The emergence of Mobility as a Service (MaaS) highlights the need for personalized and exploration-oriented recommendation that account for users' subjective preferences and multisensory experiences. However, existing approaches lack real-world sensory and contextual data to support such personalization. To address this gap, we developed FreePOST, a location-based information collection system that enables users to submit photos together with multisensory and location impressions tied to specific places. In a field study spanning over three months in Kyoto, 69 participants contributed more than 8,000 posts. The collected data were visualized in real time via a web application with an interactive map interface. Because the sensory tags assigned to locations may depend not only on the characteristics of the place itself but also on the personality traits of the participants, we also collected personality and behavioral data using the Big Five Inventory and the Brief Sensation Seeking Scale. Our results demonstrate the ability to effectively capture Kyoto's sensory landscapes and context-aware experiential features, providing a valuable foundation for integrating subjective and multisensory data into future personalized search and recommendation in MaaS contexts. This demo will showcase FreePOST's core functions of multisensory data submission and interactive map visualization.

Research suggests that using PowerPoint, particularly slides featuring bullet points, elicits positive responses from students and is widely accepted in lectures globally. Archiving slides on digital platforms, such as SlideShare, allows for their reuse and analysis. However, the high costs associated with slide creation have led many Japanese higher education institutions to favor chalkboard-based teaching, resulting in insufficient lecture archiving. To address this issue, the consideration of slide automation services has emerged, although concerns regarding potential errors in generated slides persist. Therefore, there’s a need for systems supporting slide editing stages. This study proposes a system designed to detect and prompt edits for errors in slide content, aiming to reduce the burden of slide creation for educators and facilitate lecture slide archiving. The system utilizes subject terminologies as normative models to detect editing points, considering the parallel and hierarchical relationships among terms.

Web advertising services have exhibited consistent growth over the years. However, the conventional methods of web advertising recommendations, relying on keyword matching with search queries and browsing histories, encounter challenges when it comes to effectively targeting users with hidden or latent interests. In contrast, the use of mobile device location data in advertising recommendations often centers around physical store proximity. To address these limitations, our research aims to enhance web advertising recommendations by analyzing latent user interests through real-world behavioral data. This study specifically investigates the influence of area size on user behavioral analysis and its subsequent impact on the accuracy of predicting visit probabilities. We achieve this by extracting the user’s activity range from user behavior (movement) log data and geotagged tweets. Subsequently, we tally the places visited by the user, considering spot attributes, and convert this data into feature vectors. Utilizing these feature vectors in conjunction with various classification methods, we build learning models. In this paper, we present and evaluate these learning models employing different area sizes, verifying their accuracy in predicting user visits to specific stores.

In recent years, online learning has gained popularity in Japan, increasing opportunities for remote study using lecture slides. However, a common issue in Japanese education is that students tend to not ask questions in class. To address this issue, we propose an online learning support system informed by research on "critical thinking" that facilitates the process of students' questioning and thinking. In this study, we aimed to clarify the relationship between the questions generated by students and the class slides. We did this by analyzing the hierarchical relationship of the keywords that appear in the slides. We also propose a method of recommending appropriate lecture slides for the questions that we hope will encourage students to ask meaningful questions and engage in productive discussions.

In recent years, the number of people who run for the purpose of improving their health and physical fitness has been increasing, but it is not easy to continue running. Therefore, we believe that it is very important to develop a running support system. In our previous study, we developed a running support system and an application that enables users to run with a virtual runner created by using their past running data in an acoustic augmented reality space. However, this system has the problem that it can only race against the past running records of oneself or one's acquaintance, and can only race against a single running record. Therefore, we considered it necessary to develop a system that allows users to race against an unspecified number of users and that allows users to arbitrarily select the distance and number of times they wish to race. In this paper, we propose an online marathon system that enables large-scale online races and examine the effect of the number of competitors on user motivation.

