12141135
Construction of classification trees via the classification-hierarchy table
Abstract The classification-tree method developed by Grochtmann and coworkers is a black box testing technique to assist test engineers to construct test cases systematically from the functional specifications, via the construction of classification trees. This paper supplements their studies by proposing a methodology to construct classification trees systematically from given sets of classifications and their associated classes, via the notion of the classification-hierarchy table. The intuition of the classification-hierarchy table is to capture the hierarchical relationship for every pair of distinct classifications from which classification trees can be constructed.
14782445
On the effectiveness of test case allocation schemes in partition testing
In partition testing, the input domain is divided into two or more disjoint subdomains according to some criteria and then test data are selected from each subdomain. Hence we must design a partitioning scheme that governs how to divide the input domain, and a test case allocation scheme that controls how to allocate test cases to the subdomains. Given any partitioning scheme, the test case allocation scheme plays a crucial part in the effectiveness of testing. This paper develops guidelines for comparing different test case allocation schemes. This provides solutions to the class of problems where various test case allocation schemes under the same partitioning scheme are compared for the effectiveness of testing. We illustrate with examples several potential applications of our findings.
295636
Agent-Based Forwarding Strategies for Reducing Location Management Cost in Mobile Networks
An important issue in location management for dealing with user mobility in wireless networks is to reduce the cost associated with location updates and searches. The former operation occurs when a mobile user moves to a new location registration area and the network is being informed of the mobile user's current location; the latter operation occurs when there is a call for the mobile user and the network must deliver the call to the mobile user. In this paper, we propose and analyze a class of new agent-based forwarding schemes with the objective to reduce the location management cost in mobile wireless networks. We develop analytical models to compare the performance of the proposed schemes with existing location management schemes to demonstrate their feasibility and also to reveal conditions under which our proposed schemes are superior to existing ones. Our proposed schemes are particularly suitable for mobile networks with switches which can cover a large number of location registration areas.
16174340
A new encryption algorithm for image cryptosystems
Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.
17774823
Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing
Virtual screening (VS) has become a preferred tool to augment high-throughput screening(1) and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge databases of chemical compounds containing millions of molecular structures and their associated data. Thus, scaling traditional applications such as classification, partitioning, and outlier detection for huge chemical data sets without a significant loss in accuracy is very important. In this paper, we introduce a data mining framework built on top of a recently developed fast approximate nearest-neighbor-finding algorithm(2) called locality-sensitive hashing (LSH) that can be used to mine huge chemical spaces in a scalable fashion using very modest computational resources. The core LSH algorithm hashes chemical descriptors so that points close to each other in the descriptor space are also close to each other in the hashed space. Using this data structure, one can perform approximate nearest-neighbor searches very quickly, in sublinear time. We validate the accuracy and performance of our framework on three real data sets of sizes ranging from 4337 to 249 071 molecules. Results indicate that the identification of nearest neighbors using the LSH algorithm is at least 2 orders of magnitude faster than the traditional k-nearest-neighbor method and is over 94% accurate for most query parameters. Furthermore, when viewed as a data-partitioning procedure, the LSH algorithm lends itself to easy parallelization of nearest-neighbor classification or regression. We also apply our framework to detect outlying (diverse) compounds in a given chemical space; this algorithm is extremely rapid in determining whether a compound is located in a sparse region of chemical space or not, and it is quite accurate when compared to results obtained using principal-component-analysis-based heuristics.
418775
Progressive image watermarking
Progressive transmission of images is very useful in many applications, especially in image transmission over the Internet. To view an image, people would want to see part of the image while the image transmission is in progress, rather than having to wait until the end of the image transmission. On the other hand, the ease of transmission and copying of images creates the need to use digital watermarking to embed the copyright information seamlessly into the media. We propose a progressive image watermarking scheme. In this scheme, the watermark is embedded in such a way that we can retrieve part of it even when the watermarked image is still being transmitted. As transmission progresses, the retrieved watermark has a decreasing bit error rate. Our proposed methods not only transmit the watermarked image progressively, but also intelligently select watermark embedding locations robust to various attacks.
15532502
Illumination normalization for face recognition and uneven background correction using total variation based image models
We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L/sup 1/ model: minimizing the total variation of the output cartoon while subject to an L/sup 1/-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.