Frequent Set Mining (FSM), an important phase of Association Rule Mining, is the process of generating frequent sets that satisfy a specified minimum support threshold. This paper explores FSM in temporal data domain ...
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It is vital to support concurrent applications sharing a wireless sensor network in order to reduce the deployment and administrative costs, thus increasing the usability and efficiency of the network. We describe Mel...
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ISBN:
(纸本)1595933433
It is vital to support concurrent applications sharing a wireless sensor network in order to reduce the deployment and administrative costs, thus increasing the usability and efficiency of the network. We describe Melete1, a system that supports concurrent applications with efficiency, reliability, flexibility, programmability, and scalability. Our work is based on the Maté virtual machine [1] with significant modifications and enhancements. Melete enables reliable storage and execution of concurrent applications on a single sensor node. Dynamic grouping is used for flexible, on-the-fly deployment of applications based on contemporary status of the sensor nodes. The grouping procedure itself is programmed with the TinyScript language. A group-keyed code dissemination mechanism is also developed for reliable and efficient code distribution among sensor nodes. Both analytical and simulation results are presented to study the impact of several key parameters and optimization techniques on the code dissemination mechanism. Simulation results indicate satisfactory scalability of our techniques to both application code size and node density. The usefulness and effectiveness of Melete is also validated by empirical study. Copyright 2006 ACM.
Road extraction plays an important role in many applications such as car navigation, but the manual extraction of roads is a laborious, tedious task. To speed the extraction of roads, an approach based on particle fil...
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Road extraction plays an important role in many applications such as car navigation, but the manual extraction of roads is a laborious, tedious task. To speed the extraction of roads, an approach based on particle filtering to extract automatically roads from high resolution imagery is proposed. Particle filtering provides a statistical framework for propagating sample-based approximations of posterior distributions and has almost no restriction on the ingredients of the model. We integrate the similarity of grey value and the edge point distribution of roads into particle filtering to deal with complex scenes. To handle road appearance changes the tracking algorithm is allowed to update the road model during temporally stable image observations. A fully automatic initialization strategy is used. Experimental results show that the proposed approach is a promising and fully automatic method for extracting roads from images, even in the presence of occlusions.
Testing and assessment remain an integral part of instructional systems design for traditional classroom based courses as well as online training courses. The goal of testing is to determine if learning objectives hav...
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Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of da...
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Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data. In this paper, we propose a fuzzy based neuro-genetic algorithm – Fuzzy based Evolutionary Approach to Self Organizing Map(FEASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. The algorithm was tested on real stock market data of companies like Intel, General Motors, Infosys, Wipro, Microsoft, IBM, etc. The algorithm consistently outperformed regression model, backpropagation algorithm and Kohonen network in predicting the stock market values.
The accuracy of Input speech signal is very essential as the speech interface is now desired for several domains. This paper attempts to propose a standard procedure for speech recognition. It also propose an algorith...
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The accuracy of Input speech signal is very essential as the speech interface is now desired for several domains. This paper attempts to propose a standard procedure for speech recognition. It also propose an algorithm to outline the cause for errors in recognition based on several related works and suggest an error recognition and repair procedures . The research work suggests that it is practically possible to predict the misrecognized utterances with a high degree of accuracy from 1. an utterance's sound file, 2. the language model being employed, 3. and recognizer outputs such as confidence, In addition, there is empirical data upon which to base successful repair strategies in relation to these misrecognitions
With the constant development of enterprises, the cost of developing new business becomes higher than that in the past, but there is still no better strategy to integrate old businesses together. Comparing integrated ...
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With the constant development of enterprises, the cost of developing new business becomes higher than that in the past, but there is still no better strategy to integrate old businesses together. Comparing integrated tactics such as EAI (enterprise application integration), SOI (service-oriented integration), etc., combining the most popular technology SOA (services oriented architecture), pouring the relevant experience into integration according to the idea proposed by BOA (business oriented architecture) and utilizing different kinds of agents to deal with different businesses. The author will introduce a sandwich model to solve the problem of business integration. And at the end of this paper, the feasibility, advantages, and disadvantages of the model are regarded
Group communication is the basis for many recent multimedia and web technology applications. Compared to unicast, Multicast faces a great challenge of providing security requirements like data confidentiality, data in...
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e-learning has become an important issue in the practice and research recently. A tool or system of distance learning should provide automatic functionalities to decrease the overload of instructors and students compa...
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e-learning has become an important issue in the practice and research recently. A tool or system of distance learning should provide automatic functionalities to decrease the overload of instructors and students compared with the traditional learning style. In this paper, we present an English chat room system in which students can discuss course contents by interacting with teachers and students. First, the novel mechanism provides Learning_Angel Agent and Semantic Agent that act as supervisors that constantly handle queries online. Next, the mechanism provides a QA subsystem that acts as an assistant. The Learning_Angel Agent can detect syntax errors made by online users. The Semantic Agent can check the semantic meaning of each sentence. Sometimes, learners make semantic level mistakes. This implies that they do not understand the course topic or some particular issue. The semantic agent can, thus, give some suggestions for corrections to the users and analyze the data in the learner corpus. Moreover, when users query the system or other online users, this system will attempt to find answers from the knowledge ontology or learner corpus that is stored in the records of previous users' comments. Furthermore, if enough QA paus can be accumulated, the FAQ can act as a powerful learning tool for learners.
Steganography and steganalysis are important areas of research in recent years involving a number of applications. Steganography is the science of embedding information into the cover image without causing statistical...
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Steganography and steganalysis are important areas of research in recent years involving a number of applications. Steganography is the science of embedding information into the cover image without causing statistically significant perturbations to the cover image. Steganalysis is the technology that attempts to defeat steganography by detecting the hidden information and extracting or destroying it. In this paper we present twenty four bits BMP image color pair analysis with variable threshold (CPAVT) to detect stego-object with 10% payload. In earlier works 20% payload was used in close color pair analysis. It is observed that with new variable threshold technique, the performance parameters, i.e. false detection rate (FDR) and false alarm rate (FAR) are better in comparison with the earlier works.
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