GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents.     

A study on effects of security risks on acceptance of enterprise cloud service: moderating of employment and non-employment using PLS multiple group analysis

This paper analyzed the effects of security risk factors fit for cloud computing paradigm on the acceptance of enterprise cloud service with intent to illuminate the factors for vitalizing the adoption of corporate cloud services in the future. The acceptance intention was set as a dependent variable. Independent variables were set in reference to the technology acceptance theory. Security risks were categorized into compliance risk, information leakage risk, troubleshooting risk and service discontinuation risk to design a model for analysis.     

Enhanced security in internet voting protocol using blind signature and dynamic ballots

In this paper, we introduce an internet voting protocol which satisfies desired security requirements of electronic voting. In the newly proposed protocol, we allow the adversaries to get more power than in any previous works. They can be coercers or vote buyers outside, and corrupted parties inside our system. These adversaries also have ability to collude with each other to ruin the whole system. Our main contribution is to design an internet voting protocol which is unsusceptible to most of sophisticated attacks. We employ the blind signature technique and the dynamic ballots instead of complex cryptographic techniques to preserve privacy in electronic voting. Moreover, we also aim at the practical system by improving the blind signature scheme and removing physical assumptions which have often been used in the previous works.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Tensor Clustering for Rendering Many‐Light Animations

Rendering animations of scenes with deformable objects, camera motion, and complex illumination, including indirect lighting and arbitrary shading, is a long‐standing challenge. Prior work has shown that complex lighting can be accurately approximated by a large collection of point lights. In this formulation, rendering of animation sequences becomes the problem of efficiently shading many surface samples from many lights across several frames. This paper presents a tensor formulation of the animated many‐light problem, where each element of the tensor expresses the contribution of one light to one pixel in one frame. We sparsely sample rows and columns of the tensor, and introduce a clustering algorithm to select a small number of representative lights to efficiently approximate the animation. Our algorithm achieves efficiency by reusing representatives across frames, while minimizing temporal flicker. We demonstrate our algorithm in a variety of scenes that include deformable objects, complex illumination and arbitrary shading and show that a surprisingly small number of representative lights is sufficient for high quality rendering. We believe out algorithm will find practical use in applications that require fast previews of complex animation.     

Motion synthesis with decoupled parameterization

In real-time animation systems, motion interpolation techniques are widely used for their controllability and efficiency. The techniques sample the parameter space using example motions, and interpolate them to compute the blend weights corresponding to the given parameters. A main problem of the techniques is that, as the dimension n of the parameter space increases, the number of required example motions increases exponentially, i.e. O(c ⁿ ). To resolve the problem, this paper proposes to use two decoupled parameter spaces for controlling the upper body and the lower body separately. At each frame time, a parameterized motion space produces a source frame, and the target frame is synthesized by splicing the upper body of one source frame with the lower body of the other. In order to have the two source frames correlated with each other, a time-warping scheme has been developed. Furthermore, in order to handle the dynamic properties of the parameter samples of the upper body, we have developed an approximation technique for quickly determining the sample positions in its parameter space. This decoupled parameterization method alleviates the complexity problem, e.g. from O(c ⁶) to O(c ³), while providing the users with the capability of convenient control over the character.     

Design and Implementation of a Ubiquitous Robotic Space

This paper describes a concerted effort to design and implement a robotic service framework. The proposed framework is comprised of three conceptual spaces: physical, semantic, and virtual spaces, collectively referred to as a ubiquitous robotic space. We implemented a prototype robotic security application in an office environment, which confirmed that the proposed framework is an efficient tool for developing a robotic service employing IT infrastructure, particularly for integrating heterogeneous technologies and robotic platforms.