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.
207171808
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
In emerging applications such as location-based services, sensor monitoring and biological management systems, the values of the database items are naturally imprecise. For these uncertain databases, an important query is the Probabilistic k-Nearest-Neighbor Query (k-PNN), which computes the probabilities of sets of k objects for being the closest to a given query point. The evaluation of this query can be both computationally- and I/O-expensive, since there is an exponentially large number of k object-sets, and numerical integration is required. Often a user may not be concerned about the exact probability values. For example, he may only need answers that have sufficiently high confidence. We thus propose the Probabilistic Threshold k-Nearest-Neighbor Query (T-k-PNN), which returns sets of k objects that satisfy the query with probabilities higher than some threshold T. Three steps are proposed to handle this query efficiently. In the first stage, objects that cannot constitute an answer are filtered with the aid of a spatial index. The second step, called probabilistic candidate selection, significantly prunes a number of candidate sets to be examined. The remaining sets are sent for verification, which derives the lower and upper bounds of answer probabilities, so that a candidate set can be quickly decided on whether it should be included in the answer. We also examine spatially-efficient data structures that support these methods. Our solution can be applied to uncertain data with arbitrary probability density functions. We have also performed extensive experiments to examine the effectiveness of our methods.
2027694
Efficient Similarity Join over Multiple Stream Time Series
Similarity join (SJ) in time-series databases has a wide spectrum of applications such as data cleaning and mining. Specifically, an SJ query retrieves all pairs of (sub)sequences from two time-series databases that epsiv-match with each other, where epsiv is the matching threshold. Previous work on this problem usually considers static time-series databases, where queries are performed either on disk-based multidimensional indexes built on static data or by nested loop join (NLJ) without indexes. SJ over multiple stream time series, which continuously outputs pairs of similar subsequences from stream time series, strongly requires low memory consumption, low processing cost, and query procedures that are themselves adaptive to time-varying stream data. These requirements invalidate the existing approaches in static databases. In this paper, we propose an efficient and effective approach to perform SJ among multiple stream time series incrementally. In particular, we present a novel method, Adaptive Radius-based Search (ARES), which can answer the similarity search without false dismissals and is seamlessly integrated into SJ processing. Most importantly, we provide a formal cost model for ARES, based on which ARES can be adaptive to data characteristics, achieving the minimum number of refined candidate pairs, and thus, suitable for stream processing. Furthermore, in light of the cost model, we utilize space-efficient synopses that are constructed for stream time series to further reduce the candidate set. Extensive experiments demonstrate the efficiency and effectiveness of our proposed approach.
1494369
Incorporating Gesture and Gaze into Multimodal Models of Human-to-Human Communication
Structural information in language is important for obtaining a better understanding of a human communication (e.g., sentence segmentation, speaker turns, and topic segmentation). Human communication involves a variety of multimodal behaviors that signal both propositional content and structure, e.g., gesture, gaze, and body posture. These non-verbal signals have tight temporal and semantic links to spoken content. In my thesis, I am working on incorporating non-verbal cues into a multimodal model to better predict the structural events to further improve the understanding of human communication. Some research results are summarized in this document and my future research plan is described.
14544468
Using Exact Locality Sensitive Mapping to Group and Detect Audio-Based Cover Songs
Cover song detection is becoming a very hot research topic when plentiful personal music recordings or performance are released on the Internet. A nice cover song recognizer helps us group and detect cover songs to improve the searching experience. The traditional detection is to match two musical audio sequences by exhaustive pairwise comparisons. Different from the existing work, our aim is to generate a group of concatenated feature sets based on regression modeling and arrange them by indexing-based approximate techniques to avoid complicated audio sequence comparisons. We mainly focus on using exact locality sensitive mapping (ELSM) to join the concatenated feature sets and soft hash values. Similarity-invariance among audio sequence comparison is applied to define an optimal combination of several audio features. Soft hash values are pre-calculated to help locate searching range more accurately. Furthermore, we implement our algorithms in analyzing the real audio cover songs and grouping and detecting a batch of relevant cover songs embedded in large audio datasets.
2193712
Similarity Joins of Text with Incomplete Information Formats
Similarity join over text is important in text retrieval and query. Due to the incomplete formats of information representation, such as abbreviation and short word, similarity joins should address an asymmetric feature that these incomplete formats may contain only partial information of their original representation. Current approaches, including cosine similarity with q-grams, can hardly deal with the asymmetric feature of similarity between words and their incomplete formats. In order to find this type of incomplete format information with asymmetric features, we develop a new similarity join algorithm, namely IJoin. A novel matching scheme is proposed to identify the overlap between two entities with incomplete formats. Other than q-grams, we reconnect the sequence of words in a string to reserve the abbreviated information. Based on the asymmetric features of similar entities with incomplete formats, we adopt a new similarity function. Furthermore, an efficient algorithm is implemented by using the join operation in SQL, which reduces pairs of tuples in similarity comparison. The experimental evaluation demonstrates the effectiveness and the efficiency of our approach.
