The network monitoring problem, crucial to many applications from outbreak prevention to online rumor management, demands an optimal set of monitors to detect the spreading of infections or rumors over a network. We t...
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ISBN:
(纸本)9783030299118;9783030299101
The network monitoring problem, crucial to many applications from outbreak prevention to online rumor management, demands an optimal set of monitors to detect the spreading of infections or rumors over a network. We tackle this problem through solving a type of facility location problem where the monitored nodes are selected to minimize their distance to other nodes. Existing methods for this problem either consume prohibitively long time for large networks, lack of reasonable theoretical performance guarantees, or are very difficult to implement. We propose a new algorithm, csav, which combines a novel technique to reduce the search space with an iterative improvement mechanism. Our algorithm outputs a logarithmic number of monitors in (O) over tilde (vertical bar E vertical bar) time. We perform empirical analysis over both synthesized and real-world networks as well as three propagation models. The results show that csav achieves superior performance over a number of benchmark algorithms. In particular, it produces outputs that are comparable to the well-established local search at only a fraction of its running time. Our approach is hence a scalable and time-efficient method for the network monitoring problem.
In this paper, we present a new algorithm for the Depth first search traversing based on Hashing. The data in the nodes are stored in a hash table and corresponding identifier or key is stored in the tree. Thus, whate...
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ISBN:
(纸本)9783642194221
In this paper, we present a new algorithm for the Depth first search traversing based on Hashing. The data in the nodes are stored in a hash table and corresponding identifier or key is stored in the tree. Thus, whatever might be the data;the entries in the tree will be only integral numbers. This pre processing mark existence and location of node before searching, this causes to faster searched results. Experimental results show that proposed algorithm is simpler and faster of traversing the graph that are repeatedly queried for different goals or paths. Implementation is carried out in Java, and compared with standard DFS and BFS.
Our previous work brought some interesting results of the discrete Quantum Walks in the regime of Weak Measurement (QWWM or QWWV). Using the knowledge of such results of QWWM, we are now exploring the search algorithm...
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ISBN:
(纸本)9781628410600
Our previous work brought some interesting results of the discrete Quantum Walks in the regime of Weak Measurement (QWWM or QWWV). Using the knowledge of such results of QWWM, we are now exploring the search algorithms and investigating the factors associated with such walk. The study of such factors like dimensionality, connectivity of the dataset and the strength of disorder or percolation are already studied by others in the context of general quantum walks. It is our interest to show the similarities and/or differences of such factors of general quantum walks with QWWV. The subject of decoherence in quantum walks is another challenging research topic at present. We are also exploring the topic of decoherence in QWWM or QWWV.
In many applications of directed graph theory, it is desired to obtain a list of the simple cycles of the graph. In this paper, a new search algorithm for finding the simple cycles of any finite directed graph is pres...
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Industrial Cyber Physical Systems (CPSs) are naturally complex. Manual configuration of CPS product lines is error-prone and inefficient, which warrants the need for automated support of product configuration activiti...
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ISBN:
(纸本)9789897582325
Industrial Cyber Physical Systems (CPSs) are naturally complex. Manual configuration of CPS product lines is error-prone and inefficient, which warrants the need for automated support of product configuration activities such as decision inference and decision ordering. A fully automated solution is often impossible for CPSs since some decisions must be made manually by configuration engineers and thus requiring an interactive and step-by-step configuration solution. Having an interactive solution with tool support in mind, we propose a search-based solution (named as Zen-DO) to support optimal ordering of configuration steps. The optimization objective has three parts: 1) minimizing overall manual configuration steps, 2) configuring most constraining decisions first, and 3) satisfying ordering dependencies among variabilities. We formulated our optimization objective as a fitness function and investigated it along with four search algorithms: Alternating Variable Method (AVM), (1+1) Evolutionary Algorithm (EA), Genetic Algorithm, and Random search (a comparison baseline). Their performance is evaluated in terms of finding an optimal solution for two real-world case studies of varying complexity and results show that AVM and (1+1) EA significantly outperformed the others.
AI research has developed numerous methods to solve state space problems. During the recent times, one Such strategy, search Enhancements has performed a pivotal role in solving complex real world problems. Many diffe...
