Yoga pose classification is critical for intelligent environments, health, and fitness applications. This study investigates resource-efficient implementations of classification models using contrastive learning frameworks, including SimCLR, MoCo, and BYOL. We evaluate their performance across varying levels of labeled data, focusing on accuracy, computational efficiency, and robustness. MoCo offers a balanced tradeoff with 87.59% accuracy at 50% labeled data, while BYOL achieves strong results with faster inference. SimCLR, suitable for real-time applications due to faster training, consumes more memory and has slower inference. We apply data augmentation and normalization in the preprocessing pipeline to enhance generalization and address challenges like limited data and class imbalance. These techniques improve the model’s resilience and learning efficiency. Our findings guide scalable, energy-efficient, user-centered yoga pose classification models for intelligent environments.

Video cameras are prevalent in large cities but their use outside of public safety remains limited due to legitimate privacy concerns. Nevertheless, the rich information they can capture appears incredibly promising for large-scale smart city applications, as they can function as very powerful and versatile sensors. This ambivalence raises the question of whether such image data can be used in a privacy-responsible manner. Encryption-based solutions assume the end server can be trusted with keeping data safe; data leaks show us this assumption does not necessarily hold true. Traditional image obfuscation methods such as pixelization or blurring on the other hand fail to offer both sufficient privacy and utility. As such, privacy approaches that can provide privacy protection directly on the data itself while retaining practical utility are required. We here extend two such notions, differential privacy and \( k \) -anonymity, to image data, and extensively evaluate the resulting privacy-utility tradeoff on cross-camera person re-identification and attribute recognition data. Our results show that our proposed approaches can significantly reduce the privacy-sensitivity of image data at source while retaining decent utility for vision-based smart city applications.

Engaging in discussions that involve diverse perspectives and exchanging arguments on a controversial issue is a natural way for humans to form opinions. In this process, the way arguments are presented plays a crucial role in determining how engaged users are, whether the interaction takes place solely among humans or within human-agent teams. This is of great importance as user engagement plays a crucial role in determining the success or failure of cooperative argumentative discussions. One main goal is to maintain the user’s motivation to participate in a reflective opinion-building process, even when addressing contradicting viewpoints. This work investigates how non-verbal agent behavior, specifically co-speech gestures, influences the user’s engagement and interest during an ongoing argumentative interaction. The results of a laboratory study conducted with 56 participants demonstrate that the agent’s co-speech gestures have a substantial impact on user engagement and interest and the overall perception of the system. Therefore, this research offers valuable insights for the design of future cooperative argumentative virtual agents.

Mental health has a huge impact on humans, affecting both psychological and physical well-being. Excessive stress can lead to depression, reduced productivity, and even suicidal tendencies. Stress also impacts appetite and sleep quality, potentially leading to other health issues. However, stress accumulation often goes unnoticed until it severely impacts health, highlighting the need for daily stress level assessment. This study aims to estimate daily distress levels through natural conversations with a smart speaker. We utilize the audio-visual data of users interacting with a smart speaker on a daily basis, extract features from different modalities through analysis, and predict distress changes in daily life using questionnaire responses as labels. In the experiment, participants interacted with a smart speaker placed in their bedrooms, simulating daily life. Webcam recordings captured facial expressions, voice, and heart rate data, which were preprocessed for analysis. Predictions for happiness, depression, and anxiety levels were made using data from questionnaires filled out after each recording session, with scores ranging from 0 to 18. Results from the 14-day experiment with seven participants, aged 22 to 24, revealed MAEs of 2.04, 2.59, and 2.31 for happiness, depression, and anxiety levels, respectively. The corresponding RMSEs were 2.63, 3.20, and 2.91.