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one’s physical health. However, even if we understand that exercising regularly has many benefits to our body and brain, it is not easy for people to stay motivated. To deal with this issue, in our previous research, we developed a running support system based on the users’ own records. Our system enables users to run and compete against virtual runners in an audio-based augmented reality space. However, in the AR running support system, competing with real users is more immersive than competing against one’s own running records. In this study, we aim to improve the motivation of running users, not only using the users’ own records, we also use the GPS information and time data from other real runners. We believe that by using information from real runners, our virtual running rival generation/recommendation method can improve the enthusiasm of users. Therefore, we propose a rival recommendation approach for our acoustic-based AR running support system considering the athletic ability of users. In addition, as a preliminary analysis, we conduct a questionnaire survey to clarify users’ needs for the future development of our running support system.

As an important foundation of the smart city, MaaS (Mobility as a Service) provides a seamless passenger experience using multiple modes of transport from the beginning until the end of a journey, and its developments have attracted keen interest from the industry in recent years [1] [2] . Furthermore, as the last mile of the MaaS, electric bicycle is an important and environmental-friendly transportation [3] [4] .

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.

advertisement (ad) recommendation services for mobile users is rapidly increasing. The conventional ways of recommending ads are based on the analysis of users' explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on an analysis of the user's latent interest on web browsing history which categorized positive and negative behaviors. In this paper, we adapt the method of the linked pages to real world locations using geo-tagged tweets. By several evaluations, we discuss the possibility to recommend ads according to the user's current location.

With the spread of internet television, many studies have been conducted to recommend relevant information for TV programs. Such as NHK Hybridcast is a new TV service that provides relevant information on the same screen during a TV program broadcast. However, current services cannot recommend supplementary information for TV programs based on user viewing behavior. Therefore, in this paper, we propose a video viewing support system to recommend supplementary information using geographical relationships based on user interaction.

In recent years, an evolutionary change occurred in the digital world when various social networking sites became popular. This led to a big increase of users who share their activities in digital form. A huge amount of digital information has become available, providing researchers with a unique insight into the behavior and activities of entire population. The use of Geo-tagged social media data became an emerging trend to represent user actions and behaviour at specific geographical locations. However, the inaccuracies of Geo-tagged data from social media often limits the utility of this data source in micro scale analysis (at the street or place of interest level (POI)). Our study supports how social media data could be matched accurately with specific physical locations. More specifically, our investigation includes Geo-tweet data and image data from flickr to map accurately physical location using machine leaning and deep learning techniques. In this paper, a preliminary discussion of this work is provided, using Geo-tagged data from Twitter and Open StreetMap in cities such as San Francisco and London as well as for image mapping using Flickr data in cities such as San Francisco and Kyoto.

Nowadays routing systems can provide optimal routes in terms of time and travel distance. However, they do not consider special needs of certain group of users. For example, people recovering from alcohol and drug addiction may want to travel a route that is alcohol and drug-free. In this demonstration, we propose a system we built that helps with this special need. We detect if a street is related to alcohol and drug by exploiting Web open data, including Foursquare, microblog tweets, Google Street View images, and crime data. We calculate an alcohol and drug relevance score using unsupervised methods, to be used in route ranking. Our system prototype is ready to be tested for the cities of San Francisco and Kyoto.

The vast and growing amount of publicly available real-time information from social network services such as Twitter could provide many benefits for improving public health and safety, especially towards the area of crime prevention. While prior studies have leveraged such data to help in the prediction of criminal incidents, we have developed a crime investigation tool which utilizes Twitter data to aid in crime analysis. The tool provides contextual information about crime incidents by visualizing the spatial and time-based characteristics of a crime and its context using data from nearby tweets and from the criminal history of a target place. In addition, sentiment analysis is also carried out with the identified tweets to further examine the negative characteristics of the spatial areas related to the different crimes in question. A demonstration prototype of this tool was developed as a web application for the area of San Francisco.

In recent years, running has become increasingly popular as an effective exercise activity which could help improve and maintain one's physical health. However, it is generally difficult to motivate people to adhere to their fitness regimens and persist in such activities. To help address this problem, we developed a Gamified Acoustic AR running support system where we use the previous running records of a user to project a "Virtual runner" into an augmented reality space for runners to compete against. The presence of the virtual runner is conveyed through the sound of running footsteps and breathing: users hear the sound of the virtual runner while they are running and are able to virtually compete against themselves in real time. In this paper, we describe how such a system could be implemented and discuss the results of an experiment study which highlights the effectiveness of our Gamified AR system.

