In this paper, we propose a new model for securing agent-based systems in which agents are equipped with reasoning capabilities allowing them to interact with each other. the agents can reason about the reputation of ...
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ISBN:
(纸本)9780769527765
In this paper, we propose a new model for securing agent-based systems in which agents are equipped with reasoning capabilities allowing them to interact with each other. the agents can reason about the reputation of each other using their argumentation systems. the reputation is dealt with as a quantitative value computed using a set of parameters based on the interaction histories and the notion of social networks. the problem of securing autonomous interacting agents in a distributed setting is core to a number of applications, particularly the emerging semantic grid computing-Eased applications such as e-business. Current approaches fail to adequately address the challenges of security in these emerging applications. these approaches are either centralized on mechanisms such as digital certificates, and thus are particularly vulnerable to attacks, or are not suitable for argumentation-based agent systems in which agents use advanced reasoning capabilities.
We present Sensos, a sensor node operating system withthe device management scheme for sensor nodes with multiple sensor elements. As the number of sensor elements attached to a single sensor node increases, programm...
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the proceedings contain 200 papers. the topics discussed include: programming highly parallel reconfigurable architectures for public-key cryptographic applications;two novel resource management schemes for integrated...
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ISBN:
(纸本)0769527760
the proceedings contain 200 papers. the topics discussed include: programming highly parallel reconfigurable architectures for public-key cryptographic applications;two novel resource management schemes for integrated wireless networks;analysis of time based random waypoint mobility model for wireless mobile networks;study on worm poisoning technology;a direct-time queue analytical model based on dynamic random early drop;dynamic two-layer signature-based IDS with unequal databases;proactive model for mitigating Internet denial-of-service attacks;cell-based distributed addressing technique using clustered backbone approach;interference aware routing in sensor networks;genetic algorithm for energy efficient clusters in wireless sensor networks;a mobile context dissemination middleware;polygonal approximation of 2-D binary images;a probabilistic approach to source code authorship identification;and neural networks in cultivation.
Cellular neural networks (CNN) are very useful for image processing tasks [1],[2]. One problem with CNN networks is the lack of a programming method to realize a processing task. the cloning templates entirely specifi...
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Cellular neural networks (CNN) are very useful for image processing tasks [1],[2]. One problem with CNN networks is the lack of a programming method to realize a processing task. the cloning templates entirely specifies the programming of a CNN net. there are a lot of cloning templates for several tasks [3]-[4], got by mathematical analysis or heuristically [4]-[9]. However for some specific tasks is very difficult to find the correct templates. In this paper a procedure for finding cloning templates for image processing tasks is described, using a gradient method. A set of CNN templates obtained using the proposed procedure is shown.
Computer tomography (CT) is a very competitive medical imaging field, where worldwide manufactures are constantly improving the state of the art investing huge amounts of money to assure sales. therefore, warranties a...
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Computer tomography (CT) is a very competitive medical imaging field, where worldwide manufactures are constantly improving the state of the art investing huge amounts of money to assure sales. therefore, warranties and maintenance contracts avoids hospital and university research labs in Latin America to have information access and to perform research experiments where CT hardware and software are involved. Despite this, university CT research labs are important to exist in order to create the "know-how" and to preserver the knowledge. this paper addresses important issues to create CT scanner experiments using donated equipment out of commercial companies support that not only take advantage of the hardware-software state of the art but in addition the experiments are used to improve the universal knowledge in the reconstruction algorithms field. One particular example is described where CT raw data is acquired and then parallel programming will be applied to obtain CT images at very low cost, improving quality and reducing reconstruction time.
this paper present a new circuit designed to support high accuracy reference voltages over a nearly full range of the power supply. To achieve this, the circuit is designed to be efficient utilizing a CMOS floating ga...
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this paper present a new circuit designed to support high accuracy reference voltages over a nearly full range of the power supply. To achieve this, the circuit is designed to be efficient utilizing a CMOS floating gate memory fabricated in 1.2 mum CMOS process. the memory stores voltages as charge on the floating gate of a pMOS transistor. the output voltages of the circuit are easily programming by simply modifying the value of the floating gate through the tunnelling and injection hot electrons mechanisms. Also, the circuit can drive a resistive load withthe advantage of reduced both silicon area and dissipated power on chip.
One of the most important tasks in multi-objective optimization is "trade-off analysis" which aims to make the total balance among objective functions. the trade-off relation among alternatives can be shown ...
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Support vector machines (SVM) for binary classification have been developed in a broad field of applications. But normal SVM algorithms are not suitable for classification of large data sets because of high training c...
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Support vector machines (SVM) for binary classification have been developed in a broad field of applications. But normal SVM algorithms are not suitable for classification of large data sets because of high training complexity. this paper introduces a novel two-stage SVM classification approach for large data sets: minimum enclosing ball (MEB) clustering is introduced to select the training data from the original data set for the first stage SVM, and a de-clustering technique is then proposed to recover the training data for the second stage SVM. then we extend binary SVM classification to case of multiclass. the novel two-stage multi-class SVM has distinctive advantages on dealing with huge data sets. Finally, we apply the proposed method on several benchmark problems, experimental results demonstrate that our approach have good classification accuracy while the training is significantly faster than other SVM classifiers.
We present an evolutionary algorithm for the inference of context-free grammars from positive and negative examples. the algorithm is based on genetic programming and uses a local optimization operator that is capable...
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