To achieve smart cities, leveraging data from cameras, which are often readily-installed IoT devices, can offer precious insights on the behavior of pedestrians and play a crucial role in designing and maintaining efficient transportation, appropriate infrastructure, or attractive tourism facilities. Such pedestrian flow data collection is often achieved through cross-camera person re-identification. This task is heavily privacy-invasive task by design, benefiting from rich visual data, which thus carries highly sensitive personal details about individuals. We here study how image data can be protected upfront, and introduce a novel image differential privacy mechanism leveraging both pixelization and color quantization for this purpose. Our extensive experiments show that through its random noise additions, our mechanism can obfuscate data more effectively than standard image obfuscation methods while retaining high utility for cross-camera re-identification, preserving reasonable re-identification metrics and demographic information even under low privacy budgets.

Numerical calculations using an abacus (also known as Soroban) require understanding numerical representations through beads, and the manipulation of beads in multiple methods and sequences necessitates long-term repetitive learning to master. Traditional abacus instruction involves checking the correctness of answers and observing learners to identify and address mistakes or difficulties in bead manipulation. However, this depends heavily on human resources and requires considerable effort. Therefore, this study proposes a learning support system for abacus learning using a commercially available abacus and a tabletop interface to provide coaching content based on realtime estimation of the abacus input values using a document camera.

Enabling federated learning in opportunistic networks unlocks the potential for machine learning in challenging environments like disaster zones and remote regions. However, the divergent models induced by dynamic node encounters, combined with complete parameter overlap in model-homogeneous training lead to catastrophic interference, which disrupts training progress. Furthermore, when whole models must be transmitted, nodes with shorter contact duration are limited from participating in the training process. To address these challenges, we propose a different approach for training neural networks in opportunistic settings that leverages independent subnetworks and sequential training. We partition the original neural network into non-overlapping subnetworks and assign each to a unique node. These subnetworks are then trained and exchanged repeatedly during node encounters, exposing them further to diverse datasets. As a consequence, we achieve parallel and conflict-free progress while minimizing participation costs. Our experiments demonstrate that continuous training and subnetwork accumulation foster the development of a more robust model. Moreover, by utilizing pre-trained backbones as feature extractors, we achieve a test accuracy of 75.06% on MEDIC's disaster damage severity assessment task, demonstrating that the approach can be adopted in resource-constrained and dynamic scenarios in the real world.

Regulating human flow is essential to reducing congestion in areas where people gather. A digital twin that realistically simulates human flow helps for this purpose. To realize a realistic human flow simulation mechanism, it is essential to take into account people's attributes. However, existing simulation methods use only location-specific information to predict people's behavior, thus do not reflect the routines that appear in people's actual lives. In this paper, we propose a human flow simulation using synthetic population data that help extract the attributes of people living in a target area. In the proposed method, we simulate the movement of people with each attribute like office workers, students, etc. every 15 minutes using the synthetic population data and the hourly transition probability matrix between PoIs (Points of Interest) by computing the transition probability matrix from hourly PoI-level congestion (people count) in the target area using people trajectory data included in the point-type fluid population data commercially available and applying a Markov chain to the congestion. The proposed simulation mechanism is based on the data assimilation of the actual PoI congestion vector (how many people were staying in each PoI) obtained from the point-type fluid population data and the virtual PoI congestion vector generated from the prediction of people's movement using the attribute information in the synthetic population data at regular time intervals. The data are assimilated at regular intervals to obtain highly accurate PoI-level congestion forecasts. The results of the mobility simulation for office workers showed that the maximum cosine similarity with the actual PoI congestion was 0.96 after 12 hours even when the actual PoI congestion vector is known only for a part of the area (one mesh).

Predicting crowd flow is crucial for decision-making to mitigate various risks. For instance, in social problems such as traffic congestion and over-tourism, countermeasures can be taken by predicting crowd flows in advance. Typically, people visit multiple Points of Interest (PoI) for various purposes. Previous work has proposed methods to incorporate the behavioral characteristics of people in different areas, such as dining areas or office areas, into machine learning models. However, they have not considered the specific behavioral characteristics associated with each PoI, such as when restaurants or train stations experience peak periods. Recently, there has been an increase in the ability to handle large amounts of location information, leading to a growth in the volume of individual trace data. In this study, we propose a crowd flow prediction method that aggregates large-scale individual trace data of movements between PoIs and considers the behavioral characteristics associated with each PoI. We define this information as time series PoI stay counts generated from trace data collected from mobile phones. Using this, we developed a machine learning model to predict the number of people in an area (mesh) over the next several minutes to hours. This prediction is based on the number of people staying at each PoI (category) in neighboring areas (meshes). We applied this approach to densely populated areas in central Tokyo, where congestion is a significant concern, and conducted validation. The results showed that the method utilizing time series PoI stay counts improved prediction accuracy by up to 50% compared to methods that did not use it. Additionally, the Mean Absolute Percentage Error (MAPE) for predicting the number of people staying 1 hour later was only 2.57%.

