In language learning, adults seem to be superior in their ability to memorize knowledge of new languages and have better learning strategies, experiences, and intelligence to be able to integrate new knowledge. However, unless one learns pronunciation in childhood, it is almost impossible to reach a native-level accent. In this research, we take the difficulties of learning tonal pronunciation in Mandarin as an example and analyze the difficulties of tone learning and the deficiencies of general learning methods using the cognitive load theory. With the tasks designed commensurate with the learner’s perception ability based on perception experiments and small-step learning, the perception training app is more effective for improving the tone pronunciation ability compared to existing apps with voice analysis function. Furthermore, the learning effect was greatly improved by optimizing the app interface and operation procedures. However, as a result of the combination of pronunciation practice and perception training, pronunciation practice with insufficient feedback could lead to pronunciation errors. Therefore, we also studied pronunciation practice using machine learning and aimed to train the model for the pronunciation task design instead of classification. We used voices designed as training data and trained a model for pronunciation training, and demonstrated that supporting pronunciation practice with machine learning is practicable.

Economic activities heavily rely on social infrastructure, such as bridges, tunnels, public structures, and buildings. In Japan, regular inspections are mandated by law to ensure these assets remain functional. This kind of requirement is rare worldwide. Similar regulations exist only in major cities in the United States and Canada. These inspections often focus on detecting issues that are not visible to the naked eye, such as cavities within concrete walls. The most widely used method for such inspections is the hammering test, in which inspectors analyze the sound variations produced when a hammer strikes a surface. By interpreting these auditory changes, they can assess the structural integrity and identify hidden defects within a structure. While effective, the accuracy of this method strongly depends on the inspector’s sensory perception, which varies with individual experience and skill. Furthermore, because the test is conducted manually, it is time-consuming, especially for large-scale structures. The most critical problem is the shortage of skilled inspectors due to retirement. It is difficult to meet the demand for inspections. To solve this problem, this paper proposes an AI-based hammering test system that analyzes and evaluates structural health by detecting abnormal sounds during inspections. This system allows even less-experienced workers to accurately identify defective areas. The focus of this study is on the application of AI-driven sound analysis for inspecting adhesive-applied tile walls, a common feature in modern apartment buildings. By integrating AI technology, this approach promises to improve the consistency and quality of inspections, reduce reliance on human expertise, and significantly enhance the overall efficiency of the inspection process.

This research paper discusses the current state of Internet of Things (IoT) security in relation to smart bot technology and suggests actions to mitigate IoT weaponization threats. One of the most dangerous cyberattack activities is infecting IoT devices with malware to gain control of their operations. This allows attackers to combine the computational power of infected devices to form a botnet, which can then be used to carry out Distributed Denial of Service (DDoS) attacks against specified IP addresses. The aim is to overwhelm the target IP address servers with requests and prevent them from servicing legitimate requests from other users, thereby degrading the availability of the target's services and resources. This paper aims to analyze IoT DDoS attacks or weaponization of IoT to determine the most effective methods of eliminating such threats and preventing serious damage to IoT infrastructure and business-related data. We will describe what IoT weaponization malware is, outline its features, and discuss the most common IoT malware currently present on the Internet. We will then analyze the Mirai source code to fully understand its qualities, including attack types and operations, that allowed it to remain effective.

We have been studying a sightseeing support application using Bluetooth Low Energy (BLE) beacons and mobile phones since 2014 to support sightseeing in Nikko, one of Japan’s World Heritage sites. Travelers can receive information relating to their position by using this application. We tried to use some psychological effects to increase travelers’ satisfaction when they receive such information. Moreover, we tried to emphasize travel memories of the place (scenery, culture, food, etc.) to encourage them to revisit or inspire their friends to visit it. Our tests prove the usefulness of our sightseeing support application for enhancing the travel experience. However, we had not yet developed a function to provide information that met the travelers’ intentions or needs. Moreover, it is hazardous to use a smartphone during walking, especially in an unfamiliar place. If a traveler uses a smartphone during walking, they might miss the beautiful scenery, historic buildings, and exotic atmosphere. We tried to adapt our sightseeing support application for use with a wearable device (smartwatch) to avoid using a smartphone during walking and introduced a prospect theory to evaluate information according to personal interest and behavior. Our experiments proved that our application provided the most appropriate sightseeing information to travelers and prevented danger during walking.