190958
Reinforcement learning and aggregation
Reinforcement learning (RL) is a learning technique that provides a means for learning an optimal control policy when the dynamics of the environment under consideration is unavailable [L.P. Kaelbling et al., 1996, R.S. Sutton and A.G. Barto, 1998]. While RL has been successfully applied in many single or multiple agents systems [S. Arai et al., 2000, H.R. Berenji and D.A. Vengerov, 2000, M. Tan, 1993, Y. Nagayuki et al., 2000], the learning quality is greatly influenced by learning algorithms and their parameters. Setting of the parameters of RL algorithms is something of a black art, and small differences in these parameters can lead to large differences in learning qualities. Determining the best algorithm and the optimal parameters can be costly in terms of time and computation. Even if the cost is acceptable, the robustness of learning is still a question. In order to address the difficulty, an aggregated multiagent reinforcement learning system (AMRLS) is proposed to deal with the RL environment as a multiagent environment. A maze world environment is used to validate the AMRLS. Experimental results illustrate that compared with normal Q(/spl lambda/)-learning and SARSA(/spl lambda/) algorithms, the AMRLS increases both the learning speed and the rate of reaching the shortest path.
262231
Monochromatic and bichromatic reverse skyline search over uncertain databases
Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this paper, we model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach with various experimental settings.
8704395
Summarization Graph Indexing: Beyond Frequent Structure-Based Approach
Graph is an important data structure to model complex structural data, such as chemical compounds, proteins, and XML documents. Among many graph data-based applications, sub-graph search is a key problem, which is defined as given a query Q, retrieving all graphs containing Q as a sub-graph in the graph database. Most existing sub-graph search methods try to filter out false positives (graphs that are not possible in the results) as many as possible by indexing some frequent sub-structures in graph database, such as [20, 22, 4, 23]. However, due to ignoring the relationships between sub-structures, these methods still admit a high percentage of false positives. In this paper, we propose a novel concept, Summarization Graph, which is a complete graph and captures most topology information of the original graph, such as sub-structures and their relationships. Based on Summarization Graphs, we convert the filtering problem into retrieving objects with set-valued attributes. Moreover, we build an efficient signature file-based index to improve the filtering process. We prove theoretically that the pruning power of our method is larger than existing structure-based approaches. Finally, we show by extensive experimental study on real and synthetic data sets that the size of candidate set generated by Summarization Graph-based approach is only about 50% of that left by existing graph indexing methods, and the total response time of our method is reduced 2-10 times.
9576587
Non-Threshold based Event Detection for 3D Environment Monitoring in Sensor Networks
Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.
7517195
Spotting Significant Changing Subgraphs in Evolving Graphs
Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. In this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This problem is challenging since it is hard to define changing regions that are closely related to the actual changes (i.e., additions/deletions of edges/nodes) in graphs. We formalize the problem, and design an efficient algorithm that is able to identify the changing subgraphs incrementally. Our experimental results on real datasets show that our solution is very efficient and the resultant subgraphs are of high quality.
8931794
Speed up semantic search in p2p networks
Peer-to-peer architectures become popular in modern massively distributed systems, which are often in very large scale and contain a huge volume of heterogeneous data. To facilitate the information retrieval process in P2P networks, we consider semantic search approach, where syntax-based queries are shipped to peers based on semantic correlations. Motivated by an interesting experience in Web information retrieval, we propose a novel ontology-based scheme to measure similarity of peer interests accurately and consistently in a decentralized way, and group peers under a scalable hierarchical overlay network. Given queries, our approach either floods them within local peer groups or guides them towards remote groups based on the similarity of interests. Our work overcomes the limitations of the existing P2P hybrid-search approaches by avoiding costly data popularity measurement. Performance evaluation and comparison against baseline algorithms show that our approach provides a better solution for information retrieval in large-scale P2P networks.