Recently, short cooking videos such as Kurashiru and DELISH KITCHEN are attracting attention. These cooking videos can help people learn the essentials of cooking in a short time. However, it is difficult to understand cooking operations by watching a video only one time. Therefore, in this paper, we propose a novel cooking support system to extract cooking video's difficulty by extracting cooking ingredients and operations based on their appearance time from open captions of videos. We also show the effectiveness of our proposed cooking video's difficulty extraction method.

For a tourist who wishes to stroll in an unknown city, it is useful to have a recommendation of not just the shortest routes but also routes that are pleasant. This paper demonstrates a system that provides pleasant route recommendation. Currently, we focus on routes that have much green and bright views. The system measures pleasure scores by extracting colors or objects in Google Street View panorama images and re-ranks shortest paths in the order of the computed pleasure scores. The current prototype provides route recommendation for city areas in Tokyo, Kyoto and San Francisco.

This paper proposes a novel system which utilizes information from a social network services to suggest food venues to users based on crowd preferences. To recommend an appropriate food venue for each crowd preference, the system ranks food venues in each region by using an improved collaborative filtering method based on the differences between locations and languages in geo-tagged tweets. A key feature of the proposed system is the ability to suggest food venues in regions where very few geo-tagged tweets are available in a specific language by using the weighted similarity by others' preferences. To implement the system, more than 26 million tweets from European countries were collected and analyzed based on 6 languages and 7 regions. Afterwards, we provide an evaluation of the ranked venues proposed by the system based on 89 French speakers in 7 European countries.

Currently, movie trailers are edited using various methods. However, the length of each trailer is at most several minutes, and the scenes used for editing and the types of effects are limited because a trailer is created for a certain target audience. Therefore, it is difficult to edit a trailer that caters to the different preferences of various users. Moreover, potential audience may be lost if the trailer is not enticing enough. To solve this problem, we define seven editing biases that occur when movies are summarized and edited into trailers. We investigate whether these biases can be used to generate a movie trailer catering to various viewer preference.

This paper presents TweeVist, a geo-tweet visualization system to support users grasp event happens over time and space from tweets while they browse any web pages based on spatio-temporal analysis. TweeVist presents a tag cloud of tweets in different time periods are associated with web pages based on detected events. In order to detect events, the system extracts normal events (e.g., crowded restaurants, crowded facilities in Walt Disney World) happen at anytime and anywhere by utilizing machine learning algorithms, and it also extracts unusual or special events (e.g., time sales, disasters) by comparing current situation to those normal regularities. Thus, TweeVist can effectively visualize a summary of tweets in a tag cloud to help users immediately gain a quick overview of current situation or events through time and space while they browse a web page, and it can also effectively present a list of most related tweets to help users easily obtain more detailed information. Furthermore, TweeVist provides a communication function, which allows users to chat with other users who browse the same web pages, or Twitter users who follow an account of TweeVist.

Navigation in unfamiliar cities often requires frequent map checking, which is troublesome for wayfinders. We propose a novel approach for improving real-world navigation by generating short, memorable and intuitive routes. To do so we detect useful landmarks for effective route navigation. This is done by exploiting not only geographic data but also crowd footprints in Social Network Services (SNS) and Location Based Social Networks (LBSN). Specifically, we detect point, area, and line landmarks by using three indicators to measure landmark's utility: visit popularity, direct visibility, and indirect visibility. We then construct an effective route graph based on the extracted landmarks, which facilitates optimal path search. In the experiments, we show that landmark-based routes out-perform the ones created by baseline from the perspectives of the lap time and the number of references necessary to check self-positions for adjusting route directions.

Pub/Sub communication model becomes a basis of various applications, e.g. IoT/M2M, SNS. These application domains require new properties of the Pub/Sub infrastructure, for example, supporting a large number of devices with widely distributed manner, handling emergency messaging with priority control and so on. In order to meet the demands, we proposed Software Defined Network Aware Pub/Sub (SAPS) which utilize the both Application Layer Multicast (ALM) and SDN, especially Open Flow based multicast (OFM). A simulation was done for evaluating the hybrid architecture in traffic and transmission delay reduction, and then the issues to be solved in the current design were discussed.

