7935679
Deferrable Scheduling for Temporal Consistency: Schedulability Analysis and Overhead Reduction
The deferrable scheduling algorithm for fixed priority transactions (DS-FP) is demonstrated to be a very effective approach for minimizing real-time update transaction workload while maintaining temporal validity of real-time data. This paper examines the schedulability of deferrable scheduling and presents a sufficient condition. While its online scheduling overhead is a concern, we propose a hyperperiod based DS-FP approach satisfying the temporal validity constraint with low overhead. The algorithm, namely DEferrable Scheduling with Hyperperiod by Schedule Adjustment (DESH-SA), adjusts the DS-FP schedule in an interval so that the adjusted schedule in the interval can be repeated infinitely. Our experiments demonstrate that DESH-SA can reduce scheduling overhead of DS-FP and also achieve update workload near optimal
7999297
A deferrable scheduling algorithm for real-time transactions maintaining data freshness
Periodic update transaction model has been used to maintain freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus in the past studies such as the more-less scheme by Xiong and Ramamrithan (2004) in which update transactions are guaranteed by the deadline monotonic scheduling algorithm by Leung and Whitehead (1982) to complete by their deadlines. In this paper, we propose a novel algorithm, namely deferrable scheduling, for minimizing imposed workload while maintaining temporal validity of real-time data. In contrast to previous work, update transactions scheduled by the deferrable scheduling algorithm follow a sporadic task model. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical analysis of its processor utilization, which is verified in our experiments. Our experimental results also demonstrate that the deferrable scheduling algorithm is a very effective approach, and it significantly outperforms the more-less scheme in terms of reducing processor workload
15030913
Automatic Real-Time Barcode Localization in Complex Scenes
Many applications exist for automatically finding and reading barcodes in complex scenes with a camera. The key problem is to search barcodes in a complex scene and supply them for a reading subsystem. However, illumination, rotation, perspective distortion and multiple barcodes circumstances make barcode localization difficult. By jointly analyzing texture and shape, we propose a realtime barcode localization method that is robust to the above problems. A user only needs to put a barcode in front of a camera at around 15 cm for common Web cameras, and the system then automatically locates and decodes the barcode. This paper only focuses at automatic localization. We will demonstrate the complete system live at the conference.
42412948
Hallucinating 3D Faces
Super resolution technique could produce a higher resolution image than the originally captured ones. However, a few works have been done in 3D models. In this paper, we focus on generating the 3D face model of higher resolution from one input of 3D face model. In our method, the 3D face models including the training samples and input model are remeshed to construct consistent mesh to find out the correspondence among the models. The super resolution then is performed in the remeshed models to reconstruct the high resolution model. The experiments using USF HumanID 3D face database of 100 3D face models are carried out, and demonstrate the presented algorithm is promising.
9276774
Privacy-preservation for gradient descent methods
Gradient descent is a widely used paradigm for solving many optimization problems. Stochastic gradient descent performs a series of iterations to minimize a target function in order to reach a local minimum. In machine learning or data mining, this function corresponds to a decision model that is to be discovered. The gradient descent paradigm underlies many commonly used techniques in data mining and machine learning, such as neural networks, Bayesian networks, genetic algorithms, and simulated annealing. To the best of our knowledge, there has not been any work that extends the notion of privacy preservation or secure multi-party computation to gradient-descent-based techniques. In this paper, we propose a preliminary approach to enable privacy preservation in gradient descent methods in general and demonstrate its feasibility in specific gradient descent methods.
5965700
Analysis of topological characteristics of huge online social networking services
Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks.
10475402
Human face orientation detection using power spectrum based measurements
This work presents a new approach to human face orientation detection. We start the work with establishing a frequency domain model to analyze the orientation distribution of the oriented patterns in an image, according to the power spectrum of its bi-valued image. This model is derived from the perspective of the rendering rules in classical computer graphics. On the assumptions of the crossed distribution rule and symmetry rule of facial textures, we use the model above to define a series of measurements, called power spectrum based measurements, to quickly determine the orientation of a human face. The experimental results on MIT, ORL and BiolD face databases demonstrate our approach is effective and promising.