Kakenhi are funds that provide broad support for scientific research based on the free ideas of the researchers themselves, and covers a wide range of academic studies spanning from basic to applied research. Both faculty members and researchers actively apply to Kakenhi for grants, and many are approved. The grants obtained from Kakenhi are also distributed to researchers in other institutions (co-investigators) for collaborative research work.
Similarly, many NII faculty members also participate as co-investigators in the Kakenhi-funded projects of researchers at other institutions.

Applications Accepted(FY2021)

No. of applications accepted Amount
(in thousands of yen)
Project Leader
(Principal Investigator)
73 388,186
(Other institutions → NII)
57 60,700

[Model Cases of Research Funded by Kakenhi]

Research on master biometric information protection and utilization platform

 Grant-in-Aid for Scientific Research (A) 

Principal Investigator: ECHIZEN, Isao
Professor, Information and Society Research Division

With the proliferation of high-performance cameras and microphones, biometric data defining human faces, voices, gaits, fingerprints, veins, irises, and other characteristics can now be captured and recorded remotely and shared in cyberspace. This poses the threat of "spoofing," i.e., breaches of biometric authentication to commit fraud or identity theft. For this kind of spoofing, it was previously necessary to restore the biometric data of a person from the captured image or recorded audio, but now with advances in machine learning, it is possible to generate biometric data that can be recognized as matching multiple persons from publicly available biometric data sets (i.e., master biometrics) without restoring biometric data of a specific person.
This study aims to establish a biometric data protection and utilization platform that prevents spoofing by detecting master biometric data while at the same time continuing to guarantee the usefulness of biometric data sets used to generate such information and "neutralizing" the inherent threat posed by biometric data sets.


Explainable next-generation media forensics technologies based on fake media detection and automatic fact verification

 Grant-in-Aid for Scientific Research (A) 

Principal Investigator: YAMAGISHI, Junichi
Professor, Digital Content and Media Sciences Research Division

In the current age of "infodemics," fake media in the form of video, audio, and text that resemble the real thing can be generated easily with machine learning, resulting in floods of fake news and other inaccurate information.
To counter this threat, this study proposes a pioneering next-generation media analysis technology to help ensure the publication of accurate media and information and support effective decision-making. It firstly proposes a liveness detection method that improves the explanatory power of authenticity judgments by identifying and indicating the falsified areas and methods of fake media as evidence. Next, the study proposes a new detection method that, in principle, incorporates the ability to deal with unknown fake media generation methods, so that the method is capable of robustly dealing with constantly changing media generation techniques.
An approach to learning this detection method is also suggested. Additionally, the study aims to make advances in automatic fact verification, for automated fact checking, and to integrate this with media analysis technology.


Development of ODR system by AI

 Grant-in-Aid for Scientific Research (A) 

Principal Investigator: SATOH, Ken
Professor, Principles of Informatics Research Division

This study aims at using AI technology to develop an efficient online dispute resolution (ODR) system for general use, by applying a previously developed civil court dispute resolution system (for legal professionals such as lawyers and judges) to each phase (diagnosis, negotiation, mediation, and evaluation) of an interactive ODR system.
The study will run for three years.
To begin, the work will focus on identifying technical and legal problems in the ODR, and then, based on these, examining support functions for each phase of the ODR.
Finally, a prototype system that integrates the support functions of each phase will be developed, and its usefulness will be verified in trials with general users.

Robust AI by integration of knowledge representation and machine learning

 Grant-in-Aid for Scientific Research (A) 

Principal Investigator: INOUE, Katsumi
Professor, Principles of Informatics Research Division

In Artificial Intelligence (AI) research, pattern recognition capabilities have improved dramatically in recent years, thanks to advances in the development of machine learning (ML). However, for advanced intelligence tasks involving symbolic processing, knowledge representation and reasoning (KR) have been used.
This study integrates the two technologies of ML and KR, which up to now have been studied independently, to establish a technological foundation for building a next-generation AI system that is both explainable and robust.
For this purpose, three research goals were set:
(1) to improve the explainability and updatability of ML methods by deploying KR techniques;
(2) to develop robust KR methods supported by ML techniques;
(3) to develop groundbreaking AI applications through the integration of ML and KR.

Exploration of super multi-view construction techniques for creating light fields in a real space in which visual obstacles are cancelled out

 Grant-in-Aid for Challenging Research (exploratory) 

Principal Investigator: KODAMA, Kazuya
Associate Professor, Digital Content and Media Sciences Research Division

Although plagued by pillars and walls that greatly obstruct views, cramped multitenant buildings have been diverted as inexpensive community spaces, becoming sustainable centers of community that powerfully support new countercultural activities such as theater, music, and film-from longstanding live music venues to theaters where numerous idol groups have been nurtured.
Now, in the new era of social distancing required for pandemic control, it is essential to resolve these visual problems to enable more efficient use of compact urban spaces by further recycling cramped city spaces.
This study sets out to construct a super multi-view system for freely inputting and outputting light rays through the space in front of and behind shielding objects, for the purpose of achieving a virtual transparency of visual obstacles.

Type Systems for Verification of Temporal and State-dependent Properties in the Presence of Various Computational Effects

 Grant-in-Aid for Early-Career Scientists 

Principal Investigator: SEKIYAMA, Taro
Associate Professor, Information Systems Architecture Science Research Division

Ensuring high reliability of real software needs program verification for various sets of programming features, called computational effects, that address program states.
This research aims to study type-based verification for two kinds of properties of computation effects: temporal properties, which state that computational effects are invoked in a certain correct order, and state-dependent properties, which state that computation effects are invoked in a certain valid state.
Verifying these properties makes it possible to confirm whether programs use computational effects and maintain their states correctly.

Constructing Reading Comprehension Datasets to Evaluate Discourse-level Language Understanding

 Grant-in-Aid for Early-Career Scientists 

Principal Investigator: SUGAWARA, Saku
Assistant Professor, Digital Content and Media Sciences Research Division

In recent years, reading comprehension tasks have been widely used to evaluate natural language understanding systems. However, existing datasets have questions created based on descriptive or factual context, which makes it difficult to test the capability of understanding multiple sentences or the entire text.
This study aims to address this limitation by defining types of discourse-level linguistic phenomena and inferences that are relevant to understanding the relations between sentences.
By collecting passages that contain such features from various domains, our goal is to create more advanced reading comprehension datasets and develop systems that perform well on them.

Understanding Finger-Braille Interaction

 Grant-in-Aid for Scientific Research (B) 

Principal Investigator: BONO, Mayumi
Associate Professor, Information and Society Research Division

The purpose of this study is to shed light on the transmission and comprehension mechanisms of finger-Braille communication.
The project aims at creating a research environment that enables linkage analysis and analysis of speech content, by developing a technique for transcribing and building a database of finger-Braille interactions.
People with deafblindness are affected by visual and auditory impairments.
Finger-Braille is a means of communication used principally by persons with "blind-first deafblindness," who first lose their sight and later their hearing.
In the finger-Braille method, six fingers of the person with deafblindness (index to ring fingers of each hand), which are likened to the six keys on a Braille typewriter, are tapped directly ("Tokyo Deafblind Association" website).
In this study, conversational and interactional analyses are performed on finger-Braille dialogue data that have already been recorded.
To enable these analyses, it was essential to develop a method of transcribing the interaction by matching the positions of the finger-Braille strokes to the speech occurring simultaneously.
The analysis results obtained with this method will be shared with the deafblind community.
The possibility of extending this line of research will be examined using the method of party research.