A typical CS1 class involves students working on solving programming problems. Before the pandemic, this occurred in a computer laboratory with a teacher who could quickly assist students having difficulty with their work. Sometimes, there is a need for this intervention even without the student asking for help. An experienced teacher can sense the growing frustration of a student through their overall demeanor. A teacher can also watch how a student codes to provide quick hints to address potential problems. This kind of intervention is challenging to do in an online learning setting. A typical online meeting software provides a small and limited view of a student, often crowded with all the other students. As such, the visual cues of frustration can be easily lost in the noise. Not being able to see the student's code easily is also a problem. The system we are developing aims to create an online IDE that leverages gaze tracking and code logging to automatically identify these struggling students. In the first phase of the research, a learning model will be trained on students' gaze and code logs in line with their overall class performance. The second phase of the research will then use this model to predict the frustration level of student users. Collaboration and gamification strategies will be explored in the final stage of the research that would assist interventions of not just teachers but also classmates who are willing to help.

Human users tend to selectively ignore information that contradicts their pre-existing beliefs or opinions in their process of information seeking.These "self-imposed filter bubbles" (SFB) pose a significant challenge for cooperative argumentative dialogue systems aiming to build an unbiased opinion and a better understanding of the topic at hand.To address this issue, we develop a strategy for overcoming users' SFB within the course of the interaction.By continuously modeling the user's position in relation to the SFB, we are able to identify the respective arguments which maximize the probability to get outside the SFB and present them to the user.We implemented this approach in an argumentative dialogue system and evaluated in a laboratory user study with 60 participants to show its validity and applicability.The findings suggest that the strategy was successful in breaking users' SFBs and promoting a more reflective and comprehensive discussion of the topic.

We present an approach to evaluate argument search techniques in view of their use in argumentative dialogue systems by assessing quality aspects of the retrieved arguments. To this end, we introduce a dialogue system that presents arguments by means of a virtual avatar and synthetic speech to users and allows them to rate the presented content in four different categories (Interesting, Convincing, Comprehensible, Relation). The approach is applied in a user study in order to compare two state of the art argument search engines to each other and with a system based on traditional web search. The results show a significant advantage of the two search engines over the baseline. Moreover, the two search engines show significant advantages over each other in different categories, thereby reflecting strengths and weaknesses of the different underlying techniques.

In the tourism sector, user-generated information and communication among tourists are perceived to be more effective and reliable contents. In addition, the collection of dynamic tourism information with high spatio-temporal resolution is required to provide comfortable tourism in response to the changing tourism style with the advancement of information technology. Participatory sensing, which can collect various types of information, is a useful method by which to collect these contents. However, continuous participation of users is essential in participatory sensing, and it is one of the most important points to stimulate participation motivation. In the tourism situation, we also need to pay attention to the total tourist satisfaction of participants. In this study, we investigate the effects of task allocation interfaces and user types on the efficiency of tourism information collection, tourism behavior and satisfaction in gamified participatory sensing. Two types of task allocation interfaces (free selection and agent interaction) were designed and implemented, and a sightseeing experiment was conducted with 10 participants at an actual sightseeing spot (Nara, Japan). As a result, we found that there was no difference in the effect of each interface on sightseeing satisfaction, but the characteristics of the collected data that, free selection allows for the collection of quantitative data and agent interaction allows for the efficient collection of data needed by the system, were different. In addition, we found that different user types had different tendencies for their contribution to sensing and their interface preferences.