5117817
Enhanced random search based incremental extreme learning machine
Recently an incremental algorithm referred to as incremental extreme learning machine (I-ELM) was proposed by Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892], which randomly generates hidden nodes and then analytically determines the output weights. Huang et al. [G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] have proved in theory that although additive or RBF hidden nodes are generated randomly the network constructed by I-ELM can work as a universal approximator. During our recent study, it is found that some of the hidden nodes in such networks may play a very minor role in the network output and thus may eventually increase the network complexity. In order to avoid this issue and to obtain a more compact network architecture, this paper proposes an enhanced method for I-ELM (referred to as EI-ELM). At each learning step, several hidden nodes are randomly generated and among them the hidden node leading to the largest residual error decreasing will be added to the existing network and the output weight of the network will be calculated in a same simple way as in the original I-ELM. Generally speaking, the proposed enhanced I-ELM works for the widespread type of piecewise continuous hidden nodes.
96016
Fast algorithm for large-scale UMTS coverage planning with soft handover consideration
Coverage planning by means of controlling cell Common Pilot Channel (CPICH) power is an important task in deploying UMTS networks. In addition to determining coverage, CPICH power heavily influences the amount of Soft Handover (SHO). Non-uniform cell CPICH power allows for significant power saving and thereby higher capacity for traffic channels. However, optimizing non-uniform CPICH is challenging in terms of computational effort, if the resulting coverage pattern is required to satisfy a desired level of SHO. We present a very fast algorithm for this planning problem. The algorithm has utilized the fact that SHO is strongly correlated with cell overlap. Using overlap as a very good approximation of SHO, the algorithm can compute a near optimal coverage pattern within a few seconds even for large networks. Next, the solution is further polished to deal with SHO accurately. Simulation results show that our solution strategy is able to perform non-uniform CPICH optimization very time-efficiently, making it possible to tackle large scale UMTS coverage planning under SHO consideration.
22510443
Universal Approximation and QoS Violation Application of Extreme Learning Machine
Neural networks have been successfully applied to many applications due to their approximation capability. However, complicated network structures and algorithms will lead to computational and time-consuming burdens. In order to satisfy demanding real-time requirements, many fast learning algorithms were explored in the past. Recently, a fast algorithm, Extreme Learning Machine (ELM) (Huang et al. 70:489–501, 2006) was proposed. Unlike conventional algorithms whose neurons need to be tuned, the input-to-hidden neurons of ELM are randomly generated. Though a large number of experimental results have shown that input-to-hidden neurons need not be tuned, there lacks a rigorous proof whether ELM possesses the universal approximation capability. In this paper, based on the universal approximation property of an orthonormal method, we firstly illustrate the equivalent relationship between ELM and the orthonormal method, and further prove that neural networks with ELM are also universal approximations. We also successfully apply ELM to the identification of QoS violation in the multimedia transmission.
14619623
Incremental extreme learning machine with fully complex hidden nodes
Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879-892] has recently proposed an incremental extreme learning machine (I-ELM), which randomly adds hidden nodes incrementally and analytically determines the output weights. Although hidden nodes are generated randomly, the network constructed by I-ELM remains as a universal approximator. This paper extends I-ELM from the real domain to the complex domain. We show that, as long as the hidden layer activation function is complex continuous discriminatory or complex bounded nonlinear piecewise continuous, I-ELM can still approximate any target functions in the complex domain. The universal capability of the I-ELM in the complex domain is further verified by two function approximations and one channel equalization problems.
29666362
Algebraic Laws for BSP Programming
The Bulk-Synchronous Parallel (BSP) model of computation reflects the capabilities and costs of communication on a wide range of general-purpose parallel architectuers. Thus it allows general-purpose parallel software and hardware to be developed independently of one another; much as the von Neumann model provides the same facility for sequential computation. This paper presents a simple programming notation for shared-memory programming, based upon BSP constructs. The notation is defined formally by its effect upon process states. Algebraic laws are given which allow program derivation. A further set of laws allow transformation of finite programs to a normal form.