Masayuki Murata

Masaaki Yamauchi, Yiyuan Liang, Hiroko Hara et al.

Smart home automation that adapts to a user's emotional state can enhance psychological safety in daily living environments. This study proposes an emotion-aware automation framework guided by the emotional Biologically Inspired Cognitive Architecture (eBICA), which integrates appraisal, somatic responses, and behavior selection. We conducted a proof-of-concept experiment in a pseudo-smart-home environment, where participants were exposed to an anxiety-inducing event followed by a comfort-inducing automation. State anxiety (STAI-S) was measured throughout the task sequence. The results showed a significant reduction in STAI-S immediately after introducing the avoidance automation, demonstrating that emotion-based control can effectively promote psychological safety. Furthermore, an analysis of individual characteristics suggested that personality and anxiety-related traits modulate the degree of relief, indicating the potential for personalized emotion-adaptive automation. Overall, this study provides empirical evidence that eBICA-based emotional control can function effectively in smart home environments and offers a foundation for next-generation affective home automation systems.

Masaaki Yamauchi, Masahiro Tanaka, Yuichi Ohsita et al.

Internet-of-things (IoT) devices are vulnerable to malicious operations by attackers, which can cause physical and economic harm to users; therefore, we previously proposed a sequence-based method that modeled user behavior as sequences of in-home events and a base home state to detect anomalous operations. However, that method modeled users' home states based on the time of day; hence, attackers could exploit the system to maximize attack opportunities. Therefore, we then proposed an estimation-based detection method that estimated the home state using not only the time of day but also the observable values of home IoT sensors and devices. However, it ignored short-term operational behaviors. Consequently, in the present work, we propose a behavior-modeling method that combines home state estimation and event sequences of IoT devices within the home to enable a detailed understanding of long- and short-term user behavior. We compared the proposed model to our previous methods using data collected from real homes. Compared with the estimation-based method, the proposed method achieved a 15.4% higher detection ratio with fewer than 10% misdetections. Compared with the sequence-based method, the proposed method achieved a 46.0% higher detection ratio with fewer than 10% misdetections.

Takio Uesugi, Yoshinobu Shijo, Masayuki Murata

Securing the traceability of products in the supply chain is an urgent issue. Recently, supply chain systems that use public blockchain (PBC) have been proposed. In these systems, PBC is used as a common database shared between supply chain parties to secure the integrity and reliability of distribution information such as ownership transfer records. Thus, these systems secure a high level of traceability in the supply chain. However, the distribution information, which can be private information, is made public since the information recorded in PBC can be read by anyone. In this paper, we propose a method for preserving privacy while securing traceability in a supply chain system using PBC. The proposed method preserves privacy by concealing the distribution information via encryption. In addition, the proposed method ensures distribution among legitimate supply chain parties while concealing their blockchain address by using a zero-knowledge proof to prove their authenticity. We implement the proposed method on Ethereum smart contracts and evaluate cost performance based on transaction fees. The results show that the fee per party is at most 2.6 USD.