Location-based games (LBGs) such as Pokémon Go integrate gameplay with real-world locations, creating novel player experiences. Motivated by the idea that new gameplay forms can emerge by rethinking the game field, we have proposed WWW-based games (WBGs) as a new approach to implementing location-based gaming within the WWW. WBGs utilize URLs and web content as game elements, allowing players to interact with web environments in new ways. To explore its feasibility and impact, we developed Text Monster, a game in which players capture monsters inhabiting web pages and compete for control over websites. User studies with two versions of the game demonstrated that WBG is technically feasible and engaging, as players voluntarily participated and exhibited strategic behaviors. Our findings suggest that WBG can influence web usage patterns and introduce new possibilities for interactive web-based experiences.

Non-native speakers (NNS) often face challenges gaining the speaking floor in conversations with native speakers (NS) of a common language. To help NNS to contribute more, we developed a conversational agent that opens up the speaking floor either automatically, after NS have taken a certain number of consecutive speaking turns, or manually, upon NNS request. We compared these automatic and manual agents to a control condition in a laboratory study in which one NNS collaborated with two NS using English as a common language. Participants (N=48) communicated over video conferencing from separate locations in a research institution to collaborate on three survival tasks. Based on data gathered from the experiments, the automatic agent encouraged NNS to participate more, which previous studies had attempted but failed to achieve. Excerpts from group discussions further showed the crucial role of the automatic agent on NNS participation. Interview results suggested that while NNS appreciated the automatic agent's help to participation, NS perceived the agent's interruption as unfair because they thought all members were speaking equally, which was not the case. The mismatch in their perceptions further emphasizes the need to intervene, and we provide design implications based on the results.

Multiparty collaboration using a common language is often challenging for nonnative speakers (NNS). Conversation can move forward rapidly, with terms and references unfamiliar to NNS often going unexplained because NNS do not request clarification due to cognitive overload or face concerns. Language difficulties may further lead to NNS having a low level of participation in a conversation, which could be a loss for multilingual teams. To help NNS resolve potential confusions due to unfamiliar language use without risking face concerns, we created a conversation agent that asked clarification questions intended to help NNS follow and participate in multiparty conversations. We conducted a within-subjects laboratory experiment with 17 triads of 2 NS and 1 NNS, who performed a series of collaborative tasks under three conditions: a) no agent, b) a high-level agent that resembles a NNS with good command of English, and c) a low-level agent that resembles a NNS with poor English skills. Results suggest that NS made significantly more clarifications in both agent conditions than without an agent. In the high-level agent condition, NNS reported an increase in understanding after the agent's interruption and spoke significantly more. Further, NNS evaluated their communication competence in English highest in the low-level agent condition and lowest in the control condition. Our findings suggest several directions to improve the tool to better facilitate multilingual multiparty communication.

The disinfection of environmental surfaces is an effective countermeasure for COVID-19. In this paper, we use a thermographic camera and lightweight background image processing and propose a method that detects and visualizes the places touched by a person. Our method will support effective disinfection activities.

Sensor networks allow us to collect data, such as camera images, over a wide area. Understanding the sensing area by aggregating and processing the data from multiple sensors is promising. Deep Learning (DL) is a powerful method for interpreting data. However, communication cost on the sensor network and computational cost on the server are two substantial problems of aggregating and processing data from multiple sensors. We therefore propose divided processing of the DL between a server and a powerful Edge-Heavy Sensor (EHS). In our study, we reduced transmission data to twelve times lower than the amount of raw input data while maintaining a 4.5% decrease in the DL's recognition accuracy.

The datafication of spatio-temporal city-wide events is one essential factor for smart management of the city. For this purpose, the combination of real-time event detection on edge sensor nodes mounted public vehicles, and event accumulation on a server is one realistic and efficient solution. We can analyze the accumulated data to understand complex phenomena occurring in entire the city. In this paper, we introduce a novel datafication procedure of city-wide events by sensor mounted garbage trucks and evaluated the preliminary implementation of event detection system on actual vehicle-mounted sensors.

This paper proposes a new method that makes it easy for us to construct a positioning model for device-free passive indoor localization by using model transfer techniques. With device-free passive indoor positioning, a wireless sensor network is used to detect the movement of a person based on the fact that RF signals transmitted between a transmitter and a receiver are affected by human movement. However, because device-free passive indoor positioning relies on machine learning techniques, we must collect labeled training data at many training points in an end user's environment. This paper proposes a method that transfers a signal strength model used for locating a person obtained in another environment (source environment) to the end user environment. With the transferred models, we can construct a positioning model for the end user environment inexpensively. Our evaluation showed that our method achieved almost the same positioning performance as a supervised method that requires labeled training data obtained in an end user's environment.

In this study, we examine how remote collaborators' upper body view affects collaboration when people engage in multiparty fluid tabletop activities across distances. We experimentally investigated the effects of upper body view on four-person group tabletop collaboration, two-by-two at identical locations: shared tabletop vs. shared tabletop plus upper body view. Although previous research has often failed to illustrate the advantages of showing remote participants' upper body view, our study showed that task performance was significantly higher in conditions with upper body view. Furthermore, participants with upper body view tended to take a step away from their remote partners to effectively glance at them while taking a comparable perspective of the tabletop objects. Detailed analysis of the video recordings revealed that upper body view was effective for fluid tabletop collaboration because it helped achieve joint perspective and helped estimate the timing and rough location of subsequent tabletop activity.

In this paper, we present t-Room, the next generation video communication system we are developing. Our approach is to build rooms with identical layouts, including walls of display panels on which users and physical or virtual objects are all shown at life-size. In this way, the user space enclosed by t-Room's surrounding displays can be shared as a common space at any other site. In other words, the enclosed spaces overlap each other. This configuration effectively provides symmetric reproduction of the audio-visual information surrounding local and remote users and objects. The feeling provided by t-Room is different from that by conventional videoconferencing systems, since there is no spatial barrier separating users such as the video screen of a conventional videoconferencing system. Furthermore, t-Room benefits in every way from Next Generation Network (NGN) technology: QoS, service productivity, and security. We view t-Room as a future form of telephone service.

The goal of our research is to support cooperative work performed by stakeholders sitting around a table. To support such cooperation, various table-based systems with a shared electronic display on the tabletop have been developed. These systems, however, suffer the common problem of not recognizing shared information such as text and images equally because the orientation of their view angle is not favorable. To solve this problem, we propose the Lumisight Table. This is a system capable of displaying personalized information to each required direction on one horizontal screen simultaneously by multiplexing them and of capturing stakeholders' gestures to manipulate the information.