Regularized Boolean operations have been widely used in 3D modeling systems. However, evaluating Boolean operations may be quite numerically unstable and time consuming, espe?cially for iterated set operations. A novel and unified tech?nique is proposed in this paper for computing single and iter?ated set operations efficiently, robustly and exactly. An adap?tive octree is combined with a nested constructive solid geom?etry (CSG) tree by this technique. The intersection handling is restricted to the cells in the octree where intersection actu?ally occurs. Within those cells, a CSG tree template is in?stanced by the surfaces and the tree is converted to plane?based binary space partitioning (BSP) for set evaluation;More?over, the surface classification is restricted to the cells in the octree where the surfaces only come from a model and are within the bounding?boxes of other polyhedrons. These two ways bring about the efficiency and scalability of the opera?tions, in terms of runtime and memory. As all surfaces in such a cell have the same classification relation, they are clas?sified as a whole. Robustness and exactness are achieved by integrating plane?based geometry representation with adaptive geometry predicate technique in intersection handling, and by applying divide?and?conquer arithmetic on surface classifica?tion. Experimental results demonstrate that the proposed ap?proach can guarantee the robustness of Boolean computations and runs faster than other existing approaches.

With the fast-growth of mobile social network, people’s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-Interaction Disparity (LID), we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid communitydetection algorithm using LID for discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people’s different social circles in different places with high efficiency.

In this paper, we propose a novel image recompression framework and image quality assessment (IQA) method to efficiently recompress Internet images. With this framework image size is significantly reduced without affecting spatial resolution or perceptible quality of the image. With the help of IQA, the relationship between image quality and image evaluation scores can be quickly established, and the optimal quality factor can be obtained quickly and accurately within a pre determined perceptual quality range. This process ensures the image’s perceptual quality, which is applied to each input image. The test results show that, using the proposed method, the file size of images can be reduced by about 45%-60% without affecting their visual quality. Moreover, our new image-recompression framework can be used in to many different application scenarios.

Integration of the cloud desktop and cloud storage platform is urgent for enterprises. However, current proposals for cloud disk are not satisfactory in terms of the decoupling of virtual computing and business data storage in the cloud desktop environment. In this paper, we present a new virtual disk mapping method for cloud desktop storage. In Windows, compared with virtual hard disk method of popular cloud disks, the proposed implementation of client based on the virtual disk driver and the file system filter driver is available for widespread desktop environments, especially for the cloud desktop with limited storage resources. Furthermore, our method supports customizable local cache storage, resulting in user-friendly experience for thin-clients of the cloud desktop. The evaluation results show that our virtual disk mapping method performs well in the read-write throughput of different scale files.

A high- performance, distributed, complex- event processing engine with improved scalability is proposed. In this new engine, the stateless processing node is combined with distributed storage so that scale and performance can be linearly expanded. This design prevents single node failure and makes the system highly reliable.

With user-generated content, anyone can be a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is becoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts of data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this information using natural language processing and data-mining techniques.

AB-chromaticity histogram analysis works well most of the time, but it may not work well when the color cast is not severe. To overcome this problem, we propose an improved, two-step automatic cast-detection method. First, we compute the RGB color variance to evaluate the quality of the input image. If this variance is very small, we extract near-neutral color areas and compute the local ab-chromaticity histogram. We use this local ab-chromaticity histogram to evaluate the quality of the input image. This method has been tested in ZTE’s video surveillance system. The results show that the proposed method produces better results based on subjective evaluation and is more efficient in various conditions.

As cloud computing gains in popularity, data migrated off premises is exposed to more threats than ever before. This is because data is out of control of the owner while floating in the cloud. Traditional device-centric security systems are not efficient enough and need to be evolved to data-centric protection systems. Cloud telecommunications services require security measures in three domains: data storage, processing, and transmission. Data stored in the cloud requires a mechanism to protect it; data in transit needs to be protected either at the service or transmission level; and data being processed needs to be protected during the processing stage. In this paper, we propose a security model based on a new method of security domain division to provide on-demand, dynamic, and differentiated protection for cloud-based telecommunications services.

Mobile search is beset with problems because of mobile terminal constraints and also because its characteristics are different from the traditional Internet search model. This paper analyzes cloud computing technologies—especially mass data storage, parallel computing, and virtualization—in an attempt to solve technical problems in mobile search. The broad prospects of cloud computing are also discussed.