Generating and Recomposing Contents using ...

Generating and Recomposing Contents using Networked Sensor Information Kosuke Numa1 , Kenro Aihara2 , Hideaki Takeda2 , Katsuaki Tanaka1 , Mina Akaishi1 , and Koichi Hori1 1

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RCAST, The University of Tokyo, 4–6–1 Komaba, Meguro, Tokyo, Japan, numa (at) ai.rcast.u-tokyo.ac.jp National Institute of Informatics, 2–1–2 Hitotsubashi, Chiyoda, Tokyo, Japan

Abstract. In this paper, we introduce our current two projects — (1) automatic content generation based on networked sensors and (2) dynamic recomposition of contents —, then we integrate these methods and propose a new framework for people’s expressing support.

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Introduction

Nowadays, we people are living in rapid increasing numbers of digital contents and feeling difficulty in managing these flooding information. We need a way to find and select wanted information, to arrange them, to think over them, and to create and publish new information. Ohmukai and his group proposed a system which supports whole processes from collecting to publishing information on the Web [1]. In their work, they aimed to support information management processes by focusing on people behind information. It can be useful regarding online activities. But now, with spreading mobile devices, we need to utilize information in certain place and in certain situation in our daily lives. A new framework for information activities in this networked sensing environment is required. In our research, we dare to focus on people’s expressing phase. On creating content, we need to collect other’s information and arrange them. It is often said that “to write is to think.” It seems that all activities regarding people’s information management are included in the creation process. This paper is organized as follows: We introduce our current two projects. Automatic content generation method based on sensor information is shown in Section 2 and our dynamic content recomposition approach are discussed in Section 3. In Section 4, we propose an integrated framework that recomposes contents using sensor information.

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ActionLog: Automatic Content Generation based on Sensor Information

We have been developed a series of systems called ActionLog. ActionLog is a framework for associating the real world context to the Weblog.

Fig. 1. Usage scenario of the ActionLog system

ActionLog system manages to capture actions and metadata of their situations by using sensors and cooperating with other systems. Some metadata, like time and place can be obtained directly from sensors, but others should be inferred from sensor information. For example, we can obtain nearby people by integrating locational information of multiple users. Then the system generates an entry for each action with sentences indicating the metadata of the action as a draft. Furthermore, the system shows related information, e.g., entries by other people with the same or similar situations. This is achieved by integrating and calculating metadata. The ActionLog usage scenario is as follows (Figure 1). The user walks around and does something with a wearable device like a mobile phone. She notifies the device when she wants to record an action. After she goes back home, she browses with a PC a list of drafts, each of which corresponds to her actions, and edits and publishes some of them. We have developed several ActionLog applications and applied it for academic conference support [2, 3]. We have installed KIOSK PCs at the conference site. Conference participants touch a card reader with their own name card with RFID tag, and the system infers what she is doing, e.g., attending a session or making a presentation. Generated drafts are published after edited by the user. Published entries can be browsed according to their metadata (Figure 2). In order to utilize our system in open and daily environment, we proposed a version using GPS-enabled mobile phone [4]. Now we are developing a system and designing a service using smart card tickets called PASMO3 . 3

http://www.pasmo.co.jp/en/

Fig. 2. Presentation-based aggregated view of ActionLog

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Dynamic Recomposition of Contents

Managing contents based on metadata is surely effective to find information you want. This way, however, is too organized for finding new possibilities of contents. Contents are placed in changing contexts and structures or relationships among them also change momently. When supporting information management for creative activities, we need to consider the dynamics of content creation activities. In the ActionLog system, draft contents are generated by just applying static templates to captured sensor data. In this section, we discuss a way to recompose contents according to the context dynamically. 3.1

Expression Liquidization and Crystallization

We have developed a cycle model which consists of the expression liquidization and crystallization processes [5, 6]. An expression has one static form, but is interpreted based on contexts, which differ according to situations or states of people and expressions. Contexts are relationships among units of partial expression and between them and units of external knowledge. These relationships always change. As expression liquidization, we call decomposition of expressions into units in proper granularity with every possible connection among each, and