With the advance of video-on-demand (VOD) services such as Netfix, users are able to watch many kinds of videos anytime and anywhere. While watching a video, recently, users often search related information about it through the Web by using mobile PC. However, users cannot satisfactorily understand and enjoy it because the video keeps playing when they search about it. It is necessary to detect various questions of the video to supplement their related information about each scene for automatic search. However, only one video includes various topics of each scene, furthermore, viewers have different levels of knowledge. Therefore, we have developed a novel automatic video reinforcing system, called TV-Binder, it generates new video contents from one video stream related to viewers' interests and knowledge by adding other related contents (i.e., YouTube videos, images or maps) and by removing unnecessary original scenes, based on topics of each scene. As a result, viewers can satisfy and joyfully watch modified video contents without searching anything. At first, our system extract topics and detect their scenes of a video stream by using closed captions. The system then searches other necessary contents and determines unwanted original scenes based on popularity rating of each original scene and level of detail (LOD) controlling under time pressure. Through this, TV-Binder can automatically generate video contents are classified into four quadrants by two axes, one is digest and detailed videos, the other one is videos for experts with knowledge about particular topics and ordinary viewers without special knowledge. In this paper, we discuss our automatic video reinforcing system and an evaluation of its effectiveness.

Microblogging platforms such as Twitter have been recently frequently used for detecting real-time events. The spatial component, as reflected by user location, usually plays a key role in such systems. However, an often neglected source of spatial information are location mentions expressed in tweet contents. In this paper we demonstrate a novel visualization system for analyzing how Twitter users collectively talk about space and for uncovering correlations between geographical locations of Twitter users and the locations they tweet about. Our exploratory analysis is based on the development of a model of spatial information extraction and representation that allows building effective visual analytics framework for large scale datasets. We show visualization results based on half a year long dataset of Japanese tweets and a four months long collection of tweets from USA. The proposed system allows observing many space related aspects of tweet messages including the average scope of spatial attention of social media users and variances in spatial interest over time. The analytical framework we provide and the findings we outline can be valuable for scientists from diverse research areas and for any users interested in geographical and social aspects of shared online data.

Nowadays, many universities utilize groupware support for students to post and share their e-reports, and the students can browse and vote other students' reports in e-learning. Teachers then need to evaluate all students' reports, but this will require a great deal of time and effort for a fair evaluation of the reports. Therefore, we propose an e-report scoring method based on student peer evaluation by considering the relationship between voting and posting time of the e-reports, to promote the quality of the votes and prevent unfair votes. Then, the method can provide scores of reports based on a voting graph by analyzing students who vote the reports. In this paper, we perform a student peer evaluation using groupware based on voting with a "Like" button in a course practice, and compare the results with teachers' evaluation.

Microblogging platforms such as Twitter have recently received much attention as great sources for live web sensing, real-time event detection and opinion analysis. Previous works usually assumed that tweets mainly describe "what's happening now". However, a large portion of tweets contains time expressions that refer to time frames within the past or the future. Such messages often reflect expectations or memories of social media users. In this work we investigate how microblogging users collectively refer to time. In particular, we analyze half a year long portion of Japanese and four months long collection of US tweets and we quantify collective temporal attention of users as well as other related temporal characteristics. This kind of knowledge is helpful in the context of growing interest for detection and prediction of important events within social media. The exploratory analysis we perform is possible thanks to the development of visual analytics framework for robust overview and easy detection of various regularities in the past and future-oriented thinking of Twitter users. We believe that the visualizations we provide and the findings we outline can be also valuable for sociologists and computer scientists to test and refine their models about time in natural language.

Various TV programs, such as travel and educational programs, often introduce tourist spots, or historical places. However, viewers find it difficult to grasp the surroundings of these spots or places, how the locations are related, and distances between them, when instantaneously moving between the scenes by switching video streams. Therefore, we built an interface using the geographical metadata of the video streams, called TV-Milan, that automatically presents geographic contents (i.e., maps, photos, Street View, etc.) synchronized with the TV program.

