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In this paper, we propose the bitwise structured prediction model for lossless image coding, especially for the oscillatory regions. The learning-based model utilizes the regular features obtained from the predicted local data. At first, the pixel-wise prediction is decomposed into the bitwise ones. In each bit plane, the prediction of the current bit is simplified to the max margin estimation for...
This paper explores an emerging method with deep roots in machine learning and game theory that has been applied to a number of signal processing applications. This competitive algorithm-based framework is particularly attractive for applications in which there is a large degree of uncertainty in the statistics and behavior of the signals of interest. Problems of prediction, equalization and adaptive...
Many computer systems and applications must be “non-stop” that is, provide continuous and uninterrupted services. Meanwhile, Service-Based Applications (SBAs) should be dynamic in terms of adap¬tability in order to operate and evolve in highly dynamic environments. Thus, in general, non-stop service-based applications require the ability to be updated without interruption. Flurry of researches on...
Symbiotic simulation is a paradigm in which a simulation system and a physical system are closely associated with each other. This close relationship can be mutually beneficial. The simulation system benefits from real-time measurements about the physical system which are provided by corresponding sensors. The physical system, on the other side, may benefit from the effects of decisions made by the...
Digital systems have been rapidly evolving within highly dynamic and unstructured environments, where the lack of a central authority forces entities to interact with each other through collaboration and negotiation. Digital agents often use Trust models in order to compute the level of trustworthiness of the partner they want to collaborate with. Unfortunately, due to the evolution speed of open...
Reliable recognition of activities from cluttered sensory data is challenging and important for a smart home to enable various activity-aware applications. In addition, understanding a user's preferences and then providing corresponding services is substantial in a smart home environment. Traditionally, activity recognition and preference learning were dealt with separately. In this work, we aim to...
The prediction task in national language processing means to guess the missing letter, word, phrase, or sentence that likely follow in a given segment of a text. Since 1980s many systems with different methods were developed for different languages. In this paper an overview of the existing prediction methods that have been used for more than two decades are described and a general classification...
In this study, the appropriateness of TRA, TPB and TAM to predict the first-line and middle manager IT usage intention is compared due to the gap found in prior research and the practical need for more information in this area. Hierarchical regression analysis is conducted with the sample of 156 first-line and middle managers distributed in 73 companies. The results show that TRA is most appropriate...
Managing quality during the development, operation, and maintenance of software(-intensive) systems and services is a challenging task. Although many organizations need to define, control, measure, and improve various quality aspects of their development artifacts and processes, nearly no guidance is available on how to select, adapt, define, combine, use, and evolve quality models. Catalogs of models...
We present a framework for audio background modeling of complex and unstructured audio environments. The determination of background audio is important for understanding and predicting the ambient context surrounding an agent, both human and machine. Our method extends the online adaptive Gaussian Mixture model technique to model variations in the background audio. We propose a method for learning...
In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantization (R-Q) model-based H.264/AVC macroblock layer rate control for low bit rate real-time applications. The context-adaptive prediction scheme simultaneously exploits both spatial...
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