Street parking in prohibited areas has become a social problem, especially in urban and tourist areas. In addition, because street parking can cause traffic congestion and accidents, real-time detection is required. The detection of street parking has been previously implemented on the basis of comparisons of videos recorded by fixed-point cameras. However, this approach has a limited detection area and low accuracy. In this demonstration, we present a system that recognizes street parking in real-time using a model trained by dashboard camera videos, which are widely used. The trained model was constructed by collecting data on 1,765 vehicles from dashboard camera videos. We use the Jetson TX2 as an edge device for vehicle recognition and processing. We create and demonstrate a dashboard camera device by attaching a camera and sensor module to the Jetson TX2.

In recent years, many studies and development of Cyber Physical Systems (CPS) have been carried out to feed back analysis results to human users in a physical space by using machine learning, aiming to analyze a huge amount of information obtained from physical space in a cyber space. To apply CPS to sports, a lot of studies have been conducted on sensing and recognizing actions and movements of athletes using machine learning. In this study, we focus on fishing as a sport, and propose a fishing CPS that recognizes anglers' actions in real-time and provides information on the past useful actions that are linked to fishing results depending on time and place as a decision support when the anglers do not make catch. In addition, this paper reports on the development of an IoT (Internet of Things) device that acquires positional information, acceleration and gyroscope information, and a web system that displays results of real-time activity recognition along with the place and time by animation for realizing the fishing CPS. We have evaluated the developed IoT device and web system from the viewpoint of practical use. As a result, we have confirmed that the GPS and acceleration sensors, in the actual breakwater environment, were constantly transmitting data to a server via UDP communication for 4 hours and 40 minutes.

In recent years, there has been a growing interest in travel applications that provide on-site personalized tourist spot recommendations. While they are generally useful, most of the available apps are focused on helping tourists make decisions only on the next spot to visit. This may cause that the tourists miss attractive spots to visit in the future. Due to the lack of awareness on the spots to go afterwards, they are unable to visit the spots they wanted to visit later, hence their overall tourism satisfaction decreases. In this study, we introduce an on-site tourism recommendation system, ISO-Tour, which can be used on the spot during the tour and allows users to consider multiple spots to visit next taking into account the trade-off between satisfaction of the next spot and that of the subsequent spots visited in the future.

In this paper, we aim to reveal the relationship between the number of people infected with COVID-19 in each ward in Tokyo and the changes in human mobility behavior using demographic information (population density, number of restaurants, etc.) and mobility data collected from GPS data of residents in Tokyo's 23 wards. The results confirmed that changes in human mobility behavior extracted from mobility data in each ward were an important feature related to the number of people infected by COVID-19 on the previous day's difference. These results suggest that the transition of the number of infected people in COVID-19 is largely due to human mobility behavior.

By the spread of active learning and group work, the ability to collaborate and discuss among the participants becomes more important than before. Although several studies have reported on that micro facial expressions and body movements give psychological effects to others during conversation, most of them are lacking in quantitative evaluation and there are few datasets about group discussion. In this research, we proposed a highly reproducible system that helps to make datasets of group discussions with multiple devices such as an omnidirectional camera (360-degree camera), an eye tracker and a motion sensor. Our system operates those devices in one-stop to realizing synchronized recording. To confirm the feasibility, we built the proposed system with an omnidirectional camera, 4 eye trackers, and 4 motion sensors. Finally, we succeeded to make a dataset by recording 8 times group meeting by using our developed system easily.

Following the increase in demand for "smart tourism," various tourist information becomes available. Current tourist guidance systems can provide many possible routes around the city, which are usually based on an optimal distance and time or popularity of the place. To design more enjoyable tourist routes, we should be aware of tourist's perception of the urban environment. In this paper, we propose the methodology of estimating the tourist emotions and satisfaction level by analysing physiological features, such as head movement and eye gaze. Features derived from raw sensor data have the correlation up to 0.58 with emotion and satisfaction labels. This study also shows the differences in feature/label dependencies between different touristic areas and highlights the challenges of tourist satisfaction estimation.