653913
Utilizing gestures to better understand dynamic structure of human communication
<b>Motivation:</b> Many researchers have highlighted the importance of gesture in natural human communication. McNeill [4] puts forward the hypothesis that gesture and speech stem from the same mental process and so tend to be both temporally and semantically related. However in contrast to speech, which surfaces as a linear progression of segments, sounds, and words, gestures appear to be nonlinear, holistic, and imagistic. Gesture adds an important dimension to language understanding due to this property of sharing a common origin with speech while using a very different mechanism for transferring information. Ignoring this information when constructing a model of human communication would limit its potential effectiveness.
 <b>Goal and Method:</b> This thesis concerns the development of methods to effectively incorporate gestural information from a human communication into a computer model to more accurately interpret the content and structure of that communication. Levelt [5] suggests that structure in human communication stems from the dynamic conscious process of language production, during which a conversant organizes the concepts to be expressed, plans the discourse, and selects appropriate words, prosody, and gestures while also correcting errors that occur in this process. Clues related to this conscious processing emerge in both the final speech stream and gestures. This thesis will attempt to utilize these clues to determine the structural elements of human-to-human dialogs, including sentence boundaries, topic boundaries, and disfluency structure. For this purpose, the data driven approach is used. This work requires three important components: corpus generation, feature extraction, and model construction.
 <b>Previous Work:</b> Some work related to each of these components has already been conducted. A data collection and processing protocol for constructing multimodal corpora has been created; details on the video and audio processing can be found in the <i>Data and Annotation</i> section of [3]. To improve the speed of producing a corpus while maintaining its quality, we have surveyed factors impacting the accuracy of forced alignments of transcriptions to audio files [2]. These alignments provide a crucial temporal synchronization between video events and spoken words (and their components) for this research effort. We have also conducted measurement studies in an attempt to understand how to model multimodal conversations. For example, we have investigated the types of gesture patterns that occur during speech repairs [1]. Recently, we constructed a preliminary model combining speech and gesture features for detecting sentence boundaries in videotaped dialogs. This model combines language and prosody models together with a simple gestural model to more effectively detect sentence boundaries [3].
 <b>Future Work:</b> To date, our multimodal corpora involve human monologues and dialogues (see http://vislab.cs.wright.edu/kdi). We are participating in the collection and preparation of a corpus of multi-party meetings (see http://vislab.cs.wright.edu/Projects/Meeting-Analysis). To facilitate the multi-channel audio processing, we are constructing a tool to support accurate audio transcription and alignment. The data from this meeting corpus will enable the development of more sophisticated gesture models allowing us to expand the set of gesture features (e.g., spatial properties of the tracked gestures). Additionally, we will investigate more advanced machine learning methods in an attempt to improve the performance of our models. We also plan to expand our models to phenomena such as topic segmentation.
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.
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.
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.
24767232
The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System
This paper presented a special design and implementation of human detection based on SVM (support vector machine) and this method is used in intelligent video surveillance system. In order to simplify the design of the SVM classifier and improve efficiency of machine learning, both a grid vector representation and a center radiating vector representation are proposed to abstract features of the object. The sample data is obtained through processing and analysis including human and no-human which forms the training input to SVM. Finally, we used the trained recognizer to identify whether there is somebody broken into the object region. If there is, the automatic warning device gives the alarm, which guarantees a real-time surveillance.
7211665
Dynamic data dependence tracking and its application to branch prediction
To continue to improve processor performance, microarchitects seek to increase the effective instruction level parallelism (ILP) that can be exploited in applications. A fundamental limit to improving ILP is data dependences among instructions. If data dependence information is available at run-time, there are many uses to improve ILP. Prior published examples include decoupled branch execution architectures and critical instruction detection. In this paper, we describe an efficient hardware mechanism to dynamically track the data dependence chains of the instructions in the pipeline. This information is available on a cycle-by-cycle basis to the microengine for optimizing its performance. We then use this design in a new value-based branch prediction design using available register value information (ARVI). From the use of data dependence information, the ARVI branch predictor has better prediction accuracy over a comparably sized hybrid branch predictor With ARVI used as the second-level branch predictor the improved prediction accuracy results in a 12.6% performance improvement on average across the SPEC95 integer benchmark suite.
33211849
Hardness results and approximation algorithms for (weighted) paired-domination in graphs
Let G=(V,E) be a simple graph without isolated vertices. A vertex set S@?V is a paired-dominating set if every vertex in V-S has a neighbor in S and the induced subgraph G[S] has a perfect matching. In this paper, we investigate the approximation hardness of paired-domination in graphs. For weighted paired-domination, an approximation algorithm in general graphs and an exact dynamic programming style algorithm in trees are also given.
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.
616754
Automated Planning of CPICH Power for Enhancing HSDPA Performance at Cell Edges with Preserved Control of R99 Soft Handover
We present and demonstrate a novel approach for automated planning of common pilot channel (CPICH) power in mobile networks with co-existing HSDPA and R99 services. CPICH power allocation greatly influences the cell coverage pattern. A conventional strategy is to uniformly allocate a constant proportion of the total power to CPICH. We study non-uniform CPICH and optimize its allocation for enhancing HSDPA performance. We focus on HSDPA performance at cell edges, where user throughput is typically very low. Our approach is based on a linear-integer mathematical model; solving the model results in a power allocation that both ensures CPICH coverage and optimizes HSDPA performance at cell edges. Moreover, the model allows for precise control of soft handover (SHO) regions for R99 service. Experimental results show that our approach yields significant enhancement of HSDPA performance at cell edges with preserved R99 SHO control.