39700388
Surface Spatial Index Structure of High-Dimensional Space
This paper proposes a spatial index structure based on a new space-partitioning method. Previous research proposed various high dimensional index structures. However, when dimensionality becomes high, the effectiveness of the spatial index structure disappears. This problem is called the “curse of dimensionality”. This paper focuses on the fact that the volume of high dimensional space is mostly occupied by its surface and then proposes a new surface index structure. The utility of this new surface spatial index structure is illustrated through a series of experiments.
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.
24794561
Finding Short Patterns to Classify Text Documents
Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
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.
12775848
Analyzing Inconsistency Toward Enhancing Integration of Biological Molecular Databases
The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.
10261174
Microarchitecture-aware floorplanning using a statistical design of experiments approach
Since across-chip interconnect delays can exceed a clock cycle in nanometer technologies, it has become essential in high performance designs to add flip-flops on wires with multi-cycle delays. Although such a wire pipelining strategy allows higher operating frequencies, it can reduce the delivered performance of a microarchitecture, since the extra flip-flops inserted may increase the operation latencies and stall cycles. Moreover, the addition of latencies on some wires can have a large impact on the overall performance while other wires are relatively insensitive to additional latencies. This varying sensitivity suggests the need for a throughput-aware strategy for pipelining the interconnects that interacts closely with the physical design step, which determines the lengths of these multicycle wires. We use a statistical design of experiments strategy based on a multifactorial design, which intelligently uses a limited number of simulations to rank the importance of the wires. When applied at the floorplanning level, our results show improvements both in the overall system performance and in the total wire length when compared with an existing technique.
62165917
Statistical landscape features for texture classification
This paper proposes the use of information derived from the graph of a texture image function for texture description. The graph of an image function is a rumpled surface in the three-dimensional space that appears like a landscape. Four novel texture feature curves are used to characterize the texture. This method is named as statistical landscape features (SLF). SLF achieves a very high correct classification rate of 94.53% on the entire Brodatz set. Besides the very good performance, another remarkable advantage of the proposed method is that it has no parameter to tune.
6045052
Basis pursuit for tracking
This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
265743
SOA Collaboration Modeling, Analysis, and Simulation in PSML-C
This paper extends the ebXML Web services development framework to introduce a new collaboration framework, which is based on the collaboration specification language PSML-C (Process Specification and Modeling Language for Collaboration), CCSOA (Consumer-Centric Service-Oriented Architecture), and DDSOS (Dynamic Distributed Service-Oriented Simulation) framework. Since collaborations are inevitable and play a critical role in SOA, an effective framework will greatly reduce the effort for rapid and adaptive service composition, simulation, evaluation, and collaboration. The PSML-C collaboration framework provides a service-oriented infrastructure for process collaboration specification, modeling, design, code generation, simulation, deployment, execution, and management. This paper presents the concepts, architecture, enabling techniques, and illustrative examples that demonstrate the concepts and the techniques
17475296
Statistical geometrical features for texture classification
Abstract This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method's performance under additive noise is also shown to be the best of the four.