10107398
Yet Another Induction Algorithm
Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate that the rules produced by our induction algorithm have high consistency and simplicity.
11734040
Analyzing Security Protocols Using Association Rule Mining
Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.
6262105
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse of dimensionality” problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of “curse of dimensionality”; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
33072384
Side Chain Conformational Preferences in Protein Structures
2536834
Extensible detection and indexing of highlight events in broadcasted sports video
Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used.
2475006
Content-based video indexing for sports applications using integrated multi-modal approach
To sustain an ongoing rapid growth of video information, there is an emerging demand for a sophisticated content-based video indexing system. However, current video indexing solutions are still immature and lack of any standard. This doctoral consists of a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple audio-visual modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s).
14057550
Exploration on the commonality of hierarchical clustering algorithms
Many clustering algorithms have been devised. The published account of each algorithm emphasizes how it is different from previous algorithms. However, similarities among agglomerative hierarchical algorithms are greater than commonly supposed. For example, several algorithms perform merging by the single link (SLINK, OPTICS) and some algorithms perform merging by the edge cut criterion (CHAMELEON, ROCK). Some algorithms use the square of the original adjacency matrix (OPTICS, ROCK). Our goals are to compose a not very long list of methods used by the various algorithms; to locate each algorithm in this space of methods; and to devise new algorithms that improve upon the previous methods.
5924956
On joining and caching stochastic streams
We consider the problem of joining data streams using limited cache memory, with the goal of producing as many result tuples as possible from the cache. Many cache replacement heuristics have been proposed in the past. Their performance often relies on implicit assumptions about the input streams, e.g., that the join attribute values follow a relatively stationary distribution. However, in general and in practice, streams often exhibit more complex behaviors, such as increasing trends and random walks, rendering these "hardwired" heuristics inadequate.In this paper, we propose a framework that is able to exploit known or observed statistical properties of input streams to make cache replacement decisions aimed at maximizing the expected number of result tuples. To illustrate the complexity of the solution space, we show that even an algorithm that considers, at every time step, all possible sequences of future replacement decisions may not be optimal. We then identify a condition between two candidate tuples under which an optimal algorithm would always choose one tuple over the other to replace. We develop a heuristic that behaves consistently with an optimal algorithm whenever this condition is satisfied. We show through experiments that our heuristic outperforms previous ones.As another evidence of the generality of our framework, we show that the classic caching/paging problem for static objects can be reduced to a stream join problem and analyzed under our framework, yielding results that agree with or extend classic ones.
16804055
SCAN: A Dynamic, Scalable, and Efficient Content Distribution Network
We present SCAN, the Scalable Content Access Network. SCAN combines dynamic replica placement with a self-organizing application-level multicast tree to meet client QoS and server resource constraints. It utilizes an underlying distributed object routing and location system (DOLR) as an essential component.Sim ulation results on both flash-crowd-like synthetic workloads and realWeb server traces show that SCAN deploys close to an optimal number of replicas, achieves good load balance, and incurs a small delay and bandwidth penalty for update multicast relative to static replica placement on IP multicast. We envision that SCAN could enhance a number of different applications, such as content distribution and peer-to-peer file sharing.
669399
Camera Calibration: a USU Implementation
The task of camera calibration is to estimate the intrinsic and extrinsic parameters of a camera model. Though there are some restricted techniques to infer the 3-D information about the scene from uncalibrated cameras, effective camera calibration procedures will open up the possibility of using a wide range of existing algorithms for 3-D reconstruction and recognition. The applications of camera calibration include vision-based metrology, robust visual platooning and visual docking of mobile robots where the depth information is important.
3264554
An Analytical Piecewise Radial Distortion Model for Precision Camera Calibration
The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This article presents a new piecewise radial distortion model with easy analytical undistortion formula. The motivation for seeking a piecewise radial distortion model is that, when a camera is resulted in a low quality during manufacturing, the nonlinear radial distortion can be complex. Using low order polynomials to approximate the radial distortion might not be precise enough. In contrast, higher order polynomials suffer from the inverse problem. With the new piecewise radial distortion function, more flexibility is obtained and the radial undistortion can be performed analytically. Experimental results are presented to show that with this new piecewise radial distortion model, better performance can be achieved than using the single function. Further, a comparable performance with the conventional polynomial model using two coefficients can also be accomplished.