30505871
User Interest Detection on Web Pages for Building Personalized Information Agent
In this paper we propose a vision-based approach to detecting user’s interests on web pages. This approach first splits a web page into several blocks of topics according to visual cues and then analyzes the layout structure of the page based on these blocks. The user’s interests are detected according to his browsing behaviors on the blocks and the layout structure of the web page. A personalized information agent is also developed to automatically extract new content from those web pages according to the user’s interests. Our experiments show that our proposed approach to detecting user’s interests on web pages achieves satisfactory accuracy and the personalized information agent significantly saves user’s browsing time and improves user’s browsing experience.
140108792
Online Segmentation of Freehand Stroke by Dynamic Programming
3075495
LOT-based adaptive image watermarking
A robust, invisible watermarking scheme is proposed for digital images, where the watermark is embedded using the block-based Lapped Orthogonal Transform (LOT). The embedding process follows a spread spectrum watermarking approach. In contrast to the use of transforms such as DCT, our LOT watermarking scheme allows larger watermark embedding energy while maintaining the same level of subjective invisibility. In particular, the use of LOT reduces block artifacts caused by the insertion of the watermark in a block-by-block manner, hence obtaining a better balance between invisibility and robustness. Moreover, we use a human visual system (HVS) model to adaptively adjust the energy of the watermark during embedding. In our HVS model, each block is categorized into one of four classes (texture, fine-texture, edge, and plain-area) by using a feature known as the Texture Masking Energy (TME). Blocks with edges are also classified according to the edge direction. The block classification is used to adjust the watermark embedding parameters for each block.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
2987131
Interface-Based Rate Analysis of Embedded Systems
Interface-based design is now considered to be one of the keys to tackling the increasing complexity of modern embedded systems. The central idea is that different components comprising such systems can be developed independently and a system designer can connect them together only if their interfaces match, without knowing the details of their internals. We use the concept of rate interfaces for compositional (correct-by-construction) design of embedded systems whose components communicate through data streams. Using the associated rate interface algebra, two components can be connected together if the output rate of one component is "compatible" with the input rate of the other component. We formalize this notion of compatibility and show that such an algebra is non-trivial because it has to accurately model the burstiness in the arrival rates of such data streams and the variability in their processing requirements. We discuss how rate interfaces simplify compositional design and at the same time help in functional and performance verification which would be difficult to address otherwise. Finally, we illustrate these advantages through a realistic case study involving a component-based design of a multiprocessor architecture running a picture-in-picture application
5743866
The powerrank web link analysis algorithm
The web graph follows the power law distribution and has a hierarchy structure. But neither the PageRank algorithm nor any of its improvements leverage these attributes. In this paper, we propose a novel link analysis algorithm "the PowerRank algorithm", which makes use of the power law distribution attribute and the hierarchy structure of the web graph. The algorithm consists two parts. In the first part, special treatment is applied to the web pages with low "importance" score. In the second part, the global "importance" score for each web page is obtained by combining those scores together. Our experimental results show that: 1) The PowerRank algorithm computes 10%-30% faster than PageRank algorithm. 2) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm.
101051
Simultaneous training of negatively correlated neural networks in an ensemble
This paper presents a new cooperative ensemble learning system (CELS) for designing neural network ensembles. The idea behind CELS is to encourage different individual networks in an ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. In CELS, the individual networks are trained simultaneously rather than independently or sequentially. This provides an opportunity for the individual networks to interact with each other and to specialize. CELS can create negatively correlated neural networks using a correlation penalty term in the error function to encourage such specialization. This paper analyzes CELS in terms of bias-variance-covariance tradeoff. CELS has also been tested on the Mackey-Glass time series prediction problem and the Australian credit card assessment problem. The experimental results show that CELS can produce neural network ensembles with good generalization ability.