9886444
Computing Semantic Similarity between Skill Statements for Approximate Matching
This paper explores the problem of computing text similarity between verb phrases describing skilled human behavior for the purpose of finding approximate matches. Four parsers are evaluated on a large corpus of skill statements extracted from an enterprise-wide expertise taxonomy. A similarity measure utilizing common semantic role features extracted from parse trees was found superior to an information-theoretic measure of similarity and comparable to the level of human agreement.
20260133
Temporal aggregates for Web services on the semantic Web
In this paper we describe how we encode our temporal aggregates ontology in OWL for Web services on the semantic Web. We also present one example to show how to use the ontology to represent temporal aggregates information.
13046627
Adaptive frame skipping based on spatio-temporal complexity for low bit-rate video coding
Abstract Conventional rate control schemes focus on the adjustment of quantization values to retain a certain buffer level, and buffer adaptive frame skippings are used to avoid buffer overflow at low bit-rates. In this paper, an adaptive frame-skipping scheme is proposed to achieve the balance between spatial and temporal quality, and to maintain the buffer level. The occurrence of frame skipping is jointly dependent on the temporal and spatial complexity of the video, and on the fullness of the buffer. This helps to achieve a balanced spatial and temporal quality and to enhance the overall perceptual quality. Experimental results show that the new scheme is simple but very effective, with large average PSNR gains and consistently improved visual quality, and the improvement in perceptual quality is much significant than that in average PSNRs.
11110859
A locally-adaptive algorithm for measuring blocking artifacts in images and videos
Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring blocking artifacts in images and videos. It exhibits unique and useful features: 1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; 2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; 3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; 4) the blocking artifacts measure is well-defined in the range of 0-10. Experiments on various still images and videos show that this blockiness measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.
981043
Learning Event Durations from Event Descriptions
We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.
8588212
Temporal Aggregates in OWL-Time
In this paper, we describe our approach for representing temporal aggregates in OWL-Time, and a systematic way of mapping iCalendar recurrence sets to OWL-Time temporal sequences. We present several examples of natural language expressions for temporal aggregates and how they would be represented in OWL-Time; some examples can be described in both iCalendar and OWL-Time, while others can only be represented in OWL-Time. We also illustrate how we can translate from a natural language sentence to our representation.
12021051
COE: A General Approach for Efficient Genome-Wide Two-Locus Epistasis Test in Disease Association Study
The availability of high density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this paper, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.
7408751
Quantitative Association Analysis Using Tree Hierarchies
Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements, and then they examine the association between these groups and the specified target variables without considering the inter-group similarities or alternative groupings. This can lead to cases where the strength of association depends significantly on arbitrary clustering choices. In this paper, we propose a tree-based method for quantitative association analysis. Tree hierarchies derived from sample similarities represent many possible sample groupings. They also provide a natural way to incorporate domain knowledge such as ontologies and to identify and remove outliers. Given a tree hierarchy, our association analysis evaluates all possible groupings and selects the one with strongest association to the target variable. We introduce an efficient algorithm, TreeQA, to systematically explore the search-space of all possible groupings in a set of input trees, with integrated permutation tests. Experimental results show that TreeQA is able to handlelarge-scale association analysis very efficiently and is more effective and robust in association analysis than previous methods.
14988396
Exploring the energy-time tradeoff in high-performance computing
High-performance computing is and has always been performance oriented. However, a consequence of the push towards maximum performance is increased energy consumption, especially at supercomputing centers. Moreover, as peak performance is rarely attained, some of this energy consumption results in little or no performance gain. In addition, large energy consumption costs the government a significant amount of money and wastes natural resources. This paper investigates the tradeoff between energy and performance. Through the use of processors that support frequency and voltage scaling, we measured the performance and energy consumption of programs from three popular benchmark sets. We took multiple measurements for each program using different frequency and voltage settings. Results show that for many programs, a significant decrease in energy is possible with a small increase in time. We believe that this justifies further investigation into parallel HPC (e.g., MPI) applications.
2768791
Finding representative set from massive data
In the information age, data is pervasive. In some applications, data explosion is a significant phenomenon. The massive data volume poses challenges to both human users and computers. In this project, we propose a new model for identifying representative set from a large database. A representative set is a special subset of the original dataset, which has three main characteristics: It is significantly smaller in size compared to the original dataset. It captures the most information from the original dataset compared to other subsets of the same size. It has low redundancy among the representatives it contains. We use information-theoretic measures such as mutual information and relative entropy to measure the representativeness of the representative set. We first design a greedy algorithm and then present a heuristic algorithm that delivers much better performance. We run experiments on two real datasets and evaluate the effectiveness of our representative set in terms of coverage and accuracy. The experiments show that our representative set attains expected characteristics and captures information more efficiently.
23103906
Modeling of Fermentation Process Based on QDPSO-SVM
Utilizing the ability simple in application and quick in convergence of quantum delta-potential-well-based particle swarm optimization (QDPSO) algorithm and the high generalization ability of support vector machine (SVM), selecting the appropriate state variables, a dynamic time-varying model has been built. Using the model and algorithm to per-estimate some biochemical state variables which can not be measured on-line, and to optimise some operational variables. It is proved that the method is efficiency through the practical application of penicillin fermentation process.