6331812
Fine-Grained Power Gating Based on the Controlling Value of Logic Elements
Leakage power consumption of logic elements has become a serious problem, especially in the sub-100-nanometer process. In this paper, a novel power gating approach by using the controlling value of logic elements is proposed. In the proposed method, sleep signals of the power-gated blocks are extracted completely from the original circuits without any extra logic element. A basic algorithm and a probability-based heuristic algorithm have been developed to implement the basic idea. The steady maximum delay constraint has also been introduced to handle the delay issues. Experiments on the ISCAS'85 benchmarks show that averagely 15-36% of logic elements could be power gated at a time for random input patterns, and 3-31% of elements could be stopped under the steady maximum delay constraints. We also show a power optimization method for AND/OR tree circuits, in which more than 80% of gates can be power-gated.
14979419
QoS-aware routing based on bandwidth estimation for mobile ad hoc networks
Routing protocols for mobile ad hoc networks (MANETs) have been explored extensively in recent years. Much of this work is targeted at finding a feasible route from a source to a destination without considering current network traffic or application requirements. Therefore, the network may easily become overloaded with too much traffic and the application has no way to improve its performance under a given network traffic condition. While this may be acceptable for data transfer, many real-time applications require quality-of-service (QoS) support from the network. We believe that such QoS support can be achieved by either finding a route to satisfy the application requirements or offering network feedback to the application when the requirements cannot be met. We propose a QoS-aware routing protocol that incorporates an admission control scheme and a feedback scheme to meet the QoS requirements of real-time applications. The novel part of this QoS-aware routing protocol is the use of the approximate bandwidth estimation to react to network traffic. Our approach implements these schemes by using two bandwidth estimation methods to find the residual bandwidth available at each node to support new streams. We simulate our QoS-aware routing protocol for nodes running the IEEE 802.11 medium access control. Results of our experiments show that the packet delivery ratio increases greatly, and packet delay and energy dissipation decrease significantly, while the overall end-to-end throughput is not impacted, compared with routing protocols that do not provide QoS support.
11705945
A TCP Based Performance Test Method and Its Application
With the increasing scale of software and the exploding amount of data, software performance problem has become more and more serious, drawing more and more people to study the way of testing softwarepsilas performance. Although there are many performance test methods focusing on B/S structured system, other systems are ignored. In this paper, the common performance test procedure is given first, and then a method is presented to show how to perform such a procedure on an application based on the TCP. This method, which involves three main phases including ldquoCapture and filter out useless packagesrdquo, ldquoParameterizationrdquo, and ldquoTesting and monitoringrdquo, provides an adaptive approach to test a system built upon the TCP. At last, the methodpsilas application is based on a typical C/S structure validation is proved in an experiment, in which the target application is based on a typical C/S structure.
2677048
Convergence analysis of convex incremental neural networks
Recently, a convex incremental algorithm (CI-ELM) has been proposed in Huang and Chen (Neurocomputing 70:3056–3062, 2007), which randomly chooses hidden neurons and then analytically determines the output weights connecting with the hidden layer and the output layer. Though hidden neurons are generated randomly, the network constructed by CI-ELM is still based on the principle of universal approximation. The random approximation theory breaks through the limitation of most conventional theories, eliminating the need for tuning hidden neurons. However, due to the random characteristic, some of the neurons contribute little to decrease the residual error, which eventually increase the complexity and computation of neural networks. Thus, CI-ELM cannot precisely give out its convergence rate. Based on Lee’s results (Lee et al., IEEE Trans Inf Theory 42(6):2118–2132, 1996), we first show the convergence rate of a maximum CI-ELM, and then systematically analyze the convergence rate of an enhanced CI-ELM. Different from CI-ELM, the hidden neurons of the two algorithms are chosen by following the maximum or optimality principle under the same structure as CI-ELM. Further, the proof process also demonstrates that our algorithms achieve smaller residual errors than CI-ELM. Since the proposed neural networks remove these “useless” neurons, they improve the efficiency of neural networks. The experimental results on benchmark regression problems will support our conclusions.
8657338
Improved pronunciation features for construct-driven assessment of non-native spontaneous speech
This paper describes research on automatic assessment of the pronunciation quality of spontaneous non-native adult speech. Since the speaking content is not known prior to the assessment, a two-stage method is developed to first recognize the speaking content based on non-native speech acoustic properties and then forced-align the recognition results with a reference acoustic model reflecting native and near-native speech properties. Features related to Hidden Markov Model likelihoods and vowel durations are extracted. Words with low recognition confidence can be excluded in the extraction of likelihood-related features to minimize erroneous alignments due to speech recognition errors. Our experiments on the TOEFL® Practice Online test, an English language assessment, suggest that the recognition/forced-alignment method can provide useful pronunciation features. Our new pronunciation features are more meaningful than an utterance-based normalized acoustic model score used in previous research from a construct point of view.