We present a new CAPTCHA which is based on identifying an image's upright orientation. This task requires analysis of the often complex contents of an image, a task which humans usually perform well and machines generally do not. Given a large repository of images, such as those from a web search result, we use a suite of automated orientation detectors to prune those images that can be automatically set upright easily. We then apply a social feedback mechanism to verify that the remaining images have a human-recognizable upright orientation. The main advantages of our CAPTCHA technique over the traditional text recognition techniques are that it is language-independent, does not require text-entry (e.g. for a mobile device), and employs another domain for CAPTCHA generation beyond character obfuscation. This CAPTCHA lends itself to rapid implementation and has an almost limitless supply of images. We conducted extensive experiments to measure the viability of this technique.

We present a logs-based comparison of search patterns across three platforms: computers, iPhones and conventional phones. Our goal is to understand how search users differ from computer-based search users, and we focus heavily on the distribution and variability of tasks that users perform from each platform. The results suggest that search usage is much more focused for the average user than for the average computer-based user. However, search behavior on high-end phones resembles computer-based search behavior more so than search behavior. A wide variety of implications follow from these findings. First, there is no single search interface which is suitable for all phones. We suggest that for the higher-end phones, a close integration with the standard computer-based interface (in terms of personalization and available feature set) would be beneficial for the user, since these phones seem to be treated as an extension of the users' computer. For all other phones, there is a huge opportunity for personalizing the search experience for the user's mobile needs, as these users are likely to repeatedly search for a single type of information need on their phone.

Entering search terms on mobile phones is a time consuming and cumbersome task. In this paper, we explore the usage patterns of query entry interfaces that display suggestions. Our primary goal is to build a usage model of query suggestions in order to provide user interface guidelines for mobile text prediction interfaces. We find that users who were asked to enter queries on a search interface with query suggestions rated their workload lower and their enjoyment higher. They also saved, on average, approximately half of the key presses compared to users who were not shown suggestions, despite no associated decrease in time to enter a query. Surprisingly, users also accepted suggestions when the process of doing so resulted in an increase in the number of total key presses.

The difficulty of entering queries from impoverished keyboards impedes the use of web search on mobile devices. On average, it takes a mobile user approximately 60 seconds to enter a query from a 9-key keypad [1]. In this paper, we explore the use of contextual signals to facilitate query entry on mobile phones. We present a query prediction system which offers automatically generated word completions as the user is typing her query. The query prediction system redefines the prediction dictionary after considering contextual signals such as the application being used (e.g. search vs. general text messaging), the inferred location of the user, the time of day and day of week. We demonstrate a 46.4% improvement in query entry, measured by number of key presses needed to enter queries. We found that the two contextual signals that make the largest impact are knowledge of the application being used and the location of the user.

We present a large scale study of search patterns on Google's mobile search interface. Our goal is to understand the current state of wireless search by analyzing over 1 Million hits to Google's mobile search sites. Our study also includes the examination of search queries and the general categories under which they fall. We follow users throughout multiple interactions to determine search behavior; we estimate how long they spend inputting a query, viewing the search results, and how often they click on a search result. We also compare and contrast search patterns between 12-key keypad phones (cellphones), phones with QWERTY keyboards (PDAs) and conventional computers.