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ISBN:
(纸本)0780392477
AI research has developed numerous methods to solve state space problems. During the recent times, one Such strategy, search Enhancements has performed a pivotal role in solving complex real world problems. Many different properties and taxonomies for these search enhancements appear in the literature. This work presents a new parameter for the classification of search enhancements with the intent to add a new dimension to the process of creating new enhancements as well as to develop a better understanding. This classification is based oil the semantics of the state space graph (or tree) generated and the problem domain. It is shown that semantics of a problem domain has been a vital aspect of the search enhancements. One semantic based search enhancement, the False-Move is described in this paper. This search enhancement in conjunction with the A* algorithm is used to solve the 8-puzzle problem and the results are presented.
The domain of Artificial Intelligence (AI) search algorithms is a well-studied area. A variety of AI search algorithms have been developed in the past decades. Some of them are considered to be among the main AI searc...
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ISBN:
(数字)9783030233679
ISBN:
(纸本)9783030233679;9783030233662
The domain of Artificial Intelligence (AI) search algorithms is a well-studied area. A variety of AI search algorithms have been developed in the past decades. Some of them are considered to be among the main AI search algorithms such as Best First search, A*, Iterative Deepening search, etc. The others use these main algorithms as a basis and include more heuristics to generate newer algorithms such as Iterative Deepening A* (IDA*), Branch and Bound search, etc. the innovation of this research is a new approach to the AI search algorithms, we called it indexed search. The methods assigns integer indices to the states of the problem and instead of searching for the goal state, it searches for the index of the goal state. Once the index of a goal state found, we convert that index from decimal base to the base of the branching factor of the problem, and this new number is the solution path from goal to the start node. The new approach eliminates the Closed List which employed for most AI search algorithms to store the explored nodes. The new approach also generates a solution path faster than the respective versions of them and it also use less memory space.
Weight-based multi-objective optimization requires assigning appropriate weights using a weight strategy to each of the objectives such that an overall optimal solution can be obtained with a search algorithm. Choosin...
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ISBN:
(纸本)9783319099408;9783319099392
Weight-based multi-objective optimization requires assigning appropriate weights using a weight strategy to each of the objectives such that an overall optimal solution can be obtained with a search algorithm. Choosing weights using an appropriate weight strategy has a huge impact on the obtained solutions and thus warrants the need to seek the best weight strategy. In this paper, we propose a new weight strategy called Uniformly Distributed Weights (UDW), which generates weights from uniform distribution, while satisfying a set of user-defined constraints among various cost and effectiveness measures. We compare UDW with two commonly used weight strategies, i.e., Fixed Weights (FW) and Randomly-Assigned Weights (RAW), based on five cost/effectiveness measures for an industrial problem of test minimization defined in the context of Video Conferencing System Product Line developed by Cisco Systems. We empirically evaluate the performance of UDW, FW, and RAW in conjunction with four search algorithms ((1+1) Evolutionary Algorithm (EA), Genetic Algorithm, Alternating Variable Method, and Random search) using the industrial case study and 500 artificial problems of varying complexity. Results show that UDW along with (1+1) EA achieves the best performance among the other combinations of weight strategies and algorithms.
The DARPA/AFRL "Moving and Stationary Target Acquisition and Recognition" (MSTAR) program is developing a model-based vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The...
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ISBN:
(纸本)0819431958
The DARPA/AFRL "Moving and Stationary Target Acquisition and Recognition" (MSTAR) program is developing a model-based vision approach to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The motivation for this work is to develop a high performance ATR capability that can identify ground targets in highly unconstrained imaging scenarios that include variable image acquisition geometry, arbitrary target pose and configuration state, differences in target deployment situation, and strong intra-class variations. The MSTAR approach utilizes radar scattering models in an on-line hypothesize-and-test operation that compares predicted target signature statistics with features extracted from image data in an attempt to determine a "best fit" explanation of the observed image. Central to this processing paradigm is the search algorithm, which provides intelligent control in selecting features to measure and hypotheses to test, as well as in making the decision about when to stop processing and report a specific target type or clutter. Intelligent management of computation performed by the search module is a key enabler to scaling the model-based approach to the large hypothesis spaces typical of realistic ATR problems. In this paper, we describe the present state of design and implementation of the MSTAR search engine, as it has matured over the last three years of the MSTAR program. The evolution has been driven by a continually expanding problem domain that now includes 30 target types, viewed under arbitrary squint/depression, with articulations, reconfigurations, revetments, variable background, and up to 30% blocking occlusion. We believe that the research directions that have been inspired by MSTAR's challenging problem domain are leading to broadly applicable search methodologies that are relevant to computer vision systems in many areas.
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