This paper presents TWinChat, a Twitter and Web user interactive chat system to support simultaneous communication between microbloggers and Web users in real-time through both the contents of microblogs and Web pages. TWinChat provides a question answering interface attached to Web pages, which allows Web users to chat with Twitter users in real-time while presenting tweets that are associated with Web pages, i.e., simultaneous cross-media communication. In order to map heterogeneous media, the system extracts relationship between tweets and Web pages by generating queries based on location names. Thus, our system can effectively present messages from Web users to help Twitter users immediately obtain useful information or knowledge, and it also can effectively present tweets from the Twitter users to help the Web users easily grasp the current situation in real-time.

Nowadays, mutual evaluation in education is an available method that students in the learning community use to evaluate each other. The method calculates scores of students’ reports by considering who evaluates those reports. In this paper, we propose a student's mutual evaluation method using the PageRank algorithm, an appropriate evaluation method that helps teachers to easily understand whose report is the best from the students’ viewpoints. In particular, we perform students’ mutual evaluation based on a groupware by utilizing a “Like” function in a course practice. As a result, it was able to not only provide an overall rating from the students’ sum of votes but also, by considering who voted, to promote the reliability of the students as evaluators for their mutual evaluation.

This paper proposes an interactive 3D animation system specifically aiming efficient control of human motion. However there are various commercial products for creating movies and game contents, those are still difficult to deal with for non-professional users. To ease the creation process and encourage to utilize 3D animation for the general users, e.g., in the field of such as education, medicine and so on, we propose a system using Kinect. The data of skeleton models of human motion estimated by Kinect is processed to generate Motion Graph and finally restructure the data automatically for 3D character models. We also propose an efficient 3D animation viewing system based on touch interface for tablet device, which enables intuitive control of multiple motions of the human activity. To evaluate the effectiveness of the method, we implemented a prototype system and created several 3D animations.

Recently importance of tablet devices with touch interface increases significantly, because they attract not only mobile phone users, but also people of all generations including small kids to elderly people. One key technology on those devices is 3D graphics; some of them exceed the ability of desktop PCs. Although currently 3D graphics are only used for game applications, it also has a great potential for other purposes. For example, an interactive 3D animation system for tablet devices has been developed recently and attracts many people. With the system, multiple 3D animations are played depending on view direction and user's gesture input. However, the system still has two critical issues: the first one is a complicated process for data creation, and the other is a conflict on several gestures overlapped at the same position. In the paper, the first issue is solved by using a realtime range sensor and the second one is solved by considering the relationship between the 3D object and the input 2D gesture. The effectiveness of the system was proved by making the real system and creating and manipulating the actual 3D animations.

We present a novel system that combines the advantages of social communication and Web search by simultaneously discovering important pages and users. First, the system provides a communication interface attached to pages, which allows users to talk with each other in real time while browsing the same page, i.e., page-centric communication. Then, the system can efficiently provide two ranking lists of pages and users by analyzing a hybrid structure of hyperlinks (page-page relationship) and social links (page-user relationship and user-user relationship). Thus, users can efficiently search for important pages as well as important users related to their queries through the ranking function, and immediately obtain useful information or knowledge from not only pages themselves but also from other users.

The abundance of content on the web and the lack of quality control require more refined approaches in analyzing online information. In this paper, we propose evaluating the extent to which web search results cover important and recent news related to real-world objects. Our method allows for identifying search results that provide comprehensive overviews of major events related to user queries or that contain most recent information.

Information recommendation and filtering techniques have been studied intensively. Traditional tour recommendation systems, which can be considered one of information recommendation systems, usually calculate the shortest path in terms of time or distance. Recently, tour recommendation systems for more general purposes have become an important research topic. In this paper we propose an efficient tourist route search system which not only recommends the path simply connecting several tourist spots, but also recommends the path with beautiful scenic sights. We focus on the visibility of scenic sights between one tourist spot and another, which is an important factor for choosing a driving route, but has not been considered in traditional tour recommendation systems. To automatically retrieve tourist spots, we propose a personalized tourist spot recommendation technique using the Web information. Although, for some regions, databases of the famous spots exist and are published, such regions are limited and usually outdated. Our method automatically extracts spots from the Web, thus our system is versatile and up-to-date for large regions. To find a route with attractive scenery, we calculate scores for paths based on the visibility of scenic sights. After generating route candidates using GIS, a 3D virtual space is constructed and the Z-Buffer method is used to decide the visibility of scenic sights for each route candidate. We implemented a prototype and tested the effectiveness of the system.

