We introduce a novel simple interaction technique for finding and browsing data in a large hierarchical database using only two keys.Finding data from a huge hierarchical database takes time because users should traverse the tree using a mouse or a keyboard, and click a "select" button to check the content. If the data was not appropriate, they should go to the next entry and perform the selection task again.In the past, when only a small number of "channels" were available from broadcast TV stations, users could simply rotate a television dial to select a channel and watch a program. We introduce a simple interaction technique called "Gear", with which users can select an entry from a large hierarchical database and browse it instantly, just like we could select a channel and enjoy a program using old television dials.

We introduce a simple and powerful visual interaction technique for managing strong passwords. Passwords have been used for authentication for decades, but appropriate handling of passwords is difficult because people can easily forget passwords and they can be easily attacked. Better authentication methods have been investigated, and various visual interaction methods have been proposed, including the DAS (draw-a-secret) method. Using DAS, users can log into a service just by drawing a secret pattern on the screen, but remembering complex secret patterns is as difficult as remembering passwords. We developed EpisoDAS, with which users can generate strong passwords based on their secret episodic memories with a simple DAS interface. A user can draw a secret pattern and generate a password, based on their secret episodic memories that they cannot easily forget.

A number of people are exchanging e-mail messages everyday using mobile phones and PDAs. as well as PCs. E-mail is useful not only for one to-one communication but group communication through mailing lists. However, conventional mailing lists are not as widely used as they should be, because creating and maintaining a mailing list is not an easy task. We propose a simple and powerful mailing list service system called QuickML, with which people can easily create a mailing list and control the member account only by sending e-mail messages. QuickML allows people to enjoy group communication at any place, at any time, and by anyone.

We introduce an inexpensive position input device called the FieldMouse, with which a computer can tell the position of the device on paper or any flat surface without using special input tablets or position detection devices. A FieldMouse is a combination of an ID recognizer like a barcode reader and a mouse which detects relative movement of the device. Using a FieldMouse, a user first detects an ID on paper by using the barcode reader, and then drags it from the ID using the mouse. If the location of the ID is known, the location of the dragged FieldMouse can also be calculated by adding the amount of movement from the ID to the position of the FieldMouse. Using a FieldMouse in this way, any flat surface can work as a pointing device that supports absolute position input, just by putting an ID tag somewhere on the surface. A FieldMouse can also be used for enabling a graphical user interface (GUI) on paper or on any flat surface by analyzing the direction and the amount of mouse movement after detecting an ID. In this paper, we introduce how a FieldMouse can be used in various situations to enable computing in real-world environments.

Pen-based computing has not yet taken off, partly because of the lack of fast and easy text input methods.The situation is even worse for people using East Asian languages, where thousands of characters are used and handwriting recognition is extremely difficult.In this paper, we propose a new fast text input method for pen-based computers, where text is not composed by entering characters one by one, but by selecting words from a menu of candidates created by filtering the dictionary and predicting from context.Using our approach, users can enter Japanese text more than hvice as fast as recognition-based and other existing text input methods.User studies and detailed analysis of the method are also given.

We propose a new pen-based text input method, which is an integration of software keyboards, handwriting recognition, and marking menus. With our method, a user selects a word from a list of candidate words filtered from a dictionary by specifying spelling, pronunciation, or the shape of its characters. Users can easily switch between using a software keyboard and using handwriting recognition systems, by tapping or writing strokes in the same area. Users can also show a menu of candidate words around the pen by stopping pen movement during operation.

Although various visualizationtechniques have been proposed for information retrieval tasks, most of them are based on a single strategy for viewing and navigating through the information space, and vague knowledge such as a fragment of the name of the object is not effective for the search.In contrast, people usually look for things using various vague clues simultaneously.For example, in a library, people can not only walk through the shelves to find a book they have in mind, but also they can be reminded of the author's name by viewing the books on the shelf and check the index cards to get more information.To enable such realistic search strategies, we developed a multiple-view information rerneval system where data visualization, keyword search, and category search are integrated with the same smooth zooming interface, and any vague knowledge about the data can be utilized to narrow the search space.Users can navigate through the information space at will, by modifying the search area in each view.

We propose a simple and powerful predictive interface technique for text editing tasks. With our technique called the dynamic macro creation, when a user types a special “repeat” key after doing repetitive operations in a text editor, an editing sequence corresponding to one iteration is detected, defined as a macro, and executed at the same time. Although being simple, a wide range of repetitive tasks can be performed just by typing the repeat key. When we use another special “predict” key for conventional prediction techniques in addition to the repeat key, wider range of prediction schemes can be performed depending on the order of using these two keys.

We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.