No abstract

Sketch recognition systems are currently being developed for many domains, but can be time consuming to build if they are to handle the intricacies of each domain. In order to aid sketch-based user interface developers, we have developed tools to simplify the development of a new sketch recognition interface. We created LADDER, a language to describe how sketched diagrams in a domain are drawn, displayed, and edited. We then automatically transform LADDER structural descriptions into domain specific shape recognizers, editing recognizers, and shape exhibitors for use in conjunction with a domain independent sketch recognition system, creating a sketch recognition system for that domain. We have tested our framework by writing several domain descriptions and automatically generating a domain specific sketch recognition system from each description. r

A highly secure, foolproof, user authentication system is still a primary focus of research in the field of User Privacy and Security. Shoulder-surfing is an act of spying when an authorized user is logging into a system, and is promoted by a malicious intent of gaining unauthorized access. We present a gaze-assisted user authentication system as a potential solution to counter shouldersurfing attacks. The system comprises of an eye tracker and an authentication interface with 12 pre-defined shapes (e.g., triangle, circle, etc.) that move simultaneously on the screen. A user chooses a set of three shapes as a password. To authenticate, the user follows the paths of three shapes as they move, one on each frame, over three consecutive frames.The system uses either the template matching or decision tree algorithms to match the scan-path of the user's gaze with the path traversed by the shape. The system was evaluated with seven users to test the accuracy of both the algorithms. We found that with the template matching algorithm the system achieves an accuracy of 95%, and with the decision tree algorithm an accuracy of 90.2%. We also present the advantages and disadvantages of using both the algorithms. Our study suggests that gaze-based authentication is a highly secure method against shoulder-surfing attacks as the unique pattern of eye movements for each individual makes the system hard to break into.

Sketch recognition attempts to interpret the hand-sketched markings made by users on an electronic medium. Through recognition, sketches and diagrams can be interpreted and sent to simulators or other meaningful analyzers. Primitives are the basic building block shapes used by high-level visual grammars to describe the symbols of a given sketch domain. However, one limitation of these primitive recognizers is that they often only support basic shapes drawn with a single stroke. Furthermore, recognizers that do support multistroke primitives place additional constraints on users, such as temporal timeouts or modal button presses to signal shape completion. The goal of this research is twofold. First, we wanted to determine the drawing habits of most users. Our studies found multistroke primitives to be more prevalent than multiple primitives drawn in a single stroke. Additionally, our studies confirmed that threading is less frequent when there are more sides to a figure. Next, we developed an algorithm that is capable of recognizing multistroke primitives without requiring special drawing constraints. The algorithm uses a graph-building and search technique that takes advantage of Tarjan's linear search algorithm, along with principles to determine the goodness of a fit. Our novel, constraint-free recognizer achieves accuracy rates of 96% on freely-drawn primitives.

Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems.