429,643 research outputs found
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device,
non-intrusive and automatic-learning system for smartphone user authentication.
Operating in the background, our system implicitly observes users' phone
pick-up movements, the way they bend their arms when they pick up a smartphone
to interact with the device, to authenticate the users.
Our SPU outperforms the state-of-the-art implicit authentication mechanisms
in three main aspects: 1) SPU automatically learns the user's behavioral
pattern without requiring a large amount of training data (especially those of
other users) as previous methods did, making it more deployable. Towards this
end, we propose a weighted multi-dimensional Dynamic Time Warping (DTW)
algorithm to effectively quantify similarities between users' pick-up
movements; 2) SPU does not rely on a remote server for providing further
computational power, making SPU efficient and usable even without network
access; and 3) our system can adaptively update a user's authentication model
to accommodate user's behavioral drift over time with negligible overhead.
Through extensive experiments on real world datasets, we demonstrate that SPU
can achieve authentication accuracy up to 96.3% with a very low latency of 2.4
milliseconds. It reduces the number of times a user has to do explicit
authentication by 32.9%, while effectively defending against various attacks.Comment: Published on ACM Symposium on Access Control Models and Technologies
(SACMAT) 201
nbodykit: an open-source, massively parallel toolkit for large-scale structure
We present nbodykit, an open-source, massively parallel Python toolkit for
analyzing large-scale structure (LSS) data. Using Python bindings of the
Message Passing Interface (MPI), we provide parallel implementations of many
commonly used algorithms in LSS. nbodykit is both an interactive and scalable
piece of scientific software, performing well in a supercomputing environment
while still taking advantage of the interactive tools provided by the Python
ecosystem. Existing functionality includes estimators of the power spectrum, 2
and 3-point correlation functions, a Friends-of-Friends grouping algorithm,
mock catalog creation via the halo occupation distribution technique, and
approximate N-body simulations via the FastPM scheme. The package also provides
a set of distributed data containers, insulated from the algorithms themselves,
that enable nbodykit to provide a unified treatment of both simulation and
observational data sets. nbodykit can be easily deployed in a high performance
computing environment, overcoming some of the traditional difficulties of using
Python on supercomputers. We provide performance benchmarks illustrating the
scalability of the software. The modular, component-based approach of nbodykit
allows researchers to easily build complex applications using its tools. The
package is extensively documented at http://nbodykit.readthedocs.io, which also
includes an interactive set of example recipes for new users to explore. As
open-source software, we hope nbodykit provides a common framework for the
community to use and develop in confronting the analysis challenges of future
LSS surveys.Comment: 18 pages, 7 figures. Feedback very welcome. Code available at
https://github.com/bccp/nbodykit and for documentation, see
http://nbodykit.readthedocs.i
Characterizing Search Behavior in Productivity Software
Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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