17759170
General Cost Models for Evaluating Dimensionality Reduction in High-Dimensional Spaces
Similarity search usually encounters a serious problem in the high-dimensional space, known as the "curse of dimensionality". In order to speed up the retrieval efficiency, most previous approaches reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). In addition, random projection is proposed as an approximate dimensionality reduction (ADR) technique to answer the approximate similarity search instead of the exact one. However, so far little work has formally evaluated the effectiveness and efficiency of GDR, LDR, and ADR for the range query. Motivated by this, in this paper, we propose general cost models for evaluating the query performance over the reduced data sets by GDR, LDR, and ADR, in light of which we introduce a novel (A)LDR method, partitioning based on randomized search (PRANS). It can achieve high retrieval efficiency with the guarantee of optimality given by the formal models. Finally, a B+-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost models on both real and synthetic data sets and demonstrate the efficiency and effectiveness of the proposed PRANS method.
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.
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.
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.
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.
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.
36282948
Extremal problems on consecutive L(2, 1)-labelling
For a given graph G of order n, a k-L(2,1)-labelling is defined as a function f:V(G)->{0,1,2,...,k} such that |f(u)-f(v)|>=2 when d"G(u,v)=1 and |f(u)-f(v)|>=1 when d"G(u,v)=2. The L(2,1)-labelling number of G, denoted by @l(G), is the smallest number k such that G has a k-L(2,1)-labelling. The consecutive L(2,1)-labelling is a variation of L(2,1)-labelling under the condition that the labelling f is an onto function. The consecutive L(2,1)-labelling number of G is denoted by @[email protected]?(G). Obviously, @l(G)=<@[email protected]?(G)=<|V(G)|-1 if G admits a consecutive L(2,1)-labelling. In this paper, we investigate the graphs with @[email protected]?(G)=|V(G)|-1 and the graphs with @[email protected]?(G)[email protected](G), in terms of their sizes, diameters and the number of components.
17397024
Multi-level secure video streaming over SRTP
In this paper, we propose a set of solutions to four different levels on data selection for encrypting Motion JPEG video frames over SRTP. These solutions are based on our intensive analysis of the JPEG image compression procedure. We select the most important data which contains most information of an image for encryption; in consequence provide a set of balances between security and computation for different hardware environments. Extensive experiments are conducted and performance data has been analyzed to evaluate the proposed solutions. Results show that these solutions are well suited in different scenarios with various computation power and security requirements.
17212464
A unified SWSI-KAMs framework and performance evaluation on face recognition
The Kernel method is an effective and popular trick in machine learning. In this paper, by introducing it into conventional auto-associative memory models (AMs), we construct a unified framework of kernel auto-associative memory models (KAMs), which makes the existing exponential and polynomial AMs become its special cases. Further, in order to reduce KAM's connect complexity, inspired by ''small-world network'' recently described by Watts and Strogatz, we propose another unified framework of small-world structure (SWS) inspired kernel auto-associative memory models (SWSI-KAMs), which, in principle, makes KAMs simpler in structure. Simulation results on the FERET face database show that, the SWSI-KAMs adopting kernels such as Exponential and Hyperbolic tangent kernels have advantages of configuration simplicity while their recognition performance is almost as good as or even better than corresponding KAMs with full connectivity. In the end, the SWSI-KAM adopting Exponential kernel with different connectivities was emphatically investigated for robustness based on those face images which were added random noises and/or partially occluded in a mosaic way, and the experiments demonstrate that the SWSI-KAM with Exponential kernel is more robust in all cases of network connectivity of 20%, 40% and 60% than both PCA and recently proposed (PC)^2A algorithms for face recognition. cognition.
23064856
Equivalent Relationship of Feedforward Neural Networks and Real-Time Face Detection System
In this paper, we mainly investigate a fast algorithm, Extreme Learning Machine (ELM), on its equivalent relationship, approximation capability and real-time face detection application. Firstly, an equivalent relationship is presented for neural networks without orthonormalization (ELM) and orthonormal neural networks. Secondly, based on the equivalent relationship and the universal approximation of orthonormal neural networks, we successfully prove that neural networks with ELM have the property of universal approximation, and adjustable parameters of hidden neurons and orthonormal transformation are not necessary. Finally, based on the fast learning characteristic of ELM, we successfully combine ELM with AdaBoost algorithm of Viola-Jones in face detection applications such that the whole system not only retains a real-time learning speed, but also possesses high face detection accuracy.
42950226
Automated Design Approach for Analog Circuit Using Genetic Algorithm
The technology of electronic design automation (EDA) has improved the efficiency of design process, however, designer is still required much special knowledge of circuit. During the past decade, using genetic algorithm (GA) to design circuit had attracted many experts and scholars. However, too much more attention was focus on a circuit's function and many other factors had been neglected which caused the circuit had little applicability. This paper proposes an automated design approach for analog circuit based on a multi-objective adaptive GA. The multi-objective fitness evaluation method, which can dynamic adjust parameter, is selected. And a parallel evolution strategy which separates evolution of circuit structure and element value is adopted but also organically combined them by weight vectors. The experimental results indicate that this approach obviously be able to improve the evolution efficiency and could generate numbers of suitable circuits.