So far, there have been many techniques and applications developed for handling 3D contents efficiently on the Web. However, there is currently no application that successfully makes 3D contents pervasive on the Web. One of the main reasons for this can be considered be the fact that there are only a few pages on the Web where a 3D object is sufficiently annotated with meta-information; meta-information is fundamental for the Web. In this paper, (1) a semi-automatic annotation module which supports users in creating 3D contents with rich meta-information by collecting and filtering meta-data on the Web using 3D information; and (2) a view-dependent information representation module which shows information corresponding to the viewing direction of 3D objects are presented. In those modules, view-dependent information representation module is most important in our system, because such system is highly suitable to show meta-information to users, however, not proposed yet. We implemented the proposed system and conducted experiments which show that it is possible to realize semi-automatic annotation of meta-data on 3D objects and efficiently present view-dependent meta-information to users.

Recently, along with the rapid growth of the Web, the preservation efforts have also increased. As a consequence, large amounts of past Web data are stored in Web archives. This historical data can be used for better understanding of long-term page topics and characteristics. In this paper, we propose an interactive visualization system called Page History Explorer for exploring page histories. It allows for roughly portraying evolution of pages and summarizing their content over time. We use a temporal term cloud as a structure for visualizing prevailing and active terms appearing on pages in the past.

We developed a mechanism for photographing people and annotating their behavior along with nearby elements using multiple embedded cameras. In addition, we developed a method of dynamically integrating and presenting the recorded content. As conventional cameras are used to photograph objects selected by users, taking pictures that include users while holding the camera is difficult. Security camera systems take pictures that include people and nearby elements, but such systems cannot show the intentions of the people being photographed. After detecting the intentions and behaviors of subjects with radio frequency identification (RFID) tags, our system selects the best camera from cameras located in an area, and then the camera photographs the subject and the surrounding area. In addition, these photos can be annotated with information about the context and movement history of the subject. We created a prototype of our system and determined its effectiveness experimentally.

We developed a mechanism for photographing people and annotating their behavior along with nearby elements using multiple embedded cameras. In addition, we developed a method of dynamically integrating and presenting the recorded content. As a conventional camera is used to photograph objects selected by a photographer, taking pictures that include him while holding the camera is difficult. Security camera systems take pictures that include people and nearby elements, but such systems cannot take the user aspect of intentions and interest of the people. After detecting the intentions and behaviors of subjects using radio frequency identification (RFID) tags, our system selects the best camera from cameras located in an area, and then the camera photographs the subject and the surrounding area. In addition, these photos can be annotated with meta datainformation about the context and interest of the subject.

We describe a browser for the past web. It can retrieve data from multiple past web resources and features a passive browsing style based on change detection and presentation. The browser shows past pages one by one along a time line. The parts that were changed between consecutive page versions are animated to reflect their deletion or insertion, thereby drawing the user's attention to them. The browser enables automatic skipping of changeless periods and filtered browsing based on user specified query.

A novel Web application called "my portal viewer (MPV)" has been developed to provide Web users with higher quality content, which is needed due to a rapidly growing amount of content on the Web. It provides fused news to the user, based on two viewpoints, through a user friendly interface and the user's preferences. MPV automatically selects and merges content from many news pages, based on the user's interest and knowledge, after gathering these pages from various news Web sites. Our unique approach is that the layout of the MPV page is applied to the users' favorite news portal page and a part of the original content is replaced by the fused content. Whenever a user accesses an MPV page after browsing other news pages, he/she can acquire the desired content efficiently because MPV presents a refreshed page, based on the user's behavior, which reflects his/her interests and knowledge. In addition to the MPV framework, methods that are based on user reference for replacing and selecting have been developed using an HTML table model.