Fig. 3. Expression Liquidization and Crystallization

as expression crystallization, new expression formation from decomposed partial units based on new relationships within the context (Figure 3). When an expression is merged against an expression and when a context is merged against a context, the original context will be broken down and liquidization will be enhanced. Placing others expressions into a context changes the context and the values/meanings of expressions. Once a new expression created, it raises a new context, and then the new context stimulates her again. 3.2

Framework for Activating Expression Life Cycle

Figure 4 depicts our proposed framework for activating this cycle. Automatic draft generation and creation through interaction with generated draft are the key elements of the framework. Draft generation phase helps the expression crystallization process; but this support is not direct one — finally a human decision is required. Created expressions and modifications for generated drafts are decomposed into units and analyzed possible relations among them; this is the expression liquidization process. A new expression changes the nebulous knowledge, and then different drafts will be generated. These generated drafts stimulate users and cultivate newer expressions again. This interactive and incremental process is supposed to activate the expression cycle. 3.3

Preliminary Workshop

We have conducted several preliminary practices based on the framework [6]. Recently, various types of workshops are held in many fields for participatory learning and creative endeavors. We installed our systems in a participatory workshop. Here we introduce one of our recent practices called Shonan Photo-attached Acrostic Workshop. For the workshop, we designed a new format of expression

Fig. 4. Automatic Draft Generation and Interactive Creation

called photo-attached acrostics to highlight the process of decomposing and recomposing. Acrostic is “a poem or other writing in an alphabetic script, in which the first letter, syllable or word of each line, paragraph or other recurring feature in the text spells out another message4 .” We modified it to include pictures for each sentence. Participants take and select photos, write sentences whose first letters match a message given. Here a pair of sentence and photo should correspond and both photos and sentences should be along a theme given. In our first practice, we decided the theme as “Shonan” – the name of a region along a coast in central Japan, and recruited participants related to – e.g., living around, working around, or was born around – Shonan area. In the workshop, participants create an expression using their own photos at first. Then next, they are divided into groups and collaborate to create new expressions by remixing their photos. The system supported this remixing process. Figure 5 shows the scenes in the workshop. The installed system consists of four parts (see Figure 6): expression database, expression input interface, expression re-composing engine, and expressing support interface. The expression input interface is used by participants. They input their works, which are created in manual and analog manner. The expressing support 4

Acrostic – Wikipedia, the free encyclopedia: http://en.wikipedia.org/wiki/Acrostic

Fig. 5. Photo-attached Acrostic Workshop

Fig. 6. Architecture of Installed Information System

interface is used by the facilitator. The interface shows the draft expressions, which are generated from the expression recomposing engine (see Figure 7). The participants’ expressions are morphologically analyzed and the relations [7] among words are calculated. Based on the word network, the system presents candidates for new expressions.

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Framework for Dynamic Recomposition of Contents using Networked Sensor Information

The framework for dynamic recomposition described above was based on the contents themselves. Contexts, however, are formed not only by surficial expressions and structures of contents but also by situations in which contents

Fig. 7. Photo-attached Acrostic Creation Support Interface

were created and accessed. Networked sensor information will be useful to reach contexts. In this section, we expand and integrate our two current works, and propose a new framework for supporting people’s expressing activities with consideration of dynamics of real world contexts. Key functions of the framework are as follows: – Inferring situations and contexts from real world networked sensors and binding these metadata to contents. – Relating contents based on surficial expression and real world metadata. – Presenting related contents and candidates of partial expression based on relations for creation support. For the purpose of dynamic recomposition of contents, following points should be considered (but these are remained as future works): – How and what to capture as users’ situation data Designing interaction with systems, and natural and casual sensing devices are required. – How to abstract sensor data Law sensor data are too primitive, so some kind of abstraction is required. This process includes combining multiple law data and inferring what kind of situation is represented. – How to relate situations dynamically In order to extract related situations, contents, and users, a way to relate situations dynamically depending on contexts is required.

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Conclusion

In this paper, we introduced our current two projects and proposed an integrated framework for people’s expressing support. The main feature of the framework is that a system treats not only static situations but also momently changing contexts.

Acknowledgements This work has been partially supported by a grant from the Japan Science & Technology Agency under CREST Project, and also has been partially supported by a grant from the Ministry of Economy, Trade and Industry under Information Grand Voyage Project.

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