19448510
A Genetic Approach for Gene Selection on Microarray Expression Data
Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class prediction to reduce the curse of dimensionality. We propose a novel genetic approach that selects a subset of predictive genes for classification on the basis of gene expression data. Our genetic algorithm maximizes correlation between genes and classes and minimizes intercorrelation among genes. We tested the genetic algorithm on leukemia data sets and obtained improved results over previous results.
17681470
New Usage of Sammon's Mapping for Genetic Visualization
It is a hard problem to understand the fitness landscape of a problem as well as the evolution of genetic algorithms. For the purpose, we adopt Sammon's mapping for the investigation. We demonstrate its usefulness by applying it to the graph partitioning problem which is a well-known NP-hard problem. Also, through the investigation of schema traces, we explain the genetic process and the reordering effect in the genetic algorithm.
15089682
Lagrange Multiplier Method for Multi-campaign Assignment Problem
It is crucial to maximize marketing efficiency and customer satisfaction in personalized marketing. In this paper, we raise the multiple recommendation problem which occurs when performing several personalized campaigns simultaneously. We formulate the multi-campaign assignment problem to solve this issue and propose methods for solving the problem. The most notable element is the Lagrange multiplier method. Roughly speaking, Lagrange multiplier reduces problem complexity with a minor impact on optimality. However, it is not easy to find Lagrange multipliers in exact accord with problem constraints. We use a genetic algorithm for finding optimal Lagrange multipliers. Through the experiments, we verify the effectiveness of the problem formulation and our genetic approach.
3094791
Multicampaign assignment problem
It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple recommendation problem, which occurs when performing several personalized campaigns simultaneously. We formulate the multicampaign assignment problem to solve this issue and propose algorithms for the problem. The algorithms include dynamic programming and efficient heuristic methods. We verify by experiments the effectiveness of the problem formulation and the proposed algorithms.
10542004
Investigation of the Fitness Landscapes and Multi-parent Crossover for Graph Bipartitioning
An empirical study is performed on the local-optimum space of graph bipartitioning. We examine some statistical features of the fitness landscape. They include the cost-distance correlations and the properties around the central area of local optima. The study revealed some new notable results about the properties of the fitness landscape; e.g., the central area yielded fairly good quality in the local-optimum space. We performed an experiment on a spectrum of different exploitation strengths of the central areas. From the results, it seems attractive to exploit the central area, but excessive or insufficient exploitation is not desirable.
12076
Multi-attractor gene reordering for graph bisection
We propose a new gene reordering scheme for the graph bisection problem. Our gene reordering starts with two or more vertices to capture the clustering structure of graphs effectively. We devised a chromosome repairing method for hybrid genetic search, which helps exploit clusters when combined with gene reordering. Experimental tests showed that the suggested reordering scheme significantly improves the performance of genetic algorithms over previous reordering methods.
61949387
Quantizer design and distributed encoding algorithm for source localization in sensor networks
In this paper, we propose a quantizer design algorithm that is optimized for source localization in sensor networks. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to achieve a certain source localization accuracy. We show that this goal can be achieved more efficiently when "application-specific" quantizers are used. Our proposed quantizer design algorithm uses a cost function that takes into account the distance between the actual source position and the position estimated based on quantized data. We also propose a distributed encoding algorithm that is applied after quantization and achieves rate savings by merging quantization bins without any degradation of localization performance. The merging technique in the encoding algorithm exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these algorithms to a system where an acoustic sensor model is employed for localization. For this case, we introduce the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations show the improved performance of our quantizer over traditional quantizer designs. In addition, they show rate savings (32.8%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used. Our results also show that an optimized bit allocation leads to significant improvements in localization performance with respect to a bit allocation that uses the same number of bits for each node.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
1232413
Secure group services for storage area networks
Storage Area Networks, with their ability to offer high data availability, reliability and scalability, are a promising solution for the large scale storage needs of many enterprises. As with any distributed storage system, a major design challenge for a Storage Area Network (SAN) is to provide data integrity and confidentiality. In this paper we propose a solution which addresses these core security requirements. In particular, we focus on mechanisms that enable efficient key distribution to allow scalable data sharing. Our scheme uses strong cryptographic techniques to achieve data security and integrity. Further, we delegate the bulk of the cryptographic processing to the SAN entities (e.g., switches, routers or other network elements), thereby removing bottlenecks at the disks and causing minimal inconvenience to the hosts. By recognizing the peer nature of the group of SAN entities, we propose efficient group key mechanisms that do not involve any centralized key distribution servers. This allows our scheme to be scalable and be free from any single point of failure or attack.
14346631
Remote Software-Based Attestation for Wireless Sensors
Wireless sensor networks are envisioned to be deployed in mission-critical applications. Detecting a compromised sensor, whose memory contents have been tampered, is crucial in these settings, as the attacker can reprogram the sensor to act on his behalf. In the case of sensors, the task of verifying the integrity of memory contents is difficult as physical access to the sensors is often infeasible. In this paper, we propose a software-based approach to verify the integrity of the memory contents of the sensors over the network without requiring physical contact with the sensor. We describe the building blocks that can be used to build a program for attestation purposes, and build our attestation program based on these primitives. The success of our approach is not dependent on accurate measurements of the execution time of the attestation program. Further, we do not require any additional hardware support for performing remote attestation. Our attestation procedure is designed to detect even small memory changes and is designed to be resistant against modifications by the attacker.
33372839
3-D Facial Expression Recognition-Synthesis on PDA Incorporating Emotional Timing
This paper describes a pipeline by which facial expression of the face is recognized and then 3-D facial animation is synthesized in the remote place based upon timing information of the facial. The system first detects a facial area within the given image and then classifies its facial expression into 7 emotional weightings. Such weighting information, transmitted to the PDA via a mobile network, is used for non-photorealistic facial expression animation. A cartoon-like shading is used to render a 3-D avatar that conveys a familiar and yet unique facial character, even without employing extensive polygons. It turns out that facial expression animation using emotional curves is more effective in expressing the timing of an expression comparing to the linear interpolation method. The emotional avatar embedded on a mobile platform has some potential in conveying emotion between peoples in Internet.