When we model dynamic phenomena, it is important to properly incorporate their probabilistic uncertainty. In particular, there is an increasing need for modeling and analysis methods for rare events that cause severe impact, e.g., extreme wind power fluctuations due to gusts. For the modeling of the stochasticity in dynamical systems, Gaussian noise is often used because of its analytical tractability. However, it cannot represent outliers because of its rapidly decaying tails. In this context, this article introduces a modeling and analysis method using stable distributions. This framework is capable of modeling rare events while retaining the favorable properties equipped with the Gaussian. As applications, we also describe our results on stochastic linearization analysis and privacy protection in dynamical systems.

Three-dimensional (3D) audio is a technology for reproducing acoustic events in an arbitrary environment and for creating virtual reality. In this technology, multiple loudspeakers or a head-related transfer function that is defined between a sound source and a point in the ear canal is used. This paper focuses on studies, technologies, and services based on the head-related transfer function. There are many studies on the head-related transfer function, and research infrastructures such as public head-related transfer function databases are also available. Furthermore, high-performance and inexpensive devices and software make us familiar with spatial audio. In this paper, the fundamentals, applications, related equipment, and services for spatial audio based on the head-related transfer function are reviewed, and the future prospects of spatial audio are described.

By the achievements in science and technology, we are enjoying a rich life that has never been seen in the past. On the other hand, the sophistication of science and technology has led to a high complexity and large scale of systems that support society and life. Our lives are greatly affected by these sudden failures and malfunctions. As the news of system failures are well reported everyday, it is becoming more and more important to improve the reliability of the “system” that surrounds us and to consider the design and maintenance of highly reliable systems. Research on system reliability (system operating probability) began in the 1960s. In this paper, we focus on the consecutive k-system in which the system fails (operates) when a part of the system fails (operates), and give an overview of the research surrounding the design and maintenance of consecutive-k systems.

In this paper, the cooperation between technological development and social implementation is described on the basis of the author's experience in natural language processing research and development of language resources. First, I describe the social implementation project of natural language processing technology currently underway. Next, I discuss the issues and directions of natural language processing technology for social implementation. After describing the importance of data in artificial intelligence systems, I introduce our experience in language data development so far. I also describe the AI Data Consortium we established recently for sharing data with society. I also explain the related activities on international standardization. Finally, I discuss human language understanding and artificial intelligence from a broader perspective.

The rapid and universal proliferation of wireless network technology and the considerable development of artificial intelligence technology together have brought forth the era of Internet of Things (IoT). Although each IoT device has limited computational capability, an IoT network as a whole, which realiges the role of data collection from devices, can be regarded as a rich computational resource. In this paper, we introduce several attempts to embed a neural network into a wireless network and conduct neural computing, which contribute to the realization of a high-performance, energy-efficient, and low-latency IoT system.

Gait has been considered as a biometric cue for identifying a person, and hence, gait recognition has recently become an active research area. Unlike other biometrics, gait recognition is still applicable even if the target person is captured at a distance from the camera without being aware of the camera, and therefore, it is expected to applied to be forensics using CCTV footage. In this article, we introduce basic approaches to gait recognition, as well as recently deep-learning frameworks for it. Moreover, we address our practice of gait recognition in forensics. Finally, we conclude this article with perspectives on gait recognition in the future.

In 1985, Miller and Koblitz independently introduced elliptic curve cryptosystems, a type of public key cryptosystem. Elliptic curve cryptosystems use the fact that elliptic curves become a group to realize an ID-based cryptosystem for the first time, by applying a bilinear map on an elliptic curve. Furthermore, in recent years, isogenies on elliptic curves have been used to realize post-quantum cryptosystems. Elliptic curves are indeed treasures for solving various cryptographic problems. Elliptic curves have been applied to resolve many theoretical problems and are merely a theoretical breakthrough. The charm of elliptic curves is that they are highly practical. To verify the correctness of a blockchain, the elliptic curve DSA signature (ECDSA) is used, since the signature size of ECDSA is very short. Furthermore, the elliptic curve realizes a post-quantum cryptosystem. In this paper, we discuss various breakthroughs achieved by using elliptic curves as well as international standardization related to elliptic curves.

To realize future large-scale quantum computation, we must perform a design procedure called “quantum circuit design.” Quantum circuit design can be considered as solving optimization problems similar to the conventional circuit design procedure. In this paper, we explain some costs that are optimized in quantum circuit design. Among such costs, we especially explain some known methods of optimizing nearest-neighbor cost (NNC) that have been studied intensively in recent years.