Securing the sensitive data stored and accessed from mobile devices makes user authentication a problem of paramount importance. The tension between security and usability renders however the task of user authentication on mobile devices a challenging task. This paper introduces FAST (Fingergestures Authentication System using Touchscreen), a novel touchscreen based authentication approach on mobile devices. Besides extracting touch data from touchscreen equipped smartphones, FAST complements and validates this data using a digital sensor glove that we have built using off-the-shelf components. FAST leverages state-of-the-art classification algorithms to provide transparent and continuous mobile system protection. A notable feature is FAST 's continuous, user transparent postlogin authentication. We use touch data collected from 40 users to show that FAST achieves a False Accept Rate (FAR) of 4.66% and False Reject Rate of 0.13% for the continuous post-login user authentication. The low FAR and FRR values indicate that FAST provides excellent post-login access security, without disturbing the honest mobile users.

Pervasive cross-disciplinarity has been a key factor in the genomics revolution.

No abstract

In this paper, we present a novel approach of using the integrated GPU to accelerate conventional operations that are normally performed by the CPUs, the bulk memory operations, such as memcpy or memset. Offloading the bulk memory operations to the GPU has many advantages, i) the throughput driven GPU outperforms the CPU on the bulk memory operations; ii) for on-die GPU with unified cache between the GPU and the CPU, the GPU private caches can be leveraged by the CPU for storing moved data and reducing the CPU cache bottleneck; iii) with additional lightweight hardware, asynchronous offload can be supported as well; and iv) different from the prior arts using dedicated hardware copy engines (e.g., DMA), our approach leverages the exiting GPU hardware resources as much as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times in micro benchmarks while still using less resources. Using eight real world applications and a cycle based full system simulation environment, the results showed 30% speedup for five, more than 20% speedup for two of the eight applications.

In affective user studies, visual interfacing of data has received little attention. Such interfaces can support qualitative understanding, conveying insight about static and temporally evolving information; static information is exemplified by demographic data, while temporally evolving information is exemplified by physiological signals. In this paper we present User Portrait -an abstraction and visualization method that condenses the essence of a study's data in a single figure. It is an inverted pyramid design, where the information abstraction is communicated on the top view, while the details are displayed on a need-to-know basis. The method has been applied to a longitudinal study of student affect vs. exam performance, effectively visualizing its voluminous data set.