Hearables, wearable earphone-type devices, are often operated via voice commands. However, voice control faces limitations such as sensitivity to environmental noise, difficulty in silent environments, and privacy concerns. Silent speech interaction (SSI) has been proposed to address these issues, with methods using IMUs and ultrasound showing promise. Yet, SSI using ear canal pressure remains underexplored. In this study, we propose a novel SSI method that recognizes silent speech by analyzing pressure changes in the ear canal, measured by a built-in barometric sensor. These signals are classified using machine learning techniques such as SVM, DTW, and kNN. Experiments were conducted with seven participants using five predefined commands related to music control. The proposed method achieved an average recognition accuracy of 72.0
Future communication systems are complex infrastructures with virtualized software services that are updated regularly as requirements evolve. This paper addresses the adaptation of microservices under dependency and efficiency constraints. We formalize the evolution of microservices as a combination of two subproblems, the Microservice Dependency Problem and the Evolution Planning Problem, both of which are difficult to solve optimally. We then propose a method based on the Binate Covering Problem (BCP) with branch-and-bound, and introduce a novel algorithm that finds the deployment steps towards a desired new configuration. Our proposed method, DEP-DS, is then compared with two heuristics on three sample datasets from historical radio access network update records run on Kubernetes. We further show that BCP with greedy search is faster but finds fewer solutions to the evolution plan. Overall, DEP-DS is able to find solutions to all samples, generates deployment plans within an average time of 1–2 s, and the plans are similar to other heuristics in terms of CPU usage.
In this paper, we propose a song recommendation method for DJs that focuses on song lyrics. A DJ plays and connects a variety of songs. There are two ways to connect songs in DJ: based on musical similarities and based on semantic similarities. DJs of J-POP and anime songs tend to use connecting methods based on semantic similarity. In this paper, we focus on the lyrics of songs to support DJs of J-POP and anime songs. The task is to recommend the next song for the currently selected song. To train our model, we create a dataset from actual set lists played at DJ events. We fine-tune the Japanese pre-trained BERT in two ways: a Bi-Encoding model and a Cross-Encoding model. We experiment with Japanese pre-trained BERT as a baseline. In MRR, the Cross-Encoding model showed superior scores. In user tests, the Bi-Encoding model showed superior scores.
This study proposes a system to support the establishment of midfoot strike (MFS), a foot strike pattern where the heel and forefoot contact the ground simultaneously. While MFS is considered to reduce injury risk and improve running performance, it is difficult to acquire and maintain consistently. To promote MFS acquisition, runners must become aware of their ground contact balance and the distribution of plantar pressure during running. The proposed system enhances this awareness by providing intermittent visual feedback, which consists of insoles with pressure sensors placed at the heel and the ball of the foot and an LED mounted on the brim of a cap. When pressure values exceed a threshold, the LED lights up in red (heel) or blue (ball of the foot). Rather than delivering constant feedback, the system provides feedback at fixed intervals, encouraging users to estimate their foot strike internally and later verify its accuracy. This cycle of estimation and correction helps runners gradually learn to identify and correct deviations from MFS on their own. Even after the system is removed, users are expected to have learned to maintain MFS independently. An experiment was conducted to evaluate the system’s effectiveness. The results showed that participants who used the system could detect and correct deviations from MFS earlier than those who did not. This suggests that the intermittent feedback is effective for supporting the acquisition and retention of MFS during running.
Human skeleton data plays a crucial role in healthcare, rehabilitation, and human-computer interaction. However, traditional motion capture methods are often expensive and highly dependent on controlled environments. To address these limitations, we propose a novel framework based on Diffusion and Transformer models that reconstructs full-body skeleton sequences from foot pressure data collected via smart insoles.
