2307602
A Multi-Dimensional K-Anonymity Model for Hierarchical Data
For improving the usability of the anonymous result, it is important to comply with the hierarchical structure when generalizing quasi-identifying attributes with hierarchical characteristics. We propose an unrestricted multi-dimensional anonymization model which combines global recoding and local recoding methods. The bottom-up anonymization algorithm with the minimal coverage subgraph constraint and the anonymization metric are proposed. The experiment results justify the effectiveness and scalability of this model.
1252893
A Configurable Web Service Performance Testing Framework
More and more softwares based on web service technologies are developed. Before their releases on the Internet, it is necessary to evaluate these systems' performance, especially their response time under different workload pressures. However, existing performance testing benchmarks and tools for web service applications are difficult to adapt to various user-specific testing purposes. This paper proposes a configurable web service performance testing framework which contains client module, application server module and database module. Client module, by using the network cooperation method that one central client drives several other clients, adapts to a great number of concurrent customers to request web services. Application server module contains web services under testing and external supporting web services, each of which is configured as a plug-in. The process to realize mixed ratio of web service interactions is similar to dealing cards and adapts to different commercial application characteristics. In database module, the data model including table and attribute dependence can be customized, and the data scale initialization can be resized according to the topology of above dependence. As such, this framework allows testers to dynamically define their data model, customize their scale of database, configure their transaction characteristics, deploy their application strategies and confirm their performance metrics..
20021213
Trust and Privacy in Dissemination Control
Dissemination control is concerned with controlling electronic resources even after they have been delivered to a legitimate recipient. This paper proposes a secure resource dissemination system framework in which resource providers and recipients are interacted mutually in an anonymous, accountable, and trustworthy way, while security constraints on diverse resources can be continuously protected along resource distribution. We specify the three-party context-aware trust management and privacy protection mechanism based on part behavior using Dempster-Shafer (D-S) theory. We also discuss main logic functions and procedures in the resource dissemination and usage transaction process.
16607650
Performance Analysis of Multi-channel DCA MAC Protocol in Cognitive Wireless Mesh Networks
The spectrum resource gets scared with the development of wireless communication. Users in cognitive wireless mesh network need to switch among multi-channels to communicate. In this paper, we propose a mathematical model of multi-channel DCA protocol in cognitive wireless mesh network. This model is based on Bernoulli and M/M/K queuing model. From the validation by compare simulation with analytical result, we can see our model and analysis is simple and accurate.
37867009
Spatial Clustering with Obstacles Constraints Using Ant Colony and Particle Swarm Optimization
Spatial clustering is an important research topic in Spatial Data Mining (SDM). This paper proposes an Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use improved ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel PKSCOC based on PSO and K-Medoids to cluster spatial data with obstacles. The PKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).
14221074
Accurate and efficient processor performance prediction via regression tree based modeling
Computer architects usually evaluate new designs using cycle-accurate processor simulation. This approach provides a detailed insight into processor performance, power consumption and complexity. However, only configurations in a subspace can be simulated in practice due to long simulation time and limited resource, leading to suboptimal conclusions which might not be applied to a larger design space. In this paper, we propose a performance prediction approach which employs state-of-the-art techniques from experiment design, machine learning and data mining. According to our experiments on single and multi-core processors, our prediction model generates highly accurate estimations for unsampled points in the design space and show the robustness for the worst-case prediction. Moreover, the model provides quantitative interpretation tools that help investigators to efficiently tune design parameters and remove performance bottlenecks.
3173726
Approximate GCDs of polynomials and sparse SOS relaxations
The problem of computing approximate GCDs of several polynomials with real or complex coefficients can be formulated as computing the minimal perturbation such that the perturbed polynomials have an exact GCD of given degree. We present algorithms based on SOS (Sums Of Squares) relaxations for solving the involved polynomial or rational function optimization problems with or without constraints.
206574982
Battle Damage Assessment based on Bayesian Network
With the quickening rhythm of the war and the complication of battlefield environment,the battle damage assessment(BDA) has had a more important effect on the commander's decision.Because of the complexity of modern battlefield and the uncertainty of battle damage information,it is more and more difficult to determine the damage level of target.Bayesian network is a reasoning and predicting tool coming into vogue only in recent decades.It can correctly reason a problem of interest based on the uncertainty and incomplete information,which is suited to evaluate the battle damage based on the uncertainty and incomplete target damage information.According to the study of BDA based on Bayesian network,the building and updating process of Bayesian network model were discussed.The feasibility and validity of Bayesian network for BDA were analyzed by taking airport runway for example.