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Crowdsourcing has become an effective tool to utilize human intelligence to perform tasks that are challenging for machines. In the integrated crowdsourcing systems, the requesters are non- monopolistic and may show preferences over the workers. We are the first to design the incentive mechanisms, which consider the issue of stimulating the biased requesters in the competing crowdsourcing market....
Background estimation can be regarded as a problem to construct the background from a series of video frames including moving objects in the scene. Scene background estimation is the essential prerequisite, or at least can be helpful for many applications such as video surveillance, video segmentation, and privacy protection for videos. To perform this task, in this paper we propose a robust framework...
Inspired by the mechanism of human brain three-stage memory model and on the basis of our previous work, in this paper we present a novel spinning tri-layer-circle memory based Gaussian mixture model (STLCM-GMM). In this model, three circle memory spaces are defined to store and process the pixels and the Gaussians used in the segmentation framework respectively. With three circle memory spaces spinning,...
Customer churn management becomes increasingly critical for telecommunication companies in the competitive mobile market. For retaining customers before they switch to competitors, an accurate customer churn analysis model is important to predict the potential lost customers in two or three months. Two month window is practical for telecommunication companies to design strategies to retain potential...
Contact acoustic nonlinearity (CAN) between rough interfaces in solids are mainly caused by nonlinear stress-strain relationship between contact interfaces. According to classical Hertz theory, unidirectional normal contact stress between two rough interfaces was built as functions of interface parameters on roughness. Nonlinear spring stiffness was further deduced from such functions. Then two rectangular...
Foreground detection is widely used in many applications of computer vision and artificial intelligence. In this paper, a novel online algorithm of detecting moving objects in complex scenes is proposed based on incremental nonnegative matrix factorization (INMF). In this algorithm, a new video frame is modeled as a linear combination of basis vectors of background subspace, plus a sparse term which...
Sequential data modeling has received growing interests due to its impact on real world problems. Sequential data is ubiquitous -- financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in sequential data modeling is how to capture the strong hidden correlations among complex features in high volumes. The sparsity and skewness...
Mixed-model assembly lines have been widely used to satisfy the ever-increasing variety of customer requirements. However, the successful application of this production model greatly relies on the level of inventory management, which determines the efficiency and operating cost. Considering the flexibility to unexpected events, this paper proposes a dynamic part supply method to deliver the required...
The huge data traffic demand is a challenge for the next generation of wireless network. Since the majority of data is public information, a novel wireless intelligent pushing mechanism is presented by pushing public information in the way of point to multipoint (P2M) in network idle time, using limited spectrum. Analytic Hierarchy Process (AHP) is employed to establish the performance evaluation...
To detect and resolve distributed deadlocks in the generalized model, a few algorithms have been proposed. Most of them are distributed algorithms which are based on the diffusing computation technique where propagation of probes and backward propagation of replies are required. On the contrary, centralized algorithms send the dependency information directly to the initiator. However, existing algorithms...
This article provides an overview of the challenges of big data, in the aspects of data scale, data heterogeneity, data timeliness, and demand for deep analyzing, in correspondence with four features of big data. It also presents an overview of trends of big data analytics both in theory and technique, with the development of testing benchmark, visualization technology, advanced and mixed architecture,...
In order to guarantee data reliability in distributed storage systems, erasure codes are widely used for the desirable storage properties. Nevertheless, the codes have one drawback that overmuch data are needed to repair a failure, resulting in both large bandwidth consuming in the network and high calculation pressure on the replacement node. For repair bandwidth problem, researchers derive the tradeoffs...
Cloud computing provides a convenient platform for big data computation such as machine learning and data mining. However, privacy conscious users often encrypt their data with their own keys before uploading them to the cloud. Existing techniques for computation on encrypted data are either in the single key setting or far from practical. In this paper, we show how two non-colluding servers can leverage...
To solve the problem of detecting salient moving object in the video shot by static camera, a new spatio-temporal object detection algorithm is proposed in this paper. Firstly, Hypercomplex Fourier Transform(HFT) is used to the current video frame to achieve the static salient region; then, the moving salient region is detected by an improved three frames difference algorithm; finally, the static...
In this paper, a method for the alignment of face images is proposed. We first extract SIFT features from the set of images, match features between each two images. Then we cluster the image according to the number of matching keypoints,in each category. We use the congealing algorithm to train and generate the model. The experiments show that the features are invariant to image scaling and rotation,...
Simulated annealing (SA) is one of the popular approaches to predict protein structures. SA is prohibitive because it usually consumes much computing time and is likely to fall into local minimum points. We proposed a parallel SA algorithm based on a Graph Process Unit (GPU) technique to improve the efficiency and accuracy of the protein structure prediction. First, we analyze the SA algorithm based...
In order to meet the big data challenge of today's society, several parallel execution models on distributed memory architectures have been proposed: MapReduce, Iterative MapReduce, graph processing, and dataflow graph processing. Dryad is a distributed data-parallel execution engine that model program as dataflow graphs. In this paper, we evaluated the runtime and communication overhead of Dryad...
In this paper, two independent component analysis (ICA) based algorithms are proposed for blind multiuser detection (BMUD) in DS-CDMA systems. The first algorithm is Per-processing for Noisy ICA Based Blind Multiuser Detection that can reduce the noise in the detection system but it still has the accumulation of error. The second algorithm is Estimation and Elimination of Noise for ICA Based Blind...
The evolution of Internet topology is not always smooth but sometimes with unusual sudden changes. Consequently, identifying certain patterns of the unusual topology evolution is critical for Internet topology modeling and simulation. We analyze IPv6 Internet topology evolution in IP-level graph to demonstrate how it changes in uncommon ways to restructure Internet. After evaluating changes of average...
We present a novel unsupervised learning method for human action categories from video sequences using Latent Dirichlet Markov Clustering (LDMC). Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The algorithm automatically learns the probability distributions of the words and the intermediate topics corresponding...
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