the impact using a simulation approach with a macro artificial economy model that incorporates multilayered feedback paths and encompasses a complete flow of funds mechanism. The analysis revealed conditional impacts depending on the BI implementation period. A boom can stagnate or a recession can recover, depending on the public’s work motivation. Particularly, when work motivation is high and the economy expands, BI implementation may slow down economic activities.