16672435
Modeling and Learning Vague Event Durations for Temporal Reasoning
This paper reports on our recent work on modeling and automatically extracting vague, implicit event durations from text (Pan et al., 2006a, 2006b). It is a kind of commonsense knowledge that can have a substantial impact on temporal reasoning problems. We have also proposed a method of using normal distributions to model Judgments that are intervals on a scale and measure their interannotator agreement; this should extend from time to other kinds of vague but substantive information in text and commonsense reasoning.
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.
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.
9260921
Complexity adaptive quantization for intra-frames in very low bit rate video coding
Conventional rate control schemes focus on the problem of finding an optimal quantization value for P- and B-frames. No rate control is available for the encoding of I-frames. This could pose big problems due to the large number of bits an I-frame could generate, and due to the fact that the number of bits varies drastically from sequence to sequence, depending on their image complexity. This problem becomes severe especially at very low bit rate. Therefore, a mechanism to allocate data bits to an I-frame according to its complexity is indispensable in order to have constant coding quality. This work presents a mechanism to establish for I-frames a generic relationship between the quantization value, data bits, and their image complexity. Experimental results show that this generic relationship provides a fairly accurate estimation of quantization value for an I-frame at given data bits and image complexity, and is very useful in controlling the data bits generated by an I-frame.
39269573
An adaptive edge-preserving artifacts removal filter for video post-processing
We propose an adaptive edge-preserving artifacts removal filter (EPARF) for post-processing video compressed using block-based motion compensation so as to improve visual quality. The proposed filter is based on half-quadratic regularization, and it can adaptively remove both blocking and ringing artifacts while preserving object edges. The proposed algorithm has been applied to video sequences compressed by the MSRA scalable video coding (SVC) codec with block-based motion-compensated temporal filtering (MCTF). Experimental results demonstrate that EPARF can significantly improve both subjective and objective quality.
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.
207290821
Discriminating between photorealistic computer graphics and natural images using fractal geometry
Rendering technology in computer graphics (CG) is now capable of producing highly photorealistic images, giving rise to the problem of how to identify CG images from natural images. Some methods were proposed to solve this problem. In this paper, we give a novel method from a new point of view of image perception. Although the photorealistic CG images are very similar to natural images, they are surrealistic and smoother than natural images, thus leading to the difference in perception. A part of features are derived from fractal dimension to capture the difference in color perception between CG images and natural images, and several generalized dimensions are used as the rest features to capture difference in coarseness. The effect of these features is verified by experiments. The average accuracy is over 91.2%.
2696556
Dynamic Modeling of Biotechnical Process Based on Online Support Vector Machine
Due to the complexity and high non-linearity of biotechnical process, most simple mathematical models cannot describe the behavior of biochemistry systems very well. Therefore, dynamic modeling of biotechnical process is indispensable. Support vector machine (SVM) is a novel machine learning method, which is powerful for the problem characterized by small sample, non-linearity, high dimension and local minima, and has high generalization. But currently most support vector machine regression (SVR) training algorithms are offline, which could not be suit for time-variant system. So an improved SVM called online support vector machine was presented to modeling for the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments. Some models based on SVM were also presented. The result shows that the modeling based online SVM is superior to modeling based on SVW.
9189430
Exploring the energy-time tradeoff in MPI programs on a power-scalable cluster
Recently, energy has become an important issue in high-performance computing. For example, supercomputers that have energy in mind, such as BlueGene/L, have been built; the idea is to improve the energy efficiency of nodes. Our approach, which uses off-the-shelf, high-performance cluster nodes that are frequency scalable, allows energy saving by scaling down the CPU. This paper investigates the energy consumption and execution time of applications from a standard benchmark suite (NAS) on a power-scalable cluster. We study via direct measurement and simulation both intra-node and inter-node effects of memory and communication bottlenecks, respectively. Additionally, we compare energy consumption and execution time across different numbers of nodes. Our results show that a power-scalable cluster has the potential to save energy by scaling the processor down to lower energy levels. Furthermore, we found that for some programs, it is possible to both consume less energy and execute in less time when using a larger number of nodes, each at reduced energy. Additionally, we developed and validated a model that enables us to predict the energy-time tradeoff of larger clusters.
16312564
A new hybrid model for time-series prediction
This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.
18783473
A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO
An unmanned aerial vehicle (UAV) assignment requires allocating vehicles to destinations to complete various jobs. It is a complex assignment problem with hard constraints, and potential dimensional explosion when the scenarios become more complicated and the size of problems increases. Moreover, the non-stationary UAV assignment problem, studied in the paper, is more difficult, since dynamic scenarios are introduced, e.g. change of the number, or different task requirements of targets and vehicle, etc. In this paper, a "Constraint-First-Objective- Next" model is proposed for the non-stationary problem. The proposed model can effectively handle constraints as an additional objective, including constraints expressed by nature language, and is flexible enough to be combined with kinds of intelligent computation algorithms. A local version of PSO is cooperated with the proposed model to solve non-stationary UAV assignment problems. Numerical experimental results illustrate that it can efficiently achieve the optima and demonstrate the effectiveness.