10648050
Searching musical audio datasets by a batch of multi-variant tracks
Multi-variant music tracks are those audio tracks of a particular song which are sung and recorded by different people (i.e., cover songs). As music social clubs grow on the Internet, more and more people like to upload music recordings onto such music social sites to share their own home-produced albums and participate in Internet singing contests. Therefore it is very important to explore a computer-assisted evaluation tool to detect these audio-based multi-variant tracks. In this paper we investigate such a task: the original track of a song is embedded in datasets, with a batch of multi-variant audio tracks of this song as input, our retrieval system returns an ordered list by similarity and indicates the position of relevant audio track. To help process multi-variant audio tracks, we suggest a semantic indexing framework and propose the Federated Features (FF) scheme to generate the semantic summarization of audio feature sequences. The conjunction of federated features with three typical similarity searching schemes, K-Nearest Neighbor (KNN), Locality Sensitive Hashing (LSH), and Exact Euclidian LSH (E2LSH), is evaluated. From these findings, a computer-assisted evaluation tool for searching multi-variant audio tracks was developed to search over large musical audio datasets.
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.
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.
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.
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.
14259203
E-government Web Mining Tool Design and Implementation Based on the Semantic Web
E-government is innovation and change of administrative activities for hundreds of years. This paper proposed a E-government's Web mining method based on the Semantic Web. It first describes the Design and Implementation of the Web spider, tool of colleting network data collection, including the basic structure of the work processes and procedures of the Web spider, the storage structure and form Web spider collected, the network topology discovery algorithms and the major technical difficulties and details of implementation of Web spider. In actual test, the system runs effective and efficient. Finally this paper points out the works that need to improve and upgrade.
14606163
Gene intrusion on a society of Hill-Climbers
In this paper, a new algorithm of Gene Intrusion on a Society of Hill-Climbers (GI-SoHC) is introduced. In GI-SoHC, the genes which contribute most to the best fitness of the best climber in each generation are selected and inserted to other climbers for replacing their own genes, such that the society can have faster climbing speed and better climbing locations.The experiment results show that with Gene Intrusion, climbers generally and averagely perform better in that they climb more quickly than the original Historical Society of Hill-Climbers, especially in the early stage of the climbing process.
39027040
Fabric Handle Clusters Based on Fuzzy Clustering Algorithm
Fabric handle depends on physical and mechanical properties, which are measured by the two well-know systems, i.e., Kawabata's Evaluation System for Fabrics (KES-F) by Japan and Fabric Assurance by Simple Testing (FAST) by Austria. However, the two systems are too expensive and time-costing. A new comprehensive handle evaluation system for fabrics and yarns (CHES-FY) developed based on the single-test multiple indicator measuring principle can reflect the weight, bending, friction and tension basic mechanical behavior as well as handle through only one pulling-out test. The present paper is to select characteristic indexes from the pulling-out force and distance curve acquired by the CHES-FY system, and the characteristics make a vector which expresses the handle of the fabric based on Karhunen-Loeve (K-L) transformation; the other is to make clustering on handle of measured fabrics based on K-means fuzzy clustering. The experiments of thirty fabrics and comparisons between experimental and theoretical results were conducted, which shows that K-means fuzzy clustering algorithm is effective and accurate in sorting fabric handle based on the features selected from the pulling-out force and distance curve of the CHES-FY system.
8353452
OPHMR: An Optimized Polymorphic Hybrid Multicast Routing Protocol for MANET
We propose in this paper an optimized, polymorphic, hybrid multicast routing protocol for MANET. This new polymorphic protocol attempts to benefit from the high efficiency of proactive behavior (in terms of quicker response to transmission requests) and the limited network traffic overhead of the reactive behavior, while being power, mobility, and vicinity-density (in terms of number of neighbor nodes per specified area around a mobile node) aware. The proposed protocol is based on the principle of adaptability and multibehavioral modes of operations. It is able to change behavior in different situations in order to improve certain metrics like maximizing battery life, reducing communication delays, improving deliverability, etc. The protocol is augmented by an optimization scheme, adapted from the one proposed for the optimized link state routing protocol (OLSR) in which only selected neighbor nodes propagate control packets to reduce the amount of control overhead. Extensive simulations and comparison to peer protocols demonstrated the effectiveness of the proposed protocol in improving performance and in extending battery power longevity
7600762
Automatic Scoring of Children's Read-Aloud Text Passages and Word Lists
Assessment of reading proficiency is typically done by asking subjects to read a text passage silently and then answer questions related to the text. An alternate approach, measuring reading-aloud proficiency, has been shown to correlate well with the aforementioned common method and is used as a paradigm in this paper. 
 
We describe a system that is able to automatically score two types of children's read speech samples (text passages and word lists), using automatic speech recognition and the target criterion "correctly read words per minute". Its performance is dependent on the data type (passages vs. word lists) as well as on the relative difficulty of passages or words for individual readers. Pearson correlations with human assigned scores are around 0.86 for passages and around 0.80 for word lists.