Apple Inc.

About: Apple Inc. is a company organization based out in Herzliya, Israel. It is known for research contribution in the topics: Signal & User interface. The organization has 15687 authors who have published 22600 publications receiving 624507 citations. The organization is also known as: Apple Computer, Inc. & Apple Computer Inc.

T-Cube: a fast, self-disclosing pen-based alphabet

Abstract: An interface for entering text to a pen-based computer is described. The technique proposes a new alphabet, where each letter is a flick gesture. These flick gestures are selfdisclosing using pie menus. An experiment determined the speeds of executing the flick gestures and the transition speeds between gestures. An assignment of characters to gestures is developed and evaluated. Audio feedback is used to convey whether a gesture was wellor badly-formed. A longitudinal study showed clear progress on a learning curve. The method is compared to soft keyboards, handwriting recognition systems, and unistrokes.

Systems and methods for adjusting a display based on the user's position

Abstract: An electronic device for providing a display that changes based on the user's perspective is provided. The electronic device may include a sensing mechanism operative to detect the user's position relative a display of the electronic device. For example, the electronic device may include a camera operative to detect the position of the user's head. Using the detected position, the electronic device may be operative to transform displayed objects such that the displayed perspective reflects the detected position of the user. The electronic device may use any suitable approach for modifying a displayed object, including for example a parallax transform or a perspective transform. In some embodiments, the electronic device may overlay the environment detected by the sensing mechanism (e.g., by a camera) to provide a more realistic experience for the user (e.g., display a reflection of the image detected by the camera on reflective surfaces of a displayed object).

Automatic method for scoring and clustering prototypes of handwritten stroke-based data

Abstract: A system and method for processing stroke-based handwriting data for the purposes of automatically scoring and clustering the handwritten data to form letter prototypes. The present invention includes a method for processing digitized stroke-based handwriting data of known character strings, where each of the character strings is represented by a plurality of mathematical feature vectors. In this method, each one of the plurality of feature vectors is labelled as corresponding to a particular character in the character strings. A trajectory is then formed for each one of the plurality of feature vectors labelled as corresponding to a particular character. After the trajectories are formed, a distance value is calculated for each pair of trajectories corresponding to the particular character using dynamic time warping method. The trajectories which are within a sufficiently small distance of each other are grouped to form a plurality of clusters. The clusters are used to define handwriting prototypes which identify subcategories of the character.

Portable multifunction device, method, and graphical user interface for managing communications received while in a locked state

Abstract: A computer-implemented method, performed at a portable multifunction device with a display, includes: receiving a plurality of communications via a plurality of communication modalities while the device is in a locked state; and detecting an unlock interaction by the user with the device. In response to detecting the unlock interaction, the method further includes unlocking the device and presenting a communication, or information about the communication, in the plurality of communications that was received while the device was in the locked state. The presented communication or information is presented in accordance with the corresponding communication modality.

Arterial travel time forecast with streaming data: A hybrid approach of flow modeling and machine learning

Abstract: This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming global positioning system (GPS) probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA. The hybrid model provides a distinct advantage over pure statistical or pure traffic theory models in that it is robust to noisy data (due to the large volumes of historical data) and it produces forecasts using traffic flow theory principles consistent with the physics of traffic. Validation of the model is performed in two different ways. First, a large scale test of the model is performed by splitting the data source into two sets, using the first to produce the estimates and the second to validate them. Second, an alternate validation approach is presented. It consists of a 3-day experiment in which GPS data was collected once per second from 20 drivers on four routes through San Francisco, allowing for precise calculation of actual travel times. The model is run by down-sampling the data and validated using the travel times from these 20 drivers. The results indicate that this approach is a significant step forward in estimating traffic states throughout the arterial network using a relatively small amount of real-time data. The estimates from the model are compared to those given by a data-driven baseline algorithm, for which the authors achieve a 16% improvement in terms of the root mean squared error of travel time estimates. The primary reason for success is the reliance on a flow model of traffic, which ensures that estimates are consistent with the physics of traffic.