Mining diversity on social media networks

The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. Based on the approach, we can measure not only a user’s sociality and interest diversity but also a social media’s user diversity. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive.     

Performance of TZCD-MBOK watermarking scheme in T-DMB systems

Recently, watermarking based data transmission techniques using terrestrial digital TV signal have been proposed since they are also cost-free and can overcome the limitations of GPS. However, in the previous watermarking based methods, the detection accuracy is low and additional data rate is too low. Thus, we propose the throughput enhancement method by employing the TZCD-MBOK watermarking technique in T-DMB system. By applying the proposed scheme to T-DMB, it allows additional data transmission for disaster broadcasting and improves efficiency of data transmission in shadow region and indoor to mobile environment through watermarking spread code. From the simulation results, we confirm the proposed watermarking scheme affected on the existing T-DMB signal. Moreover, it was also confirmed that the system capacity increases as the power of additional watermarking signal rose. The results of the paper can be applied to wireless multimedia digital broadcasting systems.     

Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.     

A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

Rate-smoothed schedule with tolerable data dropping for video coding stream

Most algorithms of smoothing schedule compute the required bit rate of video transmission to satisfy all the transmitted data. In this paper, our proposed tolerable data dropping algorithm can adjust transmitting data to fit available bit rate. MPEG-4 with fine grained scalability (FGS) can support partial data dropping to adapt to available bandwidth network. The algorithm is based on the minimum variance bandwidth allocation (MVBA) algorithm proposed by Salehi et al. to compute the bit rate such that still ensuring that the buffer never underflows and overflows for MPEG-4 FGS streams under the limited bandwidth resource. We prove that our proposed algorithm, named MVBADP, is smoother than the MVBA algorithm. The experimental results show the peak rate, the number of rate changes, and the ratio of total dropping data, and the PSNR for four test sequences with different content characteristics. They are varied by buffer sizes and tolerable dropping ratios. We found that the MVBADP algorithm can reduce the peak rate and the number of changes when the transmitted data are dropped by tolerable dropping ratio, especially on the video sequences with the high motion and complex texture characteristic and larger size change of the consecutive frame.     

3D human modeling from a single depth image dealing with self-occlusion

This paper presents a 2D to 3D conversion scheme to generate a 3D human model using a single depth image with several color images. In building a complete 3D model, no prior knowledge such as a pre-computed scene structure and photometric and geometric calibrations is required since the depth camera can directly acquire the calibrated geometric and color information in real time. The proposed method deals with a self-occlusion problem which often occurs in images captured by a monocular camera. When an image is obtained from a fixed view, it may not have data for a certain part of an object due to occlusion. The proposed method consists of following steps to resolve this problem. First, the noise in a depth image is reduced by using a series of image processing techniques. Second, a 3D mesh surface is constructed using the proposed depth image-based modeling method. Third, the occlusion problem is resolved by removing the unwanted triangles in the occlusion region and filling the corresponding hole. Finally, textures are extracted and mapped to the 3D surface of the model to provide photo-realistic appearance. Comparison results with the related work demonstrate the efficiency of our method in terms of visual quality and computation time. It can be utilized in creating 3D human models in many 3D applications.     

Types and associated type families for hardware simulation and synthesis

In this article we overview the design and implementation of the second generation of Kansas Lava. Driven by the needs and experiences of implementing telemetry decoders and other circuits, we have made a number of improvements to both the external API and the internal representations used. We have retained our dual shallow/deep representation of signals in general, but now have a number of externally visible abstractions for combinatorial and sequential circuits, and enabled signals. We introduce these abstractions, as well as our abstractions for reading and writing memory. Internally, we found the need to represent unknown values inside our circuits, so we made aggressive use of associated type families to lift our values to allow unknowns, in a principled and regular way. We discuss this design decision, how it unfortunately complicates the internals of Kansas Lava, and how we mitigate this complexity. Finally, when connecting Kansas Lava to the real world, the standardized idiom of using named input and output ports is provided by Kansas Lava using a new monad, called Fabric. We present the design of this Fabric monad, and illustrate its use in a small but complete example.     

A fast two-step block type decision algorithm for intra prediction in H.264/AVC high profile

Intra coding in H.264/AVC can significantly improve the compression efficiency but at the cost of high computational complexity. To reduce the complexity, this paper presents an efficient block type decision algorithm for intra prediction. In H.264/AVC high profile, three kinds of block types are supported. This algorithm determines the optimal block type by two steps. The first step is based on the fact that the block type of intra prediction is highly dependent on the smoothness of macroblock. An edge-based feature is introduced to characterize the smoothness, by comparing it with two thresholds impossible block type is firstly filtered out. Then the second step is based on the correlation of block type chosen for different chroma modes. Experimental results show that the proposed fast algorithm can achieve 72.8% time saving on average for encoding the all intra-frame sequence with average 0.75% bit rate increase and 0.05dB PSNR degradation when comparing with the reference software.